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... Knowledge of Age-Related Physiological Changes and Fall Risk in Older Adults Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Sara Young, PT, DPT Copyright April 3, 2024 By: Sara Young, PT, DPT All rights reserved Approved by: Elizabeth S. Moore, PhD Committee Chair ______________________________ Kurt Jackson, PT, PhD Committee Member ______________________________ Heidi H. Ewen, PhD, FGSA, FAGHE Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ AGE-RELATED PHYSIOLOGICAL CHANGES Knowledge of Age-Related Physiological Changes and Fall Risk in Older Adults Sara Young Department of Interprofessional Health and Aging Studies, University of Indianapolis 1 AGE-RELATED PHYSIOLOGICAL CHANGES 2 Abstract With an increasing population of older adults at risk for falls, it is imperative to understand aspects of fall risk and fall prevention. One area of fall risk that has yet to be examined is adults knowledge about age-related physiological changes and how this knowledge relates to fall risk. This non-experimental study using a cross-sectional design explored community-dwelling adults knowledge of age-related physiological changes that occur as a normal part of the aging process. The study investigated knowledge scores of adults over the age of 50 years regarding typical age-related physiological changes and the relationships among knowledge, activity level, fall history, and other demographic factors. The study found that adults had a general knowledge of age-related physiological changes with a mean knowledge score of 83.50 (SD = 9.59) out of 100. There was a very weak correlation (r = .14) between knowledge and activity level. There was a weak but statistically significant correlation (r = .19, p = .036) between knowledge and fall history. Significant correlations were found between knowledge and education level (r = .32) and between knowledge and participant age (r = -.25). Multiple linear regression showed that the strongest correlated predictive variables of knowledge score were participants education level (HS or lower (r = -.290, p = .001); Graduate degree or higher (r = .188, p =.025)) and age (r = .331, p = .012). The most significant knowledge gaps were with topics about changes in sensation, proprioception, and cellular effects within the body. The highest levels of knowledge were about changes in hormones, muscle mass, protein for strength, and changes in vision. Knowledge gaps were largest between age and education level. This information could help fall prevention programs target specific information and population groups who have gaps in knowledge about age-related physiological changes related to fall risk. Keywords: fall risk, activity level, age-related changes, older adults AGE-RELATED PHYSIOLOGICAL CHANGES 3 Acknowledgements God has been with me through this whole journey, I would not have made it through without Him. So many times, I felt stuck or overwhelmed and would say a simple prayer, God, I cannot do this on my own, I need your help. At that, the task no longer seemed impossible, and I found the answers I was looking for. Next, I would like to express my appreciation to my family for sacrificing my time while working on homework and my dissertation. My husband and children stepped up and assisted with extra chores, cooked many dinners, and waited patiently for me as I finished, just one more paragraph. Also, a big thank you to my dad for sharing my survey with everyone he knows. I am exceedingly grateful to my dissertation committee, who so diligently read and advised each draft of each section of my dissertation. Though she was unable to finish the work on my dissertation, I am thankful for all the input and advice from Dr. Ewen on the development of my survey and for helping to clean my data to run the statistics. I appreciate Dr. Moore for stepping in to fill both roles of committee chair and analysis expert for the remainder of my dissertation process and for all the great editing advice. Dr. Moore helped me to shape my writing into a scholarly work. I am honored to have Dr. Jackson serve as my content expert. His expertise in research on falls, older adults, and neurological diagnosis was invaluable to the contribution of this dissertation. Lastly, I would not have made it this far without the University of Indianapolis faculty, who pushed us to do our best and prepared us to write a dissertation. Also, my classmates who journeyed alongside me inspired me and challenged me to do my best to become a better researcher and writer. AGE-RELATED PHYSIOLOGICAL CHANGES 4 Table of Contents Abstract ........................................................................................................................................... 2 Acknowledgements ......................................................................................................................... 3 Table of Contents ............................................................................................................................ 4 Knowledge of Age-Related Physiological Changes and Fall Risk in Older Adults ....................... 7 Problem Statement ...................................................................................................................... 8 Purpose Statement ....................................................................................................................... 8 Research Questions ..................................................................................................................... 8 Objectives .................................................................................................................................... 9 Significance of the Study ............................................................................................................ 9 Literature Review.......................................................................................................................... 10 Age-Related Changes ................................................................................................................ 10 Hormonal Changes ................................................................................................................ 11 Muscular Changes ................................................................................................................. 12 Effects of Age-Related Changes............................................................................................ 12 Measurement of Age-Related Changes ................................................................................. 13 Importance of Understanding Age-Related Muscle Loss ......................................................... 14 Perceptions of Aging ................................................................................................................. 15 Perceptions of Fall Risk......................................................................................................... 16 Further Research Needs ............................................................................................................ 17 Method .......................................................................................................................................... 17 Study Design ............................................................................................................................. 17 Participants ................................................................................................................................ 17 Data ........................................................................................................................................... 18 Operational Definition of Variables ...................................................................................... 20 Instruments ................................................................................................................................ 20 Knowledge of Age-Related Physiological Changes Questionnaire ...................................... 20 Physical Activity Scale for the Elderly.................................................................................. 21 Procedures ................................................................................................................................. 22 Recruitment ........................................................................................................................... 22 Screening ............................................................................................................................... 23 Informed Consent .................................................................................................................. 23 AGE-RELATED PHYSIOLOGICAL CHANGES 5 Data Collection ...................................................................................................................... 24 Data Management .................................................................................................................. 25 Statistical Analysis .................................................................................................................... 26 Results ........................................................................................................................................... 27 Objective 1 ................................................................................................................................ 28 Objective 2 ................................................................................................................................ 28 Objective 3 ................................................................................................................................ 29 Objective 4 ................................................................................................................................ 29 Objective 5 ................................................................................................................................ 29 Discussion ..................................................................................................................................... 30 Objective 1: Knowledge of Age-Related Physiological Changes ............................................. 31 Objective 2: Knowledge and Activity Level ............................................................................. 34 Objective 3: Knowledge and Fall History ................................................................................. 35 Objective 4: Knowledge and Participant Demographics .......................................................... 36 Objective 5: Predictive Variable of Knowledge Scores ............................................................ 38 Limitations ................................................................................................................................ 38 Conclusion................................................................................................................................. 39 Future Research ......................................................................................................................... 40 References ..................................................................................................................................... 42 Appendix A ................................................................................................................................... 57 Appendix B ................................................................................................................................... 64 Appendix D ................................................................................................................................... 66 Appendix E ................................................................................................................................... 67 Appendix F.................................................................................................................................... 68 AGE-RELATED PHYSIOLOGICAL CHANGES 6 List of Tables Table 1: Participant Demographics ........................................................................................... 50 Table 2: Knowledge of Age-Related Physiological Changes Questionnaire Outcomes .......... 51 Table 3: Knowledge of Age-Related Physiological Changes Questionnaire ............................ 52 Table 4: Correlation Between Knowledge of Age-Related Physiological Questionnaire Scores and Demographics ..................................................................................................................... 55 Table 5: Multilinear Regression for Knowledge of Age-Related Physiological Questionnaire ................................................................................................................................................... 56 AGE-RELATED PHYSIOLOGICAL CHANGES 7 Knowledge of Age-Related Physiological Changes and Fall Risk in Older Adults The population of adults 65 years and older is growing rapidly each year, with more than a 30% increase in the past decade (United States Census Bureau, 2020). Alarmingly, one in four of these older adults will fall each year (Center for Disease Control and Prevention [CDC], 2020). Around three million of these falls will lead to an injury requiring treatment in an emergency department, and more than 32,000 deaths will occur due to a fall (CDC, 2020). With the increasing percentage of older adults and the high risk for falls, there is an urgent need for fall prevention interventions. Understanding the need for fall prevention at a younger age may help decrease fall risk as adults age. Fall risk results from intrinsic and extrinsic factors that place an individual in a position of instability. Common extrinsic risk factors include environmental hazards, weather conditions, and medication interactions (Gell et al., 2020; Gross et al., 2021). Intrinsic physiological changes include loss of muscle mass, hormone dysregulation, bone mineral density loss, neurodegeneration, visual changes, and vestibular dysfunction (Gabriel et al., 2022; Maggi et al., 2018; Voelcker-Rehage, 2008; Volpi et al., 2004). This study focused on the intrinsic physiological factors that result in fall risk. Normal aging includes loss of muscle strength and a gradual slowing of bodily functions. Muscle breakdown can begin as early as the third decade of life (Volpi et al., 2004). Physiological factors leading to muscle loss are at the molecular, cellular, and motor unit levels (Wilkinson et al., 2018). These age-related changes can be slowed or reversed with proper nutrition and exercise (Holloszy, 2000; Volpi et al., 2004; Wilkinson et al., 2018). To slow or reduce age-related muscle loss, adults need to be aware of the normal physiological changes that occur with aging. Research has shown that middle-aged adults recognize the loss of strength among older adults; however, middle-aged adults may not recognize their loss of strength (Diehl AGE-RELATED PHYSIOLOGICAL CHANGES 8 et al., 2021). Understanding physiological changes such as muscle mass loss, bone density loss, neurodegeneration, visual degradation, and vestibular dysfunction will allow for greater prevention and accommodation of these physiological changes. Improving physical strength helps to accommodate the other intrinsic physiological changes associated with aging and extrinsic factors that lead to fall risk. Problem Statement Studies have been conducted to understand perceptions of aging such as losses or gains in certain abilities and perspectives as individuals age (Sabatini et al., 2022). Research has also shown a decline in physical functioning and performance with age but has not measured participant awareness of these age-related physiological changes (Lin et al., 2011; Stijntjes et al., 2017). Current research has focused on individuals after they have become frail, individuals that have had falls, and participant reception to fall risk programs. There is currently a gap in the research about adults knowledge of age-related physiological changes that can lead to impairments in physical abilities and eventually place individuals at risk for falls. Purpose Statement The purpose of this study was to understand the knowledge adults over 50 years have regarding age-related physiological changes and how that knowledge may be related to activity level, fall history, and other demographic factors. This study will provide insight into reaching adults who are entering older age to help moderate physiological decline and combat the loss of physical abilities that can result from normal aging. Research Questions 1. What level of knowledge do adults over 50 years have regarding common age-related physiological changes? AGE-RELATED PHYSIOLOGICAL CHANGES 9 2. Is knowledge of age-related physiological changes related to physical activity? 3. Is knowledge of age-related physiological changes related to fall history as indicated by a history of falls in the previous 12 months? 4. Are there correlations between knowledge of age-related physiological changes and participant demographics? 5. Can knowledge of age-related physiological changes be predicted by fall history, physical activity level, or participant demographics? Objectives 1. To determine the level of knowledge of age-related physiological changes in adults aged 50 years and older, through the Knowledge of Age-Related Physiological Changes Questionnaire (KAPCQ). 2. To determine if knowledge of age-related physiological changes measured by the KAPCQ is related to physical activity level measured by the Physical Activity Scale for the Elderly (PASE). 3. To determine if knowledge of age-related physiological changes measured by the KAPCQ is related to fall history as measured by participant response of a history of falls in the preceding 12 months. 4. To determine relationships between the knowledge of age-related physiological changes measured by the KAPCQ and participant demographics. 5. To determine if knowledge of age-related physiological changes can be predicted by fall history, physical activity level, or participant demographics. Significance of the Study Identifying adults knowledge of age-related physiological changes and how knowledge AGE-RELATED PHYSIOLOGICAL CHANGES 10 level may be related to physical activity level and fall history will allow researchers to understand gaps in knowledge that need to be targeted for future education. Programs can then be developed to target knowledge gaps and the physiological changes that occur as a normal part of aging. Comparing knowledge relationships with demographic variables will allow future education efforts to target groups with the most significant knowledge gaps regarding age-related physiological changes and fall risk. Literature Review With one in four older adults sustaining a fall each year, much research has focused on fall risk including physiological, environmental, and medical factors (CDC, 2023). With more than 32,000 deaths due to falls, it is imperative to understand all aspects of fall risk and prevention (CDC, 2020). An area that has not yet been explored is older adults knowledge of age-related physiological changes related to aging that increase fall risk. By having knowledge of age-related physiological changes that occur with aging, actions can be taken to address physical changes that lead to fall risk. Further, education can be developed to fill gaps in knowledge needed to prevent fall. Age-Related Changes Research has shown that age-related muscle loss begins as early as the third decade of life (Kenny et al., 2008; Larsson et al., 2019; Volpi et al., 2004). Aging causes changes throughout the body that affect the cells, hormones, organs, and body systems. Age-related changes remain at a steady, low rate until about the fifth to sixth decade of life (Holloszy, 2000; Kenny et al., 2008; Larsson et al., 2019; Volpi et al., 2004). In normal aging, there is a loss of muscle strength of about 12-15% per decade after the age of 50 years (Kenny et al., 2008). Older AGE-RELATED PHYSIOLOGICAL CHANGES 11 adults experience a 30-40% loss in strength and muscle mass by the age of 80 years (Holloszy, 2000). Cellular changes can lead to a breakdown in various systems throughout the body. Cellular Changes Primary aging occurs due to cellular and structural changes within the body (Holloszy, 2000). Water loss within the body occurs with aging, which affects the hydration of the cells throughout the body (Woodhead & Yochim, 2022). As the cells lose fluid, their function and ability to regenerate decline. Neurochemical changes occur at the cellular level leading to slowed or decreased synapses at the neuromotor junction, causing denervation of muscle fibers that lead to muscle atrophy (Voelcker-Rehage, 2008; Wilkinson et al., 2018). Further investigation of agerelated changes reveals biological changes within macromolecules, mitochondria, cellular systems, and homeostatic imbalances (Gladyshev & Gladyshev, 2016). These biological changes lead to a breakdown in multiple systems throughout the body that can cause disease, degrade muscles, cause loss of visual acuity, and lead to deficiencies in the vestibular system creating a greater fall risk (Gabriel et al., 2022; Gladyshev & Gladyshev, 2016; Saxon et al., 2022). Hormonal Changes Andropause is a reduction in testosterone production in men over the age of 65 years (Gharahdaghi et al., 2019; Volpi et al., 2004). The drop in testosterone decreases muscle protein synthesis, muscle mass, and strength (Gharahdaghi et al., 2019; Volpi et al., 2004). Men have greater muscle mass during early life and thus maintain more significant amounts of muscle mass compared with women into later life (Holloszy, 2000). Significant decreases in estradiol among post-menopausal women can lead to loss of bone mineral density and may contribute to changes in muscle mass (Benedetti et al., 2018; Volpi et al., 2004). Hormonal changes with aging may increase insulin resistance and cause glucose intolerance (Volpi et al., 2004). Insulin resistance AGE-RELATED PHYSIOLOGICAL CHANGES 12 can lead to diabetes that is associated with peripheral neuropathy and other degradations to the body increasing fall risk. Resistance to insulin leads to decreased protein synthesis reducing protein levels in muscles causing weakness and increased fall risk (Volpi et al., 2004). Muscular Changes Amino acids play an important role in muscle protein synthesis and breakdown. Atrophy of muscle tissue occurs when muscle protein breakdown occurs at a higher rate than muscle protein synthesis (Wilkinson et al., 2018). With aging, amino acids are diminished in their ability to inhibit muscle protein breakdown along with a diminished ability for muscle protein synthesis (Wilkinson et al., 2018). This reduction in the function of amino acids leads to muscle loss and an overall decrease in strength (Wilkinson et al., 2018). Age-related losses in muscle fibers and muscle mass increase intermuscular fat mass (Holloszy, 2000; Wilkinson et al., 2018). Increased inflammation associated with aging has also shown a loss in muscle fibers that causes a decrease in the number of motor units (Wilkinson et al., 2018). The changes in muscular structure lead to an overall loss of strength that affects physical function. There is a greater reduction in physical performance with age, especially after the sixth decade of life (Lin et al., 2011). The muscle breakdown results in muscle strength loss and is highly associated with increased fall risk. Effects of Age-Related Changes Losses in muscle mass, hormone regulation, and functioning of body systems lead to changes in physical ability. For men, as testosterone levels gradually decrease with age, protein synthesis, muscle mass, and strength decline (Gharahdaghi et al., 2019; Volpi et al., 2004). For women, the decrease in estradiol levels with menopause may have minimal effects on muscle loss (Volpi et al., 2004). AGE-RELATED PHYSIOLOGICAL CHANGES 13 Neurodegeneration from neurochemical changes and reduced brain size associated with aging leads to cognitive decline (Voelcker-Rehage, 2008; Woodhead & Yochim, 2022). The decline in cognition has been associated with a slower gait speed and a decrease in grip strength (Stijntjes et al., 2017; Zammit et al., 2019). Neurodegenerative age-related changes cause a decline in motor performance, leading to decreased spatial acuity, manual dexterity, grip strength, and proprioception (Adamo et al., 2009; Voelcker-Rehage, 2008). Physiological changes throughout the various systems in the body are a part of the normal aging process. Namely, muscle mass and strength loss lead to instability during gait and impaired balance (Gabriel et al., 2022; Saxon et al., 2022). Visual changes such as glaucoma, cataracts, and macular degeneration affect balance and increase fall risk (Maggi et al., 2018). Age-related changes within the inner ears vestibular system led to balance impairments (Gabriel et al., 2022). Age-related changes associated with muscle loss create improper weight shifting, collapse while standing, gait instabilities, excessive movements needed to maintain balance, and inability to correct slips and trips (Burm et al., 2021; Gabriel et al., 2022). Gait quality is a strong indicator for fall risk, with poor gait quality associated with greater fall risk (Weijer et al., 2018). Secondary aging occurs due to environmental factors that enhance or inhibit the normal aging process (Holloszy, 2000). Genetics does play a role in the aging process, but that was not explored in this study. Measurement of Age-Related Changes Physiological assessment of age-related changes can be measured by visual contrast sensitivity, proprioception, quadriceps strength, reaction time, postural sway, and dynamic balance control (Delbaere et al., 2010). Poor quadriceps strength strongly predicted fall risk in older adults (Delbaere et al., 2010). Loss of mobility associated with age-related changes can be AGE-RELATED PHYSIOLOGICAL CHANGES 14 characterized by decreased muscle mass, strength, bone mass, bone density, and chronic conditions (Diehl & Wahl, 2010). The Falls Efficacy Scale has been shown to accurately measure the fear of falling (Delbaere et al., 2010). Attitudes about aging have been measured with an Awareness of Age-Related Change (AARC) 50-question survey and a shortened 10-question survey to assess the perception of agerelated gains and losses (Diehl & Wahl, 2010; Kaspar et al., 2019). The survey focused on the participants thoughts on losses and gains in physical health, social health, and feelings about aging. The AARC does not address an individuals knowledge about age-related physiological changes, such as muscle loss, cellular changes, or hormonal effects on aging. The Physical Functioning Scale assesses how physical health is associated with limitations in daily activities (Windsor et al., 2022). These assessments measure other aspects of the aging process but do not address the knowledge of age-related physiological changes. Importance of Understanding Age-Related Muscle Loss Awareness of age-related muscle loss is important for understanding the need to combat muscle loss. Muscle mass can be increased with a combination of a diet that is high in protein and completing a weight and resistance training program on a regular basis (Holloszy, 2000; Wilkinson et al., 2018). A high-protein diet can assist with the loss of protein synthesis that occurs with aging (Wilkinson et al., 2018). Weight and resistance training lead to increased muscle fibers and assist with protein synthesis (Wilkinson et al., 2018). Adaptations to weight training can occur into the later decades of life (Hollosvy, 2000). Research has shown that with three months of a weight training program, 85-year-old individuals could increase their strength by 20-40% (Holloszy, 2000). Strength and resistance training has also been shown to improve AGE-RELATED PHYSIOLOGICAL CHANGES 15 bone mineral density and increase bone mass which improves overall strength (Benedetti et al., 2018). Activities to promote cortical and subcortical functions can maintain or improve neurodegeneration and enhance overall function and physical performance (Voelcker-Rehage, 2008). Loss of muscular strength was highly correlated with the ability to perform work-related tasks and with work-related injuries (Kenny et al., 2008). Resistance training is important to reduce the effects of age-related changes to maintain a strong capacity for the ability to work (Kenny et al., 2008). Perceptions of Aging The AARC questionnaire measures awareness of internal and external changes that lead to ideas of positive or negative associations of aging (Diehl & Wahl, 2010). Studies using the AARC focused on positive and negative views of aging versus perceptions of physiological changes associated with aging. The perceptions measured by the AARC included aging goals, changes in physical appearance, health, and functional status but did not measure knowledge of specific physiological changes associated with aging (Diehl & Wahl, 2010). Further research using the AARC showed that perceptions of physical and social loss were stable from ages 40-65 years and began to decline after age 65 (Diehl et al., 2021). Lower levels of well-being correlated with perceptions of age-related losses, while age-related gains were associated with personal growth and relationships (Kaspar et al., 2019; Sabatini et al., 2020). Those who perceived more significant losses with aging had higher levels of poor health and physical activity (Kaspar et al., 2019; Sabatini et al., 2022; Windsor et al., 2022). One study explored young adults aged 18-22 years and older adults aged 64-100 years and their perceptions of strength, balance, endurance, hand-eye coordination, gross motor AGE-RELATED PHYSIOLOGICAL CHANGES 16 coordination, flexibility, physical activity, vigorous exercise, moderate exercise, and musclestrengthening activities over each decade (Lineweaver et al., 2018). Participants consistently responded that physical function declines with aging, with older adults viewing physical aging more positively than young adults (Lineweaver et al., 2018). This study did not explore the knowledge of actual physiological aspects of aging, as it looked at the perceptions young adults and older adults had of each decade of age. Stress is another factor that can affect aging. Higher stress levels correlated with lower perceptions of physical health and negative self-perceptions of aging (Witzel et al., 2022). Perceptions of Fall Risk Individuals with more significant physiological deficits have greater fall risk awareness (Delbaere et al., 2010; Kiyoshi-Teo et al., 2020; de Souza et al., 2022). Older adults attribute fall risk to physical impairments, aging, difficulty walking, medical conditions, and environmental hazards (de Clercq et al., 2021). Health condition is also a significant factor in the perception of fall risk, with reports of weakness in the legs and declining health as contributors (Kiyoshi-Teo et al., 2020). Fatigue and pain also contribute to an individuals perceptions of fall risk (Naseri et al., 2020). Fear of falling is associated with fall risk and is higher in individuals with lower physical activity levels (Wang et al., 2021). Older adults in one study perceived falls as a threat to ones identity, independence, and social interactions (Gardiner et al., 2017). Adults in this study attributed falls to not being careful and reported reducing fall risk by being careful (Gardiner et al., 2017). Fall risk perception studies have not explored if the awareness of muscle loss and other physiological changes within the body are related to reports of weakness and poor health. AGE-RELATED PHYSIOLOGICAL CHANGES 17 Further Research Needs Individuals with more significant co-morbidities and lower perceptions of health are more aware of age-related changes affecting physical ability (Delbaere et al., 2010; Kiyoshi-Teo et al., 2020). Research has not explored the understanding of physiological aging and the ability to combat age-related changes to improve strength and reduce fall risk. Physiological age-related changes lead to a breakdown in the cells, hormones, muscles, and neurological systems. Adults need to understand these changes as they age to prevent or slow the effects of aging. Research has not examined how adults aged 50 years and older view or understand physiological changes that begin as early as age 30 years. The importance of this study is to determine the amount of knowledge adults have about specific physiological changes associated with aging that increase fall risk. It is also important to determine if the knowledge about physiological changes is related to physical activity level, fall history, and demographic factors such as education level, income, race, living situation, gender, and age. This information could allow future research and educational programs to be developed that target adults who would benefit the most from a better understanding of the effects of age-related physiological changes. Method Study Design A non-experimental study using a cross-sectional design was used to explore communitydwelling adults knowledge of age-related physiological changes that occur as a normal part of aging. The study occurred between June 1, 2023, and August 31, 2023. Prior to data collection, the study was approved by the University of Indianapolis Institutional Review Board. Participants The target population for this study was adults 50 years and older living in the AGE-RELATED PHYSIOLOGICAL CHANGES 18 community. Participants were eligible for inclusion in the study if they were over the age of 50 years; were currently able to ambulate in their home and community (with or without an assistive device) and if they considered themselves to be in fair or better health as measured by a subjective question on a Likert scale. Individuals were excluded from the study if they required assistance from another person to ambulate; had a neurological condition that affected their gait; had a recent hospitalization of more than one night in the preceding three weeks; were homebound, and do not go out in the community; or were unable to make their own decisions. The study was designed to assess normal age-related changes; thus, if an individual had a neurological condition, was homebound and dependent on others, or had been recently hospitalized, their perspectives of knowledge of age-related changes could have been skewed toward impairments. An a priori sample size determination was conducted using G*Power. The calculation was based on using an ANOVA f multiple linear regression fixed model, R2 deviation from zero (Faul et al., 2007). A medium effect size of 0.15 was selected based on the results from a similar study that used the PASE (Washburn et al., 1993). In addition, the following parameters were used: an alpha of .05, power of 0.80, and six predictors. Based on the calculation it was estimated a minimum sample size of 98 would be sufficient to power the study at 80%. To account for possible missing data, the minimum sample size was increased by 20% to a minimum sample size of 118. Data The following data were collected through Qualtrics, an online survey platform. Knowledge of age-related physiological changes (dependent variable) Participants physical activity level (independent variable) AGE-RELATED PHYSIOLOGICAL CHANGES Fall history (independent variable) Potential confounding variables included 19 o Biological sex (female or male) o History of falls (yes or no; If yes, number of falls in the last 6 months and the last 12 months, and injury from falls) o Age (years) o Community setting in which the participant lived most of the year (rural, urban, or suburban) o Highest education level (some high school or less, high school diploma or GED, some college, associate or technical degree, bachelors degree, graduate, or professional degree (MA, MS, MBA, PhD, JD, MD, DDS, etc.)) o Income level (<$25,000; $25,000 to $49,999; $50,000 to $74,999; $75,000 to $99,999; $100,000 to $149,999; or $150,000 or more) o Ethnicity and race (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, White or Caucasian) o Medical comorbidities diagnosed by a healthcare practitioner (self-reported diabetes, hypertension, high cholesterol, heart disease, pulmonary disease, and other) o Occupation (list current or most recent occupation with additional self-reported categories to choose one of the following for type of work activity: primarily sitting while working, a mixture of sitting and light activity while working, primarily light activity while working, a mixture of light and heavy activity while AGE-RELATED PHYSIOLOGICAL CHANGES 20 working, or primarily heavy activity while working) o Marital status (married, single, widowed, divorced, or other) Operational Definition of Variables Knowledge of age-related physiological changes was operationalized as the score received on the KAPCQ. Participants physical activity level was operationalized as the score on the PASE, while fall history was determined by reports of falls and injuries from falls in the last 6 months and the last 12 months. For this study, a fall was defined as an unintentional loss of balance resulting in a fall to the ground or to a lower surface. Injury from a fall was defined as bodily damage lasting more than one week after a fall. Instruments Knowledge of Age-Related Physiological Changes Questionnaire Age-related physiological changes are defined as the changes in body systems from normal aging, such as changes within the cells, hormones, organs, and muscles (Benedetti et al., 2018; Gladyshev & Gladyshev, 2016; Voelcker-Rehage, 2008; Volpi et al., 2004; Wilkinson et al., 2018). Age-related physiological changes can cause disease, degrade muscles, decrease visual acuity, impair the vestibular system, impair sensation, decrease balance, decrease reaction time, and place individuals at a higher fall risk (Adamo et al., 2009; Gabriel et al., 2022; Maggi et al., 2018; Saxon et al., 2022; Voelcker-Rehage, 2008; Volpi et al., 2004). The KAPCQ assesses the knowledge individuals have about age-related physiological changes. Knowledge was assessed using a Likert scale of agreement level on statements about each of the age-related physiological changes to allow for a rating scale of answers. The questionnaire was piloted with a small sample size representative of the study population. Based on feedback from the pilot sample, the questionnaire was revised and re-tested and was revised again with minor changes to AGE-RELATED PHYSIOLOGICAL CHANGES 21 the current questionnaire. The questionnaire included 20 five-point Likert questions coded on a scale of one to five. A score of five indicated strong agreement with the statement on each age-related physiological change. Reverse coding was completed for questions written with the knowledge of change when negatively worded to reflect disagreement. Responses to the questions were summed with scores ranging from 20 to 100. Predictive scores were determined as data was collected. As data was collected, descriptive statistics and correlation tests were used to determine the reliability and validity of the instrument. See Appendix A for the Age-Related Changes and Physical Activity Survey, which includes the KAPCQ. Physical Activity Scale for the Elderly The PASE is a self-reported measure of physical activity in individuals 65 years and older. It looks at activities during the previous 7-day period in three categories leisure, household, and work-related. It is weighted with scores for each category (Washburn et al., 1993). Leisure activity includes six primary questions about various activities, the type of activities performed, the frequency of completing the activities, and the average duration of each activity. Household activities have two questions about light and heavy housework and the type of housework completed. Work-related activity inquiries about work for pay or as a volunteer, number of hours per work, and activity rigor. Each PASE activity has a weighted score multiplied by the activitys frequency. Participants can score between 0 and 793, with 0 being no activity and 793 being the highest total activity possible for all the categories. Developers of the PASE found moderate correlations with participants health status and physiological measures such as grip strength (r = .37), static balance (r = .33), leg strength (r = .25), age (r = -.34), perceived health status (r = -.34), and sickness impact profile score (r = -.42) AGE-RELATED PHYSIOLOGICAL CHANGES 22 (Washburn et al., 1993). Reliability statistics showed a correlation coefficient of .75 for testretest reliability (Washburn et al., 1993). Crockett et al. (2021) found that high PASE scores above 203 showed no significant correlation (r = .11) with the Physiological Profile Assessment, indicating that PASE scores above 203 showed a high level of physical activity. The study also found that the higher the PASE score, the lower the risk of falling (Crokett et al., 2021). Dinger et al. (2004) found high intraclass correlation coefficients between the PASE and leisure activity self-rating scores and between the PASE and electronically monitored physical activity levels. In a separate study by Ngai et al. (2012), the researchers found higher reliability than the original researchers of the PASE with a correlation coefficient of .81. The study also showed moderate associations with other tests that measure fall risks, such as the SF-36, grip strength, single-legstance, five times sit-to-stand, and the 10-m walk test (Ngai et al., 2012). Studies conducted in Japan, China, and Norway using the PASE showed significantly moderate correlations between the PASE and physical function (r = .35 - .47) and high-reliability coefficients of .73 - .81 (Hagiwara et al., 2008; Loland, 2002; Ngai et al., 2012). These findings demonstrate strong reproducibility of the PASE. The original author, Dr. Richard Washburn, granted permission to use the PASE. See Appendix B for correspondence of permission. Procedures Recruitment Convenience and snowball sampling was used to target adults through visits by the primary researcher (S. Y.) to community centers, churches, and organizations with programs designed for adults aged 50 years and older. The primary researcher visited community centers in multiple counties in Ohio when community programs were beginning or ending to distribute study information. Recruitment flyers with a QR code and website link to the survey were AGE-RELATED PHYSIOLOGICAL CHANGES 23 available for potential participants to use with family and friends (Appendix D). Participants were also recruited through social media posts and emails with links to the survey. See Appendix E for a sample of the social media post and Appendix F for the email Script. Individuals who completed the survey were offered the chance to participate in a drawing for a $10 Amazon egift card (Appendix C). Screening The first page of the Qualtrics survey had screening questions to determine eligibility to participate in the study. Participants had to meet the inclusion criteria to be directed to the next page of the survey, the informed consent document. The Qualtrics survey was designed to take participants directly to the last page of the survey if any of their answers indicated one or more of the following exclusion items: their level of health was poor or very poor, their ability to walk in their home was poor or very poor, their ability to walk in their community was poor or very poor, they had a neurological condition, they used an assistive device for mobility in their home or community, they had a recent hospitalization of more than one night in the last three weeks, or another person helped them to fill out the survey. Surveys of participants who did not meet the inclusion criteria based on the screening process were deleted from Qualtrics. Informed Consent Participants provided electronic informed consent before they were accepted into the study. The informed consent document was on the first page of the survey following the screening questions. In addition, potential participants had to agree to participate in the study by clicking an agreement box in the Qualtrics survey. Individuals who did not agree to be in the study were taken to the end of the survey and were not allowed to participate. AGE-RELATED PHYSIOLOGICAL CHANGES 24 Data Collection Data were collected using an online survey distributed through Qualtrics. The electronic survey remained open for three months, until the minimum sample size of 118 was reached. The primary researcher visited recruitment sites and provided handouts with information about the study. Individuals interested in completing the survey had the option of completing the survey online using a QR code or an internet link directly to the survey. Participants recruited through social media and email completed the survey through a website link using the Qualtrics survey. Recruitment handouts were used by participants to share with family and friends. Participants who completed the survey using the recruitment handout scanned a QR code or used the website link provided on the handout. To ensure survey security and only one participant submission, the following measures were activated for the Qualtrics survey. The security settings in Qualtrics were set to prevent multiple submissions by the same participant by detecting duplicate responses through cookies placed during the survey submission. The respondent could not enter the survey again once it was submitted. Bot detection was used to prevent robot or mass responses from a single source. The security scan monitor protected the identities of the participants as well as prevented email scanning and provided fraud protection. Participants had to complete an interactive question to prove they were not a robot. Relevant ID also helped prevent multiple submissions by assessing respondent metadata and completing fraud detection. Incentive Drawing. An online incentive drawing was built into Qualtrics. At the end of the online survey, a question asked the participants if they wanted to enter a drawing for a chance to win one of twenty $10 Amazon e-gift cards. If the respondent answered no, they were thanked for participating in the survey, and the survey was terminated. If the respondent AGE-RELATED PHYSIOLOGICAL CHANGES 25 answered "yes, they were directed to the drawing page. Participants names and contact information were collected on the drawing page. Once the survey closed, the data from the drawing page was downloaded and exported separately to an Excel file by the primary researcher. Participants line location in Excel served as their numerical drawing number. Twenty winning numbers were randomly selected using the www.random.org random number generator website. The drawing winners were notified via email from the primary researchers University email with the $10 digital Amazon e-gift card attached. In addition, the notification email was set to request a read receipt. A master list of all drawing winners, date of email notifications, return of read-receipt emails, and prize amounts were stored in an Excel file on the primary researchers password-protected laptop until no longer needed. The information was destroyed once it was no longer needed. Data Management The primary researcher reviewed the data in Qualtrics and deleted any information or data on participants that meet exclusion criteria for the study. The primary researcher downloaded data meeting the inclusion criteria from Qualtrics into SPSS statistical software once the survey closed. Data was explored, looking for data input errors and missing data. In addition, scores from the instruments used were calculated. The Qualtrics survey had the security settings turned on not to record participants IP Address, location data, or contact information. Electronic data files were stored on the primary researchers password-protected computer. At the close of the study, the survey was deleted from Qualtrics. Once the data were no longer needed for the study, it was deleted from the SPSS software. The submission for the drawing of the Amazon gift cards was kept separate from the survey data and was deleted once the drawing was AGE-RELATED PHYSIOLOGICAL CHANGES 26 completed. Statistical Analysis Descriptive statistics were conducted on the study sample. Nominal data were reported as frequencies and percentages, ordinal data as medians and interquartile ranges, normally distributed interval and ratio data as means and standard deviations, while non-normally distributed interval and ratio data were reported as medians and interquartile ranges. A Cronbachs alpha analysis test was run on the data collected from the KAPCQ to determine internal consistency as data was collected. For objective 1, to determine the level of knowledge of age-related physiological changes in adults over 50 years, descriptive statistics using means and standard deviations were used as the data were collected to determine predictive scores for knowledge level. Correlation tests were conducted for objective two, to determine the relationship between knowledge of age-related physiological changes and the total PASE score using a Pearson correlation test. To address objective three, a point biserial correlation was run to determine the relationship between knowledge of age-related physiological changes and fall history. Correlation tests, point biserial, Spearman rho, and Pearson, as appropriate, were used for objective four to determine the relationship between knowledge of age-related physiological changes and demographic variables. Correlations were considered weak with values between 0 and .30, moderate between .30 and .70, and strong with values between .70 and 1.0 (Ratner, 2009). Multiple linear regression was used to determine if activity level and fall history were significant predictors of knowledge of age-related physiological changes. Confounding variables were added to the regression model if a correlation coefficient of .25 or greater was found AGE-RELATED PHYSIOLOGICAL CHANGES 27 between the variable and knowledge of age-related physiological changes score. The assumptions associated with multiple regression were tested as outlined by (Field, 2018). The presence of multicollinearity among the independent variables were tested in two ways: correlation tests between the dependent and each independent variable and by checking collinearity Tolerance/VIF values in coefficient tables. Multicollinearity was assumed with correlations greater than .70 or a Tolerance value less than 0.1. The Durbin-Watson test statistic between knowledge level and fall history and between knowledge level and activity level indicated independence of observations with values of 2.0 indicating zero autocorrelation, values below 2.0 indicating positive autocorrelation, and values above 2.0 indicating negative autocorrelation. A scatterplot of the independent variables with the dependent variable were used to determine linear relationships between the variables using a visual inspection. Homoscedasticity was checked by inspecting a scatterplot of the standardized residuals against the standardized predicted values from the regression analysis. A scatterplot was also used to ensure that there were no significant outliers between the dependent variable and the independent variables. The normality of the data was determined using the Shapiro-Wilk tests and visual inspection of Q-Q plots and histograms. Data were analyzed using IBM SPSS Statistics for Windows, Version 28.0 (IBM Corp., Armonk, NY). All comparisons were two-tailed, and a significance level of less than .05 was considered statistically significant. Results A total of 148 individuals participated in the study; however, 27 of those individuals did not meet the inclusion criteria, so their data were not included in the analysis. This left a sample size of 121 participants. Of the 121 participants, 11 had missing data but completed enough of AGE-RELATED PHYSIOLOGICAL CHANGES 28 the survey to compute scores. The median age of participants was 62 years, with a 25th percentile of 55 years and a 75th percentile of 68 years. Participants ranged in age from 50-86 years old. There was a higher percentage of females (66.9%) than males. Twenty-three (19%) participants reported falling in the previous 12 months, and 15 (12.4%) reported falling in the previous 6 months. A detailed report of participant demographics can be found in Table 1. The internal consistency of the KAPCQ was tested using Cronbachs alpha. The instrument had high internal consistency, = .88. Each item showed a strong relationship with the total KAPCQ score, and the estimates of the alpha value did not change if any of the items were deleted. Descriptive statistics for each KAPCQ statement response demonstrated that the median scores ranged from 3 to 5, where larger values were associated with greater levels of knowledge. See Table 2 for the level of knowledge with each statement and Table 3 for the number of participant responses for each level of agreement for the KAPCQ statements. The highest percentage of responses were strongly agree and agree for all statements. Objective 1 The first objective was to determine the level of knowledge of age-related physiological changes. The KAPCQ had a mean score of 83.50 (SD 9.59) with a 95% CI [81.76, 85.24] and a median score of 84.00 (25th percentile 77.00, 75th percentile 91.00). Objective 2 The second objective was to determine if there was a relationship between knowledge of age-related physiological changes and participant activity level. The Pearson correlation results showed there was a very weak correlation between KAPCQ and PASE scores (r = .14) that was not statistically significant (p = .143). AGE-RELATED PHYSIOLOGICAL CHANGES 29 Objective 3 The third objective was to determine if knowledge of age-related physiological changes was related to fall history. A point biserial correlation was run to determine if KAPCQ scores were related to fall history. There was a weak but statistically significant correlation (r = .19, p = .036) between KAPCQ scores and fall history within the last 6 months. There was no significant correlation between KAPCQ scores and fall history within the last 12 months (r = .11, p = .253). Objective 4 The fourth objective was to determine if there was a relationship between the KAPCQ and participant demographics. Point biserial correlations were run with KAPCQ for gender, race, marital status, and community setting. Spearman correlations were run between KAPCQ individually with participant age, education level, and income level. For KAPCQ scores and participant demographics, the correlation coefficients ranged from .02 to .32, with significant correlations between KAPCQ scores and level of education and KAPCQ scores and participant age. See Table 4 for a detailed description of the relationship between KAPCQ scores and participant demographics. Objective 5 Multiple linear regression was run to understand the effect of fall history, physical activity level, age, and education on the knowledge of age-related physiological changes. Participant age and education level significantly correlated with the KAPCQ scores; therefore, they were added as possible predictors to the regression model. Most assumptions of multiple regression analyses were met. The independence of observation assumption was met with a Durbin-Watson of 1.82. A linear relationship between KAPCQ score and each independent variable was found through a visual inspection of partial regression plots. The assumption of AGE-RELATED PHYSIOLOGICAL CHANGES 30 homoscedasticity was met with a horizontal line on a scatterplot with the standardized residuals against the standardized predicted values. The no multicollinearity assumption was met, given that none of the correlation coefficients between the independent variables were greater than .85. There was one significant outlier. One individual had a standardized residual greater than 3 SD below the mean. The histogram and P-P Plot showed normal distribution with variables close to the diagonal line on the P-P plot and a normal curve on the histogram. The strongest correlated predictive variables of knowledge score were participants education level, HS or lower (r = -.29, p = .001); Graduate degree or higher (r = .19, p =.025) and age (r = .33, p = .012). The multiple regression model statistically significantly precited knowledge of agerelated physiological changes F(7, 102) = 3.95, p < .001 and explained 21% of the variance (R2 = .21). Two variables, age and education level, high school diploma/GED or less, added statistically significantly to the prediction, (p = .009 for both predictors). When all other independent variables were held constant, for every year increase in age, KAPCQ scores decreased by 0.30. In addition, if the education level was high school/GED or less, relative to other education levels, and when all other independent variables were held constant, KAPCQ scores decreased by 8.04. Results are presented in Table 5. Discussion The purpose of this study was to understand the level of knowledge adults 50 years and older have regarding age-related physiological changes. Further, the study explored whether participants knowledge level was related to activity level, fall history, and participant demographics. Statistically significant correlations were found between knowledge level and each of the following: fall history, age, and education level. AGE-RELATED PHYSIOLOGICAL CHANGES 31 Objective 1: Knowledge of Age-Related Physiological Changes To determine an individuals knowledge of age-related physiological changes, participants completed a survey with 20 statements regarding various aspects of age-related physiological changes. Agreements with the true statements and disagreements with false statements were recorded on a five-point scale with scores of four and five indicating appropriate knowledge of the age-related change. Higher mean scores out of 100 possible points indicated higher levels of knowledge of age-related changes. With a score of four out of five or higher indicating knowledge of the change, an overall mean score of 80% or higher indicated knowledge of age-related changes. The mean score of participants in this study was 83.5; further, 61.3% of participants scored an 80 or higher on the KAPCQ, indicating basic knowledge of agerelated changes. Previous studies found that adults over the age of 70 had greater awareness of general age-related changes and that younger adults perceived changes to only occur with advanced aging (Diehl et al., 2021; Lineweaver et al., 2018). These perceptions are not accurate given prior research that has shown age-related changes can occur as early as the third decade of life (Kenny et al., 2008). This indicates that there is knowledge of age-related changes, but this knowledge may not always be accurate. This could explain some of the variation seen in the KAPCQ survey. Questions regarding sensation and proprioception demonstrated the lowest knowledge level. Proprioceptive awareness and the ability to accurately place ones limb in space declines with aging (Adamo et al., 2009). Questions about the vestibular system and balance also showed lower knowledge levels. Muscular and vestibular age-related changes often cause balance impairments that increase fall risk (Gabriel et al., 2022; Saxon et al., 2022). Previous research has not examined the knowledge of adults regarding proprioception and sensory loss with aging. AGE-RELATED PHYSIOLOGICAL CHANGES 32 Knowledge in these areas may be lower due to a lesser understanding of how sensory changes impact movement and balance. Twenty-eight percent of participants did not agree that individuals over 65 are at a higher risk of falling; however, statistics show that more than 25% of adults ages 65 and older have a fall each year (CDC, 2023). Further, these falls are the primary cause of injuries in older adults (CDC, 2023). Knowledge levels were lower regarding the effects of aging on the cells within the body. The ability of cells to regenerate and function declines with aging, causing changes throughout each body structure (Holloszy, 2000; Woodhead & Yochim, 2022). The knowledge of cellular changes may be lower due to individuals having less knowledge of cellular functions within the body. Previous research has explored individuals perceptions of decline in physical function, but has not examined participants knowledge of body systems decline (Diehl & Wahl, 2010; Lee et al., 2023; Lineweaver et al., 2018) Highest levels of knowledge and agreement were with statements about changes in hormones, muscle mass, protein required for strength, and vision changes. Participants agreed that muscle mass decreases with aging but were less aware that this loss begins before the fourth decade of life. Age-related muscle loss begins as early as the third decade of life (Kenny et al., 2008; Larsson et al., 2019; Volpi et al., 2004). This could be attributed to the belief of older and younger adults that muscle loss only occurs later in life (Lineweaver et al., 2018). Study participants agreed that protein is required for strength but had lower levels of agreement about the bodys ability to use protein with aging. With advanced aging, the body loses the ability to breakdown protein and utilize the protein for muscle mass which is required to maintain strength (Wilkinson et al., 2018). Participants also strongly agreed that hormones AGE-RELATED PHYSIOLOGICAL CHANGES 33 change with aging but had lower levels of agreement with the effects of hormone changes on strength. Hormonal changes in men and women effect the bodys ability to use protein to maintain muscle mass which causes decreased strength (Gharahdaghi et al., 2019; Volpi et al., 2004). This lack of knowledge may come from a decreased awareness of how the body uses protein for strength. Previous studies have explored the effects of protein synthesis with aging and have explored individuals knowledge of muscle loss but have not examined the knowledge of proteins role in age-related muscle loss. Knowledge regarding muscle loss and the effects of muscle loss on balance that is associated with age-related changes should be a focus of fall prevention education. Learning that the body is unable to use protein as efficiently with aging may help older adults understand the importance of increasing protein intake and maintaining an exercise program emphasizing increased muscle mass (Holloszy, 2000; Wilkinson et al., 2018). Though strength plays a role in maintaining balance, other body systems effect balance such as: vision, vestibular, and coordination centers that control reaction time and precision of movement (Delbaere et al., 2010; Gabriel et al., 2022; Maggi et al., 2018). Understanding the impact of these system changes on balance may help individuals accommodate and make appropriate adaptations to prevent falls. Though participants strongly agreed that vision changes with aging, not as many showed agreements with vision changes affecting balance. Vision deficits associated with aging have been found to be one of the largest predictors and contributors to fall risk (Maggi et al., 2018). Previous studies have explored the relationship of visual changes on balance but have not explored individuals knowledge of that relationship. Participants may recognize that their vision declines with aging, but do not necessarily associate that change with fall risk. AGE-RELATED PHYSIOLOGICAL CHANGES 34 The results of this survey may be useful for healthcare professionals that are working in fall prevention and education to address these potential knowledge gaps. Objective 2: Knowledge and Activity Level There was not a significant correlation (r = .14) between knowledge level and physical activity level as measured by the PASE. There was a tendency for those with higher PASE scores to have higher knowledge scores; however, this was not consistent. Multiple individuals had high PASE but low knowledge scores. Conversely, many individuals had low PASE scores but high knowledge scores. This was not consistent with previous research that showed a significant correlation with knowledge of muscle loss changes associated with individuals who participated in a regular exercise program (Lee et al., 2023). This variation could be due to the addition of other age-related changes in the KAPCQ that were not part of previous studies. This also demonstrates that there may be a gap in knowledge about age-related changes that lead to fall risk beyond strength. Knowledge levels in this study were lower regarding sensation, balance, hormone changes affecting strength, and the bodys ability to use protein for strength. With overall knowledge being less in these areas, this could explain the inconsistency with activity level and knowledge level. Further research needs to be done to explore if increased knowledge in these areas contributes to increased physical activity levels. Participant scores on the PASE ranged from 63.3 to 437.7 with higher scores indicating higher levels of physical activity. Scores on the PASE above 203 indicated greater levels of physical activity and lower risk for falls (Crockett et al., 2021). Of the 121 study participants, 112 participants completed the questionnaire for the PASE score. Of those 112 participants, 52.7% had scores higher than 203 on the PASE. The mean knowledge scores for participants who had PASE scores less than 203 were slightly lower (82.7) than participants who had PASE AGE-RELATED PHYSIOLOGICAL CHANGES 35 scores more than 203 (84.4), but this was not a statistically significant difference. The PASE measures physical activity through daily activities that include household chores and workrelated activities, as well as participation in an exercise program. Comparison of knowledge and regular participation in a moderate or higher level of exercise program may have yielded more significant results. This may be more consistent with Lee et al.s (2023) research that found individuals who regularly exercised had more knowledge about muscle loss. Further research should be conducted to determine a relationship between participation in a regular exercise program and knowledge of age-related physiological changes. Objective 3: Knowledge and Fall History Participants history of falls within the last 6 months had a weak but statistically significant correlation with knowledge scores on the KAPCQ. However, the participants history of falls within the last 12 months did not show a statistically significant correlation with knowledge scores. Of the 121 participants, 23 reported having had a fall within the last 12 months and 15 reported having had a fall within the last 6 months. Of the 23 participants reporting a fall in the last 12 months, five reported having two or more falls and eight reported having an injury from a fall. Of the 15 participants who reported having a fall within the last 6 months, 2 reported having two or more falls. Mean scores for those who reported a fall in the previous 6 months were higher (88.3) than the mean scores of those who reported not having a fall (82.8). This fits with research that showed those with a history of falls had a greater awareness of physiological deficits (Delbaere et al., 2010; de Souza et al., 2022; Kiyoshi-Teo et al., 2020). This could be that individuals who have had falls become more aware of their deficits and are provided fall risk education (Francis-Coad et al., 2021). Those with greater deficits are more AGE-RELATED PHYSIOLOGICAL CHANGES 36 aware of their impairments and may show greater knowledge of age-related changes (KiyoshiTeo et al., 2020; Lee et al., 2022). Other research showed that older adults were more aware of deficits but did not attribute those deficits to age-related changes (Sabatini et al., 2022). Though this study showed a correlation to having falls with increased knowledge, it is important to understand the need to educate older adults on age-related changes prior to having a fall. The individuals in this study may have had more knowledge after having a fall due to the need to prevent further falls. Knowledge of age-related changes may be needed to prevent falls rather than waiting until an individual falls to provide education. Objective 4: Knowledge and Participant Demographics Participant demographics of age, race, gender, marital status, community setting, education level, and income level were used to determine correlations between knowledge level and demographics. Age and education were the only two demographics with statistically significant correlations with knowledge level. Participants ranged in age from 50 to 86 years old, with an average age of 62. Participants between 50 and 64 years had a higher mean knowledge score (85.2) than participants over 65 years (81.1). Participant knowledge scores showed a statistically significant decrease with older age, demonstrating less knowledge of age-related physiological changes in older adults. This was not consistent with one study that found greater awareness of age-related changes in individuals over age 70 (Diehl et al., 2021). There was another study that did show similar findings with knowledge of muscle loss with aging where participants aged 65 to 74 had higher knowledge than those aged 75 to 84 (Lee et al., 2023). Studies have shown that awareness of the effects of aging increases after age 65, but that younger adults view age-related changes as occurring at an earlier point in life compared to older adults (Diehl et al., 2021; Lineweaver et al., 2018). Interestingly, one study found that older AGE-RELATED PHYSIOLOGICAL CHANGES 37 adults recognized they had a decline in function; however, those participants did not attribute that decline to age-related changes (Sabatini et al., 2022). This could be attributed to older adults lower knowledge of age-related physiological changes. This further indicates the need for greater education on age-related physiological changes, how those changes can lead to fall risk, and what can be done to prevent falls based on those changes. Education level significantly correlated with knowledge of age-related physiological changes. Those with a high school diploma or general education development equivalent or lower had the lowest mean scores on the KAPCQ. Each education level category increased correspondingly to an increase in mean score on the KAPCQ. Other studies collected information on participant education level but did not specifically report on correlations between education level and awareness of age-related changes. Other studies have shown a significant correlation between higher levels of education and higher levels of awareness or knowledge about age-related changes (Diehl & Wahl, 2010; Lee et al., 2023; Lineweaver et al., 2018; Sabatini et al., 2022). Lower education levels were also associated with lower levels of physical activity (Lin et al., 2011). This information is important to be able to target individuals with lower levels of education and ensure the information is presented at a level that is appropriate for their education level. Race, gender, marital status, community setting, and income level did not have statistically significant correlations with knowledge level. In this study, only 7% of participants were of diverse racial backgrounds; therefore, it did not allow for an adequate measure of knowledge level relative to race. No significant differences were found between the mean scores of men and women, and no correlations between knowledge level and gender. This is consistent with other research that found no difference between perceptions of aging among men and AGE-RELATED PHYSIOLOGICAL CHANGES 38 women (Lee et al., 2023; Lineweaver et al., 2018). Other studies have indicated lower awareness levels of age-related changes associated with socioeconomic status combining education and income (Sabatini et al., 2020; Stijntjes et al., 2017). Objective 5: Predictive Variable of Knowledge Scores Knowledge of age-related physiological changes was predicted through a regression model by participants age and education level. The regression model did not show a predictive correlation with fall history, this could be attributed to a small sample size reporting fall history. As detailed above, there were inverse relationships with increased age having lower knowledge levels and higher education levels having higher knowledge levels. This is consistent with previous research that has shown higher levels of awareness and physical activity level with younger populations and those with higher levels of education (Diehl & Wahl, 2010; Lin et al., 2011; Lineweaver et al., 2018; Sabatini et al., 2022; Stijntjes et al., 2017). This predictive model is important to appropriately target education about age-related physiological changes associated with aging. Limitations Primary limitations of this study were sample size, convenience sampling, limited diversity of participants, limited participant fall risk information, and the use of a novel questionnaire. The study primarily used convenience and snowball sampling to recruit participants. This yielded enough participants to power the study but was at the minimal level for a study with a medium effect size. This recruitment type also led to low diversity within the sample population. Only 7% of the participants reported racial diversity thus the study could not adequately determine knowledge of age-related physiological changes in diverse populations. AGE-RELATED PHYSIOLOGICAL CHANGES 39 Further, only 2% of participants in this study reported being under the poverty line and only 9% reported living in urban areas. Only 23 of the participants reported having a fall in the past 12 months and 15 in the last 6 months. This provided a limited sample for the study to determine the relationship between fall history and knowledge levels of age-related physiological changes. A larger sample of participants with a fall history may show a significant correlation to predict knowledge of agerelated changes based on participants fall history. Though the study asked questions about the number of falls and a history of injury from falls, this did not provide sufficient data to understand the participants fall risk level. A standardized fall assessment questionnaire may provide better information about knowledge level and fall risk. Lastly, the KAPCQ questionnaire was created for this study. Reliability of the questionnaire was determined as data was collected. Though the questionnaire showed a high internal consistency, this was not known at the start of the study. A more established assessment may have yielded different results; however, there were no established questionnaires of this type. Conclusion Falls are a major health concern in the United States with an increasing population of adults aged 65 and older (Caplan & Rabe, 2023). More than 14 million of these older adults sustain a fall each year (CDC, 2023). Knowledge of age-related physiological changes that contribute to falls may play a role in the prevention of falls; thus, it is important to understand the type of knowledge and the target population needed to prevent falls. This study found gaps in knowledge about protein metabolism as well as cellular changes that are a normal part of the aging process. Addressing these knowledge gaps could promote increased protein intake and AGE-RELATED PHYSIOLOGICAL CHANGES 40 participation in exercise programs that target increased strength in adults as they age. This study also found decreased knowledge levels in individuals with lower education levels, thus it is important to target populations with lower education levels and ensure information is provided at the appropriate comprehension level. Participants also demonstrated a lower level of knowledge about changes in sensation, balance, and the effects of hormone changes on strength. These are areas that would also be beneficial for fall prevention education programs. Understanding gaps in knowledge will allow fall prevention programs to better tailor education to meet the needs of the participants. Educational programs should prioritize addressing these knowledge gaps in populations with lower education levels and reaching a diverse audience. Future Research Future research should focus on knowledge levels in a greater diversity of racial and socioeconomic populations, comparing knowledge to standardized fall risk assessments, and determining the effects of increased knowledge on fall prevention. A study with a larger sample size, including a more diverse population, would yield more valid results on differences in knowledge levels between demographic groups. This type of research may show statistical differences in knowledge levels among individuals of different racial backgrounds and different socioeconomic backgrounds. There are many aspects of fall risk that are important to understand, among them are an individuals knowledge about fall risk and the ability to prevent falls. Age-related changes in hormones, cellular structure, and muscle loss lead to decreased balance and instability, increasing ones risk for falling (Holloszy, 2000; Wilkinson et al., 2018). Understanding these changes allows one to implement strategies to improve strength and stability to decrease fall risk AGE-RELATED PHYSIOLOGICAL CHANGES 41 (Holloszy, 2000; Wilkinson et al., 2018). If individuals understand how changes within their body lead to fall risk, they may be willing to make changes to combat the effects of aging and reduce their fall risk. Further research needs to be conducted to see if older adults who are given more education about age-related physiological changes implement that education and make necessary lifestyle changes to reduce fall risk. Knowledge of age-related changes should also be compared with standardized fall risk assessments to better determine a correlation between knowledge level and fall risk. This study has shown a weak but statistically significant correlation between fall history and knowledge of age-related changes. Qualitative studies may be helpful to better determine an individual's knowledge of age-related changes and how that knowledge impacts fall risk and fall prevention strategies. Longitudinal studies should be conducted to determine the effects of increasing a participants knowledge of age-related changes on fall prevention participation and reduction in fall risk. AGE-RELATED PHYSIOLOGICAL CHANGES 42 References Adamo, D. E., Alexander, N. B., & Brown, S. H. (2009). 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Journal of Targeting, Measurement and Analysis for Marketing, 17, 139-142. https://doi.org/10.1057/jt.2009.5 Sabatini, S., Silarova, B., Martyr, A., Collins, R., Ballard, C., Anstey, K. J., Kim, S., & Clare, L. (2020). Associations of awareness of age-related change with emotional and physical well-being: A systematic review and meta-analysis. Gerontologist, 60(6), e477-e490. https://doi.org/10.1093/geront/gnz101 Sabatini, S., Ukoumunne, O. C., Ballard, C., Collins, R., Corbett, A., Brooker, H., & Clare, L. (2022). Exploring awareness of age-related changes among over 50s in the UK: Findings from the PROTECT study. International Psychogeriatrics, 34(9), 789-803. https://doi.org/10.1017/S104161022100123X Saxon, S. V., Etten, M. J., & Perkins, E. A. (2022). Physical change and aging: A guide for the helping professions (7th ed.). Springer. AGE-RELATED PHYSIOLOGICAL CHANGES 48 Stijntjes, M., Aartsen, M. J., Taekema, D. G., Gussekloo, J., Huisman, M., Mesker, C. G. M., de Craen, A. J. M., & Maier, A. B. (2017). Temporal relationship between cognitive and physical performance in middle-aged to oldest old people. Journals of Gerontology: Medical Sciences, 72(5), 662-668. https://doi.org/10.1093/gerona/glw133 United States Census Bureau. (2020, June 25). U.S. census bureau releases 2019 population estimates by demographic characteristics. https://www.census.gov/newsroom/pressreleases/2020/65-older-population-grows.html United States Department of Health. (2021). Regulations, policy, & guidance: 2018 requirements (2018 Common Rule). https://www.hhs.gov/ohrp/regulations-andpolicy/regulations/45-cfr-46/revised-common-rule-regulatory-text/index.html#46.104 Voelcker-Rehage, C. (2008). Motor-skill learning in older adults A review of studies on agerelated differences. European Review of Aging Physical Activity, 5, 5-16. https://doi.org/10.1007/s11556-008-0030-9 Volpi, E., Nazemi, R., & Fujita, S. (2004). Muscle tissue changes with aging. 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The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Aging Research Reviews, 47, 123-132. https://doi.org/10.1016/j.arr.2018.07.005 Windsor, T. D., Abbott, M. J., Cations, M., Howard, A. J., & Wilton-Harding, B. (2022). Subjective perceptions of age-related gains buffer negative associations of perceived agerelated losses with health, well-being, and engagement. International Journal of Behavior Development, 46(2), 118-124. https://doi.org/10.1177/01650254211039025 Witzel, D. D., Turner, S. G., & Hooker, K. (2022) Self-perceptions of aging moderate associations of within- and between-persons perceived stress and physical health symptoms. Journal of Gerontology: Psychological Sciences, 77(4), 641-651. https://doi.org/10.1093/geronb/gbab228 Woodhead, E. L., & Yochim, B. (2022). Adult development and aging: A foundational geropsychology knowledge competency. Clinical Psychology: Science and Practice, 29(1), 16-27. https://doi.org/10.1037/cps0000048 Zammit, A. R., Robitaille, A., Piccinin, A. M., Muniz-Terrera, G., & Hofer, S. M. (2019). Associations between aging-related changes in grip strength and cognitive function in older adults: A systematic review. Journal of Gerontology: Medical Sciences, 74(4), 519527. https://doi.org/10.1093/gerona/gly046 AGE-RELATED PHYSIOLOGICAL CHANGES Table 1 Participant Demographics Characteristic N % Gender Male 40 33.1 Female 81 66.9 Level of education Some HS or less 1 .5 HS diploma or GED 12 9.9 Some college, no degree 20 16.5 Associates or tech degree 23 19.0 Bachelors degree 30 24.8 Graduate/profess degree 35 28.9 Race White/Caucasian 112 96.4 Black/African American 5 2.6 Native American 1 .5 Asian 1 .5 Native Hawaii/Pacific Island 1 .5 Marital status Married 95 79.2 Widowed 7 5.8 Divorced 17 14.2 Never married 1 .8 Community setting Urban 11 9.1 Suburban 65 53.7 Rural 44 36.4 Senior community 1 .8 Income level Less than $25,000 2 1.9 $25,000 - $49,000 17 15.7 $50,000 $74,000 25 23.1 $75,000 $99,000 16 14.8 $100,000 $149,000 26 24.1 $150,000 and higher 22 20.4 Preferred not to answer 13 Note. HS = High School; GED = General Education Development 50 AGE-RELATED PHYSIOLOGICAL CHANGES 51 Table 2 Knowledge of Age-Related Physiological Changes Questionnaire Outcomes Mdna 25th percentile 75th percentile Risk for falls 4 3 5 Cell function 4 3.5 5 Cell production 4 3 5 Loss of sensation 4 3 4 Proprioception 3 2 4 Hormones women 5 4 5 Hormones men 5 4 5 Hormones and strength 4 4 5 Protein for strength 5 4 5 Protein loss 4 3 5 Decline in muscle mass 5 4 5 Loss of muscle after age 40 4 4 5 Age effect on vision 5 4 5 Vision and balance 4 4 5 Inner ear changes 4 3 4 Inner ear related to balance 4 4 5 Loss of balance 4 3 4 Decreased stability 4 4 4.5 Increased risk for falls 4 4 5 Reaction time 4 4 5 Question topic Note. a Median scores for level of agreement for each item on a scale of 1 to 5, with 5 being the highest level of agreement. AGE-RELATED PHYSIOLOGICAL CHANGES 52 Table 3 Knowledge of Age-Related Physiological Changes Questionnaire (N = 121) Question Topics Risk for falls SA A NA/D D SD Missing Cell functions a SA A NA/D D SD Cell production a SA A NA/D D SD Loss of sensation SA A NA/D D SD Proprioception SA A NA/D D SD Hormones women SA A NA/D D SD Hormones men SA A NA/D N % 37 49 11 12 11 1 30.6 40.5 9.1 9.9 9.1 0.8 45 46 12 13 5 37.2 38.0 9.9 10.47 4.1 36 48 20 13 4 29.8 39.7 16.5 10.7 3.3 20 46 40 13 2 16.5 38.0 33.1 10.7 1.7 9 42 39 19 12 7.4 34.7 32.2 15.7 9.9 89 27 4 0 1 73.6 22.3 3.3 0 0.8 63 49 5 52.1 40.5 4.1 AGE-RELATED PHYSIOLOGICAL CHANGES D SD Hormones and strength a SA A NA/D D SD Protein for strength SA A NA/D D SD Protein loss a SA A NA/D D SD Decline in muscle mass SA A NA/D D SD Loss of muscle after age 40 SA A NA/D D SD Age effect on vision a SA A NA/D D SD Vision and balance SA A NA/D D SD Inner ear changes SA 53 2 2 1.7 1.7 50 45 15 9 2 41.3 37.2 12.4 7.4 1.7 83 33 2 1 2 68.6 27.3 1.7 0.8 1.7 33 52 19 13 4 27.3 43.0 15.7 10.7 3.3 69 47 3 1 1 57.0 38.8 2.5 0.8 0.8 43 64 7 6 1 35.5 52.9 5.8 5.0 0.8 80 32 4 3 2 66.1 26.4 3.3 2.5 1.7 53 45 13 6 3 43.8 37.2 10.7 5.0 2.5 19 15.7 AGE-RELATED PHYSIOLOGICAL CHANGES 54 A 61 50.4 NA/D 36 29.8 D 3 2.5 SD 2 1.7 Inner ear related to balance SA 39 32.2 A 53 43.8 NA/D 25 20.7 D 2 1.7 SD 2 1.7 Loss of balance SA 22 18.2 A 67 55.4 NA/D 20 16.5 D 10 8.3 SD 2 1.7 Decreased stability SA 30 24.8 A 71 58.7 NA/D 14 11.6 D 4 3.3 SD 2 1.7 Increased risk for falls SA 54 44.6 A 56 46.3 NA/D 7 5.8 D 2 1.7 SD 2 1.7 Reaction time SA 55 45.5 A 53 43.8 NA/D 8 6.6 D 2 1.7 SD 3 2.5 Note. SA = strongly agree; A = agree; NA/D = neither agree nor disagree; D = disagree; SD = strongly disagree. a Reverse coded to show level of knowledge AGE-RELATED PHYSIOLOGICAL CHANGES 55 Table 4 Correlation Between Knowledge of Age-Related Physiological Questionnaire Scores and Demographics (N = 121) Demographics r p Age -.25 .007 Gender .02 .807 Education level .32 <.001 Race -.01 .918 Marital status -.08 .414 Community setting .02 .851 Income level .15 .119 AGE-RELATED PHYSIOLOGICAL CHANGES 56 Table 5 Multilinear Regression for Knowledge of Age-Related Physiological Questionnaire (N = 110) Model B (Constant) SE t p 95% CI Lower Upper 103.00 7.96 12.94 < .001 87.21 118.79 -0.30 0.11 -2.67 .009 -0.53 -0.08 < HS, HS/GED -8.04 3.02 -2.66 .009 -14.02 -2.05 Some college -4.70 2.73 -1.72 .088 -10.12 0.71 Associate or technical -2.12 2.60 -0.82 .417 -7.27 3.04 -0.30 2.35 -0.13 .900 -4.96 4.37 Age Education degree Graduate/professional degree Note. B = unstandardized coefficient; SE = standard error; HS = high school; GED = general education development. AGE-RELATED PHYSIOLOGICAL CHANGES 57 Appendix A Knowledge of Age-Related Changes and Physical Activity Survey Purpose of the Study The purpose of this study is to understand the knowledge that adults over the age of 50 years have regarding age-related changes to the body and how that knowledge relates to activity level, fall risk, and other factors such as age, sex, race, education, and income level. This study will help us to understand how your knowledge of common age-related changes can affect your ability to stay active and prevent falls. Consent Information Participation in this survey is voluntary, you may stop this survey at any time or skip any questions that you do not wish to answer. We will not ask for your name or other personally identifiable information. Your survey information will remain anonymous. If you would like us to provide you with some important educational information regarding fall prevention and exercises for improving your mobility you can optionally provide us with your email address. You do not need to complete the survey to receive this information, you can simply give us your email and we will send you the information. Your email address will not be given to any thirdparties or used for any other reason. I agree to participate in this survey acknowledging the risks and benefits of completing the survey and know that I can stop the survey at any time: YES or NO 1. What is your current age? ______ 2. What is your biological sex? a. Male b. Female 3. What type of community best describes where you live the majority of the year? a. Senior living community AGE-RELATED PHYSIOLOGICAL CHANGES 58 b. Urban area c. Suburban area d. Rural area 4. What race and/or ethnicity do you identify yourself as? a. Asian or Asian American b. African or African American c. Hispanic or Latino d. Native American e. Caucasian f. Other 5. What is your highest level of education? a. 8th grade or less b. Some high school c. High school graduate d. Some college e. Associates degree f. Bachelors degree g. Masters degree h. Doctoral degree 6. What is your annual income level? a. < $10,000 b. $10,000 30,000 c. $30,000 60,000 d. $60,000 100,000 e. > $100,000 7. What is or was your most recent occupation? _____________________ Which of the following best describes the type of work you do or most recently did? a. Primarily sitting while working b. Mixture of sitting and light activity while working c. Primarily light activity while working d. Mixture of light and heavy activity while working e. Primarily heavy activity while working 8. What is your marital status? a. Married b. Single c. Widowed d. Divorced e. Other 9. How do you rate your level of health compared to 5 years ago? a. Excellent AGE-RELATED PHYSIOLOGICAL CHANGES 59 b. Good c. Fair d. Poor e. Very poor 10. How would you describe your ability to walk in your home? a. Excellent b. Good c. Fair d. Poor e. Very poor 11. How would you describe your ability to walk in your community? a. Excellent b. Good c. Fair d. Poor e. Very poor 12. Do you have a neurological condition that affects your ability to walk, such as a stroke, MS, spinal cord injury, or other disorder? YES or NO a. If so, what type of neurological condition? _____________________________ 13. Do you use an assistive device when walking in your home or community (cane, walker, crutches, wheelchair)? YES or NO a. If yes, what type of device? _____________________________ 14. Have you been diagnosed with one or more of the following medical conditions? (check all that apply) a. Diabetes b. Hypertension (high blood pressure) c. High cholesterol d. Heart disease e. COPD (chronic obstructive pulmonary disease) f. Other (please list) __________________________ 15. Have you had a fall in the last 6 months, defined as an unintentional loss of balance resulting in a fall to the ground or to a lower surface? YES or NO a. If yes how many? ____ 16. Have you had a fall in the last 12 months, defined as an unintentional loss of balance resulting in a fall to the ground or to a lower surface? YES or NO a. If yes how many? ____ 17. Have you ever had an injury from a fall, defined as an unintentional loss of balance resulting in a fall to the ground or to a lower surface causing bodily damage that lasted for more than one week? YES or NO AGE-RELATED PHYSIOLOGICAL CHANGES 60 a. If yes, what kind of injury? __________________ 18. Have you ever participated in a fall prevention program? YES or NO 19. In the last 3 weeks have you been in the hospital for more than one night? YES OR NO 20. On a scale from 1 10 (10 being the best) how would you rate your overall health compared to your peers? ____ 21. Are you receiving help from another person to fill out this survey? ____ YES OR NO Knowledge of Physiological Age-Related Changes Questionnaire Circle one of the following for each of the questions below (Completely agree, agree, agree somewhat, disagree, completely disagree) 22. Individuals over the age of 65 are at a higher risk of having falls compared to individuals under the age of 65. Strongly agree agree neither agree/disagree disagree strongly disagree 23. Cells within the body function the same way in advanced age as they do in young age. Strongly agree agree neither agree/disagree disagree strongly disagree 24. Age does not influence the ability for cells to reproduce. Strongly agree agree neither agree/disagree disagree strongly disagree 25. With advanced aging it is common to lose some of the ability to feel with the tips of the fingers and with the feet. Strongly agree agree neither agree/disagree disagree strongly disagree 26. It is a normal part of the aging process to have a more difficult time knowing where the feet are when walking without looking at them. Strongly agree agree neither agree/disagree disagree strongly disagree 27. It is normal for women to have changes in hormones through different stages of life. Strongly agree agree neither agree/disagree disagree strongly disagree 28. It is normal for men to have changes in hormones through different stages of life. Strongly agree agree neither agree/disagree disagree strongly disagree 29. Changes in hormones do not affect muscular strength. AGE-RELATED PHYSIOLOGICAL CHANGES Strongly agree agree neither agree/disagree 61 disagree strongly disagree 30. Protein is important for the health of muscles and for strength as we age. Strongly agree agree neither agree/disagree disagree strongly disagree 31. The bodys ability to use protein for muscular strength does not change with aging. Strongly agree agree neither agree/disagree disagree strongly disagree 32. Overall muscle mass declines with aging. Strongly agree agree neither agree/disagree disagree strongly disagree 33. Individuals over the age of 40 start to lose muscular strength as part of the aging process. Strongly agree agree neither agree/disagree disagree strongly disagree 34. Age does not have an effect on vision. Strongly agree agree neither agree/disagree disagree strongly disagree 35. Loss of vision effects balance. Strongly agree agree neither agree/disagree disagree strongly disagree 36. Changes within the inner ear are common with aging. Strongly agree agree neither agree/disagree disagree strongly disagree 37. Age-related changes in the inner ear cause a decrease in balance. Strongly agree agree neither agree/disagree disagree strongly disagree 38. Loss of balance is expected with aging. Strongly agree agree neither agree/disagree disagree strongly disagree 39. Decreased stability when walking is associated with aging. Strongly agree agree neither agree/disagree disagree strongly disagree 40. Normal changes associated with aging can increase risk for falls. Strongly agree agree neither agree/disagree disagree strongly disagree 41. The ability to react quickly when falling or to catch an object that is falling is lessened as one ages. Strongly agree agree neither agree/disagree disagree strongly disagree PASE (Washburn et al., 1993) Leisure Time Activity 1. Over the past 7 days, how often did you participate in sitting activities such as reading, watching TV, or doing handcrafts? AGE-RELATED PHYSIOLOGICAL CHANGES (0) Never (1) Seldom 62 (2) Sometimes (3) Often 1a. On average, how many hours per day did you engage in these sitting activities? (1) Less than 1 hour (2) 1-2 hours (3) 2-4 hours (4) More than 4 hours 2. Over the past 7 days, how often did you take a walk outside your home or yard for any reason? For example, for fun or exercise, walking to work, walking the dog, etc (0) Never (1) Seldom (2) Sometimes (3) Often 2a. On average, when taking a walk, how many hours did you spend walking? (1) Less than 1 hour (2) 1-2 hours (3) 2-4 hours (4) More than 4 hours 3. Over the past 7 days, how often did you engage in light sport or recreational activities such as bowling, golf with a cart, shuffleboard, fishing from a boat or pier or other similar activities? (0) Never (1) Seldom (2) Sometimes (3) Often 3a. Please list these activities: 3b. On average, how many hours did you engage in these light sport or recreational activities on the days you completed them? (1) Less than 1 hour (2) 1-2 hours (3) 2-4 hours (4) More than 4 hours 4. Over the past 7 days, how often did you engage in moderate sport and recreational activities such as doubles tennis, ballroom dancing, hunting, ice skating, golf without a cart, softball or other similar activities? (0) Never (1) Seldom (2) Sometimes (3) Often 4a. Please list these activities: 4b. On average, when engaging in moderate activities, how many hours did you engage in these moderate sport or recreational activities? (1) Less than 1 hour (2) 1-2 hours (3) 2-4 hours (4) More than 4 hours 5. Over the past 7 days, how often did you engage in strenuous sport and recreational activities such as jogging, swimming, cycling, singles tennis, aerobic dance, skiing (downhill or cross-country) or other similar activities? (0) Never (1) Seldom (2) Sometimes (3) Often 5a. Please list these activities: 5b. On average, how many hours per activity did you engage in these strenuous sport or recreational activities? (1) Less than 1 hour (2) 1-2 hours (3) 2-4 hours (4) More than 4 hours AGE-RELATED PHYSIOLOGICAL CHANGES 63 6. Over the past 7 days, how often did you do any exercises specifically to increase muscle strength and endurance, such as lifting weights or pushups, etc.? (0) Never (1) Seldom (2) Sometimes (3) Often 6a. Please list these activities: 6b. On average, how many hours per session did you engage in these strenuous sport or recreational activities? (1) Less than 1 hour (2) 1-2 hours (3) 2-4 hours (4) More than 4 hours Household Activity 7. During the past 7 days, have you done any light housework, such as dusting or washing dishes? YES or NO 8. During the past 7 days, have you done any heavy housework or chores, such as vacuuming, scrubbing floors, washing windows, or carrying wood? YES or NO 9. During the past 7 days, did you engage in any of the following activities? Please answer YES or NO for each item. a. Home repairs like painting, wallpapering, electrical work, etc. YES or NO b. Lawn work or yard care, including snow or leaf removal, wood chopping, etc. YES or NO c. Outdoor gardening: YES or NO d. Caring for another person, such as children, dependent spouse, or another adult: YES or NO Work-related Activity 10. During the past 7 days, did you work for pay or as a volunteer? YES or NO 10a. How many hours per week did you work or volunteer: _____ hours per week 10b. What type of work did you do: _______________________________________ AGE-RELATED PHYSIOLOGICAL CHANGES Appendix B February 27, 2023 Sara Young University of Indianapolis This letter provides permission to use the Physical Activity Scale for the Elderly developed by our group (Washburn et al, J Clin Epidemiol, 1993 Feb;46(2);153-162) in your study on the association between knowledge of age-related physiological changes and fall risk and physical activity. Best for success with the project. Sincerely, Richard A. Washburn, PhD, FACSM Senior Research Scientist Division of Physical Activity and Weight Management Department of Internal Medicine The University of Kansas Medical Center & The University of Kansas-Lawrence 64 AGE-RELATED PHYSIOLOGICAL CHANGES 65 Appendix C Drawing Contact Information Thank you for taking the time to participate in this survey. Would you like to be entered into a drawing for a chance to win a $10 Amazon e-gift card? Yes or No (Respondents who select Yes will be directed to the next page) Please enter your name: _____________________________________ Please enter an email address where you can be contacted if you win one of the $10 Amazon e-gift cards. Your email will not be sold to any third-party vendors, you will not be solicited, and the email will only be used for drawing purposes. Email: _____________________________________ AGE-RELATED PHYSIOLOGICAL CHANGES 66 Appendix D Recruitment Handout My name is Sara Young, I am a Doctor of Health Science student at the University of Indianapolis. I am completing my dissertation on the impact of the knowledge of age-related physical changes on fall risk and levels of physical activity. I am reaching out to adults over the age of 50 years. The purpose of this research is to determine whether there is an association between fall risk and physical activity compared with an individuals knowledge of age-related physical changes. This survey is completely voluntary and anonymous. Individuals who choose to participate in this survey may stop at any time and are not obligated to complete the survey. Please feel free to share this survey with friends and family. Participants are welcome to enter their name and contact information separate from the survey for a chance to win one of twenty $10 Amazon e-gift card. The survey will take approximately 15 minutes to complete. To participate in the study, you can access an electronic version of the survey using the QR code below. Paper copies of the survey are available upon request. (QR code to Qualtrics survey) (IRB approval information will go here) If you have questions about this research, please contact Sara Young at young@uindy.edu or Dr. Elizabeth Moore at moorees@uindy.edu. Thank you, Sara Young University of Indianapolis 937-470-41818 young@uindy.edu AGE-RELATED PHYSIOLOGICAL CHANGES 67 Appendix E Sample Social Media Post My name is Sara Young, I am a Doctor of Health Science student at the University of Indianapolis. I am completing my dissertation on the impact of the knowledge of age-related physical changes on fall risk and levels of physical activity. I am reaching out to adults over the age of 50 years who may be interested in completing a brief survey. The purpose of this research is to determine whether there is an association between fall risk and physical activity compared with an individuals knowledge of age-related physical changes. This survey is completely voluntary and anonymous. Individuals who choose to participate in this survey may stop at any time and are not obligated to complete the survey. Please feel free to share this survey with friends and family. Participants are welcome to enter their name and contact information separate from the survey for a chance to win one of twenty $10 Amazon e-gift card. The survey will take approximately 15 minutes to complete. To participate in the survey, click on the following link (Link to Qualtrics survey) (IRB approval information will go here) If you have questions about this research, please contact Sara Young at young@uindy.edu or Dr. Elizabeth Moore at moorees@uindy.edu. AGE-RELATED PHYSIOLOGICAL CHANGES 68 Appendix F Sample Email To Whom it May Concern, My name is Sara Young, I am a Doctor of Health Science student at the University of Indianapolis. I am completing my dissertation on the impact of the knowledge of age-related physical changes on fall risk and levels of physical activity. I am reaching out to adults over the age of 50 years who may be interested in completing a brief survey. The purpose of this research is to determine whether there is an association between fall risk and physical activity compared with an individuals knowledge of age-related physical changes. This survey is completely voluntary and anonymous. Individuals who choose to participate in this survey may stop at any time and are not obligated to complete the survey. Please feel free to share this survey with friends and family. Participants are welcome to enter their name and contact information separate from the survey for a chance to win one of twenty $10 Amazon e-gift card. The survey will take approximately 15 minutes to complete. To participate in the survey, click on the following link (Link to Qualtrics survey) (IRB approval information will go here) If you have questions about this research, please contact Sara Young at young@uindy.edu or Dr. Elizabeth Moore at moorees@uindy.edu. Thank you, Sara Young University of Indianapolis 937-470-41818 young@uindy.edu  ...

... Effect on Self-Perception of Competency Using Simulation to Teach the Quality Improvement (QI) Principle of Change Management Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Jennifer J. Skelton, MBA, CPHQ Copyright December 5, 2023 By: Jennifer J. Skelton, MBA, CPHQ All rights reserved Approved by: Heidi H. Ewen, Ph.D., FGSA, FAGHE Committee Co-Chair ______________________________ Laura Santurri, Ph.D., MPH, CPH, aPHR Committee Co-Chair ______________________________ O. Elijah Asagbra, Ph.D., MHA, CPHQ Committee Member ______________________________ Elizabeth S. Moore, Ph.D. Committee Member ______________________________ Accepted by: Lisa Borrero, Ph.D., FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, Ph.D. Dean, College of Health Sciences University of Indianapolis ______________________________ QUALITY IMPROVEMENT EDUCATION 1 Effect on Self-Perception of Competency Using Simulation to Teach the Quality Improvement (QI) Principle of Change Management Jennifer J Skelton Department of Interprofessional Health & Aging Studies, University of Indianapolis Author Note I have no known conflict of interest to disclose. Correspondence concerning this report should be addressed to Jennifer J. Skelton, 11661 N. Old State Rd. Gentryville, IN 47537. Email: skeltonj@uindy.edu QUALITY IMPROVEMENT EDUCATION 2 Abstract Background: For many U.S. healthcare management positions, a master's prepared individual is customary, but the opportunity to minimize diversity in graduate preparation on critical Quality Improvement (QI) skill sets remains. Objective: This study aimed to determine if there was a significant difference in the Self-Perception of Change Management Competency (SPCMC) after administering the Change Management Simulation Power and Influence V3 (Judge & Hill, 2020). This study also aimed to compare the competency scores of healthcare industry students and non-healthcare industry students after the simulation. Method: Study participants included a convenience sample of graduate business administration students from a mid-sized midwestern university enrolled in the programs required management course. Pretest and posttest data analysis included paired t-tests. Two-group comparison data analysis included the independent ttest. Additional competency statement analysis was performed, and trends were identified. Results: This study found a statistical difference between mean pretest and posttest sum scores on the SPCMC instrument. There was no difference in mean SPCMC posttest scores between non-healthcare industry and healthcare industry students. Discussion: The simulation intervention increased the self-perception of competency, demonstrating simulation was an effective teaching method. However, the simulation was least impactful in educating students on using change management tools to analyze employee acceptance, influence, or resistance and collaborating with coworkers to plan, carry out change, and create buy-in. The simulation notably improved healthcare industry students' self-perception of competency in discussing impacts and responses to workplace changes. Keywords: quality improvement, change management, healthcare administration, graduate education QUALITY IMPROVEMENT EDUCATION 3 Acknowledgments I usually am not a patient traveler, and "enjoy the journey" doesn't often resonate with me. However, my journey to complete a doctoral degree has been different due to the love and unwavering support of family, friends, and mentors. If I had received any resistance when I first announced my return to school, I might have abandoned the endeavor before even starting. But, my husband, Jeremy, who often believes in me more than I believe in myself, has never questioned this final destination, and I am eternally grateful for that. To my daughters (Samantha, Summer, and Shelby), my parents (Gary, Paula, and Nell), and my sister, Shannon, thank you for the many mental health check-ins along the way. We have shared lots of laughter and even some tears; I sincerely appreciate the emotional support. And to my Deaconess Women's Hospital colleagues (Lisa, Tina, Allie, Stephanie, Chris, and so many more) who didn't even hint they thought I might be crazy for trying, I sincerely thank you. I could not have come so far without the new supportive relationships I have built in the last few years. It has been a blessing to collaborate with educators who have helped me uncover new passions for being a researcher and teacher: Dr. Dinko Bai, Dr. Jack Smothers, Ms. Teri Short, Dr. Julie Gahimer, Dr. Lisa Borrero, Dr. Diana Harrison, and Dr. Melanie Lambert. Your encouragement has changed me, and I promise to pay it forward. And finally, with most profound admiration and respect, I thank my dissertation committee members, Dr. Laura Santurri, Dr. Elizabeth Moore, Dr. Elijah Asagbra, and Dr. Heidi Ewen. Their consistent guidance and unending patience in answering my questions demonstrated that I was never alone on this journey. Each is a generous giver of their time and experience, and I will never forget this support. QUALITY IMPROVEMENT EDUCATION 4 Reflecting on the special people in my life, I recognize this achievement belongs to them as much as it does to me. And, on this day, I can genuinely say I enjoyed this journey. With the deepest gratitude, Jennifer J. Skelton December 5, 2023 QUALITY IMPROVEMENT EDUCATION 5 Table of Contents Abstract Acknowledgments Table of Contents List of Tables List of Figures Introduction Background Problem Statement Purpose Statement Hypotheses Significance of the Study Literature Review Stakeholders Definitions of Change Management Methods for QI Education Delivery Implementation Readiness Sustainability Resistance to Change Method Study Type and Study Design Participants Setting Data Demographic and Participant Characteristics Outcome Variables Operational Definition Instrument Procedures Recruitment Informed Consent Data Collection Intervention Data Management Statistical Analysis Results Discussion Study Limitations Future Research Opportunities Conclusion References Tables Figures Appendix 2 3 5 6 7 8 8 9 9 10 11 11 12 16 17 20 21 22 23 24 24 25 25 25 25 26 26 27 27 27 28 28 28 29 30 34 38 40 41 42 51 60 61 QUALITY IMPROVEMENT EDUCATION 6 List of Tables Table 1 Study Participant Demographic Characteristics (N = 96) Table 2 Comparing the Means of Two Related Groups (All Participants N=96) Table 3 Comparing the Means of Two Related Groups (Subgroup of Healthcare Industry Students N=20) Table 4 Differences Between Groups (Non-Healthcare Industry Students and Healthcare Industry Students) QUALITY IMPROVEMENT EDUCATION List of Figures Figure 1 Mean Pretest Scores by Question by Group 7 QUALITY IMPROVEMENT EDUCATION 8 Effect on Self-Perception of Competency Using Simulation to Teach the Quality Improvement (QI) Principle of Change Management It has been over 20 years since the Institute of Medicine released the report on healthcare quality in America, Crossing the Quality Chasm (2001), calling for redesigning healthcare processes to support improvement and reduce medical errors (Institute of Medicine, 2001). During this time, agencies have responded. In 2007, the Institute for Healthcare Improvement developed the Triple Aim framework to guide this redesign (Berwick et al., 2008). In 2014, proposals to expand this framework to a Quadruple Aim were introduced to recognize a fourth key contributor to quality: staff member burnout due to complex and ever-changing systems (Bodenheimer & Sinsky, 2014). Graduate programs have also responded to this call to action by increasing healthcare professionals' training and education in quality and safety. In 2016, a joint effort between the Commission on Accreditation of Healthcare Management Education (CAHME) and educators representing specialized quality and safety graduate programs met to begin talks to standardize this educational effort by creating program accreditation standards. According to Tekian et al. (2021), there are currently 25 patient safety and healthcare quality graduate programs worldwide, with 17 of the programs located in North America. In 2017, the National Association for Health Care Quality (NAHQ) defined the first set of standardized competencies for those working as healthcare quality professionals (NAHQ, 2017). Even with a continued focus on standardized competencies in quality and safety concentration graduate programs, there is still an opportunity to standardize further quality improvement (QI) education in other undergraduate and graduate programs in the United States. Although a master's degree has long been the standard credential for many healthcare management positions, the diversity in preparation results in various skill sets (Anderson & QUALITY IMPROVEMENT EDUCATION 9 Garman, 2018). According to Sadowski et al. (2018), a few graduate programs report interdisciplinary or interprofessional education in leadership training. Yet, interprofessional teamwork and a strong understanding of the psychology of change are required for QI work (Bonin, 2018). QI education provides an excellent opportunity to combine healthcare leadership training, change management, and team-building skills (Goodman et al., 2022). In a poll conducted by Porter et al. (2016), health administrators indicated that skillsets like power and influence were highly needed. These skillsets are also essential for elements of QI education such as process improvement and leadership. Since the proposal of the Quadruple Aim, burnout has continued to be a significant healthcare employee issue. Healthcare leaders must address the human side of change management in QI (Agency for Healthcare Research and Quality, 2017; Raudensk et al., 2020). Problem Statement The desire to meet the tenets of the Quadruple Aim and the increasing complexity of healthcare has created an opportunity to advance change management principles and tools among today's emerging healthcare leaders. There is the opportunity to provide a standardized educational intervention to students in graduate programs to allow them to learn change management tools for increased engagement and teamwork and to better support operational and clinical quality initiatives. Purpose Statement The purpose of the study was to identify effective ways to provide graduate program learners with feedback on the QI competencies of change management. QUALITY IMPROVEMENT EDUCATION 10 Hypotheses H0: There will be no significant difference between pretest and posttest QI assessment scores after administering the change management simulation educational intervention HA: There will be a significant difference between pretest and posttest QI assessment scores after administering the change management simulation educational intervention H0: There will be no significant difference between pretest and posttest QI assessment scores for students who work primarily in healthcare after administering the change management simulation educational intervention HA: There will be a significant difference between pretest and posttest QI assessment scores for students who work primarily in healthcare after administering the change management simulation educational intervention H0: There will be no significant difference in the posttest QI assessment scores between the students with work experience in the healthcare professional industry and the nonhealthcare professional industry group after administering the change management simulation educational intervention HA: There will be a significant difference in the posttest QI assessment scores between the healthcare professional industry student group and the non-healthcare professional industry student group after administering the change management simulation educational intervention Objectives 1. To determine if there was a significant difference in QI scores for all students, as measured by the Self-Perception of Change Management Competency Assessment after QUALITY IMPROVEMENT EDUCATION 11 completion of the Change Management Simulation Power and Influence V3 educational intervention 2. To determine if there was a significant difference in QI scores in a subgroup of healthcare industry graduate students, as measured by a Self-Perception of Change Management Competency Assessment after completion of the Change Management Simulation Power and Influence V3 educational intervention 3. Compare the QI skills assessment scores of healthcare and non-healthcare graduate students after completing the Change Management Simulation Power and Influence V3 educational intervention Significance of the Study This proposed study has provided valuable insight for healthcare institutions where human resources are finite. Leaders must know how to get the most value from their teams and make the most sustainable QI gains. Healthcare leaders, staff, patients, and health educators benefit from a standardized educational delivery method that improves change management skills and subsequently improves quality outcomes. Literature Review In the 1980s and 1990s, John P. Kotter led business leaders from many industries through the complex process of changing their organizations to achieve success (Kotter, 1995). Kotter (1995) observed that few corporate change efforts were successful, and most change efforts fell short. In 1995, in response to his observations, Kotter defined eight critical steps to transforming an organization through successful change management (Kotter, 1995). After 2001 and the renewed focus on quality improvement process redesign, healthcare systems and health researchers reiterated the similar difficulty of implementing and sustaining change through QUALITY IMPROVEMENT EDUCATION 12 healthcare quality improvement projects. Lennox et al. (2018) identified that many initiatives failed to maintain improvement long enough to realize the full benefits. Moussa et al. (2019) found that of 35 studies of healthcare practices utilizing change facilitators for implementing innovation into practice, 12 reported positive results, 3 reported non-significant results, and 20 reported mixed results. On a smaller project improvement scale, Carman et al. (2019) identified that step eight of Kotter's model, anchoring new approaches into the organization's culture, was a needed improvement for their clinics. There are many causes of change failure; however, the most common can be traced to resistance to change (Amarantou et al., 2017). A better understanding of the different types of resistance, as well as the behavior and motivation of the employees, can lead to new ways to encourage change compliance (Amarantou et al., 2017). While there is much focus on improving healthcare quality in the United States, there remains an opportunity to standardize change management education as a critical component of quality improvement education. Many stakeholders are interested in improving quality and sustaining meaningful change; thus, raising the competency levels in the change management skills of future leaders is necessary. Stakeholders Healthcare systems are interested in improving the quality of healthcare for patients. The Centers for Medicare & Medicaid Services (CMS) hospital value-based purchasing (VBP) program adjusts hospitals' payments for selected services based on the quality of care delivered. This federal government program incentivizes hospitals to eliminate or reduce medical errors, adopt evidence-based care standards, improve the patient experience, increase cost transparency, and lower costs (CMS, 2021). The program is funded by reducing hospitals' fiscal year base operating Medicare severity diagnosis-related group payments by 2% and then encouraging QUALITY IMPROVEMENT EDUCATION 13 hospitals to meet required thresholds to earn this back (CMS, 2021). Large health systems or hospitals with a substantial percentage of patients with Medicare insurance coverage can be the equivalent of millions of dollars or a significant portion of an operating budget. A well-prepared workforce must navigate this high-risk healthcare landscape, including leaders with change management skills as part of their quality improvement education. McKimm et al. (2020) recommend relationship-building partnerships between health administrators, health professionals, policymakers, communities, and academic institutions to meet this continuous need for quality improvement. Other government agencies are also key stakeholders. The Agency for Healthcare Research and Quality (AHRQ) is just one of 12 agencies within the Department of Health and Human Services (HHS) working to improve the quality of health in the U.S. (AHRQ, 2022). The AHRQ's mission is to produce evidence to make healthcare safer, of higher quality, equitable, affordable, and accessible (AHRQ, 2022). AHRQ has created guidance modules to inform and train leaders on the core competencies needed. Core competencies include checklists of questions for consideration and help healthcare decision-makers anticipate pitfalls and review past mistakes to overcome resistance to change (AHRQ, 2021). In addition to AHRQ, another government agency, the National Institutes of Standards and Technology (NIST), supports the Baldridge Performance Excellence program. The original goal of the Malcolm Baldridge National Quality Improvement Act of 1987 was to advocate for quality management as a critical component of sustaining business competitiveness in global markets. The scope was expanded to healthcare and educational organizations in 1999 (NIST, 2019). Self-assessment using the Malcolm Baldridge Award Criteria for Performance Excellence has become an effective tool for QUALITY IMPROVEMENT EDUCATION 14 organizations to self-evaluate their strategic strengths and weaknesses in the promotion of organizational change management (Ford & Evans, 2001). The Institute for Healthcare Improvement (IHI) is another agency that works to improve quality, safety, and value in healthcare by building practical improvement capabilities based on evidence in organizations associated with healthcare systems (IHI, 2022). Since 2006, IHI has had in place a framework to help guide healthcare leaders in the spread of local improvements to system-wide change (Massoud et al., 2006). A white paper advises leaders to consider spreading innovation throughout their organizations and develop a plan to assess readiness, decisionmaking, and infrastructure (Massoud et al., 2006). The changes from examining organizational processes can require changing organizational infrastructure, including process improvements, to achieve results (Ford & Evans, 2001). The National Association for Healthcare Quality (NAHQ) is essential in quality and process improvement education for healthcare professionals. This professional association works to bridge the gap between formal educational programs and workforce development. It has developed the NAHQ Healthcare Quality Competency Framework, which defines a set of standardized competencies for professionals working on quality improvement (Schrimmer et al., 2019). According to Schrimmer et al. (2019), healthcare leaders should understand the quality competencies necessary for safe, high-quality care in their organizations. The future of healthcare success relies on a coordinated competent workforce. Other key stakeholders in quality improvement education are the graduate healthcare programs that teach future healthcare leaders the necessary skills to lead in a complex, continuous improvement work environment. One such association, the Association of University Programs in Health Administration (AUPHA), is a global network of universities, faculty, and QUALITY IMPROVEMENT EDUCATION 15 organizations dedicated to improving healthcare through excellence in healthcare management education (AUPHA, 2022). AUPHA provides a self-study certification program encouraging university undergraduate programs to meet standardized criteria. The criteria require programs to address a healthcare leader's role in QI practices and outcomes, providing a solid QI foundation for emerging healthcare leaders (AUPHA, 2022). Also, university programs that offer master's or doctoral level degrees in health administration can join AUPHA as a member to network and share knowledge and expertise. The Commission on Accreditation of Healthcare Management Education (CAHME) is an accrediting body for graduate programs in healthcare management in the United States and Canada (CAHME, 2021). CAHME requires accredited programs to standardize by meeting rigorous leadership competency requirements to ensure graduates are well-prepared for management responsibilities (CAHME, 2021). However, many more non-CAHME accredited graduate management education programs develop leaders with degrees such as Master of Business Administration (MBA), Master of Health Administration (MHA), Master of Public Health (MPH), and Master of Science in Healthcare Administration (MS-HCA). In 2014, the American College of Healthcare Executives recognized the challenge of this lack of standardization among graduates and issued a policy statement endorsing accreditation as optimal (Anderson & Garman, 2018). However, Fick et al. (2017) found that health administration graduates were rated unfavorably by Fellow of the American College of Healthcare Executive (FACHE) credentialed U.S. hospital CEOs for a subset of skills, including change leadership. It is critical, then, to standardize and define change management competency. QUALITY IMPROVEMENT EDUCATION 16 Definitions of Change Management In human relations, change management competency is defined as the ability to create dissatisfaction with the status quo, communicate steps of a process toward change, and overcome resistance (Peacock, 2017). Student leaders most frequently learn three commonly used change models for improved competency; however, there are many models: Lewin's Three Stages of Change, Prosci's ADKAR Steps for Individual Change, and Kotter's Eight Steps for Change Management (Varkey & Antonio, 2010). Kurt Lewin, a physicist, wrote in 1947 about creating change with a three-step process. Any level of permanent change requires unfreezing the present, moving the group to a new level, and freezing the group at the new level (Hussain et al., 2018). Jeff Hiatt first published a white paper titled "The Perfect Change" in 1999 and, after continued research, published the Prosci ADKAR model (Prosci, n.d.). According to the ADKAR model, organizational change can only occur when individuals change. Thus, ADKAR is an acronym for the five outcomes the individual needs to achieve for the transition to be successful: awareness, desire, knowledge, ability, and reinforcement (Prosci, n.d.). John P. Kotter, the author of "Leading Change" in 1995, created the 8 Steps for Change model (Kotter, 1995). The model continues to evolve, and in 2012, four change principles supported eight enhanced change processes, now known as eight accelerators, that create a network for change (Kotter, 2012). The eight steps include: creating a sense of urgency, building a guiding coalition, forming a strategic vision and initiatives, enlisting a volunteer army, enabling action by removing barriers, generating short-term wins, sustaining acceleration, and instituting change (Kotter, 2012). Moosa et al. (2021) define change management as the processes involved in changing individual and team behavior or systems to bring about positive change at a personal, QUALITY IMPROVEMENT EDUCATION 17 organizational, or community level. The researchers used science mapping to perform a bibliographic analysis. They identified that research in the field of change management was on the rise and could be classified into broad categories: engineering, information, and communication technology, organizational aspects, leadership aspects, and human aspects. The results show a heavy focus on change management research in business and healthcare between 2008 and 2014. Moreover, during this time, there was greater attention to change management's organizational, human, and leadership aspects (Moosa et al., 2021). NAHQ focuses on personal competency development and defines change management competencies for the healthcare professional as the ability to draw people into support of changes required to achieve performance improvement outcomes (NAHQ, 2017). One component of a competent professional is the ability to communicate the vision, expectations, and improvement results to key stakeholders (NAHQ, 2017). To be considered advanced in this communication competency, one should be able to analyze stakeholder acceptance, influence, and resistance and then adapt communication styles and use human factor approaches to leverage or increase support (NAHQ, 2017). NAHQ recognizes that performance and process improvement are an organization's core functional responsibilities to achieve optimal performance levels. Miltner et al. (2021) found that 85% of NAHQ members report working in the performance and process improvement domain. Thus, it is relevant for all healthcare leaders to support these efforts by having the skills to build awareness of the need to change, communicate the vision, monitor accountability, and provide the tools for effective change implementation (NAHQ, 2017). Methods For QI Education Delivery A variety of methods have been used to deliver quality improvement education. Goodman et al. (2022) developed an in-person experiential learning model leading to positive QUALITY IMPROVEMENT EDUCATION 18 learning outcomes for an interprofessional group of healthcare students. In this collaborative partnership, learners complete online modules and are then coupled with mentors who oversee their participation in team-based work (Goodman et al., 2022). Project-based quality improvement curriculums have also been utilized. Through projects, participants in the clinical safety and effectiveness course at the Long School of Medicine at the University of Texas Health San Antonio achieved changes in clinical practice, patient benefits, and positive returns on investment for the institution. This educational program format has been sustained for over 10 years (Patterson et al., 2022). Chelagat et al. (2020) utilized a leadership development program, Leading High-performing Healthcare Organizations (LeHHO), which integrated classroom knowledge alongside real health delivery challenge projects. This form of leadership training and coaching built around health improvement projects improved key quality outcomes (Chelagat et al., 2020). They also found these outcome improvements to be sustained for over five years (Chelagat et al., 2020). Another educational method, simulation, can be used to assess the performance and competency of individuals and teams and has received attention in healthcare over the last twenty years (Gaba, 2007). The technology available in simulation can range from non-technical verbal scenario discussions to high-tech use of mannequins for mastering complex clinical trauma and surgical scenarios (Gaba, 2007; Pringle et al., 2010). Simulation can also provide a systematic training and assessment tool for non-clinical healthcare personnel (Gaba, 2007). Two early meta-analysis studies demonstrated evidence that supported using simulations as an additional tool to instruct healthcare management principles (Pringle et al., 2010). Another benefit of simulation and other competency-based education (CBE) is that it allows students to advance at their own pace and provides them with personal and flexible learning opportunities QUALITY IMPROVEMENT EDUCATION 19 (Foster & Jones, 2020). Research on simulations and games in management education can be traced back many years, and international research on the use of simulation and games in management education continues to grow (Hallinger & Wang, 2020). While research on gamebased learning grows, there is conflicting evidence on the benefits of learning outcomes (Sanchez & Lee, 2022). There are five key challenges in the literature with research using simulation and gamebased learning: blurred definitions of games and game-based training, overlooking the importance of game characteristics, misunderstanding the mechanisms and processes in which humans learn, having unclear definitions and non-standardized measures for learning outcomes, and misinterpreting and overstating research results for game-based learning (Sanchez & Lee, 2022). To address these challenges, Sanchez and Lee (2022) recommend caution when interpreting results from research where procedural knowledge outcomes and effectiveness are reported, but only affective outcomes like satisfaction and enjoyment were measured. Pringle et al. (2010) studied using a simulation tool to teach healthcare change management. They found it to be a valuable opportunity, allowing students to experience firsthand the difficulty of making meaningful change (Pringle et al., 2010). Pringle et al. (2010) also found that simulation promoted critical and reflective thinking. In one study of students in the Master of Healthcare Administration (MHA) program at Winston-Salem State University, Foster and Jones (2020) found a positive change in students' overall self-assessed competency level utilizing a CBE for the course. However, after performing a bibliometric review of almost 60 years of research on simulation-based learning, Hallinger and Wang (2020) found that simulation-based learning (SBL) is being used internationally to teach a wide range of management subjects, but primarily in business education. They found only a small cluster of QUALITY IMPROVEMENT EDUCATION 20 authors studying SBL in professions beyond business, such as medicine. There is still a continued need to research simulations in management fields outside of business education (Hallinger & Wang, 2020). Implementation Readiness An individual's readiness for change can be impacted by fear of the unknown, comfort with current processes, or exhaustion from a barrage of changes (Folaron, 2005). Readiness to embrace change may stem from more than just a lack of desire. Physical and emotional capacity may inhibit the capability to change, as does the appropriate amount of time, people, and supplies (Folaron, 2005). In the COVID-19 era, Blok et al. (2022) found low engagement in quality improvement among frontline nurses. Nurses reported a lack of time, heavy workload, and lack of adequate resources as the highest barriers to quality work engagement (Blok et al., 2022). In addition to low levels of engagement, Raudensk et al. (2020) identified factors associated with mental health outcomes in healthcare professionals from the overwhelming impact of COVID-19. They recognized that patient care quality could suffer if burnout, the longterm stress reaction marked by emotional exhaustion, disengagement, and lack of personal accomplishment, continued (Agency for Healthcare Research and Quality, 2017; Raudensk et al., 2020). Healthcare leaders continue to push for the implementation of innovation and continuous quality improvement but must do so without alienating their teams. To do this, effective implementation and change management leaders must recognize the significance of the psychology of change (Deming, 1994). Readiness to accept new change may be affected by an individual's experience with change and the associated outcome, positive or negative. Often, time is needed to move through emotional readiness (Folaron, 2005). QUALITY IMPROVEMENT EDUCATION 21 Leaders then must identify the factors that can potentially influence a healthcare practitioner's implementation behaviors, such as the complexity of the change itself, the setting of change, the organizational capacity and resources, and the practitioners' attitudes (Huijg et al., 2014). Woiceshyn et al. (2017) extended the concepts by adding integrated and fragmented implementation modes to implementation readiness. When comparing five hospitals within the same health system, the two hospitals that had successfully implemented the organizational change did so by having leaders that framed and communicated the innovative ideas as opportunities. The leaders also sought to integrate the innovation with existing projects that made sense for staff and reduced staff feeling overwhelmed by overlapping initiatives (Woiceshyn et al., 2017). Goldman and Wong (2020) recommend that future QI training include assessing "soft skills" such as skillfully addressing change resistors, negotiating group decision support, and fostering interprofessional collaboration for successful QI implementation. Sustainability For high-quality patient outcomes, it is not enough for healthcare leaders to implement innovative change; change must be sustained. Initiatives that fall short are highly wasteful of human and monetary resources (Lennox et al., 2018). Lennox et al. (2018) reviewed 62 publications that reported approaches to support or influence healthcare sustainability. Although multiple sustainability theories were identified, common sustainability theories included diffusion of innovations, complexity, ecological, and open systems theory (Lennox et al., 2018). Lennox et al. (2018) found that although 40 different constructs were identified, only six were included in over 75% of the approaches: available resources, demonstrating effectiveness, monitoring progress over time, stakeholder participation, integrating existing programs, and building training and capacity. Hung et al. (2019) reinforce these findings by identifying the QUALITY IMPROVEMENT EDUCATION 22 importance of employee participation in the meaningful sustainment of process redesign and improvement efforts. Thus, the sustainability of quality improvement initiatives requires careful planning and thoughtfulness for the people involved (Lennox et al., 2018). Resistance to Change When planning for successful and sustainable change, leaders must develop the skills to predict and overcome the obstacles that impede change (Folaron, 2005). Such obstacles are employee resistance, inability to prioritize so that too many changes coincide, and half-hearted commitment by other leaders to change (Varkey & Antonio, 2010). Amarantou et al. (2017) found that resistance to change can be conceptualized as a three-component construct: the disposition to change, attitudes toward change, and the anticipated impact. Resistance to change is mainly affected by employee empowerment in decision-making and positive employee-manager relationships (Amarantou et al., 2017). Relationship building mediates resistance through its impact on the employee's disposition to change and the anticipated effect of change (Amarantou et al., 2017). Job perception and the quality of the change communication had no significant impact on resistance to change (Amarantou et al., 2017). Therefore, anticipating, recognizing, and responding to resistance is crucial to change management (Peacock, 2017). Avoiding peoples' opposition can ultimately outweigh other efforts to enhance change management. By increasing dissatisfaction with the status quo and sharing the future vision, a healthcare leader can reduce the likelihood of derailing or diminishing the change initiative (Peacock, 2017). Further understanding of opposition points, such as unwanted loss or gains of various forms of power, helps shed light on why and how opposition to change may be encountered (Peacock, 2017). QUALITY IMPROVEMENT EDUCATION 23 This study adds to the body of literature by studying one way to educate emerging healthcare leaders on becoming better at the management-related activities of change management. This pretest-posttest study design assessed management students for increased selfperception of competency in change management skills using the Self-Perception of Change Management Competency (SPCMC) instrument. Secondarily, a subgroup of healthcare industry management students was assessed for increased self-perception of competency in change management skills. Finally, comparisons between groups of healthcare industry students and non-healthcare industry students were assessed to evaluate the effectiveness of simulation education for each group. Understanding the key stakeholders, the required change management skills, and the factors contributing to implementation readiness and project sustainability is essential for future QI project success. This study addressed the gaps in the literature and built upon previous researcher recommendations. The study tested if simulation education could effectively build change management competency for emerging healthcare leaders. Previous research has focused on content-related activities, such as information dissemination and employee involvement. In contrast, management-related activities such as identifying and planning resistance potential have not been extensively investigated (Amarantou et al., 2017). This study provided valuable insight for teaching leaders how to make sustainable quality improvements. Method This study aimed to identify effective ways to provide graduate program learners feedback on the QI competencies of change management (NAHQ, 2017). The study took place between May and September 2023. Before data collection began, the study was approved by the University of Indianapolis Institutional Review Board (IRB) on March 9, 2023 (approval number QUALITY IMPROVEMENT EDUCATION 24 01840). A letter of cooperation was also signed by Dr. Jack Smothers, MBA Program Director and Interim Assistant Dean at the University of Southern Indiana. Study Type and Design This quasi-experimental study used a pretest-posttest study design, and participants were assessed for a difference in the perceived competency of change management skills using a QI assessment tool. Secondarily, a sub-set of participants who identified as students with healthcare work experience were assessed for a difference in the perceived competency of change management skills using a QI assessment tool. Finally, QI score differences were evaluated for the two groups of students: health industry students and non-health industry students. Knowledge gained from this study provided valuable insight for teaching emerging healthcare leaders how best to make sustainable QI gains. Participants Participants for this study consisted of a convenience sample of graduate business administration students at the University of Southern Indiana (USI). Students were enrolled in the online course Management (MNGT) 611 Leadership Skills & Innovation during the Summer 1 2023 or Fall 1 2023 semesters. Historical enrollment estimates show approximately 150-200 students were expected to enroll in Summer 1, and 200-250 students were expected to enroll in Fall 1. Actual enrollment numbers were 97 students and 262 students, respectively. Previously, about 20-30% of students in the course were employed in the healthcare field. In this study, 20 (20.8%) of students were employed in the healthcare field. Using the G*Power 3 software program (Faul et al., 2007) for sample size estimation, it was determined that the minimum sample size needed would be 35 study participants. The computed required sample size was determined by utilizing the statistical test, Wilcoxon signed- QUALITY IMPROVEMENT EDUCATION 25 rank test (matched pairs) from the t-test family, and setting the effect size to medium 0.50, the alpha to .05, and the power to 0.80. Setting USI is a public higher education institution located on a 1400-acre campus in Evansville, Indiana, and it enrolls almost 10,000 undergraduate and graduate students. The Master of Business Administration program consists of 30 credit hours and can be completed in person or online. MNGT 611 is a required course in the core curriculum for the Master of Business Administration program. Due to this program requirement, there is diversity among students with regard to background, profession, and years of experience (Foroughi et al., 2018). Data The following data was collected. Demographic and Participant Characteristics Professional industry (Categorical: Healthcare, Technology, Construction, Retail, Manufacturing, Other) Years of professional experience Geographic region of professional employment (Categorical: U.S. Northeast, U.S. Southwest, U.S. West, U.S. Midwest, Outside of the U.S.) Undergraduate degree major (Open-ended free text) Age (years) Gender (Categorical: Male, Female, Non-binary/third gender, Prefer not to say) Outcome Variables Mean pretest score from the Self-Perception of Change Management Competency Assessment Sum Score QUALITY IMPROVEMENT EDUCATION 26 Mean posttest score from the Self-Perception of Change Management Competency Assessment Sum Score Operational Definition Change management competency was operationalized as the score obtained on the Selfperception of Change Management Competency Assessment. Instrument The Self-Perception of Change Management Competency Assessment was developed based on the NAHQ Healthcare Quality Competency Framework, which is grounded in competency statements defined by the NAHQ HQ Essentials (NAHQ, 2017). NAHQ's work reflects empirical aspects of QI in the field and seeks to understand to what level work is being performed in healthcare quality improvement (NAHQ, 2022). Email permission to reference this material was received. This instrument required students to rate their competency level for 12 change management abilities on a scale of 1-4, with 1 representing novice, 2 representing emerging proficiency, 3 representing competent, and 4 representing mastery. A ranking list of skills and a question inquiring about supervisor ranking was added to combat the DunningKruger Effect, the cognitive bias that may limit one's ability to analyze one's thoughts on performance (Kruger & Dunning, 1999). For each participant, the competency level for each of the 12 abilities was calculated based on the difference between the chosen competency on the pretest and posttest. After all competency values were calculated, the sum score for each student was obtained. Like student scores, each ability statement was scored to investigate trends in individual competency statement responses. The face validity of the instrument was established by obtaining feedback from the thesis committee members. The final Flesch-Kincaid Grade Level score was 9.4, and survey readability QUALITY IMPROVEMENT EDUCATION 27 was improved by requesting input from four graduate students in the Winter I course, prior to testing. Internal consistency reliability was measured by performing Cronbach's alpha analysis. Procedures Recruitment All students enrolled in the online course MNGT 611 Leadership Skills & Innovation during the Summer I 2023 and Fall I 2023 semesters were invited to participate in the study. Dr. Jack Smothers, MBA Program Director, Interim Assistant Dean, and course instructor, shared IRB-approved student recruitment verbiage with all enrolled students during week one of the course via university email and the learning management system regarding the opportunity to participate. Informed Consent Because the change management intervention was taught during week two of the accelerated online course, the professor shared the opportunity to participate in the research study during week one of each course. Students were provided with a URL link to a Qualtrics survey, and the informed consent was the landing page for the assessment. To proceed to the survey, potential participants must have provided informed consent. If a student did not wish to participate after reading the informed consent form, they were directed to the end of the assessment. The informed consent highlighted that participation in the research study was optional, and students could stop participating anytime. Students were informed of the purpose of the research, how study results were used, and the risks and benefits of participation. The consent process included statements to ensure that students understood that participation in the study was voluntary and in no way related to their grades in the course. There was no QUALITY IMPROVEMENT EDUCATION 28 compensation for participation; however, students could submit their email addresses for the opportunity to be randomly selected for one of four $25 gift cards. Data Collection The course professor provided students with a link to the pretest assessment via Qualtrics, an online survey platform, during week one of the course before the change management simulation lesson. Students had one week to respond to the pretest assessment questions. After the change management simulation exercise lesson was complete, students received another link to the Qualtrics posttest assessment. Student participants had two weeks to respond; a reminder message was sent mid-week. Students created their unique participant ID number by combining the first three letters of their mother's family name and the last four digits of their cell phone number. Participants' unique study identification numbers allowed pretest and posttest survey matching and helped protect participants' confidentiality. Intervention The intervention delivered to students was the Change Management Simulation: Power and Influence V3 by William Q. Judge and Linda A. Hill published by Harvard Business Publishing Education (Judge & Hill, 2020). This virtual simulation exercise allows the student to play one of two roles in a manufacturing firm in four different scenarios. Students role-play as a change agent to gain insight into why individuals might resist change, better appreciate the change agent's power, and how to avoid common missteps (Judge & Hill, 2020). Data Management Raw data from the server hosting the University of Indianapolis Qualtrics application was downloaded into MS Excel files for consistency checking. The participant identification number was utilized to protect the confidentiality of participants. MS Excel data was stored on a QUALITY IMPROVEMENT EDUCATION 29 password-protected laptop and backed up to an external password-protected hard drive. The primary researcher cleaned the data before statistical analysis and included stripping geolocator and IP address identifiers produced by Qualtrics. Pretest-posttest assessments were collated by using students' self-created, unique identifiers. Statistical Analysis Descriptive statistics were first used to describe the sample and explore the sample's initial results and pretest and posttest assessment test scores. Nominal variables were reported as frequencies and percentages. Ratio variables were reported as means and standard deviations if the data were normally distributed. If the data were not normally distributed, then data was reported as medians and interquartile ranges. The presence of missing data, values of central tendency (mean and median), and dispersion of data (standard deviation, interquartile range, and minimum and maximum values) were explored. Examining the minimum and maximum scores allowed for the analysis of extreme values and checking for data entry errors (Kellar & Kelvin, 2013). Fisher's test for skewness and kurtosis was utilized to check for potential issues with data dispersion (Kellar & Kelvin, 2013). The normality of the data was determined using the Shapiro-Wilk test results and visual inspection of the Q-Q plots and histograms. The following inferential statistics were used to analyze the primary and secondary research questions. The interval and ratio data were normally distributed, and the sample size was at least 30; thus, the paired t-test was used for analysis. Another set of tests addressed the tertiary research question. Based on the categorical response to the demographic question of primary professional industry, students were assigned to one of two groups: Healthcare or NonHealthcare. The independent t-test was conducted on the posttest assessment scores from both QUALITY IMPROVEMENT EDUCATION 30 groups. A Levene's test was conducted to determine that variances were equal. The effect size was calculated and interpreted based on Cohen's recommendations (Cohen, 1992). Each change management ability statement was then further explored to investigate trends in mean differences by statement. Data was analyzed using IBM SPSS Statistics for Windows, Version 28.0 (IBM Corp., Armonk, NY). All comparisons were two-tailed, and a significance level less than .05 was considered statistically significant. Results Participant Characteristics There were 193 responses to the pretest survey and 150 to the posttest survey. Of those surveys, 50 were excluded due to a lack of informed consent or incomplete, and 101 were excluded because the pretest survey could not be matched to a posttest survey. After the exclusions, 96 surveys remained for analysis. The total number of students enrolled in the Summer I and Fall I sessions was 359, for a 27% response rate. Table 1 presents a summary of participant demographic characteristics. Comparing the Means of Two Related Groups Self-Perception of Change Management Competency All Student Participants Data for pretest and posttest were normally distributed as determined by visual inspection of the histogram and the Q-Q plot. The Self-Perception of Change Management Competency (SPCMC) mean and standard deviation before the simulation intervention were 25.70 (7.83) and 29.24 (7.16) following the simulation intervention. A paired samples t-test indicated statistically significant differences between the two scores, t(95) = -6.10, p < .001. The 95% confidence interval of the difference between the means ranged from -4.70 to -2.39. The Cohens d effect QUALITY IMPROVEMENT EDUCATION 31 size was medium (d = 0.62). The null hypothesis was rejected, and it was inferred that there was a statistically significant difference between pretest and posttest SPCMC scores after administering the simulation educational intervention (see Table 2). The mean difference of 3.54 between pretest and posttest SPCMC scores indicates an improvement in student self-perception of competency after participating in the simulation educational intervention. Self-Perception of Change Management Competency Subgroup of Healthcare Industry Students Differences in the means of pretest and posttest SPCMC scores for students who identified healthcare as their primary industry were analyzed. The data for both time periods were normally distributed; thus, a paired samples t-test was performed. The SPCMC mean and standard deviation before the simulation intervention were 24.45 (7.90) and 30.20 (8.43) following the simulation intervention. The mean scores differed statistically, t(19) = -3.43, p = .003. The 95% confidence interval of the difference between the means ranged from -9.26 to 2.24. The post-hoc effect size is large (d = 0.77). The null hypothesis was rejected; thus, there was a statistically significant change in SPCMC scores after the simulation educational intervention among the subgroup of healthcare industry students (see Table 3). Higher posttest scores indicate that healthcare students gained in self-perception of competency after participating in the simulation educational intervention. Differences Between Groups The SPCMC mean, and standard deviation for the healthcare students was 30.20 (8.43) and 28.99 (6.82) for the non-healthcare student group. An independent samples t-test indicated the two groups did not differ statistically, t(94) = -0.67, p = .503 at an alpha level of .05. The 95% confidence interval of the mean difference between the groups ranged from -4.79 to 2.37. QUALITY IMPROVEMENT EDUCATION 32 The Cohens effect size was small (d = 0.17). The null hypothesis was retained, and it was inferred that there was no difference in SPCMC posttest scores between healthcare and nonhealthcare management students (see Table 4). Internal Consistency of Survey Items Measuring Self-Perception of Change Management Cronbachs alpha was used to assess the degree of overall correlation (internal consistency) of the survey items measuring self-perception of the QI concept of change management. Cronbachs alpha values can range from a low score of 0 to the highest score of 1.0. The SPCMC survey instrument consisted of 12 items on change management ability on which respondents rated their competency assessment using a 4-point Likert response scale. The Likert response choices were coded as 4 = Master, 3 = Competent, 2 = Emerging proficiency, and 1 = Novice. Cronbachs alpha was acceptable, with a value of .93 for the 12 questions (Taber, 2017). The standard deviations were fairly equal across the 12 questions. On the 4-point Likert scale, item SPCMC3: I can describe the value of a needed change and how it applies to my coworkers, had the highest mean value (2.65) and the lowest item-total correlation. If this item were removed from the scale in this study sample, the Cronbachs alpha value would rise to .94. (SPCMC2): I can apply a standard change management model or framework to support workplace improvements, had the lowest mean value (1.66). SPCMC8, I know how to apply change management tools relevant to the separate phases when making a workplace change, had the highest item-total correlation. Removing SPCMC8 would lower the Cronbachs alpha value to .92. Overall, the internal consistency was good, and removing items would not substantively change the overall Cronbach alpha value. The Cronbach analysis was then repeated on data for the subgroup of healthcare industry students. Cronbachs alpha was higher in this subgroup than in the total population, with a value QUALITY IMPROVEMENT EDUCATION 33 of .95 for the 12 questions. For the subgroup of healthcare industry students, the internal consistency could be improved to .96 by removing the item SPCMC5, I can explain the stages of behavior that may occur when experiencing a workplace change and what to expect at each stage. Removing other competency statements would result in either no change or a decrease in Cronbachs alpha score. Participant and Change Management Competency Statement Analysis Seventy-three (76.0%) student participants increased their overall competency scores. The average increase in score across participants was 3.5. However, 23 participants (24.0%) had lower total posttest summary scores than pretest summary scores. Before the Change Management Simulation Power and Influence V3 lesson, students started with the highest mean scores in SPCMC3, I can describe the value of a needed change and how it applies to my coworkers, and SPCMC7, I can collaborate with participants in my workplace to plan and carry out change and create buy-in at 2.65 and 2.57 respectively. The Change Management Simulation Power and Influence V3 exercise was the least effective in educating students on these two competencies as well as a third, SPCMC9, I know how to use change management tools (e.g., Stakeholder Analysis, Elevator Speech) to analyze employee acceptance, influence, or resistance to change with the average improvement for each question being .17, .14, and .14. Before the Change Management and Simulation Power and Influence V3 lessons, students started with the lowest scores in SPCMC2 I can apply a standard change management model or framework (e.g., Lewin, Kotter, Rogers, Kubler-Ross) to support workplace improvements. Additional exploratory analysis of the change management competency statements revealed that the mean of every question demonstrated improvement from pretest to posttest scores (see Table 2). Research study participants showed the most improvement in mean score QUALITY IMPROVEMENT EDUCATION 34 on SPCMC5: I can explain the stages of behavior that may occur when experiencing a workplace change and what to expect at each stage. However, the subgroup of healthcare industry students showed the most improvement in SPCMC6: I can discuss how the use of change management principles and tools impacts peoples responses to workplace changes. Further analysis of change management competencies 5 and 6 was performed to determine if there was a significant difference in scores for these questions between the subgroup of healthcare industry students and the subgroup of non-healthcare students. Since the data were not normally distributed, a non-parametric test, Mann Whitney U, was used to compare the changes in SPCMC scores for the two competencies. The median change in the SPCMC6 score for the non-healthcare participants was 0, and the median change in the SPCMC6 score for the healthcare participants was 1.0. The Mann-Whitney U test, Z = -2.84, p = .005, indicated that the null hypothesis was rejected. The median change in SPCMC6 scores between the two groups did differ significantly at an alpha .05 level. The subgroup of healthcare industry students had a lower pretest mean score on each change management competency statement than the nonhealthcare industry students except SPCMC2, I can apply a standard change management model or framework (e.g., Lewin, Kotter, Rogers, Kubler-Ross) to support workplace improvements (see Figure 1). Discussion The purpose of this study was to meet three primary objectives. First, to understand if the Change Management Simulation Power and Influence V3 was an effective educational intervention that led to a significant change in perception of QI skills as measured by the SPCMC instrument in the Management 611 course students. The second objective was to determine if the Change Management Simulation Power and Influence V3 was an effective QUALITY IMPROVEMENT EDUCATION 35 intervention that led to a significant change in the perception of QI skills as measured by the SPCMC instrument in the subgroup of healthcare industry students in the Management 611 course. The third objective was to determine if there was a significant difference in the SPCMC posttest scores between the subgroup of healthcare industry students and the subgroup of nonhealthcare industry students. The paired sample t-test revealed a statistically significant difference in scores obtained before and after the Change Management Simulation Power and Influence V3 for all student participants and the subgroup of healthcare industry students (see Tables 2 and 3). The SPCMC scores increased. There was no statistically significant difference in SPCMC posttest scores between the subgroup of non-healthcare industry students and the subgroup of healthcare industry students (see Table 4). Stakeholders The subgroup of healthcare industry students in this study comprised people with diverse undergraduate backgrounds, both clinical and non-clinical. Their undergraduate backgrounds included majors in actuarial mathematics, agribusiness economics, nursing, biology, biomedical engineering, healthcare administration, marketing, speech-language pathology, chemistry, pharmacy, psychology, and theology. The subgroup of healthcare industry students brought to class a lower perception of change management competency than the subgroup of non-healthcare industry students, as they had a lower mean pretest sum score on each competency statement except one (see Figure 1). Thus, an opportunity remains to standardize the QI training for diverse emerging healthcare leaders in graduate school programs. In 2014, the American College of Healthcare Executives recognized the challenge of the lack of standardization among MBA, MHA, MPH, and MS-HCA graduates (Anderson & Garman, 2018), degrees of those often found in healthcare leadership positions. The Change Management Simulation Power and Influence V3 QUALITY IMPROVEMENT EDUCATION 36 improved the self-perception of competency in the QI skill of change management, thus providing a method of standardized instruction. Posttest scores between the two groups did not differ significantly, demonstrating that the Change Management Simulation Power and Influence V3 aligned the group of health industry students' posttest scores with those of non-healthcare students. Studer (2003) found that healthcare organizations that reduce leader variability were rewarded with improved outcomes, efficiencies, and innovation, essential benefits to all stakeholders in healthcare. Definitions of Change Management Although definitions of change management competency vary, Moosa et al. (2021) found greater attention to change management research in business and healthcare between 2008 and 2014, emphasizing organizational, human, and leadership definitions. NAHQ furthered this definition by focusing on personal competency and defined change management competency for the healthcare professional as the ability to draw people into support of changes required to achieve performance improvement outcomes (NAHQ, 2017). NAHQ also found it relevant for all healthcare leaders to have skills to build awareness of the need to change, communicate the vision, monitor accountability, and provide the tools for effective change (NAHQ, 2017). By this definition, the study results support using the Change Management Simulation Power and Influence V3 for change management skill training for emerging leaders. Research participants demonstrated the most improvement in self-perception of change management competencies 5 I can explain the stages of behavior that may occur when experiencing a workplace change and what to expect at each stage," 6 I can discuss how the use of change management principles and tools impact peoples' response to workplace changes," and 11 "I can coach leaders on change management processes and tools." The subgroup of healthcare QUALITY IMPROVEMENT EDUCATION 37 industry students showed the most improvement in change management competency 6, "I can discuss how the use of change management principles and what to expect at each stage." The difference in means for this competency statement significantly differed between the subgroups of healthcare industry and non-healthcare industry students. Thus, the Change Management Simulation Power and Influence V3 simulation was particularly effective in improving healthcare professional students' self-perception of competency in the ability to draw people into support of changes required, build awareness for the need to change, and understand the tools for effective change as defined by NAHQ (2017). Methods For QI Education Delivery A variety of methods have been used to deliver quality improvement education. Experiential learning, project-based learning, competency-based instruction, and simulation learning have all been tried, and there is a continued need for research simulations in management fields outside of business education (Goodman et al., 2022; Foster & Jones, 2020; Patterson et al., 2022; Pringle et al., 2010). This research study adds to Pringle et al.'s (2010) earlier work in that simulation can provide a safe environment to experience the difficulty of making meaningful change and promote critical thinking. The statistically significant change in sum scores between the pretest and posttest and the increase in SPCMC sum scores demonstrates that the simulation was an effective educational method for improving the student's selfperception of competency in the QI skill of change management. Implementation, Sustainability, and Resistance to Change Leaders must identify factors that can potentially influence healthcare team members' implementation behaviors, such as the complexity of the change itself, the setting of change, the organizational capacity and resources, and the practitioners' attitudes (Huijg et al., 2014). QUALITY IMPROVEMENT EDUCATION 38 Amarantou et al. (2017) found that resistance to change can be conceptualized as a threecomponent construct: the disposition to change, attitudes toward change, and the anticipated impact. Healthcare leaders can continue to push for continuous quality improvement without alienating their teams by recognizing the significance of psychology in change (Deming, 1994). The Change Management Simulation Power and Influence V3 simulation was least helpful in educating students on SPCMC9: I know how to use change management tools (e.g., Stakeholder Analysis, Elevator Speech) to analyze employee acceptance, influence, or resistance to change and SPCMC7: I can collaborate with participants in my workplace to plan and carry out change and create buy-in. The difference in paired means for each statement was 0.14. Thus, this study demonstrated the continued need to develop further educational interventions that teach emerging healthcare leaders how to push for the implementation of innovative ideas and move their teams through the emotional readiness for change (Folaron, 2005). Leaders must also identify the factors that can potentially influence sustainability and resistance behaviors (Huijg et al., 2014). Goldman and Wong (2020) recommended that future QI training include assessing soft skills such as skillfully addressing change resistors and supporting group decision-making. This study demonstrated that there remains an opportunity to redesign future simulation educational interventions. Study Limitations There were several limitations in this study. One limitation of this study was the low power of having only 20 responses from students in the subgroup of healthcare industry students. Researchers conducting a study with low power have a high probability of committing a type II error, that is, saying no statistically significant difference exists between groups when differences do exist (Kellar & Kelvin, 2013). Using the G*Power 3 software program (Faul et al., 2007) for QUALITY IMPROVEMENT EDUCATION 39 sample size estimation, it was determined that the minimum sample size needed in the healthcare student group was 67 study participants. The computed required sample size was determined by utilizing the statistical test, Wilcoxon-Mann-Whitney test (two groups) from the t-test family, and setting the effect size to medium 0.50, the alpha to .05, and the power to 0.80. Another limitation of this study to consider was the impact of the Dunning-Kruger Effect (Kruger & Dunning, 1999). Sixty-six (68.8%) study participants saw an increase in their posttest scores, and seven (7.3%) participants saw no change. However, twenty-three (23.9%) study participants had posttest sum scores, which declined in the self-perception of competence in change management. Of the 23 students whose posttest sum scores declined, 18 identified as non-healthcare professional students with various undergraduate backgrounds, including business, international relations, engineering, and math. The mean pretest score of the twentythree participants was 31, which is higher than the mean of the other participants at 24, indicating they started with a higher self-perception of competence in their change management skills. Although a self-ranking statement and supervisor statement were added before and after the SelfPerception of Change Management Competency instrument questions, students may have displayed a cognitive bias in which people overestimate their knowledge or ability in a specific area, known as the Dunning-Kruger Effect (Kruger & Dunning, 1999). Another limitation of the study was the use of convenience sampling. The self-perception of competency from the chosen sample may only reflect the characteristics of the graduate business students at this university and their demographic profile. The participant group had 74.0% professional experience in the Midwest, 56.3% were female, and the mean age was 34.8 years, with 9.4 years of experience. Because selection was non-random, nonprobability sampling did not allow the estimation of sampling errors and may be subject to a sampling bias QUALITY IMPROVEMENT EDUCATION 40 (Bhattacherjee, 2012). Although an introductory explanatory video by the researcher, follow-up reminders, gift card drawings, and assurances that data was anonymous and private were all techniques used to encourage study participation, student participants were lost. Future Research Opportunities There are several future research opportunities as a result of this study. Future research should include further study on the reliability and validity of this novel survey instrument. Administration of the survey in another of this universitys MBA management courses could improve the test-retest reliability of the instrument. Also, the internal reliability, as measured by Cronbachs alpha, was higher in the subgroup of healthcare industry students than the whole group, and further exploration of this concept is warranted. The survey instrument utilized in this study was developed based on the NAHQ Healthcare Quality Competency Framework and grounded in NAHQ's work in the healthcare QI field. A correlation study between NAHQs competency work and the self-perception of competency could lead to a better understanding of the concurrent criterion-related validity of the instrument. Collecting survey data from multiple universities management courses with data analyzed for correlations and exploratory factor analysis could improve construct validity. Additional research opportunities include repeating this study in more graduate healthcare administration programs to perform a more complete analysis of healthcare professionals' results. Also, the study could be repeated using a different simulation educational intervention, modified to include training on using change management tools (e.g., Stakeholder Analysis, Elevator Speech) to analyze employee acceptance, influence, or resistance to change in a healthcare scenario. Several other customizable simulation technologies, for example, ExperiencePoint and Mursion, are available. QUALITY IMPROVEMENT EDUCATION 41 Another opportunity for future research would be to compare the survey responses to the Change Management Simulation Power and Influence V3 performance scores and students' efforts of persistence measured by the number of simulation attempts. This comparison and the analysis of demographic data collected to search for correlations between improvement scores and gender, undergraduate background, age, geographic location, and years of professional experience would also add to the literature and build upon earlier research by Pringle et al. (2010). Pringle et al. (2010) sought to understand the influence of repeat simulation testing, gender, and additional instructions on simulation performance scores. They found a gender effect, with males performing better than females (Pringle et al., 2010). Conclusion This study provided valuable insight into an effective way to improve the self-perception of competency of emerging leaders in the QI principle of change management. The Change Management Simulation Power and Influence V3 raised the self-perception of competency in the QI skill of change management in students in a graduate business management course. The Change Management Simulation Power and Influence V3 was also an effective intervention for raising the self-perception of change management competency in a subgroup of healthcare industry students. Finally, there was no statistically significant difference in SPCMC posttest scores between the non-healthcare and healthcare industry student subgroups. QUALITY IMPROVEMENT EDUCATION 42 References Agency for Healthcare Research and Quality. (2022). About AHRQ. AHRQ. Retrieved on September 20, 2022, from https://www.ahrq.gov/cpi/about/index.html Agency for Healthcare Research and Quality. (2017). Physician burnout. AHRQ. https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/clinicians-providers/ahrqworks/impact-burnout.pdf Amarantou, V., Kazakopoulou, S., Chatzoudes, D., & Chatzoglou, P. (2018). Resistance to change: an empirical investigation of its antecedents. Journal of Organizational Change Management, 31(2), 426-450. https://doi.org/10.1108/jocm-05-2017-0196 Anderson, M. M., & Garman, A. N. (2018). Preparing future healthcare leaders through graduate education: Impact of program accreditation on quality improvement. Journal of Allied Health, 47(2), 121-125. 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How do U.S. hospital CEOs perceive leadership competencies of health administration graduates? [Poster presentation]. AUPHA Annual Meeting, Long Beach, California, USA. Retrieved from https://cahme.org/healthcare-management-education-accreditation/resources/researchproof-points/ Folaron, J. (2005). The human side of change leadership. Quality Progress, 38(4), 39-43. Ford, M. W., & Evans, J. R. (2001). Baldrige assessment and organizational Learning: The need for change management. Quality Management Journal, 8(3), 9-25. https://doi.org/10.1080/10686967.2001.11918963 Foroughi, A., Smothers, J., Bai, D., & Khayum, M. (2018). Launching an accelerated online MBA program: Assuring quality with scale, based on principles of effective course design. Journal of Higher Education Theory and Practice, 18(6), 58-71. Foster, M. R. B., & Jones, C. M. (2020). The effects of competencybased education delivery methods on competency level: A quantitative study. The Journal of Competency-Based Education, 5(4), Article e1226. https://doi.org/10.1002/cbe2.1226 QUALITY IMPROVEMENT EDUCATION 45 Gaba, D. M. (2007). The future vision of simulation in healthcare. Simulation in Healthcare, 2(2), 126-135. https://doi.org/10.1097/01.SIH.0000258411.38212.32 Goldman, J., & Wong, B. M. (2020). Nothing soft about 'soft skills': Core competencies in quality improvement and patient safety education and practice. BMJ Quality and Safety, 29(8), 619-622. https://doi.org/10.1136/bmjqs-2019-010512 Goodman, C. W., Justo, J., Merrow, C., Prest, P., Ramsey, E., & Ray, D. (2022). An experiential learning collaborative on quality improvement for interprofessional learners. Journal of Interprofessional Care, 36(2), 327-330. https://doi.org/10.1080/13561820.2021.1901673 Hallinger, P., & Wang, R. (2020). Analyzing the intellectual structure of research on simulationbased learning in management education, 1960-2019: A bibliometric review. The International Journal of Management Education, 18(3), Article e100418. https://doi.org.10.1016/j.ijme.2020.100418 Huijg, J. M., Gebhardt, W. A., Dusseldorp, E., Verheijden, M. W., van der Zouwe, N., Middelkoop, B. J. C., & Crone, M. R. (2014). Measuring determinants of implementation behavior: Psychometric properties of a questionnaire based on the theoretical domains framework. Implementation Science, 9, Article e33. https://doi.org/10.1186/1748-5908-933 Hung, D. Y., Gray, C. R., Truong, Q. A., & Harrison, M. I. (2019). Sustainment of lean redesigns for primary care teams. Quality Management in Health Care, 28(1), 15-24. https://doi.org/10.1097/QMH.0000000000000200 Hussain, S. T., Lei, S., Akram, T., Haider, M. J., Hussain, S. H., & Ali, M. (2018). Kurt Lewin's change model: A critical review of the role of leadership and employee involvement in QUALITY IMPROVEMENT EDUCATION 46 organizational change. Journal of Innovation & Knowledge, 3(3), 123-127. https://doi.org/10.1016/j.jik.2016.07.002 Institute for Healthcare Improvement. (2022). Vision, mission, and values. IHI. Retrieved on September 20, 2022, from https://www.ihi.org/about/Pages/IHIVisionandValues.aspx Institute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. The National Academies Press. https://doi.org/10.17226/10027. Judge, W. Q., & Hill, L. A. (2020). Change management simulation: Power and influence V3. Harvard Business Publishing. https://hbsp.harvard.edu/product/7611-HTM-ENG Kellar, S. P., & Kelvin, E. A. (2013). Munro's statistical methods for health care research (6th ed.). Lippincott Williams & Wilkins. Kotter, J. P. (1995). Leading change: Why transformation efforts fail. Harvard Business Review 73, 59-67. Kotter, J. P. (2012). Accelerate! Harvard Business Review. Retrieved on September 21, 2022, from https://hbr.org/2012/11/accelerate Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 11211134. https://doi.org/10.1037/0022-3514.77.6.1121 Lennox, L., Maher, L., & Reed, J. (2018). Navigating the sustainability landscape: A systematic review of sustainability approaches in healthcare. Implementation Science, 13(1), Article 27. https://doi.org/10.1186/s13012-017-0707-4 Massoud, M.R., Nielsen, G.A., Nolan, K., Nolan, T., Schall, M.W., & Sevin, C. (2006). A framework for spread: From local improvements to system-wide change [White paper]. IHI Innovation Series. QUALITY IMPROVEMENT EDUCATION 47 https://www.ihi.org/resources/Pages/IHIWhitePapers/AFrameworkforSpreadWhitePaper. aspx McKimm, J., Redvers, N., El Omrani, O., Parkes, M. W., Elf, M., & Woollard, R. (2020). Education for sustainable healthcare: Leadership to get from here to there. Medical Teacher, 42(10), 1123-1127. https://doi.org/10.1080/0142159X.2020.1795104 Miltner, R., Pesch, L., Mercado, S., Dammrich, T., Stafford, T., Hunter, J., & Stewart, G. (2021). Why competency standardization matters for improvement: An assessment of the healthcare quality workforce. The Journal for Healthcare Quality, 43(5), 263-274. https://doi.org/10.1097/JHQ.0000000000000316 Moosa, V., Khalid, A. H., & Mohamed, A. (2021). Intellectual landscape of research on change management: a bibliometric analysis. Management Research Review, 45(8), 1044-1059. https://doi.org/10.1108/mrr-04-2021-0256 Moussa, L., Garcia-Cardenas, V., & Benrimoj, S. I. (2019). Change facilitation strategies used in the implementation of innovations in healthcare practice: A systematic review. Journal of Change Management, 19(4), 283-301. https://doi.org/10.1080/14697017.2019.1602552 National Association for Healthcare Quality. (2017). HQ essentials: Competencies for the healthcare quality professional. NAHQ. National Association for Healthcare Quality. (2022). Healthcare quality and safety workforce report: New imperatives for quality and safety mean new imperatives for workforce development. NAHQ Intelligence. https://nahq.org/wp-content/uploads/2022/09/NAHQWorkforce-Report-2022.pdf QUALITY IMPROVEMENT EDUCATION 48 National Institutes of Standards and Technology. (2019, November 15). Baldridge performance excellence program: History. Retrieved on September 29, 2022, from https://www.nist.gov/baldrige/how-baldrige-works/about-baldrige/history Patterson, J. E., Martin, S., Hutcherson, L., Toohey, J., Bresnahan, L., Garza, C., Alsip, B., & Shine, K. (2022). The clinical safety and effectiveness course: Ten years of experiential training in quality improvement for practicing professionals. American Journal of Medical Quality, 37(3), 227-235. https://doi.org/10.1097/JMQ.0000000000000025 Peacock, M. (2017). Human resource professional's guide to change management: Practical tools and techniques to enact meaningful and lasting organizational change. Business Expert Press. Retrieved September 5, 2022, from https://ebookcentral.proquest.com/lib/uindy-ebooks/detail.action?docID=4865275 Porter, J. A., Haberling, K., & Hohman, C. (2016). Employer desired competencies for undergraduate health administration graduates entering the job market. The Journal of Health Administration Education, 33(3), 355-375. Pringle, J. D., Nippak, P., & Isaac, W. (2010). Simulations as an instruction tool to teach healthcare change management: The influence of repeat simulation testing, gender, and additional instructions on performance scores? The Journal of Health Administration Education, 27(1), 27-43. Prosci. (n.d.). Who we are. Retrieved on September 21, 2022, from https://www.prosci.com/about Prosci. (n.d.). Change management: Driving change success by preparing, equipping, and supporting individuals to thrive through change. Retrieved on September 21, 2022, from https://www.prosci.com/change-management QUALITY IMPROVEMENT EDUCATION 49 Raudensk, J., Steinerov, V., Javurkov, A., Urits, I., Kaye, A. D., Viswanath, O., & Varrassi, G. (2020). Occupational burnout syndrome and post-traumatic stress among healthcare professionals during the novel coronavirus disease 2019 (COVID-19) pandemic. Best Practice & Research Clinical Anaesthesiology, 34(3), 553-560. https://doi.org/10.1016/j.bpa.2020.07.008 Sadowski, B., Cantrell, S., Barelski, A., O'Malley, P. G., & Hartzell, J. D. (2018). Leadership training in graduate medical education: A systematic review. Journal of Graduate Medical Education, 10(2), 134-148. https://doi.org/10.4300/JGME-D-17-00194.1 Sanchez, D. R., & Lee, C. A. (2022). Understanding the challenges of game-based training: Recommendations for moving research forward in game-based learning. In O. Bernardes, V. Amorim, & A. Moreira (Eds.), Handbook of research on the influence and effectiveness of gamification in education (pp. 541-578). IGI Global. https://doi.org/10.4018/978-1-6684-4287-6.ch027 Schrimmer, K., Williams, N., Mercado, S., Pitts, J., & Polancich, S. (2019). Workforce competencies for healthcare quality professionals: Leading quality-driven healthcare. Journal for Healthcare Quality, 41(4), 259-265. https://doi.org/10.1097/JHQ.0000000000000212 Studer, Q. (2003). Hardwiring excellence: Purpose, worthwhile work, making a difference. Fire Starter Publishing. Taber, K. S. (2017). The Use of Cronbach's Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2 QUALITY IMPROVEMENT EDUCATION Tekian, A., Infante, A. F., & Valenta, A. L. (2021). Master's programs in patient safety and healthcare quality worldwide. Journal of Patient Safety, 17(1), 63-67. Varkey, P., & Antonio, K. (2010). Change management for effective quality improvement: A primer. American Journal of Medical Quality, 25(4), 268-273. https://doi.org/10.1177/1062860610361625 Woiceshyn, J., Blades, K., & Pendharkar, S. R. (2017). Integrated versus fragmented implementation of complex innovations in acute health care. Health Care Management Review, 42(1), 76-86. https://doi.org/10.1097/HMR.0000000000000092 50 QUALITY IMPROVEMENT EDUCATION 51 Table 1 Study Participant Demographic Characteristics (N = 96) Category Subcategory Number (%) of Participants Male 41 (42.7%) Female 54 (56.3%) Prefer not to say 1 (1.0%) 20-29 34 (35.4%) 30-39 34 (35.4%) 40-49 18 (18.8%) 50-59 10 (10.4%) Construction 2 (2.1%) Healthcare 20 (20.8%) Work primarily in what Manufacturing 22 (22.9%) industry Other 36 (37.5%) Retail 11 (11.5%) Technology 5 (5.2%) 1-5 40 (41.7%) 6-10 24 (25.0%) 11-15 15 (15.6%) 16-20 7 (7.3%) 21-25 5 (5.2%) 26-30 4 (4.2%) 30+ 1 (1.0%) Outside of the U.S. 1 (1.0%) U.S. Midwest 71 (74.0%) U.S. Northeast 9 (9.4%) U.S. Northwest 2 (2.1%) U.S. Southeast 10 (10.4%) U.S. Southwest 3 (3.1%) Gender Age (years) a Years of experience in primary industry Where most of work experience has taken place Note. a Participants had a mean age of 34.8 years and 9.4 years of professional experience. QUALITY IMPROVEMENT EDUCATION 52 Table 2 Comparing the Means of Two Related Groups (All Participants N=96) Pretest M SPCMC1: I can compare and Posttest SD M SD 2.13 0.81 2.41 1.66 0.79 2.65 p ES 0.71 Mean Diff 0.28 .001 0.34 2.00 0.89 0.34 <.001 0.41 0.85 2.81 0.69 0.17 .052 0.20 2.33 0.91 2.65 0.74 0.31 <.001 0.40 2.14 0.87 2.55 0.78 0.42 a <.001 0.48 2.09 0.92 2.48 0.74 0.39 <.001 0.43 2.57 0.78 2.71 0.71 0.14 .091 0.17 contrast different change models. SPCMC2: I can apply a standard change management model or framework (e.g., Lewin, Kotter, Rogers, Kubler-Ross) to support workplace improvements. SPCMC3 I can describe the value of a needed change and how it applies to my coworkers. SPCMC4 I know how to encourage a change management strategy through the workplace. SPCMC5 I can explain the stages of behavior that may occur when experiencing a workplace change and what to expect at each stage. SPCMC6 I can discuss how the use of change management principles and tools impacts peoples' response to workplace changes SPCMC7 I can collaborate with participants in my workplace to plan and carry out change and create buy-in. QUALITY IMPROVEMENT EDUCATION SPCMC8 I know how to apply 53 2.05 0.85 2.41 0.82 0.35 <.001 0.43 2.11 0.94 2.25 0.83 0.14 .123 0.16 2.06 0.83 2.39 0.75 0.32 <.001 0.40 1.82 0.87 2.21 0.87 0.39 <.001 0.47 2.08 0.88 2.39 0.84 0.30 <.001 0.36 25.70 7.83 29.24 7.16 3.54 <.001 0.62 change management tools relevant to the separate phases when making a workplace change. SPCMC9 I know how to use change management tools (e.g., Stakeholder Analysis, Elevator Speech) to analyze employee acceptance, influence, or resistance to change. SPCMC10 I can evaluate the impact of change efforts (e.g., impact analysis and assess change readiness). SPCMC11 I can coach leaders on change management processes and tools. SPCMC12 I know how to implement a variety of strategies to reduce the barriers that can block lasting change. SPCMC Sum Score Note. SPCMC = Self-Perception of Change Management Competency; SD = standard deviation; Diff = difference; ES = effect size. Students raised their perceived level of competency for each change management statement on a scale from 1 4. 1 = Novice a person new to or inexperienced in a field, 2 = Emerging proficiency gaining knowledge or becoming skillful in a field, 3 = Competent have the necessary ability, knowledge or skill to be successful in a field, 4 = Master have the comprehensive knowledge or skill to command a field. A paired-samples ttest, t(95) = -6.10, p < .001, indicated that the two times differed statistically at an alpha .05 QUALITY IMPROVEMENT EDUCATION level. This table demonstrates the improvement in the SPCMC Sum Score was statistically significant. a Students demonstrated the highest differences in paired means for this competency statement. 54 QUALITY IMPROVEMENT EDUCATION 55 Table 3 Comparing the Means of Two Related Groups (Subgroup of Healthcare Industry Students N=20) Pretest M SD SPCMC1 I can compare and contrast Posttest M SD Mean Diff p ES 1.95 0.69 2.50 0.69 0.55 .008 0.67 1.70 0.80 2.10 0.97 0.40 .088 0.40 2.60 0.68 2.85 0.75 0.25 .135 0.35 2.30 0.87 2.70 0.66 0.40 .028 0.53 2.00 0.65 2.55 0.83 0.55 .008 0.67 1.75 0.85 2.70 0.73 0.95 a <.001 1.01 different change models. SPCMC2 I can apply a standard change management model or framework (e.g., Lewin, Kotter, Rogers, Kubler-Ross) to support workplace improvements. SPCMC3 I can describe the value of a needed change and how it applies to my coworkers. SPCMC4 I know how to encourage a change management strategy through the workplace. SPCMC5 I can explain the stages of behavior that may occur when experiencing a workplace change and what to expect at each stage. SPCMC6 I can discuss how the use of change management principles and tools QUALITY IMPROVEMENT EDUCATION 56 impacts peoples' response to workplace changes SPCMC7 I can collaborate with 2.55 0.76 2.85 0.75 0.30 .055 0.46 1.95 0.89 2.35 0.88 0.40 .119 0.37 1.95 0.95 2.40 0.94 0.45 .035 0.51 2.00 0.86 2.50 0.89 0.50 .021 0.56 1.75 0.85 2.35 0.93 0.60 .01 0.64 1.95 0.83 2.35 0.88 0.40 .057 0.45 participants in my workplace to plan and carry out change and create buy-in. SPCMC8 I know how to apply change management tools relevant to the separate phases when making a workplace change. SPCMC9 I know how to use change management tools (e.g., Stakeholder Analysis, Elevator Speech) to analyze employee acceptance, influence, or resistance to change. SPCMC10 I can evaluate the impact of change efforts (e.g., impact analysis and assess change readiness). SPCMC11 I can coach leaders on change management processes and tools. SPCMC12 I know how to implement a variety of strategies to reduce the barriers that can block lasting change. QUALITY IMPROVEMENT EDUCATION SPCMC Sum Score 24.45 57 7.90 30.20 8.43 5.75 .003 0.77 Note. SPCMC = Self-Perception of Change Management Competency; SD = standard deviation; Diff = difference; ES = effect size. The subgroup of healthcare industry students raised their perceived level of competency for each change management competency statement on a scale from 1 4. 1 = Novice a person new to or inexperienced in a field, 2 = Emerging proficiency gaining knowledge or becoming skillful in a field, 3 = Competent have the necessary ability, knowledge or skill to be successful in a field, 4 = Master have the comprehensive knowledge or skill to command a field. A paired-samples t-test, t(19) = -3.43, p = .003, indicated that the two times differed statistically at an alpha .05 level. This table demonstrates that the SPCMC Sum Score improvement for the subgroup of healthcare industry students was statistically significant. a The subgroup of healthcare industry students demonstrated the highest differences in paired means for this competency statement. QUALITY IMPROVEMENT EDUCATION 58 Table 4 Differences Between Groups (Non-Healthcare Industry Students and Healthcare Industry Students) NonHealthcare N = 76 M SD SPCMC1 I can compare and contrast Healthcare N = 20 M SD Mean Diff p ES 2.38 0.71 2.50 0.69 0.12 .506 0.17 1.97 0.88 2.10 0.97 0.13 .577 0.14 2.80 0.67 2.85 0.75 0.05 .785 0.07 2.63 0.76 2.70 0.66 0.07 .715 0.09 2.55 0.77 2.55 0.83 0.00 .989 0.00 2.42 0.74 2.70 0.73 0.28 .134 0.38 different change models. SPCMC2 I can apply a standard change management model or framework (e.g., Lewin, Kotter, Rogers, Kubler-Ross) to support workplace improvements. SPCMC3 I can describe the value of a needed change and how it applies to my coworkers. SPCMC4 I know how to encourage a change management strategy through the workplace. SPCMC5 I can explain the stages of behavior that may occur when experiencing a workplace change and what to expect at each stage. SPCMC6 I can discuss how the use of change management principles and tools impacts peoples' response to workplace changes QUALITY IMPROVEMENT EDUCATION SPCMC7 I can collaborate with 59 2.67 0.70 2.85 0.75 0.18 .318 0.25 2.42 0.80 2.35 0.88 0.07 .731 0.09 2.21 0.81 2.40 0.94 0.18 .368 0.23 2.36 0.71 2.50 0.89 0.15 .442 0.19 2.17 0.86 2.35 0.93 0.18 .416 0.21 2.39 0.83 2.35 0.88 0.05 .833 0.05 28.99 6.822 30.20 8.43 1.21 .503 0.17 participants in my workplace to plan and carry out change and create buy-in. SPCMC8 I know how to apply change management tools relevant to the separate phases when making a workplace change. SPCMC9 I know how to use change management tools (e.g., Stakeholder Analysis, Elevator Speech) to analyze employee acceptance, influence, or resistance to change. SPCMC10 I can evaluate the impact of change efforts (e.g., impact analysis and assess change readiness). SPCMC11 I can coach leaders on change management processes and tools. SPCMC12 I know how to implement a variety of strategies to reduce the barriers that can block lasting change. SPCMC Posttest Sum Score Note. An independent t-test, t (94) = -0.67, p = .503, indicated the two groups' posttest scores did not differ statistically at an alpha level of .05. The difference between the groups is not statistically significant. QUALITY IMPROVEMENT EDUCATION 60 Figure 1 Mean Pretest Scores by Question by Group Mean Score Pretest Non-Healthcare Industry Students Mean Score Pretest Healthcare Industry Students 3 2 1 0 Note. The subgroup of healthcare industry students brought to class a lower perception of change management competency than the subgroup of non-healthcare industry students, as they had a lower mean pretest sum score on every competency statement except SPCMC2. QUALITY IMPROVEMENT EDUCATION 61 Appendix Self-Perception of Change Management Competency C1. Change management is a systematic approach to drawing people into support of needed workplace changes. However, research shows many workplace changes do not last over time. This creates an opportunity to standardize how to teach processes, tools, and techniques to manage the people side of business. This survey is intended to gather information about how you think about your change management skills. Thank you for taking 5-10 minutes to participate in this brief 20 question academic survey. Individual responses will be kept confidential, and survey data will be reported in aggregate. There are no risks involved, and it 'won't cost you anything besides your time. There are no direct benefits to you, but your participation will help us in our research. We understand if you 'aren't interested, or 'don't feel comfortable participating. If you would like to volunteer, please read the following information carefully before beginning the questionnaire. CONSENT TO PARTICIPATE IN RESEARCH STUDY STUDY TITLE: Assessing the Effect on Student Competency Using Simulation to Teach the Quality Improvement Principle of Change Management PRINCIPAL INVESTIGATOR: Heidi H. Ewen, Ph.D. CONTACT DETAILS: Tel: 1 (317) 791-4425 Email: ewenh@uindy.edu STUDENT INVESTIGATOR: Jennifer J. Skelton CONTACT DETAILS: Email: skeltonj@uindy.edu PURPOSE AND DURATION: This study involves research on change management skill education and self-perceived competency. The purpose of this study is to determine if there is a significant difference in self-perceived competency in a convenience sample of graduate business administration students. This study also aims to compare the competency scores between students from various industries. We hope to encourage further investigation and provide valuable insight into effective ways to improve the competency of emerging leaders in the quality improvement principle of change management. We expect that the questionnaire will take less than 10 minutes of your time. PROCEDURES: You will be responding to 7 demographics questions and 13 questions regarding your self-perception of change management competency before and after the change management simulation exercise. RISKS AND DISCOMFORT: There are no foreseeable risks or discomfort associated with this study. BENEFITS: There are no direct benefits to you, but you may enjoy learning more about change management skills. COMPENSATION: You will not be compensated for participating in this study. However, you QUALITY IMPROVEMENT EDUCATION 62 will have the opportunity to enter a drawing for one of 4 $25 gift cards after completion of both the pre and post lesson surveys. CONFIDENTIALITY: Participants will create a unique study identification number which will allow pretest and posttest survey matching and protect 'participants' confidentiality. All survey data will be kept confidential. FUTURE RESEARCH: Your data will not be used or distributed for future research studies even though there is no way for your data to be linked with any information that could identify you. WITHDRAWAL OF PARTICIPATION: Your participation is voluntary and in no way associated with your grade in this course. Should you decide at any time during the study that you no longer wish to participate, you may withdraw your consent and discontinue your participation. REQUEST FOR MORE INFORMATION: You may ask more questions about the study at any time. Please e-mail the principal investigator at ewenh@uindy.edu or call 1 (317) 791-4425 with any questions. In addition, if you have questions about your rights as a participant or any other pertinent questions, you may contact the Human Research Protections office by either emailing hrpp@uindy.edu or calling 1 (317) 781-5774 or 1 (800) 232-8634 ext. 5774. If you would like to volunteer, please select one of the options below: Yes, I voluntarily consent to participation and wish to proceed No, I do not consent to participation Demographics C2. Please type the first three letters of your mother's family name and the last four numbers of your phone number to create a unique Participant ID# _______ D1. Your age (years)______ D2. Gender ______ (Categorical: Male, Female, Non-binary/third gender, Prefer not to say) D3. In what industry do you primarily work? (Categorical: Healthcare, Technology, Construction, Retail, Manufacturing, Other) D4. How many years of experience do you have in this industry? ____ D5. Where has most of your work experience been in this industry? (Categorical: U.S. Northeast, U.S. Southeast, U.S. Northwest, U.S. Southwest, U.S Midwest, Outside of the U.S.) D6. Undergraduate degree major(s) and/or minor ___________ QUALITY IMPROVEMENT EDUCATION 63 D7. Please rank the following change management skill competencies in the order you feel are strongest (1= strongest skill, 4 = weakest skill) _____ I can manage coworkers that oppose change by supporting their mental, emotional and physical needs _____ I can compare the different change management models and select the most appropriate one for the situation _____ I communicate well to share with all participants the value of change _____ I can influence and facilitate change in others by selling ideas, coaching, and listening Survey Instructions For the following 13 statements, please rate your perceived level of competency for each change management ability on a scale from 1 4: 1= Novice a person new to or inexperienced in a field 2= Emerging proficiency gaining knowledge or becoming skillful in a field 3= Competent have the necessary ability, knowledge, or skill to be successful in a field 4= Master have the comprehensive knowledge or skill to command a field Begin Survey SPCMC1. I can compare and contrast different change models 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC2. I can apply a standard change management model or framework (e.g., Lewin, Kotter, Rogers, Kubler-Ross) to support workplace improvements 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC3. I can describe the value of a needed change and how it applies to my coworkers 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master QUALITY IMPROVEMENT EDUCATION 64 SPCMC4. I know how to encourage a change management strategy through the workplace 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC5. I can explain the stages of behavior that may occur when experiencing a workplace change and what to expect at each stage 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC6. I can discuss how the use of change management principles and tools impacts 'peoples' response to workplace changes 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC7. I can collaborate with participants in my workplace to plan and carry out change and create buy-in 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC8. I know how to apply change management tools relevant to the separate phases when making a workplace change 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC9. I know how to use change management tools (e.g., Stakeholder Analysis, Elevator Speech) to analyze employee acceptance, influence, or resistance to change 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC10. I can evaluate the impact of change efforts (e.g., impact analysis and assess change readiness) 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC11. I can coach leaders on change management processes and tools 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master QUALITY IMPROVEMENT EDUCATION 65 SPCMC 12. I know how to implement a variety of strategies to reduce the barriers that can block lasting change 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master SPCMC13. How would my supervisor rank my skill level for managing workplace changes? 1= Novice 2 = Emerging proficiency 3 = Competent 4 = Master Exit1 PRE Would you like to submit your email address for a chance to win a $25 gift card? Yes, I would like to enter my email address No, I do not wish to enter my email address Exit1POST Would you like to submit your email address for a chance to win a $25 gift card? (Please enter the same email address submitted at the conclusion of the pretest survey.) Yes, I would like to enter my email address No, I do not wish to enter my email address We thank you for your time spent taking this survey. Your response has been recorded.  ...

... Acute Effects of Beetroot Juice on Locomotor Economy and Capacity in Chronic Stroke Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Jennifer Lotter, PT, NCS Copyright 2/28/2024 By: Jennifer Lotter, PT, NCS All rights reserved Approved by: Stephanie A. Miller, PT, PhD, NCS Committee Chair ______________________________ T. George Hornby, PT, PhD Committee Member ______________________________ Stephen J. Carter, MS, PhD Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE Acute Effects of Beetroot Juice on Locomotor Economy and Capacity in Chronic Stroke Jennifer K. Lotter Department of Interprofessional Health & Aging Studies, University of Indianapolis Author Note This study was registered with ClinicalTrials.gov (Identifier NCT5720013). The authors report no known conflicts of interest to disclose. Funding source provided by start-up funds from the Locomotor Recovery Lab, Indiana University. 1 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 2 Abstract People with stroke are often older with a decreased quality of life, often due to limited mobility (Benjamin et al., 2018). In addition, their aging vascular systems may not be as responsive to exercise, which impedes their ability to participate in established interventions at higher cardiovascular intensities to improve mobility, specifically via walking ability (Hornby, Reisman, et al., 2020). Dietary nitrate (NO3-) in the form of beetroot juice (BRJ) has been shown to have positive effects on oxygen uptake (VO2) during exercise, presumably through nitric oxide (NO) mechanisms across varying populations, including healthy older adults but also in people with comorbidities common to those with stroke (e.g., heart failure, hypertension, obesity)(Behrens et al., 2020; Coggan et al., 2018; Dominguez et al., 2017). The primary objective of this study is to examine the effects of an acute dose of BRJ on VO2 during steadystate gait (i.e., gait economy) in individuals with chronic stroke. This double-blinded repeated measures cross-over study (n = 19) assessed VO2 during steady-state walking (primary measure) in participants (age 50-89) with chronic stroke comparing an acute dose of BRJ vs. placebo (PLA). Steady-state heart rate (HR), steady-state ratings of perceived exertion (RPE), and peak VO2 (VO2peak) during a maximal-exertion graded treadmill test, peak HR, and peak RPE were secondary outcomes. Repeated measures ANOVA revealed no significant interaction of steadystate VO2 (p = 0.27; effect size?) for conditions of time and group. Also, no significant interactions were noted for all secondary measures during submaximal or maximal exertion tests. Despite an increase in NO precursors, including NO3- and nitrite (NO2-) following BRJ consumption (p = <0.001 and p < 0.005, respectively), an acute dose of BRJ did not appear to improve gait economy as there was no change in VO2 during steady-state walking. EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 3 Acute Effects of Beetroot Juice on Locomotor Efficiency and Capacity in Chronic Stroke Stroke is a leading cause of long-term disability, with an incidence of 800,000 annually in the United States (Virani et al., 2020). Primary physical impairments for individuals post-stroke include weakness and postural instability, which interfere with locomotor function and physical activity (Virani et al., 2020). Reduction in mobility for individuals with chronic stroke contributes to systemic deconditioning and decreased aerobic capacity, both of which can exacerbate locomotor impairments. Given the consequence of significant disability and attendant medical costs, prior research has focused on minimizing the direct and indirect effects of stroke. The functional consequences of reduced aerobic capacity are commonly manifested as a loss of mobility independence. Likewise, gait economy is defined as the rate of oxygen uptake (VO2) per unit distance. This outcome has also been linked to decreased community mobility due to limited gait function for individuals post-stroke (Moore et al., 2010). Previous data suggest measures of gait economy or surrogate indices of cardiovascular demands (i.e., physiological cost index) are associated with locomotor function (Leddy et al., 2016; Macko et al., 2005). Alternatively, improvements in gait economy and efficiency (i.e., reduced VO2 at matched speeds) following exercise training at higher intensities (up to 85% age-predicted maximum HR) may be associated with gains in walking ability in both clinical and community settings (Moore et al., 2010). Though higher-intensity exercise training represents a non-pharmacologic strategy to improve gait economy, further research is needed to uncover potential options that can supplement exercise to restore locomotor function among chronic stroke survivors. Dietary supplements rich in NO3-, commonly consumed as BRJ, have received much attention in the last decade due to their beneficial effects on multiple performance indicators among healthy and chronic disease populations. Following ingestion, NO3- is first converted to EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 4 NO2- by commensal bacteria in the oral cavity. The one-electron reduction from NO2- to nitric oxide (NO), a potent vasodilator, is expedited in conditions of low oxygen tension and/or low pH (Carter et al., 2020). Previous work has shown improved exercise performance with beetroot juice (BRJ), as evidenced by reduced oxygen cost during submaximal and maximal exertion exercise in people diagnosed with peripheral arterial disease, obesity, and heart failure (Behrens et al., 2020; Coggan et al., 2018; Dominguez et al., 2017; Pekas et al., 2021). However, the acute effects of BRJ on exercise performance in individuals with a history of stroke have not been examined. Problem Statement High-intensity training (HIT) focused on stepping activities has been shown to improve locomotor function in clinical and community settings in individuals post-stroke, with positive effects on muscle strength, balance, peak aerobic capacity, and economy (Holleran et al., 2014; Hornby et al., 2019; Hornby, Reisman, et al., 2020; Leddy et al., 2016). Despite these improvements, exercise at these higher intensities is often challenging for this patient population. In addition to neurological deficits, common co-morbidities of stroke, including obesity, physical inactivity, and cardiovascular limitations, can impede the ability to achieve higher aerobic intensities (Feigin et al., 2016). Recent studies using BRJ supplementation have demonstrated increased aerobic endurance (e.g., time to exhaustion, lowered oxygen cost) in individuals with heart failure (i.e., both preserved and reduced ejection fraction) (Coggan et al., 2018; Zamani et al., 2015), obesity (Behrens et al., 2020), and hypertension (Vanhatalo et al., 2010). Accordingly, pre-exercise BRJ supplementation may benefit those with a history of stroke by lowering the energetic cost of walking. While the exact physiological mechanisms remain unclear, ostensibly, such enhancements may be related to an increase in NO bioavailability and EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 5 improved mitochondrial function via increased calcium sensitivity (Coggan et al., 2021; Coggan & Peterson, 2016). However, prior work has yet to examine the effects of acute BRJ supplementation on walking performance in individuals with chronic stroke. Understandably, even a modest improvement in walking performance may offer translational value, given the impact of mobility independence on quality of life., Purpose Statement This study evaluated the effects of acute BRJ supplementation gait economy in participants with chronic stroke. If BRJ supplementation lowers VO2 and perceived exertion during exercise in patients with chronic stroke, participation in aerobic exercise may increase, where the individual can reap benefits from the exercise. Hypotheses 1. Compared to the placebo condition, acute supplementation with BRJ will not affect the gait economy during steady-state among individuals with a history of stroke. (Ho) 2. Compared to the placebo condition, acute supplementation with BRJ will not affect the gait economy during steady-state among individuals with a history of stroke. (Ha) Significance of the Study The results of this study could positively impact chronic stroke survivors and the medical community, especially in physical therapy. Stroke is a leading cause of disability in the United States (Murray et al., 2013), and using an effective treatment strategy to limit a person's disability is vital. Decreasing disability in stroke survivors is likely to positively impact life satisfaction and reduce the burden on the healthcare system. Restoring physical function could also affect the potential of stroke survivors' ability to return to work, live more independently, EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 6 and have a higher quality of life. If pre-exercise BRJ supplementation increases exercise capacity, individuals with stroke may participate in aerobic activities to improve their general health. Literature Review In the United States, stroke is the primary cause of long-term disability, in which incidence and severity rise with advancing age (Virani et al., 2020). Experts attribute much of the risk to systemic, age-related changes affecting the vascular wall's functional performance, including endothelial dysfunction and arterial stiffening (Donato et al., 2018; Lakatta & Levy, 2003; Seals & Alexander, 2018; Seals et al., 2011; Sindler et al., 2014). Regrettably, such changes also frequently coincide with obesity and physical inactivity, which positively correlate with stroke incidence (Virani et al., 2020). Using a systematic review of validated outcome measures, Wesselhoff and colleagues (2018) showed that individuals post-stroke exhibit impaired community mobility, as evidenced by 30-83% of normative or non-stroke participants. These findings underscore the need to uncover novel strategies designed to attenuate the mobility constraints experienced by stroke survivors. To this end, over the past two decades, studies have evaluated the use of BRJ, a dietary supplement rich in NO3-, for its potential health and performance benefits. Multiple reports have demonstrated a broad range of acute benefits by BRJ, many relevant to individuals post-stroke. The following subsections discuss the age-related changes in vascular health, the relevance of NO in stroke, and finally, the potential utility of BRJ in clinical populations. Vascular Aging and Stroke EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 7 Stroke incidence is linked to the accretion of atherosclerotic plaque and chronological age (Virani et al., 2020). As the radius of an arteriole readily affects blood flow, cerebrovascular narrowing can lead to local hypoxia and nutrient deprivation both of which can irreversibly damage central nervous system tissue (Paneni et al., 2017). On the other hand, a healthy vascular endothelium synthesizes several bioactive molecules (e.g., prostacyclin, endothelin-1) that modulate vessel tone to ensure appropriate end-organ tissue perfusion and blood pressure maintenance (Carter et al., 2020). With advancing age, the mechanisms responsible for modulating vascular function are not well-persevered and can readily compromise adequate blood flow. Declining NO bioavailability, for one, can potentiate local inflammation involved with prothrombotic effects and initiation of atherosclerosis (Paneni et al., 2017). Accordingly, it is important to acknowledge how vascular aging can impact endothelial function and atrial compliance. It is possible that increasing NO bioavailability could positively affect the overall function of those diagnosed with overt cardiovascular disease. Endothelial Dysfunction The term "endothelial dysfunction" is characterized by structural and functional changes linked to cardiovascular diseases, a significant stroke risk factor (Seals & Alexander, 2018; Virani et al., 2020). During the aging process, the vascular endothelium demonstrates a decreased ability to react to a variety of inputs from the body (e.g., increased blood flow, inflammation responses, decreased immune responses, and oxidative stress resulting in dysfunction) (Donato et al., 2018). Oxidative stress occurs when there is an imbalance of superoxide radicals, referred to as reactive oxygen species (ROS) (Pizzino et al., 2017). The increase of ROS occurs from mitochondrial respiration, environmental factors such as pollution, and a decreased ability to clear these super-anions as aging occurs (Grote et al., 2019; Pizzino et EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 8 al., 2017). An increase in ROS disrupts NO formation through the L-Arginine-NOS pathway by uncoupling the nitric oxide synthases (NOS) (Izzo et al., 2021). This decrease in NO production leads to an impairment wherein the endothelium is unable to dynamically maintain homeostasis. This, in turn, increases the incidence of atherosclerosis and stroke in an aging population (Izzo et al., 2021; Pizzino et al., 2017; Seals et al., 2011). Arterial Stiffening Large artery stiffening increases vascular resistance, which increases myocardial workload (Donato et al., 2018; Seals et al., 2014). Due to a loss in elastin and the development of fibrotic tissue, aging arteries exhibit a decreased ability to accommodate changes in blood flow (LaRocca et al., 2017). Importantly, increased aortic stiffness reduces the ability to act as a secondary pump because the artery cannot expand as blood is pushed from the left ventricle, followed by recoil to allow for more consistent blood flow (Donato et al., 2018). The resultant effect widens the gap between systolic blood pressure (SBP) and diastolic blood pressure (DBP) (Donato et al., 2018), which jeopardizes end-organ tissue perfusion and tissue oxygenation (Donato et al., 2018; Lakatta & Levy, 2003; Paneni et al., 2017). Though age-related changes to the vascular system are a natural phenomenon, it is vital to consider interventions that may augment the positive effects of exercise. As such, a potential option could be supplementation with NO3--rich BRJ that may enhance NO bioavailability. Nitric Oxide NO is a free radical with a short half-life that regulates several physiological functions (Hord et al., 2009). NO plays an essential role in vasodilation for vascular function through various actions (e.g., smooth muscle relaxation) (Donato et al., 2018). NO causes vasodilation of the blood vessels in response to changes in blood flow, where the increase in shear stress EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 9 activates endogenous production of NO (Donato et al., 2018; Sandoo, 2010). NO attenuates platelet aggregation along the vascular endothelium and promotes vasodilation by acting on the calcium channels to elicit smooth muscle relaxation. (Moncada & Higgs, 2006; Sandoo, 2010). NO also potentially improves skeletal muscle activity by opening the calcium channels and inducing increased calcium sensitivity, especially in type II muscle fibers (Coggan et al., 2018). This improvement in skeletal muscle activation can enhance exercise performance through increased twitch force, maximal power, rate of developing force, and maximal shortening velocity (Coggan et al., 2018; Lundberg et al., 2011). Improvements in vasodilation and tissue perfusion notwithstanding, increased skeletal muscle activation via NO could benefit those with stroke by elevating joint powers needed to enhance walking function. L-Arginine-NOS Pathway The L-arginine- nitric oxide synthase (NOS) pathway produces NO, which NO3- and NO2- are considered end-products of the unused NO in the body (Lundberg et al., 2008; McDonagh et al., 2019). In this pathway, NO is produced within the body when NOS catalyzes L-arginine, an amino acid, and molecular oxygen. NO oxidizes to NO3- and NO2- if unused in the body (Lundberg et al., 2011). Three different categories of NOS exist within the body that direct the actions of NO (Hord et al., 2009). These categories involve neuronal functions, inflammatory and immune responses, and endothelial homeostasis (Hord et al., 2009). Endothelial NOS is the enzyme that most directly affects vascular homeostasis since it regulates vascular function during varying stimuli (e.g., exercise) (Hord et al., 2009). In the L-arginine-NOS pathway, NO3- and NO2- are considered a potential reservoir for NO production where these end-products are reconverted to NO, ostensibly during physical exertion (Lundberg et al., 2011; Sandoo, 2010). EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 10 Reduced oxygen tension triggers the conversion of NO2- to NO, which acts on the vascular endothelium (Tejero et al., 2019). To counter NO overproduction within the system, the ROS uncouples the eNOS, which is vital to converting NO2- to NO during instances of reduced oxygen (Landmesser et al., 2003). With advancing age, oxidative stress disrupts the L-arginineNOS pathway (Seals et al., 2011). The resultant decrease in NO bioavailability diminishes the responsiveness of the vascular system to adjust to the demands placed on it. The resultant effects of reduced NO bioavailability directly impair physical function and exercise tolerance which is concerning for individuals with a history of stroke. Nitrate-Nitrite-Nitric Oxide Pathway Researchers have discovered that dietary NO3- is converted to NO2- and eventually NO through an enterosalivary pathway (Lundberg et al., 2009). This pathway functions independently of oxygen and may possess practical relevance for individuals with stroke when physically active. In the past, levels of NO3- and NO2- in the body have been a concern due to a potential link to gastrointestinal cancers (Hord et al., 2009). However, this stance may shift due to recent investigations where NO bioavailability improves during exercise via the NO3--NO2-NO pathway. This pathway has also become an area of interest because it seems to elicit attendant health benefits, especially in older, chronic disease populations. Inorganic NO3- is found in BRJ and leafy green vegetables. When consumed, NO3- is reduced to NO2- via bacterial nitrate reductases in the mouth, which is then swallowed and proceeds to the stomach (Bonilla Ocampo et al., 2018). In the stomach, some of the NO2- is further reduced to NO, where there are potential cytoprotective actions to prevent ulcers in the stomach (Lundberg et al., 2008). The surviving NO3- and NO2- in the stomach are then absorbed into the circulatory system, where various options for reduction to NO are available, including EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 11 low oxygen tension and low pH environments. Notably, such conditions often arise with advancing age and during physical exertion (Bonilla Ocampo et al., 2018; Lundberg et al., 2008; Woessner et al., 2018). NO and Beetroot Juice BRJ is a rich source of inorganic nitrate (Hord et al., 2009). The DASH (Dietary Approaches to Stop Hypertension) Diet, which often includes a high vegetable intake, tends to correspond with nitrate consumption that exceeds the World Health Organization's acceptable daily intake recommendations by 550% (Hord et al., 2009). Given the noted blood pressurelowering effects, a possible reexamination of nitrate/nitrite recommendations may be needed in adults post-stroke. There are some possible increased risks to infants, and the manner of consuming nitrates (e.g., fruits and vegetables versus processed meats) may also be an important factor to consider in consuming dietary nitrates (Hord et al., 2009). The idea of improved NO bioavailability through the NO3--NO2--NO pathway is novel in the stroke population. It may improve exercise capability as many in this population have oxidative stress, making the endogenous L-arginine pathway less effective. NO has demonstrated many health benefits, including decreasing blood pressure (Jajja et al., 2014), improving muscle contractility (Coggan & Peterson, 2018), and increasing exercise tolerance (Bailey et al., 2015) in individuals without neurologic injury. These actions may provide an ergogenic aid to exercise capabilities in patients with stroke. Such effects may work through the combined effects of augmenting vasodilation, tissue perfusion, and improved skeletal muscle contractility (Coggan & Peterson, 2018; Donato et al., 2018; Lundberg et al., 2008). Beetroot Juice and Hypertension EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 12 Endothelial dysfunction is a precursor to many cardiovascular diseases, including stroke, due to the disruption in vasodilation (Bonilla Ocampo et al., 2018). The risk of hypertension increases with advancing age, and there is a shift toward waning NO bioavailability linked to systemic inflammation and oxidative stress (Carter et al., 2020). As such, supplementation with BRJ may aid individuals post-stroke by transiently aiding vascular health through the reduction of NO3- to NO (Bonilla Ocampo et al., 2018). In able-bodied individuals, recent reports suggest chronic supplementation of BRJ (e.g., six to seven days) is more effective than acute supplementation in lowering blood pressure (Bonilla Ocampo et al., 2018; Eggebeen et al., 2016). Kapil et al. (2015) found significantly decreased blood pressure, improved endothelial function, and decreased arterial stiffness after ingesting a BRJ supplement of about 6.4 mmol nitrate daily for four weeks. Bonilla-Ocampo et al. (2018) also support this dosage and report that an acute dose of 5 to 8 mmol of inorganic NO3is required to reduce blood pressure in adults. There appears to be a dose-response to lowering effects of systolic and diastolic blood pressures, and according to Wylie et al. (2013), the changes in blood pressure were not different between 8.4 and 16.8 mmol of NO3-. Secondary characteristics may also contribute to the effectiveness of the BRJ supplement. For instance, males appear more responsive to BRJ (Bonilla Ocampo et al., 2018). Also, older adults may have more considerable diastolic changes when compared to younger adults, as older adults may exhibit a decreased ability to access NO endogenously (Stanaway et al., 2019). The BRJ supplement can provide NO to the vascular system in this population, allowing for improved vasodilation (Stanaway et al., 2019). Bonilla-Ocampo et al. (2018) also report that higher resting blood pressure demonstrates more responsiveness to the BRJ supplement. This concept is essential to understand when assessing the effectiveness of BRJ on blood pressure. EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 13 For instance, Jakubcik et al. (2021) found no differences in blood pressure following a BRJ supplement; however, the participants had lower blood pressure than other studies. Also, Oggioni et al. (2018) found no changes in systolic or diastolic blood pressure in 20 healthy older adults following a seven-day BRJ supplementation. The lack of significance may be attributed to the absence of any pre-existing co-morbidities. The work of Rogerson et al. (2022) also notes no significant findings on blood pressure and microvascular endothelial function in healthy young and older participants between taking BRJ or PLA supplements. However, there is some evidence to support more extensive blood pressure changes with dietary NO3- in people with obesity (Bezerra, 2019; Bonilla Ocampo et al., 2018; Jajja et al., 2014), which may be due to the increase in cardiovascular disease within this population; however, the exact cause is unclear. In acute or chronic supplementation, dietary nitrate appears more effective at lowering blood pressure in people with poor baseline health status. Beetroot Juice and Clinical Populations Heart failure is also a common co-morbidity of stroke and consists of two major categories depending on the proportion of ejection fraction. Some researchers report that people with heart failure may have decreased NO available throughout their bodies through the Larginine- NOS pathway (Coggan & Peterson, 2016). Thus, BRJ may be an effective supplement to improve exercise capacity (e.g., ventilatory measures and muscle function) in heart failure, as the NO3--NO2--NO pathway supports NO bioavailability during exercise. NO synthesized by eNOS increases vasodilation (Woessner et al., 2020) and tissue perfusion such that physiologic reserve may be increased during walking. Another benefit of NO for heart failure patients is improving skeletal muscle function. Evidence also supports that the NO3--NO2--NO pathway can improve muscle contractility by opening the calcium channels to allow for various factors, such EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 14 as twitch force, rate of force development, estimated maximal shortening velocity, and maximal power (Coggan & Peterson, 2018). Researchers have shown that acute BRJ consumption improves VO2peak during a maximal exertion exercise test in those with heart failure with a preserved ejection fraction (HFpEF) by decreasing peripheral vascular resistance (Zamani et al., 2015). Interestingly, Eggebeen et al. (2016) found that one week of BRJ supplementation increased time to exhaustion in submaximal exercise in HFpEF but was unchanged with an acute dose of BRJ. There were similar results of increased VO2peak, peak power, and time to exhaustion during maximal exercise in people with heart failure and reduced ejection fraction (HErEF), but no changes in VO2 in steady-state exercise (Coggan et al., 2018). In addition, Woessner et al. (2020) reported an increase in cardiac output, stroke volume, and decreased total peripheral resistance after a thirteen-day BRJ supplement in participants with HErEF. This study suggests that in the HFrEF population, NO can affect cardiac muscle contractility, as evidenced by the increase in stroke volume and cardiac output (Woessner et al., 2020). To support the theory of increased skeletal muscle function, a study found an increase in peak power and peak torque in lower extremity skeletal musculature after one acute dose of dietary nitrate in participants with HFrEF, suggesting NO may positively affect contractility; however, the exact pathway remains unclear (Coggan et al., 2015). Although there appears to be consistent exercise benefits of dietary nitrate for those with heart failure, it is difficult to generalize these results due to variability in dosage (both in concentration and number of days), exercise testing protocols, and small sample sizes. While there appears to be more evidence that BRJ can have beneficial effects on blood pressure, stroke volume, and cardiac output in people with heart failure, a study with a similar research design and dosage found that nine days of a BRJ supplement did not affect these outcomes in those with HFrEF (Hirai et al., EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 15 2017). These mixed findings highlight the need for further research into the potential utility of dietary nitrate among chronic disease populations. Peripheral arterial disease (PAD) due to atherosclerotic accretion is another common comorbidity of stroke (Noor et al., 2019). Due to the potential effect of promoting vasodilation, BRJ may enhance functional (e.g., walking) outcomes in this population. PAD is often associated with decreased endothelial dysfunction, local tissue hypoxia, and reduced NO bioavailability, which is common in cardiovascular diseases (Kenjale et al., 2011). As NO is the end product of inorganic dietary nitrate, BRJ supplementation may be beneficial to the PAD population. Kenjale et al. (2011) found that participants could exercise longer before experiencing claudication pain and an increase in time to exhaustion during exercise following an acute dose of BRJ compared to a placebo. In contrast, Van der Avoort et al. (2021) found no effect on mean claudication time, peak walking time, blood pressure, arterial stiffness, and gastrocnemius oxygenation, despite changes in NO3- and NO2- plasma levels following a nitraterich breakfast or acute nitrate supplement. However, many participants were smokers, which can interfere with the conversion of NO3- to NO2- in the mouth (Bailey et al., 2016) and thus impact the potential benefits of dietary nitrate supplementation. Another study found improved endothelial function when analyzing popliteal and brachial flow-mediated dilation, significant improvement in maximal walking distance, and time to completion. (Pekas et al., 2021). The same group also found an improvement in 6-minute walking distance in another study but no differences in pain-free walking time between BRJ and PLA (Pekas et al., 2023). Taken together, current data suggests BRJ aids walking function in PAD likely through the combined effects acting on the vascular system. Summary EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 16 Nitric oxide generation through the NO3--NO2--NO pathway has demonstrated variable results on exercise capacity in chronic disease populations. Nevertheless, there are consistent findings of improved exercise tolerance (e.g., increased time to exhaustion), lower blood pressure, and improved muscle contractility during sub-maximal and maximal exertion tests. Such effects offer potential translational utility to individuals with a history of stroke. More specifically, people with stroke frequently demonstrate a decrease in walking function with subsequent restrictions in community participation that negatively impact quality of life. Established practice guidelines in physical therapy to improve walking function in chronic stroke recommend HIT, which includes large amounts of stepping practice at high to vigorous cardiovascular intensities (Hornby, Henderson, et al., 2020; Hornby, Reisman, et al., 2020). HIT after stroke can prove challenging due to this population's high incidence of cardiovascular and physical co-morbidities (Virani et al., 2020) and reduced volitional control of the lower extremities. Of additional concern, the endogenous bioavailability of NO can be further limited in this population, confounding an established treatment intervention to improve walking capability in stroke survivors. Through the NO3--NO2--NO pathway, BRJ may promote vasodilation, tissue oxygenation, and skeletal muscle contractility, thereby improving walking function. The translational implications possibly include increased participation in free-living physical activity. Importantly, BRJ supplementation could also improve skeletal muscle activation during exercise, especially in type II fibers used primarily in high-intensity interval training, further improving the ability to perform HIT gait training after stroke. BRJ may attenuate hypertension in the stroke population and increase safety during exercise. Also, BRJ may improve tissue oxygenation via endothelial dilation, increasing stroke volume and VO2peak during moderate-to- EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 17 vigorous exercise, specifically high-intensity gait training. To date, prior work has yet to examine the effects of acute BRJ supplementation on walking performance among individuals with chronic stroke. Methods Study Type and Design A randomized, double-blinded, repeated-measures crossover design was employed to study the effect of acute BRJ supplementation on exercise capacity in participants with chronic stroke. The study had two intervention arms; one contained an over-the-counter BRJ supplement, and the other a nitrate-deplete PLA that tasted similar to the BRJ supplement manufactured by the same company (James White Co.). The study design consisted of a baseline assessment and two intervention sessions. During the first intervention session, the participant performed graded and submaximal exercise tests before and after taking the assigned supplement (BRJ or PLA). There was a washout period of 7 days before the second intervention session, during which the participant performed the same protocol but with the other condition. Research staff and participants were blinded to supplement type during each testing session. A designated researcher randomized the participant to the BRJ or PLA for the initial intervention after completing the baseline assessment. The study conformed to guidelines set forth by the Indiana University Institutional Review Board and an established reliance agreement with the University of Indianapolis Institutional Review Board. Participants The research staff used a convenience sample of individuals post-stroke recruited from the outpatient clinics of local rehabilitation centers. Inclusion criteria consisted of the following: 1) individuals with chronic (> six-month duration) hemiparesis following unilateral stroke, 2) 50- EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 18 89 years old, 3) ability to ambulate at least 0.3 m/s, with or without using an assistive device or bracing below the knee, 4) ability to read and speak English, and 5) ability to follow three-step commands. Exclusion criteria included 1) the presence of cerebellar deficits, 2) any other neurological or orthopedic deficits that would limit walking ability, 3) uncontrolled cardiovascular, respiratory, and/or metabolic disease, 4) use of nitrates or proton-pump inhibitors, 5) smoking (Bailey et al., 2016), and 6) unable to receive physical therapy sessions between the two intervention sessions for gait-related impairments. Setting The study occurred at the Locomotor Recovery Lab (LRL) at RHI and is associated with the Indiana University School of Medicine. Data Participant Demographics Patient demographics consisted of the following: age (years), sex ( male, female), race (White, African American, American Indian and Alaska Native, Asian, Pacific Islander, two or more races), height (cm), weight (kg), time since stroke (months), type of stroke (ischemic versus hemorrhagic), area of stroke (e.g., middle cerebral artery distribution), the side affected (right versus left), Berg Balance Scale score, Activities of Balance Confidence Scale, Lower Extremity Fugl-Meyer motor score, over ground gait speed at self-selected and fastest speeds (meter/second), the SF-36 questionnaire score, assistive device used (yes or no), below-knee bracing (yes or no), medications grouped into types, including anti-hypertensive agents, antidepressants or anxiolytics, and anti-spastic medications. Demographics were tabulated and inspected for trends across participants. The Berg Balance Scale, Activities of Balance Confidence Score, and gait speeds are core outcome measures related to walking function EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 19 recommended by the Academy of Neurologic Physical Therapy (Moore et al., 2018). The lower extremity Fugl-Meyer motor scores are also used extensively in research as an indicator of function and have been used as a predictor of functional ability when assessed within the first 30 days after stroke (Duncan, 1992). Primary Outcomes Researchers evaluated changes in VO2 during steady-state treadmill testing before and after BRJ or PLA ingestion at a specific pace (70% of peak treadmill speed), which was hypothesized to be lower following BRJ versus PLA. Secondary Outcomes Secondary outcomes included peak VO2, heart rate (HR), and RPE (Scherr et al., 2013) during steady-state and graded treadmill tests. NO3- and NO2- levels in the venous blood were data points of interest for this study, and blood samples were collected at baseline before BRJ or PLA ingestion and approximately 2.5 hours after ingestion prior to post-testing. Instruments Clinical Assessments Researchers performed a battery of clinical evaluations during baseline assessment. Lower Extremity Fugl-Meyer Assessment (LE-FMA) The LE-FMA is a standardized general assessment of lower extremity power, coordination, reflex excitability, and joint individuation that is tested in different positions (i.e., sitting, supine, standing) by a licensed physical therapist (Sullivan et al., 2011). Berg Balance Scale (BBS) The BBS is a standardized 14-item assessment of static and dynamic standing balance (Berg et al., 1995). EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 20 Self-selected Gait Speed (SSS) and Fastest-possible (FS) Gait Speed Researchers determined gait speed (SSS and FS) by walking over the GaitRite walkway embedded with pressure sensors to detect spatiotemporal footfall patterns. Starting 1-2 m from the mat, participants walked at their "normal, comfortable pace" and "as fast as they safely can" at least two times, with data recorded during each walk. A physical therapist guarded the participant in the event of a balance perturbation (Moore, 2017). 6-Minute Walk Test (6MWT) The 6MWT measures the distance traveled during 6 consecutive minutes over a pre-specified pathway. Heart rate was monitored throughout the walk using a pulse oximeter with a forehead sensor, and a physical therapist guarded the participant in case of loss of balance (Moore, 2017). Activities-specific Balance Confidence (ABC) Scale - The ABC Scale is a 20-item subjective assessment of confidence in remaining standing during various tasks (Powell & Myers, 1995). Medical Outcomes Short-Form 36 Questions (SF-36) - The SF-36 is a 36-item subjective assessment of participation and quality of life, which assesses physical and cognitive/mental limitations to activities in the home and community (Ware & Sherbourne, 1992). Stepping activity - The patients wore a StepWatch (Modus, Inc, Washington, DC), a small accelerometer, on their paretic leg for 5-7 days to estimate physical activity (daily stepping activity) in the home and community (Henderson et al., 2022; Hornby et al., 2022). Graded Exercise Test (GXT) - Previous studies from the LRL have involved single-day testing and repeated training paradigms to assess the comparative efficacy of various exercise strategies. Experimental protocols in the present study utilized specific testing procedures and equipment developed and typically employed in these research studies. The GXT involved EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 21 walking on a motorized treadmill at a gradually increasing speed while evaluating electrocardiograms using a 12-lead arrangement coupled with indirect calorimetry to estimate the metabolic and cardiopulmonary demands of walking. Participants wore a safety harness attached to an overhead suspension system while walking on the treadmill, with speeds starting at 0.1 m/s for 1 minute and increasing by 0.1 m/s every minute. Termination of the GXT followed recommendations the American College of Sports Medicine and included the following criteria: loss of balance or unstable gait, ECG abnormality consistent with absolute exercise test termination, patient report of chest pain (angina), excessive handrail use, elevated blood pressures above 240/115 mmHg for systolic and diastolic (respectively) or patient request to stop (Hornby et al., 2019; Riebe, 2017). The fastest treadmill speed achieved for a full minute determined the peak treadmill speed and metabolic/cardiopulmonary measures were assessed at that peak speed (Hornby et al., 2019). Submaximal Treadmill Test - Assessment of metabolic demands during steady-state walking utilized a similar set-up and equipment described for the GXT. Participants walked on a treadmill for 6 minutes at 70% of the peak speed obtained during the graded exercise text. Analysis of previous data from published studies indicates SS walking speeds are typically ~70% of peak treadmill speeds, and participants should be able to maintain this pace for the 6-minute duration (Hornby et al., 2019). Researchers analyzed metabolic and cardiopulmonary data during minutes 4-6, during which most individuals achieve a steady-state metabolic rate. Blood Samples - Researchers obtained venous blood samples as described by Coggan et al. (2018) to measure NO3- and NO2-. Laboratory staff took blood samples via an antecubital venous catheter, centrifuged them to separate the plasma, and stored them at -37 C. When ready for further analysis, researchers thawed the sample. Samples were mixed with a 1:1 ratio of plasma EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 22 and methanol, centrifuged at 4 C for 10 min, and analyzed for plasma NO3- and NO2- using a dedicated high-performance liquid chromatography (HPLC) system. Cardiopulmonary/Metabolic Measures - Researchers used a portable indirect calorimeter (K5 Cosmed, Chicago, IL) to determine cardio-metabolic measures for this study. This system has been used extensively in metabolic testing in exercise physiology research, and previous models indicate excellent inter-rater and intra-rater reliability and validity [minimal detectable change (MDC) = 3.8%] (Guidetti et al., 2018). Researchers collected gases breath-by-breath and analyzed for volumes and O2 and CO2 concentrations (partial pressures). Data derived from these measures included the rate of VO2 and VCO2 production, minute ventilation (VE), and respiratory exchange ratio (RER = VCO2/VO2). Procedures Recruitment The LRL used available connections from various sources, including outpatient rehabilitation services at the Rehabilitation Hospital of Indiana (RHI) and other local hospital systems, to recruit participants for this study. The lab also has established relationships with local physiatrists who refer people with chronic stroke and with previous participants who may be willing to participate in additional research. Screening Initially, the LRL research staff screened potential participants over the phone or in person and completed the consenting process in person. Researchers determined the participant's gait speed after consent. The research staff knew the study design, protocol, and inclusion/exclusion criteria. In addition, the research staff has experience screening previous studies administered by the LRL. EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 23 Informed Consent The study design required informed consent from participants due to the experimental intervention. The proposed study also included a vulnerable population as they may have variable physical disabilities and potential cognitive impairments. The study took place through Indiana University, which required a decision-making tool when obtaining informed consent. This document allowed the research staff to ensure compliance during the consenting process. For example, a potential participant could freely ask questions, and the staff used a teach-back method to provide understanding and checked the Indiana registry to ascertain if the person had a state-appointed guardian. The research staff, including the director of the LRL, another full-time researcher, three research physical therapists, and one research assistant, were responsible for obtaining informed consent when appropriate and using the decision-making tool provided by Indiana University. The research staff explained the study using the abovementioned parameters and performed screening to ensure the potential participant was eligible for the study. The research staff scheduled the baseline assessment and intervention sessions after participants signed the informed consent, reviewed relevant medical records, and received a signed medical clearance from the participant's physician. Randomization Randomization of study conditions (BRJ or PLA) occurred during the study's first phase after the participant completed the baseline assessment. The research staff obtained medical clearance and records after the participant signed the informed consent document. Data Collection EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 24 During the first session, participants arrived at the LRL for baseline assessments at times convenient to them. The research staff took baseline blood pressure (BP) and heart rate to ensure safety and ability to continue with testing according to criteria defined by the American College of Sports Medicine (Riebe, 2017). The staff also completed a collection of patient demographic characteristics, including the following: age (years), sex (male, female), race (White, African American, American Indian and Alaska Native, Asian, Pacific Islander, two or more races), height (cm), weight (kg), time since stroke (months), type of stroke (ischemic, hemorrhagic), area of stroke (e.g., middle cerebral artery distribution), the side affected (right, left), BBS score (Berg et al., 1995), 6MWT (Eng et al., 2002), Lower Extremity Fugl-Meyer(Smith et al., 2017), over ground gait speed at self-selected and fastest speeds (meter/second)(Wade et al., 1987), the Activities of Balance Confidence Scale questionnaire (Powell & Myers, 1995), the Short Form 36 (SF-36) questionnaire (Brazier et al., 2002), an assistive device used (yes, no), below-knee bracing (yes, or no), medications grouped into types, including anti-hypertensive agents, antidepressants or anxiolytics, and anti-spastic medications. Participants performed the GXT during the first session to determine the speed of the submaximal treadmill test used in subsequent sessions with EKG monitoring to ensure safety, as outlined previously. The participant then attended two subsequent intervention sessions after baseline assessments at least one day between baseline to avoid fatigue from previous testing. They were instructed not to use antibacterial mouthwash or chew gum on the same day as the intervention sessions as this can disrupt the reduction of NO3- to NO2- (Oliveira-Paula et al., 2019). When the participant arrived at the lab, the research staff took HR and blood pressure to ensure safety. The participants then completed the submaximal treadmill test at a workload aligned with 70% peak treadmill speed for 6 minutes, followed by the GXT after a ten-minute rest break. Before EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 25 ingesting the BRJ or PLA, participants had venous blood samples taken as previously outlined. Participants then waited in the hospital lobby for two to three hours after ingestion to allow for peak NO3- and NO2- levels during testing (Karimzadeh et al., 2022). Before the second round of exercise testing, the Indiana University laboratory staff took another venous blood sample. The participant then performed the submaximal treadmill test followed by the GTX following the same protocol. Intervention The intervention in the study was a BRJ supplement or PLA. Beet It Sport Nitrate 400 shot (Beet It, James White Drinks, Ipswich, UK) contains at least 400 mg of nitrate in each shot. The participants ingested two 70 mL shots of the supplement (140 mL total volume). The company also produced a placebo supplement that tasted and looked similar to the BRJ supplement and was of equal volume. Participants also ingested two placebo shots to maintain blinding to the intervention type. Data Management The research staff maintained identifiable information on password-protected programs through the Indiana University programs designed for research studies and Indiana University computers. Researchers kept any physical files in locked file cabinets accessed only by the research staff. Paper testing packets contained de-identified information, such as using a specific codeword for each participant and known only to the research staff. The research staff transferred the information from the testing packets to the Redcap system from Indiana University. Research staff updated the data into Redcap throughout the study. When the study was complete, data was exported from Redcap to Excel files using de-identified code names. The EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 26 LRL office is also only accessed by research staff and locked during non-office hours. In addition, the researchers maintained data in compliance with IRB policy. Statistical Analysis A priori calculations yielded a sample size of 15, assuming a large effect size (0.8) based on published data (Bailey et al., 2009; Dominguez et al., 2017) and powered at 82%. If powered to 91%, a sample size of 19 would be required (Power and Precision, Baltimore, MD). Researchers used descriptive statistics to evaluate participant demographics, clinical characteristics, and primary and secondary outcomes. Researchers reported information per data type. They used frequencies and percentages for nominal data, whereas means and standard deviations were used for normally distributed interval and ratio data. Shapiro-Wilk test was used to determine if the data was normally distributed. A two (time) by two (condition) repeated measures analysis of variance (ANOVA) was used to examine outcome measures separately between the intervention and placebo groups and within both groups. Researchers used post hoc comparisons using the Bonferroni correction in the event of significant main effects. A few instances of one of the data sets within an ANOVA analysis were not normally distributed. For example, when determining differences for steady-state VO2, the data was normally distributed for pre- and post-testing in the BRJ group and pre-testing in the PLA group but not normally distributed in the post-testing PLA group. The repeated measures ANOVA was used to determine significant differences for each outcome as it is a robust measure despite a few instances of data not normally distributed. The study used an intention-to-treat protocol to analyze the data by carrying the last value forward for missing data. One participant had missing data during the sub-maximal treadmill test, data was extrapolated to determine VO2 during the last three minutes of the test. Data was EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 27 analyzed with IBM SPSS Statistics for Windows or Mac, Version 29 (IBM Corp., Armonk, NY). All comparisons were two-tailed, with an alpha level of less than .05 considered statistically significant. There were no established values for MDC or minimal clinically important difference (MCID) for changes in VO2 during the GTX. Therefore, effect sizes used Cohen's d for paired t-tests and partial eta squared for ANOVAs of these outcome measures calculated and interpreted based on recommendations provided by Cohen (1992). Results Demographics and Baseline Clinical Characteristics Twenty-two participants started the study, with nineteen (n = 19) completing all testing sessions. Data collection occurred from February 2023 through November 2023. Two were withdrawn due to exclusion criteria not initially detected during the screening process. One participant dropped out before baseline testing. Participants were randomized to BRJ or PLA for the initial intervention after baseline testing. Eleven consumed the BRJ first, and eight started with the PLA. Figure 1.0 refers to the CONSORT diagram to display the study flow. Table 1 refers to baseline demographic information for participants based on the initial intervention group. Table 2 displays baseline clinical characteristics by initial intervention group as well. One minor adverse event occurred when a participant scraped the top of his foot on the treadmill during the GXT; however, the harness system kept him from falling. Measures During Steady State For the primary outcomes of steady-state VO2, there was no significant interaction between intervention and time (F(1,18) = 1.32, p = 0.27). Average heart rates during the final three minutes of the submaximal treadmill test were also not different between groups over time (F(1,18) = .15, p = 0.71). There were no significant interactions for steady-state RPE (F(1,18) = EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 28 0.03, p = 0.87). Results from the repeated measures ANOVA tested during the sub-maximal treadmill test can be found in Table 3 and Figure 2. Measures During Rest Heart rates and blood pressure were obtained during the 10-minute rest between the submaximal treadmill test and the GXT, five and ten minutes after the submaximal treadmill test. A significant treatment and time interaction for heart rates at the 5-minute measure, F(1,18) = 6.76, p = .02. The BRJ group demonstrated a significant difference in heart rate between pre and post-conditions, F(1,18) = 10.62, p = .004. While the PLA group did not experience a significant difference in HR pre and post-conditions at the five-minute rest period, F(1,18) = 1.16, p = .30. The HRs at five minutes increased from pre to post in the BRJ group. No significant interactions were found at the ten-minute rest HR and blood pressures at the five- and ten-minute rest periods for either SBP or DBP (Table 3). Measures During Graded Treadmill Test Cardiopulmonary variables were also examined during the GXT, with specific comparisons during the last thirty seconds of the peak treadmill speed can be found in Table 3 and Figure 3. No significant interactions were found for VO2peak (F(1,18) = .12, p = .73) and HRs (F(1,18) = .35, p =.56) were not different between groups. Similarly, changes in RPE (F(1,18) = 3.22, p = .09) at peak treadmill speeds. Nitrate and Nitrite Plasma Levels NO3- and NO2- levels were examined to determine absorption into the circulatory system. Two-way repeated measure ANOVAs were used to determine differences in NO3- and NO2levels between the BRJ and PLA conditions. Findings can be found in Table 4. The baseline NO3- and NO2- concentrations were not statistically significant between BRJ and PLA, F(1,18)= EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 29 140.62, p = .21; F(1,18) = 1.20, p = .18 respectively. NO3- and NO2- were significantly higher in the BRJ group from pre- to post-testing, while no significant changes were noted in the PLA group. Both NO3- and NO2- in the BRJ group were significantly higher than the PLA group at post-testing. treatment. Correlation Between VO2 and NO2Potential associations between the percent change in nitrite levels and the percent change in steady-state VO2 were also examined using Pearsons product-moment correlation. Not all the data was normally distributed using Shapiro-Wilks test; however, the result was similar when outliers were removed to normalize the distribution of data. No statistically significant relationship between steady-state VO2 and NO2 (p > 0.05). Figure 4 demonstrates this correlation. Discussion Steady-State VO2. The primary outcome of interest was a potential change in VO2 during steady-state walking in the chronic stroke population, representing an improved gait economy. To date, this is the first study to examine the effects of an acute BRJ supplement on individuals with chronic stroke. However, the findings do not support our primary hypothesis that BRJ would improve gait economy during steady-state walking for individuals with chronic stroke. Previous research has revealed varying findings of BRJ on submaximal VO2. For example, the effects of BRJ have been tested in athletes as well as in individuals with various health conditions (e.g., heart failure, peripheral arterial disease, hypertension) (Bailey et al., 2009; Betteridge et al., 2016; Dominguez et al., 2017; Kocoloski, 2018). Selected research suggests that BRJ is more effective in untrained people versus well-trained athletes (Bescos et EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 30 al., 2011; McIlvenna et al., 2019) owing to the notion that athletes are not limited by insufficient NO bioavailability. Behrens et al. (2020) found improvement in submaximal VO2 following an acute BRJ supplement in the obese population using an electronically braked cycle ergometer, which is a 7% improvement in VO2 when compared to the PLA (BRJ with dietary NO3- depleted), sodium nitrate solution, and a control (no supplement). The participants in this study fell into the untrained category as their average VO2 peak during the GXT was ~20 ml VO2/kg/min. with ~5,500 steps/day, indicating low or poor fitness and a relatively sedentary lifestyle. If BRJ was more effective in untrained people, we would expect to find larger changes in the steady-state gait economy after BRJ supplementation. Conversely, several studies have found no changes in steady-state VO2 following ingestion of BRJ (Esen et al., 2022; Kocoloski, 2018; Lansley et al., 2011; MacLeod et al., 2015). One study in healthy males found no changes in submaximal VO2 after a single dose (Betteridge et al., 2016), although this study had a smaller dose of dietary NO3- , although blood plasma levels of NO2- were similar to previous studies (Betteridge et al., 2016) highlighting the lack of understanding of dietary NO3- dosage necessary to induce changes. Rather, improvements in submaximal VO2 may be more consistent after several days of BRJ supplementation (Bailey et al., 2009; Eggebeen et al., 2016). For example, Bailey et al. (2009) found a 19% reduction of submaximal VO2 during a cycle ergometer test after six days of supplementation. Similarly, Eggebeen et al. (2016) examined if an acute or a seven-day supplementation affected submaximal VO2 in people with HFpEF, revealing no changes during the acute dose but a 24% improvement after repeated doses. These findings demonstrate high NO3- to NO2- conversion variability among healthy and clinical populations. EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 31 Findings from previous work were used to strengthen the design of our study. For example, Wylie et al. (2013) determined that single, 140 mL, and 280 mL doses of BRJ could elicit a decrease in steady-state VO2 in healthy, active men, the former of which was utilized in our study. The BRJ sample contained 14.56 mmol NO3-, which has been found to elicit ventilatory changes in other studies (Bailey et al., 2016; Carriker et al., 2016; Shannon et al., 2016). The research design also contained a rest period of 2-3 hours after ingestion before testing to measure the optimal bioavailability of NO through the NO3- - NO2- - NO pathway (Wylie et al., 2013).. Similarly, this study used a BRJ and PLA (James White) that is commonly used in most research studying effects of BRJ. Given these conditions and the randomized doubleblinded study design, the current study reduces many threats to internal validity. In addition, no changes in measures to indicate increased vasodilation were present (e.g., HR during exercise tests, SBP, and DBP) following BRJ. While we did find a significant increase in HR in the BRJ group at five minutes rest, there were no differences in blood pressure at this point and of either measure at the 10-minute mark. One potential difference between the current study and previously published findings may be the use of anti-hypertensive medication. All of our participants were prescribed anti-hypertensive agents, which may blunt responsiveness to the effects of dietary nitrate. In addition, many individuals are also prescribed statins, which can potentiate some of the effects of endothelial cells, including increasing NO availability and improved anti-oxidative function (Endres, 2006). Selected research has demonstrated a limited ability of BRJ to lower blood pressure in people already medicated or those who are normotensive (Broxterman et al., 2019; Kerley et al., 2018; Kukadia et al., 2019), which may contribute to the current findings. Maximal Exertion Measures EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 32 Peak cardiopulmonary and exertion measures (VO2, HR, and RPE) during the graded treadmill test were also of interest. Physical therapy practice recommends high-intensity gait training to improve walking ability in people post-stroke(Hornby, Reisman, et al., 2020). If BRJ reduces subjective measures of exertion or improves peak cardiopulmonary and walking performance, then BRJ may facilitate greater training volumes or at higher intensities. However, this study found no significant differences during the graded treadmill test at the final 30 seconds of the fastest speed completed. There was a trend of lower RPE in favor of BRJ (p = .09), and the sample size may not have been large enough to detect a difference. However, the notion that BRJ may affect subjective exercise tolerance may be promising. For example, in individuals with obesity, both Behrens et al. (2020) and Rasci et al (2018) found an improvement in time to exhaustion during cycle ergometer testing. Similarly, a study on peripheral arterial disease found a 17% increase in peak walking time after an acute BRJ supplement (Kenjale et al., 2011). While significant changes in RPE were not observed, power calculations suggest another 2 participants would have been required to observe significant differences. Limitations This study is the first to examine the effects of BRJ on a population affected by an upper motor neuron diagnosis. We attempted to account for the reduced volitional motor function and walking ability in the population tested by selecting a paradigm allowing participants to complete all procedures (e.g., submaximal assessments at 70% of peak treadmill speed). Another limitation of this study is that all but one participant was on anti-hypertensive medications, which, while unavoidable, may have reduced the ability to observe significant changes in cardiopulmonary function in this sample. Another limitation is the sample size, and a slightly larger population would have been necessary to observe selected changes in RPE, EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE although much larger samples would be required to see differences in other physiologic outcomes. Conclusion The present study aimed to assess whether BRJ supplementation might have beneficial effects on walking costs in individuals with chronic stroke. However, we found no significant improvements in gait economy via a decrease in VO2 during steady-state. There was a trend towards a lower RPE during maximal exertion, but these changes were not significant. Further research is needed to examine if larger doses or chronic BRJ supplementation may have beneficial effects on metabolic and subjective outcomes. 33 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 34 References Bailey, S. J., Blackwell, J. R., Wylie, L. J., Holland, T., Winyard, P. G., & Jones, A. M. (2016). 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Medical Care, 30(6), 473-483. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=1593914 Wesselhoff, S., Hanke, T. A., & Evans, C. C. (2018). Community mobility after stroke: a systematic review. Topics in Stroke Rehabilitation, 25(3), 224-238. https://doi.org/10.1080/10749357.2017.1419617 Woessner, M. N., Levinger, I., Allen, J. D., McIlvenna, L. C., & Neil, C. (2020). The Effect of dietary inorganic nitrate supplementation on cardiac function during submaximal exercise in men with heart failure with reduced ejection fraction (HFrEF): A pilot study. Nutrients, 12(7). https://doi.org/10.3390/nu12072132 Woessner, M. N., McIlvenna, L. C., Ortiz de Zevallos, J., Neil, C. J., & Allen, J. D. (2018). Dietary nitrate supplementation in cardiovascular health: an ergogenic aid or exercise therapeutic? American Journal of Physiology: Heart and Circulatory Physiology, 314(2), H195-H212. https://doi.org/10.1152/ajpheart.00414.2017 Wylie, L. J., Kelly, J., Bailey, S. J., Blackwell, J. R., Skiba, P. F., Winyard, P. G., Jeukendrup, A. E., Vanhatalo, A., & Jones, A. M. (2013). Beetroot juice and exercise: pharmacodynamic and dose-response relationships. Journal of Applied Physiology, 115(3), 325-336. https://doi.org/10.1152/japplphysiol.00372.2013 Zamani, P., Rawat, D., Shiva-Kumar, P., Geraci, S., Bhuva, R., Konda, P., Doulias, P. T., Ischiropoulos, H., Townsend, R. R., Margulies, K. B., Cappola, T. P., Poole, D. C., & Chirinos, J. A. (2015). Effect of inorganic nitrate on exercise capacity in heart failure EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE with preserved ejection fraction. Circulation, 131(4), 371-380; discussion 380. https://doi.org/10.1161/CIRCULATIONAHA.114.012957 49 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE Figure 1: CONSORT diagram 50 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 51 Table 1: Demographic Information at Baseline BRJ PLA n=11 n=8 Age (years) 61 (7.6) 64 (4.3) Duration post-stroke (years) 2.9 (2.2) 7.5 (8.8) Weight (kg) 88 (12) 98 (8.3) Sex (male/female) 8/3 6/2 6/5 5/3 5/6 6/2 10/1; 90.9/0.09 6/2; 75/25 6/5; 55/45 3/5; 37.5/62.5 6/5; 55/45 3/5; 37.5/62.5 Ankle foot Orthosis, number (Y/N) Assistive Device, number [(Y/N); percentages] Medications: Antihypertensives; number [(Y/N), percentages] Medications: Anti-depressants or anxiolytics, number [(Y/N), percentages] Medications: Anti-spastics, number [(Y/N), percentages] Note: Mean scores with standard deviations are in parentheses. BRJ=Beetroot Juice Intervention; PLA=Placebo intervention; kg = kilogram EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 52 Table 2: Clinical Assessments at Baseline BRJ PLA Fugl-Meyer- lower limb (a.u.) 23 (4.9) 24 (3.7) Gait speed, self-selected (m/s) 0.77 (0.31) 0.75 (0.18) Gait speed, fast speed (m/s) 1.0 (0.42) 0.99 (0.38) Average steps per day 5608 (5146) 5416 (4619) Activities of Balance Confidence Scale (a.u.) Berg Balance Scale (a.u.) 74 (12) 69 (17) 44 (8.5) 46 (3.5) 6 MWT (meters) 320 (166) 315 (155) SF-36 (physical) (a.u.) 31 (6.3) 30 (6.7) SF-36 (mental) (a.u.) 42 (9.9) 49 (10) Note: Mean scores with standard deviations are in parentheses. BRJ = Beetroot Juice Intervention; PLA = Placebo Intervention; 6 MWT = 6 Minute Walk Test; SF-36 = Short Form Health Survey Form; m/s = meters/second; a.u. =arbitrary units. EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 53 Table 3: Outcome Measures Primary Variable SS VO2 (mLO2/kg/min) Secondary Variables SS HR (BPM) SS RPE (Borg) Rest 5 SBP (mm Hg) Rest 5 DBP (mm Hg) Rest 5 HR (BPM) Rest 10 SBP (mm Hg) Rest 10 DBP (mm Hg) Rest 10 HR (BPM) Peak VO2 (mLO2/kg/min) Peak HR (BPM) Peak RPE (Borg) BRJ PLA Crossover (p values) Time X Pre Post Pre Post Time 15 3.0 14 2.3 15 3.6 15 3.4 0.80 0.27 107 17 108 20 109 19 110 21 0.40 0.71 13 2 13 2 13 3 13 3 0.10 0.87 129 14 129 16 129 16 129 13 0.91 0.90 79 6 79 6 79 7 81 7 0.55 0.26 74 12 79 15 76 15 78 16 0.04 * 0.02 * 128 13 125 11 126 10 126 14 0.53 0.75 80 5 79 7 79 5 79 6 0.67 0.61 75 10 77 14 75 13 77 14 0.29 1.00 20 5.1 21 5.5 20 5.8 21 6.9 0.25 0.73 130 23 132 25 130 22 133 24 0.24 0.56 19 1 19 2 19 1 19 1 0.89 0.09 Group Note: BRJ = beetroot juice; PLA = placebo; SS = steady-state; VO2 = oxygen uptake; HR = heart rate; RPE = rate of perceived exertion; Rest 5 = rest at five minutes after steady-state test; Rest 10 = rest at ten minutes after steady-state test; SBP = systolic blood pressure; DBP = diastolic blood pressure; Peak = last 30 seconds of final completed minute of the maximal exertion treadmill test. * = p <0.05 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE Figure 2 Steady-State Outcomes 54 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE Figure 3 Peak Outcomes 55 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 56 Table 4: Nitrate and Nitrite Plasma Levels BRJ NO3 - (M) NO2- (M) PLA Crossover (p values) Pre Post Pre Post 31.36 564.76 27.52 29.05 15.80 1102.74 16.25 12.13 0.49 0.23 0.87 0.52 0.44 0.21 0.51 0.26 Note: NO3- = nitrate; NO2- = nitrite; M = micrometer; * = p< 0.05 Time Time X Group <0.001 * <0.001 * 0.007 * 0.005 * EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE Figure 4: Correlation Between Percent Change in Steady-State VO2 and NO2- Note: VO2 = oxygen consumption; NO2- = nitrite; BRJ = beetroot juice; PLA = placebo 57 EFFECT OF BEETROOT JUICE ON EXERCISE IN CHRONIC STROKE 58  ...

... Physical Therapists Knowledge, Attitudes, and Practices Related to Postoperative Scars Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Elizabeth Geiger Harvey, PT, DPT, MSR, PCS Copyright April 4, 2024 By: Elizabeth Geiger Harvey, PT, DPT, MSR, PCS All rights reserved Approved by: Elizabeth S. Moore, PhD Committee Chair ______________________________ Heidi H. Ewen, PhD, FGSA, FAGHE Committee Member ______________________________ Esther de Ru, PT, OMT, PPT Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ POSTOPERATIVE SCARS KAP Table of Contents Abstract ...................................................................................................................................... 4 Acknowledgements ..................................................................................................................... 5 Chapter 1: Physical Therapists Knowledge, Attitudes, and Practices Related to Postoperative Scars ...........................................................................................................................................7 Problem Statement ............................................................................................................................ 8 Purpose Statement ............................................................................................................................ 9 Significance of the Study ............................................................................................................... 10 Chapter 2: Literature Review .................................................................................................... 10 Scar Formation ................................................................................................................................ 13 Atrophic Scars ................................................................................................................... 13 Hypertrophic Scars............................................................................................................. 14 Keloid Scars....................................................................................................................... 15 Scar Treatments .............................................................................................................................. 15 Soft Tissue Massage .......................................................................................................... 16 Vibration Therapy .............................................................................................................. 17 Instrument-Assisted Soft Tissue Massage........................................................................... 17 Dry Needling ..................................................................................................................... 18 Tape ................................................................................................................................... 18 Topicals ............................................................................................................................. 19 Chapter 3: Method .................................................................................................................... 21 1 POSTOPERATIVE SCARS KAP Study Design ................................................................................................................................... 21 Participants ...................................................................................................................................... 21 Data .................................................................................................................................................. 21 Operational Definitions .................................................................................................................. 22 Instruments ...................................................................................................................................... 23 Procedures ....................................................................................................................................... 23 Recruitment ....................................................................................................................... 23 Informed Consent .............................................................................................................. 24 Data Collection .................................................................................................................. 24 Data Management .............................................................................................................. 25 Statistical Analysis ............................................................................................................. 25 Inferential Analyses ........................................................................................................... 25 Chapter 4: Results ..................................................................................................................... 26 Comparison of Knowledge Scores ................................................................................................ 26 Comparison of Attitude Scores ...................................................................................................... 28 Comparison of Practice Scores ...................................................................................................... 31 Comparison of Total Scores ........................................................................................................... 33 Chapter 5: Discussion ............................................................................................................... 36 Study Limitations ............................................................................................................................ 41 Implications ..................................................................................................................................... 41 Chpater 6: Conclusion ............................................................................................................... 44 References ................................................................................................................................ 46 2 POSTOPERATIVE SCARS KAP Abstract 3 POSTOPERATIVE SCARS KAP 4 Despite the high rate of complications and comorbidities that exist with postoperative scars, there is little evidence to demonstrate how physical therapists (PTs) in the United States (U.S.) perceive the relevance of this type of scar or how they are managed. This study examined PTs' knowledge, attitudes, and practices related to postoperative scars using a cross-sectional survey called the Postoperative Scar Survey. It was deployed February 21-March 3, 2023, to a convenience sample of PTs licensed in the U.S. In total, 750 completed surveys were used for data analysis. The primary outcomes were scores derived from knowledge, attitudes, and practices and a summed total score. A statistically significant difference was found in scar tissue engagement based on PT characteristics of sex, patient population, practice setting, region, specialty certification, and years of experience. PTs most engaged in scar tissue treatment were females, those with specialty certification in womens health, those practicing in a private outpatient setting in the Western U.S., and those treating a broad range of patients (both adult and pediatric). Over 81% (n = 572) agreed that postoperative scars should be evaluated by a PT, indicating a high awareness of the impact scars can have on patient outcomes. Patient disparities were found in the pediatric and geriatric populations and the military setting. This is the first survey to assess PTs engagement in postoperative scar tissue. Keywords: postoperative scar, physical therapy, linear scar, survey POSTOPERATIVE SCARS KAP 5 Acknowledgments Nothing great is ever accomplished in isolation. Thank you, Dr. Moore, for acting as committee chair and helping me navigate the challenges of the doctoral project. I appreciate you helping me publish the first study on how sternotomy scars, left untreated, can impact motor development in pediatrics. Dr. Ewen, your early and continued support, as well as your personal interest in this topic led to the creation of the Postoperative Scar Survey. I appreciate the time and assistance you dedicated to making this project possible. Your patience, encouragement, and support have been unwavering. I will always be grateful for this opportunity to work with you. I didnt realize until our last conversation just how important this survey could be to my career. You are a statistics genius! Thank you for believing in me and seeing the big picture that I could not. Esther de Ru, this project would not have been possible without your expertise on the skin and your passion for making a difference in the world. Thank you. To the faculty- Dr. Santurri, Dr. Borrero, Dr. Gahimer, Dr. Nitche. I began this journey with high expectations, which has not disappointed me. I have gleaned so many personal and professional lessons from each of you. Thank you for the personal attention and dedication you have shown. The DHS program has fostered friendships, created growth, and offered new opportunities. A special thank you to Dr. Wakeford, who showed excessive patience and fortitude in guiding me through the ethics process. To those who worked tirelessly behind the scenes, most especially Teri Short and Dr. Wakeford. Your support for students does not go unnoticed. Much gratitude is extended to the UIndy Writing Lab, which helped me fine-tune my writing and offered personal encouragement. I am grateful to my professional colleagues who have supported me along this journey. POSTOPERATIVE SCARS KAP 6 Thank you, Judee Macias-Harris, for helping me understand the importance of scar tissue and the integumentary system. Thank you to Alison Taylor and Yichen Su for your tireless curiosity and dedication to changing the world as we know it! I stand among giants, and I am inspired. Thank you to Dr. Stephanie Phares and Earlene Masi for being my partners in the trenches of this journey and sticking with me to the end. You will always hold a special place in my heart! I cannot express my gratitude to my family, husband, three sons, parents and siblings, patients, and coworkers. Education has always been held in the highest regard and forever encouraged. Thank you for your patience and support throughout this entire endeavor. You have been the most patient and supportive of all. Thank you for always cheering me on and believing that I can make a difference in this world. Tracy Brandt, you prayed for me through many challenging times. Dora Brown, Laura Caddell, Leeanna McBee, and the Renewal Staff thank you for being patient and supportive during some trying times. Cujo, thank you for reminding me that there is more to life. You faithfully sat by my side for every assignment and edit. Without my patients, this journey would never have happened. Walking with you through trials and surgical procedures, you have shown me resilience and strength. Thank you for putting your faith in me and allowing me to help guide you through your healing process. You have taught me so much. I hope that together, we can change how scars are managed in the pediatric population. Finally, to God be the glory, great things He has done! He called me to this journey at UIndy and has sustained me. I pray that everything I do pleases Him and makes Him smile. He is so good to me! Elizabeth Geiger Harvey POSTOPERATIVE SCARS KAP 7 Physical Therapists Knowledge, Attitudes, and Practices Related to Postoperative Scars Scar tissue is the endpoint of soft tissue damage, including surgery or lesions. Despite advances in surgical techniques, postoperative scars or traumatic lacerations can cause dermal lesions that generate a significant patient burden (Abd-Elseyad et al., 2022; Alvira-Lechuz et al., 2017; Andrews et al., 2016; Chamorro et al., 2017; Cincinnati Children's Hospital Medical Center, 2018; Coentro et al., 2019; Davies et al., 2017; Duquennoy-Martinot et al., 2016; Eid & Abdelbasset, 2022; Fu et al., 2019; Grigoryan & Kampp, 2020; Kelly et al., 2019; Krakowski et al., 2016; Lauridsen et al., 2014; Lyons et al., 2018; Ngaage & Agius, 2018; Nesbitt et al., 2017; Nischwitz et al., 2020; Olsson et al., 2018; Patel et al., 2014; Rabello et al., 2014; Scott et al., 2022; Sidgwick et al., 2015; Vuotto et al., 2018). Weiser et al. (2008) reported an incidence of 234 million surgical scars annually, with 100 million in the United States (U.S.) alone (Krakowski et al., 2016). Over 12 million traumatic lacerations occur yearly (Block et al., 2015). Of these injuries, 38-70% of the scars were estimated to become pathological (Abd-Elseyad et al., 2022; Ferriero et al., 2015). Problematic scars can significantly impact on quality of life physically and psychologically (Davies et al., 2016; Duquennoy-Martinot et al., 2016; Fu et al., 2019; Gilbert et al., 2022; Gottrand et al., 2016; Kelly et al. 2019; Lee et al., 2018; Lubczyska et al., 2023; Olsson et al., 2018; Rullander, 2015; Vuotto et al., 2018; Wasserman et al., 2016). Frequent comorbidities are pain, tenderness, pruritus, nerve damage, contracture, poor thermoregulation, and decreased skin mobility (Alvira-Lechuz et al., 2017; Bae et al., 2015; Deflorin et al., 2020; Fu et al., 2019; Kelly et al., 2019; Lee et al., 2018; Krakowski et al., 2016; Lubczyska et al., 2023; Mundy et al., 2016; Rullander, 2015; Sidgwick et al., 2015; Uher et al., 2018; Vercelli et al., 2015; Wang et al., 2020; Wasserman et al., 2016; Wilgus et al., 2020). These deficits can also POSTOPERATIVE SCARS KAP 8 impact motor control (Alvira-Lechuz et al., 2017; Harvey, 2022; Le Touze et al., 2020; Matur, 2017; Takayuki & Ostry, 2010; Uher et al., 2018), which may be especially apparent in the pediatric population still in the process of motor development (Duquennoy-Martinot et al., 2016). Psychological consequences of scars can include social barriers (Duquennoy-Martinot et al., 2016; Gottrand et al., 2016; Krakowski et al., 2016; Olsson et al., 2018; Padilla-Espaa et al., 2014; Rullander, 2015; Sitohang et al., 2021; Vuotto et al., 2018), depression, anxiety, stigmatization, and decreased quality of life (Dastigir et al., 2021; Deflorin et al., 2020; Fu et al., 2019; Gottrand et al., 2016; Hambraeus et al., 2020; Jiang et al., 2021; Krakowski et al., 2016; Lubczyska et al., 2023; Mundy et al., 2016; Ngaage & Agius, 2018; Olsson et al., 2018; Patel et al., 2014; Padilla-Espaa et al., 2014; Rullander, 2015; Sidgwick et al., 2015; Vercelli et al., 2015; Vuotto et al., 2018; Wang et al., 2020; Wilgus et al., 2020). The emotional impact of scars can be as detrimental as chronic diseases such as diabetes, resulting in body dysmorphia and increased suicidal tendencies, especially in the pediatric population (Duquennoy-Martinot et al., 2016; Lubczyska et al., 2023; Padilla-Espaa et al., 2014; Patel et al., 2014; Rullander, 2015). Problem Statement Physical therapists often work with patients before and after surgery (Abd-Elseyad et al., 2022; McClelland & Davidson, 2016). Scars' physical and psychological comorbidities are frequently underreported and overlooked by the medical community (Krakowski et al., 2016; Ngagge & Agius, 2018); however, patients spend over 20 billion dollars annually seeking treatment (Block et al., 2015). Conservative scar tissue treatment ranges from physical methods such as massage (Abd-Elseyad et al., 2022; Crowle & Harley, 2020; Gottrand et al., 2016; Lee et al., 2018; Lubczyska et al., 2023; Masanovic, 2013; Poddighe et al., 2024; Scott et al., 2022; POSTOPERATIVE SCARS KAP 9 Shin & Bordeaux, 2012; Wasserman et al., 2019; Wilk et al., 2015) to the use of topical agents (i.e., silicone sheets) (Abd-Elseyad et al., 2022; Block et al., 2015; Deflorin et al., 2020; Fijan et al., 2019; Ocampo-Candiani et al., 2014; Wang et al., 2020). Despite this, there is little evidence in the literature on PTs' approach to postoperative scar tissue management (Abd-Elseyad et al., 2022; Lubczyska et al., 2023). Purpose Statement Physical therapists minimize the importance of postoperative scars and do not regularly assess or treat postoperative scar tissue. This may be pronounced among different patient demographics (i.e., adult versus pediatric), which may be due to limited knowledge of evidencebased assessment and treatment methods. Additional variations in practice patterns may exist due to PT characteristics (i.e., sex, practice setting, geographic location). Therefore, the purpose of this study is to survey PTs in the U.S. to document their knowledge, attitudes, and practices of postoperative scar tissue. Research Question The research question was: What are PTs' knowledge, attitudes, and practices regarding postoperative scar tissue, and how do they vary by PT characteristics? The following objectives were used to answer the research question. 1. To determine if PT's knowledge, as measured with the Postoperative Scar Survey, varied by sex, patient population, practice setting, U.S. region, and specialty certification. 2. To determine if attitude, as measured with the Postoperative Scar Survey, varied by sex, patient population, practice setting, U.S. region, and specialty certification. 3. To determine if practice, as measured with the Postoperative Scar Survey, varied by sex, patient population, practice setting, U.S. region, and specialty certification. POSTOPERATIVE SCARS KAP 10 4. To assess whether PTs' engagement in postoperative scar tissue, as measured with the Postoperative Scar Survey, varied by sex, patient population, practice setting, U.S. region, and specialty certification. It is expected that PTs minimize the importance of postoperative scars and do not regularly assess or treat them. Differences may be higher among patient characteristics, adult versus pediatric populations, or PT demographics, such as geographical location or experience level. This may be due to limited knowledge of evidence-based assessment and treatment methods or time restrictions placed on clinicians due to productivity demands. Significance of the Study With the high incidence of aberrant scars following surgery or lacerations, it is crucial to understand PTs' beliefs and practices regarding their treatment. Documenting this information may help develop specific evidence-based practice guidelines for practitioners and PT students. Exploring these aspects of patient care will aid in furthering research efforts. Literature Review The integumentary system is PTs' first and foremost point of contact with their patients. It is the largest organ and is highly innervated (Adameyko & Freid, 2016; Chapman & Moynihan, 2009; Nguyen & Soulika, 2019; Takayuki & Ostry, 2010; Tobin, 2006), comprising 6% of an individual's total weight (Tobin, 2006). The skin consists of two main layers. The epidermis is the outermost layer and is avascular (Harvey, 2018; Tobin, 2006). The inner layer is the dermis, which houses specialized sensory organs, the circulatory system, and superficial lymphatics (Harvey et al., 2017; Harvey, 2018; Tobin, 2006). During embryologic development, the ectoderm gives rise to the skin and other critical systems, such as the central and peripheral nervous systems. It is closely connected to the nervous system, thus called the outer brain POSTOPERATIVE SCARS KAP 11 (Adameyko & Freid, 2016; Tobin, 2006) or the diffuse brain (Chapman & Moynihan, 2009). The skin is designed for protection and regulation. It protects against environmental pathogens, regulates body temperature, defends against ultraviolet radiation, and provides kinesthetic awareness through specialized sensory neurons, providing postural control and pain perception (Alviar-Lechuz et al., 2017; Lubczyska et al., 2023; Matur & ge, 2017; McGlone et al., 2014; Takayuki & Ostry, 2010; Tobin, 2006). It is described as the entry point to the central nervous system, capable of impacting perception, emotional regulation, and attentiveness (McGlone et al., 2014; Tobin, 2006). Cutaneous sensory nerves and free nerve endings deliver afferent information to the hypothalamus (Alviar-Lechuz et al., 2017; Tobin, 2006), and deficits in this information cause motor skill impairments (Matur, 2017). Damage to this system through trauma or surgery can cause aberrant wound healing, diminishing the system's effectiveness in functioning in these various capacities (Lubczyska et al., 2023; Nischwitz et al., 2020; O'Reilly et al., 2021; Rabello et al., 2014), which is to keep the system in homeostasis (Tobin, 2006). Scar tissue can adhere to underlying neural and myofascial structures, even deep into the organs (Alvira-Lechuz et al., 2017; Biology Dictionary, 2017; Bove et al., 2017; Lubczyska et al., 2023). Postoperative adhesions in the abdominal region (i.e., cesarean section) can lead to pathological attachments to the organs and bowels; however, immediate return to movement and manual therapy effectively prevent scar tissue adhesions (Bove et al., 2017; Lubczyska et al., 2023; Olszewska et al., 2023; Poddighe et al., 2024). Additionally, drainage or gastrointestinal tubes leave scars that penetrate deep into the viscera, often creating anxiety in patients and symptoms associated with tethering adhesions, including lymphatic chording (Chen et al., 2022; Nishioka et al., 2020; Xu et al., 2021). Genetic, lifestyle, and environmental factors can impact wound healing (Ogawa, 2021; Patel et al., 2014; Wang et al., 2020). Scars move through a typical recovery sequence, beginning POSTOPERATIVE SCARS KAP 12 only 48 hours after injury (Sidgwick et al., 2015). These stages are the inflammatory phase, marked by coagulation at the wound site and release of immune cells, proliferation where new collagen binds the site together and remodel the skin, followed by the maturation phase, characterized by remodeling of the extracellular matrix (Patel et al., 2014; Nischwitz et al., 2020; Wang et al., 2020). These aberrant scars fall into the following categories: atrophic, hypertrophic, keloid, or mixed presentation (Lubczyska et al., 2023; Nischwitz et al., 2020; Patel et al., 2014; Rabello et al., 2014; Sitohang et al., 2021; Wang et al., 2020). Up to 70% of postoperative scars are pathological (Ferrerio et al., 2015), usually due to prolonged inflammation of the reticular layer of the dermis (Nischwitz et al., 2020; Ogawa, 2021; Wang et al., 2020), which is exacerbated by excessive skin stretch (Lubczyska et al., 2023; Ogawa, 2021; O'Reilly et al., 2021). The many pathologies of scar tissue demand continuous evolution of evaluation and treatment methods to remedy the dysfunction scars can cause (DeFlorin et al., 2020; Ogawa, 2021; Vercelli et al., 2015; Wang et al., 2020). Substantial evidence exists on the incidence and repercussions of postoperative scars. The physical and psychological impact of scar tissue evident in the literature (Cincinnati Children's Hospital Medical Center, 2018; Grigoryan & Kampp, 2020; Krakowski et al., 2016; Lauridsen et al., 2014; Lubczyska et al., 2023; Ngaage & Agius, 2018; Nesbitt et al., 2017; Nischwitz et al., 2020; Patel et al., 2014; Rabello et al., 2014; Sidgwick et al., 2015). There are also recommendations for evaluating and treating postoperative scars, but few articles indicate PTs practice patterns. A recent study from Poland cites the goal of manual work as limiting pain and reducing functional limitations (Lubczyska et al., 2023). Over 1,400 articles published in the last ten years review how PTs manage burn scars; however, the literature lacks research indicating how PTs clinically address postoperative scars. Although there is a strong emphasis on POSTOPERATIVE SCARS KAP 13 wound care curricula (Gibbs et al., 2019; Gibbs & Furney, 2013), little is known about evidencebased physical therapy practice for scar management. Many linear scar assessment methods and conservative treatment approaches are available to PTs with various levels of efficacy, but their use in rehabilitation is not well documented. Scar Formation A scar is a natural product of the integumentary system closing a wound; however, as stated before, this process often becomes aberrant (Coentro et al., 2019; Eid & Abdelbasset, 2022). Pathological scars can come in many forms many types of scar tissue, such as atrophic, hypertrophic, or keloids (Eid & Abdelbasset, 2022; Grigoryan & Kampp, 2020; Lubczyska et al., 2023; Nischwitz et al., 2020; Ogawa, 2022; Patel et al., 2014; Sidgwick et al., 2015; Wang et al., 2020). Environmental and genetic factors can impact the wound-healing process. Pathologic scars can lead to physical comorbidities such as contracture (Cincinnati Children's Hospital Medical Center, 2018; Krakowski et al., 2016; Patel et al., 2014), discoloration, pruritis, chronic pain (Grigoryan & Kampp, 2020; Lauridsen et al., 2014; Ngaage & Agius, 2018; Nesbitt et al., 2017; Nischwitz et al., 2020; Rabello et al., 2014; Sidgwick et al., 2015). Abdominal adhesions can also form postoperatively, causing infertility, bowel obstruction, and pain (Bove et al., 2017; Alvira-Lechuz et al., 2017; Biology Dictionary, 2017). Atrophic Scars Atrophic scars are defined as thinning or a loss of collagen in the epidermal layer, creating a depression in the skin (Patel et al., 2014; Sitohang et al., 2021). Traction occurs from the loss of elastin, collagen, and dermal fat, resulting in skin depression such as a divot (Patel et al., 2014). These scars typically arise from acne; other causes of cutaneous cellular damage include cysts, burns, and surgery (Patel et al., 2014; Sitohang et al., 2021). Patients are more at POSTOPERATIVE SCARS KAP 14 risk of developing atrophic scars if they have Ehlers-Danlos syndrome, macular atrophy, or previous episodes of atrophic scars (Patel et al., 2014). Closing a wound with sutures can cause this type of scar. Hypertrophic Scars Hypertrophic scars are a global problem with impacts such as contractures and decreased function (Eid & Abdelbasset, 2022; Nischwitz et al., 2020; O'Reilly et al., 2021; Rabello et al., 2014). They are more prevalent in people 10-30 years old due to increased elastin and skin tension, higher collagen production, and greater risk of trauma (Rabello et al., 2014). This scar tissue comprises Type III collagen and is characterized by raised tissue, no more than 4 mm, that is contracted, discolored (e.g., red or pink), and does not extend beyond the original boundaries of the wound (Rabello et al., 2014). Angiogenesis creates vertically oriented blood vessels in the dermal layer, adding to the color and raised presentation (Eid & Abdelbasset, 2022; Nischwitz et al., 2020; Rabello et al., 2014). These are also more likely to occur if the incision runs perpendicular to the Langer lines or the skin's natural tension lines, such as at the sternum (Eid & Abdelbasset, 2022). Surgeons carefully consider these tension lines when planning a procedure, as they can significantly impact healing. For example, this is why cesarean incisions are performed horizontally rather than vertically when planned. It is also why incisions following median sternotomy are often problematic (Harvey, 2022). The main symptoms following surgery are pruritis, pain, erythematous or dilation of the blood vessels (Rabello et al., 2014). Keloid Scars The most aggressive pathological scars are keloids, which have a strong genetic component (Huang & Ogawa, 2021; Ogawa, 2022) and are ubiquitous following surgery (Huang & Ogawa, 2021). Lubczyska et al. (2023) reported an incidence of 4.5-6%. The recurrence rate POSTOPERATIVE SCARS KAP 15 after surgical revision ranges from 40-100%, depending on the location and inherent risk (Sidgwick et al., 2015). These scars are more common in those with darker skin, Fitzpatrick scale III-V (e.g., 10 to 16%) (Ogawa, 2022; Rabello et al., 2014), women, especially if pregnant (Ogawa, 2022), and those with Rubinstein-Taybi syndrome (Ogawa, 2022). They extend beyond the borders to invade surrounding tissue and appear shiny and discolored (e.g., pink, purple, or brown) (Nischwitz et al., 2020; Ogawa, 2022; Rabello et al., 2014). Keloids consist of irregular type I and II collagen from extended inflammation and overactive myofibroblasts (Nischwitz et al., 2020; Rabello et al., 2014; Wang et al., 2020). Scars are exacerbated by excessive skin tension during the healing process, leading to increased keloids on the knees, elbows, lower abdominal region, scapular region, and chest wall (Hosseini et al., 2022; Huang & Ogawa, 2021; Moorgat et al., 2015; Ogawa, 2022; Sidgwick et al., 2015). Scar Treatments Assessment is the first step to appropriate intervention. The assessment can inform the selected treatment intervention based on the wound healing stage and patient goals. The goal of postoperative scar management is to return the patient to their pre-operative function and attend to the somatopsychic consequences of the scar (Gottrand et al., 2018; Lubczyska et al., 2023). Conservative scar tissue treatment ranges from physical techniques to topical agents (AbdElseyad et al., 2022; Block et al., 2015; Dastagir et al., 2021; Deflorin et al., 2020; Eid & Abdelbasset, 2022; Lubczyska et al., 2023; Shin & Bordeaux, 2011; Wasserman et al., 2019). Many studies advocate a multi-modality approach to scar management rather than one gold standard for intervention (Lubczyska et al., 2023). Techniques for treating scar tissue include soft tissue massage (STM), instrument-assisted soft tissue massage (IASTM), dry needling, tape, topicals, and phototherapy. POSTOPERATIVE SCARS KAP 16 Soft Tissue Massage Non-invasive manual techniques such as scar massage have strong efficacy in decreasing symptoms of acute and chronic postoperative scar adhesions, such as improved tissue mobility, posture, range of motion, pressure tolerance, decreased pelvic organ prolapse, and reduced pain (Abd-Elseyad et al., 2022; Bove et al., 2017; Crowle & Harley, 2020; Deflorin et al., 2020; Eid & Abdelbasset, 2022; Lubczyska et al., 2023; Olszewska et al., 2023; Poddighe et al., 2024; Scott et al., 2022; Wasserman et al., 2019). Scar massage, performed either with the hands or a tool, increases circulation and adds physical pressure, producing non-nociceptor stimulation, which decreases pain (Abd-Elseyad et al., 2022; Deflorin et al., 2020; Gilbert et al., 2022; Grigoryan & Kampp, 2020; Karwacinska et al., 2012; Lubczyska et al., 2023; Olszewska et al., 2023; Poddighe et al., 2024; Scott et al., 2022; Shin & Boardeaux, 2011; Wasserman et al., 2019). Another form of mechanical massage is vacuum massage, or cupping (Moortgat et al., 2016; Moortgat et al., 2020). This treatment uses suction to lift the scarred skin to mobilize a skin fold and has been shown to improve the elasticity and texture of scars, especially burns (Moortgat et al., 2016; Moortgat et al., 2020). This therapy increases the number of fibroblasts and collagen fibers and their orientation (Moortgat et al., 2016; Moortgat et al., 2020). Cesarean section and mastectomy scars have shown an excellent response to STM, effectively reducing many comorbidities of scar tissue (Comesaa et al., 2017; Crowle & Harley, 2020; Gilbert et al., 2022; Kelly et al., 2019; Kelly-Martin et al., 2018; Koller, 2020; Kramp, 2012; Martingano, 2016; Olszewska et al., 2023). If a patient is at risk of keloid scarring, additional tension to the scar is contraindicated (Hosseini et al., 2022; Moorgat et al., 2015; Ogawa, 2022). POSTOPERATIVE SCARS KAP 17 Vibration Therapy Focal vibration (FV), a form of IASTM, is direct soundwave therapy applied to a target tissue (Brewin et al., 2017; Celletti et al., 2017; Uher et al., 2018; Vegar & Imtiyaz, 2014). FV creates a vibratory force through mechanical or battery-operated frequencies (Brewin et al., 2017). Two of the best-documented benefits of FV are pain relief (Barati et al., 2021; Celletti et al., 2017; Feltroni et al., 2018; Garcia et al., 2022; Manfredi, 2012; Uher et al., 2018) and increased local circulation (Ballard et al., 2019; Imtiyaz et al., 2017; Nakagami et al., 2007; Uher et al., 2018). It may be preferred to STM as five minutes of FV is equivalent to 15 minutes of STM (Imtiyaz et al., 2017; Pastouret et al., 2016), increasing local skin circulation through vasodilation (Imtiyaz et al., 2017; Nakagami et al., 2007; Pastouret et al., 2016). Vibration therapy may decrease pain by increasing oxygen and improving lymphatic flow, thus decreasing cytokines (Veqar & Imtiyaz, 2014). Celletti et al. (2017) have shown that this therapy may effectively regain upper limb function post-mastectomy. Instrument-Assisted Soft Tissue Massage Instrument-assisted soft tissue massage (IASTM) is a popular soft tissue adhesion treatment with short-term benefits (Cheatham et al., 2016; Seffrin et al., 2019). Focal vibration is another form of IASTM that uses mechanical frequencies to impact scar tissue (Brewin et al., 2017). Vibration has the advantage that 5 minutes is equivalent to 15 minutes of massage (Imtiyaz et al., 2017). Dry Needling Dry needling is within the scope of practice of physical therapists in many states and is used to decrease pain and improve the appearance of scars (Dunning et al., 2014; Lubczyska et al., 2023; Rozenfeld et al., 2020). A systematic review shows that this non-ablative technique offers promising results for treating all scar types and is safe (Alster & Li, 2020; Bonati et al., POSTOPERATIVE SCARS KAP 18 2017; Cohen & Elbuluk, 2016; Al-Qarqaz & Al-Yousef, 2018; Lubczyska et al., 2023; Ramaut et al., 2018; Sitohang et al., 2021). This process uses tiny, sterile needles to stimulate collagen production by provoking an increase in growth factor and elastin in the dermal vasculature systems (Sitohang et al., 2021). Some studies show effectiveness in persons with darker skin with minimal side effects (Alster & Li, 2020; Al-Qarqaz & Al-Yousef, 2018; Lubczyska et al., 2023). Caution is advised as other reviews associate this treatment with the risk of prolonged recovery time, discoloration, and scarring (Cohen & Elbuluk, 2016). Tape Elastic tapes (e.g., Kinesio tape) are a newer modality with preliminary evidence of scar improvement across different populations and remodeling stages (Cheatham et al., 2021; Eid & Abdelbasset, 2022; Grigoryan & Kampp, 2020; Harvey, 2022; Karwacinska et al., 2012; Lubczyska et al., 2023; O'Reilly et al., 2021; Prusinowska et al., 2014). Elastic tapes only deform longitudinally; thus, a potential mechanism of action is a lift in the superficial fascia while limiting tension across the scar edges (Eid & Abdelbasset, 2022; Hosseini et al., 2022; Ishii et al., 2021; Lemos et al., 2014; Lubczyska et al., 2023; Moorgat et al., 2015; Tu et al., 2016). Biomechanical tension causes compression; this stimulates a biochemical response to increase collagen production (Hosseini et al., 2022; Moorgat et al., 2015). Windisch et al. (2017) showed a localized increase in superficial circulation following total knee replacement. Another study showed decreased pain and opioid use following median sternotomy (Klein et al., 2015; Brockman & Klein, 2018). In a recent survey, 74% of rehabilitation clinicians use Kinesio tape for post-injury applications, believing that it stimulates mechanoreceptors (77%), improves circulation (69%), and modulates pain (60%) (Cheatham et al., 2021). Additionally, paper tape is helpful for POSTOPERATIVE SCARS KAP 19 abdominal or truncal scars to prevent shearing, which can lead to hypertrophic or keloid formation (Atkinson et al., 2005; Grigoryan & Kampp, 2020; Ishii et al., 2021; Lubczyska et al., 2023; O'Reilly et al., 2021). According to Ishii et al. (2021), woven tapes show better efficacy than paper tape in postoperative scars, possibly due to moisture permeability. Topicals Common topical agents for surgical scar remodeling are onion extract and silicone sheeting, which have solid preliminary efficacy, especially for the prevention of keloid scars (Block et al., 2015; Deflorin et al., 2020; Eid & Abdelbasset, 2022; Grigoryan & Kampp, 2020; Jiang et al., 2021; Sidgwick et al., 2015; Wananukul et al., 2013; Ward et al., 2019; Zoumalan, 2018). Silicone gel is made of chains that combine oxygen and silicon atoms, which may help hydrate the skin (Ward et al., 2019). Onion extract application improves erythema, pruritis, and pliability with fewer side effects than corticosteroid injections (Zoumalan, 2018). It should be noted that it can cause localized skin irritation (Grigoryan & Kampp, 2020). Overall, the current evidence is low for silicone gel sheets have shown high efficacy in changing the volume, elasticity, color, and firmness in hypertrophic scars and keloids (Block et al., 2015; Eid & Abdelbasset, 2022; Grigoryan & Kampp, 2020; Jiang et al., 2021; Ward et al., 2019). The mechanism may be that it provides a seal, thus improving hydration to the stratum corneum and affecting fibroblast activity (Block et al., 2015; Eid & Abdelbasset, 2022; Grigoryan & Kampp, 2020; Jiang et al., 2021). Lin et al. (2020) demonstrated it to be more advantageous than paper tapes, while Jiang et al. (2021) have shown it slightly more effective than onion extract. Vitamin E, when applied topically to the wound, may provide antioxidants that help with wound healing (Grigoryan & Kampp, 2020). A typical medium for vitamin E is Aquaphor, as POSTOPERATIVE SCARS KAP 20 pure vitamin E can cause contact dermatitis (Grigoryan & Kampp, 2020). While there is little definitive evidence for its efficacy with postoperative scars, it may decrease inflammation by reducing fibroblastic activity (Grigoryan & Kampp, 2020). General guidelines for the prevention of further damage from ultraviolet light can occur through the regular use of sunscreen (>50 SPF) on scarred skin (Eid & Abdelbasset, 2022). This should be observed throughout the maturation process to decrease the melanoma risk and hyperpigmentation and improve overall cosmesis (Eid & Abdelbasset, 2022), PhotoTherapy Light-emitting diode (LED), also known as low-level laser or photobiomodulation, has come to the forefront as a non-invasive approach to treating scar tissue (Eid & Abdelbasset, 2022; Fernndez-Guarino et al., 2023). Postoperative hypertrophic and keloid scars are exceptionally responsive to LED treatment, with less stiffness and volume after treatment with less recurrence (Deflorin et al., 2020; Eid & Abdelbasset, 2022; Fernndez-Guarino et al., 2023; Fu et al., 2019; Kurtii et al., 2021; Mamalis et al., 2014). The primary mechanism of action is the modification of fibroblastic activity (Eid & Abdelbasset, 2022; Fernndez-Guarino et al., 2023; Fu et al., 2019; Mamalis et al., 2014) or an overall decreased scar tissue vascularity, which reduces cytokines and growth factor levels (Eid & Abdelbasset, 2022). LED therapy is low-risk and cost-effective (Eid & Abdelbasset, 2022; Fernndez-Guarino et al., 2023; Fu et al., 2019; Kurtii et al., 2021; Mamalis et al., 2014). Method Study Design This study was a quantitative, non-experimental research using a cross-sectional design. The Postoperative Scar Survey was the instrument used to assess PTs' knowledge, attitudes, and POSTOPERATIVE SCARS KAP 21 practices regarding postoperative scar tissue. The survey used Qualtrics, an online survey platform. The survey was open from February 21 to March 3, 2023. The University of Indianapolis Institutional Review Board approved the study (Uindy IRB #01827) Participants The population of interest was PTs licensed to practice in the U.S. Convenience sampling was used to identify potential study participants. For inclusion in the study, the participant had to be a PT licensed in the U.S. and be able to read English. Exclusion criteria included a) physical therapy students b) other rehabilitation professionals. Sample Size. A sample size estimate was calculated using G*Power 3.1. The calculation was based on conducting a two-tailed independent t-test with the following parameters: medium effect size of 0.50, an alpha of .05, power of 0.8, and a 1:1 allocation ratio. Based on these parameters, a minimum sample size of 128 and group sizes of 64 were needed. A larger sample size was collected to increase the likelihood that the results could be generalizable to PTs practicing in the U.S. Data The following demographic data were collected: sex (male, female, other) practice setting (private outpatient clinic, public outpatient clinic (i.e., state, county), hospital-based facility, skilled nursing facility, home health, academic/research institution, military facility, industrial/occupational health services, outpatient pediatrics, early intervention) patient population (adults, pediatrics, both adult and pediatric patients) POSTOPERATIVE SCARS KAP 22 specialty certification (none, cardiovascular or pulmonary, clinical electrophysiology, geriatrics, pediatric, neurology, oncology, orthopedics, sports, women's health, wound management) number of years in PT practice (listed in five-year increments up to >30) primary practice region (i.e., South, Northeast, West, Midwest) Study outcomes were derived from knowledge, attitude, and practices (KAP) scores as measured with the Postoperative Scar Survey. Operational Definitions The concepts of KAP scores were operationalized as domain scores obtained from the Postoperative Scar Survey (Harvey et al., in progress). The domain scores were summed up to a total score that measures a PTs engagement in scar tissue. The higher the score, the more engaged a therapist is towards scars. Knowledge: A PT's understanding of the concept of postoperative scar treatment, comorbidities of postoperative scars, and its importance to overall health Attitude: How a PT thinks, feels, or acts regarding postoperative scars, which may be influenced by values and understanding Practice: The action a PT takes to evaluate and treat postoperative scar tissue Instruments The Postoperative Scar Survey measures PTs' knowledge of postoperative scar tissue assessment and treatment strategies, attitudes toward treating scars, and practice patterns. The revised survey consists of 45 items. Reliability through factor analysis was as follows: knowledge ( .82) with ten items, attitude ( .79) containing seven items, and practices ( .80) with nine items (Harvey et al., in progress). The Postoperative Scar Survey elicited three content domains that represent PTs KAP. The domains consist of items with true/false and Likert scale POSTOPERATIVE SCARS KAP 23 responses having a higher value and negative responses with a lower value. Before scoring, negatively worded items were coded or reverse coded, as indicated, and then summed. The summation of the direct and reverse scores of the summed domains estimates how engaged or disengaged the individual is regarding postoperative scar treatment and was analyzed. Higher scores indicate more engagement, and lower scores indicate less engagement. Each domain was assessed individually, then summed for a total score and evaluated for differences among stated PT characteristics. The instrument is available upon request. Procedures Recruitment Multimodal recruitment strategies were used. The primary researcher (E. H.) actively recruited PTs at the 2023 APTA Combined Sections Meeting (CSM) by handing out flyers with survey QR codes generated through Qualtrics, beginning on February 21, 2023. After the conference, the survey link was posted on professional social media sites such as Facebook groups for PTs and the special interest groups for each section of the APTA. Incentives were offered in the form of a drawing for one of five $20 Amazon gift cards. Participants who wanted to participate in the drawing were allowed to sign up at the end of the survey. Informed Consent Informed consent was required for participation. The informed consent document was the landing page when an individual used the QR code or the link to access the survey. Participants could not proceed to the study without giving informed consent. No vulnerable populations or psychologically harmful events were anticipated from survey participation (Manti & Licari, 2018). POSTOPERATIVE SCARS KAP 24 Data Collection Data were collected digitally through Qualtrics. A captcha was used to ensure that robots did not infiltrate the survey. The primary researcher actively recruited participants from different specialty groups during the APTA CSM to ensure equal opportunity for each group to be represented in the survey. The primary researcher closed the survey on March 3, 2023, as a sufficient sample size had been obtained. A separate Qualtrics survey was created to collect information for those wishing to participate in the drawing for five $20 Amazon gift certificates. Individuals who completed the survey and wanted to participate in the drawing were directed to a new survey page not linked to the Postoperative Scar Survey, where they could agree to be in the drawing. An incentive statement was included to inform participants of participation parameters so they understood they would not be compensated for participating in this study. Participants were asked to include their email addresses when distributing the electronic Amazon gift card if they were selected to receive it. A random number generator was used to draw participant names for the five gift cards after the study and emailed to the winners. Data Management Data from Qualtrics were exported into a data analysis file, where it was explored for missing data and manipulated to calculate instrument scores. Data were de-identified by deleting IP addresses, and geotags documenting geocoordinates were removed. All electronic files were located on a password-protected computer. The primary researcher will retain a copy of the clean data for up to ten years to allow for the publication of findings. At the end of this time, files will be destroyed. POSTOPERATIVE SCARS KAP 25 Statistical Analysis Descriptive statistics were used to describe the sample and report the outcomes. Nominal data was reported as frequencies and percentages, while normally distributed interval and ratio data were stated as means and standard deviations. The normality of data was determined using the Shapiro-Wilk test and visual inspection of histograms, box plots, and Q-Q plots. Inferential Analyses Inferential statistical analysis was used to determine if there were differences in PTs' KAP and total scores by provider characteristics. An independent t-test was used to determine if there were significant differences in knowledge scores by sex. In contrast, one-way ANOVA tests were used to compare the following practice characteristics: patient population, years of experience, specialty area board certification, primary U.S. region of practice, and primary practice setting. If a one-way ANOVA result was statistically significant, Bonferroni or Tamhanes T2 post hoc tests were used to determine the statistically significant pairwise comparisons. Data were analyzed using IBM SPSS Statistics for Windows, Version 28.0 (IBM Corp., Armonk, NY). All comparisons were two-tailed, and a significance level of less than .05 was considered statistically significant. Results A total of 958 individuals responded to the survey. Of those, 945 gave informed consent to participate in the study. Listwise deletion was performed only to include complete survey responses, leaving 750 participants. Nearly equal numbers of males and females completed the survey, and over half of the participants had ten or fewer years of experience (55.9%). Most participants worked with adult patients (42.9%); the highest reported practice setting was early POSTOPERATIVE SCARS KAP 26 intervention (20.7%). Most practiced in the Midwest (29.9%). See Table 1 for more descriptive characteristics of the sample. Comparison of Knowledge Scores Objective 1 was to determine if knowledge scores varied by sex, patient population, practice setting, U.S. demographics, and specialty certification. An independent samples t-test was used to investigate differences in knowledge by sex. There was a statistically significant difference in knowledge scores between males and females, with females having greater scores on knowledge t(2, 750 = -8.23, p <.001. One-way ANOVA tests were used to assess differences in knowledge among the therapist specialties, practice locations, patient populations, and years of experience. These results are presented in Table 2. There was a statistically significant difference in mean knowledge scores by patient population, F(2, 731) = 34.93, p < .001. Tamhane T2 post hoc tests showed that PTs who worked with pediatric patients differed significantly and had lower mean knowledge scores than those who treated pediatric and adult patients or those who treated adult patients only. There was a mean difference between those working with pediatrics and those working with adults of 0.88 (p < .001) and those working with a mixed population by 0.69 (p < .001). This suggests that those with a diverse patient population have a better understanding of scar tissue. Significant differences were found in years of experience F(6, 754) = 5.41, p < .001. Tamhane T2 post hoc tests showed significant mean difference of 1.48 between PTs with 30 years or more experience and those with < five years of experience (p < .001) mean difference of 1.28 (p = .01) with those with six to ten years of experience, mean difference of 1.79 (p < .001) with those who have 11 to 15 years of experience, and a mean difference of 1.66 (p = .003) with those POSTOPERATIVE SCARS KAP 27 who have 16-20 years of experience. There was no significant mean difference between those with >30 years of experience and those with 21-25 or 26-30 years. Thus, those with at least 21 years of experience understand scar tissue assessments and treatments more. Regional differences were also seen in knowledge scores, F(3, 758) = 2.86, p < .004. Practitioners in the Western U.S. had the highest mean knowledge score, and Bonferroni post hoc tests showed the mean differed significantly from the Northeast by 0.58 (p = .05). There were no other significant differences between regions comparisons. Significant differences were found among specialty certifications with F(10, 739) = 11.95, p < .001. Women's health specialists scored the highest, indicating more knowledge regarding post-operative scar tissue. Tamhane post hoc tests show that the mean score differs significantly from cardiology/pulmonary by 1.81 (p < .001), from clinical electrophysiology by 1.62 (p < .001), geriatrics by 2.41 (p < .001), pediatrics by 1.62 (p < .001), neurology by 2.34 (p < .001), oncology by 1.76 (p < .001), orthopedics by 1.37 (p = .004), sports by 1.78 (p < .001), and wound care by 1.06 (p = .02). Wound management had the second-highest score. It differed significantly from those without specialty certification with a mean difference of -0.85 (p = .004), geriatrics by a difference of 1.35 (p < .001), neurology by 1.31 (p < .001), and womens health by -1.06 (p = .02). Geriatrics had the lowest mean score. This indicates that those with specialty certification in womens health and wound management have the greatest understanding of assessment and treatment options for postoperative scar tissue. Significant differences were found among practice settings, F(8, 752) = 10.58, p < .001. PTs in the private outpatient setting scored the highest. Mean knowledge scores above five were seen in all settings except military facilities. (See Figure 1). Bonferroni post hoc tests showed that mean knowledge scores among PTs in private outpatient differed significantly from early POSTOPERATIVE SCARS KAP 28 intervention by 1.63 (p < .001), home health by 1.55 (p < .001), the military setting by 1.99 (p < .001), and occupational health by 1.43 (p < .001). Those in the public outpatient setting held the second highest score, and mean scores differed significantly from the early interventional setting by 1.39 (p < .001), home health by 1.36 (p < .001), occupational health by 1.21 (p = .02), and the military setting by 1.76 (p < .001). Both outpatient settings hold the highest mean knowledge scores, while the military setting scored the lowest among all settings. The military, early intervention, occupational, and home health settings scored the lowest mean score, with the military having the lowest score (See Figure 2). Thus, those in outpatient practice settings better understand post-operative scars than those in other settings. Comparison of Attitude Scores Objective 2 was determining if attitude scores varied by sex, patient population, practice setting, U.S. demographics, and specialty certification. An independent samples t-test was used to investigate differences in attitude scores by sex. There were statistically significant differences in attitude scores by sex, with females having a higher score, t(736.36) = -9.27, p < .001. One-way ANOVA tests were used to determine differences among therapists by patient population, years in practice, specialty area board certification, primary U.S. region of practice, and primary practice setting. These results are presented in Table 3. There were statistically significant differences in attitude scores by patient population, F(2, 741) = 19.75, p < .002. Tamhane T2 post hoc tests showed significant differences between PTs treating adults and those treating pediatric patients, with a mean difference of 3.78 (p < .001). There was also a significant mean difference between those treating a diverse population of adult and pediatric patients and those only treating pediatrics, with a difference of -2.68 (p < .001). Those PTs working only with pediatric patients had the lowest mean scores. No other significant differences were found in post POSTOPERATIVE SCARS KAP 29 hoc tests. This signifies that those who treat adults feel more strongly about treating scars than those who treat children. Significant differences were found in years of practice with F(6, 754) = 5.01, p < .001. Tamhane T2 post hoc tests show differences between 30+ years of experience and those with 0 to 5 years with a mean difference of 6.00 (p < .001), those with 6 to 10 years of experience with a mean difference of 6.21 (p < .001), 11-15 years of experience with a mean difference of 7.36 (p < .001), 16 to 20 years of experience with a difference of 6.48 (p < .001), 21-25 years of experience with a mean difference of 5.91 (p < .001)but no difference with those with 26 to 30 years. Thus, those with more than 26 years of experience have more favorable attitudes toward treating post-operative scars. Significant differences were found among specialty certifications with F(10, 728) = 15.21, p < .001. The highest attitude score was among those without certification, and the lowest was in geriatrics. Tamhane post hoc tests show that those with no certification differ from all other certifications and have more positive attitudes toward scar treatment. Post hoc tests showed that the mean difference between those PTs without specialty certification and cardiology/pulmonary by 6.07 (p < .001), clinical electrophysiology by 6.22 (p < .001), geriatrics by 7.11 (p < .001), pediatric specialists by 5.06 (p < .001), neurology specialists by 5.35 (p < .001), orthopedics by 4.07 (p < .001), sports by 4.88 (p < .001), and wound by 2.39 (p = .003), but not from women's health. Those specializing in womens health and wound management had higher mean scores than other specialties. Therefore, more positive attitudes toward treating postoperative scars were those without specialty certification and in womens health. Statistically significant differences were found using mean attitude scores with equal variances among practice regions within the U.S. F(3,746) = 5.87, p < .001. Bonferroni post hoc POSTOPERATIVE SCARS KAP 30 tests show that the Western region differed from the Midwest by 2.45 (p < .001), with the Western region having the highest mean score. There was no difference between the other regions. Statistically significant differences were found in the mean attitude score among practice settings with F(8,740) = 16.71, p < .001. The private outpatient setting held the highest mean score, and the military setting had the lowest. Mean attitude scores above 30 were seen in public and private outpatient clinics, hospital-based facilities, and academic research institutions. Scores below 25 were only found among those who worked in military facilities, and this group had lower scores than all other settings. (See Figure 3). Tamhane post hoc tests show that the mean scores of PTs in private outpatient settings differed significantly from those in early intervention by 5.24 (p < .001), home health by 4.94 (p < .001), military setting by 7.90 (p < .001), and occupational health by 4.38 (p < .001), but did not differ from public outpatient or SNFs. The military setting had the lowest mean score, and post hoc tests showed that they differed significantly from every other setting. It differs from the academic setting by -6.13, from early intervention by -2.66 (p = .036), from hospital setting by -6.67 (p < .001), home health by -2.96 (p = .014), occupational health by -3.52 (p = .003), private outpatient by -7.90 (p < .001), public outpatient by -6.81 (p < .001), and SNF by -5.92 (p < .001). Thus, those in the private outpatient setting have the highest beliefs and attitudes toward managing scars. Comparison of Practice Scores Objective 3 was to determine if practice scores varied by sex, patient population, practice setting, years of experience, U.S. demographics, and specialty certification. An independent samples t-test was used to investigate differences in practice by sex. Results show no statistically significant differences in practice scores by sex t(-.725) = -0.55, p = .136. POSTOPERATIVE SCARS KAP 31 One-way ANOVA tests were used to determine differences among therapists by patient population, years in practice, specialty area board certification, primary U.S. region of practice, and primary practice setting. These results are presented in Table 4. Statistically significant differences were found among patient populations, with the highest scores among PTs who practice with adults and pediatrics having the highest mean score, while the lowest scores were among those practicing exclusively with pediatric populations F(7, 736) = 35.39, p < .001. Post hoc Bonferonni tests show that those working with adults differ from those PTs working with pediatric patients by 2.77 (p < .001) and those working with a diverse population by -2.15 (p < .001). Statistically significant differences in practice scores were found among PTs based on years of experience, F(6, 738) = 6.01, p < .001. The highest practice scores were in the group with the least experience (0 to 5 years). Bonferroni posthoc tests show that this mean score was significantly higher than those with the lowest two scores: PTs with 11 to 15 years with a mean difference of 3.25 (p < .001) and 16 to 20 with a difference of 3.61 (p < .001) years of practice. No significant difference existed between those with 0 to 5 years of experience and all other groups. Statistically significant differences were found among the mean average with equal variances among specialty certifications, F(6, 719) = 6.22, p < .001. Womens health, wound management, and sports specialists had the highest mean practice scores (see Figure 5). Tamhane post hoc tests show that those with women's health certification differ significantly from those without specialty certification by 5.37 (p < .001), from cardiology/pulmonology certification by 5.66 (p < .001), clinical electrophysiology by 4.97 (p < .001), from geriatric specialists by 7.99 (p < .001), from pediatric specialists by 5.25 (p < .001), neurology by 4.97 (p < .001), oncology POSTOPERATIVE SCARS KAP 32 specialists by 6.23 (p < .001), and orthopedic specialists by 5.63 (p < .001), but not from wound management or sports specialists. Those with wound management certification differ from those without specialty certification by 5.09 (p < .001), from cardiology/pulmonology specialists by 5.39 (p < .001), from clinical electrophysiology by 4.53 (p < .001), from geriatric specialists by 7.7 (p < .001), from pediatrics by 4.98 (p < .001), from neurology specialists by 4.69 (p <. 001), oncology by 5.96 (p < .001), orthopedic specialists by 5.36 (p< .001), but did not differ from womens health or sports specialists. Geriatric and oncology specialists had the lowest mean scores. Thus, those with womens health or wound management certification are more engaged with assessing and treating scars. Statistically significant differences were found in practice scores among primary regions of practice, F(3, 736) = 6.51, p < .001. Tamhane post hoc tests show that the Western U.S. held the highest mean score and differs from the Midwest by 1.64 (p < .02), and the Northeast differs from the Midwest by 1.79 (p = .01) (See Table 4). Statistically significant differences were found among practice scores in the practice setting, F(8, 735) = 9.59, p < .001. The highest mean practice scores were among PTs in skilled nursing facilities (SNF), followed by public and private outpatient clinics; the lowest was among those in early intervention and military facilities (See Figure 6). Tamhane post hoc tests show that mean practice scores for those SNFs differed significantly from early intervention by 4.99 (p < .001), hospital setting by 2.56 (p = .004), home health by 3.42 (p = .006), and military setting by 6.47 (p < .001), but did not differ from the other settings. The mean scores for PTs working in military settings were the lowest. Post hoc tests showed that the mean score for PTs in the military setting differed significantly from those in an academic setting by -4.82 (p < .001), hospital setting by -3.90 (p = .002), occupational health by -5.59, private outpatient by -6.09 (p < POSTOPERATIVE SCARS KAP 33 .001), public outpatient by -6.31 (p < .001), and SNF by -6.47 (p < .001), but did not differ from early intervention or home health settings. This means that those in the SNF setting assess and treat scars the most, while PTs in the military settings evaluate and treat the least. Comparison of Total Scores The fourth objective was to determine if total scores varied by sex, patient population, practice setting, years of experience, U.S. demographics, and specialty certification. An independent samples t-test was used to investigate differences in practice by sex. There was a statistically significant difference in practice scores by sex, t(738) = -6.42, p < .001, with females having the highest score. ANOVA tests were used to determine differences among therapists by patient population, years in practice, specialty area board certification, years of experience, primary U.S. region of practice, and primary practice setting. These results are presented in Table 5. There were statistically significant differences in total scores by patient population, but variances were not equal, F(2, 731) = 34.93, p < .001. The highest mean score was among PTs treating both adult and pediatric patients. PTs working with adults only had the next highest score, and pediatric therapists had the lowest total score. Tamhane T 2 post hoc tests show that PTs working with adults only differ significantly from those working with pediatrics by 7.03 (p < .001) but did not differ from those working with a diverse population. Those working with pediatrics differed from those working with a diverse population by -8.51 (p < .001). Thus, those treating adults are more engaged with post-operative scar management. Statistically significant differences were found in years of practice, but unequal variance, F(6,733) = 5.53, p < .001. PTs with more than 30 years of experience had the highest mean total score, while those with 11 to 15 years of experience had the lowest (see Table ). Tamhane post POSTOPERATIVE SCARS KAP 34 hoc tests show that those with more than 30 years of experience differ significantly from those with 11 to 15 years by 10.00 (p < .001) and those with 16 to 20 years of experience by 9.52 (p = .006) but did not differ from all others. This implies that those with more than 30 years of experience are the most engaged with scar management. Statistically significant differences were found among specialty certifications but unequal variances, F(10, 718) = 15.25, p < .001 (see Figure 7). Those with women's health certification had the highest mean score, followed closely by those with wound care certification, each having a score greater than 70 (see Table 5). Tamhane post hoc tests show that those with specialty certification in womens health differ from cardiology/pulmonology by 13.26 (p < .001), clinical electrophysiology by 12.00 (p < .001), geriatric specialists mean score by 16.99 (p < .001), pediatric by 11.68 (p < .001), neurology by 12.37 (p < .001), oncology by 13.05 (p < .001), orthopedics by 10.76 (p < .001), and sports specialists by 8.58, but not wound care specialists. Those with wound care specialty differed significantly from those with cardiology/pulmonology by 9.88 (p < .001), clinical electrophysiology by 8.62 (p < .001), geriatric specialists by 13.60 (p < .001), pediatric specialists by 8.29 (p < .001), neurology by 8.98 (p < .001), oncology by 9.67 (p < .001), and orthopedic specialists by 7.37 (p < .001), but not sports, women's health, or those without specialty certification. This indicates that these groups have a higher overall engagement with post-operative scars. The lowest score was among those with geriatric certification. Statistically significant differences were found among the primary region of practice, but variances were not equal, F(3, 736) = 6.51, p < .001. Tamhane post hoc analysis showed that the West differed from the Midwest by 4.82 (p < .001). No other regions differed significantly from one another. POSTOPERATIVE SCARS KAP 35 Statistically significant differences were found among primary practice settings, but variances were unequal, F(8, 730) = 18.14, p < .001. The highest mean total score was among PTs in the private outpatient setting, with the lowest in the military facilities. Tamhane post hoc tests show that the private outpatient setting differed significantly from early intervention by 11.64 (p < .001), the hospital setting by 4.16 (p = .016), home health by 9.68 (p < .001), military setting by 16.22 (p < .001), occupational health by 6.62 (p = .004), but did not differ significantly from the SNF, public, or academic settings. This indicates that PTs in the outpatient clinic environment are the most active in post-operative scar intervention. The lowest total score was in military facilities, which differed from all other settings except early intervention. The military setting differed from the academic setting by -12.21 (p < .001), hospital setting by -12.07 (p < .001), home health by -6.54 (p = .009), occupational setting by -9.59 (p < .001), private outpatient by -16.22 (p < .001), public outpatient setting by -15.14 (p < .001), and SNFs by 13.65 (p < .001), but did not differ from early intervention (see Figure 8). Discussion The literature has emphasized burn scars (Gibbs et al., 2019; Gibbs & Furney, 2013), but little can be found on how PTs evaluate or treat postoperative scars (Abd-Elseyad et al., 2022). The present study sought to characterize the KAP of PTs licensed in the U.S. to see if there are differences among those characteristics (i.e., sex, patient population, years of experience, board certification, practice region, and practice setting). The Postoperative Scar Survey was launched at the 2023 APTA CSM to obtain uniform data across varying PT demographics (Harvey, Ewen, Moore, & DeRu, in progress). The strong participation in the survey indicates that this topic is essential in physical therapy practice. Over 81% (n = 572) of therapists agreed that PTs should evaluate postoperative scars. This is a significant finding that warrants priority in PT education and practice. Generally, scoring higher in one KAP area resulted in a higher score in other areas. POSTOPERATIVE SCARS KAP 36 According to the APTA, females comprise 68% of the profession (APTA, 2023). Females scored higher than males across every domain except practices. Interestingly, women's health literature is the main area of scar treatment research (Comesaa et al., 2017; Crowle & Harley, 2020; Gilbert et al., 2022; Kelly et al., 2019; Kelly-Martin et al., 2018; Koller, 2020; Kramp, 2012; Martingano, 2016; Ocampo-Candiani et al., 2014; Wasserman et al., 2018). Women's health specialists also held the highest scores for every area except practice, where they were almost equal to wound specialists. The exact percentage is unknown, but more females act as women's health specialists, which may connect these findings. According to the APTA, 65% of board-certified specialists are female (APTA, 2023). Following the trend seen in KAP, women's health specialists had the highest scores. This may be due to the heightened nature of postoperative care in women's health populations with corresponding higher knowledge and attitude scores. Additionally, pelvic floor therapists would be poised to address comorbidities from scars among transgender or gender-diverse patients. This patient population is at risk for physical and emotional issues, requiring heightened attention to scar tissue comorbidities (Kamal & Keuroghlian, 2023). The patient's perception of their scars may also increase scar engagement in this demographic, especially with a high incidence of cesarean section, mastectomy, and breast reconstruction. There is new insight regarding the connection between cesarean scars and function through the abdominal scar score, which can predict pelvic floor function after cesarean section (Yang et al., 2023). This research used the POSAS to assess the scar and a functional pelvic floor assessment (Yang et al., 2023). Scars are the result of wound healing. Board-certified specialists in wound care came in a close second to women's health specialists in KAP and total scores. Prioritizing scar tissue as an extension of a wound is a natural progression. Their specialty practice guidelines include scar POSTOPERATIVE SCARS KAP 37 tissue management (APTA, 2020). Only 23 certified wound care specialists are currently in the country (APTA, 2023). In contrast, those with geriatric specialty certification scored the lowest across all domains, indicating that PTs specializing in this population may not prioritize scar tissue. Also, common barriers to healthcare in this population are limited finances and concern with quality of care (Kurichi et al., 2017). Limited accessibility or comorbidities could lessen attention to scars. Additionally, oncology and cardiovascular/pulmonary specialties scored low despite many surgical procedures in these patient populations. This could be due to the lifethreatening nature of the medical issues in this patient population, making postoperative scar tissue a lesser priority than functional outcomes. The patient population also had a significant bearing on scar engagement. PTs serving a diverse population of adult and pediatric patients scored higher across all domains, possibly indicating a broader scope of evaluation and treatment techniques. These therapists may better understand the impact of scar tissue on function as they see patients across their lifespan. In contrast, pediatric therapists scored the lowest. This is a significant finding since 70% of surgical scars or traumatic lacerations occur in the pediatric population (Krakowski et al., 2015) and may have an impact on motor skill development if not addressed (Harvey, 2022). Pediatric PTs approach practice more from a sensory-motor reference and may underestimate the physical and psychological impact of scars.' This is important as scars can impact physical and emotional development (Alvira-Lechuz et al., 2017; Duquennoy-Martinot et al., 2016; Harvey, 2022; Lubczyska et al., 2023; Matur, 2017; Takayuki & Ostry, 2010; Uher et al., 2018). They also come from a foundational neurologic focus, which could account for the lower scores. Additionally, there is limited research regarding approved scar tissue treatment techniques for pediatric populations. Generally, modalities are more accepted in the adult POSTOPERATIVE SCARS KAP 38 population, with more research to support their safety and efficacy. Another consideration is that there may not be a long-term projection for the issues that scars can cause, physically and psychologically, in the pediatric population (Davies et al., 2016; Duquennoy-Martinot et al., 2016; Fu et al., 2019; Gilbert et al., 2022; Gottrand et al., 2016; Kelly et al., 2019; Lee et al., 2018; Olsson et al., 2018; Rullander, 2015; Vuotto et al., 2018; Wasserman et al., 2016). There may be a lack of understanding of the physiological healing in children, producing a scar with dense collagen that can disable and disrupt growth (Le Touze et al., 2020). The pediatric patient will grow physically with their scar. If fibrotic scar tissue is not addressed, traction can cause asymmetrical growth and disturbed motor development (Alvira-Lechuz et al., 2017; DuquennoyMartinot et al., 2016; Harvey, 2022; Le Touze et al., 2020; Lubczyska et al., 2023; Matur, 2017; Takayuki & Ostry, 2010; Uher et al., 2018). Massery (2009) has pioneered the implications of postural deformation from deep, visceral scars and body systems to function. Therapists practicing early intervention scored low across all domains, lending more power to this finding. This could be due to the prioritization of other therapeutic tasks or a lack of understanding of the long-term impact of scar tissue on physical and psychological outcomes for the patient, including a higher risk of body dysmorphia and suicide (Duquennoy-Martinot et al., 2016; Padilla-Espaa et al., 2014; Patel et al., 2014; Rullander, 2015). This could also be because pediatric therapists only see children and do not see them into adolescence or adulthood, where tethered tissues may cause pain, infertility, or other orthopedic issues. The survey showed that practice settings were significant for KAP. According to the U.S. Census, 42% of PTs practice in outpatient environments (APTA, 2023). Those in private and public outpatient settings have the highest total KAP scores, possibly indicating an interest in scar tissue treatment in this patient demographic. Patients getting outpatient therapy may place POSTOPERATIVE SCARS KAP 39 higher importance on their scars, primarily in orthopedic or women's health-related procedures. Therapists in outpatient settings may better understand the impact of scar tissue on function and possess the manual skills and modalities to treat those issues. Other settings that would see many surgical procedures did not score as well. The military setting scored the lowest on all factors; however, it has a high incidence of surgery, with over 95,461 procedures annually (Dalton et al., 2022). There are many factors to consider in the military sector, including access, eligibility, and prevalence of comorbidities. A recent study showed that 53.8% of active service members and veterans choose to use other healthcare coverage over Veterans' Affairs (V.A.) services due to concerns with the quality of care, inconvenience, limited services, or red tape (Vankar, 2023). Those who do engage with the V.A. are increasingly complex with chronic medical issues ranging from cancer to diabetes (Rasmussen & Farmer, 2023). This could cause interventions to focus on more lifesaving measures and critical care. Additionally, the low scar engagement could be attributed to the COVID-19 pandemic and reported an overall reduction in access to direct care and surgical intervention, with a 36% reduction in surgical knowledge and skills compared to before the pandemic (Schoenfield et al., 2023). This is twice as high as trends in the civilian sector (Schoenfield et al., 2023). Additionally, a recent study showed that 33% of women do not engage with the V.A. for reasons such as trauma (especially sexual assault) and military-style versus healthcare feel (Evans et al., 2024). The female military population not seeking V.A. services could utilize public or private outpatient settings. If so, this could contribute to the higher scores among therapists in these settings. The 2021 U.S. Census shows that 3,475,888 military personnel and civilian support comprise our population, making this a significant problem (Department of Defense, 2021). POSTOPERATIVE SCARS KAP 40 Lastly, years of experience and region of practice also had significance. More experience was significant in scores; those with more than 21 years in practice had higher knowledge scores, and those with 25 years had higher attitude scores. Surprisingly, those with less than five years of experience had the highest practice scores, which may be attributed to new PTs eager to apply new skills and explore modalities. This could also be due to the growing evidence of fascia and its interconnection with muscle function. While scar tissue assessment and treatment are not a core component of DPT programs, this could be due to the holistic approach to rehabilitation and a desire to account for the patients perception of their surgical scars. Therapists in the Western U.S. scored highest among all domains. This region boasts the highest number of PTs with specialty board certification, especially California (APTA, 2023). This elevated number of PTs engaging in specialty certification may account for the higher scores in this region. Physical therapists in the Midwest had the lowest total KAP scores. This could be due to many factors, including access to facilities, less dense population, or higher importance of other goals. More research is necessary to determine why there is less emphasis on scar tissue in this region. Study Limitations There were limitations in the current study. Though every effort was made for a fair and equal representation of PTs, the cross-sectional nature of the study limits the ability to generalize findings across all PTs (Wang & Chen, 2020). The survey is descriptive, and it is not possible to provide causal relationships, thus, further research is needed (Wang & Cheng, 2020). Additionally, survey revisions occurred after the pilot, but a second test pilot was not completed before launching the survey in 2023. The survey was also long, with 41 questions, taking an average of 15.7 minutes to complete. Dry needling is a treatment technique approved in many POSTOPERATIVE SCARS KAP 41 states for PT use and is a modality used for scar treatment; however, this was not explored due to restrictions in some states. Future efforts should include this technique. Implications This study demonstrates the importance of postoperative scar engagement among U.S. PTs while showing differences among therapists' sex, patient populations, setting, region, specialization, and experience, which require further investigation. The survey highlighted underserved populations, mainly pediatric, geriatric, and military settings. The least-engaged settings were those supported by government funding, from Medicaid and Medicare to the V.A., while the most engaged are those receiving services from private or public outpatient settings, which may be due to access or finances. This highlights a potential health disparity in the treatment of scars. Wound care is a foundational component of DPT programs; however, scar management education is usually left to personal discretion. Physical therapists can apply for specialty board certification in wound management and fellowships or residencies in wound care, indicating that this type of treatment requires specific analysis and knowledge. As of 2023, there are only 23 PTs who hold wound care certification (APTA, 2023). Otherwise, therapists can take continuing education courses on scar management. This study showed that scar management is needed across diverse populations and stages of life. Since it applies to many patients, scar management could be woven into wound care education in DPT programs. Those with specialty certification in wound care could be leaders in this endeavor. Incorporating scar tissue management across stages of healing into DPT programs would equip students with assessment and treatment strategies across the lifespan. This could include a lack of patient interest or a higher prioritization of other goals due to limited time or support. POSTOPERATIVE SCARS KAP 42 While wound care is a critical education component, treating scar tissue is mainly left to continuing education efforts. With the overwhelming response from PTs, this topic should also hold crucial importance in the DPT programs, equipping the next generation. Pediatric PTs and the early intervention setting scored low across all KAP subscale scores. Future efforts should focus on developing a pediatric scar linear assessment to guide intervention research in this population, which is lacking in the literature. Specific education for these PTs should address recommendations to follow pediatric clients through adolescence to monitor for functional or psychological concerns during periods of rapid growth (Le DuquennoyMartinot et al., 2016; Touze et al., 2020), especially with the high occurrence of scars in this population (Krakowski et al., 2015). The military setting also requires targeted exploration to determine why engagement is limited. Understanding the obstacles to managing scar tissue in this setting would guide intervention or give support where necessary. A similar study is needed for therapists working in the geriatric population. More research should be conducted among geriatric specialists and settings, including the V.A., to understand the low levels of engagement. The Postoperative Scar Survey could clarify issues in these environments. Additionally, knowledge of scar tissue is included in the patient's goals and concerns to help guide interventions. Therapists in outpatient settings and those with women's health or wound care specialties scored high across domains and thus may be poised to lead the education efforts in this area as they are most engaged in scar tissue. Lastly, global consensus exists regarding burns (Agency for Clinical Innovation. 2017; Koyro et al., 2021) as well as wound management and scar prevention (Fernndez-Guarino et al., 2023; Monstrey et al., 2014; National Major Trauma Rehabilitation Group, n.d.). There are also POSTOPERATIVE SCARS KAP 43 international guidelines for physical agents used in wound management, focusing on chronic open wounds to limit scars but not specific to the treatment of scars once the wound is closed (Fernndez-Guarino et al., 2023; Monstrey et al., 2014). Internationally, recently, recommendations have been made for the use of STM for patients who have had a cesarean section (Olszewska et al., 2023). There is also an emphasis on PTs using a multi-modality approach to scars, as healing depends on many factors (Fernndez-Guarino et al., 2023; Lubczyska et al., 2023). Identifying current U.S. practices among PTs engaged in scar treatment is critical to determining preferred assessments and modalities. Comparing U.S. practices to international PT practices regarding postoperative scars could bring a broader understanding. Experts could be identified to gain consensus on assessment and treatment techniques to develop professional guidelines. Conclusion This study is the first to provide an overview of how U.S. PTs engage with postoperative scar tissue. Looking at the summed total scores, PTs with the highest total score are female, treating a broad range of patients (both adult and pediatric), practicing in private outpatient settings, in the Western U.S., have more than 26 years of experience, and have specialty certification in women's health. The therapists least engaged are males, working with pediatric patients, practicing in the military setting, in the Midwest, with less than 26 years of experience, and with geriatric certification. While there is research on burn management, this is the first survey to assess PT engagement with postoperative scars. The recent revival of interest in muscle connections and myofascial chains has transitioned the rehabilitation model to a holistic view of the patient. Dr. Guimberteau's (2014) groundbreaking work in fascia demonstrated how everything is seamlessly connected POSTOPERATIVE SCARS KAP 44 anatomically. This study shows more therapists have embraced assessing the whole person, looking for scars as a potential cause of disorders elsewhere. This is especially true of females; colleagues in women's health care and private practice with patients of all ages were more interested in looking at scars. The area most lacking in postoperative scar engagement is the military setting across all scores. This is followed closely by pediatric and geriatric populations. This new information may help to guide education towards demographics to encourage postoperative scar treatment. Understanding the preferred strategies of PTs, developing a pediatric assessment, and knowing barriers are critical in moving forward. From this research, the development of evidence-based treatment strategies across patient populations and settings, with an understanding of the impact of scars on function, should be emphasized. 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Journal of Drugs & Dermatology, 17(4), 421-425. POSTOPERATIVE SCARS KAP 66 Table 1 Sample Demographic Descriptive Statistics (N = 761) PT Characteristic N Frequency (%) Sex Male 398 52.2 Female 365 47.8 Adult 387 51.3 Pediatric 159 21.1 Adult and Pediatric 209 27.7 0-5 258 33.8 6-10 249 32.6 11-15 120 15.7 16-20 50 6.6 21-25 41 5.4 26-30 12 1.6 >30 33 4.3 None 122 16.0 Cardiovascular/Pulmonary 42 5.5 Clinical Electrophysiology 70 9.2 Geriatrics 46 6.0 Primary Patient Population Years in Practice Specialty Area Board Certification POSTOPERATIVE SCARS KAP 67 Pediatrics 88 11.5 Neurology 52 6.8 Oncology 64 8.4 Orthopedics 76 10.0 Sports 36 4.7 Womens Health 29 3.8 Wound Management 126 14.2 South 190 24.9 Midwest 280 36.8 Northeast 162 21.3 West 129 17.0 Academic/Research Institution 117 15.4 Early Intervention 67 8.8 Hospital-based Facility 192 25.2 Home Health Services 80 10.5 Military Facility 40 5.2 Occupation Health Setting 55 7.2 Private Outpatient Clinic 115 15.1 Public Outpatient Clinic 54 7.1 Skilled Nursing Facility 40 5.2 Primary U.S. Region of Practice Primary Practice Setting Subscale Scores POSTOPERATIVE SCARS KAP Knowledge 68 6.0 1.79 Attitude 29.62 5.52 Practice 32.57 5.72 Note. U.S. = United States POSTOPERATIVE SCARS KAP 69 Table 2 Comparison of Knowledge Scores by Sample Demographics(N=755) N M SD Sex p < .001 Male 398 5.44 1.71 Female 364 6.51 1.88 Primary Patient Population < .001 Adult 387 6.18 1.91 Pediatric 159* 5.30 1.91 Adult and Pediatric 210 6.00 1.67 Years in Practice < .001 0-5 256 5.86 1.80 6-10 249 6.02 1.72 11-15 120 5.56 1.92 16-20 50 5.70 2.11 21-25 41 6.10 2.35 26-30 12 7.42 1.93 >30 33 7.18 1.53 Specialty Area Board Certification None < .001 117 6.09 1.93 Cardiovascular and Pulmonary 43 5.47 1.67 Clinical Electrophysiology 70 5.66 1.72 Geriatrics 46 4.87 1.75 POSTOPERATIVE SCARS KAP 70 Pediatrics 88 5.66 2.00 Neurology 52 4.90 1.81 Oncology 64 5.52 2.02 Orthopedics 76 5.91 1.79 Sports 36 5.50 1.46 Womens Health 29 7.28 1.33 125 6.22 1.34 Wound Management Primary Region of Practice in U.S. < .04 South 190 6.01 1.87 Midwest 280 5.84 1.92 Northeast 163 5.76 1.73 West 129 6.34 1.89 Primary Practice Setting Academic/Research Institution < .001 117 6.09 1.93 67 5.16 1.69 Hospital-based Facility 192 6.15 1.69 Home Health Services 80 5.24 1.71 Military Facility 40 4.80 1.94 Occupation Health Setting 55 5.36 1.76 Private Outpatient Clinic 115 6.79 1.73 Public Outpatient Clinic 55 6.56 1.42 Skilled Nursing Facility 40 6.0 1.48 Early Intervention Note. U.S. = United States POSTOPERATIVE SCARS KAP 71 Comparison of Table 3 Attitude Scores by Sample Demographic (N = 750) N M SD Sex p < .001 Male 391 27.93 5.05 Female 359 31.49 5.44 Primary Patient Population < .001 Adult 382 30.42 5.70 Pediatric 157 27.24 5.11 Adult and Pediatric 205 29.93 5.04 Years in Practice < .001 0 to 5 253 29.70 5.00 6 to 10 245 29.45 5.26 11 to 15 118 28.30 5.66 16 to 20 50 29.16 6.61 21 to 25 40 29.60 6.92 26 to 30 12 32.58 5.76 >30 32 35.16 3.75 POSTOPERATIVE SCARS KAP 72 Specialty Area Board Certification None < .001 119 33.40 4.83 Cardiovascular/Pulmonary 42 27.33 4.89 Clinical Electrophysiology 67 27.18 5.06 Geriatrics 45 26.29 4.94 Pediatrics 88 28.34 5.30 Neurology 52 28.06 5.14 Oncology 62 28.02 6.18 Orthopedics 75 29.33 5.67 Sports 36 28.53 4.70 Womens Health 28 33.04 4.60 125 31.01 4.17 Wound Management Primary Region of Practice in U.S. < .001 South 184 29.61 5.58 Midwest 277 28.82 5.59 Northeast 162 29.79 5.31 West 127 31.27 5.29 POSTOPERATIVE SCARS KAP 73 Comparison of Primary Practice Setting <.001 Academic/Research Institution 115 30.33 5.46 Early Intervention 67 26.87 4.59 Hospital-based Facility 186 30.87 5.64 Home Health Services 79 27.16 5.29 Military Facility 39 24.21 3.43 Occupation Health Setting 54 27.72 4.71 Private Outpatient Clinic 114 32.11 5.14 Public Outpatient Clinic 55 31.02 4.43 Skilled Nursing Facility 40 30.13 4.59 Note. U.S. = United States Table 4 Practice Scores by Sample Demographic (N = 745) N M SD Sex .136 Male 391 32.45 5.93 Female 354 32.70 5.52 739 32.53 5.73 Adults 381 32.51 5.82 Pediatrics 154 29.75 5.82 Both Adults and Pediatrics 204 34.67 5.10 Primary Patient Population Years in Practice p <.001 <.001 POSTOPERATIVE SCARS KAP 74 0 to 5 251 33.76 5.17 6 to 10 245 32.87 5.67 11 to 15 119 30.69 6.02 16 to 20 50 30.24 5.62 21 to 25 38 32.47 5.79 26 to 30 12 30.75 5.12 30 years or more 30 31.70 6.72 Specialty Area Certification <.001 None 116 31.49 5.78 Cardiovascular and Pulmonary 41 31.06 5.64 Clinical Electrophysiology 69 32.06 6.58 Geriatrics 44 28.86 5.66 Pediatrics 86 31.60 5.80 Neurology 52 31.88 5.50 Oncology 62 30.63 5.99 Orthopedics 75 31.23 4.64 Sports 36 34.61 4.85 Womens Health 28 36.86 3.76 Wound Management 125 36.58 3.33 Total 734 32.54 5.74 Primary Region of Practice South <.001 183 68.20 10.95 POSTOPERATIVE SCARS KAP 75 Comparison of Midwest 272 66.28 10.99 Northeast 160 69.00 10.29 West 125 71.10 8.81 Primary Practice Setting <.001 Academic Research Institution 112 33.03 5.44 Early Intervention 66 29.71 6.09 Hospital-Based Facility 185 32.14 6.06 Home Health Services 79 31.28 5.51 Military Facility 39 28.23 4.86 Occupational Health Setting 55 33.82 5.56 Private Outpatient Clinic 113 34.33 5.04 Public Outpatient Clinic 55 34.55 4.63 Skilled Nursing 40 34.70 3.95 Table 5 Total Scores by Sample Demographic a (N=740) N M SD Sex < .001 Male 387 65.83 10.55 Female 353 70.70 10.09 734 68.12 10.63 377 69.18 10.62 Primary Patient Population Adults p < .001 POSTOPERATIVE SCARS KAP 76 Pediatrics 154 62.15 10.37 Both Adults and Pediatrics 203 70.67 9.12 Years in Practice < .001 0 to 5 249 69.35 9.31 6 to 10 244 68.33 10.22 11 to 15 118 64.62 11.18 16 to 20 50 65.10 11.97 21 to 25 38 68.42 13.68 26 to 30 12 70.75 11.19 30 years or more 29 74.62 9.88 Specialty Area Certification < .001 None 115 71.97 10.32 Cardiovascular and Pulmonary 41 63.95 9.75 Clinical Electrophysiology 66 65.21 10.97 Geriatrics Pediatrics 44 86 60.22 65.53 9.54 10.73 Neurology 52 64.85 9.60 Oncology 62 64.16 12.36 Orthopedics 75 66.45 9.45 Sports 36 68.64 8.57 Womens Health 28 77.21 7.90 Wound Management 124 73.83 6.62 Primary Region of Practice < .001 POSTOPERATIVE SCARS KAP 77 Comparison of South 183 68.20 10.95 Midwest 272 66.28 10.99 Northeast 160 69.00 10.29 West 125 71.10 8.81 Primary Practice Setting < .001 Academic Research Institution 112 69.39 10.22 Early Intervention 66 61.76 10.16 Hospital-Based Facility 183 69.25 10.13 Home Health Services 79 63.72 10.41 Military Facility 39 57.18 7.87 Occupational Health Setting 54 66.78 10.19 Private Outpatient Clinic 112 73.40 9.45 Public Outpatient Clinic 54 72.31 8.27 Skilled Nursing Facility 40 70.83 7.74 Note. U.S. = United States POSTOPERATIVE SCARS KAP a The higher the score, the greater the involvement with postoperative scars. 78 POSTOPERATIVE SCARS KAP Figure 1 Mean Average Knowledge Score by Specialty Board Certification (N = 750) Figure 2 Mean Average Knowledge Score by Practice Setting (N = 750) 79 POSTOPERATIVE SCARS KAP Figure 3 Mean Average Attitude Score by Specialty Board Certification (N = 750) 80 POSTOPERATIVE SCARS KAP Figure 4 Mean Average Attitude Score by Practice Setting (N = 750) 81 POSTOPERATIVE SCARS KAP Figure 5 Mean Average Practice Score by Specialty Certification (N = 750) 82 POSTOPERATIVE SCARS KAP Figure 6 Mean Average Practice Score by Practice Setting (N = 750) 83 POSTOPERATIVE SCARS KAP 84 POSTOPERATIVE SCARS KAP Figure 7 Mean Average Total Score by Practice Setting (N = 750) Figure 8 Mean Average Total Score by Specialty Certification (N = 750) 85 POSTOPERATIVE SCARS KAP 86  ...

... Reliability and Agreement Between Goniometer and Inclinometer Ankle Range of Motion Measurements Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Colleen Cobey, PT, MS Ex Phys Copyright December 4, 2023 By: Colleen Cobey, PT, MS Ex Phys All rights reserved Approved by: Elizabeth S. Moore, PhD Committee Co-Chair _____________________________ Edward R. Jones, PT, DHSc Committee Co-Chair ______________________________ Mary Sesto, PT, PhD Committee Member _____________________________ Sara Scholtes, PT, DPT, PhD Committee Member _____________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 1 Reliability and Agreement Between Goniometer and Inclinometer Ankle Range of Motion Measurements Colleen Cobey Department of Interprofessional Health and Aging Studies, University of Indianapolis AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 2 Abstract Ankle joint range of motion is an important measurement for clinicians making decisions regarding appropriate interventions. The reliability of these measurements and the instruments used to obtain these measures is critical to this process. The goniometer is the most common clinical measurement tool, but the inclinometer is another possibility for the clinician. The purpose of this study was to investigate the reliability of a digital goniometer and a digital inclinometer in addition to determining the level of agreement of the two instruments in healthy adults with no previous ankle injury. Two Doctor of Physical Therapy students measured active ankle dorsiflexion and plantarflexion range of motion of 60 volunteers between 20 and 63 years of age. Intrarater reliability was good to excellent for the digital goniometer compared to poor to moderate for the digital inclinometer. Interrater reliability was good for the digital goniometer and excellent for the digital inclinometer. Intrarater minimal detectable change values for dorsiflexion using the digital goniometer were 4.6-5.9, and for the digital inclinometer, 10.2-15.4. Minimal detectable change values for the digital goniometer PF were 6.8-7.2, and for the digital inclinometer, they were 20.0-21.0. Bland-Altman plots demonstrated a lack of agreement between the two instruments for both range of motion measurements. The results of this study showed that although interrater reliability was good to excellent, the instruments should not be used interchangeably in the clinical setting. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 3 Acknowledgments I would like to express my sincere gratitude to the individuals who were integral to my success in this research project. To my committee members, Drs. Moore, Jones, Sesto and Sholtes for their support, guidance, expertise and patience throughout the entire process. They were instrumental in providing insights and constructive feedback to improve the overall integrity of this work. To the University of Wisconsin Doctor of Physical Therapy students who took part in this project. Their time and talents were integral to the data collection. Thank you for your questions and feedback during the project. To the University of Wisconsin Doctor of Physical Therapy program chair who provided her support throughout the entire process. I would also like to thank my colleagues who offered insights as well as answering countless questions. Finally, thank you to the University of Indianapolis for the opportunity and resources to finish this research project. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 4 TABLE OF CONTENTS Abstract ........................................................................................................................................... 2 Acknowledgments........................................................................................................................... 3 Introduction ..................................................................................................................................... 6 Literature Review............................................................................................................................ 8 Universal Goniometer ................................................................................................................. 9 Inclinometer .............................................................................................................................. 11 Ankle Joint Measurement ......................................................................................................... 12 Comparison of Measurement Tools .......................................................................................... 14 Clinical Relevance .................................................................................................................... 15 Method .......................................................................................................................................... 16 Study Design ............................................................................................................................. 16 Recruitment and Informed Consent .......................................................................................... 17 Participants and Setting............................................................................................................. 17 Procedures ................................................................................................................................. 18 Instruments ................................................................................................................................ 19 Examiners ................................................................................................................................. 19 Research Assistants ................................................................................................................... 20 Procedures ................................................................................................................................. 20 Pilot Study............................................................................................................................. 20 Positioning and Testing......................................................................................................... 21 Digital Instrument Protocol................................................................................................... 22 Statistical Analysis .................................................................................................................... 24 Results ........................................................................................................................................... 26 Examiner Comparison .............................................................................................................. 26 Intrarater Reliability .................................................................................................................. 27 Interrater reliability ................................................................................................................... 27 Agreement ................................................................................................................................. 28 Discussion ..................................................................................................................................... 28 Intrarater reliability ................................................................................................................... 29 Intrarater Standard Error of Measurement and Minimal Detectable Change ....................... 31 Interrater reliability ................................................................................................................... 32 Interrater Standard Error of Measurement and Minimal Detectable Change ....................... 33 Agreement between the two instruments .................................................................................. 34 AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 5 Mean Range of Motion Values ................................................................................................. 35 Clinical Implications ................................................................................................................. 36 Limitations ................................................................................................................................ 36 Future Research Directions ....................................................................................................... 38 Conclusion ................................................................................................................................ 38 Tables ............................................................................................................................................ 52 Table 1 ...................................................................................................................................... 52 Table 2 ...................................................................................................................................... 53 Table 3 ...................................................................................................................................... 54 Table 4 ...................................................................................................................................... 55 Table 5 ...................................................................................................................................... 56 Figures........................................................................................................................................... 57 Figure 1 ..................................................................................................................................... 57 Figure 2 ..................................................................................................................................... 58 Figure 3 ..................................................................................................................................... 58 Figure 4 ..................................................................................................................................... 59 Figure 5 ..................................................................................................................................... 60 Appendices .................................................................................................................................... 61 Appendix A ............................................................................................................................... 61 Appendix B ............................................................................................................................... 64 Appendix C ............................................................................................................................... 67 Appendix D ............................................................................................................................... 69 AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 6 Reliability and Agreement Between Goniometer and Inclinometer Ankle Range of Motion Measurements Introduction Performing ankle range of motion (ROM) measures is an important part of the physical therapy examination in patients with ankle and lower extremity dysfunction (Martin & McPoil, 2005). A universal goniometer (UG) is a conventional clinical tool used to measure ROM (Gajdosik & Bohannon, 1987). Joint ROM measures informs clinical decisions by identifying impairments, evaluating limitations in activities, and assessing progress over time. The ankle joint is unique since limits in ankle ROM can negatively influence function in many activities of daily living due to its contributions during gait and balance (Basnett et al., 2013). Limited ankle dorsiflexion (DF) ROM has been shown to have adverse effects on dynamic balance (Basnett et al., 2013; Menz et al., 2005) as well as lower scores on the Timed Up and Go Test (Jung & Yamasaki, 2016), which is a predictor of recurrent falls in communitydwelling older adults (Sai et al., 2010). Reliability of ankle ROM measurements using the UG varies from low to excellent (Delitt & Sinacore, 1989; Diamond et al., 2002; Elveru et al., 1998; Martin & McPoil, 2005; Youdas et al., 1993). Intrarater reliability is generally higher than interrater reliability for ankle DF and plantarflexion (PF) ROM measurements (Jonson & Gross, 1997). Using the UG requires clinicians to locate and standardize specific anatomical landmarks while maintaining appropriate positioning of the goniometer arms (Norkin & White, 2016). Potential sources of error when using the UG include incorrectly identifying landmarks, misreading the goniometer, and inadequate stabilization of the extremity as both hands are needed to align the UG (Bohannon et AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 7 al., 1989; Gajdosik & Bohannon, 1987; Martin & McPoil, 2005). Identifying a tool that minimizes sources of error may improve the reliability of measuring the DF and PF of the ankle. Another instrument used to measure ankle ROM is the gravity inclinometer, which does not require the same degree of positioning as the UG. There is minimal research on its use at the ankle, with no consensus on where to position the inclinometer and a lack of consistency in positioning the patient for measurements (Dickson et al., 2012). Konor, Morton, Eckerson, and Grindstaff (2012) found the inclinometer to be reliable when assessing the ROM of the ankle DF in a weight-bearing lunge position. Despite this finding, minimal evidence is available regarding the inclinometer's reliability for ankle active ROM in a non-weight-bearing position (Dickson et al., 2012). Studies are lacking in comparing the UG and gravity inclinometer of the ankle in a non-weight-bearing position for active ROM of DF and PF. Determining agreement between the two measurement tools may improve measurement consistency, ultimately improving patient outcomes. The purpose of this study was to investigate whether the UG or the gravity inclinometer is more accurate in measuring ankle DF and PF ROM. To address this, the following objectives will be met. 1. Determine the level of agreement of active ankle DF and PF ROM measurements between the goniometer and inclinometer in healthy adults with no previous ankle injury. 2. Determine the intrarater and interrater reliability of the goniometer and inclinometer. The results of this study may give clinicians another evidence-based alternative for measuring ankle DF and PF ROM using an inclinometer, as well as determining whether the two devices can be used interchangeably. In addition, the inclinometer is easier to manipulate as the AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 8 exact positioning of two goniometric arms do not need to be monitored (Gerhardt & Rondinelli, 1990), which could positively impact joint measurement precision. Literature Review Performing joint ROM measures is an essential part of a physical therapy examination and assists the clinician in making accurate clinical diagnoses. Green and Heckman (1994) reported that joint mobility measures are an integral part of the physical therapy examination and diagnostic process because they provide information on the severity and progression of chronic disorders. More recently, Dickson et al. (2012) explained that proper assessment of ROM limitations assists clinicians in implementing effective interventions. These joint-specific measurements allow monitoring of intervention effectiveness, determination of patient progress, and changes in patient status (Martin & McPoil, 2005). Reliability of joint ROM is required when making clinical decisions based on these measurements (Riddle et al., 1987). Therefore, the reliability of the instrument must be established. Clinical decision-making depends on reliable measurements to understand relationships and determine if improvement has occurred. Reliable measurements are also essential when communicating results to other clinicians using the information to make clinical decisions (Portney & Watkins, 2009). Ankle joint ROM reliability is meaningful when considering lower extremity function. It has been shown that decreased motion at the ankle joint can adversely impact function and indirectly negatively impact neighboring joints (Sueki et al., 2013). Jung and Yamasaki (2016) investigated lower extremity ROM and strength in older women. They found that PF ROM was a significant explanatory factor in the Timed Up and Go Test scoring, and DF ROM was for the forward reach test (Jung & Yamasaki, 2016). In addition to the direct effects of restricted ankle ROM on functional tests, researchers found that ankle ROM impairments can lead to dysfunction AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 9 in other joints. Backman and Danielson (2011) reported that impaired DF ROM was found to be a risk factor for developing patellar tendinopathy in young elite basketball players. Similarly, Malliaris et al. (2006) found that limited ankle DF ROM may be a risk factor for developing patellar tendinopathy in adult volleyball players. Although these studies did not discuss the functional impact of the patellar tendinopathy in their participants, Backman and Danielson (2011) did find that 36.5 degrees of weight-bearing DF was the critical ROM cutoff point in those who were at a higher risk of developing patellar tendinopathy. As a result, the ability to accurately measure ankle ROM in the sagittal plane is essential for rehabilitation assessment and as a potential preventative screening tool. Universal Goniometer The most common instrument used to measure joint ROM is the UG (Reese & Bandy, 2010), which may be due to its portability and low cost (Lea & Gerhardt, 1995). The UG allows clinicians to interpret the results of joint ROM and apply this information to determine appropriate intervention strategies (Gajdosik & Bohannon, 1987). It is typically made of a clear plastic 360-degree round protractor and two connecting arms used to confirm anatomical alignment with specific bony landmarks (Norkin & White, 2016). The starting point to measure ROM is a position that places the joint in an anatomically neutral position, thereby standardizing the measurement procedure for future consistency and replication over time (Norkin & White, 2016). Joint ROM measures are recorded in degrees from a neutral starting position, understood as zero degrees. Despite its widespread use and cost-effectiveness, there are some challenges when using the UG. Norkin and White (2016) point out that the UG requires technical skill as the clinician must visually estimate the fulcrum of the joint being measured and palpate specific anatomical AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 10 landmarks for the two goniometer arms. Wolfenberger et al. (2002) noted that identifying anatomical landmarks and UG positioning may contribute to measurement discrepancies found in the literature. Fish and Wingate (1985) investigated various sources of measurement error when measuring the elbow joint. They found that incorrect alignment of the UG and incorrect identification of anatomical landmarks contributed to measurement error (Fish & Wingate, 1985). Gajdosik and Bohannon (1987) cite an increased risk of error when reading the goniometer. Another challenge when using the UG is that two hands are required to manipulate each arm, which limits the clinicians ability to stabilize the proximal portion of the limb while isolating the distal segment to obtain a ROM measure (Green et al., 1998; Lea & Gerhardt, 1995) as a result of using two hands to manipulate each arm of the UG, applying a consistent amount of force when assessing passive ROM is challenging (Rome, 1996). In their goniometry review of clinical measurement, Gajdosik and Bohannon (1987) state that difficulty in applying the correct amount of force in passive ROM measurements can increase measurement error. Comparing reliability studies of goniometric measures has been challenging due to the variable study designs and measurement procedures (Norkin & White, 2016). In addition, variability is reported in the literature depending on the specific joint under investigation as well as whether the motion is active or passive. For example, the reliability of ankle ROM measurements using the UG varies from low to excellent reliability (Diamond et al., 1989; Elveru et al., 1998; Van Gheluwe, Kirby, Roosen, & Phillips, 2002; Youdas et al., 1993). Intrarater reliability is generally higher than interrater reliability for ankle DF and PF ROM measurements (Jonson & Gross, 1997). For other joints, Horger (1990) investigated the reliability of the UG in wrist flexion and extension active and passive ROM and found excellent intrarater reliability (intraclass correlation coefficient [ICC] > .90) for these motions. Simoneau AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 11 et al. (2000) compared hip internal and external rotation measurements performed in sitting and prone positions and found good to excellent interrater reliability between two testers (ICC = .76 .98). Lenssen et al. (2007) studied UG measurement in 30 patients three days after a total knee arthroplasty and found good interrater reliability for active and passive knee flexion (ICC = .86 .89) and moderate interrater reliability (ICC = .62 .64) for passive and active knee extension. A recent review of elbow UG measurements found fair to excellent intrarater reliability (ICC = .55 .99) for flexion measurements and low to excellent (ICC = .45 .99) for extension measures (van Rign et al., 2018). The variability and limitations of goniometric measurements have led to the development of alternative devices in clinical practice to measure joint ROM, such as smartphone applications, digital goniometers, electro-goniometers, specialized devices for specific joints, and digital inclinometers. Smartphone applications utilize an internal inclinometer in clinical settings to measure joint ROM (Wellmon et al., 2016). The current challenge with the use of smartphone applications is determining the reliability and validity of the specific type of smartphone and the specific application utilized (Keogh et al., 2019). Inclinometer An inclinometer is another measurement tool that relies on gravity to determine joint ROM (Norkin & White, 2016). Gravity-based inclinometers are not cost-prohibitive and, therefore, a viable clinical option (Kolber et al., 2011), and the training required to use them is similar to that of a goniometer (Kobler et al., 2012). From a standardized neutral starting position, the inclinometer is placed distally along a limb segment and zeroed out prior to measurement. The joint is then moved, and a measurement is taken from the inclinometer in degrees, similar to the reading of a UG. An advantage of the inclinometer is that only one hand is required to stabilize the tool over a bony landmark AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 12 compared to using two hands with a goniometer (Norkin & White, 2016). Since the inclinometer only requires one hand for placement, the clinician would have the other hand to stabilize a limb segment or provide force application during passive ROM. Additionally, the inclinometer position does not depend on the palpation of several bony landmarks or visual estimation of the joint fulcrum, which have been shown to contribute to measurement error. Instead, it uses the identification of one bony landmark or a specific position on a limb to place the inclinometer (Fish & Wingate, 1985). Researchers have investigated the reliability of inclinometers and have found good-toexcellent intrarater reliability of non-weight-bearing ankle DF active ROM measurements with an ICC = .83 to .97 (Venturni et al., 2006) and good intrarater reliability of shoulder measurements with ICC = .88 to .89 (Kolber et al., 2011). Dobija and Jankowski (2015) found interrater reliability for the inclinometer to be excellent for DF (ICC = .90 .91) and good for PF (ICC = .86 .72). Tavares et al. (2017) measured ankle DF and PF with a goniometer and inclinometer and reported good reliability with ICCs above .80. However, these results were for both DF and PF and discernment between the two measures were not reported. Ankle Joint Measurement While the goniometer is the most common clinical tool used to measure joint ROM, its reliability in measuring ankle ROM has been questioned. Studies by Clapper and Wolf (1988), Ness et al. (2018), and Youdas et al. (1993) examined goniometer reliability during active ROM of the ankle DF and PF in patients with orthopedic diagnoses as well as healthy participants. Youdas et al. (1993) found moderate to excellent intrarater reliability of the goniometer for ankle DF (ICC = .64 to .92) and poor-to-excellent intrarater reliability for PF (ICC = .47 to .96) in patients with various orthopedic diagnoses. They also reported poor interrater reliability (ICC = AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 13 .28 and .25) for goniometer DF and PF (Youdas et al., 1993). Some of this variability may have been due to a lack of measurement standardization, which could have created different results (Bohannon et al., 1989). Clapper and Wolf (1988) found excellent intrarater reliability of the goniometer for active ROM ankle DF (ICC = .92) and PF (ICC = .96) in healthy adults. Using the goniometer, Ness et al. (2018) only investigated active PF ROM in non-weight-bearing healthy adults. They found excellent intrarater reliability (ICC = .90 .98) and poor-to-excellent interrater reliability (ICC = .46 .91) (Ness et al., 2018). Passive ROM of the ankle joint using the goniometer has also been evaluated. Elveru et al. (1998) investigated the reliability of passive ankle DF and PF ROM measures in patients with orthopedic and neurologic disorders. They found excellent intrarater reliability for DF (ICC = .90) and good intrarater reliability for PF (ICC = .86) (Elveru et al., 1998). For interrater reliability, Elveru et al. (1998) found moderate reliability for DF and PF (ICC = .50, .72, respectively). Passive ankle DF ROM reliability was assessed by Diamond et al. (1989) as well as Johnson and Gross (1997), and these studies found moderate-to-excellent intrarater reliability (ICC = .74 - .96). Johnson and Gross (1997) reported moderate intrarater (ICC = .74) and interrater (ICC = .65) reliability scores for passive DF ROM. As a result of the variability in reliability studies using the goniometer, researchers have investigated the use of inclinometers to improve the measurement reliability of ankle DF and PF. Most inclinometer reliability studies of the ankle joint have focused on DF motion in a weightbearing lunge position, as it is theorized that this position may correlate better with functional activities such as walking, stair climbing, and running (Koner et al., 2012). In this position, the inclinometer is aligned with the anterior tibia and measures the angle between the tibia and vertical (Konor et al., 2012; Van der Worp et al., 2014). The inclinometer may be placed on the AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 14 tibial tuberosity to determine the 0 0-degree starting position. The subject lunges forward to the end of available ROM, and an inclinometer reading is taken to determine DF ROM. A challenge when comparing different inclinometer weight-bearing ankle DF studies is that different inclinometer placements are utilized, and this lack of standardization makes comparison difficult. The advances in technology in clinical practice have allowed clinicians to perform ROM measurements with digital versions of the goniometer and inclinometer. The availability of digital readings decreases the chance of reading and visual errors if the device is not read with the examiners eyes directly aligned with the fulcrum of the measurement tool (Goodwin et al., 1992). For this reason, the digital goniometer (DG) and digital inclinometer (DI) were utilized in this study to eliminate one source of measurement error. Comparison of Measurement Tools A limited number of studies address the agreement of the goniometer and inclinometer for active ROM in a non-weight-bearing position. Venturni et al. (2006) compared the intrarater and interrater reliability of the goniometer and inclinometer during active ankle DF in a nonweight-bearing position between 4th-year physical therapy students. Intrarater goniometry reliability was reported to be excellent (ICC = .91 .97), and intrarater inclinometer reliability was reported to be good to excellent (ICC = .83 .91). Interrater goniometry reliability was good (ICC = .72) as was the inclinometer (ICC = .83). Dickson et al., (2012) found good to excellent intrarater and interrater reliability for AROM PF (ICC = .76 - .99) but used a different inclinometer placement. Dobija et al. (2015) investigated PROM DF and PF and found good to excellent intrarater and interrater reliability (ICC = .84 - .97). Investigating agreement between different devices is important in team-based approaches during patient care, with various healthcare specialists performing measurements on the same AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 15 individual. Current studies have demonstrated higher intrarater reliability measures for the goniometer and inclinometer, so it is recommended that the same clinician repeat measurements and the measurement devices should not be used interchangeably (Goodwin et al., 1992; Norkin & White, 2016; Rome, 1996) which may impede clinical efficiency in a multi-clinician practice. However, if interrater reliability was higher using the inclinometer, this may be advantageous in a clinical setting when patients are shared between clinicians. Comparing the goniometer and inclinometer at different joints, Roach et al. (2013) assessed the reliability of the digital inclinometer and goniometer and found significant differences in all hip measurements between the two devices except for hip extension on the right side. These researchers concluded that the two devices should not be used interchangeably. Kolber and Hanney (2012) assessed shoulder motion with a digital inclinometer and goniometer and found significant differences between the two devices. They, therefore, recommended that the same device be utilized when performing repeated shoulder ROM (Kolber & Hanney, 2012). For clinical efficiency, finding a measurement device with high interrater reliability would be helpful. Clinical Relevance The reliability of ROM data is essential to a clinical assessment of musculoskeletal function, and the values obtained are used to make decisions about therapeutic interventions. Ankle ROM measurements are frequently assessed not only in patients with primary ankle impairments but also with impairments in other lower extremity joints because of biomechanical relationships between them (Sueki et al., 2013). Being able to reliably measure ankle sagittal plane movements for various lower quarter dysfunction is an essential task for the treating clinician. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 16 Researchers continue to investigate the reliability of ROM measurement tools so that clinicians can utilize them confidently. Instruments should be clinically affordable, easy to use, and provide accurate measurements. Although the goniometer continues to be the most common measurement tool, information regarding tools that may outperform the goniometer is becoming more prevalent in the literature. For example, the American Medical Association (1993) now advocates that the lumbar, thoracic, and cervical spine ROM be measured using an inclinometer. This potential shift in clinical measurement is also evident in increased measurement options reported in the literature, such as gravity, fluid, and digital inclinometers, as well as smartphone applications (Hambly et al., 2012; Kachingwe et al., 2005; Rheault et al., 1988). This may be a response to the variations found in goniometer reliability studies (Gatt & Chockalingam, 2011) and the search for a practical solution for clinical use. This shift is also evident in current measurement textbooks, which now cover a variety of devices for joint ROM measurements. Norkin and Whites (2016) most recent textbook edition of joint measurement emphasizes the use of the goniometer and introduces the gravity inclinometer and the electrogoniometer. As a result, the search to determine the most reliable tool to measure sagittal plane DF and PF will have implications in the clinic and classroom instruction. Since clinicians today have more choices for clinical measurement tools, determining if the tools agree will inform clinicians regarding appropriate measurement tool choice. Method Study Design This study compared the agreement and reliability between two instrumented methods of measuring ankle DF and PF ROM, utilizing a digital goniometer (DG) and a digital inclinometer (DI). Intrarater and interrater reliability and the agreement between the two instruments when AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 17 performing active ankle DF and PF ROM were assessed. In addition, the standard error of measurement (SEM) and minimal detectable change (MDC) were calculated. Data were collected between January and April of 2022. Before participant recruitment, the University of Wisconsin Education and Social/Behavioral Science Institutional Review Board (ED/SBS IRB) approved this study, including a reliance agreement with the University of Indianapolis. Recruitment and Informed Consent Volunteer participants were recruited through flyers posted in university buildings and a posting on a digital signage board located in the Doctor of Physical Therapy program building with instructions to contact the principal researcher (C.C.) via email or phone. The study principal investigator (C.C.) confirmed the eligibility of potential participants via email. Eligible participants were then scheduled for research participation. On the scheduled day of participation, participants met with the principal investigator (C.C.) in a private room where the study protocol was reviewed, verbal informed consent was obtained, and each participant completed their demographic information. Each participant also was given a paper copy of the consent form for future reference. Once participants finished their demographic sheet, they randomly selected an envelope that determined which examiner they started with, which device was used first, and which direction was performed first. Participants and Setting A convenience sample of sixty healthy adult men and women (39 women and 21 men) 18 years of age or older were recruited as study participants on a volunteer basis. Participants were excluded if they reported any of the following: ankle injury or any complaints of pain AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 18 surrounding the ankle joint within the last three months, a history of ankle surgery within the last year, any history of inflammatory arthropathies such as systemic lupus erythematosus (SLE), rheumatoid arthritis, or diabetes which can negatively impact joint function (Naidoo et al., 2015; Otter et al., 2016). Before starting the study, a power analysis was performed to determine the number of participants required. Based on sample size estimates, 60 participants were recruited to achieve an acceptable reliability of .80. The calculation was based on a significance level of p = .05, a power (1-) of .80, and an accepted reliability (P0) of .70 (Walter et al., 1998). This sample size estimate also included an expected 10% dropout rate. Participants were asked to wear shorts or clothing that could be manipulated to expose the leg from above the knee to the foot. They were then seated at the end of the examination table with the knee partially flexed to prevent limitation of ankle DF ROM from soft tissue restrictions. The study took place in the University of Wisconsin-Madisons Doctor of Physical Therapy program facilities. The examination space was organized with a barrier screen so that each rater was blinded to the other rater to remove the possibility of a Hawthorne effect (Kottner et al., 2011). Procedures Demographic data collected on participants included age, height, gender, weight, and body mass index (BMI) presented in Table 1. The digital goniometer and inclinometer measurements of active ROM ankle DF and PF of the right ankle were recorded in degrees of motion on data collection forms. Data collected from sessions 1 and 2 were entered into a Microsoft Excel Worksheet by the principal researcher (C.C.) on a secure encrypted laptop computer. Data was manually rechecked following each data entry cycle to ensure the accuracy AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 19 of data transfer from the data collection sheets to the Microsoft Excel Worksheet. Participants were de-identified using a unique sequence based on blocked randomization to prevent individual identification and allow data linking between each testing session. Instruments Two standard high/low tables (Armedica Manufacturing, Greenwood, AR) were used for all measurements. Active ROM of ankle DF and PF was assessed with a DG and a DI. The DG was a Jamar Plus digital goniometer (Patterson et al.) 12.5-inch universal, clear plastic goniometer with digitally displayed 0.1 increments ranging from 0.0 to 999.9 (Figure 1). The DI (Figure 2) was an Acumar digital inclinometer (Lafayette Instrument Company , Lafayette, IN) with 1 increments ranging from 0.0 to 190 with two feet to allow firm contact against the plantar surface of the foot as well as securing of the instrument. In addition, a bubble level (model 83-3, Empire, Mukwonago, WI) was attached to the DI to ensure that the participants ankle was in the standard neutral position prior to measurements and that the level agreed with the zeroing out of the inclinometer at a 0 reading (Figure 3). Examiners ROM measurements were performed by two second-year Doctor of Physical Therapy (DPT) students from the University of Wisconsin DPT Program who successfully completed coursework in ROM measurement techniques and clinical internship experiences. Each examiner performed a total of twelve measurements on each participant. Three DF measurements and three PF measurements using a DG and three DF measurements and three PF measurements using a DI for a total of twelve measurements performed on one participant for each examiner. The examiners were blinded to the measurement results of the DG and DI, as well as the measurement results from the other examiner. The DG measurement dial was covered with AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 20 opaque paper, so the 360 increments were not visible except for the red 0 to ensure standardization of the initial start position. The digital output was also covered with opaque paper. The DI digital output was also covered with opaque paper, and the digital output was turned away from the examiners during measurement. When the examiner completed each measurement, the device was handed to a research assistant who documented the angle reading. Research Assistants Documentation of all ROM measurements for each measurement tool was performed by two research assistants who were second-year physical therapy DPT students from the University of Wisconsin DPT Program who successfully completed coursework in ROM measurement techniques and clinical internship experiences. ROM measurements were documented by the research assistants on each participants unique data collection sheet, and those data sheets were given to the principal investigator (C.C.) once the measurements were completed for each participant that day. Procedures Pilot Study Before data collection, a pilot study was conducted with the two examiners, recorders, and participants. The purpose was to standardize performance and procedures, identify bony landmarks, become familiar with the measurement tools, and standardize the instructions to the participants. In addition, the examiners were instructed to use the DI at the ankle joint, which included standardization of the inclinometer placement and the starting position. The standardized ROM measurement protocol utilized for DG measures is described by Norkin and White (2016). AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 21 Positioning and Testing Participants were taken into the physical therapy lab space by the principal investigator (C.C.) and directed to either Examiner A or B, depending on their random selection. Participants were then seated on an examination table in a short sitting position and asked to remove their right sock and shoe. In short sitting, with their lower leg exposed above the knee, their knee flexion angle was measured with a UG and recorded. The examination procedure was explained to participants by examiners A and B prior to the start of measurements, and any questions were answered. Next, examiners A or B identified the predetermined landmarks for placement of the DG and DI for the participant and marked the landmarks with Crayola washable markers (Crayola, Easton, PA). Each examiner removed the marks for that participant upon completion of data collection. The DI landmarks used were the fibular head, the lateral malleolus, and the fifth metatarsal, as described by Norkin and White (2016). The DI landmark used was the center of the malleolus and the plantar surface of the calcaneus for placement of the inclinometer. Once landmarks were identified and marked, participants performed active ROM ankle DF and PF three times before testing to become familiar with the movement directives and decrease the effects of tissue creep (Sobolewski et al., 2013). Three DF and PF measurements were completed in one session by each examiner with each device for twelve measurements per examiner, then repeated within two weeks utilizing the same procedures. Both examiners performed DG and DI measurements on each participant and were blinded to each others measurements and their own measurement results. Both research assistants documented all measurements, including knee flexion range of motion, confirming that the participants' knees were flexed greater than 20 degrees to eliminate the effect of any soft AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 22 tissue tightness restricting ankle ROM (Norkin & White, 2016). A belt was placed around the mid-lower leg with a towel between the leg and belt for comfort and secured to the table to ensure that the testing leg remained in this position. This position eliminated any restriction in ROM caused by posterior soft tissues, allowing the ankle to move freely. The research assistants confirmed knee flexion of greater than 20 and measured the angle with a goniometer before the ankle ROM measurements. The knee flexion measurements were recorded on the participant data sheet. The following variables were counterbalanced to reduce the risk of an order effect and to improve internal validity (Allen, 2017): 1) examiners (Examiner A, Examiner B), 2) ROM direction (DF, PF), and 3) measurement devices (DG, DI). Randomization was performed using Research Randomizer (Version 4.0; Urbaniak & Plous, 2013). Digital Instrument Protocol (see Appendix B). Following the active ROM warm-up, the examiners aligned the DG based on the landmarks identified with the ankle in a resting position. The examiners then instructed the participant to move the ankle into the starting neutral position until the DG read 0 degrees on the 360-degree goniometer face, with the ankle at a right angle. The examiner then pressed the zero button to standardize the starting position. This is the standard starting position for goniometric ankle ROM measurements in the sagittal plane, according to Norkin and White (2016). To zero the DI, it was placed against the underside of the high/low table along the frame, and a bubble level attached to the DI was used to confirm the neutral zero position. The research assistant who read the digital readout at zero degrees confirmed the starting position. Once the research assistant confirmed the anatomical starting position, the examiner zeroed out the DI as described by the manufacturers to standardize the starting position, which was identified as zero degrees. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 23 This zeroing procedure ensures that the measurement device starts at zero degrees prior to every measurement. Once the device was zeroed out, active ROM DF and PF measurements on the right ankle for the participant were taken. Soucie et al. (2011) found no side-to-side differences in ankle sagittal plane ROM in subjects aged 2 to 69 years, so the right ankle was used for all measurements. Following the first measurement, the participants relaxed their ankles and rested for 15 seconds while each research assistant recorded the results. The instrument was then reset, and the above procedure was repeated for three measurements. The above procedure was then repeated in the second direction with the same device. When the examiner finished the measurements of DF and PF with the first measurement device, they repeated the same procedure for measuring active ROM DF and PF with the second measurement device. Once completed, the examiners erased the markings with an alcohol wipe. The participant remained in the seated position on the examination table while the examiners and research assistants switched to measure the other participant, and the procedure was repeated. When each examiner finished taking measurements of each participant, the participants were escorted out of the examination space by the principal investigator (C.C.). The same procedure was followed for the next participants until all participants were tested for that day. The examiners then explained the testing procedure to the participant, asking for any questions and receiving verbal consent to make temporary marks on the lower leg. The participants were instructed to move their right ankle up and down three times as a warm-up and familiarization of the movement. The examiner then asked the participant to move their ankle in the direction determined by randomization as far as possible and maintain this position for up to 10 seconds, replicating the amount of time it would take to obtain measurements in the clinic. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 24 The instructions given to the participant were Pull your ankle up towards the ceiling as far as you can and hold it in that position until instructed to relax or Point your ankle down as far as possible and hold it in that position until instructed to relax. During the participant rest phase, the research assistants recorded the measurements. Each examiner performed measurements three times each for the DG and DI in each direction. Examiners were blinded to their results and the results of the other examiner. The DG and DI measurement numbers were unavailable to the examiners by placing opaque paper over the digital degree increments. The research assistants recorded all measurements from each device. Both measurement devices have storage capabilities for the research assistants to record the degrees of motion to the nearest 1 accurately. This was recorded onto the participant data collection sheet, which was then entered into a Microsoft Excel worksheet following each day of data collection. Since each direction was repeated three times and two measurement devices were utilized, each examiner recorded twelve measurements for DF and PF for each participant. Identical procedures were utilized for the second session between 2 and 13 days later. Statistical Analysis Data were analyzed using IBM SPSS Statistics for Windows, Version 25 (IBM Corporation; Armonk, NY). The Kolmogorov-Smirnov test was used to determine the normal distribution of data. Descriptive statistics were reported for the demographic data, including means and standard deviations for age, height, weight, and BMI. Descriptive data for the joint ROM measurement angles include means of the three measurements and standard deviations (SD); A paired sample t-test analysis was used to assess the mean difference between examiners. Bland-Altman plots were constructed to visualize the agreement of ROM measures between the DG and DI. A one-sample t-test was performed to test the mean bias. The plots graphically AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 25 display the means and differences of the two measurements, and the 95% limits of agreement (LOA) are determined from those. The 95% LOA was calculated (1.96 multiplied by the standard deviation [SD]) to infer a 95% confidence interval. The 95% LOA was used to examine the variation, with a narrow LOA indicating higher stability. The ICC was calculated for interrater and intrarater reliability from a two-way random effects model. Intrarater and interrater reliability was determined by ICC estimates and their 95% confident intervals, which were calculated using SPSS based on a mean rating (k = 3), absolute agreement, and a 2-way mixedeffects model (3,k) (Portney & Watkins, 2009). The ICC value interpretation was based on the guidelines given by Portney and Watkins (2009), in which an ICC of < .50 is poor reliability, .50-.75 is moderate reliability, .75.90 is classified as good reliability, and > .90 is excellent. Examiner comparison was analyzed using means and SD for each examiner and the significance level between the two measurements. The absolute reliability of each device was quantified using the standard error of measurement (SEM: formula = SD1 ) and the minimal detectable change (MDC: formula = SEM * 1.96 * 2) (Hanks & Myers, 2023; Portney & Watkins, 2009). The SEM measures how much a score varies from a hypothetical true score of a group. It is an important consideration during instrumented ROM measurements and reliability measures. The SEM represents the minimum level of change that demonstrates an actual improvement in ROM versus changes in measurements due to experimental error or natural variation (Portney & Watkins, 2009). The SEM addresses the absolute measurement error and indicates whether a fundamental change has occurred (Scholtes et al., 2011) versus ICC values, which measure relative estimates (Walton et al., 2011). There is an inverse relationship between the ICC and SEM value, so if a measure's reliability is higher, the measurement error would be lower (Karagiannopoulos et al., 2003). The AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 26 MDC represents the true change that has occurred in an individual (Akizuki et al., 2016), and it informs the clinician of the amount of ROM needed to be considered a true change in patient status and not due to chance or error (Haley & Fragala-Pinkham, 2006; Portney & Watkins, 2009). MDC values assist the clinician in determining what a real change is and if a patient has benefited from current therapeutic interventions. Results Sixty adults, 39 women and 21 men, mean (SD) age 31.1 (12.5) years participated in the study. Due to illness, one participant was unable to attend the second session. After collecting the data, a systematic error was identified for the first six participants due to an instrument malfunction of the DI. Statistical analysis was completed twice, once with the full data set and once without the full data set. As no differences were found between the two datasets, the results from the full data set are reported here. The statistical analysis with the limited dataset (six participants removed) can be found in Appendix C of this document. The time interval between the first and second sessions varied from 2 to 13 days (mean=5.9 [SD=2.28]). Examiner Comparison Table 2 presents ankle DF and PF measurements for each device, examiner, and session. Group comparison was completed using paired samples t-tests. A significant difference in ankle PF ROM between examiners was found when measured with the DG for both sessions. No other measurements were significantly different between the two examiners. The means for DF measurement with the DG were lower than DF measurements using the DI for both examiners during both sessions. The means for PF measurement with the DG were higher than PF measurements using the DI for both examiners during both sessions. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 27 Intrarater Reliability The ICCs, SEMs, and MDCs for intrarater reliability are shown in Table 3. DG intrarater reliability ICC for Examiner A was excellent for DF and PF, while moderate reliability was demonstrated using the DI. The intrarater reliability ICC for Examiner B using the DG was good for DF and excellent for PF. Examiner B demonstrated poor to moderate DF and PF reliability using the DI. Overall reliability was higher for both examiners when using the DG compared to the DI for ankle DF and PF ROM measurements. The SEM and MDC intrarater reliability values using the DG were lower than the DI, with the highest values recorded for MDC during PF measurement with the DI. The SEM values for DG DF were 1.7 for examiner A and 2.1 for examiner B compared to the DI, which was 3.7 for examiner A and 5.6 for examiner B. A similar trend was found for PF measurements between the DG and DI. The SEM values for DG PF were 2.6 for examiner A and 2.5 for examiner B compared to the DI which was 7.6 for examiner A and 7.5 for examiner B. MDC values for DG DF were 4.6 for examiner A and 5.9 for examiner B compared to DI DF which was 10.2 for examiner A and 15.4 for examiner B. MDC values for DG PF were 7.2 for examiner A and 6.8 for examiner B compared to DI PF which was 21 for examiner A and 20.8 for examiner B. Interrater reliability The ICCs, SEMs, and MDCs for interrater reliability for session one are shown in Table 4. DG interrater reliability between the two examiners was good for ankle DF and PF, and for the DI it was excellent during DF and PF. The SEM and MDC interrater reliability values for the DG were lower than the DI, with the highest values recorded for MDC during PF measurement with the DI. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 28 The SEM DI values for the DG and DI were similar for DF (2.1and 2.3 respectively) but were lower for the DI during PF compared to the DG. The MDC for the DG averaged 5.5 degrees for DF and 11 degrees for PF. The MDC values for the DG and DI were similar for DF (5.7 and 6.3, respectively). MDC values for PF were higher using the DG (10.6) compared to the DI (4.8). Similar results were found for session 2, which can be found in Appendix D. Agreement Bland-Altman plots were created to visually represent the data between the DI and DG when measuring DF and PF for Examiners A and B. The results are shown in Figures 3 and 4, with limits of agreement reported in Table 5. The difference between DI DF ROM and DG DF ROM (Y-axis) was plotted against the mean of DF for the DI and DG (X-axis). This was repeated for PF and analyzed for Examiners A and B. A one-sample t-test was performed to compare the means and SDs. All differences were significant (p < .05), demonstrating that the two instruments did not agree when measuring ROM DF and PF. Discussion The primary purpose of this study was to investigate the intrarater and interrater reliability of the DG and the DI and the agreement of the two instruments when measuring active ROM ankle DF and PF. Sufficient ankle ROM is essential for functional activities such as gait and stair negotiation (Ostrosky, 1994; Protopapadaki et al., 2007). In addition, clinicians rely on ROM measurements to identify impairments in joint ROM, and reliable measurements directly impact therapeutic intervention choices. Once impairments are identified, ROM measurements are used to assess therapeutic interventions' efficacy as well as document change or lack of change over time (Muir et al., 2010). Although numerous studies have investigated the reliability of the universal goniometer, few studies have compared the goniometer to the inclinometer in a AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 29 non-weight-bearing position. Most studies have focused on the weight-bearing measurement of ankle DF using the inclinometer. Therefore, the reliability of the inclinometer in non-weightbearing conditions has not been established. Furthermore, there is no comparable weight-bearing measurement for ankle PF. A comparison of the two devices is necessary to inform clinicians if the two instruments can be used interchangeably and if the DI can be used to measure ankle DF and PF ROM accurately. Intrarater reliability The results of the present study found good to excellent intrarater reliability for the DG during ankle DF and excellent intrarater reliability for ankle PF. Intrarater reliability for the DI was poor to moderate for ankle DF and moderate for ankle PF. Confidence intervals for intrarater reliability demonstrated larger variances when using the DI during DF and PF measures. These larger variances may indicate inconsistencies in the measurement obtained when using the DI, and as a result, using the measurement protocol as described in this study may not be appropriate in the clinical setting. The good to excellent intrarater reliability found in this study for the goniometer (ICC = .88 - .94) is similar to other reports in the literature for the measurement of AROM of the ankle. Clapper and Wolf (1988) reported excellent reliability using the UG to measure AROM ankle DF and PF in healthy volunteers. Venturni et al. (2006) results demonstrated good to excellent (ICC = .83 to .97) intrarater reliability of AROM DF in a non-weight-bearing position. Youdas et al. (1993) reported moderate to excellent reliability with ICC values between .64 and .92 with a median ICC of .83 for AROM DF using a UG in a study that did not standardize the testing procedure. The higher reliability ratings found in this study compared to the Youdas et al. (1993) AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 30 study could be attributed to implementing a standardized procedure, which has been shown to improve reliability ratings (Ekstrand et al., 1982). The lower intrarater reliability when using the DI compared to the DG found in this study could result from the alignment of the DI chosen for this study. There is no standardized position in the literature for placement of the inclinometer during non-weight bearing measurements, and various studies utilize different placements, further complicating where to position the DI. Studies that have assessed various alignments for the measurement of the same joint have found differences in the measurements based on the choice of alignment (Conley et al., 2012; Menadue et al., 2006). The plantar surface of the calcaneus was chosen for this study based on its proximity to the talocrural joint while also providing a stable surface to improve consistency when positioning the instrument. The rationale for proximity to the talocrural joint was an attempt to limit ROM contributions from nearby joints since it has been shown that sagittal plane motion at the talocrural joint has contributions from surrounding joints, including the subtalar joint, transverse tarsal joints, cuneonavicular joints, and the tarsometatarsal joints (Brockett & Chapman, 2016; Lundberg et al., 1989). Different placements are also utilized in other studies comparing the goniometer and inclinometer, but discussion of those differences is absent. Dickson et al. (2012) measured AROM ankle PF in a non-weight-bearing position with the goniometer in the standard location as advocated by Norkin & White (2016) and then again with an inclinometer placed on the anterior surface of the talonavicular joint. They found a 9 difference between the two instruments but did not offer a hypothesis as to why this occurred. Several studies investigating the weight-bearing lunge test for DF ROM use the standard goniometric position as previously described but place the inclinometer on various places along the anterior tibia. Dickson et al. (2012) found an 11 mean difference in DF measurement AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 31 between the goniometer and anterior placement of the inclinometer at the tibial midpoint, while Konor et al. (2012) found a 5 difference in the mean measurement of DF with the inclinometer placed at the tibial tuberosity. During active ROM PF, the plantar surface of the foot becomes more concave as the toes point down, and as a result, the shape is incongruent, which could challenge DI placement. To circumvent this issue, some studies have added projections to the DI to create improved stability and positioning on the plantar surface of the foot (Lea & Gerhardt, 1995; Tavares et al., 2017). To reduce measurement error, Rome (1996) proposed strategies for standardizing bony landmarks when measuring ankle DF ROM and defined the inferior point on the lateral part of the foot as the projected part of the heel. As a result, the projected point of the heel was utilized in this study to standardize DI placement and to avoid the issue of an uneven plantar surface. Since there is a lack of studies using this same DI placement, comparing results is impossible. Intrarater Standard Error of Measurement and Minimal Detectable Change The SEM for DF with the DG was calculated to be 1.7 and 2.1 degrees; for the DI, it was slightly higher at 3.7 and 5.6 degrees. The SEM for PF with the DG was calculated to be 2.6 and 2.5 degrees, and for the DI, it was 7.6 and 7.5 degrees. The higher values using the DI must be considered when measuring ankle ROM in the clinic using this device. The typical SEM values reported in the literature for the ankle are often reported for the universal goniometer. van Gheluwe et al. (2002) measured 30 healthy adults with a protractor and reported an SEM value of 0.7 1.6 degrees for ankle DF. Macedo and Magee (2009) reported an SEM of 2.5 degrees for DF and 4.6 degrees for PF during passive ROM testing using a universal goniometer. The higher SEM for the DI also corresponded to lower reliability for DF and PF. In addition to the lower reliability, there was a wider variation in the ICC 95% CI. The ICC value AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 32 for DF with the DI was .51, which is interpreted as moderate reliability (.50 to .75). However, the 95% confidence interval range was between .17 to .71. This large range indicates that the measurement estimate may not be as precise when using the DI. The SEM for PF with the DG was 2.6 and 2.5; with the DI, it was 7.9 and 7.5. The MDC value is another important clinical consideration when interpreting ROM values. Intrarater MDC values in this study for ankle DF using the DG were 4.6 and 5.9 degrees for examiners A and B. Using the DI, the MDC values were 10.2 and 15.4 degrees. The larger MDC values when using the DI may indicate a lack of consistency in DF measures when using the DI. The MDC values found for PF using the DG were 7.2 and 6.8 degrees for each examiner compared to 21 and 20.8 degrees when using the DI. The DI for both DF and PF demonstrated larger MDC ranges. This may impair a clinician's ability to determine if a true change has occurred given that the accepted normative value for DF is 20 degrees and for PF it is 40-50 degrees (Greene & Hackman, 1994). Macedo and Magee (2009). Reported MDC values for passive ROM using a goniometer are 6.8 degrees for ankle DF and 12.8 degrees for PF for intrarater reliability. Their higher values may have resulted from performing passive ankle ROM versus AROM in this study. Interrater reliability Interrater reliability for the DG was good for both DF (ICC = .89) and PF (ICC = .81). For the DI, it was excellent for both DF (ICC = .92) and PF (ICC = .99). The lower reliability when using the goniometer may result from aligning the tool with three different anatomical landmarks and then maintaining that same alignment throughout the measurement in this study. In contrast, the DI is placed in one position, with initial landmark confirmation of just one alignment point during the measurement. This eliminates variability that could potentially AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 33 increase measurement error (Rome, 1996). Venturni et al. (2006) also found higher interrater reliability of DF ROM measurements when using a DI compared to a universal goniometer. They attributed this to easier handling and positioning of the DI compared to the goniometer. Interrater Standard Error of Measurement and Minimal Detectable Change The interrater SEM for DF using the DG was 2.1 degrees, and using the DI, it was similar at 2.3 degrees. The PF SEM using the DG was 3.8 degrees, and using the DI, it was lower at 1.7 degrees. The lower SEM for the DI also corresponded to higher interrater reliability for DF and PF. The MDC value is another important clinical consideration when interpreting ROM values. The MDC for DF with the DG was 5.7 degrees, and for the DI, it was 6.3 degrees. The MDC for PF with the DG was 10.6 degrees, and for the DI, it was lower at 4.8 degrees. The smaller MDC values when using the DI during PF may be the result of only having to determine one landmark compared to three for the DG, combined with not influencing the plantar surface of the foot to monitor when using the DI, thereby creating a smaller MDC between the two examiners. The smaller MDC values found for PF using the DI combined with the higher reliability compared to the DG are notable. PF using the DG demonstrated a higher MDC value of 10.6 degrees, which negatively impacts a clinician's ability to determine if a true change has occurred, given that the accepted normative value for PF is 40-50 degrees (Greene & Hackman, 1994). Walsh et al. (2020) reported MDC values using the goniometer of 5 degrees DF and 17 degrees PF. Macedo et al. (2009) reported MDC values of 6.8 degrees for DF and 13 degrees for PF for the goniometer. This study found similar results for ankle DF at 5.7 degrees, but the MDC for ankle PF in this study was calculated as less (10.6 degrees). Using a DG, active DF ROM changes greater than 6 degrees, and active plantarflexion ROM greater than 11 degrees AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 34 would indicate an actual change between different testers. For the DI, the MDC for ankle DF was 6.3 degrees, and for PF, it was 4.8 degrees. The smaller MDC for DI PF compared to DG PF could be the result of using a single versus multiple landmarks when using a DG and not needing to maintain alignment with proximal and distal points when using the DI. Both variables may indicate that the DI is easier to use and manipulate for clinicians, resulting in less instrument placement variability. Agreement between the two instruments How well the instruments agree determines the ability to use them interchangeably to assess ankle ROM. This has clinical importance in situations when more than one clinician is working with the same patient or if there are different preferences regarding which instrument is used when measuring ankle ROM. Also, an instrument that a clinician uses one day may not be available during the next patient visit, so understanding if the instruments can be interchanged will influence measurement strategies for that visit. The Bland-Altman plots in this study demonstrated low measurement agreement between the two instruments, indicating that the devices cannot be used interchangeably when assessing ankle ROM (Figures 4 and 5). For Examiner A, the mean difference for DF between the DG and DI was 10 degrees, indicating a bias between the two instruments. The LOA demonstrates that these differences could range from 3.5 to 30 degrees for DF. PF for Examiner A also demonstrates a mean difference further from 0; the LOA differences could range from 12 to 47.6 degrees. These variations may indicate that the DG and DI are measuring different constructs of ankle mobility and the wide variability indicates that the two measurement devices cannot be substituted for each other. In addition, the different placement of the measurement tools that was done to limit ROM contribution from other joints, could have contributed to the differences in ROM values for each device. Although AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 35 the DG and DI are instruments that measure joint ROM, the results do not support the interchangeability of the two devices to measure ankle ROM using the methods described here. Mean Range of Motion Values The American Association of Orthopaedic Surgeons (AAOS) and the American Medical Association (AMA) are the generally accepted sources for normative values of AROM ankle DF and PF when using the goniometer (Norkin & White, 2016). Normative values when using the inclinometer at the ankle have not been established in the literature. Goniometric normative values for DF are reported as 20 degrees, and goniometric PF normative values between 40 and 50 degrees (American Medical Association, 1993; Green & Heckman, 1994). In this study, the overall average between the two examiners over both sessions when using the goniometer was 11.3 degrees for DF and 60.4 degrees for PF compared to the inclinometer, which was 21.1 degrees for DF and 43.14 degrees for PF. The inclinometer demonstrated a higher ROM value for DF and a lower ROM value for PF compared to the goniometer, confirming a difference in results between the two measurement devices. The mean active DF ROM measurements found in this study using the goniometer are lower than what is typically reported by the AAOS and AMA. This study found a mean motion of 11.3 degrees compared to the AMA normative value of 20 degrees. According to the AMA and AAOS, the normative values for goniometric measures of PF are between 40-50 degrees, which are lower than the 60.4 degrees found in this study when using the goniometer. In a large cohort study investigating normative values for joint ROM that were stratified by age and gender in healthy individuals, passive ankle DF varied between a low of 11.6 and a high of 13.8 for DF and a low of 49.4 and a high of 62.1 for PF for participants between the age of 22-69 years old (Soucie et al., 2011). These values align with the ROM values found in this study. Although AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 36 the AAOS and AMA sources are the most commonly used norms, they provide little information regarding the population demographics, whether the motion was active or passive, or any specific measurement protocols. Similar differences with ROM normative values have been reported in the literature, which can be attributed to numerous factors such as standardization of the measurement procedure, the type of measurement instrument used, the age of the participants, and anatomical landmarks used for instrument alignment. Clinical Implications In a clinical context, consideration of measurement reliability is key to making informed decisions regarding intervention choices and the assessment of treatment effectiveness over an episode of care. Studies investigating the reliability of clinical measurement tools assist clinicians in determining if a change in ROM measurements represents a true change. If true, clinicians can make decisions regarding appropriate interventions more confidently. The good to excellent intrarater reliability coupled with the smaller intrarater MDC and SEM values reported in this study when using the DG compared to the DI indicates that a clinician can have confidence in the measures obtained with the DG and that the DG is sensitive to ROM changes. The DI demonstrated larger intrarater variability, suggesting a decrease in reliability compared with the DG, and cannot be recommended when the device is placed on the plantar surface of the calcaneus. Limitations The results of this study must be interpreted within the context of its limitations. One limitation is that this study used healthy participants between 20 and 63, so results may differ when working with a symptomatic patient population or those outside the studys age range. In addition, a standardized position for placing the inclinometer at the ankle has not been AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 37 established for non-weight-bearing measurements, and there is no consensus regarding anatomical points or references used for positioning the inclinometer. Existing studies demonstrate a wide variation in the placement of the inclinometer for sagittal plane ankle measurements (Dickson et al., 2012; Rome & Cowieson, 1996; Venturni et al., 2006); therefore, differences in assessment techniques must be considered when comparing the results of other investigations. Also, differences in subject position must be considered as a variety of positions are described in the literature, including prone or supine, sitting, and whether the knee is flexed or extended. The subject position in this study was sitting with the knee flexed greater than 30 degrees to eliminate the influence of gastrocnemius tightness, and comparisons with other studies should be limited to similar subject positioning. Another factor was that the examiners did not have previous experience in the use of the DI at the ankle joint, as current academic textbooks lack directives for the use of a DI at the ankle joint (Fruth, 2018; Norkin & White, 2016; Reese & Bandy, 2016). Although a pilot study was conducted to allow examiners to practice using the DI following a standardized protocol, this lack of familiarity may have contributed to the lower intrarater reliability findings when using the DI and cannot be discounted. Students also lack experience using the inclinometer as a measurement tool for ankle ROM. A study investigating knee flexion range of motion proficiency among physical therapy students and physical therapists found that measurement error decreased with increasing experience (Akizuki et al., 2016). Menadue et al. (2006) found no difference in examiner skill level when investigating ankle inversion and eversion measurements using a UG. Since the inclinometer is a more contemporary measurement tool and not typically used to measure non-weight bearing active ROM at the ankle, another limitation may relate to a lack AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 38 of familiarity with the tool, contributing to the lower intrarater reliability scores. This may have occurred despite training during the pilot study and utilizing a standardized measurement protocol. Additional sources of DI measurement error in this study may be attributed to DI alignment with the heel and reading error when zeroing out the DI. Future Research Directions Future studies should establish a standardized position for the inclinometer when measuring ankle joint DF and PF in a non-weight-bearing position. This will allow comparison to other studies when assessing reliability across examiners and comparing instruments. Also, studying patients with ankle ROM limitations would allow clinicians to compare results to similar populations. The placement of the inclinometer in this study has not been compared against the gold standard of radiograph imaging. Future studies may be required to confirm if this placement is acceptable or if another DI placement would better capture the ROM of the ankle. Once standardization of DI placement is established, along with other studies investigating reliability and validity, future academic instruction may include inclinometer measurement at the ankle joint in the sagittal plane. Conclusion The results of this study demonstrate good to excellent intrarater reliability for ankle DF and PF ROM when using a DG and poor to moderate reliability when using a DI. For clinicians who are the sole providers during a patients episode of care, the DG demonstrates better reliability than the DI and can be used to make appropriate clinical decisions regarding change over time. Although the DI demonstrated higher interrater reliability, the lower intrarater reliability for both DF and PF suggests that DI is not an appropriate measurement tool when AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 39 using the plantar surface of the calcaneus as the landmark. Finally, there was a lack of agreement between the two measurement methods indicating that the two instruments should not be used interchangeably when measuring ankle joint ROM using the instrument placements described in this study. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 40 References Allen, M. (2017). 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Archives of Physical Medicine and Rehabilitation, 74, 1113-1118. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 52 Tables Table 1 Demographic Descriptive M SD Age (years) 31.1 12.6 Height (m) 1.73 0.11 Weight (kg) 72.9 28.9 BMI (kg/m2) 24.2 2.9 Statistics Note. BMI = body mass index. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 53 Table 2 Comparison of Examiner A and B Ankle Joint Range of Motion Measurements at Two Separate Sessions Session 1 Instrument Digital Goniometer Digital Inclinometer Digital Goniometer Digital Inclinometer Ankle ROM Examiner A Examiner B M (SD) M (SD) Ankle DF 11.3 (6.0) Ankle DF Session 2 Examiner A Examiner B p M (SD) M (SD) p 11.9 (6.4) .209 11.1 (5.1) 11.0 (5.7) .800 21.5 (7.3) 21.7 (8.6) .703 20.5 (6.3) 20.7 (7.2) .661 Ankle PF 57.5 (9.8) 63.9 (7.7) < .001 57.2 (10.5) 63.1 (8.3) < .001 Ankle PF 43.5 (14.5) 43.1 (15.7) .385 43.3 (10.3) 42.6 (11.9) .164 Note. ROM = range of motion; DF = dorsiflexion; PF = plantarflexion. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 54 Table 3 Intrarater Reliability for Ankle Dorsiflexion and Plantarflexion for Two Examiners Examiner A Instrument Digital Ankle ICC (3,k) ROM (95% CI) Ankle DF .91 Goniometer Digital Ankle DF Inclinometer MDC .71 Ankle PF .94 Excellent 1.7 4.6 .63 Reliability a SEM MDC .88 Good 2.1 5.9 Poor 5.6 15.4 Excellent 2.5 6.8 Moderate 7.5 20.8 (.791 -.928) Moderate 3.7 10.2 .51 (.172 -.706) Excellent 2.6 7.2 (.891 -.962) Ankle PF ICC (3,k) (95% CI) (.507 -.824) Goniometer Digital SEM (.849 -.947) Inclinometer Digital Reliability a Examiner B .91 (.845 -.945) Moderate (.369 -.778) 7.6 21.0 .70 (.498 -.823) Note. ROM = range of motion; ICC = intraclass correlation coefficient; SEM = standard error of the measurement; MDC = minimal detectable change; DF = dorsiflexion; PF = plantar flexion. a ICC < .50 = poor reliability; .50-.75 = moderate reliability; .75-.90 = good reliability; > .90 = excellent reliability AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 55 Table 4 Interrater Reliability for Ankle Dorsiflexion and Plantarflexion Session 1 Instrument Ankle ROM ICC (3,k) (95% CI) Reliability a SEM MDC Digital Ankle DF .89 (.816-.934) Good 2.1 5.7 Goniometer Ankle PF .81 (-.088 - .941) Good 3.8 10.6 Digital Ankle DF .92 (.868 - .953) Excellent 2.3 6.3 Inclinometer Ankle PF .99 (.979 - .992) Excellent 1.7 4.8 Note. ROM = range of motion; ICC = intraclass correlation coefficient; SEM = standard error of the measurement; MDC = minimal detectable change; DF = dorsiflexion; PF = plantar flexion. a ICC < .50 = poor reliability; .50-.75 = moderate reliability; .75-.90 = good reliability; > .90 = excellent reliability AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 56 Table 5 Bland-Altman Limits of Agreement Session 1 DG versus DI Mean Difference a SD Lower 95% CI Upper 95% CI p Examiner A DF 10.22 7.00 -3.50 23.95 < .001 Examiner A PF -13.94 13.37 -40.14 12.26 < .001 Examiner B DF 9.80 10.42 -10.62 30.22 < .001 Examiner B PF -20.73 13.71 -47.60 6.14 < .001 Note. DG = digital goniometer; DI = digital inclinometer; DF = dorsiflexion; PF = plantarflexion; CI = confidence interval. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER Figures Figure 1 Jamar Digital Goniometer 57 AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER Figure 2 Acumar Digital Inclinometer Figure 3 Empire Bubble Level, Model 83-3 58 AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 59 Figure 4 B. A. B. Bland-Altman plot Examiner A with mean difference scores and 95% limits of agreement (LOA) for the DI versus DG for session 1. The red line represents the mean difference, and the green lines represent the upper and lower LOA. (A) Examiner A ankle DF. (B) Examiner A ankle PF AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 60 Figure 5 A. B. Bland-Altman plot Examiner B with mean difference scores and 95% limits of agreement (LOA) for the DI versus DG for session 1. The red line represents the mean difference, and the green lines represent the upper and lower LOA. (A) Examiner B ankle DF. (B) Examiner B ankle PF AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 61 Appendices Appendix A Data Collection Sheet Date: Session 1: 1st set of measurements Subject ID Examiner: A B First Set of Measurements and Order (Circle appropriate device and direction that is first) Device: DG DI Direction: DF PF Resting Knee Flexion Angle: Goniometer 1st 2nd AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 1. 2. 3. Plantarflexion 1. 2. 3. Dorsiflexion 1. 2. 3. Inclinometer 1st Plantarflexion 62 2nd Dorsiflexion 1. 2. 3. Date: Session 1: 2nd set of measurements Subject ID Examiner: A B Second Set of Measurements and Order (Circle appropriate device and direction that is first) Device: DG DI Direction: DF PF AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER Inclinometer 1. 2. 3. Plantarflexion 1st 1 2. 3. Plantarflexion Dorsiflexion 1. 2. 3. Goniometer 2nd 1st 1. 2. 3. 2nd Dorsiflexion 63 AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 64 Appendix B Measurement Standard Operating Procedure Measurement Standard Operating Procedure 1. C.C. will escort the participants to the lab space and provide KN95 or medical grade masks 2. Recorder/Examiner will introduce themselves 3. Recorder will have the participant sitting at the end of a high/low table towards the right-hand edge. The knee will be off the end of the table so that the knee is bent at a right angle. 4. The research assistant will place a strap around the participants lower leg with a towel placed between the belt and leg to ensure the lower leg remains stationary during measurements. 5. The research assistant will then measure the resting angle of knee flexion and record this on the data sheet. No changes will be made unless the knee flexion angle is less than twenty degrees. 6. The examiner will then raise the high/low table so that they are at eye level for measurements 7. Examiner will explain the procedure to the participant: a. You will be in this position for 20-25 minutes b. I am going to measure your right ankle motion when you move your ankle up as well as down c. I will confirm some landmarks on your right foot, ankle and lower leg used to align the measurement tools and make a mark that is temporary, is that ok? Any concerns or questions? Goniometer landmarks to be marked on RLE: a. Proximal arm: lateral midline of the fibula, using the head of the fibula for reference b. Fulcrum: lateral malleolus c. Moving arm: parallel with the lateral aspect of the fifth metatarsal Inclinometer landmarks to be marked on right ankle: a. With the ankle in neutral, a straight line from the center of the lateral malleolus to the calcaneus AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 65 8. Prior to measurements the examiner will ask the participant to move their right ankle up and down 3x to warm-up and to become familiar with the motion. 9. For the inclinometer: a. Prior to measurement, place the inclinometer against the underside of the high/low table along the frame and using the bubble level to confirm the neutral zero position. b. With the inclinometer in the above position the examiner will press the Zero button to zero the instrument out while in the horizontal position. c. The examiner will then place the flat side of the inclinometer base on the plantar surface of the calcaneus, lining the middle of the base with the vertical line from the lateral malleolus so that the base of the inclinometer is on the calcaneus. d. The inclinometer will be facing downward, and the measurement reading will face away from the examiner. e. The examiner will then ask the participant to bring their ankle up until the bubble level is in the mid position between the two level lines. This vertical neutral position will also be confirmed by the research assistant who will view the inclinometer and when it reads 0, inform the examiner. f. Once the position is confirmed the examiner will ask the patient to perform the required range of motion. g. Once the subject has completed each ROM, the examiner will press the hold button to store the measurement and then give the device to the recorder who will then record the measurement on the data sheet. h. Once the research assistant records the measure, they will reset the device to zero. i. Steps a-g will be repeated for each measurement. 10. For the goniometer: a. The examiner will press the ON/OFF/DISPLAY button to turn the unit on b. The examiner may then need to hit the ON/OFF/DISPLAY to rotate the display if needed c. The examiner will place the proximal arm, the distal arm and the fulcrum of the goniometer over the right lower leg landmarks d. The goniometer will be placed in the starting position by aligning with the red 0 e. The examiner will then press the Zero button f. The examiner will move the goniometer until the ankle stops and then they press the HOLD button. g. The instrument will then be passed to the research assistant to record the ROM on the data sheet h. Once the measurement is recorded the research assistant will push the ZERO button to reset the device. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 66 11. Examiner directive: DF: Move you ankle/foot up towards the ceiling as far as you can and keep it there until Ive taken the measure and I will let you know when you can relax OR PF: Point your ankle/foot down as far as you can and keep it there until Ive taken the measure and I will let you know when you can relax 12. The research assistant will write the measurement down on the participant data collection sheet between each measure to allow the participant to rest for 15 seconds 13. Steps 8-12 will be repeated 2 times with the research assistant writing each ROM value on the participant data sheet 14. The research assistant will have 3 data collection points for each ROM direction. 15. The examiner will then switch devices and repeats steps 8-13 16. The examiner will remove the landmarks with an alcohol wipe to remove the lower leg markings. 17. Examiners and research assistants will then gel out of the participants station 18. The examiners and research assistants will then switch stations to measure the next participant and the recorders will switch measurement sheets 19. The examiner and/or research assistant will disinfect the instruments, sanitize the table and stabilization belt, and hand sanitize with gel prior to the next participant 20. The participant will stay in the same position and the examiner and research assistant will move to the next participant 21. When each participant has been measured by each examiner, they are finished and will leave the testing area. AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 67 Appendix C Table 3 Intrarater Reliability for Ankle Dorsiflexion and Plantarflexion for Two Examiners Partial Data Set Examiner A Instrument Digital Ankle ICC (3,k) ROM (95% CI) Ankle DF .912 Goniometer Digital Ankle DF Inclinometer MDC .702 Ankle PF .938 Excellent 1.64 4.56 .811 Reliability a SEM MDC .884 Good 2.10 5.83 Moderate 3.91 10.84 Excellent 2.46 6.82 Good 3.94 10.93 (.800 -.933) Moderate 3.40 9.41 .710 (.503 -.831) Excellent 2.52 7.0 (.894 -.964) Ankle PF ICC (3,k) (95% CI) (.490 -.826) Goniometer Digital SEM (.849 -.949) Inclinometer Digital Reliability a Examiner B .904 (.834 -.944) Good (.658 -.894) 4.37 12.11 .876 (.771 -.931) Note. ROM = range of motion; ICC = intraclass correlation coefficient; SEM = standard error of the measurement; MDC = minimal detectable change; DF = dorsiflexion; PF = plantar flexion. a ICC < .50 = poor reliability; .50-.75 = moderate reliability; .75-.90 = good reliability; > .90 = excellent reliability AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 68 Table 4 Interrater Reliability for Ankle Dorsiflexion and Plantarflexion Session 1 Partial Data Set Instrument Ankle ROM ICC (3,k) (95% CI) Reliability a SEM MDC Digital Ankle DF .909 (.843-.947) Excellent 1.77 4.9 Goniometer Ankle PF .812 (-.055 - .939) Good 3.8 10.6 Digital Ankle DF .896 (.820 - .940) Good 2.6 7.1 Inclinometer Ankle PF .972 (.952 - .984) Excellent 2.6 7.1 Note. ROM = range of motion; ICC = intraclass correlation coefficient; SEM = standard error of the measurement; MDC = minimal detectable change; DF = dorsiflexion; PF = plantar flexion. a ICC < .50 = poor reliability; .50-.75 = moderate reliability; .75-.90 = good reliability; > .90 = excellent reliability AGREEMENT BETWEEN GONIOMETER AND INCLINOMETER 69 Appendix D Table 4 Interrater Reliability for Ankle Dorsiflexion and Plantarflexion Session 2 Instrument Ankle ROM ICC (3,k) (95% CI) Reliability a SEM MDC Digital Ankle DF .882 (.670-.869) Good 1.88 5.21 Goniometer Ankle PF .816 (.182 - .932) Good 4.08 11.32 Digital Ankle DF .937 (.895 - .963) Excellent 1.71 4.75 Inclinometer Ankle PF .969 (.947 - .981) Excellent 1.97 5.45 Note. ROM = range of motion; ICC = intraclass correlation coefficient; SEM = standard error of the measurement; MDC = minimal detectable change; DF = dorsiflexion; PF = plantar flexion. a ICC < .50 = poor reliability; .50-.75 = moderate reliability; .75-.90 = good reliability; > .90 = excellent reliability  ...

... Predictive Value of Laboratory Assays Toward Autoimmune Diseases Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Kellen J. Begeman, MA, MLS(ASCP) CM Copyright 6DEC23 By: Kellen J. Begeman, MA, MLS(ASCP) CM All rights reserved Approved by: Elizabeth S. Moore, PhD Committee Chair ______________________________ Mark J. White, PhD, D(ABMM), M(ASCP) CM Committee Member ______________________________ Heidi H. Ewen, PhD, FGSA, FAGHE Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES Predictive Value of Laboratory Assays Toward Autoimmune Diseases Kellen Begeman Department of Interprofessional Health and Aging Studies, University of Indianapolis 1 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 2 Abstract Background: With an increasing number and unmet medical needs for autoimmune diseases (AD) in the United States, it is important to assess the value of laboratory assays in diagnosis. Healthcare providers must understand general and specific laboratory results that can lead to an efficient diagnosis. Methods: A non-experimental study using a retrospective design between 2018 and 2021 was completed to explore whether the presence of positive anti-ENA and antidsDNA antibodies in individuals with an autoimmune disorder (anti-ENA, N = 1,495; antidsDNA, N = 1,261) can be predicted by their demographics and selected general laboratory test results. Results: For predicting anti-ENA, multiple logistic regression analysis 2(1) =237.62, p < .001 indicated anti-nuclear antibody (ANA), complement C3, globulin, monocyte-tolymphocyte Ratio (MLR), and blood urea nitrogen (BUN) were statistically associated with the probability of a positive anti-ENA result. The regression analysis had a sensitivity of 98.6% and a specificity measure of 16.1%. Regarding anti-dsDNA, multiple logistic regression analysis 2(1) = 388.04, p < .001 indicated that complement C3, complement C4, pH, urine protein, neutrophil-to-lymphocyte ratio (NLR) were statistically associated with the probability of a positive anti-dsDNA result. The regression analysis had a sensitivity of 77.7% and a specificity measure of 64.5%. Conclusion: In patients with a positive anti-ENA, ANA, complement C3, globulin, MLR, and BUN are important factors in diagnosing AD. In patients with a positive anti-dsDNA, complement C3, complement C4, pH, urine protein, and NLR are important factors in determining SLE diagnosis. Keywords: autoimmune disease, complement c3, complement c4, pH, urine protein, neutrophil-to-lymphocyte ratio (NLR), anti-nuclear antibody (ANA), complement C3, globulin, monocyte-to-lymphocyte ratio (MLR), and blood urea nitrogen (BUN) PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES Acknowledgments 3 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 4 Table of Contents Background ............................................................................................................................................................................ 7-8 Research Problem ............................................................................................................................................................... 8-9 Purpose Statement ................................................................................................................................................................... 9 Research Question ................................................................................................................................................................... 9 Significance of the Study................................................................................................................................................. 9-10 Definition of Terms ............................................................................................................................................................... 10 Literature Review ........................................................................................................................................................... 10-11 Current Literature ................................................................................................................................................................. 11 Complete Blood Count ........................................................................................................ 11-14 Autoantibodies ..................................................................................................................... 14-16 Inflammatory Markers .......................................................................................................... 16-17 Comprehensive Metabolic Panel, Liver Enzymes, and Complement .................................. 17-18 Urinalysis ............................................................................................................................. 18-19 Gender and Age .................................................................................................................... 19-20 Gaps in Research ............................................................................................................................................................. 20-22 Filling the Gap ...........................................................................................................................22 Method ....................................................................................................................................................................................... 22 Study Type and Design ..............................................................................................................22 Participants ........................................................................................................................... 22-23 Data ...................................................................................................................................... 23-25 Operational Definitions ........................................................................................................ 25-26 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 5 Procedures ............................................................................................................................................................................... 26 Data Collection ..................................................................................................................... 26-27 Statistical Analysis ............................................................................................................... 27-28 Results ........................................................................................................................................................................................ 28 anti-ENA ............................................................................................................................. 28-29 anti-dsDNA .......................................................................................................................... 29-31 Discussion ................................................................................................................................................................................. 31 anti-ENA ............................................................................................................................. 32-34 anti-dsDNA .......................................................................................................................... 34-35 Limitations .......................................................................................................................... 35-36 Implications and Future Direction..............................................................................................36 Conclusion ................................................................................................................................................................................ 36 References ......................................................................................................................................................................... 37-54 Table 1................................................................................................................................................................................. 55-56 Table 2................................................................................................................................................................................. 57-58 Figure 1 ...................................................................................................................................................................................... 59 Figure 2 ...................................................................................................................................................................................... 59 Appendix ................................................................................................................................................................................... 60 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 6 Predictive Value of Laboratory Assays Toward Autoimmune Diseases Worldwide, 5-10% of individuals suffer from autoimmune diseases (ADs), with 15-20 million cases in the United States culminating as the third leading cause of illness and mortality (Tozzoli & Bizzaro, 2020). Autoimmunity constitutes elements of an organisms immune system attacking its own tissue or cells (Birtane, 2017). The corresponding formed antibodies, known as autoantibodies, contribute to the diseases known as ADs (Birtane, 2017). ADs currently reflect a significant unmet medical need and clinical problem due to their associated healthcare cost, chronic status, and prevalence in younger populations (Rosenblum et al., 2015). Therapy for ADs is typically continuous and lifelong, presenting an increased risk of complications in malignancies and infections (Rosenblum et al., 2015). A major contributor to healthcare costs is not only medications for the management of ADs but also the cost of laboratory screening and diagnostic testing (Julian, 2014). The initial clinical investigation remains expensive as ADs are notoriously difficult to confirm, requiring not only medical specialists but also increasingly more specific, complex, and costly laboratory analyses (Maher & Perugino, 2019; Rosenblum et al., 2015). Despite the expense, laboratory testing is extremely valuable for evaluating suspected ADs. Results have the potential to not only assist with diagnoses but also estimate disease severity and the tracking and prognosis of disease activity (Castro & Gourley, 2010). General laboratory testing can be conducted to begin to probe the possibility of an AD. Health professionals may review erythrosedimentation rate (ESR), comprehensive metabolic panel (CMP), c-reactive protein (CRP), complete blood count (CBC), and urinalysis (UA; Moore & Dalrymple, 2016). Even more specific are the detections of the actual autoantibodies to confirm PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 7 the preliminary diagnosis made from general laboratory results, and they have the most prognostic impact and value in monitoring treatment response (Birtane, 2017). Screening tests detect anti-nuclear antibodies (ANA) followed by anti-dsDNA antibodies and anti-extractable nuclear antigen (ENA) antibodies, which are generally highly specific for confirmation of a type of AD. Although the importance of the management of the disease is evident and, when evaluated correctly, can lead to prompt diagnosis and treatment, positive results can, unfortunately, complicate patient care (Birtane, 2017). Some autoantibodies are present in healthy populations, leading to false positives, which in turn can cause the administration of inappropriate and potentially damaging treatment (Birtane, 2017; Zheng et al., 2020). Research Problem The diagnostic responsibility for a potential AD lies with the attending physician as it requires deciphering testing complexities. If discrepancies are acknowledged, or results do not fit into the clinical context or physician expectations, the specimens can go through repeat analysis or be tested using another methodology or assay with its own characteristics in terms of performance and accuracy (Tozzoli & Bizzaro, 2020). Additionally, the risk of false positives increases as a physician requests further measurement of multiple antibodies (Tozzoli & Bizzaro, 2020). False positivity is demonstrated as anti-ENA detection sensitivity and specificity, respectively, according to the technique: enzyme-linked immunosorbent assay (ELISA) (50.0%, 78.9%); double immunodiffusion (31.3%, 89.5%); Hemagglutination Tests (HA) (40.9%, 87.7%) (Lora et al., 2011). Even with advancements in laboratory technology and diagnostic tools, the presence of an autoantibody does not singularly confirm AD diagnosis, nor does its absence exclude PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 8 diagnosis. A physicians knowledge of each laboratory tests diagnostic power and usage of the most meaningful assays in relation to AD is essential in complementing physical examination (Birtane, 2017). Studies have assessed the value and utility of singular and multiple variables of laboratory results to assist in diagnosing ADs. However, no other studies have evaluated the predictive value of all the essential general and specific diagnostic laboratory tests available to confirm ADs. Purpose Statement The purpose of this study is to provide healthcare professionals with the value and utility of laboratory analysis for the diagnosis of a suspected AD. Research Question The following research question will be answered to meet the study's purpose. Is there a relationship between age, gender, CRP, CBC, UA, CMP, ANA, anti-ENA, and anti-dsDNA antibodies? To answer the research question, the following objective will be addressed: To determine the predictive value of age, gender, CRP, CBC, UA, CMP, and ANA test results to the presence of anti-ENA and anti-dsDNA antibodies using multiple logistic regression analysis. Significance of the Study ADs undoubtedly contribute significantly to healthcare costs and an affected individuals quality of life and longevity (Tozzoli & Bizzaro, 2020). Complex disease demonstration in patients, coupled with the diagnostic limitations of laboratory testing, increases the potential for an incorrect diagnosis. Misdiagnosis can lead to inaccurate treatment, and with a heavy reliance on laboratory analysis, physicians cannot afford to make mistakes in interpreting results (Birtane, 2017). With guidance on the true value and utility of each laboratory test concerning the PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 9 diagnosis of an AD, physicians can feel more confident in their approach and treatment, patients can receive the correct and critical medical AD diagnosis, and the cost burden of unnecessary laboratory screening tests can be limited. Literature Review ADs possess the potential to affect multiple organs with significant variability and severity in signs and symptoms, leading to difficulty in diagnosis (Rosenblum et al., 2015). Regarding the clinical presentation, vague symptoms with slow progression increase the potential for misdiagnosis (Rosenblum et al., 2015). Further, confirmatory testing often depends on suspicion of a particular disorder and the ability to follow initial laboratory results, culminating in disease diagnosis (Castro & Gourley, 2010). All of these variables potentially increase strain on providers, use of healthcare resources, treatment due to misdiagnosis, and patient discomfort (Birtane, 2017). Proper understanding and utilization of laboratory diagnostics have extreme value in limiting the strain on patients, providers, and healthcare resources concerning suspected ADs (Birtane, 2017). A spectrum of both general and specific laboratory analyses is available to include: ESR, CMP, CRP, CBC, UA, ANA, anti-dsDNA antibodies, and anti-ENA antibodies (Moore & Dalrymple, 2016). Proper evaluation is an important part of disease management that can lead to prompt diagnosis and treatment; however, complex results can also complicate patient care (Birtane, 2017). For instance, autoantibody presence does not singularly confirm AD diagnosis, nor does its absence exclude the diagnosis (Birtane, 2017). Further complicating ADs, some detected autoantibodies are found in healthy populations. Complementing a physicians physical examination is requisitioning the most meaningful laboratory assays with their own PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 10 changing variables of diagnostic power, sensitivity, and specificity, further adding to the complexity of disease diagnosis (Birtane, 2017). Current Literature The indispensable role of diagnostic testing for both prognosis and treatment response has led to a vast amount of available in vitro diagnostic testing products (Kosack et al., 2017). With this increase has come a wealth of guidelines and recommendations for healthcare professionals to decipher in their medical decisions (Kosack et al., 2017). However, these guidelines only cover a small portion of testing, leading to a cumbersome clinical approach. This highlights the need for general guidance and specific disease-related diagnostic values, such as testing variables related to AD (Kosack et al., 2017). Complete Blood Count Multiple and singular testing variables, including those associated with blood cells, have been assessed for their value and utility in diagnosing ADs. Several studies have demonstrated a causal link between the presence or activation of monocytes and macrophages, followed by the development of AD (Ma et al., 2019). Through their pro-inflammatory or fibrogenic properties, immune cells can play a key role in the pathogenesis of certain ADs, lending credence to analyzing their presence and activity (Ma et al., 2019). The myeloid factors, including monocytes, macrophages, dendritic cells, and neutrophils, are key in local immune response and may play a part in various systemic autoimmune diseases (SADs; Morell et al., 2017). In addition, changes to the frequency and count of macrophages and monocytes are distinguishing characteristics of many ADs (Ma et al., 2019). Lymphocyte Ratios PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 11 Regarding various inflammatory diseases, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte (PLR) ratios act as indicators of clinical outcomes (Jaszczura et al., 2019). Monocyte-to-lymphocyte ratios (MLR), PLR, and NLR are all associated with ADs (Seringec Akkececi et al., 2019). PLR and NLR are useful for disease activity and inflammatory response in patients with Systemic Lupus Erythematosus (SLE) and disease activity in rheumatoid arthritis (RA; Fu et al., 2015; Seringec Akkececi et al., 2019). Not only NLR, but red cell distribution width (RDW) also shows promise in estimating disease activity related to Primary Sjgrens Syndrome (pSS; Hu et al., 2014; Seringec Akkececi et al., 2019). MLR can assess disease severity in spondyloarthritis disease (Seringec Akkececi et al., 2019). Specifically, in immunoglobulin A vasculitis (IgAV), NLR and PLR were significantly increased (Jaszczura et al., 2019). Evaluating NLR, MLR, ELR, and their association with inflammatory markers regarding patients with systemic autoimmune rheumatic diseases (SARDs), NLR and MLR were significantly increased (Yang et al., 2017). SLE showed decreased ELR and overall neutropenia, while other SARDs showed increased ELR (Morell et al., 2017; Yang et al., 2017). Additionally, increased NLR, PLR, and MPV suggest that NLR is associated with SLE activity (Qin et al., 2016). Patients with SLE combined with lupus nephritis demonstrated higher NLR values than healthy controls and those without lupus nephritis (Li et al., 2015). Wang et al. (2020) also have shown significantly higher NLR values than healthy controls within active versus inactive SLE and lupus nephritis versus non-lupus nephritis. In nearly all SARDs, NLR and MLR were closely associated with ESR and CRP (Yang et al., 2017). In autoimmune encephalitis, MLR showed significant increases in severe cases compared to mild cases (Liu et al., 2022). White Blood Cell Counts PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 12 Neutrophils demonstrate various roles in ADs to promote inflammation and contribute to AD pathogenesis in disorders including RA, SLE, pSS, multiple sclerosis (MS), and Crohns disease (CD) (Fu et al., 2021). Abnormalities, including granulocytopenia and neutropenia, can be seen in AD patients, while lymphopenia is the most common WBC count parameter regarding SLE (Fayyaz et al., 2015). Anomalies of lymphocyte subset distributions are characteristics of SLE, RA, and pSS (Carvajal et al., 2017). In RA, insulin-dependent diabetes mellitus, Crohn's disease, SLE, primary vasculitis, and pSS lymphopenia have been observed (Schulze-Koops, 2004). Patients with idiopathic CD4 lymphopenia show a high prevalence of autoantibodies associated with ADs (Perez-Diez et al., 2020). Specifically related to SLE, patients have demonstrated elevated neutrophil counts and lowered lymphocyte counts (Han et al., 2020). In SLE, Systemic Sclerosis, RA, Multiple Sclerosis, Type 1 Diabetes, Primary Biliary Cholangitis, Sjgren's Syndrome, Celiac Disease, and Inflammatory Bowel Disease, high monocyte counts have been observed (Ma et al., 2019). Elevated neutrophil counts and reduced lymphocyte counts can be seen in SLE patients leading to elevated NLR compared to healthy individuals (Han et al., 2020). Other WBC absolute counts, including eosinophils, have shown a pathogenic role in ADs (Diny et al., (2017). Basophils are involved in developing autoimmune diseases (Karasuyama et al., 2018). ADs such as lupus nephritis and RA have basophils implicated in disease pathophysiology (Cromheecke et al., 2014). Autoreactive IgE antibodies including basophils, are often detected in SLE, along with the autoreactive IgE serum level being associated with disease activity and active nephritis (Miyake et al., 2022). Platelets and Red Blood Cells Platelets also warrant consideration as a mechanism of autoimmunity through platelet activation and a source of autoantigens (ukasik et al., 2018). Both increased and decreased PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 13 platelet counts provide the benefit of another biomarker in the full spectrum of AD diagnosis (ukasik et al., 2018). Low RBC counts may indicate patients who have developed autoantibodies toward red cells (Moore & Dalrymple, 2016). SLE and SS can show acute reductions in hematocrit (HCT) (Moore & Dalrymple, 2016). Compared to healthy individuals, HCT levels showed significant decreases in patients with SLE (Yang et al., 2015). Additionally, HCT may reflect the inflammatory response and disease activity in SLE patients with the correlation shown to CRP and ESR (Yang et al., 2015) Autoantibodies AD diagnosis depends upon laboratory detection of autoantibodies directed against nuclear or cytoplasmic antigens, clinical history, and physical examination (Stamouli et al., 2013). As a characteristic of ADs is the production of autoantibodies for extractable nuclear autoantigens, their assessment helps specify various types and relates to antinuclear antibody titer and pattern (Andrade et al., 2022; Banhuk et al., 2018). Therefore, antinuclear antibodies and autoantibodies are a trademark of AD screening and diagnosis (Dinse et al., 2020; Banhuk et al., 2018). ANA by indirect immunofluorescence (IIF) is generally the initial screening assay for SARD patients (Soto et al., 2013). Patients can potentially have high autoantibody titers without correlating clinical signs or symptoms (Soto et al., 2013). Soto et al. (2013) consider it essential to evaluate ANA titers for clarity in true positive results, as low titers are present in healthy individuals and intermediate titer levels are possibly present in relatives of AD patients. This includes the dense fine-speckled (DFS) pattern, which is the most frequent pattern in the hightiter ANA-positive healthy populations (Conrad et al., 2017). Further, these assays, along with other analytes, could assist with disease prediction and earlier intervention based on assessed complications or organ involvement (Fitzler et al., 2018). PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 14 Anti-SSA/Ro antibodies demonstrated the highest frequency of detection at 41.7% (15/36) with nuclear fine speckled being the most common pattern (Banhuk et al., 2018). SLE and scleroderma were the most common pathologies in antigen-positive patients (Banhuk et al., 2018). Anti-ENA is an indicator in the diagnosis of AD. Anti-ENA reflex testing showed AD in SARDs diagnosis with patient serum demonstrating a 1:256 titer with homogenous, speckled, or multiple patterns (Wendy & Thomas, 2012). Autoantibodies are essential in the confirmation and classification of SADs (Fritzler et al., 2018). In order to detect ADs, ANA and ENA are instrumental biomarkers (Alsubki et al., 2020). Alsubki et al. (2020) established the association between these two assays, demonstrating the most common pattern exhibited as speckled for females and homogenous for males with varying frequencies to anti-Sjgren syndrome-related antigen A (SSA), anti-ribonucleoprotein antibody (RNP), and histones associated with homogenous and speckled nuclear patterns. Specifically, positive ANA is frequently associated with autoantibodies anti-Ro, anti-La, and anti-Jo1 in RA patients (Emad et al., 2021). ANA is also used to diagnose connective tissue disorder (CTD). Homogenous and peripheral patterns associated with anti-dsDNA and speckled patterns possibly predict anti-ENA ribonucleoproteins (Sharmin et al., 2014). Anti-Sm correlates to proteinuria and renal disorder, while lone anti-Sm assist C3, C4, and anti-dsDNA in AD diagnosis (Meng et al., 2018). Related to SLE, there is a wide range of clinical symptoms and reports of a large spectrum of antibodies including anti-ribonucleoproteins (Dima et al., 2018). ANA tests have a high sensitivity in diagnosing SLE but low specificity. Even healthy individuals may have a positive ANA (Dima et al., 2018). Anti-dsDNA is a good predictor of disease flare in SLE patients (Herbst et al., 2012). The feature of specific antibody positivity and the resulting clinical manifestations could grant earlier diagnosis for those cases associated with severe organ PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 15 impairment (Dima et al., 2018). Anti-ENA and SLE associations may lead to novel diagnostic testing approaches and better accuracy in evaluating disease (Joob & Wiwantkit, 2019). Anti-SM antibodies played an important role in SLE diagnosis and monitoring due to their specificity, as their presence correlated with specific subsets of organ/system involvement, including renal, neurologic, and hematologic disorder cases (Arroyo-vila et al., 2015). Depending on ethnic and demographic characteristics, these antibodies comprised between 9-49% of SLE cases (Arroyovila et al., 2015). According to Oh et al. (2020), Anti-Sm antibodies complement the diagnosis of active SLE but cannot be used as solo biomarkers to classify patients with SLE (AganovicMusinovic et al., 2012). Inflammatory Markers Inflammatory markers such as CRP and ESR are regularly used throughout primary care for inflammatory conditions, including ADs, for both diagnosis and monitoring (Watson et al., 2019). CRP along with albumin, known as acute phase reactants (ARPs), are also commonly used to measure the activity of inflammatory conditions (Seringec Akkececi et al., 2019). Specifically, serum Albumin is used as a biomarker for AD patients' disease severity and nutritional status (Weng et al., 2016). Reduced serum Albumin concentration is associated with ADs and may worsen patient health and functioning (Ward et al., 2022). The Albumin to Globulin (AG) Ratio provides some of the first evidence of an inflammatory process or patient with an AD through possible hypergammaglobulinemia (Moore & Dalrymple, 2016). Elevated globulin can indicate increased humoral immune activity and potential inflammation, which is abundant in ADs (Hashash et al., 2022). Patients with IBD, RA, and Sjgren syndrome (SS) all show increased globulin levels (Chen et al., 2021; Wang et al., 2022; Maliska et al., 2020). Erythrosedimentation Rate and C-Reactive Protein PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 16 ESR is a potential marker of disease activity in SLE and organ-specific activity, too (Stojan et al., 2013). However, multiple tests of these markers can show abnormal and discordant results without ruling out a critical pathology (Watson et al., 2019). Interpretation becomes challenging in deciphering if one abnormal test is sufficient or if the result should warrant further investigation or treatment (Watson et al., 2019). Therefore, no combination of inflammatory markers alone can rule in or rule out disease, and other clinical findings must be considered (Watson et al., 2019). Due to cheaper cost and minimally better performance, CRP carries the recommendation as the first inflammatory marker screening test to utilize (Watson et al., 2019). Regarding Crohns disease, another AD management based on CRP and albumin results may help avoid major adverse outcomes (MAO; Shiga et al., 2020). Comprehensive Metabolic Panel, Liver Enzymes, and Complement Aspartate transaminase (AST) and alanine transaminase (ALT) are two common liver function tests (LFT) that show abnormal levels of hepatocyte damage. The established association between SLE and liver damage and the ratio of AST to ALT can potentially differentiate this etiology (Liu et al., 2015). LFT may be abnormal in 65% of RA patients, involving increases in alkaline phosphatase (Cojocaru et al., 2013). A decrease in serum bilirubin levels can occur in SLE patients, potentially showing an association with the inflammatory process and renal involvement (Yang et al., 2012). Compared to healthy individuals, total bilirubin was significantly reduced in patients with pSS (Zhang et al., 2020). In nearly all antibody-mediated ADs, complement is activated (Thurman & Yapa, 2019). This is due to its importance in the immune system and potential pathogenic impact in many autoimmune systems (Holers & Banda, 2018; Galindo-Izquierdo et al., 2021). C3 hypocomplementemia has been seen PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 17 in pSS and Antiphospholipid Syndrome (Jordn-Gonzlez et al., 2020). SLE pathogenesis related to complement has been thoroughly established with C3 and C4 levels generally decreasing (Morell et al., 2021). Total complement activation, characteristically assessed by individual levels of C3, C4, and CH50, has shown a negative association with SLE. Serum complement levels show low sensitivity and specificity alone as levels are comparable between the healthy population and SLE patients (Herbst et al., 2012). Urea nitrogen in the blood can increase with impaired renal function (Krishnamurthy et al., 2023). Lupus nephritis patients presented with higher BUN than patients without the disorder (KrFFalishnamurthy et al., 2023; Nassif, 2021). Patients with Connective Tissue Disease and a positive anti-ENA demonstrated higher mean BUN values (Krishnamurthy et al., 2023). RA patients have shown increased BUN compared to a healthy control group (Krishnamurthy et al., 2023). BUN Creatinine ratio is useful for assessing and differentiating pre-renal and renal causes, including ADs (Gounden et al., 2022). SLE patients are twice as likely to experience episodes of hypocalcemia compared to healthy counterparts, demonstrating that calcium levels may contribute to the SLE disease process (Watad et al., 2017). Serum chloride has been garnering attention as a predictor of adverse outcomes in clinical settings specifically related to an association between hyperchloremia and acute kidney injury, a potential outcome of ADs (Khatri et al., 2020). In SLE and Lupus nephritis, renal potassium wasting can create sustained hypokalemia (Adomako et al., 2021). Autoimmune hepatitis can demonstrate elevations in serum proteins, which can be useful for abnormal elevation of immunoglobulin (Castro & Gourley, 2010). Regarding sodium, hyponatremia may reflect SLE disease activity and inflammation (Yamany et al., 2020). Urinalysis PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 18 Along with ANA, CBC, and serum creatinine, urinalysis is also advised in AD testing, in particular for SLE (Nashi & Shmerling, 2021). Urinalysis including creatinine ratio, proteinuria, and creatinine-clearance are useful monitors of kidney function in SLE but lack sensitivity to early disease and detection of renal flares alone (Herbst et al., 2012). Acidic pH, proteinuria, and WBCs are all self-evident markers of ADs, particularly with renal damage (Moore & Dalrymple, 2016). However, ADs, including Sjgrens, rheumatoid arthritis, autoimmune hepatitis, and SLE, can also present distal renal tubular acidosis, a defect in the urinary acidification process (Silveira et al., 2022). This well-known association characteristically causes alkaline urine (Silveira et al., 2022). With abnormal UA results from a potential AD, creatinine clearance is indicated to better understand kidney function. Current markers of lupus nephritis include proteinuria, one of the most typical manifestations of the disease (Celia et al., 2021; Morell et al., 2021). Patients with lupus nephritis and lupus-related kidney disease demonstrate increased proteinuria (Nassif, 2021; Chedid et al., 2020). The presence of bilirubin in urine is abnormal and signifies potential complications (Gounden et al., 2022). Specific gravity is decreased in dilute urine and increased in concentrated urine, which can indicate the ability of the kidney to concentrate urine and overall renal health (Gounden et al., 2022) Gender and Age When looking at gender, women receive more AD diagnoses than men, with a relationship between the age of onset and the clinical course and individual outcomes in SLE (Meng et al., 2018). A study by Stamouli et al. (2013) demonstrated a higher prevalence of autoantibodies among women over men in a sera of 3,000 patients. This corresponds to approximately 80% of women being affected by AD (Angum et al., 2020). Regarding age groups, although there is an increase in autoimmunity among the elderly, it does not always PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 19 signify increased AD prevalence (Watad et al., 2017). Additionally, a study by Kang et al. (2004) showed that neither gender nor age was found beneficial in determining positive anti-ENA and anti-dsDNA results. Age at onset tends to be decreased except for a few diseases showing some variation among disorders, with SLE being 15-55 years, RA 30-60 years, pSS 40-60 years, systemic sclerosis 30-60 years, and psoriasis 15-35 years (Angum et al., 2020; Watad et al., 2017). Acknowledging the age of onset and gender, along with other risk factors, is important in pursuing physician consultation to determine the potential diagnosis and treatment of AD (Angum et al., 2020). Gap in Research There are minimal studies that have accessed multiple markers over individual markers of suspected AD patients. A study by Seringec Akkececi et al. (2019) concluded that CRP/albumin ratio, RDW, NLR, PLR, and MLR were markers of remission for active disease, while CRP/albumin ratio and total albumin were markers of disease activity in ADs, such as Takayasu arteritis. Qu et al. (2018) assessed the combined detection of ANA, ds-DNA antibody, and complements C3 and C4 in diagnosing SLE. The study found that the markers played a complementary role in the treatment and diagnosis of SLE patients and future treatment of SLE patients (Qu et al., 2018). Kang et al. (2004) studied associations among age, gender, immunofluorescence pattern and ANA titer, anti-ENA, and anti-dsDNA antibodies. They found that ANA titer alone is useful in predicting anti-ENA antibodies, while both titer and pattern were useful predictors of anti-dsDNA antibodies (Kang et al., 2004). This is the extent of the amount of laboratory assays accessed for their value to our knowledge. The laboratory immunologist has an expanded expectation to provide for both clinical and patient needs by facilitating findings closely with healthcare professionals (Tozzoli & PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 20 Bizzaro, 2020). Diagnostic limitations and pitfalls still exist relevant to ADs, potentially leading to incorrect interpretations because of a misunderstanding of the diagnostic technique (Maher & Perugino, 2019). Additionally, some studies have pointed to an overuse of ANA testing, indicating the need for the proper identification of symptoms that call for further screening (Aygn et al., 2019). Otten et al. (2017) advocated that the current strategy for ANA analysis was not cost-effective and that the search for specific autoantibodies in patients without appropriate signs and symptoms causes confusion in interpreting unexpected positive results. ANA by indirect immunofluorescence (IFF) is inherently subjective and prone to the bias of the individual interpreting the results (Infantino et al., 2017). ANA testing represents a complication in diagnosis with low pre-test AD probability and limitations as a marker for pre-autoimmunity screening (Pisetsky et al., 2017). ANA positivity has also been dubbed an unreliable predictive marker for rheumatologic disease and may depend upon the laboratory conducting the testing (Abeles et al., 2016). Best practices in ANA diagnosis are far from certain (Hira-Kazal et al., 2015). A better understanding of ANA testing indications and interpretations may lessen costs and unnecessary referrals (Rodriguez et al., 2015). Sheth et al. (2014) argued that a significant amount of clinical ANA testing did not produce high-value-cost-conscious care, contributing to economic burden. In actuality, laboratory tests ordered inappropriately occur approximately 20% of the time, burdening patients, increasing costs, and producing false-positive results (Lesuis et al., 2017). Rheumatologists ordering behavior of the ANA assay is multi-faceted, influenced by their work experience, personality, and gender (Lesuis et al., 2017). Disease-specific patients with established SLE have shown assay variation detection of ANA in sera (Van H.L. Van B.S., 2019). However, Pregnoato et al. (2019) showed ANA as a hallmark of SLE, and commercially PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 21 available kits were reliable in the multicenter setting. Contradictory to this reliance on testing results, anti-Ro/SSA antibodies can be missed in a low percentage of samples (Sefik et al., 2015). This lack of confidence in AD testing reflects a real need for guidance. Standardization and harmonization are highlights of autoimmune diagnostics for diagnostic direction, resource allocation, and the safety of patients. It is essential to efficiently and effectively choose and utilize assays at a healthcare professionals disposal for the best patient outcome (Tozzoli et al., 2017). Filling the Gap Detection of AD requires multi-faceted judgment to successfully correlate physical findings with test results (Nashi & Shmerling, 2021). Testing suffers from the dual issues of both overuse and over-reliance (Nashi & Shmerling, 2021). AD diagnosis is challenging due to the disease complexity and the diagnostic tools used to examine them (Agmon-Levin et al., 2014). Attempts to create classification criteria for diagnosis have occurred, but these tools are not without flaws (Agmon-Levin et al., 2014). Many patients in daily practice do not align with established criteria for ADs. Having a clear guideline regarding the utility and capability of laboratory tests regarding AD could complement physical examination and clinical symptoms and potentially improve the decision-making process, cost burden, and patient outcome. No other studies known have evaluated the predictive value of the amount of essential general and specific diagnostic tools available that relate to confirming ADs at once, which has the potential to alleviate these complexities. Method Study Type and Design PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 22 A non-experimental study using a retrospective design was completed to explore whether the presence of positive anti-ENA and anti-dsDNA antibodies in individuals with an AD can be predicted by their demographics and selected general laboratory test results. The study took place from November 1, 2022 to December 6, 2023, upon institutional review board approval. Participants The population from which the samples were drawn are individuals who had biological specimens obtained and tested between 2018 and 2021 by a diagnostic laboratory company located in the United States. Potential participants were identified from a database that consists of patients who received laboratory testing from a diagnostic laboratory. To be included in the study, the individual was screened for and has the following data points recorded: age, gender, CRP, CBC, UA, CMP, ANA to anti-ENA and anti-dsDNA antibody results. Participants were excluded if they had a known co-morbid underlying condition other than an AD. Data All data were extracted from the Laboratory Information System (LIS). Independent Variables Age (years) Sex (male, female) Comprehensive Metabolic Panel (CMP) o AG Ratio o Albumin o Alkaline Phosphatase o ALT o AST PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES o Bilirubin Serum o Blood Urea Nitrogen (BUN) o BUN Creatinine Ratio o Calcium o Chloride o Globulin o Potassium o Protein o Sodium C-Reactive Protein (CRP) Complete Blood Count (CBC) o Eosinophil Absolute o Hematocrit o Lymphocytes Absolute o Monocytes Absolute o Neutrophils Absolute o Basophils Absolute o Platelets o RBC (Red Blood Cells) o RDW (Red Blood Cell Width) o WBC (White Blood Cells) o Eosinophil-to-Lymphocyte Ratio (ELR) 23 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES o Monocyte-to-Lymphocyte Ratio (MLR) o Neutrophil-to-Lymphocyte Ratio (NLR) o Platelet-to-Lymphocyte Ratio (PLR) Urinalysis (UA) o Creatinine o Occult Blood o pH o Protein Urine o Specific Gravity o WBC Esterase o Bilirubin Urine Erythrosedimentation Rate (ESR) Anti-nuclear Antibody (ANA Screen) Complement C3 Complement C4 Dependent Variables Anti-Extractable Nuclear Antigen (Anti-ENA) Screen Anti-Double Stranded DNA (Anti-dsDNA) Screen Operational Definitions Anti-ENA and anti-dsDNA by multiplex immunoassay determined the levels of autoantibodies in serum. Indirect Immunofluorescence (IFF) assay determined serum present ANAs. CRP values were collected through Latex immunoturbidimetry methodology. CBC 24 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 25 results were recorded through an automated cell counter with mixed technologies. CMP results were collected through mixed technologies. Urinalysis results were collected through a reagent strip. Automated and manual Modified Westergren methods were employed for ESR. C3 and C4 were analyzed through Turbidimetry. Procedures Informed Consent A waiver of informed consent was obtained. Only retrospective data were collected and there was no direct participant interaction. The data needed were collected from biological specimen data repositories. Informed consent can be waivered for medical records, secondary data, or specimens analyzed for information retrospectively (Niihawan et al., 2013). Illnesses are not constituted as terminal and the study has the potential to be considered for exempt review since the research involves the collection and study of existing data from pathological and diagnostic specimens, and only numeric identifiers are linked to subjects. Data Management All data extracted files from the LIS were stored on a secured external drive. Only the primary researcher (K. B.) has access to the data by accessing a computer system protected by a personal username and password. This limits the transfer of data from its original source. All extracted output files will be destroyed five years after the final publication of the results. Statistical Analysis Multiple logistic regression analyses were used to determine if laboratory test results and demographic information could predict the dependent variables, anti-ENA screen and antidsDNA screen. Any correlations between independent and dependent variables less than .001 will be selected as possible predictors and entered into the regression model. Assumptions for PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 26 multiple logistic regression were tested and interpreted as outlined by Field (2018), including a sample representative of the population, dependent variables measured on a nominal dichotomous scale with infinite probability values between 0 and 1, independent predictor variables of nominal, ordinal, interval, or ratio scale, no multicollinearity, sufficient sample size, linearity of the continuous predictor variable, and log odds of the dependent variable. The Nagelkerke R2 was used to determine how much the dependent variable was predicted by the independent variable, with an increase demonstrating goodness of fit. For the inclusion of variables, we ensured that the model improved upon each addition. Data were analyzed using IBM SPSS Statistics for Windows, Version 24.0 (IBM Corp., Armonk, NY). The assumption that the models reasonably fit the linearity assumption was based on Chi-Square significance. A significance level of less than .001 was considered statistically significant. Results Anti-ENA Multiple logistic regression analysis using stepwise variable entry indicated that antinuclear antibodies, complement C3, globulin, monocyte-to-lymphocyte ratio, and blood urea nitrogen were statistically associated with the probability of a positive anti-ENA result (Table 1). The final model achieved an acceptable fit, 2(1) = 237.62, p < .001. From the sample of 1495 patients, 1321 patients (88%) were observed to have a positive anti-ENA. The regression analysis correctly predicted 1302 of the 1321 participants with positive anti-ENA for a sensitivity of 98.60%. Also, in this sample, 174 patients did not have a positive anti-ENA. The logistic regression analysis correctly classified 28 of the 174 participants as not having a positive anti-ENA with a specificity measure of 16.10%. In the overall classification predictive model, PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 27 89% of the participants were correctly classified as anti-ENA positive (true positive) or antiENA negative (true negative). The logistic regression model showed statistically significant probability prediction and effectively classified patients with anti-ENA positive results and patients without anti-ENA positive results. The final model had a Nagelkerke R2 of .29 (coefficient of determination) in this logistic regression analysis with an overall predictive improvement with each variable entry step. The model converged in 5 iterations (-2 Log 1075.401). The final model demonstrated that a positive anti-ENA result was best predicted by antinuclear antibodies, basophils, complement C3, globulin, monocyte-to-lymphocyte ratio, red blood cells, urine pH, and blood urea nitrogen. Holding all other predictor variables constant, it was found that the odds of a positive anti-ENA occurring decreased by 75% for the absence of antinuclear antibodies. The odds of a positive anti-ENA occurring decreased by 96% for a oneunit decrease in basophils, decreased by 1% for a one-unit decrease in complement C3, increased by 143% for a one-unit increase in globulin, increased by 525% for a one-unit increase in monocyte-to-lymphocyte ratio, decreased by 36% for a one-unit decrease in red blood cell count, decreased by 32% for a one-unit decrease in urine pH, and decreased by 3% for a one-unit decrease in blood urea nitrogen (Table 1). Anti-dsDNA Multiple logistic regression analysis X2(1) =388.04 (p < .001) using stepwise variable entry resulted in a final model that indicated that complement C3, complement C4, creatinine, hematocrit, urine pH, urine protein, NLR, and basophils were statistically associated with the probability of a positive anti-dsDNA result (Table 2). From the sample of 1261 patients, 771 patients (61%) were observed to have a positive anti-dsDNA. The regression analysis correctly PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 28 predicted 599 of the 771 participants with positive anti-dsDNA for a sensitivity of 77.7%. In this sample, 490 patients were observed not to have a positive anti-dsDNA. The final logistic regression model correctly classified 316 of the 490 participants as not having a positive antidsDNA with a specificity measure of 64.5%. In the overall classification predictive model, 72.5% of the participants were correctly classified as anti-dsDNA positive (true positives) or anti-dsDNA negative (true negative). The logistic regression model showed statistically significant probability prediction and effectively classified patients with anti-dsDNA positive results and patients without anti-dsDNA positive results. The final model coefficient of determination, Nagelkerke R2 was .36 for this logistic regression analysis. Each step of the model showed overall improvement in the coefficient of determination and chi-square model fitting indices. The model converged in 3 iterations (-2 Log 1684.971). It was found that holding all other predictor variables constant, the odds of a positive anti-dsDNA occurring decreased by 2% for a one-unit decrease in complement C3, decreased by 3% for a one-unit decrease in complement C4, decreased by 18% for a one-unit decrease in creatinine, decreased by 4% for a one-unit decrease in hematocrit, decreased by 23% for a oneunit decrease in urine pH, decreased by 62% for the absence in urine protein, increased by 13% for a one-unit increase in NLR, and decreased by 98% for a one-unit decrease in basophils. Discussion The diagnosis of AD is notoriously difficult, requiring medical specialist examination and complex, costly laboratory tests (Maher & Perugino, 2019; Rosenblum et al., 2015). With the significant contribution to healthcare costs and detriment to an individuals quality of life and longevity, healthcare providers cannot afford to make mistakes in their diagnostic approach PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 29 (Rosenblum et al., 2015). There is value and utility in laboratory analysis for the proper diagnosis of suspected AD. For this reason, our study attempted to identify the association between laboratory assays related to ADs and their predictive value for better confidence in diagnosis and ultimately, better patient outcomes. Through multiple logistic regression analysis, the relationship between laboratory analytes and demographic characteristics on the presence of anti-dsDNA antibodies and anti-ENA antibodies was studied. Anti-ENA According to a multiple logistic regression model, ANA, complement C3, globulin, MLR, and BUN were significantly correlated to a positive anti-ENA result. Although anti-ENAs have strong clinical associations with several ADs, there is considerable overlap for the diseases they are associated with. Specifically related to SLE, anti-Sm is highly specific, and anti-RNP is also associated with the disease (Oh et al., 2020). These anti-ENAs were captured within this analysis creating the potential for significant analytes to be associated with SLE. In particular, BUN is used to assess renal function through the quantification of urea in the blood (Krishnamurthy et al., 2023). SLE commonly impacts renal function, therefore, the findings related to BUN may be attributable to SLE. Another consideration for the overlap with SLE relates to ANA, as patients with the disease have demonstrated variable detection of ANA in their serum (Van H.L. Van B.S., 2019). However, other studies have shown ANA to be a hallmark of SLE among commercially available kits (Pregnoato et al., 2019). Our study demonstrated significance between ANAs and anti-ENA but was not found significant toward anti-dsDNA and ANAs, demonstrating the same variability acknowledged by previous researchers. PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 30 The first marker of significance within our model is ANA which is observed in many patients with AD and one of the most common biomarkers of autoimmunity (Dinse et al., 2020). As autoantibodies are a distinctive hallmark used to classify autoimmune diseases and ANA is capable of their detection, our research aligns with their significance (Andrade et al., 2022). Our study demonstrated the absence of an ANA significantly decreased the likelihood of having a positive anti-ENA. Previous studies have shown ANA titer alone is useful in predicting antiENA antibodies (Kang et al., 2004). Within our results, a direct ANA, signifying a qualitative positive versus negative result, was significant in the determination of anti-ENA presence. Additionally, for the determination of the presence of anti-ENA, titer and pattern are not necessarily needed, but a qualitative test result will suffice. This is of importance, as titration of an ANA and producing a semi-quantitative result consumes laboratory and healthcare resources, the time of laboratory professionals, and can create delays in the diagnostic process, which impact the outcomes of AD patients. Complement, which is activated in nearly all antibody-mediated autoimmune diseases, is also present within our patient population (Thurman & Yapa, 2019). Although it is an important component of the innate immune system, it also has a pathogenic role in many autoimmune systems, which can lead to tissue damage (Holers & Banda, 2018; Galindo-Izquierdo et al., 2021). Specifically related to the positive anti-ENA disorder,pSS, C3 hypocomplementemia has been established as a clinical feature and associated with extraglandular manifestations suggesting clinical value within pSS patients (Jordn-Gonzlez et al., 2020). Antiphospholipid Syndrome, an anti-ENA positive disease, is also associated with C3 deficiency (GalindoIzquierdo et al., 2021). Our study demonstrated that with decreases in C3, the likelihood of a positive anti-ENA decreased. No other study examining multiple markers over individual PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 31 markers of suspected AD patients has demonstrated the significance of C3 in diagnosis. Importantly, C3 specifically was associated with anti-ENA rather than both C3 and C4. With the complexity of AD diagnostic tools, specifying this complement toward anti-ENA helps to simplify and define its importance in AD presence. High globulin levels can reflect increased globulin production, potentially resulting from increased humoral immune activity and/or increased inflammation, which are common in many ADs aligning with our findings (Hashash et al., 2022). For example, increased globulin levels can be seen in active IBD, RA patients, and patients with SS (Wang et al., 2022; Chen et al., 2021; Maliska et al., 2020). We demonstrated an increased probability of approximately 143% for having a positive anti-ENA from increased globulin aligning with the importance of globulin levels and ADs. Globulin is a component of the CMP commonly ordered by providers. With the significant increase in the odds of anti-ENA in patient serum with the presence of globulin, it is important to not overlook this analyte among others testing within this panel. Other than globulin, one of our most significant findings relates to the laboratory marker and ratio calculation, MLR. MLR has shown an association with ADs with some usefulness in assessing disease severity (Seringec Akkececi et al., 2019) Specifically, MLR has shown significant increases regarding patients with SARDs (Yang et al., 2017). Higher monocyte counts or increased activity of various phenotypes can be seen in SLE, Systemic Sclerosis (SSc), RA, Multiple Sclerosis (MS), Type 1 Diabetes, Primary Biliary Cholangitis, SS, Celiac Disease, and Inflammatory Bowel Disease (IBD) (Ma et al., 2019). Lymphopenia has been observed in RA, insulin-dependent diabetes mellitus, Crohn's disease, SLE, primary vasculitis, and pSS (Schulze-Koops, 2004). Within SLE, RA, and pSS, anomalies of lymphocyte subset distributions are common (Carvajal et al., 2017). Idiopathic CD4 lymphopenia patients demonstrate a high PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 32 prevalence of autoantibodies (Perez-Diez et al., 2020). These findings contribute to the variance between increases in monocytes and decreases in lymphocytes which results in increased MLR. In our study, increases in MLR improve the odds of a positive anti-ENA by approximately 525%. With such a large increase in the odds of a positive anti-ENA, MLR should be considered for use in patient assessment. This is particularly essential as a CBC is a commonly ordered laboratory test providing both monocyte and lymphocyte counts which are needed to calculate the ratio. It is important to note that generally a CBC provides the absolute cell counts for these types of white blood cells but does not provide a calculated ratio. With a high percentage increase in the possibility of a positive anti-ENA in relation to increases in MLR, it may be useful to begin to include this ratio regularly or as a reflex calculation for healthcare providers. rather than reliance on high or low flags tagged to monocytes or lymphocytes alone. Although MLR has shown an association with ADs, multi-marker studies are limited in demonstrating this. Specifically, Seringec Akkececi et al. (2019) found MLR as markers of remission for active disease, but this was only related to Takayasu arteritis. We have indicated the potential for multiple ADs under anti-ENA presence to show an association with MLR. The final marker within our model, BUN, relates to a decrease in renal function which can lead to an increase in urea nitrogen in the blood (Krishnamurthy et al., 2023). Krishnamurthy et al. (2023) demonstrated higher mean BUN values in both male and female SLE subjects with a positive anti-dsDNA compared to controls. Additionally, patients were tested for SSA(Ro), SSB(La), RNP/Sm, Jo-1, Sm, Scl-70, Chromatin, Centromere, Histone, RNA polymerase III within an anti ENA panel. Positive results for SSA(Ro), SSB(La), RNP/Sm, and Jo-1 demonstrating a Connective Tissue Disorder had higher mean values of BUN than the control group (Krishnamurthy et al., 2023). RA patients demonstrated elevated BUN levels compared to PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 33 controls (Krishnamurthy et al., 2023). With decreased BUN, we demonstrated a 3.0% decreased probability of having a positive anti-ENA. Decreases in BUN can be found in a common CMP panel, lending clarity toward the absence of anti-ENA in patients. Anti-dsDNA According to a multiple logistic regression model, complement C3, complement C4, urine pH, urine protein, and NLR were significantly correlated to a positive anti-dsDNA result. Of important note is the use of anti-dsDNA as a marker for SLE, as approximately 70-98% of patients test positive (Conti et al., 2015). Therefore, as expected, many of the significant factors show an association with SLE. Although C3 alone was found to be significant in anti-ENA positive patients, both C3 and C4 were significant in association to anti-dsDNA. This is important as complement related to SLE pathogenesis is well accepted (Morell et al., 2021). Both serum C3 and C4 levels are generally lower in SLE (Morell et al., 2021). Within our study, we demonstrated that decreases in Complement C3 and Complement C4 lowered the probability of having a positive antidsDNA value aligning with the concept of complement activation from ADs. Previous multiple marker studies to determine a suspected AD have shown that C3 and C4 assist in the detection of anti-dsDNA, and potentially even more importantly, play an essential role in the treatment and diagnosis of SLE (Qu et al., 2018). Although there is a clear risk of over-reliance on singular AD testing results, regarding SLE, our study shows that interpretations and decisions from complement measures can potentially be relied upon. Regarding the next laboratory marker, depending on the mechanism of the disease and the specific organ systems that are impacted, urine pH can fluctuate in AD patients. An acidic pH is a general marker for a potential AD with renal involvement (Moore & Dalrymple, 2016). ADs PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 34 including SLE are associated with Distal Renal Tubular Acidosis which would cause alkaline urine (Silveira et al., 2022; Ungureanu & Ismail, 2022). These observations lead to a discrepant nature regarding urine pH results for ADs. Due to this tendency, pH as an indicator, whether acidic or alkaline, should be considered with caution specifically related to SLE. Within our study, we demonstrated that an acidic urine pH decreased the probability of a positive antidsDNA favoring the potential for alkaline urine pH to be associated with ADs. Therefore, it may be more appropriate to consider an alkaline urine result specifying distal tubular involvement within the kidney over an acidic result being general toward kidney damage as a whole. Another common marker of ADs and renal involvement that we examined is urine protein (Moore & Dalrymple, 2016). In the absence of urine protein, there was a significant decrease in the possibility of a positive anti-dsDNA. SLE patients with lupus or non-lupus related kidney disease may demonstrate low-level proteinuria (Chedid et al., 2020). Current laboratory markers for lupus nephritis include proteinuria (Morell et al., 2021). It is an essential screening test as proteinuria demonstrating kidney involvement in SLE patients is one of the most typical manifestations (Celia et al., 2021). Subsequently, a rapid urine reagent strip test is the quickest way to analyze urine. As protein in urine is abnormal, proteinuria can be an inexpensive combined marker toward AD diagnosis. If an AD diagnosis is established, it is also critically important to frequently perform urinalysis for monitoring renal function (Castro & Gourley, 2010). Urine reagent strips can provide a semi-quantitative means for monitoring not only through point-of-care testing, but potentially through at-home self-administered screening for proteinuria saving time, resources, and limiting healthcare spending. This may allow for more efficient prognosis for patients who suffer from organ involvement within active SLE. This could PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 35 be particularly important for patients with early lupus nephritis by self-monitoring and seeking care when needed. Progressing to potentially our most significant finding in association with the odds of a positive anti-dsDNA occurring was NLR. NLR is widely used as a marker of immune response to infectious and non-infectious stimuli along with a novel use as a marker of inflammation (Zahorec, 2021). Previous research has also shown that increased NLR is useful to monitor disease activity and inflammation caused by SLE (Fu et al., 2015; Seringec Akkececi et al., 2019). Our study highlights the importance of blood cell ratios, specifically NLRs association to a positive anti-dsDNA, further supporting the frequent pattern seen in studies of elevated neutrophil counts and lowered lymphocyte counts compared to healthy individuals (Han et al., 2020). Our study demonstrates that increased NLR is associated with a heightened probability of a positive anti-dsDNA, which aligns with previous research on its elevation in SLE patients. Although previous studies have shown an association between NLR and SLE, our study strengthens this association with the use of multiple markers, which is lacking in past research. NLR can be quickly calculated from the neutrophil and lymphocyte count provided within the CBC, allowing providers to add another useful measurement in SLE diagnosis. As discussed previously regarding MLR and positive anti-ENA patients, it may be useful to begin to include this completed calculation with the CBC results, rather than relying on the absolute cell values and the LIS flagging the result as low or high. Limitations There are some limitations that should be considered while reviewing the data. As a retrospective study performed on de-identified laboratory results, we cannot control for the potential of comorbid conditions or therapies that might have impacted results. Although the PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 36 importance of ANA titer and pattern are known, we only included qualitative results. As discussed, anti-ENAs have considerable overlap in associations with various ADs, and our study considered a general positive anti-ENA result for the dependent variable instead of specifically analyzing singular analytes within the anti-ENA panel. Implications and Future Direction Our research implies the potential use of specific diagnostic analytes to predict ADs through not only their presence or absence but also in the value of their results. Along with physical examination, healthcare providers should consider the use of general laboratory assay results that demonstrated significance in this study before considering implementation of more expensive confirmatory testing. Specific attention should be paid to the presence of high globulin and MLR, as providers should begin to suspect the possibility of a positive anti-ENA. Regarding the possibility of an anti-dsDNA, providers may utilize an elevated NLR as an indication. Future research should consider assessing similar laboratory tests and their association with specific extractable nuclear antigens. This application could help identify if the analytes included in our study are more specific toward a particular AD. Several results on analytes that exhibited associations with ADs were not reproducible in this study. Further research is warranted to reproduce results showing which analytes included in the independent variables are significant in association with anti-ENA and anti-dsDNA. Conclusion In conclusion, to the best of our knowledge, this is the first retrospective study to assess the amount of both essential general and specific diagnostic tests available that relate to confirming ADs. The predictive value and clinical utility of laboratory analysis is apparent to assist with AD diagnosis and potentially better patient outcome. PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 37 References Abeles, A. M., Gomez-Ramirez, M., Abeles, M., & Honiden, S. (2016). Antinuclear antibody testing: Discordance between commercial laboratories. Clinical Rheumatology, 35(7), 17131718. https://doi.org/10.1007/s10067-016-3241-x Adomako, E. A., Bilal, S., Liu, Y. L., Malik, A., Van Buren, P. 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Frontiers in Immunology, 11, 562138. https://doi.org/10.3389/fimmu.2020.562138 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 54 Table 1 Multiple Logistic Regression Using Stepwise Variable Entry to Predict Anti-ENA Step 1 Antinuclear Antibodies Step 2 Antinuclear Antibodies Complement C3 Step 3 Antinuclear Antibodies Complement C3 Globulin Step 4 Antinuclear Antibodies Complement C3 Globulin Monocyte-to-Lymphocyte Ratio Step 5 Antinuclear Antibodies Complement C3 Globulin Monocyte-to-Lymphocyte Ratio Urine pH Step 6 Antinuclear Antibodies Complement C3 Globulin Monocyte-to-Lymphocyte Ratio Urine pH Blood Urea Nitrogen Step 7 Antinuclear Antibodies Complement C3 Globulin Monocyte-to-Lymphocyte Ratio Urine pH Blood Urea Nitrogen Final Model Beta (SE) OR 95% CI for OR -1.74 (0.18) 0.18 0.12 to .25 -1.59 (0.18) -0.02 (0.002) 0.18 0.99 0.14 to 0.29 0.98 to 0.99 -1.48 (0.19) -0.15 (0.002) .97 (0.17) 0.23 0.99 2.65 0.16 to 0.33 0.98 to 0.99 1.90 to 3.69 -1.45 (0.19) -0.14 (0.003) .92 (0.17) 0.24 0.99 2.50 0.16 to 0.34 0.98 to 0.99 1.78 to 3.50 1.81 (0.47) 6.13 2.45 to 15.36 -1.45 (0.19) -0.15 (0.003) .89 (0.17) 0.24 0.99 2.43 0.16 to 0.34 0.98 to 0.99 1.74 to 3.41 1.82 (0.46) 6.19 2.50 to 15.34 -0.36 (0.10) 0.70 0.58 to 0.84 -1.48 (0.19) -0.16 (0.003) .93 (0.17) 0.23 0.99 2.54 0.16 to 0.33 0.98 to 0.99 1.80 to 3.57 1.98 (0.47) 7.22 2.87 to 15.34 -0.38 (0.10) -0.03 (0.01) 0.68 0.98 0.56 to 0.82 0.96 to 0.99 -1.44 (0.19) -0.14 (0.003) .90 (0.18) 0.24 0.99 2.47 0.16 to 0.34 0.98 to 0.99 1.74 to 3.51 1.90 (0.49) 6.68 2.56 to 17.46 -0.40 (0.10) -0.03 (0.01) 0.68 0.97 0.45 to 0.83 0.95 to 0.98 Nagle kerke R2 .11 c2 87.62 .17 131.90 .21 170.81 .24 197.77 .26 211.66 .27 222.26 .28 232.68 .29 237.62 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES Antinuclear Antibodies Basophils Complement C3 Globulin Monocyte-to-Lymphocyte Ratio Red Blood Cells Urine pH Blood Urea Nitrogen -1.41 (0.19) -3.14 (1.43) -0.14 (0.003) .89 (0.18) 0.25 0.04 0.99 2.43 0.17 to 0.36 0.003 to 0.71 0.98 to 0.99 1.71 to 3.46 1.83 (0.49) 6.25 2.40 to 16.27 -0.45 (0.16) -0.39 (0.10) -0.03 (.01) 0.64 0.68 0.97 0.47 to 0.87 0.56 to 0.82 0.95 to 0.99 55 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 56 Table 2 Multiple Logistic Regression using Stepwise Variable Entry to Predict Anti-dsDNA Beta (SE) OR 95% CI for OR Complement C3 -0.03 (0.002) 0.97 0.97 to 0.98 Complement C3 Urine Protein -0.02 (0.002) -1.11 (0.15) 0.99 0.33 0.97 to 0.98 0.25 to 0.44 Complement C3 Urine Protein Neutrophil-toLymphocyte Ratio Step 4 Complement C3 Complement C4 Urine Protein Neutrophil-toLymphocyte Ratio Step 5 Complement C3 Complement C4 Urine Protein Neutrophil-toLymphocyte Ratio Basophils Step 6 Complement C3 Complement C4 Urine pH Urine Protein Neutrophil-toLymphocyte Ratio Basophils Step 7 Complement C3 Complement C4 Hematocrit Urine pH Urine Protein Neutrophil-toLymphocyte Ratio Basophils -0.02 (0.002) -1.01 (0.03) 0.98 0.36 0.98 to 0.98 0.27 to 0.49 0.13 (0.03) 1.14 1.08 to 1.20 -0.02 (0.002) -0.03 (0.01) -0.99 (0.16) 0.98 0.97 0.37 0.98 to 0.98 0.95 to 0.99 0.27 to 0.50 0.12 (0.03) 1.13 1.08 to 1.19 -0.02 (0.002) -0.03 (0.01) -0.99 (0.16) 0.99 0.97 0.37 0.98 to 0.99 0.95 to 0.99 0.27 to 0.50 0.13 (0.03) 1.13 1.08 to 1.19 -4.75 (1.34) 0.01 0.001 to 0.12 -0.02 (0.002) -0.03 (0.01) -0.25 (0.08) -0.95 (0.16) 0.98 0.97 0.78 0.39 0.98 to 0.99 0.95 to 0.98 0.67 to 0.90 0.28 to 0.52 0.12 (0.03) 1.13 1.08 to 1.19 -4.72 (1.34) 0.01 0.001 to 0.13 -0.02 (0.002) -0.03 (0.01) -0.04 (0.01) -0.26 (0.08) -0.88 (0.16) 0.99 0.97 0.97 0.77 0.41 0.98 to 0.99 0.95 to 0.98 0.94 to 0.99 0.67 to 0.90 0.30 to 0.57 0.12 (0.03) 1.13 1.07 to 1.18 -4.23 (1.37) 0.02 0.001 to 0.21 Step 1 Step 2 Step 3 Naglekerke c2 R2 .24 245.15 .29 302.33 .32 338.04 .33 352.83 .34 365.26 .35 376.77 .36 383.75 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES Final Model Complement C3 Complement C4 Creatinine Hematocrit Urine pH Urine Protein Neutrophil-toLymphocyte Ratio Basophils -0.02 (0.003) -0.03 (.01) -0.20 (0.09) -0.04 (0.01) -0.27 (0.08) -0.96 (0.17) 0.98 0.97 0.82 0.96 0.77 0.38 0.98 to 0.99 0.97 to 0.95 0.69 to .098 0.94 to 0.99 0.66 to 0.89 0.28 to 0.53 0.12 (0.03) 1.13 1.07 to 1.18 -3.93 (1.37) 0.02 0.001 to 0.29 57 .36 388.04 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 58 Figure 1 Anti-ENA Proposed Equation Logit(p)=6.07+(-1.41 x ANA) + (-.01 x Complement C3) + (.89 x Globulin) + (1.83 x MLR) + (.03 x BUN) Figure 2 Anti-dsDNA Proposed Equation Logit(p)=7.01 + (-.02 x Complement C3) +( -.03 x Complement C4) +(-.27 x pH) + (-.96 X Urine Protein) + (.12 x NLR) PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES 59 Appendix Definition of Terms and Acronyms Autoimmune disease (AD): Elements of an organisms own immune system attacking tissue or cells by the formation of autoantibodies. Comprehensive Metabolic Panel (CMP) AG Ratio Albumin Alkaline Phosphatase ALT AST Bilirubin Serum Blood Urea Nitrogen (BUN) BUN Creatinine Ratio Calcium Chloride Globulin Potassium Protein Sodium C-Reactive Protein (CRP) Complete Blood Count (CBC) Eosinophil Absolute Hematocrit PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES Lymphocytes Absolute Monocytes Absolute Neutrophils Absolute Basophils Absolute Platelets RBC (Red Blood Cells) RDW (Red Blood Cell Width) WBC (White Blood Cells) Eosinophil-to-Lymphocyte Ratio (ELR) Monocyte-to-Lymphocyte Ratio (MLR) Neutrophil-to-Lymphocyte Ratio (NLR) Platelet-to-Lymphocyte Ratio (PLR) Urinalysis (UA) Creatinine Occult Blood pH Protein Urine Specific Gravity WBC Esterase Bilirubin Urine Erythrosedimentation Rate (ESR) Anti-nuclear Antibody (ANA Screen) Complement C3 60 PREDICTIVE VALUE OF ASSAYS TOWARD AUTOIMMUNE DISEASES Complement C4 Anti-Extractable Nuclear Antigen (Anti-ENA) Screen Anti-Double Stranded DNA 61  ...

... Cardiometabolic Predictors of Cardiovascular Disease Risk in Veterans with Spinal Cord Injuries Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: John Powell Y. Robles, MS, RKT Copyright December 4, 2023 By: John Powell Y. Robles, MS, RKT All rights reserved Approved by: Elizabeth S. Moore, PhD Committee Chair ______________________________ Heidi H. Ewen, PhD, FGSA, FAGHE Committee Member ______________________________ Stephen F. Figoni, PhD, RKT, FACSM, FAKTA Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ Cardiometabolic Predictors of Cardiovascular Disease Risk in Veterans with Spinal Cord Injuries John Powell Y. Robles Department of Interprofessional Health and Aging Studies, University of Indianapolis Author Note Data collection was approved by the Department of Veterans Affairs VA Long Beach Healthcare System, Research Service (IRB# 1759687), VA Long Beach Healthcare System, Research Service (09/151), 5901 E. 7th St. Long Beach, CA. Send correspondence to author, John Powell Y. Robles, by email: Roblesuindy@uindy.edu, or John.robles@va.gov. There is no known conflict of interest to disclose. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 2 Abstract A retrospective chart review of 600 veterans with chronic (> 1 year) spinal cord injury (SCI) from outpatient therapy and annual evaluation rosters from 2022 were screened for Cardiometabolic Syndrome (CMS). 10-year Framingham Risk Scores (FRS) were estimated for n = 184 (106 tetraplegia and 78 paraplegia) patients with CMS. Age, SCI level, severity, ASIA grade, completeness, duration of SCI, SCI-adjusted obesity, hypertension, dyslipidemia, dysglycemia, and hypertriglyceridemia were included as predictors of FRS in a multiple linear regression. CMS prevalence, FRS scores (20%), Type II diabetes mellitus (T2DM), and smoking were found to be more prevalent in tetraplegia. Data exploration revealed a positively skewed distribution of FRS scores, bimodal distributions between tetraplegia (C1-C8) and paraplegia (T1-S5), and upper thoracic (T1-T6) and lower thoracic (T7-T12). A square root transformation was performed to achieve normal distribution for the dependent variable (FRS Transformed). The full regression models for combined tetraplegia and paraplegia, tetraplegia, paraplegia, upper thoracic, and lower thoracic were all statistically significant (p < .001). In addition, backward elimination and reduction for each SCI model also reached statistical significance (p < .001). Assumptions were met for all the regression equations with minor considerations for a robust independence of observations in combined tetraplegia and paraplegia regression models. Prediction equations became less robust when controlling for SCI groups compared to the combined tetraplegia and paraplegia. Reduced models displayed less robust prediction equations based on examining residuals. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 3 Acknowledgements I would like to express gratitude to the committee chair, Dr. Elizabeth S. Moore, committee statistics expert, Dr. Heidi Ewen, and committee content expert, Dr. Stephen F. Figoni, for their contributions to my dissertation, education, and clinical practice. I am sincerely grateful to my scientific mentor, Dr. Figoni, for guiding my research interests into realistic applications to help our patients. I would also like to express gratitude to Alice J. Hon, MD, the Principal Investigator of my research project, for kindly welcoming my proposal, and mentoring the development of our research into a successful Institutional Review Board approval. I am also grateful to my colleagues at the VA Long Beach Healthcare Center, along with professors and staff at the University of Indianapolis who have brought me a meaningful educational experience. Their contributions helped transcend my professional development in veteran rehabilitation, and I would not be able to reach this point in my life without their guidance and professionalism. I am especially grateful to my mother, Ida, and my father, Nicolas, for unconditionally sharing my lifelong academic experiences. I am thankful to John Maynard and his family, and Skynyrd and family for showing me resilience. I wish to thank Rita Robles, Dr. Joe Robles, and family for nurturing my relationship with Jesus Christ. I am also thankful for my relatives in the Philippines, as they have kept me in their hearts and prayers. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 4 Table of Contents Cardiometabolic Predictors of Cardiovascular Disease Risk in Veterans with Spinal Cord Injuries ........................................................................................................................................... 9 Problem Statement .................................................................................................................. 10 Purpose Statement................................................................................................................... 11 Hypotheses ............................................................................................................................... 11 Objectives ................................................................................................................................. 12 Significance of the Study......................................................................................................... 12 Definition of Terms ................................................................................................................. 12 Literature Review ....................................................................................................................... 14 Understanding SCI.................................................................................................................. 14 Aging and SCI Characteristics .............................................................................................. 14 CVD in SCI .............................................................................................................................. 15 SCI Characteristics and CVD Risk ....................................................................................... 16 Body Composition and Metabolic Markers .......................................................................... 17 Cardiometabolic Syndrome.................................................................................................... 18 Framingham Risk Score (FRS) Prediction Model ............................................................... 19 Gaps in Research ..................................................................................................................... 21 Method ......................................................................................................................................... 24 Study Design ............................................................................................................................ 24 Sample ...................................................................................................................................... 24 Data........................................................................................................................................... 25 Patient Characteristics from Chart Review ................................................................................ 25 Predictors of the Composite Regression Model (Independent Variables) ................................. 26 Outcome (Dependent Variable) ................................................................................................... 26 Operationalization of Variables .................................................................................................. 26 Instruments .............................................................................................................................. 28 Procedures................................................................................................................................ 28 Screening ...................................................................................................................................... 28 Data Collection............................................................................................................................. 29 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 5 Data Management ........................................................................................................................ 29 Statistical Analysis................................................................................................................... 30 Multiple Linear Regression and Prediction Model .................................................................... 30 Results .......................................................................................................................................... 32 Data Exploration.......................................................................................................................... 35 Regression Models for Combined Tetraplegia and Paraplegia (C1-S5) ................................... 35 Regression Models for Tetraplegia (C1-C8) Group ................................................................... 36 Regression Models for Paraplegia (T1-S5) Group ..................................................................... 37 Regression Models for Upper Thoracic (T1-T6) Group ............................................................. 38 Regression Models for Lower Thoracic (T7-T12) Group .......................................................... 39 Prediction Equations Summary .................................................................................................. 40 Discussion .................................................................................................................................... 40 Regression Model Assumptions ............................................................................................. 44 Clinical Importance................................................................................................................. 45 Study Limitations .................................................................................................................... 47 Conclusion ................................................................................................................................... 48 References .................................................................................................................................... 50 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 6 List of Tables Table 1.......................................................................................................................................... 60 Descriptive Statistics for Total Sample (N = 184)........................................................................ 60 Table 2 ...................................................................................................................................... 61 Descriptive Statistics for Tetraplegia group (n = 106)................................................................. 61 Table 3 ...................................................................................................................................... 62 Descriptive Statistics for Paraplegia Group (n = 78) .................................................................. 62 Table 4 ...................................................................................................................................... 63 Frequency Counts and Percentages of Ethnicities and Gender Reported for Each Group ......... 63 Table 5 ...................................................................................................................................... 64 Frequencies and Percentages for Different Spinal Cord Injury Levels of Tetraplegia and Paraplegia Groups (N = 184) ...................................................................................................... 64 Table 6 ...................................................................................................................................... 65 Frequencies and Percentages of Different Spinal Cord Injury Levels for the Tetraplegia Group (N = 106) ...................................................................................................................................... 65 Table 7 ...................................................................................................................................... 66 Frequencies and Percentages of Different Spinal Cord Injury Levels for the Paraplegia Group (N = 78) ........................................................................................................................................ 66 Table 8 ...................................................................................................................................... 67 Prevalence of Cardiometabolic Syndrome (CMS) and CMS Risk Factors (N = 184) ................. 67 Table 9 ...................................................................................................................................... 68 Framingham Risk Score Ranges, Diabetes, Smoking Frequencies, and Percentages ................. 68 Table 10 .................................................................................................................................... 69 Cross tabulation of CMS Risk Factors Between Tetraplegia and Paraplegia ............................. 69 Table 11 .................................................................................................................................... 70 Cross tabulation of Diabetes, Smoking, High-risk FRS (20%), and CMS of Tetraplegia and Paraplegia. ................................................................................................................................... 70 Table 12 .................................................................................................................................... 71 Cross tabulation of Diabetes, High-risk FRS (20%), and CMS when Adjusted for SCI-Obesity Cut-Off .......................................................................................................................................... 71 Table 13 .................................................................................................................................... 72 Frequency Counts and Percentages of Status at the Time of Data Collection and Causes of SCI ...................................................................................................................................................... 72 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 7 Table 14 .................................................................................................................................... 73 Frequency Counts of Causes of SCI Between Groups ................................................................. 73 Table 15 .................................................................................................................................... 74 M1Full: Composite Regression Model Output of Combined Groups (C1-S5) ............................... 74 Table 16 .................................................................................................................................... 75 M2Full: Composite Regression Model Output of Tetraplegia (C1-C8) Group.............................. 75 Table 17 .................................................................................................................................... 76 M3Full: Composite Regression Model Output of Paraplegia (T1-S5) Group ............................... 76 Table 18 .................................................................................................................................... 77 M4Full: Composite Regression Model Output of High Thoracic (T1-T6) Group .......................... 77 Table 19 .................................................................................................................................... 78 M5Full: Composite Regression Model Output of Low Thoracic (T7-T12) Group ......................... 78 Table 20 .................................................................................................................................... 79 Model Summary for each Full and Reduced Models .................................................................... 79 Table 21 .................................................................................................................................... 80 Table Summary of Partial F-Tests of Each Model a ..................................................................... 80 Table 22 .................................................................................................................................... 81 Table Summary of Prediction Equations a .................................................................................... 81 Table 23 .................................................................................................................................... 82 Summary of Shapiro-Wilk Statistics for Normal Distribution Assumption for Each Full and Reduced Models ............................................................................................................................ 82 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 8 List of Figures Figure 1 ........................................................................................................................................ 83 Consult Retrieval, Screening, Data Collection Convenience Sampling Process ......................... 83 Figure 2..................................................................................................................................... 84 Illustration of Data Processing..................................................................................................... 84 Figure 3..................................................................................................................................... 85 Histogram and Scatterplot of FRS Before Transformation .......................................................... 85 Figure 4..................................................................................................................................... 86 Histogram and Scatterplot of FRS After Square Root Transformation ........................................ 86 Figure 5..................................................................................................................................... 87 Bar Chart Frequency of High-CVD Risk (20%) in Combined Groups (C1-S5) ........................ 87 Figure 6..................................................................................................................................... 88 Bar Chart Frequency of High-CVD Risk (20%) in Tetraplegia Group (C1-C8) ....................... 88 Figure 7..................................................................................................................................... 89 Bar Chart Frequency of High-CVD Risk (20%) in Paraplegia Group (T1-S5) ......................... 89 Figure 8..................................................................................................................................... 90 Bar Chart Frequencies of High-CVD Risk (20%) in Upper and Lower Thoracic Level Injuries (T1-T12) ........................................................................................................................................ 90 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 9 Cardiometabolic Predictors of Cardiovascular Disease Risk in Veterans with Spinal Cord Injuries Spinal cord injury (SCI) is a life- changing condition that affects 18,000 new people in the United States each year (National Spinal Cord Injury Statistical Center, 2023). Mortality rates are significantly higher during the first year after SCI and complications from SCI typically contribute to related secondary health consequences, such as cardiovascular and metabolic diseases (Chamberlain et al., 2019; Jorgensen et al., 2019; Wahl & Hirsch, 2022). Different levels of SCI result in varying motor, sensory, and autonomic nervous system dysfunction that impact musculoskeletal and cardiovascular responses to physical activities (Raguindin et al., 2021). Muscle paralysis from SCI results in body composition changes, such as muscle atrophy and intramuscular fat that collectively predispose the individual to lipid abnormalities, carbohydrate intolerance, and systemic inflammation (La Fountaine et al., 2018; Gater et al., 2021). SCI research on body composition indicated that improperly regulated metabolism and energy expenditure result in obesity and visceral adipose tissue deposition. (Gill et al., 2020). Overall, the combined physiological and physical activity changes after SCI contribute to the development of chronic health conditions and susceptibility to major health risks that include cardiovascular disease (CVD) (Chamberlain et al., 2019; Jorgensen et al., 2019; Lu et al., 2018). Recent research has indicated the occurrence of CVD is underestimated and underdetected in SCI populations primarily due to the independent risk factors being based on non-SCI populations (i.e., nondisabled older white males; Barton et al., 2021; Dorton et al., 2020; Gill et al., 2021). The Framingham Risk Score (FRS) estimates an individuals chance of developing CVD. It is one of the well-known predictive algorithms derived from large non-SCI population cohorts that have been validated in multiethnic studies (DAgostino et al., 2008). More popularly CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 10 in the last two decades, SCI-studies have examined the FRS algorithm ability to predict CVD (Barton et al., 2021; Jrgensen et al., 2019). The use of a traditional and non-comparable predictive algorithm, like the FRS, that estimates risk of CVD in veterans with SCI has led to clinical disadvantage in poor risk stratification and treatment (Barton et al., 2021). Algorithms based on SCI populations have identified clusters of factors termed Cardiometabolic Syndrome (CMS) that contribute to the accelerated development of CVD and type 2 diabetes mellitus (T2DM; James et al., ,2020; Lemieux & Desprs, 2020). Investigators have reported high prevalence of CMS risk factors in individuals with SCI, which have been associated with severe injury characteristics, unhealthy changes in muscle and skeletal mass composition, and physical inactivity habitus (Gater et al., 2019; Gill et al., 2020; Gordon et al., 2021; Solinsky et al., 2022). Identification of CMS is dependent on the presence of three or more of the following CVD risk factors: abdominal (central) obesity, hypertension, insulin resistance, dyslipidemia, and pro-inflammatory markers (Nash & Gater, 2020). Therefore, it is vital to characterize the relationship between CMS and the risk of adverse outcomes in SCI populations (Nash et al., 202). Problem Statement While progress is being made in advancing clinical guidelines for screening of CMS among populations with SCI, the evidence of linking CMS to high risk of CVD occurrences has been sparse due to inconsistencies in cut-off values and variability in clinical accuracy of screening tests, and corresponding large population evidence (Gater et al., 2021; Gill et al., 2020; Nash & Gater, 2020; Nash et al., 2018; Stillman & Williams, 2019). Moreover, with an increased risk of CVD and CMS in older veterans with SCI, studies of SCI-specific risk factors continue to CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 11 be underutilized in clinical screening and risk management (Figoni & Chen, 2015; Gater et al., 2019; Gill et al., 2020; La Fountaine et al., 2018; Nash et al., 2019; Solinsky et al., 2022). CVD risk tends to be underestimated due to the differences in population risk factors, such as body mass index and lipid serum that differ between non-SCI and SCI populations (Dorton et al. 2020; La Fountaine et al., 2018; Lu et al., 2018). In addition, there is a paucity of research on predicting CVD risk using SCI-specific biomarkers despite the prevalence of CVD in chronic SCI population (Gill et al., 2020; Nash & Gater, 2020; Solinsky et al., 2022). Due to the paucity of research on CVD prediction using composite risk factors, a regression model is warranted. Purpose Statement The purpose of this study was to develop a predictive regression equation for CVD risk using SCI information and CMS risk factors while controlling for varying SCI levels. To address the study purpose, the following research question was answered: Can a regression model predict CVD risk in veterans with CMS and varying SCI levels? To answer the research question, the following objectives were addressed. Hypotheses 1. At least one predictor of the SCI model will significantly predict FRS. 2. At least one predictor of the tetraplegia model will significantly predict FRS. 3. At least one predictor of the paraplegia model will significantly predict FRS. 4. The overall significance of the full and reduced models will be statistically significant. 5. The reduced models will not be statistically different from the full models. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 12 Objectives 1. To identify significant predictors of FRS in veterans with varying SCI levels (tetraplegic and paraplegic). 2. To identify significant predictors of FRS in veterans with tetraplegia. 3. To identify significant predictors of FRS in veterans with paraplegia. 4. To determine the significance of the full model in predicting FRS. 5. To determine if the reduced model is not different from the full model in predicting FRS. Significance of the Study This study demonstrated the use of CMS predictors of 10-year CVD risk in older veterans with SCI. The prognostic model using CMS risk factors would contribute to identification of health risks allowing stratification for earlier therapeutic interventions in SCI cohorts. Enhancing therapeutic interventions for secondary health preventions would minimize burden of care, increase survival, and improve quality of life for individuals living with chronic SCI. Definition of Terms Cardiometabolic Syndrome is the co-occurrence of three (or more) risk factors according to the American Heart Association, including abdominal/central/visceral obesity, insulin resistance, dyslipidemia, hypertriglyceridemia, and hypertension (Nash et al., 2019). International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) is used to discriminate between paraplegia and tetraplegia based on sensory and motor deficits from physical examinations. Paraplegia is defined by the neurological classification of an injury to the thoracic, lumbar, or sacral spine (ISNCSCI). CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 13 Tetraplegia is defined by neurological classification of an injury to the cervical spine (ISNCSCI). The Framingham Risk Score represents the risk of a major CVD event in the subsequent 10-years using age, blood pressure, serum lipid values, and smoking (Ko et al., 2020). CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 14 Literature Review Understanding SCI An estimated 302,000 Americans are living with SCI and about 18,000 new cases occurring each year (National Spinal Cord Injury Statistical Center, 2023). The clinical outcomes of SCI depend on the severity and location of the lesion, which may include partial or complete loss of sensory and motor function below the level of injury (United Spinal Association, 2020). Cervical spinal level lesions can cause quadriplegia while thoracic spinal level lesions can cause paraplegia (Alizadeh et al., 2019). Injury etiology of SCI includes traumatic injuries, such as motor vehicle accidents or falls, and atraumatic injuries, such as neoplasm of the spinal cord or stenosis of the spinal canal (McGrath et al., 2018). Functional impairments caused by SCI include loss of voluntary control of skeletal muscles for activities of daily living (ADL), locomotion, or bowel and bladder control (United Spinal Association, 2020). Despite the advancement in acute medical care, several studies reported poorer prognosis of survival for those with high spinal level, completeness, and older age at onset of injuries (Chamberlain et al., 2019; Chhabra et al., 2021). Aging and SCI Characteristics The SCI population has been acknowledged in research as being older and with unique characteristics and socioeconomic impact compared to the able-bodied population (Bloom et al., 2019; Peterson et al., 2021). A subset of the total SCI population in the United States includes veterans who comprise about 5% of the total number of cases per year (Health Services R&D, n. d.). Altogether individuals aging with SCI, of which include both veteran and non-veteran groups, have been well-documented in research showing SCI-associated comorbidities ultimately result in secondary and chronic diseases that impact quality of life (Gater et al., 2019; McGrath CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 15 et al., 2018). Significant declines in functional abilities and life satisfaction were generally associated with older age and severity of injury (Bak et al., 2022; McGrath et al., 2019). Higher level SCI is typically associated with a greater impact on satisfaction with life. Chronic disease outcomes related to decreased energy expenditure and physical inactivity behaviors have been reported as individuals with SCI survive longer (Dorton et al., 2020). Consequently, older individuals with more severe SCI have been found susceptible to higher risk of CVD, diabetes, and respiratory complications (Alizadeh et al., 2021). CVD in SCI CVD is an umbrella term that is commonly understood as a group of endpoint disorders of the heart and blood vessels (DAgostino et al., 2008). Researchers of CVD in SCI have noted several systemic related comorbiditiesnamely, hypertension, coronary heart disease (CHD), myocardial infarction (MI), stable or unstable angina, demonstrated myocardial ischemia by noninvasive testing, coronary death, congestive heart failure, and stroke (Barton et al., 2021; Chamberlain et al., 2019; Dorton et al., 2020). The CVD spectrum also includes cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, and pulmonary embolism (American Heart Association [AHA], n. d.). The cause of immobilization predisposes individuals with SCI to comorbidities such as lipid abnormalities, carbohydrate intolerance, and atherogenic pattern for CHD (Fu et al., 2021; Lu et al., 2018). As multifactorial risk assessment of CVD from the non-SCI population was utilized in the SCI population. Findings of risk-to-mortality models identified CVD as highly prevalent among individuals with chronic SCI (Barton et al., 2021; Gill et al., 2020; Jrgensen et al., 2019; Lu et al., 2018; Wahl & Hirsch, 2022). However, despite many studies revealing CVD risk in individuals with SCI, there is evidence suggesting that the prevalence may have been CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 16 underestimated because of poor detection of major CVD markers (Barton et al., 2021; Gater et al., 2021; Raguindin et al., 2021). Other research of CVD risk in SCI population have also reported untreated risk factors and poor adherence to treatment guidelines (Chopra et al., 2018). While the prevalence of CVD risk factors has been found prevalent in SCI, Chopra et al. (2018) reported that dyslipidemia, hypertension, type II diabetes have been left untreated with appropriate medications due to poor adherence to diagnostic treatment guidelines and screening. SCI Characteristics and CVD Risk SCI characteristics are additional non-modifiable CVD risk factors have been reported in literature (Dorton et al., 2021). Level of injury may be an additional non-modifiable factor that could aggravate CVD risk (Raguindin et al., 2021). Raguindin et al. (2021) reported injury level differences between serum lipids, glucose metabolism, insulin resistance, inflammation, oxidative stress markers, and blood pressure profiles. Higher injury level (cervical spine level) tends to be associated with lower blood pressure (systolic BP, diastolic BP, mean arterial pressure MAP) compared to lower injury level (thoracic spine or lower). Wahl and Hirsch (2021) reported that paraplegics are more likely to suffer from dyslipidemia, obesity, and peripheral artery disease. Neurological level of injury and SCI characteristics have been associated with CVD risk factors and individuals with SCI were found to have higher CVD risk in comparison to non-SCI (Dorton et al., 2021; Gill et al., 2020; Raguindin et al., 2021). Similarly, Barton et al., 2021 conducted a 5-year follow-up study to observe the development of CVD event in middle-aged men, aged 40 14 years, and the underestimation of predicting future 5-year occurrence of CVD events using traditional risk factors. The authors reported that adding SCI-related factors to the regression model, including level of injury, severity of impairment, motor completeness, and CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 17 sports participation did not improve the level of discrimination. However, the determinants of increased CVD risk in SCI may be underestimated because FRS scoring system does not include SCI-specific risk factors. Body Composition and Metabolic Markers Alterations in body composition, such as decreased skeletal muscle mass, result in in low daily energy expenditure, heightening the instances of dyslipidemia, insulin resistance, decreased glucose metabolism, and obesity (Farkas et al., 2021; Gordon et al., 2021). Particularly, decreased skeletal muscle with a relative increase in adiposity, a state of insulin resistance and hyperinsulinemia take place as the consequence of inactivity and body composition changes (Dolbow et al., 2022; Nash et al., 2019; Nash et al., 2020). More findings about obesity and systemic inflammation that contribute to chronic occurrence of CVD have been found to be greater in SCI than the general population (Bloom et al., 2019). The resulting weight gain from loss of muscle function presents many challenges as inactivity continues in individuals with SCI and a high prevalence of obesity (Gater et al., 2021). Decline in fat-free mass and increase in fat mass in individuals with SCI have been associated with lower metabolism than in individuals without SCI (Ma et al., 2022). There is also evidence to suggest that endogenous anabolic hormone levels are depressed in some individuals with SCI (Sullivan et al., 2018). Depression of serum testosterone and growth hormone/IGF-I levels may exacerbate the adverse lipid and body compositional changes, reduce exercise tolerance, and have deleterious effects on quality of life (Abilmona et al., 2019). Further evidence shows that existing abnormalities in oral carbohydrate handling and the distribution of visceral adipose tissue have been reported to have an inverse relationship with serum highdensity lipoprotein (HDL) cholesterol (Gorgey et al., 2021). Moreover, a direct relationship CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 18 between serum triglyceride levels and abdominal circumference have been associated with an earlier occurrence of impaired glucose tolerance, insulin resistance, and type 2 diabetes and risk for CVD in the SCI population (Nash & Gater, 2020). Cardiometabolic Syndrome The earlier terms syndrome X or insulin resistance syndrome, referred to a compilation of risk factors that heighten the development of Type-2 diabetes mellitus (T2DM) and CVD was first described in the late 1980s (Lemieux et al., 2020). Since then, major research organizations such as the World Health Organization (WHO), the National Cholesterol Education Program (NCEP), and the International Diabetes Federation (IDF) have shaped the definition of risk factors by recognizing risk clusters, thresholds, and subsequently delineating guidelines (Weihe & Weihrauch-Bleuher, 2019). Although known by various names, SCI research recognizes the term Cardiometabolic Syndrome (CMS) as an array of risk factors related to CVD development, associated endocrine comorbid disorders, and surrogates of T2DM (Nash et al., 2018). According to the American Heart Association (AHA), evidence-based diagnosis of CMS encompasses five component hazards of central obesity, hypertension, low plasma high-density lipoprotein cholesterol (HDL-C), fasting hyperglycemia, and hypertriglyceridemia (Sasson et al., 2018). Factor analyses have been used in assessing risk of CMS in both SCI and non-SCI populations with results showing a high prevalence of CMS incidence in individuals with SCI (Gill et al., 2020; Gordon et al., 2021; Peterson et al., 2021; Solinsky et al., 2022). Individuals with acute SCI have an increased CMS risk compared non-SCI, where obesity, insulin resistance, and low HDL-C were the most common CMS risk determinants, and age was significantly associated with early CMS (Solinsky et al., 2022). Other studies have shown that patients CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 19 diagnosed with CMS have a higher likelihood of developing CVD (Gill et al., 2020; Wiest et al., 2019). Risk factor analyses have also revealed the prevalence of patterns of CMS that have been linked to CVD and T2DM (Farkas et al., 2022; Gill et al., 2020; James et al., 2020). Chopra et al. (2018) investigated blood serum of adults with chronic SCI and have found the prevalence of CVD risk factors and T2DM. The authors reported that despite the prevalence of CVD risk, dyslipidemia, hypertension, and T2DM were shown evidence of poor adherence to diagnostic and treatment guidelines. Several instigators of CMS were identified to be prevalent in obese SCI population using multifactorial risk models, such as inflammatory risk markers (Gater et al., 2021; Weist et al., 2019). Recent studies have investigated inflammatory C-reactive protein markers in SCI and was reported to be associated with high occurrences of CVD risk factors, such as obesity, hyperlipidemia, and hypercholesterolemia (Fu et al., 2020; Solinsky et al., 2022). Furthermore, inflammatory C-reactive protein and interleukin-6 markers have been reported to be prevalent in men with chronic SCI, testosterone, lower HDL, greater insulin resistance, and higher FRS scores. Leaving those CMS risk factors untreated may incite atherosclerotic plaque formation and premature occurrence of CVD (La Fountaine et al., 2018; Lu et al., 2018). Framingham Risk Score (FRS) Prediction Model Early CVD risk prediction models were based on the population cohort from the Framingham Heart Study from 1975 to 1990 (DAgostino et al., 2001). Prediction models have been derived using sex-specific multivariable risk functions of generalized CVD algorithms (DAgostino et al., 2008). Multivariable assessment has been advocated to estimate absolute CVD risk and to guide treatment factors (Khan et al., 2019). As the evidence grew, Framingham Risk Scoring (FRS) was incorporated into the Third Report of Expert Panel on Detection, CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 20 Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) (James et al., 2020). The FRS CHD risk assessment tool was later validated in multiethnic studies in the U.S. and culturally diverse populations in Canada, Europe, Mediterranean region, and Asia (Ko et al., 2020). Studies using the application of CVD risk prediction models have been used in SCI-specific primary care studies incorporating traditional risk factors, such as age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status (Barton et al., 2021; Dorton et al., 2020). FRS score have been used in assessing CVD risk determinants in SCI and non-SCI groups. In the recent decade, guidelines of cardiovascular health and prevention have improved proportionally as systematic evidence reports defined clinical practices (Dorton et al., 2021). Specifically, a pool of five large, multiethnic studies were used to develop risk prediction used by the American College of Cardiology and American Heart Associations. Atherosclerotic Cardiovascular Disease (ASCVD) Score is a pooled cohort of multiethnic studies based on Framingham Study, which have been the basis of current statin guidelines for primary CVD prevention (Ko et al., 2020). In the veteran population, homogenous studies have emerged as veteran-specific CVD prediction equations have surfaced in recent years. One of the most notable cohorts include the Million Veteran Program where CVD risk assessment was examined using electronic health records (Vassy et al., 2020). Similar studies derived from a large sample of veterans using review of healthcare records indicated calibration of statin guidelines based on the estimation of atherosclerotic CVD in a large cohort (Sussman et al., 2017). Despite the advancement in veteran-specific CVD risk guidelines, considerations for SCI-specific cardiovascular guidelines exploring non-traditional CMS risk factors have not been fully demonstrated in older veteran population with SCI (Stillman & Williams, 2019). CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 21 Gaps in Research Major contributors to heightening the risk of CVD have been identified in SCI population, such as higher prevalence of hypertension, dyslipidemia, obesity and diabetes (La Fountaine et al., 2018; Jorgensen et al., 2019). Single risk factors (i.e., hypertension or hypercholesterolemia) to more comprehensive multiple etiologies have been considered as management tools to appraise for elevated CVD risk (Jrgensen et al., 2019). However, a recent study by Barton et al. (2021) reported an underestimation of CVD morbidity and mortality in a sample of elderly patients with SCI. Precise quantification of CVD risk and prevalence is often complicated by lack of control for the varying differences in demographics and degrees of injury. While traditional risk factors have underestimated the occurrence of CVD events, using CMS risk factors have been sparsely used in prediction models for CVD risk (Barton et al., 2021; Sullivan et al., 2018). Therefore, because of the discrepancies in SCI-specific values of predictor variables, evidence of CVD risk in the older veteran population with SCI remains underestimated (Barton et al., 2021; Gater et al., 2019; Wischik et al., 2019; Yahiro et al., 2020). Researchers have reported higher prevalence of CMS and 10-year projected CVD risk in the veteran population with SCI than non-SCI (Gater et al., 2019; Yahiro et al., 2020). While many studies have examined possible causes, the way CMS accelerates the occurrence of T2DM and CVD remains unclear (Nash et al., 2018; Specht et al., 2018). Similar concerns from a pilot study by Figoni and Chen (2015) indicated the high prevalence of CVD risk factors in a sample of veterans with SCI. Furthermore, evidence of CMS prevalence from the non-SCI population has indicated a 5-fold increase in the risk of T2DM and a 2-fold risk of developing CVD over a period of 5 to 10 years (Barton et al., 2021; McGrath et al., 2019; Peterson et al., 2021). However, the identification and treatment of CVD in SCI continues to be a clinical challenge CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 22 because CVD was usually assessed separately from CMS, despite of its overlapping prevalence (Stillman & Williams, 2019). Interrater agreement between risk estimation and predictors of CVD remains crucial in assessing prognostic outcomes (Nash & Gater, 2020; Stillman & Williams, 2019; Wiest et al., 2019; Yahiro et al., 2019). Important studies recognizing the underestimation of FRS of 5-year CVD morbidity and mortality may be linked to observations made using cut-off values of traditional risk factors (Barton et al., 2021). However, while a set of potential secondary targets for CVD intervention represents a constellation of abnormal glycemic, lipid, and inflammatory factors seen in obese subjects, SCI-specific cut-offs have been underutilized when investigating underlying mechanisms between CMS and CVD risk (Chopra et al., 2018; Gater et al., 2018; La Fountaine et al., 2018; Ma et al., 2022; Wischik et al., 2019; Yahiro et al., 2020). Therefore, because CMS in elderly SCI population has been associated with an increased risk of all-cause and CVD mortality, overlapping three or more CMS components poses a threat as CVD development in individuals with SCI may occur concurrently (Barton et al., 2021; Solinsky et al., 2022). Accumulating evidence now suggests that, given the accelerated trajectory of cardiometabolic risk, the threat of CVD in individuals aging with SCI varies when adopting intervention models that recognize emerging non-modifiable risk factors (Nash et al, 2018; Stillman et al, 2019; Nash et al., 2020). More specifically, the SCI population differs from the non-SCI population and specific studies investigating prevalence of CMS between tetraplegia and paraplegia have been mixed (Chamberlain et al., 2019; Figoni et al., 2021; Lu et al., 2018; Raguindin et al., 2021). Cervical injuries interrupt autonomic signals for pain and cardiac dysfunction and, so far, the evidence of CMS in higher level or cervical injuries has been lacking CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 23 (Biering-Srensen et al., 2018). While the evidence-based studies concerning risk factors in SCI are robust, neurological injury level and severity have gained significant research interest in being a predictor for CVD as studies using neurological outcomes gain clinical significance (Farkas et al., 2022; Raguindin et al., 2021). Using national organization guidelines, identifying CMS risk factors remains the accepted countermeasure in stratifying CVD risk in older population of veterans with SCI (Nash et al., 2018). Furthermore, because exploratory analyses of SCI characteristics revealed SCI specific CVD risk factors that are closely related to CMS, opportunities for treatment measures of modifiable risk factors in people with SCI interest clinicians in rehabilitation settings (Jrgensen et al., 2019; Nash et al., 2020). Consequently, recent guidelines in the veteran population using ASCVD risk stratification have been demonstrated; however, longitudinal evidence has yet to be explored in the veteran population with SCI (Gater et al., 2019; Vassy et al., 2020; Wischik et al., 2019). However, the operational framework of existing evidence-based approaches, such as heuristic modeling and warranted utility in a changing state of treatment approaches, rehabilitation programs continue to dwell on functional outcomes instead of secondary health prevention (Nash et al., 2018; Peterson et al., 2021). More relevantly in the SCI population, assessing the prevalence of risk factor patterns in a group with unique profile characteristics could open opportunities to advancing screening and identification of secondary health conditions (Gater et al, 2021; Gordon et al., 2021). CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 24 Method Study Design The present study was a retrospective chart review that explored a composite regression model to predict 10-year CVD risk in veterans with chronic SCI. The study started on September 7, 2023, and extended to September 5, 2024. The study has been approved by the Department of Veterans Affairs VA Long Beach Healthcare System, Research Service (IRB# 1759687). Sample Patients were identified from the Spinal Cord Injury and Disorders (SCI/D) clinic at the Tibor Rubin Veterans Affairs Medical Center in Long Beach, California through convenience sampling of outpatient therapy and annual evaluation rosters. An a priori sample size calculation was conducted using G*Power 3.1 based on F-test, alpha =.05, power = 0.80, eleven predictor variables, and an effect size of 0.15, determined a minimum of N = 122 (Faul et al., 2009). 600 consults from 2022 were received from the facility coordinator and were reviewed for eligibility. Patients were eligible based on the study inclusion criteria: diagnosis of a SCI (traumatic or atraumatic), ages 18-80 years, have had either a consult for Kinesiotherapy Outpatient program or Annual Evaluation, completed neurological examination, and anthropometrics and wellness lab markers to complete CMS and FRS screenings. Exclusion criteria included the following: end stage CVD (cardiac, peripheral vascular, cerebrovascular), end stage pulmonary disease (COPD, asthma, interstitial lung disease, or restrictive lung disease), end-stage metabolic disease (renal failure), myocardial infarction within the preceding three months of the chart review, endstage cancer, hospice-care, less than six months post-limb amputation, greater than stage-2 pressure ulcer, dementia, and unstable behavior or psychiatric diagnosis. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 25 Data Patient characteristics included clinical information, such as ethnicity, anthropometrics, and wellness lab markers. Independent variables were considered as predictors and dependent variable as the outcome. Patient Characteristics from Chart Review Age (years) Gender (Male or Female) Ethnicity (African American, Asian, Caucasian, Declined to Answer, Hispanic, Pacific Islander, or Unknown) Spinal segment(s) level of injury (C1-C8, T1-T12, L1-L5, S1-S5) ASIA impairment grade (A, B, C, D, E) Completeness of injury (complete or incomplete) Injury duration (time in years from onset of SCI diagnosis to date of data collection) Height (cm) Body mass (kg) Body mass index (kg/m2) Systolic Blood Pressure (mmHg) Diastolic Blood Pressure (mmHg) Glucose (mg/dL) Hemoglobin A1c (HbA1c; mmol/L) Total cholesterol (TC; mg/dl) High-density lipoprotein cholesterol (HDL-C; mg/dl) Low-density lipoprotein cholesterol (LDL-C; mg/dl) CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Triglyceride (mg/dl) C-reactive protein (mg/L) Diagnosis of diabetes (no or yes) Smoking (no or yes) Predictors of the Composite Regression Model (Independent Variables) Age (years) Gender (male or female) Spinal segment(s) level of injury (C1-C8, T1-T12, L1-L5, S1-S5) ASIA impairment grade (A, B, C, D, E) Completeness of injury (complete or incomplete) Injury duration (time in years from onset of SCI diagnosis to date of data collection) Cardiometabolic Syndrome Indices: o SCI-adjusted obesity (no or yes) o Hypertension (no or yes) o Dyslipidemia (no or yes) o Dysglycemia (no or yes) o Hypertriglyceridemia (no or yes) Outcome (Dependent Variable) Framingham Risk Score (FRS) 10-year risk % Operationalization of Variables Spinal segment level of injury was defined as the level classification used by the International Standards for Neurological Classification of SCI (ISNCSCI) SCI grade was categorized by the motor impairment classification by ISNCSCI. 26 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 27 Severity of SCI was defined by the whether the injury was a complete or incomplete lesion to the spinal cord. Cardiometabolic syndrome (CMS; no or yes): defined as three or more cardiometabolic risk factors that include obesity, fasting hyperglycemia, hypertension, hypertriglyceridemia, and low plasma high-density lipoprotein cholesterol (HDL-C) Cardiometabolic indices: o SCI-adjusted obesity: 22 kg/m2 o Hypertension: 130/85 mmHg or use of medication for high blood pressure o Dyslipidemia is defined by: low HDL cholesterol in men: <40 mg/dl (1.03 mmol/L); and women: <50 mg/dl (1.29 mmol/L) o Dysglycemia: 100 mg/dl, fasting glucose 5.6 mmol/L or use of medication for hyperglycemia o Hypertriglyceridemia: 150 mg/dl or medication for this lipid abnormality FRS (10-year) %: o Gender (male, female) o Age (years) o Systolic Blood Pressure (mmHg) o Treatment for Hypertension (no or yes) o Smoker (no or yes) Do you smoke every day, some days, or not at all? Do you smoke now? o Diagnosis of Type II diabetes mellitus (T2DM) or diabetes (no or yes) CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Hemoglobin A1c result is 6.5% or higher 2-hour oral glucose tolerance test result is equal to or greater than 200 28 mg/dL Fasting blood sugar level is equal to or greater than 126 mg/dL Medication for T2DM or diabetes o HDL cholesterol (mg/dL) o Total cholesterol (mg/dL) Instruments BMI was calculated when the data were not available by dividing weight by height (kg/m2). Framingham risk score (FRS) was calculated provided in the Framingham Heart Study website [https://www.framinghamheartstudy.org/fhs-risk-functions/cardiovascular-disease-10year-risk/] based on Agostino et al. (2008). Statistical analyses were conducted using GNU PSPP 2.0.0 free application software provided by VA Informatics and Computing Infrastructure (VINCI). Procedures Screening The researcher retrieved the year 2022 roster of Kinesiotherapy (KT) outpatient therapy and inpatient and outpatient annual evaluation rosters from the department coordinators. All patients obtained from the lists were screened for inclusion. Each patient required a completed recent anthropometric measurements within one year of the the consultation, physical examination, and blood analyses not to exceed one year from their consultation dates. Patients were included in the study if all variables, including CMS and CVD risk factors were present for calculations. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 29 Data Collection A chart review of eligible patients was conducted by the researcher from Computerized Patient Record System (CPRS, version 1.0.30.69, Dept. of Veterans Affairs). Data retrieved from the chart review included physical examination, demographics, SCI characteristics, anthropometrics, and comprehensive blood analyses). Ten-year FRS-CVD % was measured using sex, age, systolic blood pressure (mmHg [SBP]), treatment for hypertension, smoking status, diagnosis of diabetes, high-density lipoprotein-cholesterol (HDL-C), and total cholesterol from patient chart notes and laboratory reports. CVD risk were computed from FRS calculator by the Framingham Heart Study [https://www.framinghamheartstudy.org/fhs-risk-functions/cardiovascular-disease-10-year-risk/]. After patient and injury characteristics were retrieved, the investigator reviewed their medical charts for laboratory reports and recorded values for CMS (e.g., BMI or diagnosis of obesity, glucose or HbA1c, SBP, DBP, or diagnosis of hypertension, HDL-C or diagnosis of dyslipidemia, triglyceride). CMS was recorded if at least 3 out of the 6 criteria for CMS were met. Data Management The patient annual evaluation roster (accessible to the investigator) was stored electronically in the researchers hospital computer. The investigator accessed patient records from the hospitals record system via CPRS. Patients de-identified data and data collection sheets were stored on the hospital server. FRS score calculations were performed by the FRS website [https://framinghamheartstudy.org/fhs-risk-functions/cardiovascular-disease-10-yearrisk/]; however, data scores were entered on the data collection sheet secured in the investigators network folder. Individuals who did not meet the criteria for inclusion were CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 30 excluded from the study and were not included in the final de-identified list. A copy of the roster and data collection sheets were uploaded in the Veterans Affairs Informatics and Computing Infrastructure (VINCI) intranet server for data analysis. Statistical Analysis Data were analyzed using VINCI statistical application, GNU PSPP 1.6.2. F-tests were one-tailed, t-tests were two-tailed, and all alphas were at a significance level of less than .05. Descriptive statistics included mean, standard deviation, minimum, maximum, and confidence intervals. Nominal data were reported as frequencies and percentages; ordinal data and nonnormally distributed interval and ratio data were stored as medians. Normality of data was determined using Shapiro-Wilk tests as well as visual inspection of histograms and Q-Q plots. SCI cohorts were dichotomized by group above C8 and below T1 spinal levels to identify group differences between tetraplegia and paraplegia. Multiple linear regression analysis was performed to investigate the statistical significance of the model predicting high-risk CVD. Equal variances were assessed using the Durbin-Watson statistic. Coefficient of determination was used to assess the variance of the prediction model. Correlation coefficients were determined for independent variables. Using the coefficient correlation approach, the strongest CMS predictor variables were identified and correlated with the dependent variable of FRS risk percentages score using regression coefficient. Identifying the strongest predictor variables for the regression equation were reported in model summary of the coefficient correlation report (see Appendices). Multiple Linear Regression and Prediction Model Multiple linear regression was examined for significance of the regression model in predicting of FRS. To identify potential predictors of CVD risk, backward elimination regression CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 31 analyses were conducted. The outcome variable was FRS score, while age, SCI level, SCI grade, SCI impairment, SCI severity, and CMS risk factors were predictor variables. Testing and interpreting assumptions of multiple linear regression were performed as outlined by Field (2018). Independence of observations were checked with the Durbin-Watson statistic and visual inspections of residual scatterplots. It was considered present if the value was close to 2, i.e., between 0.80 and 3.20. Homoscedasticity was determined through visual inspection of the scatterplot of the standardized residuals against the standardized predicted values of the regression analysis output. The assumption of no multicollinearity was tested using two separate methods and were considered met if all correlations between independent variables were less than .70 or if the collinearity tolerance value was less than 0.10. The assumption of no significant outliers was determined if none of the standardized residuals and/or predicted values were 3 standard deviations. To check if the residuals of the regression line were normally distributed, histograms of the standardized residuals and normality, visual inspection of the Q-Q plot were inspected. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 32 Results A total of 600 combined consults from outpatient therapy (n = 84) and annual evaluation (n = 516) rosters for the year 2022 were reviewed. Among the consults, n = 188 individuals (33 outpatient therapy and 155 annual evaluations) completed medical exams and wellness lab markers that were eligible for CMS risk factors and FRS score assessments. See Figure 1 for the chart review process used to determine eligible patients. The sample size was further reduced to the final size of n = 184 after four outliers were removed from the data set during data exploration. Table 1 summarizes the baseline patient characteristics for combined groups. The mean age was 63.37 years (standard deviation [SD] = 12.05), mean duration of having an SCI of 18.12 years (SD = 16.19), and maximum of 54 years. The combined group showed an obese group with a mean body mass index (BMI) of 28.54 kg/m2 ( 22 kg/m2) based on SCI-adjusted cut-off (Gater et al., 2021; Ma et al., 2022). Table 2 summarizes the baseline patient characteristics for the tetraplegia group (n = 106). The mean age was 64.79 years (SD = 11.58), mean duration of SCI of 16.06 years (SD = 15.57), and maximum of 54 years. Table 3 summarizes the baseline patient characteristics for the paraplegia group (n = 78). The paraplegia group showed a greater mean BMI of 29.51 kg/m2 that was also considered obese ( 22 kg/m2) based on SCI-adjusted cut-off (Gater et al., 2021; Ma et al., 2022). The mean age showed a slightly younger group with mean age of 61.44 years (SD = 12.46), slightly longer mean duration of SCI of 20.92 years (SD = 16.69), and maximum of 54 years. The paraplegia group showed a slightly heavier weight of 91.59 kg. The paraplegia group was considered obese with a mean BMI of 29.51 kg/m2 ( 22 kg/m2) based on SCI-adjusted cutoff (Gater et al., 2021; Ma et al., 2022) CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 33 Table 4 summarizes the counts and percentages of ethnicities and genders. Over-half of the sample was Caucasian (n = 103, 56%), and predominantly male (n =173, 94%). Table 5 summarizes injury characteristics for combined groups. Most individuals had tetraplegia or injuries at the cervical spinal level (n = 106) followed by thoracic (n = 63), lumbar (n = 12), and sacral (n = 3) spinal level injuries. The sample consisted of mostly ASIA grade D (44.60%) followed by A (29.30%), C (15.20%), B (9.80%), and E (1.10%) and predominantly incomplete motor impairment (72.30%). Table 6 summarizes injury characteristics for the tetraplegia group. There were more C4 level SCI (n = 37, 34%), AISA grade D impairment (n = 59, 55.70%), and incomplete SCI lesions (n = 85, 80.20%). Table 7 summarizes injury characteristics for the paraplegia group. There were more T4 (n =10, 12.80%) and T12 (n =10, 12.80%) levels of SCIs, ASIA grade A (n = 33, 42.30%), and incomplete SCI lesions (n = 45, 57.70% (see Table 7). Table 8 summarizes the prevalence of CMS, risk factor counts, and percentages. 70.65% (n = 130) met the CMS criteria ( 3 risk factors) with a median of 3 risk factors (n = 71). SCIadjusted obesity (84.20%) was most prevalent risk factor followed by hypertension (70.70%), dysglycemia (66.80%), dyslipidemia (46.20%), and hypertriglyceridemia (27.70%). Table 9 summarizes the 10-year CVD risk by FRS range differences, including high-risk 52.20% (n = 96), moderate-risk 33.20% (n =61), and low-risk 14.70% (n = 27). The mean FRS score showed a high-CVD risk of 24.59% (16.02%) and a median score of 20.10% (minimum = 0.80%; maximum = 85.50%). Other comorbidities included type 2 diabetes mellitus (T2DM) 29.30% (n =54) and smoking 7.60% (n = 14). Table 10 summarizes the cross-tabulation of CMS factors between tetraplegia and paraplegia. The tetraplegia group showed more prevalent SCI-adjusted obesity 47.30% (n =87), CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 34 hypertension 17.40% (n = 74), dsyglycemia 19.60% (n =70), dyslipidemia 27.70% (n =51), hypertriglyceridemia 17.90% (n = 33). Paraplegia showed SCI-adjusted obesity 37% (n =68), hypertension 30.40% (n = 56), dysglycemia 28.80% (n = 53), dyslipidemia 18.50% (n = 34), hypertriglyceridemia 9.80% (n = 18). Table 11 summarizes the group comparison of comorbidities, high-risk FRS score ( 20%), and CMS prevalence. Compared to individuals with paraplegia, those with tetraplegia showed greater incidence of T2DM (19%, 10.90%), high-risk FRS score (29.90%, 20.70%), and CMS (41.30%, 30.40%) and lesser incident of smoking (3.30%, 4.30%). Table 12 summarizes the prevalence of T2DM, CMS, and FRS ( 20%) in tetraplegia and paraplegia groups when controlled for individuals with SCI-adjusted obesity ( 22 kg/m2). The prevalence of T2DM, CMS, and FRS ( 20%) for obese individuals are 10.30%, 37.90%, and 27.60%, respectively. Tetraplegia with SCI-obesity displayed higher prevalence of T2DM, CMS, and FRS ( 20%). Table 13 summarizes the frequency counts of life status at the time of data collection and causes of SCI. Most patients were alive 95.70% (n =176) at the time of data collection. Eight individuals were deceased at the time of data collection since the retrieval of lab wellness markers. The causes of death were not recorded due to being outside of the scope of this study. The most common causes of SCI were vehicular accidents 29.90% (n = 55), falls 19% (n = 35), columnar degeneration 16.80% (n = 31), others (Traumatic) 7.60% (n = 14), and infections or abscess 5.40% (n =10). The least prevalent causes of SCI were arthritic disease 0.50% (n =1), stenosis 0.50% (n = 1), others (non-traumatic), genetic-related 1.10% (n =2), surgical-related 2.70% (n = 5), tumor 3.30% (n = 6), and sports 4.90% (n = 9). Group comparison of causes of SCI are summarized in Table 14. Compared to the paraplegia group, the tetraplegia showed more incidences of vehicular-related injury 16.30% (n = 30), columnar degeneration 12.50% (n = 23), CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 35 falls 12.50% (n = 12), sports related 3.80% (n = 7), surgical related 2.2% (n = 4), and more than 1 SCI (n = 1). Paraplegia showed more incidences of violence 4.90% (n= 9), other (traumatic) causes 4.30% (n = 8), infection or abscess 3.30% (n = 6), and tumor 2.70% (n = 5). Data Exploration Figure 3 shows a positively skewed distribution of FRS scores and outliers in the normal Q-Q Plot. Square root transformation of FRS (Y) was performed and n = 4 cases with extreme FRS scores were removed. Figure 4 shows the histogram after square root transformation with a normal distribution shape and Q-Q plot of transformed FRS (Y). Further data exploration of injury characteristics revealed a bimodal distribution of SCI levels that showed peaks between cervical and thoracic levels of injuries as seen in Figure 5. Tetraplegia group showed a bellshaped distribution with a peak at C4 level SCI as listed on Table 6 and seen in Figure 6. Similarly, paraplegia group showed a bell-shaped distribution with a peak at T12 (see Figure 7). However, when controlling for only thoracic group, thoracic levels revealed a bimodal distribution with peaks at upper thoracic (T4) and lower thoracic (T12) levels as listed on Table 7 and see in Figure 8. Therefore, full and reduced regression model analyses were conducted for the combined tetraplegia and paraplegia group(C1-S5), and for each tetraplegia (C1-C8), paraplegia (T1-S5), upper thoracic paraplegia (T1-T6), and lower thoracic paraplegia (T7-T12). Regression Models for Combined Tetraplegia and Paraplegia (C1-S5) Multiple linear regression was conducted to test if the outcome variable, FRS transformed, had a significant relationship with the following predictor variables: age, gender, spinal cord injury (SCI) level, ASIA Grade, SCI completeness, duration of SCI, SCI-adjusted obesity, hypertension, dysglycemia, dyslipidemia, and hypertriglyceridemia. Tables 15 and 20 showed the combined full model summary of tetraplegia and paraplegia indicating that that the CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 36 first hypothesis was met. The correlation coefficient of the combined group, R = .82, was linear and statistically significant, R2 = .67(.98), F(11, 172) = 32.18, and p < .001, at alpha p < .05 level. Age, hypertension, dyslipidemia, and hyperlipidemia were statistically significant, p < .001, p < .001, p = .006, and p = .002, respectively. Using backward elimination regression, the predictive variables with the largest significant value (p > .05) were eliminated at each iteration of the regression step until all the remaining predictors reached level of significance. The correlation coefficient of R = .81 for the reduced backward elimination regression model for tetraplegia and paraplegia. It was found that the combined model was statistically significant, R2 = .66(.98), F(5, 178) = 68.74, p < .001, at an alpha p < .05 level. It was found that age (b = 0.08, p <.001), hypertension (b = 1.27, p < .001), dysglycemia (b = 0.34, p = .045), dyslipidemia (b = 0.42, p = .005), and hypertriglyceridemia (b = .52, p = .003) predicted FRS transformed (Y). The root mean squared error (RMSE) for the reduced model (0.98) did not differ greatly from the reduced model (0.97) (see Table 16). Partial F-test (Fjnk = 1.25) showed that the variables removed from the model were not statistically significant, for critical value 2.21, to infer that the reduced model was not different from the full model; therefore, meeting the fifth hypothesis (see Table 21). Regression Models for Tetraplegia (C1-C8) Group Tables 16 and 20 show the composite model summary for tetraplegia indicating that the first, second, and third hypotheses were met when adjusted for SCI level. When controlling for tetraplegia, the third hypothesis was met for the adjusted SCI level. Multiple linear regression was conducted to test if the outcome variable, FRS transformed, has a significant relationship with the following predictor variables: age, gender, AISA Grade, SCI completeness, duration of SCI, SCI-adjusted obesity, hypertension, dysglycemia, dyslipidemia, and hypertriglyceridemia. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 37 The correlation coefficient of the tetraplegia group, R = .83, was linear and statistically significant, R2 = .68(.97), F(10, 95) = 20.65, and p < .001, at alpha p < .05 level. Age, hypertension, and dyslipidemia were statistically significant, p < .001, p < .001, and p = .009, respectively. Using backward elimination regression, the predictive variables with the largest significant value (p > .05) were eliminated at each iteration of the regression step until all the remaining predictors reached level of significance. The reduced backward elimination regression correlation coefficient, R = .80, of was statistically significant, R2 = .65(1.00), F(4, 101) = 46.15, p < .001, at an alpha p < .05 level. It was found that age (b = 0.08, p < .001), hypertension (b = 1.52, p < .001), dyslipidemia (b = 0.58, p = .004), and hypertriglyceridemia (b = 0.49, p = .028) predicted FRS transformed (Y), which meets the fourth hypothesis. The root mean squared error (RMSE) for the reduced model (1.00) did not differ greatly from the full model (0.97) (see Table 16). Partial F-test (Fjnk = 1.96) shows that the variables removed from the model did were not statistically significant, for critical value 2.37, to infer that the reduced model was not different from the full model); therefore, meeting the fifth hypothesis. (See Table 21.) Regression Models for Paraplegia (T1-S5) Group Tables 17 and 20 show the composite model summary for paraplegia indicating that the first, second, and third hypotheses were met when adjusted for SCI level. Multiple linear regression was conducted to test if the outcome variable, FRS transformed, had a significant relationship with the following predictor variables: age, gender, AISA Grade, SCI completeness, duration of SCI, SCI-adjusted obesity, hypertension, dysglycemia, dyslipidemia, and hypertriglyceridemia. The correlation coefficient of the paraplegia group, R = .84, was linear and statistically significant, R2 = .70, (.97), F(10, 67) = 15.72, and p < .001, at alpha p < .05 level. Age, gender, hypertension, and hypertriglyceridemia were statistically significant, p <. 001, p = CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 38 .042, p = .001, and p = .009, respectively. Using backward elimination regression, the predictive variables with the largest significant value (p > .05) were eliminated at each iteration of the regression step until all the remaining predictors reached level of significance. The correlation coefficient, R = .82, of the reduced backward elimination regression model for paraplegia was statistically significant, R2 = .68(.96), F(4, 73) = 38.45, p < .001, at an alpha p < .05 level. It was found that age (b = 0.08, p < .001), gender (b = 0.91, p = .047), hypertension (b = 1.06, p < .001), and hypertriglyceridemia (b = 0.80, p = .004) predicted FRS transformed (Y), which meets the fourth hypothesis. The root means squared error (RMSE) for the reduced model (0.96) did not differ greatly from the full model (0.97) (see Table 16). Partial F-test (Fjnk = 0.87) shows that the variables removed from the model did were not statistically significant, for critical value 2.53, to infer that the reduced model was different not from the full model; therefore, meeting the fifth hypothesis. (See Table 21). Regression Models for Upper Thoracic (T1-T6) Group Tables 18 and 20 show the composite model summary for upper thoracic group indicating that that the first, second, and third hypotheses were met when adjusted for SCI level. Multiple linear regression was conducted to test if the outcome variable, FRS transformed, has a significant relationship with the following predictor variables: age, gender, AISA Grade, SCI completeness, duration of SCI, SCI-adjusted obesity, hypertension, dysglycemia, dyslipidemia, and hypertriglyceridemia. The correlation coefficient of the tetraplegia group, R = .93, was linear and statistically significant, R2 = .87(.64), F(10, 20) = 12.82, and p < .001, at alpha p < .05 level. Age, duration of SCI, and hypertriglyceridemia were statistically significant, p < .001, p = .040, and p = .001, respectively. Using backward elimination regression, the predictive variables with the largest significant value (p > .05) were eliminated at each iteration of the regression step until CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 39 all the remaining predictors reached level of significance. Table 23 showed the correlation coefficient, R = .91, of the reduced backward elimination regression model was statistically significant, R2 = .83(.64), F(5, 25) = 23.90, p < .001, at an alpha p < .05 level. It was found that age (b = 0.06, p = <.001), gender (b = 1.02, p = .046), duration of SCI (b = .03, p = .004), dysglycemia (b = 0.61, p = .029), and hypertriglyceridemia (b = 1.19, p < .001) predicted FRS transformed (Y), which met the fourth hypothesis. The root mean squared error (RMSE) for the reduced model (0.64) did not differ greatly from the full model (0.63) (see Table 16). Partial Ftest (Fjnk = 0.98) showed that the variables removed from the model did were not statistically significant, for critical value 2.92, to infer that the reduced model was not different from the full model; therefore, indicating that the fifth hypothesis was met (see Table 21). Regression Models for Lower Thoracic (T7-T12) Group Tables 19 and 20 show the composite model summary for the lower thoracic group indicated that the third hypotheses were met when adjusted for SCI level. Multiple linear regression was conducted to test if the outcome variable, FRS transformed, has a significant relationship with the following predictor variables: age, gender, AISA Grade, SCI completeness, duration of SCI, SCI-adjusted obesity, hypertension, dysglycemia, dyslipidemia, and hypertriglyceridemia. The correlation coefficient of the tetraplegia group, R = .89 was linear and statistically significant, R2 = .79(.99), F(10, 21) = 7.86, and p < .001, at alpha p < .05 level. Age, gender, ASIA Grade, hypertension, and hypertriglyceridemia were statistically significant, p = .001, p = .022, p = .023, p = .002, and p = .010, respectively. Using backward elimination regression, the predictive variables with the largest significant value (p > .05) were eliminated at each iteration of the regression step until all the remaining predictors reached level of significance. The correlation coefficient, R = .86, of the reduced backward elimination CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 40 regression model was statistically significant, R2 = .74(1.00), F(6, 25) = 12.05, p < .001, at an alpha p < .05 level. It was found that age (b = 0.08, p = .006), gender (b = 1.79, p = .012), ASIA Grade (b = 0.45, p = .004), hypertension (b = 1.50, p = .006), dysglycemia (b = 1.50, p = .042), and hypertriglyceridemia (b = 1.06, p = .029) predicted FRS transformed (Y), which met the fourth hypothesis. The root mean squared error (RMSE) for the reduced model (1.00) did not differ greatly from the full model (0.99) (see Table 16). Partial F-test (Fjnk = 0.80) showed that the variables removed from the model did were not statistically significant, for critical value 3.48, to infer that the reduced model was not different from the full model; therefore, indicating that the fifth hypothesis was met (see Table 21). Prediction Equations Summary Table 22 summarizes the prediction equations of the reduced models for each SCI group characteristics. Table 22 lists the predication equations for each SCI groups: M1Reduced = combined tetraplegia and paraplegia (C1-S5); M2Reduced = tetraplegia (C1-C8); M3Reduced = paraplegia (T1-S5); M4Reduced = upper thoracic (T1-T6); M5Reduced = Lower Thoracic (T7-T12). Discussion The purpose of this study was to investigate whether a regression equation could predict 10-year CVD risk using FRS score as the outcome variable, while age, gender, SCI level, SCI grade, SCI impairment, SCI severity, years from onset of SCI, SCI-adjusted obesity, hypertension, dysglycemia, dyslipidemia, and hypertriglyceridemia as predictors. Upon data exploration, outliers were identified in the dependent variable (i.e., FRS score) that produced a positively skewed distribution, which could potentially influence the normality of the variance in the regression model. Subsequently, a square root transformation of the FRS score resulted in a normally distributed set of observations. In addition, injury characteristics revealed a bimodal CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 41 distribution between tetraplegia and paraplegia. Further, exploration of paraplegia revealed another bimodal distribution at upper thoracic and lower thoracic SCI levels. Subsequently, regression analyses identified at least one statistically significant predictor to test the first hypothesis (see Coefficients Tables 15). After controlling for SCI level, all of the predictors were statistically significant for tetraplegia and paraplegia, which also supported the second and third hypotheses (see Coefficient Tables 16 & 17). The hypothesis that each SCI group regression models will predict FRS were statistically significant (summarized in Table 20). Fifth, all of the reduced models were not found to be significantly different from the full models as determined by partial F-tests (summarized in Table 21). This study is one of the recent studies investigating SCI injury characteristics and among the first to account for CMS risk factors as predictive variables of 10-year CVD risk. High correlations and explained variances of SCI characteristics and CMS risk factors contributed to explaining the variance in transformed FRS score. Particularly, studies investigating SCI levels and CVD risk factors have been reported in a systematic review of 47 studies by Raguindin et al. (2021). According to the authors, tetraplegia had lower blood pressures, triglycerides, total cholesterol, HDL-C, and LDL-C. Similar to their findings, the present study also identified lower blood pressures, total cholesterol, HDL-C, and LDL-C. However, triglyceridemia was lower in paraplegia group. Although the findings of the regression model found that hypertension, hypertriglyceridemia, and low HDL-C were significant predictors in in tetraplegia and paraplegia models, low HDL-C was solely significant for the reduced tetraplegia model. Unlike the recent study by Barton et al. (2021) investigating high-CVD risk using FRS, triglycerides and LDL-C were found normal in a cohort of predominantly paraplegia and complete motor injuries, whereas hypertriglyceridemia was found to be a significant predictor in the paraplegia, upper thoracic, CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 42 and lower thoracic regression models in this study. Furthermore, comparing upper and lower thoracic SCI levels, the present study did not find major differences in CMS risk factors, although upper thoracic displayed dysglycemia as a positive influence on the regression model, while lower thoracic displayed dyslipidemia as a positive influence on the regression model. These findings are different from what was reported by Raguindin et al., (2021) who found no differences in LDL-C and serum glucose between upper and lower thoracic SCI. Notable studies have investigated aging and secondary health conditions in the SCIveteran population (Jrgensen et al., 2019; Gorgey et al., 2021; Nash et al., 2019; Yahiro et al., 2019). However, previous studies have found marginal differences in age groups, duration of injury, and sample sizes of tetraplegia groups (Bak et al., 2022; Figoni et al., 2021). The present study comprises an older SCI group (mean age 63.37 12.05) with longer mean duration of SCI (18.12 years 16.19). CMS was found to be more prevalent (70.65%) in this study similar to other studies (31% to 72%) (Nash et al., 2021; Solinsky et al., 2022). A greater prevalence of CMS was found in tetraplegia versus paraplegia (41.30% vs. 30.40%). Furthermore, there is substantial evidence identifying the prevalence of CMS in the older SCI-veteran population (Fyffe et al., 2019; Gater et al., 2019). A recent investigation by Gater et al. (2019) of prevalence of CMS in a large cohort of veterans (n =473) revealed a high prevalence (57%) of CMS, including complete SCI (45.9%), SCI-adjusted obesity (76.70%), diabetes (53.40%), dyslipidemia (72.30%), and hypertriglyceridemia (40.40%). Their findings revealed similar group breakdown between tetraplegia (49.60%), high (T1-T6) paraplegia (17.80%), and low (below T6) paraplegia (32.60). However, unlike the study by Gater et al., (2019), the present study revealed an older mean age, larger tetraplegia group (57.60%), with greater CMS CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 43 prevalence (71.70%), SCI-adjusted obesity (84.20%), hypertension (70.70%), and dysglycemia (66.80%). Tables 10 and 11 summarize the group differences associated with CMS. When categorizing the groups into tetraplegia and paraplegia, CMS risk factors were more prevalent in the tetraplegia group (SCI-obesity 47.30%, hypertension 40.20%, dysglycemia 38%, dyslipidemia 27.70%, and hypertriglyceridemia 17.90%; see Table 10). A follow-up study by Gater et al. (2021), reported lower prevalence of CMS (59.40%) in a younger SCI cohort (mean age 44.4 SD = 11.4 years) with shorter duration of SCI (14.4 years, SD = 11). Abdominal (central) obesity and visceral adiposity have been reported to be underestimated using non-SCI cut-offs (Gater et al., 2021; Ma et al., 2022). When using SCI-adjusted cut-off of BMI 22 kg/m2 (Gater et al., 2021), the prevalence of obesity found in this study (84.20%) was greater than the prevalence found by Solinsky et al. (2022) in a slightly younger SCI-non-veteran population (74.0%). When categorizing the present cohort into those with and without SCIadjusted obesity, T2DM, CMS, and high-risk CVD score (FRS 20%) revealed prevalence of 10.30%, 37.90%, and 27.60%, respectively, suggesting that SCI-adjusted obesity contributed moderately to health risks (see Table 12). Like other regression studies (Jrgensen et al., 2019; Raguindin et al., 2021), SCI characteristics were not found to be significant predictors in this study. However, all 5 full models showed positive influence of ASIA grade to the regression and even more significantly when controlling for lower thoracic injuries. Duration of SCI showed mixed influences on the regression for tetraplegia and paraplegia, where increased duration of SCI had a negative influence in the tetraplegia group in predicting transformed FRS. Part of the discrepancies between tetraplegia and paraplegia in regard to duration of SCI and CVD risk could be explained CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 44 with tetraplegia showing lower mean duration of SCI than paraplegia, and tetraplegia being the larger group in this study. To put succinctly, most of the tetraplegia group in this study experienced CVD risk sooner than paraplegia because tetraplegia seemed to have acquired their SCI at a later age. Further comparison can be found in Table 11, where tetraplegia was found to be more prevalent for high-risk FRS (20%) than the paraplegia group (29.90%, 20.70%). Additionally, diabetes was slightly more prevalent in the tetraplegia group versus paraplegia (19%, 10.30%), whereas smoking did not show a large difference between tetraplegia and paraplegia (3.30%, 4.30). Regression Model Assumptions Full and reduced regression models reached statistical significance (see Table 20). Backward elimination was performed on the full regression equation for significant predictors to identify significant regression coefficients. Further explorations of the statistical and visual output were conducted to examine basic linear regression assumptions. Independence of observations for the initial regression model were met based on Durbin-Watson statistics. Linearity assumptions were met based on visual inspection of scatterplots for each studentized residuals and unstandardized predicted values that were distributed across a horizontal band. The homoscedasticity assumptions were met based on the constant variance observed in the scatterplot of the studentized residuals against unstandardized predicted values, and individual inspections of the dependent variable against each independent variable. Multicollinearities were not identified based on Tolerance > .01, and VIF < 10. No significant outliers were observed on visual inspection of scatterplots, histograms, and studentized residuals greater than +/- 3 standard deviations. Assumptions of normally distributed errors (residuals) of the regression line were met CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 45 based on visual inspection of histogram standardized residuals and Q-Q plot (see Figure 4), and Shapiro-Wilk significance (see Table 23). Clinical Importance Older veterans living with chronic SCI are in an unfavorable subgroup of the SCI population with heightened CVD risk due to comorbidities from aging (Figoni & Chen, 2015). The duration of SCI found in this study suggests that SCI was acquired in later adult life where comorbidities and behavioral risk factors could contribute to a heightened tendency of health risk experienced later in life (Jrgensen et al., 2019). CVD risk estimation is important to clinicians in outpatient settings for identification and management of modifiable risk factors by therapeutic interventions (Nash et al., 2018). This study found dyslipidemia to be a significant predictor in the regression model. La Fountaine et al. (2018) previously reported serum lipid concentrations as prevalent risk factors that predicted CVD risk. Using risk prediction models to screen for underlying CVD risk could further diagnostically screen disease markers (Wagner et al., 2022). Similarly, systemic inflammation in chronic SCI from obesity and insulin resistance promote development of atherogenesis and CVD (Bloom et al., 2019). The heightened CVD risk for SCI has been underestimated; and therefore, CVD outcomes may be under-detected (Barton et al., 2021). More importantly, providing regular therapeutic interventions could be laborious and the taxing to both providers and patients. Evidence highlighting the role of guideline-driven identification and exercise interventions have been highlighted in literature (Nash et al., 2018; Nash et al., 2022). The findings about CMS and high-CVD risk screening in the present study could provide routine assertion to identification and intervention for clinicians. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 46 In the full models, removing outliers from the regression model transformed a robust equation into a relatively representative model derived from a heterogenous group. Even further, controlling for SCI groups drastically improved the reduced model version of the full models. First, the regression coefficients included in the full model represent clinical markers that could easily found in electronic health records for generalized SCI levels without controlling for either tetraplegia or paraplegia. The combined tetraplegia and paraplegia model provides a large effect size, the clinical significance of the regression equation that offers practical applications for risk screening and treatment for a wide range of SCI levels. Second, highlighting the importance of each modifiable risk factor provides targets for therapeutic interventions specific to the population in this study. CMS identification and management are among two of the important tasks in the chronic SCI outpatient settings. However, screening solely for CMS does not quantify the outcome of CVD risk, and therefore does not offer insight to the magnitude of CVD risk leading up to a terminal CVD outcome. Third, because of the predominance of older subjects and a pool of mixed SCI characteristics, caution should be taken based on the robustness and potential inaccuracies of predictions for potentially violating heteroscedasticity assumptions (Schmidt & Finan, 2018). However, because of the square root transformation, controlling for SCI levels and groups, and reduced version of the full models, the violations of heteroscedasticity have been reduced from robust equations. Overall, while transforming the prediction equation improved the assumptions, reduction by backward elimination further improved all models model providing confidence that were not statistically different as indicated by root mean squared error and partial F-tests (summarized in Table 21). Ultimately, the reduced models summarized in Table 22 could provide clinicians useful prediction equations using patient, SCI, and CMS characteristics in estimating high-CVD risks in older veterans with SCI. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 47 Study Limitations While the instruments used in this study have been widely used in CMS screening, lab wellness markers have not been routinely measured in a large number of individuals in this sample (188 out of 600 patients). Out of the total combined sample, less than one-third presented with completed cardiometabolic lab markers, which was lower than expected for an older group despite recommended guidelines (Nash et al., 2019). Research on longitudinal and prospective studies have reported different prevalence of CMS risk factors between age and SCI groups (McGrath et al., 2019; Solinsky et al., 2022). The statistically significant regression coefficients identified in this study may be representative of clinical markers for this sample and potentially the larger population. However, future studies on homogenous groups representative of an older population with tetraplegia, upper thoracic, and lower thoracic injuries may perform better than the variance observed in a heterogenous group. The combined number of individuals in tetraplegia and paraplegia groups are relatively small if categorized separately for prediction equations. The robustness observed in this study could be attributed to the larger variance in the outcome variable observed in older age, rather than equal variances across increasing age groups for the combined tetraplegia and paraplegia groups. Therefore, without sufficient sample size between tetraplegia and paraplegia, age-match group comparisons could not be performed. Furthermore, FRS score has been regarded to underestimate CVD outcomes, and therefore the actual risk for 10-year CVD risk could potentially be greater as validated in cross validation studies that use terminal or endpoint occurrences of CVD (Barton et al., 2021). Random sampling, age matching, and controlling for outliers may be warranted for future validation studies that encompass homogenous groups that were noted in this study. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 48 Conclusion The observations in this study supported the aim of developing SCI-specific predictive equation of high-CVD risk, especially when non-SCI CVD risk equations underestimate CVD events in SCI population (Barton et al., 2021; Jrgensen et al., 2019). In this study, the hypothesis of examining the predictive qualities of age, gender, SCI level, SCI grade, SCI impairment, SCI severity, years from onset of SCI, SCI-adjusted obesity, hypertension, dysglycemia, dyslipidemia, and hypertriglyceridemia as predictors were found at a statistically significant level. Once FRS score was transformed, the independent variables were explored which led to the multiple linear regression hypotheses testing. First, multiple linear regressions were conducted to test the hypothesis of whether at least one predictor variable is significant for each model that controlled for SCI groups. It was found that the first hypothesis was supported for all of the regression models in this study, namely combined tetraplegia and paraplegia (M1), tetraplegia (M2), paraplegia (M3), upper thoracic (M4), and lower thoracic (M5). First, while not all the predictors were significant in the first iteration of the full model, at least most of the predictors displayed influences on the regressions. Second, the hypothesis to identify significant predictors for veterans with tetraplegia was supported. Third, the hypothesis to identify those significant predictors for paraplegia was supported. Fourth, the hypothesis that the full models will be statistically significant was also supported. Fifth, backward elimination regressions were conducted by removing predictors that did not contribute significantly for each model. It was found that the resulting reduced models were not statistically different from each of the corresponding full model. The findings in the present study may have important clinical applications as they suggest that the overall CVD risk and CMS prevalence are elevated for each of the groups. Tetraplegia CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 49 made up the largest subgroup in this cohort with function and body composition that are different than paraplegic counterparts. Screening for specific risk factors that present in each subgroup could help facilitate specific interventions and targeted therapies. Similarly, while the paraplegia group present with more function, they also face challenges in preventing health risks with older age and longer duration of SCI. Overall, with a mean age of 63.54 years, more than half of the population faces a high 10-year risk of CVD suggesting that regular screening and monitoring, dietary, and health education are warranted. Similarly, managing risk factors for CMS could improve health status and morbidity and mortality. In conjunction with diagnostic testing, using readily available exercise testing, electrocardiogram (ECG), imaging, ultrasound studies, and pharmacotherapeutic interventions for asymptomatic individuals with high risk should be greatly considered. Coupled with health education, screening, identification, and targeted therapeutic interventions could yield a more favorable health status for older veterans with SCI. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 50 References Abilmona, S. M., Sumrell, R. M., Gill, R. S., Adler, R. A., & Gorgey, A. S. (2019). Serum testosterone levels may influence body composition and cardiometabolic health in men with spinal cord injury. 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Vasa, 51(1), 46-55. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY https://doi.org/10.1024/0301-1526/a000981 Weihe, P., & Weihrauch-Blueher, S. (2019). Metabolic syndrome in children and adolescents: Diagnostic criteria, therapeutic options and perspectives. Current Obesity Reports, 8(4), 472-479. https://doi.org/10.1007/s13679-019-00357-x Wiest, M. J., West, C., Ditor, D., Furlan, J. C., Miyatani, M., Farahani, F., Alavinia, S. M., Oh, P. I., Bayley, M. T., & Craven, B. C. (2019). Development of cardiometabolic health indicators to advance the quality of spinal cord injury rehabilitation: SCI-High Project. The Journal of Spinal Cord Medicine, 42(Suppl. 1), 166-175. https://doi.org/10.1080/10790268.2019.1613322 Wischik, D. L., Magny-Normilus, C., & Whittemore, R. (2019). Risk factors of obesity in veterans of recent conflicts: Need for diabetes prevention. Current Diabetes Reports, 19(9), 1-10. https://doi.org/10.1007/s11892-019-1191-9 Yahiro, A. M., Wingo, B. C., Kunwor, S., Parton, J., & Ellis, A. C. (2020). Classification of obesity, cardiometabolic risk, and metabolic syndrome in adults with spinal cord injury. The Journal of Spinal Cord Medicine, 43(4), 485-496. https://doi.org/10.1080/10790268.2018.1557864 59 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 60 Table 1 Descriptive Statistics for Total Sample (N = 184) M Mdn SD Min Max Age (years) 63.37 66 12.05 28 80 Duration of SCI 18.12 12 16.19 1 54 HbA1c 5.99 5.70 1.12 4.10 11.50 Glucose 110.03 100 34.96 68 354 TC 161.30 163 49.75 58 377 HDL-C 41.38 40 11.91 19 94 Triglyceride 115.80 100.50 62.18 23 320 LDL-C 96.66 89 42.72 19 275 SBP 127.24 126 19.10 73 186 DBP 75.82 75 10.28 46 102 Height (mm) 1.78 1.79 .09 1.52 2.03 Weight (kg) 89.75 87.70 20.60 48.99 179.62 BMI (kg/m2) 28.54 27.87 6.50 16.90 52.40 Note. Min = minimum; Max = maximum; SCI = spinal cord injury; HbA1c = hemoglobin A1c (mmol/L); TC = total cholesterol (mg/dl); HDL-C = high-density lipoprotein cholesterol (mg/dl); LDL-C = low-density lipoprotein cholesterol (mg/dl); SBP = systolic blood pressure (mmHg); DBP = diastolic blood pressure (mmHg); BMI = body mass index. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 61 Table 2 Descriptive Statistics for Tetraplegia group (n = 106) M Mdn SD Min Max Age (years) 64.79 68 11.58 29 80 Duration of SCI 16.06 10 15.57 1 54 HbA1c 6.05 5.70 1.16 4.40 9.90 Glucose 112.79 99 41.12 72 354 TC 158.06 154 46.55 73 314 HDL-C 40.24 40 10.58 21 81 Triglyceride 120.29 100.50 65.44 23 320 LDL-d 93.79 41.97 19 222 SBP 126.18 126.50 20.16 73 173 DBP 75.75 74 10.66 46 102 Height (mm) 1.78 1.80 0.09 1.55 2.01 Weight (kg) 40.09 38.39 9.54 22.22 73.16 BMI (kg/m2) 27.83 27.47 6.77 16.88 52.37 85 Note. Min = minimum; Max = maximum; SCI = spinal cord injury; HbA1c = hemoglobin A1c (mmol/L); TC = total cholesterol (mg/dl); HDL-C = high-density lipoprotein cholesterol (mg/dl); LDL-C = low-density lipoprotein cholesterol (mg/dl); SBP = systolic blood pressure (mmHg); DBP = diastolic blood pressure (mmHg); BMI = body mass index. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Table 3 Descriptive Statistics for Paraplegia Group (n = 78) M Mdn SD Min Max Age (years) 61.44 64 12.46 28 79 Duration of SCI 20.92 15 16.69 1 54 HbA1c 5.92 5.70 1.06 4.10 11.50 Glucose 106.27 100.50 23.96 68 195 TC 165.71 169 53.78 58 377 HDL-C 42.92 41 13.43 19 94 Triglyceride 109.71 99 57.30 26 297 LDL-C 100.56 100 43.70 21 275 SBP 128.69 126 17.59 95 186 DBP 75.91 75 9.82 54 99 Height (mm) 1.77 1.78 0.09 1.52 2.03 Weight (kg) 91.59 91.72 19.98 51.50 179.62 BMI (kg/m2) 29.51 29.01 6.01 17.94 52.36 Note. Min = minimum; Max = maximum; HbA1c = hemoglobin A1c; TC = total cholesterol; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; SBP = systolic blood pressure; DBP = diastolic blood pressure; BMI= body mass index 62 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 63 Table 4 Frequency Counts and Percentages of Ethnicities and Gender Reported for Each Group Tetraplegia Paraplegia Total Sample n (%) n (%) N (%) 30 (16.30) 13 (7.10) 43 (23.40) 7 (3.80) 4 (2.20) 11 (6) 56 (30.40) 47 (25.50) 103 (56) 2 (1.10) 4 (2.20) 6 (3.30) 0(0) 2 (1.10) 2 (1.10) Pacific Islander 7 (3.80) 6 (3.30) 13 (7.10) Unknown 4 (2.20) 2 (1.10) 6 (3.30) n (%) n (%) N (%) 6 5 11 (6) 100 73 173 (94) Ethnicities African American Asian Caucasian Declined to Answer Hispanic Gender Female Male Note. SCI = spinal cord injury. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 64 Table 5 Frequencies and Percentages for Different Spinal Cord Injury Levels of Tetraplegia and Paraplegia Groups (N = 184) SCI Bins C1-C8 a N(%) 106(57.60) T1-T12 63(34.20) L1-L5 12(6.50) S1-S5 3(1.60) AISA Grade N(%) A 54(29.30) B 18(9.80) C 28(15.20) Da 82(44.60) E 2(1.10) Severity N(%) Complete Lesion 51(27.70) Incomplete Lesiona 133(72.30) Note. SCI = spinal cord injury; ASIA = American Spinal Injury Association; C = cervical spine level; T = thoracic spine level; L = lumbar spine level; S = sacral spine level. a Denotes the most common group. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 65 Table 6 Frequencies and Percentages of Different Spinal Cord Injury Levels for the Tetraplegia Group (N = 106) Tetraplegia n(%) C1 6(5.70) C2 15(14.20) C3 13(12.30) C4a 37(34.90) C5 13(12.30) C6 8(7.50) C7 10(9.40) C8 4(3.80) AISA Grade n(%) A 21(19.80) B 10(9.40) C 15(14.20) Da 59(55.70) E 1(.90) Severity Complete Lesiona n(%) 21(19.80) Incomplete Lesion 85(80.20) Note. ASIA = American Spinal Injury Association; C = cervical spine level. a Denotes the most common group. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Table 7 Frequencies and Percentages of Different Spinal Cord Injury Levels for the Paraplegia Group (N = 78) Paraplegia T1 T2 T3 T4a T5 T6 T7 T8 T10 T11 T12a L1 L2 L3 S2 S3 n(%) 5(6.40) 1(1.30) 4(5.10) 10(12.80) 7(9) 4(5.10) 3(3.80) 6(7.70) 9(11.50) 4(5.10) 10(12.80) 6(7.70) 4(5.10) 2(2.60) 2(2.60) 1(1.30) AISA Grade Aa B C D E n(%) 33(42.30) 13(16.70) 13(16.70%) 23(29.50) 1(1.30) Severity Complete Lesiona Incomplete Lesion n(%) 33(42.30) 45(57.70) Note. ASIA = American Spinal Injury Association; T = thoracic spine level; L = lumbar spine level; S = sacral spine level. a Denotes the most common group. 66 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Table 8 Prevalence of Cardiometabolic Syndrome (CMS) and CMS Risk Factors (N = 184) CMS Prevalence Presenceb N(%) No 54(29.35) Yes 130(70.65) CMS Count Risk Factors Count N(%) 0 9(4.90) 1 16(8.70) 2 29(15.80) 3a 71(38.60) 4 42(22.80) 5 17(9.20) Prevalence of CMS Riskc Risk Factors N(%) SCI Obesity 155(84.20) Hypertension 130(70.70) Dysglycemia 123(66.80) Dyslipidemia 85(46.20) Hypertriglyceridemia 51(27.70) Note. SCI = spinal cord injury. a Denotes the most frequent number of risk factors. b Frequency counts of Yes for 3 Cardiometabolic Syndrome risk factors. c Five risk factor criteria for Cardiometabolic Syndrome. 67 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 68 Table 9 Framingham Risk Score Ranges, Diabetes, Smoking Frequencies, and Percentages FRS Range Frequency Percent Lowa 27 14.70% Intermediateb 61 33.20% Highc 96 52.20% Total 184 100.00% Type II Diabetes Mellitus (T2DM) Frequency Percent No 130 70.70% Yes 54 29.30% Total 184 100.00% Smoking Frequency Percent No 170 92.40% Yes 14 7.60% Total 184 100.00% Note. This table displays the frequency count and percentages of diabetes, smoking, and FRS risk score for each range. a Low < 10%. b Intermediate from 10 to <20%. c High 20%. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Table 10 Cross tabulation of CMS Risk Factors Between Tetraplegia and Paraplegia SCI Groups SCI-adjusted obesity Hypertension Dysglycemia Dyslipidemia Hypertriglyceridemia Total n(%) n(%) Tetraa Parab No 19(10.30) 10(5.40) Yes 87(47.30) 68(37) No 32(17.40) 22(12) Yes 74(40.20) 56(30.40) No 36(19.60) 25(13.60) Yes 70(38) 53(28.80) No 55(29.90) 44(23.90) Yes 51(27.70) 34(18.50) No 73(39.70) 60(32.60) Yes 33(17.90) 18(9.80) 106(57.60) 78(42.40) a Tetra = Tetraplegia level of spinal cord injury. b Para = Paraplegia level of spinal cord injury. 69 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Table 11 Cross tabulation of Diabetes, Smoking, High-risk FRS (20%), and CMS of Tetraplegia and Paraplegia. SCI Groups T2DMc Smoking CMSe FRS ( 20%)d Total n(%) n(%) Tetraa Parab No 71(38.60) 59(32.10) Yes 35(19) 19(10.30) No 100(54.30) 70(38) Yes 6(3.30) 8(4.30) No 30(16.30) 22(12) Yes 76(41.30) 56(30.40) Not High Risk 51(27.70) 40(21.70) High Risk 55(29.90) 33(20.70) 106(57.60) 78(42.40) a Tetra = Tetraplegia level of spinal cord injury. b Para = Paraplegia level of spinal cord injury. c T2DM = Type II Diabetes Mellitus. d FRS (20%) = Framingham Risk Score high-risk category for 10-year CVD risk. e CMS = Cardiometabolic Syndrome 70 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 71 Table 12 Cross tabulation of Diabetes, High-risk FRS (20%), and CMS when Adjusted for SCI-Obesity Cut-Off SCI Groups T2DMc CMSd FRSe ( 20%) Total n(%) n(%) Tetraa Parab No 17(58.60) 9(31.00) Yes 2(6.90) 1(3.40) No 11(37.90) 7(24.10) Yes 8(27.60) 3(10.30) Not High Risk 13(44.80) 8(27.60) High Risk 6(20.70) 2(6.90) 106(65.50) 78(34.5) a Tetra = Tetraplegia level of spinal cord injury. b Para = Paraplegia level of spinal cord injury. c T2DM = Type II Diabetes Mellitus. d CMS = Cardiometabolic Syndrome e FRS (20%) = Framingham Risk Score high-risk category for 10-year CVD risk. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 72 Table 13 Frequency Counts and Percentages of Status at the Time of Data Collection and Causes of SCI Status at Data Collection Alive Deceased Total N(%) 176(95.70) 8(4.30) 184(100) Causes of SCIa N(%) Arthritic Disease 1(.50) Columnar Degeneration 31(16.80) Fall 35(19) Genetic Related 2(1.10) Infection or Abscess 10(5.40) More than 1 SCIa 1(.50) Other (Non-Traumatic) 1(.50) Other (Traumatic) 14(7.60) Sports 9(4.90) Stenosis 1(.50) Surgical Related 5(2.70) Tumor 6(3.30) Vehicular Accident* Total 55(29.90) 184(100) Note. This table illustrates the frequency counts of alive and deceased patients by the time of data collection. * Denotes most common cause of SCI. a SCI = spinal cord injury. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Table 14 Frequency Counts of Causes of SCI Between Groups SCI Groups Causes of SCI Arthritic Disease n (%) n (%) Tetraplegia Paraplegia 1 (0.50) 0(0) Columnar Degeneration 23 (12.50) 8 (4.30) Fall 23(12.50) 12(6.50) Genetic Related 1(.50) 1(.50) Infection or Abscess 4(2.20) 6(3.30) Sports Related 7(3.80) 2(1.10) Stenosis 1(.50) 0(0) Surgical Related 4(2.2) 1(.50) Tumor 1(.50) 5(2.70) 30(16.30) 25(13.60) 4(2.20) 9(4.90) 0(0) 1(.50) Other (Traumatic) 6(3.30) 8(4.30) More than 1 SCI 1(.50) 0(0) Vehicular Accident Violence Other (Non-traumatic) Note. SCI = spinal cord injury. 73 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 74 Table 15 M1Full: Composite Regression Model Output of Combined Groups (C1-S5) Multiple Regression Results for Predicting Transformed Framingham Risk Score for M1Full (Constant) Age Gender SCI Level AISA Grade Complete SCI Duration of SCI SCI-Adjusted Obesity Hypertension Dysglycemia Dyslipidemia Hypertriglyceridemia Unstandardized Coefficients B SE B -2.33 .57 .07 .01 .48 .33 .00 .01 .15 .11 .21 .30 .00 .01 .13 .22 1.22 .18 .29 .17 .42 .15 .54 .17 p .000 .000 .140 .883 .161 .486 .789 .548 .000 .091 .006 .002 95% Confidence Interval for B -3.45 -1.21 .06 .09 -.16 1.13 -.02 .02 -.06 .37 -.39 .81 -.01 .01 -.30 .57 .86 1.58 -.05 .63 .13 .72 .20 .88 Note. SCI = spinal cord injury; AISA = American Injury Spinal Association; M1Full = Full combined model CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 75 Table 16 M2Full: Composite Regression Model Output of Tetraplegia (C1-C8) Group Multiple Regression Results for Predicting Transformed Framingham Risk Score for M2Full (Constant) Age Gender AISA Grade Complete SCI Duration of SCI SCI-Adjusted Obesity Hypertension Dysglycemia Dyslipidemia Hypertriglyceridemia Unstandardized Coefficients B -2.69 .07 .32 .32 .89 -.01 .28 1.34 .24 .54 .40 SE B .76 .01 .44 .16 .48 .01 .27 .24 .23 .20 .22 p .001 .000 .461 .055 .067 .192 .302 .000 .034 .009 .076 95% Confidence Interval for B -4.19 -1.19 .05 .09 -.54 1.19 -.01 .64 -.07 1.85 -.02 .00 -.25 .81 .86 1.82 -.22 .70 .14 .93 -.04 .83 Note. SCI = spinal cord injury; AISA = American Injury Spinal Association; M2Full = Full tetraplegia model CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 76 Table 17 M3Full: Composite Regression Model Output of Paraplegia (T1-S5) Group Multiple Regression Results for Predicting Transformed Framingham Risk Score for M3Full (Constant) Age Gender AISA Grade Complete SCI Duration of SCI SCI-Adjusted Obesity Hypertension Dysglycemia Dyslipidemia Hypertriglyceridemia Unstandardized Coefficients B -2.80 .08 1.14 .15 -.12 .01 -.32 1.06 .12 .32 .77 SE B .90 .01 .55 .16 .41 .01 .43 .29 .26 .25 .28 p .003 .000 .042 .367 .777 .362 .464 .001 .648 .194 .009 95% Confidence Interval for B -4.59 -1.01 .06 .10 .04 2.24 -.18 .47 -.94 .71 -.01 .02 -1.18 .55 .48 1.64 -.40 .64 -.17 .81 .20 1.33 Note. SCI = spinal cord injury; AISA = American Injury Spinal Association; M3Full = Full paraplegia model CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 77 Table 18 M4Full: Composite Regression Model Output of High Thoracic (T1-T6) Group Multiple Regression Results for Predicting Transformed Framingham Risk Score for M4Full (Constant) Age Gender AISA Grade Complete SCI Duration of SCI SCI-Adjusted Obesity Hypertension Dysglycemia Dyslipidemia Hypertriglyceridemia Unstandardized Coefficients B -1.62 .06 .97 .03 .02 .02 .01 .58 .60 -.36 1.26 SEB .89 .01 .54 .19 .43 .01 .53 .28 .30 .32 .32 p .085 .000 .087 .873 .956 .040 .980 .052 .056 .271 .001 95% Confidence Interval for B -3.48 .25 .03 .08 -.15 2.08 -.37 .43 -.88 .93 .00 .04 -1.09 1.12 -.01 1.16 -.02 1.22 -1.02 .30 .60 1.92 Note. SCI = spinal cord injury; AISA = American Injury Spinal Association; M4Full = Full upper thoracic model CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 78 Table 19 M5Full: Composite Regression Model Output of Low Thoracic (T7-T12) Group Multiple Regression Results for Predicting Transformed Framingham Risk Score for M5Full (Constant) Age Gender AISA Grade Complete SCI Duration of SCI SCI-Adjusted Obesity Hypertension Dysglycemia Dyslipidemia Hypertriglyceridemia Unstandardized Coefficients B -5.89 .09 2.50 .67 .14 .02 -1.11 1.95 -.84 .92 1.41 SE B 1.90 .02 1.01 .27 .66 .01 .85 .55 .46 .51 .50 p .005 .001 .022 .023 .833 .122 .207 .002 .082 .086 .010 95% Confidence Interval for B -9.84 -1.94 .04 .13 .41 4.60 .10 1.24 -1.23 1.51 -.01 .05 -2.88 .66 .80 3.09 -1.80 .12 -.14 1.98 .37 2.44 Note. SCI = spinal cord injury; AISA = American Injury Spinal Association; M5Full = Full lower thoracic model CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 79 Table 20 Model Summary for each Full and Reduced Models Models Number of Coefficients R Square Adjusted R Square SE of Estimate R RMSE 0.82 0.67 0.65 0.98 0.97 F p < .001 M1Full 11 M1Reduced 5 0.81 0.66 0.65 0.98 0.98 p < .001 M2Full 10 0.83 0.68 0.65 0.97 0.97 p < .001 M2Reduced 4 0.80 0.65 0.63 1.00 1.00 p < .001 M3Full 10 0.84 0.70 0.66 0.97 0.97 p < .001 M3Reduced 4 0.82 0.68 0.66 0.96 0.96 p < .001 M4Full 10 0.93 0.87 0.80 0.64 0.63 p < .001 M4Reduced 5 0.91 0.83 0.79 0.64 0.64 p < .001 M5Full 10 0.89 0.79 0.69 0.99 0.99 p < .001 M5Reduced 6 0.86 0.74 0.68 1.00 1.00 p < .001 Note. M1 = combined tetraplegia and paraplegia; M2 = tetraplegia; M3 = paraplegia; M4 = upper thoracic; M5 = lower thoracic; SE = standard error; RMSE = root mean square error. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 80 Table 21 Table Summary of Partial F-Tests of Each Model a Models RSSFullf RSSReducedg Number of Excluded Coefficients (j) Total Number of Coefficients (k) Sample Size (n) Fjnk Critical Values M1a 163.99 171.09 6 11 184 1.25 2.21 M2b 90.25 101.30 6 10 106 1.96 2.37 M3c 62.98 67.82 7 10 78 0.87 2.53 M4d 8.07 10.35 5 10 31 0.98 2.92 M5e 20.54 25.02 4 10 32 0.80 3.48 Note. The Partial F-test can be interpreted as testing whether the increase in variance moving from the reduced model to the full model is significant, expressed as: Fjnk = ((RSSReduced RSSFull)/k-j) / (RSSfull/n-k). a M1 = combined tetraplegia and paraplegia b M2 = tetraplegia c M3 = paraplegia d M4 = upper thoracic e M5 = lower thoracic f RSSFull = residual sum of squares full model g RSSReduced = residual sum of squares reduced model. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 81 Table 22 Table Summary of Prediction Equations a = (-1.56 + (Age*0.08) + (Hypertension*1.27) + (Dysglycemia*0.34) + M1Reduced (Dyslipidemia*0.42) + (Hypertriglyceridemia*0.52))2 = (-1.59 + (Age*0.08) + (Hypertension*1.52) + (Dyslipidemia*0.58) + M2Reduced (Hypertriglyceridemia*0.49))2 M3Reduced = (-2.52 + (Age*0.08) + (Hypertension*1.06) + (Hypertriglyceridemia*0.80))2 = (-1.69 + (Age*0.06) + (Gender*1.02) + (Duration of SCI*0.03) + (Dysglycemia*.61) M4Reduced + (Hypertriglyceridemia*1.19))2 = (-3.92 + (Age*0.08) + (Gender*1.79) + (AISA Grade*0.45) + (Hypertension*1.50) M5Reduced + (Dysglycemia*-0.95) + (Hypertriglyceridemia*1.06))2 Note. M1Reduced = combined tetraplegia and paraplegia (C1-S5); M2Reduced = tetraplegia (C1-C8); M3Reduced = paraplegia (T1-S5); M4Reduced = upper thoracic (T1-T6); M5Reduced = Lower Thoracic (T7-T12). Recommended criteria for CVD risk: Low <10%; Intermediate 10% to < 20%; High 20%. a This table lists the predication equations for each reduced SCI group models. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Table 23 Summary of Shapiro-Wilk Statistics for Normal Distribution Assumption for Each Full and Reduced Models Models Statistic df Sig. M1Full 0.99 184 0.79 M1Reduced 0.99 184 0.65 M2Full 0.99 106 0.58 M2Reduced 0.99 106 0.23 M3Full 0.98 78 0.48 M3Reduced 0.98 78 0.15 M4Full 0.97 31 0.54 M4Reduced 0.98 31 0.77 M5Full 0.96 32 0.33 M5Reduced 0.95 32 0.17 Note. M1 = Combined Tetraplegia and Paraplegia, M2 = Tetraplegia, M3 = Paraplegia, M4 = Upper Thoracic, and M5 = Lower Thoracic. 82 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 83 Figure 1 Consult Retrieval, Screening, Data Collection Convenience Sampling Process Chart Review Outpatient Therapy N = 84 Reviewed Lab Markers Combined Roster Inclusion Screening N = 600 CMS Screening Annual Evaluations Sample N = 188* FRS Scoring N = 516 Note. The diagram illustrates the convenience sampling process, starting from consult retrieval and chart review, leading to the final sample. * Denotes the total sample (n = 188) before removal of outliers (n = 4) during data exploration. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 84 Figure 2 Illustration of Data Processing. Data Processing Convenience Sampling Upload Data Set to VINCI Server Data Analyses using PSPP Statistical Analyses Removal of Outliers (n = 4) Final Sample (n = 184) Note. This diagram illustrates the steps of data processing, including data analyses, removal of outliers, transformation of dependent variable (DV), and statistical analyses. CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 85 Figure 3 Histogram and Scatterplot of FRS Before Transformation Histogram Mean = 25.68 Std. Dev. = 17.51 n = 188 40 35 Frequency 30 25 20 15 10 5 0 0 10 20 30 40 50 60 70 80 FRS Normal Q-Q Plot of FRS Expected Normal 2 1 0 -1 -2 -3 -40 -20 0 20 30 Observed Value 40 60 80 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 86 Figure 4 Histogram and Scatterplot of FRS After Square Root Transformation Histogram Mean = 4.67 Std. Dev = 1.65 N = 184 30 Frequency 25 20 15 10 5 0 0 1 2 3 4 5 FRS Transformed 6 7 8 Normal Q-Q Plot of FRS Transformed Expected Normal 2 1 0 -1 -2 -3 0 1 2 3 4 5 6 Observed Value 7 8 9 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Figure 5 Bar Chart Frequency of High-CVD Risk (20%) in Combined Groups (C1-S5) 87 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Figure 6 Bar Chart Frequency of High-CVD Risk (20%) in Tetraplegia Group (C1-C8) 88 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Figure 7 Bar Chart Frequency of High-CVD Risk (20%) in Paraplegia Group (T1-S5) 89 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 90 Figure 8 Bar Chart Frequencies of High-CVD Risk (20%) in Upper and Lower Thoracic Level Injuries (T1-T12) CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY Appendix A Picture of the FRS Calculator from the Framingham Heart Study Website 91 CARDIOVASCULAR DISEASE RISK IN SPINAL CORD INJURY 92 Appendix B Sample of the Data Sheet # SCI level SCI Level Cause of Injury DOI Years since Injury Date of lab since Injury (days) Date of lab since injury (years) # AIS Grade AIS Grade # Completeness Completeness Age  ...

... Predictors of Length of Care for Congenital Torticollis Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Heather Aker, PT, DPT Copyright 10/25/2023 By: Heather Aker, PT, DPT All rights reserved Approved by: Kathryn Martin, PT, DHSc Committee Chair ______________________________ Sam Pierce, PT, PhD Committee Member ______________________________ Elizabeth S. Moore, PhD Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Predictors of Length of Care for Congenital Torticollis Heather Aker Department of Interprofessional Health and Aging Studies, University of Indianapolis 1 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 2 Abstract This study explored whether the type of torticollis, amount of tummy time, amount of container time, and Alberta Infant Motor Scales score influence the length of the therapy plan of care. The study used a non-experimental retrospective design. Data were extracted from a rehabilitation database for clients who sought care between November 2018 and November 2021. One hundred forty-nine participants were identified. Statistically significant weak correlations were found between torticollis type and the length of the plan of care (LPOC) (r = -.32, p < .001), age at examination and LPOC(rs = -.38, p < .001), active range of motion rotation deficit and the LPOC (rs = .42, p < .001) and passive range of motion rotation deficit and the LPOC (rs = -.38, p < .001). The multiple regression model statistically significantly predicted LPOC F(4, 125) = 19.28, p < .001, adjusted R2 = .36. Three variables added to the regression model were statistically significant: age at the examination in days (p < .001), passive rotation deficit (p = .044) and active rotation deficit (p = .015). This research continues to support that therapists should consider an infants age at examination in days, passive rotation deficit, and active cervical rotation deficit when estimating an infants length of plan of care. Higher passive rotation and active rotation deficits lead to longer lengths of care. However, longer lengths of care in this study were associated with younger ages at examination, different from earlier studies. Keywords: torticollis, active cervical rotation deficit, passive cervical rotation deficit, physical therapy, length of plan of care PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 3 Acknowledgments I extend my gratitude to my dedicated dissertation committee members, Dr. Martin, Dr. Moore, and Dr. Pierce. Your insightful guidance, constructive feedback, and commitment to my academic growth have been instrumental in shaping the final outcome of this work. Thank you for helping me untangle my spaghetti bowl of thoughts. Thank you for sticking with me through countless drafts and weird life twists and turns. I am truly honored to have had the privilege of working under your mentorship. To my husband, Keith, your unwavering support has been my rock throughout this journey. Your patience, kindness, and countless fishing trips with the girls have sustained me during long mornings (or nights) of course work, research, and writing. Your belief in me, even when I doubted myself, pushed me to strive for excellence. Thank you for being my confidant, cheerleader, and partner in every sense of the word. If I didnt have you as a partner I wouldnt have made it. I want to express my heartfelt appreciation to my two wonderful daughters, Nora and Maya. Your boundless love, unsatiated need for snacks, and constant reminders of the bigger picture have kept me grounded and motivated. Your resilience during the pandemic and understanding during times when I needed to prioritize my studies are appreciated. Thank you for being my rays of sunshine! I am also thankful to my friends and family who provided encouragement and understanding throughout this journey. Your words of encouragement, gestures of support, and understanding of my occasional absences are deeply appreciated. With heartfelt appreciation, Heather PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 4 Table of Contents Predictors of Length of Care for Congenital Muscular Torticollis .......................................... 8 Purpose Statement ........................................................................................................................... 9 Research Question .......................................................................................................................... 9 Significance of the Study .............................................................................................................. 10 Literature Review ....................................................................................................................... 10 Variability in Identifying Torticollis ............................................................................................. 10 Types of Torticollis ....................................................................................................................... 11 Postural Torticollis ........................................................................................................................ 12 Muscular Torticollis ...................................................................................................................... 13 Torticollis Severity........................................................................................................................ 13 Treatment Outcomes ..................................................................................................................... 14 Factors Affecting Developmental Outcomes ................................................................................ 15 Length of Plan of Care .................................................................................................................. 18 Important Factors Related to Length of Plan of Care ................................................................... 19 Factors Not Directly Related to Length of Care ........................................................................... 20 Inconclusive Factors ..................................................................................................................... 21 Summary ....................................................................................................................................... 24 Method ......................................................................................................................................... 25 Study Design ................................................................................................................................. 25 Participants .................................................................................................................................... 25 Data ............................................................................................................................................... 26 Independent Variables .................................................................................................................. 26 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 5 Dependent Variable ............................................................................................................... 28 Inclusion and Exclusion criteria ............................................................................................ 29 Instruments ................................................................................................................................ 29 Alberta Infant Motor Scale .................................................................................................... 29 Procedures ................................................................................................................................. 30 Data Collection ...................................................................................................................... 31 Data Management .................................................................................................................. 32 Statistical Analysis ................................................................................................................ 33 Results .......................................................................................................................................... 35 General Participant Characteristics ........................................................................................... 35 Descriptive Statistics ............................................................................................................. 36 Inferential Statistics ................................................................................................................... 36 Correlation Tests.................................................................................................................... 36 Multiple Regression Results .................................................................................................. 37 Discussion and Conclusion ......................................................................................................... 38 Predictive factors of length of plan of care ............................................................................... 38 Age at examination ................................................................................................................ 38 Range of Motion .................................................................................................................... 39 Gross Motor Skills ................................................................................................................. 42 Estimating Length of Plan of Care ........................................................................................ 43 Other Correlates of the Length of Plan of Care......................................................................... 44 Torticollis Type ..................................................................................................................... 45 Torticollis Grade .................................................................................................................... 45 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 6 Muscle Function Scale Score ........................................................................................................ 46 Passive Lateral Flexion Deficit ..................................................................................................... 47 Clinical Implications ..................................................................................................................... 47 Cervical rotation range of motion ................................................................................................. 48 Supporting Families with Predictive Models ................................................................................ 48 Limitations .................................................................................................................................... 49 Future Research ............................................................................................................................ 52 Conclusion .................................................................................................................................... 53 References .................................................................................................................................... 54 Appendix A .................................................................................................................................. 70 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 7 List of Tables Table 1 Participant Characteristics: Patient factors ...................................................................... 64 Table 2 Participant Characteristics: Examination factors ............................................................. 65 Table 3 Objective 1 Correlations .................................................................................................. 67 Table 4 Objective 2 Correlations .................................................................................................. 68 Table 5 Multiple Regression ......................................................................................................... 69 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 8 Predictors of Length of Care for Congenital Muscular Torticollis Torticollis is a musculoskeletal issue characterized by side bending of the neck in one direction while resting in opposite neck rotation, often due to tight muscles (Kaplan et al., 2018). In 3.9% of births, torticollis is an incidental finding (Aarnivala et al., 2014), and it has been reported in up to 16% of healthy newborns (Stellwagen et al., 2008), depending on assessment strategy. Best practice for treating torticollis includes early referral to physical therapy for cervical muscle stretching and strengthening to correct muscle imbalances (Kaplan et al., 2018). If treatment is not started early, torticollis can result in infants having asymmetric cervical strength, problems feeding, poor balance, difficulty rolling to both sides and symmetric mobility (Kaplan et al., 2018). Therefore, physical therapists must create a plan of care that is of an appropriate length to maximize the infant's participation in symmetric motor skill development, cervical strength, and mobility. At the initial examination of children with torticollis, physical therapists estimate the length of a plan of care based on the client's expected prognosis and initial presentation. Clinicians also consider factors known to affect the length of a therapy plan of care for infants with torticollis. These include the presence of a sternocleidomastoid (SCM) tumor (Cheng et al., 2000; Cheng et al., 2001), early start to treatment (Lee et al., 2017; Petronic et al., 2010), and birth factors, including intrauterine constraint or breech presentation (Kaplan et al., 2018; Lee, Cho, et al., 2011). While it is known that these factors meaningfully influence the length of a plan of care, it is unknown if other variables, including the type of torticollis, amount of tummy time, amount of container time, or gross motor development at examination, influence the length PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 9 of the therapy plan of care. A better understanding of how these factors may affect length of plan of care will help clinicians predict and educate families on expected therapy timelines. Expanded knowledge of what factors influence therapy timelines will facilitate a therapy program's ability to facilitate family buy-in. Therapeutic alliance can be supported by promoting a family's understanding of an expected length of care (Castilla et al., 2023; OKeeffe et al., 2016). Creating a therapeutic alliance through knowledge-sharing sets the foundation for trust building, which in adult literature supports client buy-in and positive therapy outcomes (MacKay et al., 2020; VanEtten et al., 2021). Purpose Statement The purpose of this study was to explore whether the type of torticollis, amount of tummy time, amount of container time, and gross motor development as determined by the Alberta Infant Motor Scales Score (AIMS) influence the length of the therapy plan of care. In addition, the study explored if these factors, along with previously identified patient and examination factors, could predict the length of care for infants with torticollis. To address the study purpose, the following research questions were answered: Research Question What is the relationship between torticollis type, tummy time or container time participation, AIMS score, and patient age at examination to length of a therapy plan of care for infants with torticollis? To answer the research question, the following objectives will be met: 1. To determine if there is a relationship between torticollis type, tummy time or container time participation, AIMS score, patient age at examination, and the length of the therapy plan of care. PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 10 2. To determine if additional patient and examination factors influence the length of the therapy plan of care. Significance of the Study Given the frequency of torticollis referrals, this study encouraged clinicians to consider passive rotation range of motion deficit, active range of motion deficit, and age at examination as factors that all influence a length of plan of care. This study showed correlations between length of care and torticollis type, passive lateral flexion deficit, torticollis grade, and muscle function scale score difference, but the relationship was not predictive of length of care. This study demonstrated no relationship between tummy time or container time participation or AIMS score, and length of physical therapy plan of care. Understanding factors that influenced the length of a plan of care would facilitate a therapist's ability to provide more consistent care and would help families understand the length of the commitment to treatment. Literature Review Variability in Identifying Torticollis Torticollis research is limited by a lack of consistency in defining torticollis and the wide variety of ICD-10 codes used to identify the medical diagnosis. Clinical terms used interchangeably to describe torticollis include SCM tumor torticollis, congenital muscular torticollis, positional torticollis, muscular torticollis, and congenital torticollis (Kaplan et al., 2018). Kaplan et al. (2018) noted that a variety of medical professionals might identify torticollis by a variety of ICD-10 codes, including fascial asymmetry, plagiocephaly, congenital deformity of the SCM muscle, other congenital malformations of the musculoskeletal system, sternomastoid injury due to birth injury, or torticollis. Rohde et al. (2021) noted there is overall difficulty in applying consistent diagnosis terms in electronic medical records because multiple PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 11 different types of practitioners can add to and resolve diagnoses in a client's problem list. In Rohde et al.'s (2021) work with infants diagnosed with plagiocephaly, a limitation of their study was the possible lack of identification of all comorbidities due to the wide variety of practitioners who could add and delete diagnoses. Further defining and using consistent medical and clinical definitions of torticollis would lead future researchers to include a more homogeneous sample for analysis. Types of Torticollis The three major presentations of torticollis described In Kaplan et al. (2018) were postural, muscular, and SCM mass (Kaplan et al., 2018). The reported rates of torticollis were highly variable, ranging between 316% of infants depending on age at assessment (Aarnivala et al., 2014; Stellwagen et al., 2008). Postural torticollis was found in infants who actively laterally tilt one way and rotate the other, whereas infants with muscular torticollis present with passive range of motion (PROM) and active range of motion (AROM) limitations. In the most severe form, SCM mass torticollis, infants had a thickening or mass in the SCM muscle. SCM mass torticollis was outside the scope of this review as it was more well-studied and less frequently identified in clinical practice. Within torticollis research, those with more severe types like SMC tumor torticollis were often compared to groups containing both muscular and postural torticollis, leading to limited research comparing postural torticollis to muscular torticollis (Han et al., 2019; Lee, Yoon, et al., 2011). Those with muscular or postural torticollis were often considered together, or those with postural torticollis were excluded from studies (Amaral et al., 2019; Bercik et al., 2019; Oledzka et al., 2020). The PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS combination of groups and lack of a clear definition of torticollis among studies led to difficulty comparing outcomes between the two groups. Postural Torticollis Unbalanced muscular recruitment resulting in preferential positioning in one direction was considered postural torticollis. Kaplan et al.'s (2018) clinical practice guideline (CPG) supported categorizing torticollis as postural and described postural preference as synonymous with positional preference. The authors noted that postural preference was an asymmetric position the "infant gravitates to in all positions" (Kaplan et al., 2018, p. 209). Postural torticollis was often overlooked as a classification of torticollis because the symptoms may mimic plagiocephaly. In addition, because symptoms of postural torticollis were subtle, infants were often referred to therapy when they were older, and active movement limitations were more apparent (Amaral et al., 2019). Only three articles specifically included groups of infants with postural torticollis (Cheng et al., 2000; Leung et al., 2017; Watemberg et al., 2016). Cheng et al.'s work, two articles published in 2000 and 2001, demonstrated factors that were predictive of a need for surgical intervention and longer lengths of care; they include type of torticollis, rotation deficit, and age at presentation. Of the three articles identified, only Leung et al. (2017) considered factors related to infant motor outcomes. In Leung et al.'s (2017) work, infants with plagiocephaly but without a shortened SCM demonstrated quicker activation of the SCM muscle responsible for turning the infant's head toward their preferred direction. Quicker muscle activation evidenced an active movement imbalance rather than a passive preference for rotation, and the active movement imbalance indicated a strength or motor control challenge (Leung et al., 2017). The findings of Leung et al. (2017) raised questions about how strength imbalance characterizes 12 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 13 postural presentations of torticollis. This strength imbalance was also likely why infants with postural torticollis were referred later to physical therapy; younger infants have not yet fully demonstrated their cervical strength impairments. Watemberg et al. (2016) studied a group of infants born after 37 weeks with what they defined as functional asymmetry, very similar to the definition of postural torticollis. Researchers found that those with decreased motor activity on one side had longer treatment durations but no weakness or spasticity indicative of an upper motor neuron lesion. Therefore, the asymmetry found in Watemberg et al.'s (2016) study of infants with postural torticollis was supported by Leung et al.'s (2017) findings that infants who more easily turn toward their preferred side moved less symmetrically than expected of a typically developing infant. Functional asymmetries and moving easily toward the infant's preferred side were a typical clinical picture of an infant with postural torticollis versus an infant with muscular torticollis. Muscular Torticollis Infants with muscular torticollis differed from those with postural torticollis because they have a PROM limitation in addition to the AROM limitations found in postural torticollis (Kaplan et al., 2018). Range of motion limitations were noted as differences in cervical rotation and/or cervical lateral flexion, and these limitations were a greater than 5 difference in one direction versus the other as assessed passively (Kaplan et al., 2018). Muscular torticollis was delineated in severity by grading, identified by the range of motion limitation compared to the non-involved side (Kaplan et al., 2018). Torticollis Severity PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 14 Physical therapists used a torticollis severity grading tool to document severity. The CMT Classification Grades and Decision Tree for 0-12 months, see Appendix A, allowed clinicians to label a client's torticollis presentation objectively (Kaplan et al., 2018). All three types of torticollis were graded on the same severity scale (Kaplan et al., 2018). This scale had reliable intra-rater and interrater reliability for novice and experienced clinicians (Oledzka et al., 2018). Torticollis severity ranged from the least severe in grade 1 to the most severe presentation in grade 8. Infants with less severe grades of torticollis typically had resolution of their postural or range of motion limitations, which included improved control of midline cervical positions with physical therapy intervention (Petronic et al., 2010). The severity grading tool considered an infant's age, range of motion presentation, and presence of an SCM tumor (Kaplan et al., 2018). The tool identified infants of the same age with a noted postural preference or passive motion limitation less than 15 in one direction as the least severe grade of torticollis, grade 1 (Kaplan et al., 2018). When infants were graded, the severity of postural torticollis increased as a function of only the infant's age at examination. The grading of muscular torticollis increased in severity as a function of age and passive rotation motion limitations, see Figure 1 (Kaplan et al., 2018). Treatment Outcomes Historically, studies have looked at the resolution of torticollis symptoms as normalization of cervical rotation PROM and when the infant can maintain midline head positions (Lee et al., 2017; Petronic et al., 2010; Ryu et al., 2016). These benchmarks were clinically meaningful because they demonstrated that infants could fully engage with both sides of their environment visually and move equally. As physical therapy practice and research have evolved, so have discharge criteria for infants with torticollis (Kaplan et al., 2018). The five PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS criteria for discharge from physical therapy noted in the CPG were improved PROM (affected versus unaffected within 5), symmetrical active movement, age-appropriate motor skills, no head tilt, and sufficient parent education (Kaplan et al., 2018). Ideally, the resolution of torticollis and discharge were simultaneously achieved; however, that did not always happen in clinical care (Greve et al., 2022). Factors Affecting Developmental Outcomes Infants. When creating a therapy plan of care for torticollis, physical therapists compared the infant's presentation to what would be expected of a typically developing infant, especially regarding range of motion, strength, and developmental motor skills (Kaplan et al., 2018). Typically developing newborns could passively rotate their head 100-110 and laterally sidebend to 50-60 (Stellwagen et al., 2008). Differences in range of motion for infants with torticollis were limited either passively or actively from the typical range. Active motion is also a tool clinicians use to begin assessing an infants independent cervical motion. It was assessed in various positions to determine the infants ability to move with and against gravity (Kaplan et al., 2018). Lastly, Kaplan et al. (2018) identified multiple tools clinicians could use to assess developmental motor skills, including the AIMS (Piper & Darrah, 2022), Test of Infant Motor Performance (Campbell, 2005), or Peabody Developmental Motor Scales, 2nd edition (Folio & Fewell, 2000). An infant's inability to demonstrate full active motion is often limited by the force of gravity the infant is exposed to after birth. Clinically, this is noted in supine. For infants with torticollis, at assessment, they are pulled down into their position of preference by the forces of gravity, and the infant's limited strength to move against these 15 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS gravitational forces further exacerbates the tendency to stay within an easy movement pattern (Leung et al., 2017). Difficulty moving out of their position of preference and limited exposure to various positions, including tummy time, led to prolonged positioning in one direction and limited natural exposure to varied gravity influences (Felzer-Kim et al., 2020; Richard & Metz, 2014). The limited exposure to various positions further limited an infant's ability to develop symmetrical cervical, core, and upper back strength (Felzer-Kim et al., 2020; Richard & Metz, 2014). Asymmetrical strength presentations often translated into asymmetrical motor skills development and asymmetrical cervical control (Kaplan et al., 2018). Tummy Time. Tummy time was found to be critical for development (Hewitt et al., 2017). Tummy time was recommended for all infants beginning the day they come home from the hospital (American Academy of Pediatrics [AAP], 2017). Tummy time was important not only to allow infants to develop symmetrical core, cervical, and shoulder strength, but the practice of tummy time in the age of Back to Sleep also positively correlated with overall development (Hewitt et al., 2017). There was a lack of research into how much tummy time was enough, so the guidelines varied significantly between countries and organizations. For example, Salls et al. (2002) recommended at least 15 minutes daily. Still, the Australian government encouraged at least 30 minutes (Hesketh et al., 2017), and the AAP noted that infants should participate in tummy time three times per day for 35 minutes (AAP, 2017). The current torticollis CPG (Kaplan et al., 2018) recommended tummy time for infants with torticollis three times a day in alignment with hman, Nilsson, Lagerkvist et al.'s (2009) findings. In addition, parents have had trouble knowing how and what to do for tummy time with their infants, especially if infants negatively respond to prone positioning (Richard & Metz, 16 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 2014). Ricard and Metz (2014) described facilitators and barriers to tummy time. Their research demonstrated that parents' knowledge did not influence tummy time practice, and most parents did not fear tummy time. Instead, Ricard and Metz (2014) found that parents who set aside time for tummy time participated more. The most significant barrier in this study was the infant's response to tummy time. In addition, there was a relative lack of variety in the tummy time practice by the parentsmost parents did tummy time by placing the infant on a blanket on the floor, which was hypothesized as a reason for limited infant engagement. In contrast, Palmer et al. (2019) taught parents to do tummy time with their infants using a multisensory approach. Their single tummy time lesson intervention included face-to-face interaction with the parent, bringing the baby gently from supine to side then belly to promote acceptance of prone. Participants showed significant improvements in parents demonstrating improved handling skills for prone transitions and sensory input, and infants demonstrated to parents more comfort and attentiveness in prone. In addition, infant participation in tummy time minutes the week after class improved compared to controls. The study of how much time an infant spends participating in tummy time was often difficult to quantify. Most researchers to date reported tummy time via parent reports using questionnaires or diaries (Hewitt et al., 2019; Ricard & Metz, 2014). Researchers have begun exploring using movement monitoring devices and developing algorithms to determine time spent in prone; however, devices have difficulty recognizing modified prone positions like prone against a caregiver's shoulder (Hewitt et al., 2019). As such, parent report remained an acceptable method of tummy time minute 17 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 18 reporting and, for the proposed study, was used in conjunction with developmental testing to ensure a more robust picture of infant development was included. Tummy time was hindered by time spent supine or in baby-containing devices (Hewitt et al., 2017). Avruskin (2018) described container baby syndrome as a result of infants spending too much time in infant devices without appropriate time to move and explore their bodies. However, research into the implications of how much time infants spend in containers and the resultant effects on motor development was limited. Clinically, physical therapists often recommend increasing an infant's tummy time while limiting time spent in containment devices to encourage increased levels of physical activity and motor exploration. The interaction between baby container time, tummy time, and infant motor development was an emerging area of research (Avruskin, 2018). Current physical therapy practice encourages limited use of infantcontaining devices (Avruskin, 2018). No standard assessment technique was used beyond parent report for baby container time. Like issues objectively measuring tummy time, the lack of objective techniques to measure container time often led researchers to use multiple comparison measures to understand infant participation and support parent-reported outcome measures (Hewitt et al., 2019), as was the case with the proposed study. Length of Plan of Care At examination, physical therapists estimate the length of a plan of care based on the client's expected prognosis and initial presentation. Understanding the influences of infant and motor development characteristics on prognosis specific to torticollis can help clinicians predict a plan of care. Multiple studies have identified significant and insignificant factors affecting treatment duration (Han et al., 2019; Hong et al., 2016; Jung et al., 2015; Lee et al., 2015; Watemberg et al., 2016). PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Important Factors Related to Length of Plan of Care Researchers have consistently identified that early treatment of torticollis, prior to three months of age, led to earlier resolution of symptoms compared to infants referred after three months of age (Boyko et al., 2017; Cheng et al., 2001; Kaplan et al., 2018; Lee, Yoon, et al., 2011). Cheng et al. (2001) conducted a large retrospective study that categorized infants diagnosed with torticollis into three groups: SCM tumor torticollis, muscular torticollis, and postural torticollis. It was one of the first studies to show that infants with SCM tumors presented earlier and took longer to recover than the study's two other groups (Cheng et al., 2001). In the study, 92% of infants with SCM tumors were seen for torticollis by three months of age, compared to those with postural torticollis, where 81% were seen after the third month of age (Cheng et al., 2001). Clinically, as infants aged, treatment, including encouraging active range of motion to the non-preferred side, became difficult as infants gained head control. This study showed that 81% of infants with postural torticollis presented to physical therapists just as they gained cervical control and the ability to avoid interventions. Cheng et al. (2001) outlined through multivariate analysis that significant predictors of duration of treatment were having a passive rotation deficit, the type of torticollis, age at presentation to therapy over one month, and birth difficulties. Cheng et al. (2001) highlighted the importance of early treatment, even before one month of infant age. When reviewing newer studies, including Amaral et al. (2019) and Knudsen et al. (2020), authors noted that their study populations did not reflect that infants are being referred prior to 1 month of age despite Cheng et al.'s (2001) results being published over 20 years ago. 19 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Factors related to length of care with multiple studies supporting their influence include that the duration of therapy was related to the type of torticollis (specifically SCM tumors), duration of therapy was related to early treatment, and duration was related to birth difficulties (Cheng et al., 2001; Han et al., 2019; Hong et al., 2016; Knudsen et al., 2020). In relation to SCM tumors, there were specific factors that affected the length of treatment, including the presence of SCM lesions (Han et al., 2019), increased thickness of SCM compared to the noninvolved side (Hong et al., 2016; Lee et al., 2015), increased severity grade of torticollis (Knudsen et al., 2020). Knudsen et al.'s (2020) retrospective study of infants less than 6 months old with torticollis showed that infants with more severe forms of torticollis, here grade 3, had significantly increased utilization of services than infants at grade 1 and longer length of care durations. Both of those findings by Knudsen et al. (2020) indicated increased healthcare spending for infants with more severe forms of torticollis. Two other studies support the finding that older age at the start of treatment led to longer durations of care (hman et al., 2011; Petronic et al., 2010). Finally, Lee, Cho, et al. (2011) also found that birth issues such as intrauterine constraint or breech presentation were associated with longer treatment durations. Factors Not Directly Related to Length of Care Multiple factors have been identified as not directly affecting the length of care for an infant with torticollis. Amaral et al. (2019), Jung et al. (2015), and Watemberg et al. (2016) identified that the sidedness of torticollis did not affect outcomes. In all three studies, there was not a significant difference between groups. In addition, gestational age (Han et al., 2019; Jung et al., 2015) was reported as not significantly correlated with length of care differences. Lastly, sex (Amaral et al., 2019; Han et al., 2019; Jung et al., 2015) and delivery methods (Han et al., 2019; Jung et al., 2015; Watemberg et al., 2016) did not appear to affect an infant's length of care. As 20 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 21 Jung et al. (2015) reported, the correlation between gestational age and length of care was insignificant, r (116) = .11, p = .247. Clinically, for physical therapists, this would indicate that they did not need to consider the side of torticollis, gestational age at birth, sex, or delivery methods when determining the possible length of physical therapy care. Inconclusive Factors Research into factors that affect the length of torticollis care included many variables for which there were conflicting studies, such as the presence of rotation versus lateral tilt range of motion deficits (Amaral et al., 2019) and age at initial visit (Amaral et al., 2019; Jung et al., 2015; Knudsen et al., 2020; Lee et al., 2015; Watemberg et al., 2016). Amaral et al. (2019) described a retrospective study population of 160 infants where the mean time to resolution was higher for those with other findings at the examination, such as passive range of motion limitations versus those with only a cervical tilt resting posture (M = 21.5 weeks, SD = 10.6 and M = 16.2 weeks, SD = 7.6 respectively; p = .02). Amaral et al.'s (2019) study clinically demonstrated a 5-week earlier resolution for those with only a cervical tilt limitation which translates into less required physical therapy appointments, decreased cost, and would open a treatment slot for another waiting family. Contradicting Jung et al.'s (2015) findings, in a study population of 118 infants with a mean age of 68.8 days, that difference in passive motion for rotation or lateral flexion was not significantly associated with length of rehabilitation. The major difference between these two studies was that in Amaral et al. (2019), the infants were, on average, 81.2 days old at referral to physical therapy, and in Jung et al. (2015), infants were 68.8 days old. Thus, Amaral et al.'s (2019) population was 12 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS days older than the infants in Jung et al.'s (2015) study, throwing a confounding age variable into comparisons between these two studies. In addition, Amaral et al. (2019) grouped everyone who had any difference at examination outside of cervical tilt together, and Jung et al. (2015) isolated each issue by itself to determine the effect on length of care. If Jung et al. (2015) put all those with range of motion issues aside from cervical tilt, hip dysplasia, and fascial and head asymmetries together, they may have found significance. Lastly, the two studies described the end of care differently. Jung et al. (2015) measured rehabilitation duration from the start of treatment until the passive cervical motion difference was less than 5, and Amaral et al. (2019) specifically used the term resolution but did not further define what was considered in determining resolution. In studies looking at age at the initial visit as a predictor of length of therapy, there is a discrepancy between when infants are referred based on their severity of presentation. For example, Cheng et al. (2001) noted that infants with postural torticollis presented later to therapy, on average, at 143 days, compared to the SCM tumor group at 43.8 days or the muscular torticollis group at 106 days. Clinically, the difference between those with postural torticollis and those with muscular torticollis presenting at 143 versus 106 days of age is the difference between an infant who was more accepting of repositioning and stretching and one who was more set in their movement patterns at 143 days. However, in Amaral et al.'s (2019) study, the age at the start of treatment was not always positively related to the length of care. They found much more variability. Amaral et al.'s (2019) article did not specifically try to use regression to determine if this factor was predictive of length of care, instead focusing on independent t-tests and chisquare to determine if variables were significant for those infants with longer durations of care. The authors do not report support for their findings that age at referral was inconsistently related 22 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS to the duration of care. It would have been interesting if Amaral et al. (2019) had pulled out each variable (such as rotation difference or fascial asymmetry) to discuss its relative influence on length of care versus grouping all variables together and using less rigorous Chi-square tests. Pulling variables apart may have removed the significance of their findings because the numbers in each group would be smaller, and it would be more difficult to find significant results. Researchers in Knudsen et al.'s (2020) study noted that their participants were all under 6 months of age at examination, increasing the study populations' homogeneity. These researchers could not identify enough participants over 6 months of age in their database to include in the study, which also showed a trend toward earlier referral to physical therapy in the study sample. However, Petronic et al.'s (2010) earlier study of infants had a sufficient sample size across ages to represent all age groups between birth and 12 months. They found that infants who started therapy earlier had an earlier resolution of symptoms (Petronic et al., 2010). The gap in these studies was that there was a possible trend toward earlier overall referral noted in Knudsen et al. (2020), but the question remains: if infants with postural torticollis were referred early, would they still have an early resolution of symptoms, or did the results in Amaral et al. (2019) indicate there may be longer courses of care? Amaral et al.'s (2019) questionable sample and a lack of other studies to support or refute the findings creates an area for continued research. Further research is needed into the relationship between age at the start of treatment and overall population characteristics. Lastly, though not specifically identified in the literature as an inconclusive factor, the type of torticollis presented at examination was considered for children with 23 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 24 SCM tumor torticollis who were referred earlier to therapy, but it was often not addressed for less severe presentations. For example, Han et al. (2019) noted that in their study, infants with SCM tumors were referred earlier to therapy than clients without lesions, at 55.34 days old versus 146.6 days old, but do not further delineate muscular versus postural torticollis. Watemberg et al. (2016) found that for their study population of 173 infants, those presenting with postural torticollis and functional asymmetry, such as moving one hand more than the other, had longer treatment durations. However, in Petronic et al.'s (2010) study, the type of torticollis was not included in the reporting of results; thus, it is unknown if those with postural torticollis were represented more frequently in the older age groups and thus had longer episodes of care. Consequently, though research in the early 2010s indicated that early referral to physical therapy led to earlier resolution of symptoms (Petronic et al., 2010), recent literature raised the question of whether this finding was consistent within the context of newer studies having overall younger infants with less severe torticollis at referral (Amaral et al., 2019; Knudsen et al., 2020; Watemberg et al., 2016). Summary Torticollis has various presentations, including muscular torticollis and postural torticollis (Kaplan et al., 2018). There was conflicting evidence to support that early physical therapy treatment led to shorter durations of care for younger infants with postural torticollis (Amaral et al., 2019; Han et al., 2019; Kundsen et al., 2020) given that in current research, those infants presented later for treatment and early studies demonstrated infants who presented later for treatment had longer durations of care (Petronic et al., 2010). Research is needed to consider the role of type of torticollis has on length of care. This study aimed to help fill the literature's incongruency regarding length of care for those with muscular versus postural torticollis. In PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 25 addition, there was a gap in the research in understanding whether a parent's report of tummy time or time in containers and an infant's gross motor presentation as determined by AIMS score at examination would help predict an infant's length of plan of care. Research was also needed to build on findings by Watemberg et al. (2016) and Leung et al. (2017) to consider gross motor development's effect on length of care by considering tummy time and container time as examination factors that influenced a length of plan of care. Method Study Design This study was non-experimental and used a retrospective design to explore the relationship between participant and examination factors and length of plan of care. Data extraction from medical records took place from June 2022 through November 2022. The Children's Hospital of Philadelphia (CHOP) Institutional Review Board approved the study, and a reliance agreement was established between CHOP and the University of Indianapolis Human Research Protection Program. Participants Participants were infants diagnosed with torticollis referred to outpatient physical therapy at CHOP's main campus and satellite therapy offices. Infants were 12 months old or under at initial examination and diagnosed with torticollis or postural preference as identified by ICD 10 codes, Q67.0, Q67.3, Q68.0, Q79.8, P15.2, or M43.6 (Kaplan et al., 2018) and received care between November 2018 and November 2021. This period reflected participant episodes of care after the publication of the most recent CPG for torticollis (Kaplan et al., 2018). An a priori minimum sample size calculation was conducted to predict length of plan of care using G*Power 3.1 (Faul et al., 2009). The calculation was based on PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS conducting linear regression and the following parameters: five predictor variables at an alpha level of .05, a power of 0.80, and a medium effect size of f2 = 0.15. Medium effect size was used secondary to the lack of similar studies with published outcomes to base the estimation on. The resulting estimate was a minimum of 55 participants. However, due to an unknown effect size, a larger sample size was used to decrease the risk of having an underpowered study. Data Independent Variables Patient factors. Age at examination (days) Age at referral (days) Diagnosis codes (ICD-10) Sex o Male o Female Weight (g) within 1 month of examination Length (cm) within 1 month of examination Head circumference (cm) within 1 month of examination Insurance coverage, primary and secondary medical payor information o Medicaid o Children's Health Insurance Program (CHIP) o Employer-funded insurance Preferred Provider Organization (PPO) Health Maintenance Organization (HMO) 26 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Health Fund Private Point of Service (POS) Exclusive Provider Organization (EPO) 27 o Self-pay o Uninsured o Federal Insurances Federal PPO Tricare Examination factors. Caregiver report of tummy time in minutes per day at examination Caregiver report of baby container time in minutes per day at examination AIMS percentile at examination, at reassessment after three months of age, and discharge Plagiocephaly presence recorded as severity at examination based on the Children's Healthcare of Atlanta scale (Holowka et al., 2017) o Level 1: Cranial vault asymmetry index (CVAI) of <3.5% o Level 2: CVAI 3.5 to 6.25% o Level 3: CVAI 6.25 to 8.75% o Level 4: CVAI 8.75 to 11.0% o Level 5: CVAI > 11.0% Type of torticollis o Muscular torticollis: the presence of passive cervical rotation or lateral flexion deficit o Postural torticollis: the presence of active cervical rotation or lateral flexion deficit Passive cervical rotation and lateral flexion range of motion measurements at examination PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 28 Active cervical rotation and lateral flexion range of motion measurements at examination Torticollis grade (Kaplan et al., 2018) o Grade 1: Early mild: Infants between 0 and 6 months of age with only postural preference or a difference between sides in passive cervical rotation of less than 15. o Grade 2: Early moderate: Infants between 0 and 6 months of age with a difference between sides in passive cervical rotation of 15 to 30. o Grade 3: Early severe: Infants between 0 and 6 months of age with a difference between sides in passive cervical rotation of more than 30 or an SCM mass. o Grade 4: Later mild: Infants between 7 and 9 months of age with only postural preference or a difference between sides in passive cervical rotation of less than 15. o Grade 5: Later moderate: Infants between 10 and 12 months of age with only postural preference or a difference between sides in passive cervical rotation of less than 15. o Grade 6: Later severe: Infants between 7 and 9 months of age with a difference between sides in passive cervical rotation of more than 15 or between 10 and 12 months of age with a difference of 15 to 30. o Grade 7: Later extreme: Infants between 7 and 12 months with an SCM mass or between 10 and 12 months of age with a difference between sides in passive cervical rotation of more than 30. o Grade 8: Very late: Infants and children older than 12 months of age with any asymmetry, including postural preference, any difference between sides in passive cervical rotation, or an SCM mass. Dependent Variable Length of a therapy plan of care measured in days from the start of therapy to discharge from PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 29 physical therapy. The length of plan of care was measured from the initial therapy examination until the last documented therapy session. The end of a plan of care occurred with the last documented therapy session, not the resolution of symptoms. The current study focused specifically on the length of care an infant participated in; therefore, the resolution of symptoms was not assessed. Inclusion and Exclusion criteria Inclusion Criteria Medical record review required an AIMS score within 1 month of examination Passive range of motion measurements to determine the type of torticollis Tummy time minutes or container time minutes Exclusion Criteria Infants were excluded if their medical record number (MRN) was repeated Infants had comorbidities such as prematurity (< 37 weeks gestation), other neurological conditions such as stroke, autism spectrum disorder, ocular torticollis, or genetic conditions, such as trisomy 21, which were expected to result in developmental delay. Instruments Alberta Infant Motor Scale The AIMS, a norm-referenced observational assessment, measured gross motor maturation in infants. It assessed gross motor skills across prone, supine, sitting, and standing positions. Included are 58 observable skills, 21 concentrated in the prone position. This tool was widely used in pediatric physical therapy because it was inexpensive, quick, and easy to administer via observation (Luna de Albuquerque et al., 2015). In this study, the AIMS was used as a comparison measure to represent motor skill PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 30 development in conjunction with tummy time or container time minutes (Polit & Beck, 2014). The AIMS was reliable and valid for the assessment of infants (Darrah et al., 2014; Piper et al., 1992). The AIMS was re-normed in 2014, secondary to concerns that Back to Sleep recommendations may have changed the timeline of prone skill development (Darrah et al., 2014). In this sample, the original normative age values differed from the contemporary values by only one week, meaning percentile ranks broken down by week increments did not change. The AIMS was a good predictor of future motor difficulty at 4 months (Spittle et al., 2015). It has good interrater reliability across all age groups, intraclass correlation coefficient (ICC) = .97.99 (Jeng et al., 2000), and good concurrent validity with the Bayley Motor scale at 6 months, r(43) = .78, p < .001 (Jeng et al., 2000). Snyder et al. (2008) also demonstrated that novice and experienced clinicians accurately rated scores on the AIMS, ICC = .98.99. Spittle et al. (2015) identified a floor effect for infants under 3 months of age. Due to the floor effect on this test, scores at evaluation, re-evaluation, and discharge were collected as available to help support meaningful interpretation in line with Spittle et al.'s (2015) findings that increased delay was associated with increased delayed scores over time. Procedures Investigators used information from routine clinical care compiled within a CHOP rehabilitation database, Arcus. The Arcus Rehabilitation registry is an IRB-approved database at CHOP used to capture patient data for rehabilitation research (Camacho, 2022). The registry is stored and maintained in Arcus, CHOP's data library for secure data reuse and collaboration, supported by the CHOP Research Institute. Patients receiving rehabilitation services at all CHOP sites are included. All data were extracted from information in the patients' electronic medical record. The data extraction and the registry organization process were developed in partnership PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 31 with the Clinical Reporting Unit of CHOP's Department for Biomedical and Health Informatics. For this project, data available in the database were exported by data scientists who manage the database. Data scientists sent the initial extraction file to the CHOP-affiliated project investigator (S.P.), who controlled access to the registry data via a password-protected data file and managed the initial organization of possible participants. From the originally identified data set, data scientists were unable to identify those possible participants missing AIMS scores due to how the outcome measure was pulled into Arcus. S.P. removed possible participants if multiple rows of missing AIMS scores were identified, as they represented cases without AIMS scores. A random number function was then used to put possible participants in random order. Data were deidentified, translating date of birth into age at initial evaluation. MRN remained in this initial database extraction file for chart review. Data were then sent to the primary researcher (H.A.). Study identification numbers were assigned to possible participants. The primary researcher then completed chart reviews for possible participants to supplement information unavailable in the Arcus database. Chart review further informed inclusion decisions. All protected health information was stored in compliance with CHOP IRB standards. Data Collection The primary researcher was responsible for all data collection tasks. A second data file was created for data collection, which included only the study identification number and extracted data to protect participant health data. Charts were reviewed to ensure all inclusion criteria were met and referred to the medical chart for missing data from the initial database extraction. If participants charts were missing a required data element (AIMS score within one PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 32 month of examination, passive range of motion measurements, tummy time minutes or container time minutes), the missing data were noted in the data collection file, and the row of cells were highlighted red to note exclusion. The chart reviews were stopped once 152 participants with complete data sets were identified. Most commonly, tummy time or container time minutes data were missing. In addition, most included individuals had documented tummy time minutes, not container time minutes. Data Management The MRN identified each participant in the initial data extraction file which was password-protected and saved on the hospital's encrypted cloud server separate from the data collection file. The data collection file was password-protected and stored in a separate location on the hospital's encrypted cloud server. No other personal identifying factors were collected nor translated into the data collection file. Data within the data collection file were deidentified. The initial data extraction file was accessible by the primary researcher and project investigator. In alignment with the registry IRB this study fell under, S.P. controlled access to the file linking the study ID number and MRN, the initial data extraction file. The primary researcher was responsible for screening charts, extracting data, and immediately recording data into the data collection file. All investigators had appropriate training in data management and management of confidential information. The primary researcher conducted spot checks of recorded data, one case reviewed every 10 charts, to ensure data were correct and recorded consistently. The primary researcher also used a flow chart to ensure medical charts were reviewed each time, similarly, beginning with required chart elements to screen for inclusion, followed by all other variables of interest. Data will be kept for 10 years on an encrypted cloud server after completing data analysis and publication or PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 33 presentation of results. Once 10 years are over, all remaining data files will be deleted. Statistical Analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY). Descriptive statistics were reported to describe the study participants. Nominal data, sex, ICD-10 codes, insurance type, and torticollis type were reported as frequencies and percentages. Ordinal and non-normally distributed interval and ratio data, including but not limited to plagiocephaly score and torticollis severity, were reported as medians and interquartile ranges. The normality of the data was determined using Shapiro-Wilk tests and visual inspection of histograms and Q-Q plots. Interval and ratio data were not normally distributed in this study. Missing data were handled using one of the two following methods, dependent on the amount of missing data: (a) variables that had extensive missing data were omitted from analysis including weight at examination (42% missing), length at examination (48% missing), head circumference at examination (97% missing), container time minutes (94% missing), AIMS score at re-examination (82% missing), AIMS score at discharge (73% missing), active cervical lateral flexion at examination (89% missing); (b) individuals who did not have complete data for required elements were omitted (n = 238). All tests were two-tailed, and an alpha level of less than .05 was considered statistically significant. Before data analysis, data were recoded as necessary to allow for meaningful interpretation. Range of motion measurements were converted into absolute values of the difference between affected and non-affected sides to compare the severity of limitation between those presenting with right or left torticollis. In addition, the muscle function PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS scale score (MFS) was used to represent lateral neck flexor function. MFS is the measurement of choice to represent lateral neck flexion strength and is often used clinically to identify active movement by recruiting the infants' lateral righting response (hman, Nilsson & Beckung, 2009). For analysis, the absolute value of the difference between right and left measurements on MFS was used to represent the severity of active lateral flexion difference between affected and non-affected sides. Torticollis severity grade was left as is, noting that most participants fell into the grade 1 category. To allow for equal distribution of groups related to tummy time reporting, tummy time was recoded into four groups: less than 5 minutes per day, 5-15 minutes per day, 1630 minutes per day, and more than 30 minutes per day from a variety of reported time frames. Time was often recorded as a range in medical charts due to therapist documentation choices within the electronic medical record (EMR), though some clinicians entered free text data of an exact tummy time measurement per day. Insurance groups were also consolidated to allow for analysis; groups became Medicaid, private insurance, federal insurance, and self-pay. Bivariate correlations were used to determine if there were relationships between the dependent and independent variables for interval and ratio data. Shapiro-Wilk test of normality revealed that no interval or ratio data were normally distributed at a p < .05 level among participants. Normality was further verified using histograms, which showed skewed data, and Q-Q plots, which showed deviation from the reference line. Interval and ratio data relationships were assessed with Spearman rho correlation tests due to a lack of normality. Nominal data with more than two options and ordinal data (e.g., insurance type) correlations were assessed via Kendall tau-c tests. Lastly, correlations were examined using point biserial correlation tests for nominal data with two categories (e.g., torticollis type). The strength of correlations was assessed by reviewing the correlation coefficient r-values: r < .30 is a very weak correlation or none, .30 34 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS < r < .50 is a weak correlation, .50 < r < .70 is a moderate correlation, and r > .70 is a strong correlation (Moore et al., 2013). Multiple linear regression was used to identify predictors of the length of the plan of care. Independent variables and confounding variables (patient and examination factors) were added to the regression model when there was a correlation of at least .30. Assumptions for multiple regression were assessed as recommended in Field (2017) and are reported below. Results Three hundred eighty-seven randomly selected charts were initially reviewed to determine eligibility for the study. Cases were excluded for the following reasons: no torticollis diagnosis (n = 11), comorbid diagnoses at examination (n = 66), no recorded tummy time or container time (n = 140), lack of AIMs score (n = 1), and repeated MRN due to multiple episodes of care (n = 17). In addition, during statistical analysis, three individuals were excluded secondary to having lengths of the plan of care that were outside three standard deviations from the samples mean. Upon further in-depth chart review, these cases were identified as having comorbid diagnoses, including gross motor delay and autism spectrum. These three cases were excluded because their data reflected motor delay versus length of care difference arising from their initial torticollis presentation. Those with similarly long lengths of care but no other later comorbid diagnoses were included as they represented truly extended lengths of care related to torticollis presentation (n = 1). A total of 149 participants were identified as eligible to be in the study. General Participant Characteristics 35 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 36 Descriptive Statistics The 149 participants in this study were predominately male (67.8%), their median age at examination was 76 days, and their median age at referral was 64 days. The largest percentage (45.6%) of parents reported doing 515 minutes of tummy time per day. Most participants had Grade 1 torticollis, and there were slightly more cases of muscular torticollis (55.7%) than postural torticollis (44.3%). Table 1 shows additional participant demographics, and Table 2 displays examination factors. Inferential Statistics Correlation Tests Objective 1 was to determine if there is a relationship between torticollis type, tummy time or container time participation, AIMS score, patient age at examination, and the length of the therapy plan of care. Correlations between torticollis type, tummy time or container time participation, AIMS score, patient age at examination, and the length of the therapy plan of care are found in Table 3. Statistically significant correlations were found between torticollis type and the length of the plan of care (p < .001) and age at examination and the length of the plan of care (p < .001). No statistically significant correlations were identified between length of plan of care and tummy time or container time participation, nor AIMS score. Objective 2 was to determine if additional patient and examination factors influence the length of the therapy plan of care. Correlations between insurance type, PROM deficits, AROM rotation deficits, MFS score, and torticollis severity grade with length of the plan of care are found in Table 4 . Statistically significant correlations were found between PROM rotation deficit and the length of plan of care (p < .001) and AROM rotation deficit and the length of the plan of care (p < .001). Statistically significant, though weaker, correlations were also found PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS between passive lateral flexion deficit and the length of plan of care (p = .022), torticollis severity grade and the length of therapy plan of care (p < .001), and muscle function scale difference and length of plan of care (p = .015). No statistically significant correlations were found between length of plan of care and insurance type. Multiple Regression Results There was linearity as assessed by partial regression plots and a plot of studentized residuals against the predicted values. There was the independence of residuals, as assessed by the Durbin-Watson statistic of 1.99. There was homoscedasticity, as assessed by visual inspection of a plot of studentized residuals versus unstandardized predicted values. There was no evidence of multicollinearity, as assessed by tolerance values greater than 0.1. There were no studentized deleted residuals greater than 3 standard deviations. The assumption of normality was met as assessed by a Q-Q Plot. The potential predictor variables for the regression model based on correlation tests were age at examination, age at referral, torticollis type, passive rotation deficit, and active rotation deficit. There was a strong association between age at examination and age at referral (rs = .83). Due to the risk of multicollinearity between the two variables, only one was added to the regression model. Age at examination in days was chosen for the analysis as its correlation was slightly stronger with the length of plan of care than age at referral. The multiple regression model statistically significantly predicted length of care days F(4, 125) = 19.28, p < .001, adjusted R2 = .36. Three of the four variables added statistically significantly to the regression model, age at the examination in days (p < 37 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 38 .001), passive rotation deficit (p = .044) and active rotation deficit (p = .015). Results can be found in Table 5. Discussion and Conclusion The purpose of this study was to explore whether type of torticollis, amount of tummy time, amount of container time, and gross motor development as determined by AIMS score along with previously identified factors, including active range of motion deficit, passive range of motion deficit, age at examination, torticollis severity and insurance type influence the length of therapy plan of care. Multiple regression showed that of all the variables considered, only three, that correlated with length of plan of care, were statistically significantly predictive: age at examination, passive rotation deficit, and active rotation deficit. This was the first study to consider tummy time participation as a possible factor in predicting length of plan of care for infants with torticollis. Predictive factors of length of plan of care The multiple regression model developed with the three statistically significant variables explained 36% of the variability in the length of plan of care for infants with torticollis. The median length of care for this study population was 90 days, slightly less than that of Knudsen et al. (2020), who reported a median of 95 days for infants with full resolution and 117 days for those with unresolved tilt. Age at examination The median age at examination was 76 days, demonstrating a similarly aged population to Knudsen et al. (2020) and a slightly younger group than the population found in Greve et al. (2022). Overall, this study population supported the trend in recent literature, which noted that infants were being seen at younger ages and were presenting with less severe forms of torticollis PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS (Amaral et al., 2019; Greve et al., 2022; Knudsen et al., 2020). Age at examination in days was inversely related to length of plan of care in the current study. In this study, every day older the infant was at examination, the length of plan of care decreased by 1.05 days. From the results of this study, a clinician may consider that older infants may have shorter durations of care, given the inverse relationship between age at examination and length of care. These findings are similar to Amaral et al. (2019), where there was not always a direct relationship between age at examination and length of plan of care, thus challenging the common clinical consideration that length of plan of care is longer for older infants (Petronic et al., 2010). Similarly, Knudsen et al.s (2020) study showed that units billed were not correlated with age in days at evaluation. Amaral et al.s (2019) study retrospectively abstracted data from medical charts like this study. They found that infants presented around 11.6 weeks for treatment, about four weeks later than infants described in this study. Treatment in their study lasted between 126-133 days, and the mean time to resolution for those with only a cervical tilt at examination was 115 days versus 151 days for those with one or more findings at examination in addition to cervical tilt presentation. Amaral et al.s (2019) participants were slightly older than the current study, and researchers saw a similar phenomenon in this study: care plans were not always shorter for infants diagnosed at a younger age. These results continue to support the need for further research assessing if early examination leads to shorter lengths of care. Range of Motion Passive and active rotation deficits at examination were also predictive of length 39 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS of plan of care. For every degree increase in the passive rotation deficit between affected and non-affected sides, the length of care increased by 1.85 days. For every degree increase in active rotation deficit between the affected and non-affected sides, the length of care increased by .98 days. From these results, a clinician may consider that infants with increasing magnitudes of passive and active cervical rotation deficit between affected and non-affected sides will have increasingly longer episodes of care. For example, an infant with a 20-degree difference between right and left passive rotation will have a nearly 28-day longer length of care compared to an infant with a 5-degree passive rotation deficit. Taking it further, an infant with a 20-degree difference between right and left active rotation will have a nearly 15-day longer length of care than an infant with a 5-degree active rotation deficit. Prior studies considered only passive cervical rotation differences (Amaral et al., 2019; Cheng et al., 2001; Lee et al., 2013). Historically, studies assessed passive range of motion for cervical rotation secondary to Cheng et al.s (2001) finding that inter-rater reliability was better for passive rotation than passive cervical lateral flexion measures. A later study by Lee et al. (2013) found very large passive rotation deficits and described 3 groups with a range of deficits including 30 degrees or less (n = 12), 30 to 60 degrees, (n = 31), or more than 60 degrees, (n = 11), which were correlated to longer lengths of care. These findings by Lee et al. (2013) reinforced the use of passive cervical rotation as a correlate of length of care. Even more recently, Greve et al. (2022) also assessed passive rotation and lateral flexion deficits. The findings of Greve et al. (2022), similar to the current study, demonstrated smaller magnitudes of passive rotation deficits compared to Lee et al. (2013) while still noting that these range deficits were meaningful in predicting lengths of care. The cervical range of motion limitations found in Lee et al.s (2013) study may have represented typical resolving physiologic positioning of 40 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 41 newborns rather than a lasting result of torticollis. The main difference between these studies was that Lee et al. (2013) was the only prospective study with a younger population, with age at diagnosis being 36.63 days (SD = 14.80). The current study, Greve et al. (2022), and Cheng et al. (2001) were all retrospective and had older populations by about two months, possibly factoring into the observed differences in the magnitude of range of motion deficits. Passive Rotation Range of Motion Deficit. As noted above, passive rotation difference was not only predictive of length of plan of care but also correlated with a longer length of care. The higher an infants passive rotation deficit, the longer the length of plan of care. This supports prior research, which has long associated the larger the passive rotation range of motion deficit, the longer the length of care and higher the usage of resources (Cheng et al., 2001; Greve et al., 2022; Knudsen et al., 2020; Lee et al., 2013). Active Rotation Range of Motion Deficit. A unique finding in this study was that a longer length of plan of care was more strongly correlated with higher differences between affected side active rotation measurement and non-affected side active rotation measurement. Despite good reliability, even with visual estimation (Seager et al., 2020), AROM has traditionally not been used as a factor to determine length of plan of care. The limited use of AROM in studies may be because visual estimation, the simplest way of observing active range of motion clinically is not widely accepted as a valid tool to examine ROM (Greve et al., 2022). Greve et al. (2022) was one of the first studies showing AROM cervical rotation deficits throughout all studied groups, using arthrodial goniometric measurements. Lee et al.s (2013) use of passive cervical rotation as a correlate of length of plan of care after Cheng et al. (2001) PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 42 continued to bring awareness to using passive rotation as an idependent measure which has implications for length of plan of care. It has only been in recent publications that active range of motion has been considered as an independent measure affecting length of plan of care (Castilla et al., 2023). In addition, no current intervention frequency guidelines nor classification systems encourage the use of active cervical rotation range of motion measurements (Greve et al., 2022), leading to the limited research available relating active cervical rotation ROM to length of care. The results of the current study and those of Greve et al. (2022) should encourage researchers to reconsider using active cervical rotation deficit measurements in future research. Gross Motor Skills This study did not identify tummy time participation or gross motor skills at examination, assessed on the AIMS, as correlated to or predictive of length of plan of care. Measurement of tummy time was completed via parent report, which is standard practice, but prone to bias given that parents may not remember correctly, or bias may be introduced based on the power dynamic between parent and therapist (Colley et al., 2012). For this study, tummy time measurement was an interview question embedded into all torticollis evaluations at the research sites and thus was consistently asked across the studied health system. Currently, there is limited published data looking at the reliability of parent recall of tummy time (Hewitt et al., 2019). However, when looking at the wider literature base on physical activity reporting by parents for their children, parents tend to over-report physical activity levels (Colley et al., 2012). Colley et al. (2012) demonstrated in their report, which compared parent-reported activity versus accelerometer-measured moderate to vigorous physical activity, that there was a low correlation between parent-reported activity and accelerometer-measured activity. In Colley et al.s (2012) study, authors noted that parents might record 60 minutes of moderate activity if their child PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 43 participated in a soccer game, but the accelerometer only recorded the actual time the child played at a moderate or vigorous intensity. However, in studies where parents were asked to recall the age of a specific milestone, such as first steps (Majnemer & Rosenblatt, 1994), toddlers' fine motor or language skills (Miller et al., 2017) parents were more reliable. The difference between these two sets of studies is the parents ability to recall a discrete skill and date versus estimating their childs participation. Zysset et al. (2018) also found that parents were less reliable at recalling a range of preschool-age skills for which there were ambiguous qualifications. Instead, Zysset et al. (2018) recommend making skills they asked parents to recall more discrete. In the current study, parents may have been prone to overestimating tummy time participation like the participants in Colley et al.s (2012) study because they were asked an intensity question. For example, parents consider a nap on their shoulder as tummy time participation, but the infant was not actively participating in tummy time. In future studies, researchers should consider specifically identifying tummy time activities for parents, such as labeling wakeful participation in tummy time or prospectively using an accelerometer, to improve the specificity of what is considered tummy time. Estimating Length of Plan of Care The regression model developed as part of this study is not generalizable outside of this study population; however, it gives clinicians another point of reference when considering which items in their initial evaluation may be important for understanding an estimated length of care. From this study, therapists are supported in considering the child's age at examination but should use caution basing their prediction solely on this measure. Although seeing infants at younger ages is better for the resolution of symptoms, in this study, there was an inverse relationship PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS between older ages at examination and length of care. This study adds to the growing body of evidence showing that as infants present to physical therapy at younger ages, those younger infants do not always have shorter plans of care. It has been hypothesized by Amaral et al. (2019) that this discrepancy is secondary to the nature of retrospective data and the heterogeneity of the study population. In our study, there was less heterogeneity of the population as it came from one study site, leading researchers of this study to consider another hypothesis. Possibly, referring providers are more readily identifying torticollis at younger ages when infants have not yet developed significant muscle tightness. Though these infants are graded lower on the severity grading system (a function of their age at referral and range of motion passive restriction), they may be seen longer secondary to other factors, such as a possible strength imbalance, which takes longer to address as infants gain head control. Greve et al. (2022) also described this idea when they indicated that at their facility, they have extended their frequency recommendations to weekly until infants have met their active range of motion goals. Greve et al. (2022) also made an interesting point: infants in their study who did not complete an episode of care had less severe active range of motion deficits and were slightly older than the group who completed a full episode of care. The current hypothesis is that older infants may have better active range of motion, and thus, PT is not a priority for families as the infants are moving well (Greve et al., 2022). This study population, like many newly published studies, has a majority of younger, less severe presentations of torticollis (Greve et al., 2022; Kahraman et al., 2022; Knudsen et al., 2020), which is leading to a more robust understanding of infants who present with less severe passive restrictions at examination. Other Correlates of the Length of Plan of Care The two strongest correlations related to Objective 1 were age at examination, discussed 44 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS above, and torticollis type, while no other factors were significantly correlated with length of plan of care. For Objective 2, the significant correlates, from strongest to weakest, were active rotation range of motion deficit and passive rotation deficit, discussed above, as well as torticollis grade, muscle function scale score, and passive lateral flexion deficit. Torticollis Type Much of the population in this study had muscular torticollis (55.7%). The correlation between length of plan of care and postural versus muscular torticollis indicated that the length of plan of care was longer for infants with muscular torticollis. This finding supports prior studies, including Knudsen et al. (2020), which found that those with greater passive range of motion restrictions, also called muscular torticollis, had increased units billed and higher intensity services. Torticollis Grade All infants' grades of torticollis in this study were 1, 2, or 3, with no ratings higher than grade 3, like those in Knudsen et al. (2020). A longer length of care was associated with higher torticollis grade rating. This relationship was not an unexpected finding as torticollis grade rating is a function of age at evaluation and passive rotation range of motion limitation. Both age at evaluation and passive rotation limitation correlated with length of plan of care and were predictive of length of plan of care in this study. This finding supports prior research associating higher torticollis grade with higher PT units billed, higher frequency of PT services, and longer lengths of care for those with passive rotation range of motion deficits > 15 degrees between affected and non-affected sides (Greve et al., 2022; Knudsen al., 2020; Lee et al., 2013, Petronic et al., 2010). 45 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS There is a trend among more recent studies (Greve et al., 2022; Knudsen et al., 2020) that therapists are seeing infants with less severe forms of torticollis. Knudsen et al. (2020) had no participants with CMT grade 4 and above, and Greve et al. (2022) had 93% of the study population representing grades 1-3. Both studies included populations who were graded prior to the most recent update of the CPG in 2018, which has eight categories versus the previous six, allowing for better classification of older children. Despite the difference in torticollis severity rating scales used in these studies, Greve et al. (2022), Knudsen et al. (2020), and the current study, the comparisons among participants torticollis severity should not be a confounding factor because most participants in all studies were younger than six months at evaluation and thus used similar grading standards. Muscle Function Scale Score Strength measurement showed via MFS score that 70.5 % had no difference side to side, possibly influenced by this study population's younger age at examination compared to a similar study by Greve et al. (2022), who found a mean difference of one point between sides. Interestingly, in the current study, there was a statistically significant, though weak, negative correlation between the muscle function scale score difference and length of plan of care. The correlation between MFS difference and length of plan of care was limited by the small number of infants with actual differences between affected and non-affected sides (no difference 70.5%, one level difference 18.1%, two-level difference 3.4%, and missing data 8.1%). These results should be interpreted with caution. In a similar recent study by Song et al. (2020), researchers did not explore strength measures. Greve et al. (2022) found that those with full resolution of symptoms started with lower MFS score deficits compared to those who were slightly older and had partially completed an episode of care. The correlation in the current study, though weak, 46 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS raises the question of whether strength may be easier to remediate than stiffness, thus leading to the inverse correlation, especially in younger infants. It would be interesting to follow a group of prospectively recruited infants to assess the natural progression over treatment in MFS score and see if MFS score at evaluation remains negatively correlated with length of plan of care. Passive Lateral Flexion Deficit As expected from prior research, this study showed that higher passive lateral flexion deficits were very weakly correlated with longer lengths of care. This finding is similar to recent findings from Greve et al. (2022) and Song et al. (2020). Greve et al. (2022) noted significant passive lateral flexion differences between groups who completed a course of care and those who completed a partial course of care. Song et al. (2020) did not measure passive lateral flexion movement, instead choosing to report resting head tilt in supine, which is a position the infants rest in with their neck in lateral cervical flexion. They found that those with more significant head tilts at evaluation had longer lengths of care and that the degree of head tilt was predictive of length of care. Though Song et al. (2020) addressed resting lateral tilt, the study does not add significantly to the discussion of passive lateral flexion deficit as that measure was missing. Both studies indicate that lateral flexion, either passive or resting position, may give therapists clinically relevant information, but as in this study, it is not information therapists should base estimations of length of care on (Greve et al., 2022; Song et al., 2020). Clinical Implications 47 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 48 Cervical rotation range of motion The current study identified passive and active cervical rotation deficits as predictive of length of plan of care, but passive lateral flexion measurements were not. The subject's active rotation difference between sides was a median of 20 degrees, similar to that reported by Greve et al. (2022). For the current studys population, more active movement in the preferred direction versus the non-preferred direction when the infant started to show anti-gravity cervical rotation in supine was predictive of longer length of care. This finding supports the work by Kahraman et al. (2022), who noted that infants with torticollis had significantly different observed postural patterns (identified in Prectls General Movement Assessment) compared to controls without torticollis and a similar finding in Leung et al.'s (2017) work identifying active movement imbalance for infants with plagiocephaly but not SCM tightness. As clinicians examine infants earlier, this study supports observing and taking note of the infant's active cervical rotation difference between the affected and non-affected sides, highlighting the importance of considering active range of motion and using reproducible methods to record active rotation range of motion at examination. In this study, active cervical rotation was predictive of length of plan of care in a way that the other measures of active motion, like active lateral flexion, were not. Supporting Families with Predictive Models As patient data registries advance physical therapy practice, clinicians may be able to use predictive models from their specific offices to estimate for families what an expected length of care may be. From this study, an estimated length of care would be decreased based on older ages at the examination but would increase by almost a whole day for each degree of active rotation deficit between the child's affected and non-affected sides. In addition, more than a day PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 49 and a half for each degree of passive rotation difference between sides would be added to an estimated length of care. When estimating the length of care, awareness of which factors lead to a longer length of care helps estimate the family's commitment to therapy by basing expectations on the specific infants presentation. Understanding and educating families on examination factors that influence their infant's length of care will support a family's buy-in to therapy through knowledge sharingallowing the therapist to create a foundation of trust with families. Qualitatively, a PT's ability to guide a family through treatment helps families feel supported and reassured when managing an unfamiliar diagnosis in their new infant (Oledzka et al., 2020). Further understanding an expected therapy timeline and being reassured from the start of care may help parents understand their likely time commitment to therapy and facilitate buy-in to the treatment process (MacKay et al., 2020; VanEtten et al., 2021). Understanding their infants likely length of care may be especially important in cases where infants are lost to follow-up, as noted in Greve et al.s (2022) study. For example, when infants are older with less severe limitations, knowledge sharing may help families understand and frame their expectations for their infants length of care. Limitations Data extraction was completed from a registry that stores information from an EMR, including data collected by a large group of clinicians. There was no ability to conduct reliability studies for clinicians during this study due to the retrospective nature of the data. A lack of known reliability among therapists measuring ROM and MSF data inherently introduces challenges in interrater reliability in reporting the range of motion measurements and MFS score. In addition, the measurement method was not documented PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS for range of motion. The organization actively provides training on an ongoing basis to address interrater reliability related to range of motion measurement and MFS scoring through practice using an arthrodial goniometer and picture reference for scoring MFS. Regular staff training and education on standardization of practice for torticollis based on updates from the most recent CPG happens 1-2 times per year and with every new employee. These education strategies increase the likelihood that therapists across the system consistently measure range of motion and MFS scores. This study was completed with a sample of participants from a single health system and was retrospective, which may not represent the general population of infants with torticollis nationally. The current study lacks control of confounding variables, as is common in retrospective studies where data has been previously collected. Here, demographic data collected were limited to age, sex, and insurance type due to a lack of recorded race/ethnicity data in medical charts and missing data related to socioeconomic variables. The health system is increasing education on facilitating clinician recording of race/ethnicity data, which will likely improve the availability of this data for future studies. Researchers for this study used the AIMS score to assess gross motor skills because it is easy to administer and is routinely completed at the research site for infants with torticollis. However, the AIMS score is less reliable in our study population for infants with a median age of two and a half months. Spittle et al. (2015) showed that score reliability improves for infants over three months. Researchers for this study had intended to look at AIMS scores over time to understand better an infants gross motor development trajectory. Infants at risk for gross motor development issues are more reliably identified by assessing AIMS scores over time (Spittle et al., 2015) versus in this study, with only one consistently recorded score. This study could not 50 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS analyze an infants gross motor trajectory secondary to significant missing data at reevaluation and discharge. Another confounding variable not considered is the recommendation in the clinical practice guideline to follow infants with torticollis through the developmental milestone of walking (Kaplan et al., 2018), which may artificially inflate the lengths of plan of care. It is unknown for the current study how often therapists followed infants for up to 1 year because of CPG recommendations versus clinical presentation. This discrepancy may play a role in the trend noted in this study that length of care is not shorter for those diagnosed earlier. The median length of care for this study, 90 days, and the mean of Song et al.s (2020) population, 100.62 days, and Knudsen et al.s (2020) population, 95 days, demonstrate that lengths of care are relatively consistent across various studies with data collection timeframes both prior to the current CPGs publication (Knudsen et al., 2020; Song et al., 2020) and after, the current study. Lastly, external influences of the pandemic on length of plan of care could not be controlled during this study. The study includes participants from prior to the start of the pandemic, fall of 2019 through the pandemic until fall of 2021. This period was specifically chosen to highlight the time after the most recently published clinical practice guideline, in the fall of 2018, when clinicians would be more familiar with the recommendations. However, due to the pandemic, client care was paused for two weeks, and in-person treatments resumed first for urgent clients, infants, and patients appropriate for discharge. This timing may have led to longer lengths of care as therapists navigated telehealth, hybrid care, and returning to regular in-person treatment sessions. Further research into outpatient-specific pediatric data for physical therapy during 51 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS the pandemic was limited. In the studied health system, infants with torticollis were a prioritized population to bring in for evaluation and treatment during the early stages of returning to inperson care, which may have lessened the pandemics effect. However, access to care was likely limited, especially to physical therapy. According to an American Medical Association report on the financial impacts of the pandemic related to 2020 Medicare spending, physical therapy saw the biggest reduction in spending relative to other specialties at -28% (Gillis, 2021). Future Research Future research should be conducted to continue evaluating factors that predict length of plan of care for infants with torticollis who are trending younger at initial examination. Future research into more accurately recording tummy time minutes per day may be helpful in understanding if the findings here were truly unrelated to length of care or if imprecise measurement based on recall limited the results. Expanding reliable measurement techniques also includes using gross motor assessments which are more reliable at younger ages, and improving clinicians ability to objectively measure a young infants strength. In future studies, researchers should consider MFS score as well as a measure similar to Prectls General Movement Assessment (Prechtl, 1997) or possibly the Functional Symmetry Observation Scale (Rahlin et al., 2022) to more fully understand an infants independent antigravity movement and start to inform their assessment of an infants strength. Future research should focus on reliably measuring young infants' cervical strength or motor control. Measures sensitive to detecting lateral flexion or rotation movement deficits in infants younger than 3 months would help clinicians understand the role of active movement at examination on predicting length of plan of care. Future researchers may consider building on the work of Leung et al. (2017), who assessed active head righting as a measure of SCM 52 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 53 recruitment in infants younger than 3 months of age via rolling. Conclusion Physical therapists should continue to use age at examination to help determine the recommended length of physical therapy care. In addition, therapists should assess passive and active cervical rotation differences between sides. This study supports that rotation measure deficits are predictive of length of plan of care. The clinical implications of this study continue to support the need for timely referral but also caution that for our study population, those referred later may have a shorter length of therapy plan of care. Further research is needed to explore the role of active movement limitations at examination and their relationship to length of plan of care. Continued exploration of active cervical rotation limitation is needed to understand the reliability of the measurement of active rotation in infants younger than 3 months old. Clinicians should continue to assess rotation, which can help predict a longer length of care and thus build trust with families by helping families understand their likelihood of longer care. Allowing families to share the clinicians knowledge works to build a therapeutic alliance with the family, allowing them to participate in and understand a possible course of care. PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 54 References Aarnivala, H. E. I., Valkama, A. M., & Pirttiniemi, P. M. (2014). Cranial shape, size, and cervical motion in normal newborns. Early Human Development, 90(8), 425430. https://doi.org/10.1016/j.earlhumdev.2014.05.007 Amaral, D. M., Cadilha, R. P., Rocha, J. A., Silva, A. I., & Parada, F. (2019). Congenital muscular torticollis: Where are we today? A retrospective analysis at a tertiary hospital. Porto Biomedical Journal, 4(3), Article e36. https://doi.org/10.1097/j.pbj.0000000000000036 American Academy of Pediatrics. (2017, January 20). 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A., Arhab, A., Ferrazzini, V., Kriemler, S., Munsch, S., Puder, J. J., & Jenni, O. G. (2018). The validity of parental reports on motor skills performance level in preschool children: A comparison with a standardized motor test. European Journal of Pediatrics, 177(5), 715722. https://doi.org/10.1007/s00431-017-3078-6 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Table 1 Participant Characteristics (N = 149) Patient Factors Mdn 25th, 75th percentile Age at examination (days) 76.0 64.0, 93.0 Age at referral (days) 64.0 48.5, 72.0 N % Female 48.0 32.2 Male 101.0 67.8 Medicaid 20.0 13.4 Private insurance 120.0 80.5 Federal insurance 7.0 4.7 Self-pay 2.0 1.3 Torticollis 66.0 44.3 Congenital deformity of SCM 64.0 43.0 Plagiocephaly 18.0 12.1 Stiffness of joint 1.0 0.7 Gender Insurance ICD-10 Note. ICD-10 = International Classification of Disease 10th revision. 64 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Table 2 Participant Characteristics Examination factors Mdn 25th, 75th percentile AIMS score at/near examination (n = 149) 25.0 10.0, 50.0 AROM rotation difference (n = 130) 20.0 10.0, 41.3 PROM lateral flexion difference (n = 145) 5.0 0, 20.0 PROM rotation difference (n = 149) 0 0, 10.0 Muscle Function Difference (n = 137) 0 0, 0 90.0 42.0, 168.0 N % No difference side to side 105.0 70.5 1 level difference between sides 27.0 18.1 2 level difference between sides 5.0 3.4 Missing 12.0 8.1 Less than 5 minutes/day 13.0 8.7 5-15 minutes/day 68.0 45.6 16-30 minutes/day 35.0 23.5 Greater than 30 minutes/day 33.0 22.1 Level 1: CVAI of < 3.5% 37.0 24.8 Level 2: CVAI 3.5 to 6.25% 42.0 28.2 Length of plan of care MSF difference Parent reported tummy time minutes per day Plagiocephaly severity 65 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Level 3: CVAI 6.25 to 8.75% 33.0 22.1 Level 4: CVAI 8.75 to 11.0% 15.0 10.1 Level 5: CVAI > 11.0% 9.0 6.0 Missing 13.0 8.7 Grade 1 115.0 77.2 Grade 2 25.0 16.8 Grade 3 9.0 6.0 Muscular 83.0 55.7 Postural 66.0 44.3 66 Torticollis grade Torticollis type Note. ICD-10 = International Classification of Disease 10th revision; AIMS = Alberta Infant Motor Scales; AROM = Active Range of Motion; PROM = Passive Range of Motion; MSF = Muscle Function Scale; CVAI = Cranial Vault Asymmetry Index. PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS Table 3 Correlations between Independent Variables and the Length of Plan of Care (N = 149) rs p Torticollis type a, c -.32 <.001 Parent reported tummy time b -.09 .261 AIMS score b -.12 .134 Age at examination (days) b -.38 <.001 Note. AIMS = Alberta Infant Motor Scale. a Point biserial correlation b Spearman rho correlation c 1 = muscular, 2 = postural 67 PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 68 Table 4 Correlations of Independent Variables with the Length of Plan of Care N rs p Torticollis grade c,b 149 .29 <.001 Insurance typed 149 .01 .815 Passive rotation deficit 149 .32 <.001 Passive lateral flexion deficit c 149 .19 .022 Active rotation deficit c 130 .42 <.001 MFS difference a 137 -.21 .015 Note. MFS = Muscle function scale. a Point biserial correlation b 1 = grade 1; 2 = grade 2; 3 = grade 3 c Spearman rho d Kendall tau-c PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 69 Table 5 Multiple Regression of Predictors of Length of Therapy Plan of Care (N = 130) Variable B S.E. t p 95% CI Lower, Upper Constant 197.72 35.43 5.58 <.001 127.61, 267.84 Age at exam -1.05 -.30 0.26 -4.11 <.001 -1.56, -0.55 Torticollis type -23.15 -.12 16.32 -1.42 .158 -55.45, 9.14 Passive rotation deficit 1.85 .21 0.91 2.04 .044 0.52, 3.66 Active rotation deficit 0.98 .22 0.40 2.47 .015 0.19, 1.76 Note. CI = confidence interval. PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 70 Appendix A CMT Classification Grades and Decision Tree for 0-12 months 2018 Update To use this chart: The vertically aligned ovals on the left, list the factors that are most relevant to the classification process (age asymmetry noted, age of referral and PT evaluation, type of CMT); the diamonds below describe the cycle of PT examination, intervention, and reassessment. Begin in the larger rectangle with age at evaluation and type of CMT to choose. Abbreviations: PT, physical therapy; TX, treatment; SCM, sternocleidomastoid; L/R, left/right. PREDICTORS OF LENGTH OF CARE FOR CONGENITAL TORTICOLLIS 71 From Kaplan et al. (2018). Physical therapy management of congenital muscular torticollis: A 2018 evidence-based clinical practice guideline from the APTA Academy of Pediatric Physical Therapy. Pediatric Physical Therapy, 30(4), 240290. https://doi.org/10.1097/PEP.0000000000000544. Copyright 2018 by Wolters Kluwer Health, Inc. Reprinted with permission.  ...

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The Hybrid Doctor of Physical Therapy Student: A Comprehensive Exploration of Demographics and Decision-Making Factors Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Teresa Bachman, PT, DPT Copyright December 6, 2023 By: Teresa Bachman, PT, DPT All rights reserved Approved by: Laura Santurri, PhD, MPH, CPH, aPHR Committee Chair ______________________________ Kendra Gagnon, PhD, PT Committee Member Associate Professor Johns Hopkins University School of Medicine ______________________________ Elizabeth Moore, PhD Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT The Hybrid Doctor of Physical Therapy Student: A Comprehensive Exploration of Demographics and Decision-Making Factors Teresa Bachman Department of Interprofessional Health and Aging Studies, University of Indianapolis 1 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 2 Abstract Background: Hybrid DPT education has emerged as a model many educators feel holds promise in addressing some of the calls to action in DPT education. Data indicate an increasing number of hybrid DPT programs, yet very little is known about the hybrid DPT student. Purpose: This study aimed to explore hybrid DPT student demographic characteristics, tolerance for ambiguity and perfectionism factors, and key considerations in the students selection of a hybrid DPT program. Method: The study used a quantitative, non-experimental, cross-sectional design with the administration of a Qualtrics survey to explore demographics, student experience, and key considerations data. Data analysis included descriptive statistics to determine measures of central tendency. Results: The median age of the sample was 25 years, with 72.0% females and 29.3% racial minorities. 33.5% of participants were married and 13.5% had children. 54.4% were accepted to more than one program. The median score for tolerance for ambiguity was 23, and the median scores for perfectionism striving and evaluative concerns were 15.2 and 11.4, respectively. The most important considerations in selecting their hybrid DPT program were outcome factors of graduation and employment rates and NPTE pass rates. Conclusion: Hybrid DPT students look very similar to residential DPT students compared to published aggregate data, with main differences in marital status and those with children. New and expanding hybrid DPT programs should consider outcomes as a key driver in student selection of their program. Keywords: entry-level hybrid DPT education, tolerance for ambiguity, perfectionism HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 3 Acknowledgments Ive dreamed of the day that I would finally sit and write this acknowledgments page. It is hard to believe it is finally here. First, I must give all the glory, praise, and acknowledgment to my Savior, Jesus Christ. Countless prayers have gone up, and He has never failed me! I am truly blessed to be a daughter of the King, badly broken, but deeply loved. None of this would have been possible without the guidance of my amazing dissertation committee, Drs. Laura Santurri, Elizabeth Moore, and Kendra Gagnon. Laura, you have guided me with grace, patience, and an ever-calm presence during this process. Thank you! Elizabeth, your statistical knowledge and patience with me have been a God send. Thank you! Kendra, my colleague, mentor, and friend, I have been blessed beyond measure to have you on this committee. You have challenged me and supported me. Thank you! Together, as a committee, you all have helped me rise to the next level of professional. I am forever grateful. My husband and my boys have been incredibly supportive during this entire process. They have encouraged me, dealt with late nights and early morning working, supported me during my lows, and celebrated with me during the great times. My husband has kept the house running, worked full time, loved and supported me, and dried my tears. Michael, Logan, and Barrett, thank you! Finally, I have to thank my Mom, Angie. They say that a mom is always there for you, and I have found this to be the truth. Thank you, Mom, for raising me to be a strong and independent woman who strives to achieve great things. Thank you for your love and encouragement. I am blessed and grateful. Thank you all! HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 4 Table of Contents Abstract ........................................................................................................................................... 2 Acknowledgments........................................................................................................................... 3 List of Tables .................................................................................................................................. 6 List of Figures ................................................................................................................................. 7 Chapter 1: Introduction ................................................................................................................... 8 Problem Statement .................................................................................................................... 10 Purpose Statement..................................................................................................................... 10 Research Questions............................................................................................................... 10 Objectives.............................................................................................................................. 11 Significance of the Study .......................................................................................................... 11 Literature Review.......................................................................................................................... 12 DPT Education .......................................................................................................................... 12 History................................................................................................................................... 12 A Call for Change ................................................................................................................. 12 Excellence ............................................................................................................................. 13 The Rise of Hybrid DPT Education ...................................................................................... 15 Data and Outcomes............................................................................................................... 18 Terminology .......................................................................................................................... 19 Student Experience ................................................................................................................... 20 Tolerance for Ambiguity ....................................................................................................... 21 Perfectionism ........................................................................................................................ 23 Chapter 2: Method ........................................................................................................................ 25 Study Type and Design ............................................................................................................. 25 Participants ................................................................................................................................ 25 Data ........................................................................................................................................... 25 Variables ............................................................................................................................... 25 Operational Definitions of Variables.................................................................................... 28 Instruments................................................................................................................................ 28 Tolerance for Ambiguity Scale.............................................................................................. 28 Frost Multidimensional Perfectionism Scale-Brief .............................................................. 29 Procedures ................................................................................................................................. 29 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 5 Recruitment ........................................................................................................................... 29 Informed Consent .................................................................................................................. 29 Data Collection ..................................................................................................................... 30 Data Management ................................................................................................................. 30 Statistical Analysis .................................................................................................................... 30 Results ........................................................................................................................................... 31 Discussion ..................................................................................................................................... 32 Characteristic Demographic Profile of Hybrid DPT Students ............................................. 32 Tolerance for Ambiguity and Perfectionism ......................................................................... 34 Key Considerations of Importance in Selection of a Specific Hybrid DPT Program .......... 35 Limitations ................................................................................................................................ 37 Conclusion ................................................................................................................................ 37 References ..................................................................................................................................... 39 Appendix A ................................................................................................................................... 56 Appendix B ................................................................................................................................... 57 Appendix C ................................................................................................................................... 58 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 6 List of Tables Table 1: General Demographic Characteristics52 Table 2: Expanded Demographic Characteristics.53 Table 3: Key Considerations in Enrollment in a Specific Hybrid DPT Program.54 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 7 List of Figures Figure 1: Percentage of Participants per Regional Location55 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 8 The Hybrid Doctor of Physical Student: A Comprehensive Exploration of Demographics and Decision-Making Factors Doctor of Physical Therapy (DPT) educators have been challenged to assess, adapt, and advance educational practices in the ever-changing landscape of health care and graduate education. Over the last decade, commentary with calls to action have been published on issues including student debt to income ratio (Domholdt et al., 2020; Dunn, 2019; Jette, 2016), demand for qualified faculty (Brueilly et al., 2022; Domholdt et al., 2020), physical therapist workforce supply and demand (APTA and APTA Private Practice, 2023; Deusinger & Landers, 2022; Domholdt et al., 2020), declining DPT program applications (ACAPT, 2023; Deusinger & Landers, 2022; Deusinger & Sanders, 2017; Domholdt et al., 2020), the increase in the number of DPT programs (Deusinger & Sanders, 2017; Domholdt et al., 2020) thereby increasing the competition for students, and diversity of PT students and graduates (Domholdt et al., 2020). Considering these issues alone, multiple options to address them could be identified. However, the real challenge is that many of these problems are inextricably connected, and solutions cannot be considered in isolation. In recent years, a new instructional model of education has emerged that many educators feel holds promise in addressing some of the challenges: hybrid DPT education. The first hybrid DPT programs were launched in 2008 (University of St. Augustine for Health Sciences, 2016) and 2011 (Nova Southeastern University, 2011). Both programs were described as flex programs and decelerated the time to a degree. They were characterized by their blended delivery model that combined online and face-to-face instruction with no residential requirements to create flexibility for working students and those otherwise unable to attend fulltime graduate school (Nova Southeastern University, 2011; University of St. Augustine for HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 9 Health Sciences, 2016). In 2015, the first full-time, fully hybrid DPT program was launched at South College. Like the early flex programs, there was no residential requirement. However, to be responsive to workforce demands and rising concerns about the debt-to-income ratio, this new type of hybrid program accelerated the time to degree to two years (South College, 2021). In 2020, the COVID-19 pandemic rapidly shifted most programs to an online format (Gagnon et al., 2020). Since then, data indicate that at least 10 accredited institutions offer entry-level DPT education, with 25-50% of the program offered remotely and at least six institutions offering more than 50% in a remote method (ACAPT, 2022d). Literature related to hybrid DPT education programs and outcomes is limited. Existing data on hybrid DPT education are derived from case reports that describe hybrid program implementation, outline strategies implemented during the COVID-19 pandemic, and provide some early outcomes (Gagnon et al., 2020, 2022; Ortega et al., 2021). While these studies provide a useful foundation for understanding the characteristics of individual hybrid DPT programs, there is a need to more broadly understand the student experience and outcomes across multiple hybrid programs. Aggregate student demographics and outcomes for graduation rates, licensure examination pass rates, and employment rates for DPT programs are reported annually by CAPTE in the Aggregate Program Data (CAPTE, 2022). These data provide a superficial start to the collection of benchmarking metrics but are limited in their utility as they cannot be separated by program type, describe a narrow set of graduate outcomes, and do not provide information about the student experience. Shields et al. (2018) recognized the limited utility of the data collected by CAPTE and set out to collect a more robust set of data to capture information about student experience. These data included validated tools related to tolerance for ambiguity (TfA), HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 10 burnout, and perfectionism in their Physical Therapy Graduate Questionnaire (PT-GQ) benchmarking study (Dudley-Javoroski and Shields, 2022). TfA has been widely studied in medical students (Hancock & Mattick, 2020) and identified as a needed trait in DPT students (Craik, 2001; Jette, 2016a). Perfectionism has been correlated to increased stress (Richardson et al., 2022) and negative mental health diagnoses (Bogardus et al., 2022). Perfectionism has been positively associated with developing leadership (Jaworski et al., 2022) and academic achievement (Madigan, 2019). These findings support the need to consider student experience factors of TfA and perfectionism in the metrics of DPT students. However, like all other data, this information is unavailable for the hybrid DPT student. Problem Statement Hybrid DPT programs produced an estimated 1000 graduates this year, with an estimated 3400 students enrolled in hybrid DPT programs in 2023. These numbers will continue to grow as new and expanded hybrid DPT programs emerge. As the DPT education market becomes increasingly competitive, institutions offering hybrid DPT programs must learn to recruit, retain, and support their students effectively. To do this, they must know who their students are and why they chose their specific hybrid DPT education program. Purpose Statement This study aimed to explore hybrid DPT student demographic characteristics and student experience aspects of tolerance for ambiguity and perfectionism. This study also examined key considerations in the student resolution to enroll in a specific hybrid DPT program. Research Questions The following research questions are answered to address the study's purpose. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 11 1. What is the characteristic demographic profile of students enrolled in entry-level hybrid DPT educational programs? 2. What is the student experience of tolerance for ambiguity and perfectionism in entrylevel hybrid DPT educational programs? 3. What key considerations impacted students selection of a specific hybrid DPT educational program? Objectives The following objectives are addressed to answer the research questions. 1. Explore the demographic characteristics, such as age, gender, ethnicity, and relationship status of students who enroll in hybrid DPT educational programs, utilizing a newly constructed survey. 2. Explore the tolerance for ambiguity and perfectionism of students enrolled in hybrid DPT programs utilizing the Tolerance for Ambiguity (TfA) scale (Geller et al., 1993) and the Frost Multi-Dimensional Perfectionism Scale- Brief (FMPS-B) (Burgess et al., 2016). 3. Describe key considerations to students selection of a specific hybrid DPT program utilizing a newly constructed survey. Significance of the Study The results of this study provide data that can be used to benchmark hybrid DPT students characteristics and experiences against published aggregate data. This enables institutions that are considering development of a hybrid DPT program to make data-informed decisions regarding the anticipated students and why they would enroll. Ultimately, the results of HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 12 this study provide the foundation for future studies related to the outcomes of students enrolled in hybrid DPT programs. Literature Review DPT Education History The physical therapy profession has been in the United States since the early 1900s. Initially, six schools offered a post-baccalaureate certificate, awarding those who completed the program the title of Physical Reconstruction Aid (Moffat, 2003). Over the last century, the profession of physical therapy has advanced significantly. Thus, education programs for physical therapy have evolved as well. Education programs have gone from the bachelors degree level in 1978 through the masters degree level in 2008 to the doctorate level by 2015 (Moffat, 2012). Two hundred seventy-three accredited physical therapy programs now confer the DPT degree (CAPTE, 2022). A Call for Change Physical therapist educators have faced challenges throughout the entire history of the profession. These challenges have resulted in growth and changes in physical therapist education, including the progression of education from a certificate to a doctorate. The everchanging landscape of health care and graduate education requires physical therapist educators to assess, adapt continually, and advance administration and educational practices. Recently documented challenges include but are not limited to student debt to income ratio (Dunn, 2019; Jette, 2016b), demand for qualified faculty (Brueilly et al., 2022; Brueilly et al., 2007; Bliss et al., 2018), physical therapist workforce supply and demand (APTA, 2023; Deusinger & Landers, 2022), declining DPT program applications (APTA, 2022; Deusinger & Landers, 2022; HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 13 Deusinger & Sanders, 2017), an increase in the number of DPT programs (Deusinger & Sanders, 2017; Jette, 2016b), and student/graduate diversity (Nordstrom et al., 2022; Domholdt et al., 2007). Additionally, there are concerns regarding the rising cost of doing business (Jette, 2016b), curricular variation (Jette, 2016b), negative value proposition (Deusinger & Landers, 2022), and defining and achieving excellence in education (Gordon, 2011; Jensen et al., 2017a). All these issues have been discussed in the last 15 years within the APTA Academy of Education (2022) Cerasoli Lectures, with calls to action. However, as discussed by Domholdt et al. (2020), little progress has been seen in most areas. While there may be multiple solutions to address any of the challenges within DPT education, most problems are inextricably connected and cannot be considered in isolation. Healthcare and medical educators have been urged to consider novel education methods and leverage technology to replace outdated methods and improve efficiency in educational delivery (Prober & Khan, 2013; Thibault, 2020.) Leaders in physical therapy education have called for a need to establish excellence in education, unique methods of education, and changes to the curriculum to reduce the cost of physical therapist education, improve efficiencies, and provide flexibility to meet student needs (Gordon, 2011; Graham, 2015; Jette, 2016b; Portney, 2014; Wojciechowski, 2015). Excellence The response to the call for establishing excellence in physical therapist education began with defining excellence (Jensen et al., 2017a; Jensen et al., 2017b). In 2022, ACAPT launched the Center for Excellence in Academic Physical Therapy and formed an advisory committee to support a culture of excellence and reinforce the mission and strategic goals of the organization (ACAPT, 2022a). The definition of excellence and The Excellence Framework was published, HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 14 and an Institutional Profile Survey was created to collect data around shared challenges, such as building diversity, controlling student debt, increasing the value of education, and addressing shortages of qualified faculty (ACAPT, 2022a). The ACAPT (n.d.) Criteria for Excellence defines excellence as: Excellence is an aspiration rather than a destination characterized by continual improvement. An excellent academic program demonstrates a culture of excellence by continually and intentionally striving to transform learners, advance knowledge, and improve societal health. Excellence in transforming learners, advancing knowledge, and improving societal health is achieved when the academic culture supports the ongoing development and integration of three domains: Inquiry, inclusion, and innovation. Excellence is a multi-faceted construct that respects and supports differences among academic programs while inspiring ongoing self-assessment and growth. (p. 3) The Excellence Framework outlines 12 critical categories to track success (ACAPT, 2022c). Words such as transformative, influential, innovation, risk-taking, collaboration, motivated, engaged, diversity, equity, inclusion, contemporary, adaptive, authentic, and social responsibility are seen throughout the 12 critical categories. Many of these exact words are found in the published literature, including the APTA Academy of Education Cerasoli Lectures (2022), outlining the challenges and suggested solutions in physical therapist education. Additionally, ACAPT (2022b) provides DPT program guidelines to assist programs in striving toward excellence, yet none of these documents operationally define these terms. Achieving excellence in programs may be part of the solution to some of the problems faced in DPT education. However, it is unclear if this work can address the many problems within DPT education without operational definitions to support the implementation. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 15 The Rise of Hybrid DPT Education In recent years, a new instructional model of DPT education has emerged that many educators think holds promise in addressing some of the challenges: hybrid DPT education. Distance education, including online and hybrid formats across higher education, has steadily risen since 2014 (Allen et al., 2016). The first hybrid DPT programs were launched in 2008 (University of St. Augustine for Health Sciences, 2016) and 2011 (Nova Southeastern University, 2011). Both programs were described as flex programs that slowed down the time to a degree. They were characterized by their blended delivery model that combined online and face-to-face instruction with no residential requirements to create flexibility for working students and those otherwise unable to attend full-time graduate school (Nova Southeastern University, 2011; University of St. Augustine for Health Sciences, 2016). In 2015, the first full-time, fully hybrid DPT program was launched at South College. Like the early flex programs, there was no residential requirement. However, to be responsive to workforce demands and rising concerns about the debt-to-income ratio, this new type of hybrid program accelerated the time to degree to two years (South College, 2021). In 2020, the COVID-19 pandemic rapidly shifted most programs to an online format (Gagnon et al., 2020). Since then, almost all residential DPT programs have returned to in-person learning. However, research supports the benefits of remote instruction realized during the pandemic (Anderson & Dutton, 2022; Ortega et al., 2022; Plummer et al., 2021), and some programs have chosen to continue with blended instruction in the curriculum. Data indicate that at least 10 accredited institutions offer entry-level DPT education, with 25-50% of the program provided remotely and at least six institutions offering more than 50% in a remote method (ACAPT, 2022d). HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 16 Hybrid Model of DPT Education. Despite multiple programs now offering a hybrid DPT education model, there is limited published literature related to the model and its ability to address the challenges in DPT education. Most of the literature about hybrid DPT education has been conducted at the individual course level (Adams, 2013; Boucher et al., 2013; Lazinski, 2017; Veneri & Ganotti, 2014; Wassinger et al., 2021). Utilizing a flipped classroom for musculoskeletal DPT education, Boucher et al. (2013) and Wassinger et al. (2021) found improved course outcomes as evidenced by exam scores, and students in both studies were satisfied with the flipped model and most even preferred the method. In another study examining hybrid versus traditional course instruction using computer-assisted learning for a neurologic rehabilitation course, authors found average quiz grades to be improved, and final exam scores were statistically significantly higher in the hybrid group (Veneri & Ganotti, 2014). A study by Lazinski (2017) evaluated student outcomes with performance on practical assessments, online engagement, and student course evaluations for a hybrid one-credit hour lab course for performing surface palpation. Across three cohorts, only one student failed the practical assessment, students exceeded the posting requirement and page views, and qualitative comments related to student satisfaction were skewed in a positive direction. Other published literature on hybrid DPT education pertains to the perspective of students or faculty during the COVID-19 pandemic (Majsak et al., 2022; Neely et al., 2022; Ortega et al., 2021). Ortega et al. (2021) reviewed digital strategies implemented during the COVID-19 pandemic. They reported effective delivery of content, with more research needed on outcomes for the student, program, and community for programs that heavily use digital learning strategies. Majsak et al. (2022) analyzed challenges and faculty concerns about going virtual during the COVID-19 pandemic. This study found that faculty were most challenged with limited contact, HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 17 increased workload, and learning online technologies (Majsak et al., 2022). The biggest concerns from faculty included fewer hands-on labs, delays in clinical experiences, and safety during oncampus activities (Majsak et al., 2022). Neely et al. (2022) compared student clinical performance for those who received face-to-face education and those who received virtual education. The study results indicated that students in the virtual learning group had lower Clinical Performance Instrument scores and lower ratings from clinical instructors; however, none of the results were statistically significant. These study results must be taken with caution as many factors impacted the implementation of virtual learning during the COVID-19 pandemic. The instructional models explored may be described as emergency remote instruction versus thoughtfully constructed online learning experiences. Thus, the results cannot be generalized to the intentional use of the hybrid DPT education model. Only two studies address a fully hybrid instructional delivery model across a DPT program (Gagnon et al., 2022; Marinas et al., 2022). Marinas et al. (2022) studied students perception of cognitive load and the impact of gender and academic tutoring services on perceptions in an accelerated, blended DPT program. The study found no relationship between cognitive load and gender and a significant difference between students perception of cognitive load for those who received academic tutoring services and those who did not (Marinas et al., 2022). The study did not address whether students perceived a high versus low cognitive load. Gagnon et al. (2022) described the hybrid model implementation and early outcomes of a hybrid DPT education program. The case report provides insight into implementing a hybrid DPT education program inclusive of student affairs, academic affairs, faculty affairs, and institutional affairs (Gagnon et al., 2022). The outcomes of the report indicate that the program has a twoyear mean of 39% minority students enrolled versus the mean of 28% across all DPT programs HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 18 (Gagnon et al., 2022), suggesting that more than the average number of minority students may enroll in a hybrid DPT education program. Additionally, outcomes showed that most students were very or somewhat satisfied with the program, demonstrated a 96% graduation rate with 36.5% minority graduates, and a 97% ultimate two-year NPTE pass rate (Gagnon et al., 2022). The graduation rate, minority graduation rate, and the ultimate two-year pass rate either equal or exceed the average rates reported by the CAPTE data published in 2021 (Gagnon et al., 2022). There is no literature related to the specific characteristics of students enrolled in hybrid DPT programs or information about the reasons that students enroll in hybrid DPT programs. A study by Ancrum-Smalls et al. (2000) assessed factors of importance for physical therapist applicants choice of program. At the time of the study, there were only residential programs. Respondents rated the degree offered and the accreditation status as very influential (74%) (Ancrum-Smalls et al., 2000). Other factors garnering over 50% response as very influential included perceptions of the programs atmosphere, NPTE pass rate percentages, perceptions of faculty concern for student welfare, tuition costs, first impressions of the program, and distance from home (Ancrum-Smalls et al., 2000). On-campus housing, public transportation availability, student diversity, interaction with/perceptions of program undergraduate advisors, and familiarity with the campus were rated as least influential by 50% of respondents (AncrumSmalls et al., 2000). While this study is dated, it provides some foundation for factors to consider in student enrollment in hybrid DPT education programs. Data and Outcomes There is a lack of universal metrics collected in DPT programs, making it challenging to benchmark and compare program outcomes (Shields et al., 2021). Aggregate Program Data are reported by CAPTE annually (CAPTE, 2022), and ACAPT began reporting the Institutional HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 19 Profile Survey data in 2022 (ACAPT, 2022d). Neither CAPTE nor ACAPT data separate hybrid data from the aggregate for the program or student. Shields et al. (2018) began a series of studies (Dudley-Javoroski & Shields, 2022; Shields et al., 2021) to create universal metrics for DPT programs. The first study was completed at one university over eight years and utilized a survey based on the metrics and validity of the Association of American Medical Colleges Graduation Questionnaire (Shields et al., 2018). In the second study, the survey was named the Physical Therapist Graduation Questionnaire (PTGQ) (Shields et al., 2021). Data were collected and analyzed across 13% and 26.5% of programs to capture overall satisfaction, curriculum, learning environment, student experience, and student characteristics (Dudley-Javoroski & Shields, 2022; Shields et al., 2021). These three studies provide valuable information for benchmarking, but like CAPTE and ACAPT, there is no separation of data for hybrid programs or students Terminology A unique challenge to studying alternative education methods, especially those that include any online learning component, remains the incongruent terminology (Singh & Thurman, 2019). Various terms, including flipped, blended, online, distance education, and many others, are used interchangeably with the term hybrid. Numerous writings, some peer-reviewed and some not have attempted to provide standardized definitions (Allen et al., 2016; CAPTE, 2021; Malamed, 2010; Saichaie, 2020; Sener, 2015; Singh & Thurman, 2019). In addition, metaanalysis and review studies provide definitions to outline their processes for data collection (AlSamarraie et al., 2020; Cheng et al., 2019; He et al., 2021). Gagnon et al. (2020) utilized the Online Report Card (Allen et al., 2016) to distinguish blended/hybrid courses and programs as 30%-79% online with a minimum of 20% face-to-face. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 20 Additionally, Gagnon et al. (2020) referenced the terms outlined by CAPTE (2021) to support working definitions of online, blended, flipped, and traditional instruction. Interestingly, the CAPTE (2021) does not define hybrid instruction but does define blended instruction as a blend of distance education, asynchronous learning, and face-to-face synchronous experiences. CAPTE (2021) does not provide any guidance related to the amount of instruction delivered in distance, asynchronous, and face-to-face synchronously. The reality is that to obtain a proper understanding of the information in the literature related to students in hybrid education, all terms need to be utilized in searching. For this study, the definition of hybrid remained consistent with the seminal hybrid DPT program publication by Gagnon et al. (2020) as a blend of online and face-to-face delivery, with 30-79% of the program delivered online and at least 20% delivered face-to-face. For additional clarity, hybrid programs are referred to as programs that do not require full-time residence or relocation to attend the program. The term residential will be used to reference programs that are not hybrid. This term is not defined in the literature but is defined in this paper as a program that requires full-time residence in a specific geographic location to attend the program. The term residential is chosen over the CAPTE (2021) term traditional because the definition of traditional includes all face-to-face learning experiences in the classroom, lab, or community setting. Given the rise of technology and the shift in educational methods associated with the COVID-19 pandemic, it is unclear if any DPT program meets the working definition of traditional. Student Experience When considering the profile of the entry-level hybrid DPT student, it is essential to look beyond demographics. Demographics and cognitive factors such as grade point average are consistently reported in the literature, but there is a growing body of evidence related to non- HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 21 cognitive factors (Huhn et al., 2021; Richardson et al., 2022; Roll et al., 2018; Dudley-Javoroski & Shields, 2022; Van Veld et al., 2018). These include emotional intelligence, grit, self-efficacy, TfA, and perfectionism. These factors are studied about outcomes, identifying support mechanisms for students, and as a means of predicting success. To date, these non-cognitive traits have not been included in any of the aggregate data that CAPTE or ACAPT report, but Shields et al. (2018, 2019) began reporting on TfA in their benchmarking studies and subsequently began reporting on perfectionism in the second wave of benchmarking studies (Dudley-Javorski & Shields, 2022). These data have been collected under a section titled Student Experience and included data on burnout, interpersonal reactivity, and empathy. The student experience factors of TfA and perfectionism were selected for this study based on the hypothesis that either trait could impact a students resolution to enroll in a hybrid DPT program and remain consistent with aggregate published data. Tolerance for Ambiguity TfA is a concept that has been studied in healthcare education, with most of the literature derived from medical education (Caulfield et al., 2014; Gaufberg et al., 2018; Geller et al., 1990; Geller et al., 1993; Hancock & Mattick, 2019; Patel et al., 2022; Weissenstein et al., 2014) and a few studies relevant to nursing (Knight et al., 2016; McMahon & Dluhy, 2017; Pressler & Kenner, 2010). The construct of tolerance for ambiguity is difficult to define in the literature. Early research defines it as the tendency to perceive situations that are novel, complex, or insoluble, as sources of threat (Budner, 1962, as cited in Gellar, 1993, p. 990). McClain et al. (2015) also cite Budners work but define ambiguity tolerance as an individuals systematic, stable tendency to react to perceived ambiguity with greater or less intensity (Definition, paragraph 2). HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 22 Craik (2001) suggests that students need for tolerance for ambiguity in physical therapy education is related to clinical decision-making, growth in knowledge, and professional maturation. Craiks (2001) sentiments are further validated by D. U. Jette (2016b) in the Cerasoli Lecture, Unflattening, where she stated, graduates must make decisions in the face of limited information, ambiguity, and uncertainty (p. 7). Further, graduates must be prepared to work in situations where uncertainty is the norm, and critical thinking and clinical reasoning are the skills needed to overcome uncertainty (D. U. Jette, 2016b). A. M. Jette (2016a) says that a clinician can and often needs to act even in ambiguity. He further states that ambiguity about the effectiveness of physical therapy intervention is never a cause for celebration (p. 134) but promotes asking, studying, and finding answers to questions. Finally, A. M. Jette (2016a) concludes that physical therapy educators know that transmitting ambiguity based on tradition and experience is not helpful and that scientific evidence should be the standard. However, without evidence, embracing ambiguity allows educators and students to know when we know something and when we do not. He advocates for the preparation of students regarding ambiguity and relays that embracing ambiguity is just as important as employing evidence-based practice (A. M. Jette, 2016a). Three studies have examined TfA in DPT students (Dudley-Javoroski & Shields, 2022; Shields et al., 2018; Shields et al., 2021). These studies used the validated Tolerance for Ambiguity (TFA) Scale (Gellar et al., 1993). Results from the first study showed no significant difference in TfA between physical therapists and medical students (Shields et al., 2018). However, both subsequent studies showed a significantly lower TfA among DPT students than medical students. These findings suggested the need to investigate further the optimal levels of HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 23 TfA for successful outcomes in DPT education and to consider capturing TfA data before matriculating into DPT programs. Perfectionism Perfectionism has been defined in the literature since the mid-twentieth century as demanding of oneself or others a higher quality of performance than is required by the situation (Hollender, 1965, p. 94). The construct was included in the Diagnostic and Statistical Manual of Mental Disorders (DSM) III as a major diagnostic criterion (Frost et al., 1990) and is classified in the present-day DSM-5 as a lower-order facet of compulsivity (Ayearst et al., 2012). Perfectionism is multidimensional, with adaptive and maladaptive outcomes (Hewitt et al., 2003). Additionally, definitions of perfectionistic strivings and concerns were developed from a factor analysis by Frost et al. (1993). Perfectionistic striving consisted of the self-directed pursuit of self-determined high standards without high self-criticism and was associated with positive affect (Frost et al., 1993). Perfectionistic concerns consisted of the drive to obtain unrealistically high standards with excessive self-criticism and doubting self-ability and were associated with negative affect (Frost et al., 1993). Perfectionistic strivings are often grouped with adaptive perfectionism, and perfectionistic concerns are often grouped with maladaptive perfectionism. Meta-analyses have focused on maladaptive outcomes and found that perfectionistic concerns had a positive relationship with multiple psychopathological outcomes (Limburg et al., 2017) and a positive relationship with burnout symptoms (Hill & Curran, 2016). A meta-analysis by Madigan (2019) aimed to determine if perfectionism predicted academic achievement. The study found perfectionistic strivings to have a small to medium positive relationship with academic achievement, and perfectionistic concerns a small negative relationship with academic achievement (Madigan, 2019). HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 24 The impacts of maladaptive perfectionism have been studied in medical and health profession students and have been correlated with depression, anxiety, and stress (Bogardus et al., 2022; Enns et al., 2001; Henning et al., 2002). Other studies have found positive outcomes of adaptive perfectionism in shaping authentic leadership (Jaworski et al., 2022) and with baseline academic performance expectations and conscientiousness (Enns et al., 2008). Students working toward acceptance into physical therapist education programs may have to compete with others for limited slots in their programs of choice. This inherent competition may lead to perfectionistic tendencies. Richardson et al. (2022) studied perfectionism in entry-level DPT students and found that 41% were adaptive perfectionists and 25% were maladaptive perfectionists. Other literature related explicitly to DPT students and perfectionism groups DPT students with other health professions students (Bogardus et al., 2022; Filipkowski et al., 2021), making it difficult to ascertain if DPT students experience perfectionism with adaptive and maladaptive outcomes more than other healthcare professions students. Dudley-Javoroski and Shields (2022) utilized the FMPS-B in entry-level DPT students. They found that scores were generally higher for perfectionistic strivings than concerns, suggesting that DPT may experience more favorable outcomes related to adaptive perfectionism. Examining the Student Experience factors of TfA and perfectionism tendencies of students who enroll in hybrid DPT education programs may provide helpful information for administrators and faculty about student support services and evaluating outcomes based on these constructs. It is unknown if there is any correlation between TfA and perfectionism with hybrid DPT education program selection. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 25 Method Study Type and Design This study used a quantitative, non-experimental study using a cross-sectional design. The survey was conducted online using Qualtrics, an online survey program. The study took place from February 2023 to July 2023. Before participant recruitment, the study was approved by the University of Indianapolis Institutional Review Board. Participants Entry-level Doctor of Physical Therapy (DPT) students were recruited for this study using convenience sampling. Inclusion criteria included students 18 or older currently enrolled in any candidate or accredited hybrid DPT program. Exclusion criteria excluded students in a DPT program outside the United States and students in residential and post-professional programs. It is estimated that 3400 students were enrolled in one of the 19 accredited or candidate for accreditation programs in the United States in the Spring of 2023. Based on a 10% participation rate, the sample size target was 340. To increase the likelihood that the sample would represent the population, all eligible students who completed the survey were included in the study for an estimated maximum of 1,700 participants. Data Variables included student demographics, TfA scores, and FMPS-B scores. In addition, key considerations from the students perspective about enrolling in their specific hybrid DPT program were collected using a 5-point Likert scale. The variables collected are listed and defined below. Variables Variables for student characteristics included HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 26 age (years), gender (male, female, non-binary, other), sexual orientation (lesbian, gay, bisexual, transgender, queer, heterosexual, other) race (African American/Black, American Indian/Alaskan Native, Asian, Caucasian/White, Hispanic/Latino, Native Hawaiian/Other Pacific Islander, two or more races, unknown, prefer not to answer), highest advanced degree achieved (Bachelors, Masters, Clinical Doctorate, Terminal Doctorate), committed relationship (yes, no) relationship status (married or civil union living together or apart, not married or civil union living together or apart), divorced, widowed, separated, none of these apply (select all that apply) parental status- biological, adopted, foster, or stepchildren (no, no expecting, yes (1, 2, 3, 4 or more)) age of children and in the home or not, in matrix format (one, two, three, four); age of children, preschool (birth to 5 years), elementary (6-13 years), adolescent (14-18 years), adult (19+ years of age); living with you full time (365 days a year), part-time (less than 365 days a year), not living with you), caregiver for someone other than your children (yes, no), person living with you (yes, no), residential location before enrollment in the program- dropdown of states with categorization post data collection as follows (South Atlantic (DE, DC, FL, GA, MD, NC, PR, SC, VA, WV), Middle Atlantic (NJ, NY, PA), East North Central HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 27 (IL, IN, MI, OH, WI), West North Central (IA, KS, MN, MO, NE, ND, SD), West South Central (AR, LA, OK, TX), New England (CT, ME, MA, NH, RI, VT), Pacific (AK, CA, HI, OR, WA), East South Central (AK, KY, MS, TN), Mountain (AZ, CO, ID, MT, NV, UT, WY), other (write-in), relocation status for the program (yes/no), relocation location- dropdown of states with categorization post data collection as follows (South Atlantic (DE, DC, FL, GA, MD, NC, PR, SC, VA, WV), Middle Atlantic (NJ, NY, PA), East North Central (IL, IN, MI, OH, WI), West North Central (IA, KS, MN, MO, NE, ND, SD), West South Central (AR, LA, OK, TX), New England (CT, ME, MA, NH, RI, VT), Pacific (AK, CA, HI, OR, WA), East South Central (AK, KY, MS, TN), Mountain (AZ, CO ID, MT, NV, UT, WY), other (write-in), name of DPT program currently enrolled in- categorized post-data collection based on unique survey link by the primary researcher (T. B.) acceptance to other hybrid DPT programs (yes, no), acceptance to residential (non-hybrid) DPT programs (yes, no), method of learning about the program (friend/family member, internet search, PTCAS, social media (Instagram, Facebook, Twitter), undergraduate professor/advisor, online advertisement, other (write-in), tolerance for ambiguity (TfA Scale Score), perfectionism (FMPS-B subscale scores) HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 28 Operational Definitions of Variables Hybrid was defined as a program that blends online and face-to-face delivery; 30-79% of the content is delivered online with a minimum of 20% face-to-face in a physical location together (Gagnon et al., 2020). The concept of tolerance for ambiguity was operationalized using scores obtained on the TfA scale (Geller et al., 1993). Perfectionism was operationalized as the two subscale scores on the FMPS-B (Burgess et al., 2016). Student-reported key considerations of enrollment in a specific hybrid DPT program were defined using a 1-5 Likert, 1 (not important) to 5 (very important), and included university factors: reputation, tuition cost, travel cost, housing cost; program factors: mission, vision, values, program duration, faculty, location; admissions factors: online webinars, pre-admission visits, interactions with the admissions team; outcomes factors: National Physical Therapy Examination (NPTE) first and ultimate (two-year average) pass rates, diversity of study body, graduation rates, employment rates; others: free text write-in space provided. Instruments Tolerance for Ambiguity Scale The TfA scale (Appendix A) measures the tendency to see ambiguous situations as threatening (Budner, 1962, as cited in Geller, 1993). It is comprised of seven questions and utilizes a Likert scale from 1 (strongly agree) to 6 (strongly disagree) (Geller et al., 1993). Summed scores range from 7 to 42, with lower scores demonstrating decreased tolerance and higher scores demonstrating increased tolerance (Geller et al., 1993). The survey has moderate internal consistency ( = .75) and demonstrates construct validity based on the assumption that a self-inventory test is only useful if some overt manifestation of the variable can be strongly related to that test (MacDonald, 1970, as cited in Geller et al., 1993, p. 997). Geller et al. (1993) HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 29 state that the TfA scale study conducted with physicians on attitudes toward genetic testing reflected behavior correlates of ambiguity tolerance, establishing construct validity. The scale is openly available for use. Frost Multidimensional Perfectionism Scale-Brief The FMPS-B comprises two subscales: evaluative concerns and strivings (Appendix B). Each subscale consists of 4 questions (Burgess et al., 2016). Items are scored on a Likert scale from 1 (strongly disagree) to 5 (strongly agree), and each subscale score is summed separately (Woodfin et al., 2020). Scores on each subscale range from 4 to 20, with higher scores indicating more perfectionistic tendencies. The evaluative concerns scale has a Cronbachs coefficient of = .91, and the strivings scale has a Cronbachs coefficient of = .84, indicating good internal consistency (Tonta et al., 2021). The scale has construct validity with demonstrated concurrent and convergent validity with other similar scales (r = .68-.72) (Burgess et al., 2016). This scale is openly available for use. Procedures Recruitment Participants were recruited via a phone call and email through their DPT program director or program faculty. The primary researcher (T. B.) contacted all accredited or candidate hybrid DPT program directors or faculty to ask for cooperation to disseminate the survey via email to their students. Informed Consent Informed consent was included on the first page of the Qualtrics survey. Participants were required to select yes or no, indicating their understanding of the research project and consent to participate. Participants who selected yes were routed to the surveys first question. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 30 Participants who indicated no were routed to a closing page, which thanked them for their consideration. Data Collection Data were collected using a secure Qualtrics survey created by the primary researcher (Appendix C). Before data were collected with the survey, a pilot of the survey was completed utilizing DPTs who had just completed a hybrid DPT program. The survey link was customized for each program participating and was only provided to DPT program directors or faculty who agreed to survey dissemination. The survey link remained open for three months. Two reminder emails were sent to potential participants at four and eight weeks. Students were offered an opportunity to provide their name and email address in a Google Survey, linked at the end of the survey, for inclusion in a drawing for one of ten $25 Amazon gift cards after completion. Participants were also allowed to indicate their interest in being interviewed for phase two of the study. Fraud detection measures, including the prevention of multiple submissions and bot detection, were enabled in the Qualtrics survey software. Data Management All surveys were disseminated to each hybrid program using a unique survey link, and responses were given a unique study identification number for data analysis. The data were downloaded from Qualtrics into a Microsoft Excel file for data cleaning, summing, and calculation of instrument scores. The Microsoft Excel file was password-protected and stored on a password-protected hard drive. Statistical Analysis Statistical tests were run using IBM SPSS Statistics for Windows, Version 28.0 (IBM Corp., Armonk, NY). All statistical tests were two-tailed, and the significance level was set at HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 31 .05. Normality of interval and ratio data were determined using Shapiro-Wilk tests and visual inspection of Q-Q plots and histograms. Descriptive statistics were used to explore the demographic characteristics of the sample and tolerance for ambiguity and perfectionism scores. Nominal data are reported as frequencies and percentages, and normally distributed interval and ratio data are reported as means and standard deviations. Medians and interquartile ranges are reported for ordinal and non-normally distributed interval and ratio data. Results Thirteen of 19 US hybrid DPT programs, 68%, participated in this study. Four hundred twenty-four participants started surveys, and 379 surveys were more than 50% complete and included in the analysis. The sample included participants from a hybrid DPT program in every regional location in the United States (see Figure 1). The median age of the sample was 25 years (25th percentile = 23, 75th percentile = 29). The general demographic characteristics (gender, race, and education) of the sample are presented in Table 1. Expanded demographic characteristics (sexual orientation, children, relocation, and acceptance into other programs) are presented in Table 2. Of the participants with children (n = 45, 11.8%), 29 (64.4%) had more than one child. The children were primarily preschool age, (n = 38, 84.4%), or elementary age, 22 (48.8%). Twelve (3.2%) participants reported they were caregivers to someone other than their children, and 6 (50%) reported that the person they cared for lived in the same house. Most respondents heard about the program for which they enrolled through an internet search 125 (33%), the Physical Therapist Centralized Application Service 82 (21.6%), or a family/friend/coworker 87 (23%). The least reported methods of hearing about the program included social media 39 (10.3%), undergraduate professor/advising 21 (5.5%), and online advertisement 13 (3.4%). HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 32 Three hundred and seventy participants completed the TfA and FMPS-B tests. The median score on the TfA was 22 (25th percentile = 17, 75th percentile = 26). The median strivings score on the FMPS-B was 16 (25th percentile = 14, 75th percentile = 18). The median evaluative score was 11 (25th percentile = 8, 75th percentile = 14). Descriptive statistics on key considerations of a students decision to enroll in their specific hybrid DPT program showed program duration, first-time NPTE pass rates, ultimate NPTE pass rates, graduation rate, and employment rate all had a median score of 5, very important. Diversity of the student body had a median score of 3, moderately important. A lab visit before admission had a median score of 2, slightly important. All other factors had a median score of 4, important. Key considerations were collapsed, combining Likert scale ratings of very important and important, as well as ratings of slightly important and not important. See Table 3 for results. Discussion This study is the first to capture data specific to the characteristics and experiences of the hybrid DPT student. Anecdotally, there are many assumptions regarding the student who would choose and be successful in a hybrid DPT program. Some assumptions include hybrid programs increasing the diversity of students, only students who cannot get into a residential program would choose a hybrid program, and assumptions about the quality of students who would be attracted to a hybrid program. Until now, no studies have collected and reported data on the hybrid DPT student to validate those assumptions. Characteristic Demographic Profile of Hybrid DPT Students The characteristic demographics of age, sexual orientation, and racial diversity of the hybrid DPT student appear to be very similar to aggregate data. The median age of the hybrid HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 33 student was 25 years, and 91.8% reported being heterosexual. This is very close and likely not significantly different than the average age at graduation of 26.2 years and 93.2% heterosexual, as reported in the PT-GQ data by Dudley-Javoroski and Shields (2022). This study found a racial minority population of 28% and a white population of 71%, slightly different from the CAPTE (2022) aggregate data reporting a racial minority population of 30% and a white population of 65%. Early data suggested hybrid DPT programs may increase racial diversity (Gagnon et al., 2022). The differences found in this study compared to the aggregate are small. There appears to be a difference in the marital status and those with children in hybrid DPT students as observationally compared to the aggregate. This study found that 33.5% of students were married and 13.5% had children, compared to 17.5% of students reporting married and 3.8% of students with children reported in the PT-GQ data (Dudley-Javoroski et al., 2022). This equates to approximately twice the number of students in hybrid programs being married and just over 3.5 times the number reporting having children. While it is unknown if this is a statistically significant difference, this seems to be quite a difference. It is hypothesized that people who are married or have children may be more attracted to a hybrid program due to the ability to remain living in a location where they are established, their spouse may be working, their children may be in school, or where they might have support systems in place to help with families while still being able to complete a DPT program. Additionally, this study found a nearly 10% higher population of females at 72% versus 62% in the aggregated data (CAPTE, 2022). This may be due to women, more classically the family caretakers, choosing hybrid programs to further education while still being able to remain home most of the time with their families. Interestingly, less than 20% of participants reported relocating to attend their hybrid DPT program. This may be due to the cost of travel associated with attending face-to-face HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 34 activities such as orientation and lab immersions or may be due to the location of clinical education placements. Future research on demographic characteristics of hybrid DPT students should focus on obtaining raw data from both hybrid and residential DPT students with a large sample size to complete interferential statistical analysis to determine if actual statistically significant differences exist between the two groups. Additionally, there is a need for qualitative studies to further examine why students with certain characteristics choose hybrid programs. Tolerance for Ambiguity and Perfectionism TfA scores of the hybrid DPT sample were nearly identical to those of the aggregate data available. Participants' median score on the TfA scale was 22 out of 42 available points. This is very similar to aggregate data reported in the PT-GQ of a mean score of 23 (Dudley-Javoroski & Shields, 2022). Only the published studies reporting the PT-GQ data have reported these TfA in DPT students (Dudley-Javoroski & Shields, 2022; Shields et al., 2021). Despite the lack of published data, in recent years, multiple publications have stated the importance of tolerance for ambiguity in DPT students and medical students (Blanton et al., 2020; Hancock & Mattick, 2020). Additional research is needed in this area to determine if the construct of tolerance for ambiguity is different among hybrid DPT students as compared to residential DPT students and to determine if tolerance for ambiguity impacts outcomes for DPT programs and licensure examinations. Like TfA scores, participants in this study scored nearly the same scores on the FMPS-B as the published aggregate data. Data from this study showed a difference of less than .5 points between the two subscales compared to the data collected from the PT-GQ (Dudley-Javoroski & Shields, 2022). This supports the findings from Dudely-Javroski & Shields (2022), which pose HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 35 that DPT students tend to score higher in the strivings category than the concerns category, suggesting more favorable outcomes towards adaptive perfectionism. Continued research is needed surrounding non-cognitive factors related to student experience and acceptance to DPT programs to determine the impact these factors have on student outcomes. Key Considerations of Importance in Selection of a Specific Hybrid DPT Program Results of this study show that more than half of the participants were accepted into multiple DPT programs and could select the program of their choice. Over 80% of respondents accepted to multiple programs had the option to attend a residential program yet selected a hybrid program. These data suggest that when students have a choice, the majority are likely to choose a hybrid pathway. This is impactful because it puts data behind the rationale for programs to offer the hybrid education pathway and invalidates the assumption that only students who cannot get into residential programs go to hybrid programs. This study examined four categories of potential considerations and their importance in selecting a specific hybrid DPT program. The consideration categories included university factors, program factors, admission factors, and outcome factors. The outcome factors category was shown to have the most important considerations, having 4 out of the top 5 most important considerations. It is not surprising to see these considerations show up in the top 5 because CAPTE requires DPT programs to publish these outcomes on their websites, which makes this data some of the most readily available data to those selecting a program. The only other study examining factors of importance in an applicants choice of program also included the NPTE pass rate percentages as being very influential (Ancrum-Smalls et al., 2000). The other consideration in the top 5 at number 3 was a program consideration of program duration. This is interesting because 87% of participants in this study are from hybrid DPT programs that could be HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 36 considered accelerated time to degree programs with program completion in 30 months (7 semesters) or less. This suggests that a shorter program duration may be more favorable in students selection of a hybrid DPT program. The admissions factors category included 3 of the least important considerations in selecting a hybrid DPT program, with the number 1 least important- lab visit, number 4interaction with the program, and number 5- online webinar. These findings are important because programs must choose where to spend their recruitment funds. Providing staff and faculty coverage for lab visits, interactions with students, and online webinars can be costly and may be the least effective manner to use funds. Other considerations in the top 5 least important included number 2- diversity of students and number 3- housing cost. Diversity is a common topic of discussion in the recruitment and retention of DPT students; however, it seems that participants rank this as low importance in their selection of a hybrid DPT program. It is important to note that only 29.3% of respondents for this research were racial minorities, which could have impacted the importance of this factor among respondents. Housing costs may be of little importance to students because relocation is not required to complete a hybrid DPT, and only 18% of respondents reported relocating to attend their program. Therefore, the cost of housing in the physical location of the program may not be an important consideration since most participants did not relocate. The least important considerations in this study match several of the least influential factors found in the Ancrum-Smalls et al. (2000) study, with commonalities among student diversity and interaction with the program included. This research study acquired quantitative data on the important considerations for students in selecting their hybrid DPT program. Future studies should aim to collect qualitative data on this subject to determine if there were important considerations that were not listed in this study that had an HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 37 impact on students decision to enroll in their specific hybrid DPT program. Additionally, research should aim to determine why students choose hybrid programs and residential programs. Limitations There were several limitations to this study. First, this study could only obtain information from approximately 10% of the current hybrid DPT student population, which is a limited sample. A normal distribution of respondents was not achieved. All students from all hybrid DPT programs with current cohorts were invited to participate; however, access to advertise the opportunity to participate in the research was limited by the program's willingness to disseminate the call for participation and the survey link to their students. A second limitation was oversight by the researcher in collecting the specific year of the program that respondents were in during the data collection. Respondents in their first year may have responded differently to student experience questions than respondents in their second year due to a lack of maturation in tolerance for ambiguity and perfectionism. A third limitation of the study was the lack of working definitions within the survey instrument. This left room for misinterpretation of the words used in the key considerations area of the survey. Conclusion This study provides key insight into the characteristics and experiences of hybrid DPT students, benchmarked against national aggregate data. The profile of a hybrid DPT student is similar to the national DPT student population in terms of age, demographics (age, sexual orientation, race/ethnicity), tolerance for ambiguity, and perfectionism. Hybrid DPT students are more likely to be married and/or have children, and the majority are accepted to multiple programs, including residential as well as other hybrid programs. Published graduate outcomes HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 38 are the most important factor for students when choosing to enroll in their hybrid program. This study lays the foundational work for expanded qualitative studies. As competition for students continues to grow with a growing number of hybrid and residential DPT programs, this study provides evidence that can be useful for hybrid DPT programs in making decisions about how to spend recruitment funds and which type of students to focus their recruitment efforts towards. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 39 References ACAPT. (n.d.). Criteria for excellence. https://acapt.org/docs/default-source/default-documentlibrary/criteria-of-excellence-full-report-final.pdf?sfvrsn=14888ed8_2 ACAPT. (2022a). Center for excellence in academic physical therapy. https://acapt.org/resources/excellence ACAPT. (2022b). DPT program guidelines. https://acapt.org/resources/for-program-directors ACAPT. (2022c, January). Excellence framework for physical therapist education. Center for Excellence in Academic Physical Therapy. https://acapt.org/resources/excellence/excellence-framework ACAPT. (2022d, March 28). 2022 ACAPT institutional profile survey report. 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CAPTE. (2022). Aggregate program data. https://www.capteonline.org/about-capte/data-andresearch/aggregate-program-data CAPTE (n.d.). Directory of programs. Commission on Accreditation in Physical Therapy Education. Retrieved June 12, 2022, from https://www.capteonline.org/programs CAPTE. (2021). Position papers: Implementing distance education in physical therapist / physical therapist assistant programs (pp. 2125). HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 42 https://www.capteonline.org/globalassets/capte-docs/capte-position-papers.pdf Cambridge Dictionary. (n.d.). Ambiguity. https://dictionary.cambridge.org/us/dictionary/english/ambiguity Cambridge Dictionary. (n.d.). Uncertainty. https://dictionary.cambridge.org/us/dictionary/english/uncertainty Caulfield, M., Andolsek, K., Grbic, D., & Roskevensky, L. (2014). Ambiguity tolerance of students matriculating to U.S. medical schools. 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Internet Journal of Allied Health Sciences and Practice, 19(1), Article 10. https://doi.org/10.3352/jeehp.2018.15.19 Jaworski, M., Panczyk, M., Lenczuk-Gruba, A., Nowacka, A., & Gotlib, J. (2022). The trend of authentic leadership in nursing education: The key role of perfectionism and self-efficacy. International Journal of Environmental Research and Public Health, 19(4), Article 1989. https://doi.org.10.3390/ijerph19041989. Jensen, G. M., Hack, L. M., Nordstrom, T., Gwyer, J., & Mostrom, E. (2017a). National study of excellence and innovation in physical therapist education: Part 2- A call to reform. Physical Therapy, 97(9), 875-888. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 46 Jensen, G. M., Nordstrom, T., Mostrom, E., Hack, L. M., & Gwyer, J. (2017b). National study and innovation in physical therapist education: Part 1- Design, method, and results. Physical Therapy, 97(9), 857-874. Jette, A. M. (2016a). In pursuit of the ever-expanding shoreline. Physical Therapy, 96(2), 134135. https://doi.org/10.2522/ptj.2016.96.2.134 Jette, D. U. (2016b). Unflattening. Journal of Physical Therapy Education, 30(3), 4-10. https://doi.org/10.1097/00001416-201630030-00003 Killian C. B., May, F. E., & Moore, E. S. (2022). Statistical Procedures Manual for SPSS. Knight, K., Kenny, A., & Endacott, R. (2016). From expert generalists to ambiguity masters: using ambiguity tolerance theory to redefine the practice of rural nurses. Journal of Clinical Nursing, 25, 11-12. https://doi.org/10.1111/jocn.13196 Lazinski, M. J. (2017). Psychomotor skills, physical therapy, and a hybrid course: A case study. Quarterly Review of Distance Education, 18(4), 5769. Limburg, K., Watson, H. J., Hagger, M. S., & Egan, S. J. (2017). The relationship between perfectionism and psychopathology: A meta-analysis. Journal of Clinical Psychology, 73(10), 1301-1326. Madigan, D. J. (2019). A meta-analysis of perfectionism and academic achievement. Educational Psychology Review, 31, 967-989. Marinas, R., Groff, S., Panesar-Aguilar, S., & Bobbio, T. G. (2022). Students perception of cognitive load in an accelerated DPT program with a blended learning curriculum. Global Journal of Health Science, 14(2), 52-62. Majsak, M. J., Hall, C. A., Kirsch, N. R., Krencicki, D. B., Locke, E., & Hyland, N. (2022). Physical therapy education program faculty challenges, concerns, and priorities during the HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 47 COVID-19 pandemic: Looking back and moving forward. Journal of Physical Therapy Education, 36(2), 97-106. Malamed, C. (2010, June 6). Glossary of online learning terms. The eLearning Coach. https://theelearningcoach.com/resources/online-learning-glossary-of-terms/ McMahon, M. A., & Dluhy, N. M. (2017). Ambiguity within nursing practice: An evolutionary concept analysis. Research and Theory for Nursing Practice, 31(1), 56-74. https://doi.org/10.1891/1541-6577.31.1.56 Moffat, M. (2003). The history of physical therapy practice in the United States. Journal of Physical Therapy Education, 17(3), 1525. https://doi.org/10.1097/00001416-20031000000003 Moffat, M. (2012). A history of physical therapist education around the world. Journal of Physical Therapy Education, 26(1), 1323. McPoil, T. G. (2019). Is excellence in the cards? Physical Therapy, 99(10), 1281-1290. https://doi.org/ 10.1093/ptj/pzz104 Neely, L., Pabian, P., Darby, A., Tintor, M. Vatnsever, S. & Stock, M. (2022). Examining clinical readiness and performance of student on clinical education experiences: Is there an influence from virtual learning? Journal of Physical Therapy Education, Advance Online Publication. https://doi.org.10.1097/JTE.0000000000000243 Nordstrom, T., Jensen, G. M., Altenburger, P., Blackinton, M., Deusinger, S., Hack, L., Patel, R., Tschoepe, B., & VanHoose L. (2022). Crises as the crucible for change in physical therapist education. Physical Therapy, 102(7), Article pzac055. Nova Southeastern University. (2011, April 7). Nova Southeastern University launches new physical therapy program in the Tampa Bay area. News Wise. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 48 https://www.newswise.com/articles/nova-southeastern-university-launches-new-physicaltherapy-program-in-the-tampa-bay-area Ortega, M. A., Marchese, V. G., Zarro, M. J., Film, R. J., Shipper, A. G., & Felter, C. (2021). Digital and blended curriculum delivery in health professions education: An umbrella review with implications for doctor of physical therapy education programs. Physical Therapy Reviews, 27(1), 4-24. https://doi.org/10.1080/10833196.2021.2000286 Patel, P., Hancock, J., Rogers, M., & Pollard, S. R. (2022). Improving uncertainty tolerance in medical students: A scoping review. Medical Education. Advanced online publication. https://doi.org/10.1111/medu.14873 Plummer, L., Kaygisiz, B. B., Kuehner, C. P., Gore, S., Mercuro, R., Chatiwala, N., & Naidoo, K. (2021). Teaching online during the COVID-19 pandemic: A phenomenological study of physical therapist faculty in Brazil, Cyprus, and the United States. Education Sciences, 11(3), Article 130. https://doi.org/10.3390/educsci11030130 Portney, L. G. (2014). 17th Pauline cerasoli lecture: Choosing a disruptive path toward tomorrow. Journal of Physical Therapy Education, 28(3), 4-14. Pressler, J. L., & Kenner, C. A. (2010). Tolerance of ambiguity in nursing academia. Nurse Educator, 35(4), 139-140. https://doi.org/10.1097/NNE.0b013e3181e33813 Prober, C. G., & Khan, S. (2013). Medical education reimagined. Academic Medicine, 88(10), 1407-1410. QualtricsXM. (2022). Fraud detection. Support. https://www.qualtrics.com/support/surveyplatform/survey-module/survey-checker/fraud-detection/ Richardson, M. V., Miller, H., Papa, E., & Santurri, L. (2022). Perfectionism, stress, and the entry-level doctor of physical therapy student: A cross-sectional, observational study. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 49 Journal of Physical Therapy Education, 36(1), 9-16. Roll, M., Canham, L., Salamh, P., Covington, K., Simon, C., & Cook, C. (2018). A novel tool for evaluating non-cognitive traits of doctor of physical therapy learners in the United States. Journal of Educational Evaluation for Health Professions, 15(19). https://doi.org/10.3352/jeehp.2018.15.19 Saichaie, K. (2020). Blended, flipped, and hybrid learning: Definitions, developments, and directions. New Directions for Teaching & Learning, 164, 95104. https://doi.org/10.1002/tl.20428 Sener, J. (2015, July 7). Updated e-learning definitions. Online Learning Consortium. Shields, R. K., Dudley-Javoroski, S., Sass, K. J., & Becker Marcie. (2018). Benchmarking the physical therapist academic environment to understand the student experience. Physical Therapy & Rehabilitation Journal, 98(8), 658-669. https://doi.org/10.1093/pgj/pzab229 Shields, R. K., The Benchmarking Research Advances Value in Education Group, & DudleyJavoroski, S. (2021). Benchmarking in academic physical therapy: A multicenter trial using the PT-GQ survey. Physical Therapy & Rehabilitation Journal, 101(12), Article pzab229. https://doi.org/10.1093/ptj/pzab229 Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988-2018). The American Journal of Distance Education, 33(4), 289306. https://doi.org/10.1080/08923647.2019.1663082 South College. (2021). Learn more about our doctor of physical therapy program. https://www.south.edu/programs/doctor-physical-therapy/about-us/ Thibault, G. (2020). The future of health professions education: Emerging trends in the United States. FASEB BioAdvances, 2(12), 685-694. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 50 Tonta, K. E., Boyes, M., Howell, J., McEvoy, P., & Hasking, P. (2021). Measurement invariance of perfectionism measures in students with and without a history of non-suicidal self-injury. International Journal of Environmental Research and Public Health, 18(190), Article 10171. University of St. Augustine for Health Sciences. (2016, December 28). History. University of St. Augustine for Health Sciences. https://www.usa.edu/about/history-of-university-of-staugustine-for-health-sciences/ Van Veld, R., Slaven, E., Reynolds, B., Shupe, P., &Woolery, C. (2018). First-year doctor of physical therapy students demonstrate change in coping with stress. Journal of Physical Therapy Education, 32(2), 138-144. Veneri, D. A., & Ganotti, M. (2014). A comparison of student outcomes in a physical therapy neurologic rehabilitation course based on delivery mode: Hybrid vs traditional. Journal of Allied Health, 43(4), e75e81. Wassinger, C. A., Owens, B., Boynewicz, K., & Williams, D. A. (2021). Flipped classroom versus traditional teaching methods within musculoskeletal physical therapy: A case report. Physiotherapy Theory and Practice, Advance online publication. https://doi.org/10.1080/09593985.2021.1941457 Weissenstein, A., Ligges, S., Brouwer, B., Maschall, B., & Friederichs, H. (2014). Measuring the ambiguity tolerance of medical students: a cross-sectional study from the first to sixth academic years. BMC Family Practice, 15(6), 1-5. https://doi.org/10.1186/1471-2296-15-6 Woodfin, V., Binder, P. & Molde, H. (2020). The psychometric properties of the frost multidimensional perfectionism scale-brief. Frontiers in Psychology, 11. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01860/full HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Wojciechowski, M. (2015). The future of physical therapist education. American Physical Therapy Association. 51 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 52 Table 1 General Demographic Characteristics (N = 379) Demographics Gender Male Female Non-binary Race African American/Black American Indian/Alaskan Native Asian Caucasian/White Hispanic/Latino Native Hawaiian/Other Pacific Islander Two or more races Unknown Education Bachelors Masters Clinical Doctorate Terminal Degree n % 104 273 2 27.4 72.0 0.5 18 2 25 268 25 3 33 1 4.7 0.5 6.6 70.7 6.6 0.8 8.7 0.3 313 44 19 3 82.6 11.6 5.0 0.8 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 53 Table 2 Expanded Demographic Characteristics (N = 379) Demographics Sexual Orientation Heterosexual LGBTQ+ Relationship Status Committed Not Committed Divorced Separated Married, Living with Spouse Married, Living Apart Not Married, Living with Partner Not Married, Living Apart Children Yes 1 Child 2 Children 3 Children 4+ Children Expecting No Relocation Yes No Accepted to >1 DPT Program Yes No Accepted to a Residential Program Yes No Accepted to a Hybrid Program Yes No Accepted to another Hybrid and a Residential Program n % 347 31 91.8 8.2 281 98 9 1 126 1 71 83 74.1 25.9 2.4 0.8 33.2 0.3 18.7 21.9 45 16 19 8 2 6 328 11.8 4.2 5.0 2.1 0.5 1.6 86.5 69 309 18.3 81.7 206 173 54.4 45.6 171 208 45.1 54.9 67 312 38 17.7 82.3 10.0 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 54 Table 3 Key Considerations in Enrollment in a Specific Hybrid DPT Program (N = 379) Key Consideration University Factors University Reputation Tuition Cost Travel Cost Housing Cost Program Factors Program Duration Program Mission Program Vision Program Values Program Faculty Program Reputation Program Lab Location Admission Factors Online Webinar Lab Visit Interaction Outcome Factors NPTE First Time Pass NPTE Ultimate Pass Diversity of Students Graduation Rate Employment Rate Very Important/ Important n % Moderately Important n % Slightly Important/ Not Important n % Median 255 67.3 76 20.1 48 12.6 4 266 217 206 70.2 57.3 54.4 84 92 79 22.2 24.3 20.8 29 70 93 7.6 18.5 24.5 4 4 4 315 224 226 250 292 274 83.1 59.1 59.7 66.0 77.1 72.3 41 83 82 74 48 72 10.8 21.9 21.6 19.5 127 19 22 72 70 54 37 32 5.8 19.0 18.5 14.3 9.7 8.4 5 4 4 4 4 4 229 60.4 94 24.8 55 14.5 4 220 74 214 58.0 19.5 56.4 71 82 76 18.7 21.6 20.1 86 221 87 22.6 58.3 23.1 4 2 4 312 82.4 37 9.8 30 7.9 5 307 81.0 36 9.5 35 9.3 5 174 45.9 87 23 116 30.6 3 323 319 85.2 84.2 29 29 7.7 7.7 26 30 6.8 7.9 5 5 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Figure 1 Percentage of Participants per Regional Location 6.3 3.7 9.0 11.1 16.3 14.3 5.8 10.0 23.5 South Atlantic Middle Atlantic East North Central West North Central West South Central New England Pacific East South Central Mountain 55 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 56 Appendix A Tolerance for Ambiguity Scale1 Questions 1. It really disturbs me when I am unable to follow another persons train of thought. 2. If I am uncertain about the responsibilities involved in a particular task, I get very anxious. 3. Before any important task, I must know how long it will take. 4. I dont like to work on a problem unless there is a possibility of getting a clear-cut and unambiguous answer. 5. The best part of working on a jigsaw puzzle is putting in the last piece. 6. I am often uncomfortable with people unless I feel that I can understand their behavior. 7. A good task is one in which what is to be done and how it is to be done is always clear. Scale 1: Strongly agree 2: Moderately agree 3: Slightly agree 4: Slightly disagree 5: Moderately disagree 6: Strongly disagree Geller, G., Tambor, E. S., Chase, G. A., & Holtzman, N. A. (1993). Measuring physicians tolerance for ambiguity and its relationship to their reported practices regarding genetic testing. Medical Care, 31(11), 989-1001. https://www.jstor.org/stable/pdf/3766298.pdf?refreqid=excelsior%3A9e0dbfbed02582388990f6afef881c98&ab_seg ments=&origin=&acceptTC=1 1 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 57 Appendix B Frost Multidimensional Perfectionism Scale - Brief2 Please circle the number that best corresponds to your agreement with each statement below. Use this rating system: Strongly disagree 1 4 Strongly agree 5 1. If I fail at work/school, I am a failure as a person. 1 2 3 4 5 2. I set higher goals for myself than most people. 1 2 3 4 5 3. If someone does a task at work/school better than me, then I feel like I failed at the whole task. 1 2 3 4 5 4. I have extremely high goals. 1 2 3 4 5 5. Other people seem to accept lower standards from themselves than I do. 1 2 3 4 5 6. If I do not do well all the time, people will not respect me. 1 2 3 4 5 7. I expect higher performance in my daily tasks than most people. 1 2 3 4 5 8. The fewer mistakes I make, the more people will like me. 1 2 3 4 5 2 3 Scoring: Sum items for the following subscales. Do not use a total score. Striving: 2, 4, 5, 7 EC: 1, 3, 6, 8 2 Burgess, A. M., Frost, R. O., & DiBartolo, P. M. (2016). Development and validation of the frost multidimensional perfectionism scale-brief. Journal of Psychoeducational Assessment, 34(7), 620633. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Appendix C Entry-level Hybrid DPT Student Profile Survey Standard: Informed Consent (0 Questions) Standard: Purpose and Instructions (1 Question) Block: Demographics (20 Questions) Standard: Key Considerations (6 Questions) Standard: Tolerance for Ambiguity (1 Question) Standard: Perfectionism (1 Question) Standard: Gift Card Opt In (0 Questions) 58 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 59 Start of Block: Informed Consent Start of Block: Purpose and Instructions Purpose: The purpose of this survey is to gather the demographic characteristics of students enrolled in entry-level hybrid Doctor of Physical Therapy programs, examine the student experience aspects of tolerance for ambiguity and perfectionism, and identify the key considerations for students when selecting a hybrid DPT program. Instructions: This survey has four sections (Part I- Demographics, Part II- Key considerations in the selection of a hybrid DPT program, Part III- The Tolerance for Ambiguity Scale, and Part IV- Frost Multidimensional Perfectionism Scale-Brief). Please read the instructions above each section for the specifics of answering each section of questions. This survey should take you 15 minutes or less. End of Block: Purpose and Instructions Start of Block: Demographics Please complete the demographic portion of this survey by answering the following questions. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Enter your current age in years. ________________________________________________________________ What is your gender? o Male (1) o Female (2) o Non-binary (3) o Let me type- Enter gender below. (7) __________________________________________________ What is your sexual orientation? o LGBTQ+ (1) o Heterosexual (2) 60 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT How would you describe yourself? o African American / Black (1) o American Indian / Alaskan Native (2) o Asian (3) o Caucasian / White (4) o Hispanic / Latino (5) o Native Hawaiian / Other Pacific Islander (6) o Two or more races (7) o Unknown (8) o I prefer not to answer (9) 61 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 62 What is your highest degree earned? o Bachelors (1) o Masters (2) o Clinical Doctorate (Pharm D, Nursing Doctorate, Doctor of Psychology) (3) o Terminal Doctorate (Ph.D., Doctor of Education, Doctor of Veterinary Medicine, Doctor of Engineering, Juris Doctor) (4) Are you in a committed relationship? o Yes (1) o No (2) Skip To: Q28 If Are you in a committed relationship? = No HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Please select all that apply regarding your relationship. Married or in a civil union, living together (1) Married or in a civil union, living apart (2) Not married or in a civil union, living together (3) Not married or in a civil union, living apart (4) Are you divorced, widowed, or separated? Select all that apply. Divorced (1) Widowed (2) Separated (3) None of these apply (4) 63 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Do you have biological, adopted, foster, or stepchildren? o No (1) o No, but I am (or my partner is) pregnant or in the process of adopting (2) o Yes, one child (3) o Yes, two children (4) o Yes, three children (5) o Yes, four or more children (6) Skip To: Q27 If Do you have biological, adopted, foster, or stepchildren? = No Skip To: Q27 If Do you have biological, adopted, foster, or stepchildren? = No, but I am (or my partner is) pregnant or in the process of adopting 64 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 65 If you have children, what are the ages of your children, and do they live with you. Select all relevant options below. They do not live with me (1) Preschool (Birth to 5 years) (1) Elementary (6-13 years) (2) Adolescent (14-18 years) (3) Adult Children (19+ years) (4) They live with me parttime (< 365 days a year) (2) o o o o o o o o Are you a caregiver for someone other than your children? o Yes (1) o No (2) Skip To: Q9 If Are you a caregiver for someone other than your children? = No They live with me fulltime (365 days a year) (3) o o o o HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT If you are a caregiver for someone other than your children, does the person you care for live with you? o Yes (1) o No (2) 66 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 67 Select the geographical region in which you lived prior to enrolling in your hybrid DPT program. o South Atlantic (DE, DC, FL, GA, MD, NC, PR, SC, VA, WV) (1) o Middle Atlantic (NJ, NY, PA) (2) o East North Central (IL, IN, MI, OH, WI) (3) o West North Central (IA, KD, MN, MO, NE, ND, SD) (4) o West South Central (AR, LA, OK, TX) (5) o New England (CT, ME, MA, NH, RI, VT) (6) o Pacific (AK, CA, HI, OR, WA) (7) o East South Central (AL, KY, MS, TN) (8) o Mountain (AZ, CO, ID, MT, NV, UT, WY) (9) o Other, please specify (10) __________________________________________________ Did you relocate to be closer to the physical facilities of the hybrid DPT program? o Yes (1) o No (2) HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 68 Skip To: Q24 If Did you relocate to be closer to the physical facilities of the hybrid DPT program? = No Select the geographical region to which you relocated. o South Atlantic (DE, DC, FL, GA, MD, NC, PR, SC, VA, WV) (1) o Middle Atlantic (NJ, NY, PA) (2) o East North Central (IL, IN, MI, OH, WI) (3) o West North Central (IA, KD, MN, MO, NE, ND, SD) (4) o West South Central (AR, LA, OK, TX) (5) o New England (CT, ME, MA, NH, RI, VT) (6) o Pacific (AK, CA, HI, OR, WA) (7) o East South Central (AL, KY, MS, TN) (8) o Mountain (AZ, CO, ID, MT, NV, UT, WY) (9) o Other, please specify (10) __________________________________________________ HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Were you accepted into more than one DPT program? o Yes (1) o No (2) Were you accepted into another hybrid DPT program? o Yes (1) o No (2) Were you accepted into any residential (non-hybrid) DPT programs? o Yes (1) o No (2) 69 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 70 Think back to when you were a DPT applicant. How did you first hear about the DPT program that you chose? o Friend or family member (1) o Internet search (2) o PT-CAS (3) o Social Media (Instagram, Facebook, Twitter) (4) o Undergraduate professor or advisor (5) o Online advertisement (6) o Other, please specify (7) __________________________________________________ End of Block: Demographics Start of Block: Key Considerations Select the level of importance for each key consideration utilizing the 1-5 Likert scale from 1= Not Important to 5= Very Important. HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 71 Select the level of importance for each key consideration. 1: Not Important (1) University Reputation (1) Tuition Cost (2) Travel Cost (3) Housing Cost (4) o o o o 2: Slightly Important (2) o o o o 3: Moderately Important (3) o o o o 4: Important (4) o o o o 5: Very Important (5) o o o o Select the level of importance for each key consideration. 1: Not Important (1) Program Duration (1) Program Mission (2) Program Vision (3) Program Values (4) Program Faculty (5) Program Location for Onsite Labs (6) 2: Slightly Important (2) 3: Moderately Important (3) 4: Important (4) 5: Very Important (5) o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 72 Select the level of importance for each key consideration. 1: Not Important (1) 2: Slightly Important (2) 3: Moderately Important (3) 4: Important (4) 5: Very Important (5) Access to Online Webinar Regarding Program (2) o o o o o Pre-Admission Lab Visit (3) o o o o o Direct Interaction with Admissions Team (4) o o o o o HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 73 Select the level of importance for each key consideration. 1: Not Important (1) 2: Slightly Important (2) 3: Moderately Important (3) 4: Important (4) 5: Very Important (5) National Physical Therapy Exam First Time Pass Rates (1) o o o o o National Physical Therapy Exam Ultimate Pass Rates (2) o o o o o Diversity of the Student Body (3) o o o o o o o o o o o o o o o Graduation Rates (4) Employment Rate (5) In this section, please include any other key considerations that were not listed above and then select the level of importance for each key consideration. 1: Not Important (1) Other, please specify (1) Other, please specify (2) Other, please specify (3) o o o 2: Slightly Important (2) o o o 3: Moderately Important (3) o o o 4: Important (4) o o o 5: Very Important (5) o o o HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT End of Block: Key Considerations Start of Block: Tolerance for Ambiguity 74 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Please select the number that best corresponds to your agreement with each statement. Use this rating system: 1 (Strongly agree) to 6 (Strongly disagree). 75 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 1: Strongly Agree (1) 2: Moderately Agree (2) 3: Slightly Agree (3) 76 4: Slightly Disagree (4) 5: Moderately Disagree (5) 6: Strongly Disagree (6) It really disturbs me when I am unable to follow another persons train of thought. (1) o o o o o o If I am uncertain about the responsibilities involved in a particular task, I get very anxious. (2) o o o o o o Before any important task, I must know how long it will take. (3) o o o o o o I dont like to work on a problem unless there is a possibility of getting a clearcut and unambiguous answer. (4) o o o o o o The best part of working on a jigsaw puzzle is putting in the last piece. (5) o o o o o o I am often uncomfortable with people unless I feel that I can understand their behavior. (6) o o o o o o HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT A good task is one in which what is to be done and how it is to be done is always clear. (7) o o End of Block: Tolerance for Ambiguity Start of Block: Perfectionism o 77 o o o HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT Please select the number that best corresponds to your agreement with each statement below. Use this rating system: 1 (Strongly disagree) to 5 (Strongly agree). 78 HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT 1: Strongly Disagree (1) 2: Disagree (2) 3: Neutral (3) 79 4: Agree (4) 5: Strongly Agree (5) If I fail at work/school, I am a failure as a person. (4) o o o o o I set higher goals for myself than most people. (5) o o o o o If someone does a task at work/school better than me, then I feel like I failed at the whole task. (6) o o o o o I have extremely high goals. (7) o o o o o Other people seem to accept lower standards from themselves than I do. (8) o o o o o If I do not do well all the time, people will not respect me. (9) o o o o o I expect higher performance in my daily tasks than most people. (10) o o o o o The fewer mistakes I make, the more people will like me. (11) o o o o o HYBRID DOCTOR OF PHYSICAL THERAPY STUDENT End of Block: Perfectionism Start of Block: Gift Card opt In 80  ...

... Perceptions of Occupational Therapy Student Readiness in the Acute Care Setting Submitted to the Faculty of the College of Health Sciences University of Indianapolis In partial fulfillment of the requirements for the degree Doctor of Health Science By: Jackie Dusing, OTR/L Copyright April 4, 2024 By: Jackie Dusing, OTR/L All rights reserved Approved by: Lisa Borrero, PhD, FAGHE Committee Chair ______________________________ Elizabeth Stith, DHSc, OTR/L, BCG Committee Member ______________________________ Kristina Watkins, OTD, MOT, OTR Committee Member ______________________________ Accepted by: Lisa Borrero, PhD, FAGHE Director, DHSc Program University of Indianapolis ______________________________ Stephanie Kelly, PT, PhD Dean, College of Health Sciences University of Indianapolis ______________________________ INFORMED CONSENT LETTERHEAD AUGUST 2022 PERCEPTION OF READINESS 1 Perceptions of Occupational Therapy Student Readiness in the Acute Care Setting Jaclyn Dusing Interprofessional Health and Aging Studies, University of Indianapolis PERCEPTION OF READINESS 2 Abstract Background: Occupational therapy student readiness for level II fieldwork is a necessary component of ensuring they are prepared to be novice clinicians, and it is difficult to assess as students must apply didactic knowledge in real-world clinical situations. There is currently a gap in the literature regarding student perceptions of their readiness for level II fieldwork, especially in the acute care setting. Objective: The aim of this qualitative study is to better understand Doctor of Occupational Therapy students perception of readiness for a level II fieldwork in an acute care setting. Method: This basic interpretive qualitative research study utilized a purposeful sampling with students recruited from various acute care hospitals. Semi-structured interviews, lasting about 30-45 minutes, were conducted virtually with audio recording. Transcripts were de-identified, transcribed, coded, re-coded, and then had themes categorized. Participants that meet the inclusion criteria were included in the study and had interviews completed to assess students perceptions of readiness for level II fieldwork in an acute care setting. Results: Information power was achieved after 11 participants were interviewed for this study as the data was analyzed, themes emerged, and answers to the research question became evident. These themes were: coursework and preparation, acute care occupational therapy, and fieldwork. Discussion: The opportunity to have more focused education and collaboration with acute care level II fieldwork settings to facilitate the education and skills needed to perform occupational therapy services in this setting. Keywords: perception of readiness, knowledge transfer, level II fieldwork, acute care setting PERCEPTION OF READINESS 3 Acknowledgments Id like to acknowledge the many people who helped contribute to the completion of this research. First, Id like to thank and acknowledge the skillful members of my research committee: Dr. Lisa Borrero, Dr. Elizabeth Stith, and Dr. Kristina Watkins. Without their guidance, support, and expertise I would not have been able to complete this research. I am very grateful to have benefitted from your knowledge, and I sincerely thank you for all that youve done. To my family, especially my mom, friends, and colleagues, thank you for your motivation and interest in this research. Most importantly, to my husband, thank you for your constant support and encouragement. PERCEPTION OF READINESS 4 Table of Contents Title Page.........................................................................................................................................1 Abstract............................................................................................................................................2 Acknowledgments............................................................................................................................3 Table of Contents.............................................................................................................................4 Chapter 1: Introduction....................................................................................................................6 Problem Statement...............................................................................................................7 Purpose Statement...............................................................................................................7 Research Question...............................................................................................................7 Significance of the Study.....................................................................................................8 Definition of Terms..............................................................................................................8 Chapter 2: Literature Review...........................................................................................................9 Fieldwork.............................................................................................................................9 Knowledge Translation and Readiness..............................................................................11 Acute Care.........................................................................................................................14 Chapter 3: Method.........................................................................................................................17 Study Design......................................................................................................................17 Participants.........................................................................................................................18 Procedures..........................................................................................................................18 Rigor/Trustworthiness........................................................................................................23 Chapter 4: Results..........................................................................................................................24 Theme 1: Coursework and Preparation..............................................................................25 Theme 2: Acute Care Occupational Therapy.....................................................................28 PERCEPTION OF READINESS 5 Theme 3: Fieldwork...........................................................................................................36 Chapter 5: Discussion and Conclusion..........................................................................................42 Theme 1: Coursework and preparation..............................................................................43 Theme 2: Acute Care Occupational Therapy.....................................................................45 Theme 3: Fieldwork...........................................................................................................49 Limitations.........................................................................................................................54 Implications for future research and practice.....................................................................55 Conclusion.........................................................................................................................58 References......................................................................................................................................59 Table 1: Participants......................................................................................................................69 Table 2: Interview Themes and Subthemes...................................................................................70 Appendix A: Recruitment Email to Academic Fieldwork Coordinators.......................................71 Appendix B: Recruitment Posting to Online Forums and Occupational Therapy Association Email ListServ...............................................................................................................................72 Appendix C: Follow-up Recruitment Email..................................................................................73 Appendix D: Study Information Sheet..........................................................................................74 Appendix E: Interview Guide........................................................................................................77 PERCEPTION OF READINESS 6 Perceptions of Occupational Therapy Student Readiness in the Acute Care Setting Assessment of readiness for level II fieldwork is necessary to ensure occupational therapy students are prepared to be entry-level clinicians. During the level II fieldwork experience, students learn to apply concepts from the didactic portion of their education to patient care (American Occupational Therapy Association [AOTA], 2013). Readiness for level II fieldwork, while necessary, is difficult to assess. The purpose of level II fieldwork is to prepare entry-level, general occupational therapists that can treat in a variety of settings (AOTA, n.d.a). Occupational therapy practitioners (OTPs) work in a variety of settings, allowing level II fieldwork placements within these different environments. In occupational therapy education, fieldwork is used to bridge the gap between didactic knowledge and real patients (Karp, 2020). The acute care setting requires OTPs to provide quality care that responds to unexpected medical changes in the patients ability to perform their occupations, the skills needed to perform occupations, the context in which they perform their occupations, and space (AOTA, 2020; Morikawa & Amanat, 2022). In addition, acute care OTPs must navigate the sometimes-limited information from a patient on their prior level of function, social support, and home environment. Due to the fast-paced and challenging environment of acute care, occupational therapy students must be able to quickly translate their didactic knowledge to real-life patient situations (Gorman et al., 2010; Sterner et al., 2019). Understanding the students perception of readiness for level II fieldwork in an acute care setting is necessary to ensure the transfer of knowledge from the classroom into clinical skills during the fieldwork experience. Understanding students perceptions about their preparedness for fieldwork in the acute care setting will assist occupational therapy faculty and educators in better preparing students for this PERCEPTION OF READINESS setting, as well as potentially enhance the occupational therapy program curriculum to facilitate effective knowledge to clinical skills transition. Problem Statement Readiness for fieldwork placements is difficult to measure due to the students applying their didactic knowledge directly to patients (Timmerberg et al., 2019). Readiness for fieldwork in an acute care setting is just as challenging to measure due to the fast-paced nature of the setting and the depth of knowledge practitioners are required to have (Gorman et al., 2010; Sterner et al., 2019). In occupational therapy education, fieldwork is used to bridge the gap between foundational knowledge learned in the classroom to use of clinical skills with real patients (Karp, 2020). Perceptions of readiness are essential to allow for student confidence and the ability to adapt to the fieldwork setting. Although there is literature supporting student readiness for entry-level practice, there is a gap in the literature regarding occupational therapy students perception of readiness for level II fieldwork in the acute care setting. Purpose Statement The aim of this qualitative study was to better understand Doctor of Occupational Therapy students perceptions of readiness for level II fieldwork in an acute care setting, including the educational activities that facilitated their learning and perception of readiness. Research Question 1. How do Doctor of Occupational Therapy students perceive their readiness for level II fieldwork in an acute care setting? a. How do Doctor of Occupational Therapy students describe education methods that they believe impacted their readiness for level II fieldwork in an acute care setting? 7 PERCEPTION OF READINESS 8 b. How do Doctor of Occupational Therapy students describe the educational experiences that they would like to have been included in their didactic education to help them prepare for level II fieldwork in the acute care setting? Significance of the Study Understanding how occupational therapy students perceive their readiness for level II fieldwork in an acute care setting will assist in the further development of the occupational therapy curriculum and aid in implementing evidence-based educational techniques. The findings of this study will benefit multiple stakeholders within occupational therapy including students, academic educators, and fieldwork educators. Definition of Terms Knowledge transfer - implementing ideas and concepts from the didactic portion of a students education into clinical education experiences (Nichols et al., 2019). Level II Fieldwork - an in-depth experience, typically 12 weeks in length, for students to develop entry-level skills where students deliver evidence-based occupational therapy services to clients (AOTA, n.d.a). Acute care setting - a hospital that provides inpatient medical care and other related services for surgery, acute medical conditions or injuries (usually for a short-term illness or condition). (Centers for Medicare and Medicaid Services, n.d, p.1). Literature Review Level II fieldwork is a requirement of all occupational therapy programs where occupational therapy students spend 24 weeks with a licensed OTP (Accreditation Council for Occupational Therapy Education [ACOTE], n.d.). To successfully complete level II fieldwork, students must display professionalism, understand clinical and theoretical knowledge, apply PERCEPTION OF READINESS 9 didactic knowledge, and be receptive to feedback (Karp, 2020). Student readiness for level II fieldwork is a necessary component to ensure that occupational therapy students can apply their foundational knowledge within the clinical setting under the guidance of skilled fieldwork educators. In the acute care setting, students must display these qualities while also having an appreciation for the medical status of the patient and the variety of patients within the setting (Morikawa & Amanat, 2022). Understanding students perception of readiness for level II fieldwork has limited research and is essential to facilitate student confidence in the translation of foundational knowledge to clinical skills. Fieldwork The didactic portion of the occupational therapy curriculum is used to prepare students for fieldwork experiences. Level II fieldwork is a fundamental component of the occupational therapy curriculum and must include experiences in providing evidence-based occupational therapy services to clients (AOTA, n.d.a; ACOTE, 2018). The accreditation standards for Doctor of Occupational Therapy educational programs include a curriculum designed to prepare students for work as a generalist in a variety of settings and populations (ACOTE, n.d; ACOTE, 2018). Doctor of Occupational Therapy students complete their Level II fieldwork for a minimum of 24 weeks, in a full-time capacity, and the fieldwork placements must be in two different practice areas or in a maximum of four (ACOTE, n.d). ACOTE standards for educational content require that occupational therapy educational programs design learning methods to ensure these standards are met (ACOTE, 2018). The content requirement standards are left to each programs interpretation as these standards are not specific to a certain setting, and each program decides how these objectives are met. This can make it challenging for educational programs to prepare students for specific settings. PERCEPTION OF READINESS 10 Level II fieldwork allows for hands-on learning to develop clinical reasoning skills that develop when students are provided the opportunity to apply their didactic knowledge with patients. During level II fieldwork, students are provided with learning opportunities through real-life experiences that are needed for ongoing education that cannot be obtained in the classroom (Karp, 2020; Stigen et al., 2022). The goal of level II fieldwork is to develop competent, entry-level occupational therapists. Within the fieldwork placement, the relationship between the student, the fieldwork site, and the fieldwork educator has implications for the successful completion of level II fieldwork. As a part of a study on readiness, from the viewpoint of the occupational therapy academic and fieldwork educators, Karp (2020), reports that communication between occupational therapy academic educators and fieldwork educators is important for student success. The interpersonal interactions of occupational therapy students, their fieldwork educators, and the patients are instrumental in creating a successful fieldwork experience. The fieldwork educator plays an important role in the development of the students clinical reasoning skills by assuming the role of a mentor and guiding the student through the clinical experiences (Coviello, 2019). Occupational therapy assistant students mentioned that the fieldwork educator credentials, years of experience treating patients, years of experience supervising students, availability, receptiveness, and communication with students as important elements in the development of clinical reasoning skills (Coviello, 2019). Fieldwork placements and the hands-on learning from fieldwork are needed for student learning and creating the connections between didactic knowledge and clinical skills. There are limited studies on students entering clinical experiences, including occupational therapy students entering fieldwork in the acute care setting. It is important to consider the students perspective PERCEPTION OF READINESS 11 of readiness to enter level II fieldwork placements in acute care. Students may expect that teaching will occur both in the traditional, didactic program and in a hands-on, clinical setting. Fieldwork educators must facilitate a student's learning and ability to adapt their skills to provide safe and effective occupational therapy treatment in the acute care setting. Knowledge Translation and Readiness Core Competencies Readiness for clinical practice includes utilizing a core set of competencies and communication between fieldwork and academic educators to ensure a positive transition to fieldwork education for students (Karp, 2020; Timmerberg et al., 2019). Students must have a set of competencies or attributes that guide their clinical practice such as empathy, emotional intelligence, the ability to reflect, and medical professionalism (Benbassat, 2019). Professional behaviors helpful for students to have at the beginning of their level II fieldwork experiences include clinical competence, communication skills, and personality traits such as leadership skills and being independent (Campbell et al., 2015). All occupational therapy educators have a role to play in the knowledge translation of occupational therapy students (Perkins et al., 2020). It is difficult to bridge the gap between foundational knowledge and application; thus, having core competencies and greater collaboration between students, educators, and clinical instructors will aid in improving student readiness for fieldwork experiences then novice clinical practice. Student Perceptions Student perceptions of readiness for clinical practice and an ability to transfer knowledge is an essential component of the successful completion of occupational therapy education. A successful student transition from the academic setting to clinical practice requires much more PERCEPTION OF READINESS 12 than foundational knowledge. Student success also includes confidence in their abilities, eagerness to seek out learning opportunities, and understanding their limitations as found in a study of nursing students (Cantlay et al., 2017). Furthermore, inclusion of the fieldwork educator can aide in understanding gaps in student readiness for acute care. The fieldwork educators can aide in improving awareness of the shortcomings of students that occur from the academic setting to the clinical setting, and this can help to change educational practices and better prepare students (Fairburn et al., 2019). There is a relationship between student self-efficacy and the opportunities for clinical growth and decision making; as a students perception of support from their fieldwork educator increases so does the students perception of self-efficacy (Andonian, 2017a). Students with more self-efficacy may be able to better receive and implement constructive criticism and feedback on their performance which can lead to improved performance throughout their fieldwork (Andonian, 2017a). Furthermore, a students self-efficacy is tested during level II fieldwork as students are faced with applying didactic concepts with entry-level patient care situations (Andonian, 2017a). Britton, Rosenwax, and McNamara (2015) highlight the importance of ongoing clinical supervision to increase clinical confidence specifically in the acute care setting due to the fast-paced environment. Critical thinking development is the responsibility of the student, fieldwork educator, and occupational therapy academic educators. Knowledge translation includes the process of acquiring, synthesizing, and using knowledge (Govender & Mostert, 2019). For successful completion of level II fieldwork, students must be able to apply their foundational knowledge and translate this knowledge into their clinical practice. Billett (2015) states that conceptual readiness is based on students prior PERCEPTION OF READINESS 13 learning experiences, and their abilities to perform and learn are established during situations to which they are exposed. Simulation There are a variety of knowledge translation approaches. These approaches include inperson didactic education, online resources, patient simulation, and clinical audits with feedback with in-person didactic education being the most used approach (Perkins et al., 2020). With the evolution of technology, healthcare programs utilize various learning techniques to enhance the acquisition of knowledge and clinical skills to facilitate the transition from the classroom to clinical practice (Nichols et al., 2019). Careful design of didactic education and experiences can help students learn and can aid in knowledge translation into clinical education. Berg et al. (2021) found that case studies can be an effective educational approach for future healthcare providers to engage in higher-level thinking to develop clinical competency. Limitations of the current research related to readiness for clinical practice include small sample size studies as well as many studies aimed at other disciplines that are not occupational therapy causing difficulty with generalization throughout healthcare education. Further research regarding readiness for level II fieldwork is necessary and would greatly benefit from emphasizing the students perception of readiness as they are the center of their education. A set knowledge base is necessary, but if a student is not confident in their abilities to provide competent patient-centered care within the healthcare field, then those foundational competencies are not meaningful. Acute Care Acute Care Setting PERCEPTION OF READINESS 14 A definition of acute care according to Centers for Medicare and Medicaid Services (n.d.) is a hospital that provides inpatient medical care and other related services for surgery, acute medical conditions or injuries (usually for a short-term illness or condition) (p.1). A definition of acute care should include the aspects of variety of healthcare providers that provide timesensitive care to those experiencing an unexpected or emergent injury or illness that requires immediate intervention (Hirshon et al., 2013). Working in acute care has unique challenges that require healthcare providers to understand medical conditions, recognize how conditions may impact patients, and evaluate a patients ability to participate in rehabilitation within the acute care setting. Students in the acute care setting must have an understanding of medical complexity, ability to respond quickly and calmly to changes, and ability to execute strong emotional intelligence with all interpersonal interactions (Hayward et al., 2015). Healthcare providers in acute care must be able to understand the medical complexities of their patients and be able to support the patients psychosocial needs. van Belle et al. (2020) found that the most common ways nursing supported psychosocial needs of hospitalized patients include communication, compassion, empathy, keeping patients informed, and engaging with families. Within the acute care setting, students must demonstrate the ability to pivot between time constraints and focus on discharging patients to an appropriate next level of care (Hayward et al., 2015). Learning within the acute care setting can cause additional stress and can lead to limitations of applying knowledge due to the quick and ever-changing nature of that environment (Benbassa, 2019; Hayward et al., 2015). In the acute care setting, an interdisciplinary care team, including OTPs, must be able to effectively communicate needs and results to facilitate safe patient treatment and discharge. Due to the interprofessional nature of acute care, and healthcare PERCEPTION OF READINESS 15 in general, knowledge should be shared by the healthcare team to contribute to holistic nature of patient care (Wimpenny, 2013). Occupational Therapy in Acute Care OTPs in the acute care setting provide rehabilitation services to acutely ill or injured patients. OTPs working in the acute care setting are expected to have a knowledge base of body systems and structures, medical conditions and diagnoses, and lab values as well as an understanding of various test results (Coppola et al., 2019). Acute care OTPs must be able to manage various types of medical equipment and lines, understand safety precautions and restrictions, and provide patient centered care (Coppola et al., 2019). They also face environmental barriers such as space, equipment, time, and changes in patient medical status (Morikawa & Amanat, 2022). Additionally, within the acute care setting, there is often disjointed care due to time constraints and the interruptions of multiple providers at the patient's bedside (Hayward et al., 2015). As a result of these barriers, OTPs must be able to quickly adjust intervention plans and goals. The medical barriers challenge occupational therapy interventions showing the importance of understanding disease processes and their impact on performance as well as the need for grading activities to allow for optimal patient participation (Morikawa & Amanat, 2022). There is a necessity to understand the physical implications of a patient's illness or injury on their occupational performance. In addition to the clinical skills needed for successful completion of level II fieldwork, students must apply their therapeutic use of self to develop an effective client-therapist relationship with patients. OTPs using the proper therapeutic modes to handle the interpersonal events that arise within a therapy session help to strengthen the therapeutic relationship with the patient (Hussain et al., 2018). OTPs use their holistic nature to provide patients with PERCEPTION OF READINESS 16 psychosocial interventions, mindfulness techniques, and coping strategies (Morikawa & Amanat, 2022). The unique occupation-based treatment interventions provided by occupational therapists help restore function and independence with patients and facilitate appropriate discharge plans based on patient and family goals. Importance of Occupational Therapy in Acute Care It is within the scope of practice for OTPs to assess and provide treatment interventions that will increase a patients independence with their everyday occupations (Broome & Kennedy-Behr, 2021). While occupational therapy treatment interventions may directly address motor and process skills, they more often address occupations that include activities of daily living (ADLs) and instrumental activities of daily living (IADLs) (AOTA, 2020). One of the roles of an acute care OTP is to make discharge recommendations based on a patients performance of skills needed to safely return home. Within the acute care setting, OTPs are skillful in identifying if patients can safely care for themselves in preparation for discharging from the hospital (Lockwood & Porter, 2022). Providing treatment interventions that focus on a patients ability to safely complete their ADLs and IADLs has been shown to prevent hospital readmissions (Edelstein et al., 2022a). Medicare has programs in place to help hospital readmissions with the goal of the program being to improve healthcare by connecting quality of care within the hospital to reimbursement (Centers for Medicare & Medicaid Services, 2021). There is evidence to support that acute occupational therapy services can aide in reducing hospital readmissions (Edelstein et al., 2022b; Lockwood & Porter, 2022). Additionally, there is also evidence to support that occupational therapy has a statistically significant relationship between increased spending and lower readmission as well as how occupational therapy services can be used to improve hospital PERCEPTION OF READINESS 17 outcomes (Edelstein et al., 2022a; Rogers et al., 2017). It was determined that the amount of ADLs, IADLs, and self-care training provided by OTPs that patients received in the acute hospital setting had an impact on patient readmissions (Edelstein et al., 2022a). While understanding and working within the limitations of the patients medical diagnosis, OTPs assess a patients ability to perform the everyday tasks needed to live safely and independently including assessment of cognitive abilities (Rogers et al., 2017). Options for acute care fieldwork provide students with the opportunity to become entrylevel practitioners competent to provide safe and effective treatment in this setting. Timmerberg et al. (2019) reports that to improve student readiness, students should have opportunities for learning experiences and evaluation of skills prior to a clinical experience. Method Study Design A basic interpretative approach was used for this qualitative research study to improve understanding of Doctor of Occupational Therapy students perception of readiness for a level II fieldwork in an acute care setting. A basic interpretive approach was used to appreciate the unique experiences and understand the behaviors that relate to perceived readiness of Doctor of Occupational Therapy students. This basic interpretive approach draws upon phenomenology and symbolic interactionism by understanding the meaning of individual lived experiences and behaviors (Merriam, 2002). Participants Inclusion criteria for this study comprised Doctor of Occupational Therapy students from any school in the United States who were completing level II fieldwork at an acute care hospital. Exclusion criteria included Doctor of Occupational Therapy students that were not fluent in PERCEPTION OF READINESS 18 English. Achieving information power (Malterud et al., 2016) was the goal, with an estimated sample size ranging from eight to twelve participants. The researchers approach to this study supported the use of information power given that this is a narrow topic within the scope of occupational therapy and had an aim of thorough analysis of the experiences of these students (Malterud et al., 2016). The researcher utilized an analysis strategy with in-depth narrative interviews from a few selected participants. The intention was to seek a robust quality of dialogue from participants holding characteristics that are highly specific to the study, which is in keeping with the construct of information power (Malterud et al., 2016). Procedures Sampling and Recruitment The researcher used a purposeful homogenous sampling strategy, followed by snowball sampling, to select participants. Using a purposeful homogenous sampling strategy allows for focused details to be shared on a narrow population (Creswell & Poth, 2018; Patton, 1990), particularly a subgroup of Doctor of Occupational Therapy students completing their level II fieldwork in an acute care setting. Snowball sampling was also utilized after successfully recruiting one or more participants, whereby the researcher asked participants to identify potential participants that meet the inclusion criteria who were willing to provide details of their experiences completing level II fieldwork in an acute care setting. The recruitment activities began after the approval from the University of Indianapolis Institutional Review Board was received. The researcher contacted the Academic Fieldwork Coordinators at Doctor of Occupational Therapy programs that have a clinical affiliation with a local Chicago area hospital system with a description of the research study, the confidentiality of the participants, interview procedures, time commitments of participants, and researcher contact PERCEPTION OF READINESS 19 information (see Appendix A). The study information was also posted to several AOTA CommunOT online forums including AOTA General forum, the Student forum, the Academic Education forum, Rehabilitation & Disability forum, and the Community of Practice Fieldwork Educators forum. The study information was also sent out through the Illinois Occupational Therapy Association and the Indiana Occupational Therapy Association email listservs (see Appendix B). The emails and forum postings also emphasized the voluntary participation component. If students were interested in participating, they were instructed to contact the researcher directly, noting their interest. During the initial email correspondence with the researcher, potential participants were asked for their demographic information including gender, age, and the college or university they attend (see Appendix C). In this initial email correspondence, the study information sheet was shared (see Appendix D), and the researcher answered any questions. Once participants responded to the email, a tentative date for a virtual interview, via the videoconferencing platform of their choice, was scheduled for a time between week six and 12 of their fieldwork rotation. An email was sent to the participant for confirmation of the interview date and time two weeks and again one week before the tentative interview date. This email also included the link and instructions for accessing the video communication platform. Informed Consent Informed consent began with the electronic study information sheet that was shared with participants during the initial email exchange. The information sheet contained the purpose of the study, procedures for data collection, methods to protect participant confidentiality, the right of participants to voluntarily withdraw from the study at any time, and the known risks and benefits associated with participation in the study. Participants were advised that they could ask questions PERCEPTION OF READINESS 20 throughout the study. Participants were also informed that their participation in the interview would take approximately 30 to 45 minutes and that they would receive an email from the researcher approximately two weeks after the interview to verify the initial themes drawn from the data. The components of the information sheet were also reviewed verbally during the verbal consent process prior to the interview and another opportunity to ask questions was provided. Verbal consent was obtained as permission to begin the recorded interview. Once informed consent was received, each participant was be assigned a participant identification number created by the researcher to protect confidentiality. Data Collection Data was collected via individual semi-structured interviews using a videoconferencing platform such as Microsoft Teams or Zoom. At the beginning of the interview, the researcher greeted the participant, asked permission to begin recording, and began by reading the introductory paragraph of the interview guide. Verbal consent was established, and the interview began. A semi-structured interview guide that included open-ended questions developed by the researcher was utilized to conduct the interviews (see Appendix E). The interviews lasted approximately 30 to 45 minutes and included broad questions to explore perception of readiness for their fieldwork placement in acute care. Questions included how participants would describe readiness for level II fieldwork in acute care, how students prepared for level II fieldwork in acute care, and the educational experiences that were beneficial to their learning. Prompts and follow-up questions were used to gain additional insight, based on participant responses. The interviews were concluded when the interview guide questions had been exhausted and the PERCEPTION OF READINESS 21 participants had no other information to share. Upon completion of the interview, the participant was thanked and informed and encouraged to reach out to the researcher with any questions they might have. Participants were reminded that they would receive an email from the researcher containing the initial themes from the study approximately two weeks following their interview, along with a request to verify that these interpretations reflect their experiences shared (and to provide any necessary clarification). Throughout the interviews, the researcher used a memoing strategy for notes and interpretations. Specifically, memoing was utilized to record thoughts and reactions about the interview process and the development of patterns in the data (Birks et al., 2008). In addition to memoing, the researcher kept a journal for an outlet to express feelings and capture biases as a part of reflexivity to address credibility. The journal utilized participant identification numbers to protect participant confidentiality and was kept within a password-protected Google Drive. Data Management & Analysis Audio recordings of the interviews were transcribed by a local, professional stenographer. Dedoose 9.0.54 was utilized to store audio files, transcripts, and memos. Dedoose is a secure, project-specific encryption software that aids in data management, coding, and analysis in a cloud-based format (Dedoose, n.d.). As a backup, data was also stored in a password-protected Google Drive and in a password-protected file on the researchers computer; it was named according to assigned participant identification numbers. Sub-folders were created to organize data with interview recordings and transcriptions in one file and individual coded transcriptions in another file. A key matching participant names with their participant identification number was kept on a separate password-protected Google Drive and in the PERCEPTION OF READINESS 22 password-protected file on the researchers computer. Data analysis was also completed within Dedoose 9.0.54. Marshall and Rossman (2011) describe that the phases of typical data analysis include coding, creating categories, and generating themes. The coding process began with immersion in the data beginning with the re-read process. Steps used to analyze the data began with looking for patterns within the participant interviews. To identify these similarities, the transcripts were read and re-read to connect similarities that arose in the interviews (Marshall & Rossman, 2011). A line-by-line, code-recode process of coding data and then a later recoding occurred to allow for identification of new or overlapping codes (Henderson & Rheault, 2004). After coding the initial data, a codebook was created, and participant quotes were added to each individual code. The codebook was utilized for definitions of codes and to outline how codes were used. A second researcher with qualitative expertise assisted with data analysis. Specifically, during coding, transcripts were coded independently by both researchers then discussed together to identify and agree upon codes to begin developing the codebook and, ultimately, the central themes. Once initial coding was completed, categorizing and clustering coding was completed. In the analytical review of the data, Marshall & Rossman (2011) suggest that patterns between transcripts are checked against one another to identify categories. Within this process, categories were created by noting the patterns from the codes based on participants interviews. Marshall & Rossman (2011) state that the categories identified are concepts that were significant to the participants; thus, the process of categorization aided in the development of the central themes. A theme table was also used to assist in organizing data. Data analysis was also completed in conjunction with analysis of notes and memoing completed during the interviews. PERCEPTION OF READINESS 23 Birks et al. (2008) report that with the use of memoing, researchers can further explore data and interpretations. These notes were recoded to assist in providing additional context to data from interviews. Member checking emails were completed after data analysis. Participants received an email with the initial draft of themes from their interview with the option to correct if necessary. Through this member checking process, participants ensured accuracy and increased credibility when they reviewed the data, analyses, and interpretations of the researchers (Creswell & Poth, 2018). The content of the email was saved as a PDF then stored and saved in Dedoose and the backup password protected University of Indianapolis Google Drive; the email was then deleted. Rigor/Trustworthiness Rigor and trustworthiness were evaluated by attending to the four components of Gubas Model as described by Henderson and Rheault (2004). The four aspects of trustworthiness in Gubas Model include credibility, transferability, dependability, and confirmability (Henderson & Rheault, 2004). To support credibility, the researcher ensured that feelings and biases did not influence data collection or analysis through methods incorporating reflexivity. Specifically, the researcher kept a journal and engaged in memoing as an outlet to express feelings and capture biases. Credibility was also assessed by using member checking with research participants to verify the researchers understanding of the participants responses. Using a second researcher with expertise in qualitative analysis during data analysis also supported credibility. Transferability was addressed using a thick description with a thorough explanation of the progression of the research and the lived experiences of the participants included in the study (Holloway & Galvin, 2016). Transferability was also supported by including a detailed description of the participants backgrounds. The code-recode method was used to address PERCEPTION OF READINESS 24 dependability. During a code-recode, the data was coded and then recoded later allowing for identification of new or overlapping codes. There was also an audit trail of this process. To address confirmability, external audits occurred through the assistance of the second researcher. In addition, reflexivity methods described above, and the use of member checking supported confirmability. The interview guide was reviewed by the dissertation committee to assist in independent analysis and feedback. The dissertation committee also served as an external auditor and assisted with coding. Results Information power was achieved after 11 participants were interviewed for this study as the data was analyzed, themes emerged, and answers to the research question became evident. All 11 participants met the inclusion criteria as they were level II fieldwork students who were willing and able to provide details of their experiences completing level II fieldwork in an acute care setting. All participants identified as women with an average age of 26 years old (Table 1). All participants are Doctor of Occupational Therapy students from a school in the United States who were completing level II fieldwork at an acute care hospital. The participants were between weeks six and 12 of their 12-week level II fieldwork rotation with the participants having an average of eight weeks completed (Table 1). Data analysis included 28 codes to create categories within interview data, which included four parent codes and the four parent codes had 15 child codes. Data analysis included looking for patterns within the participant interviews. Based on the analysis of the hierarchical codes, three themes, and 14 sub-themes (Table 2) emerged and assisted in answering the research question. The three themes include, 1) coursework and preparation, 2) acute care occupational therapy and, 3) fieldwork. PERCEPTION OF READINESS 25 Theme One: Coursework and Preparation The first theme that emerged during the coding process was the coursework and preparation for level II fieldwork in acute care. The participants noted how their didactic education and hands-on practice aided in preparation for acute care and that there were opportunities to increase their familiarity with acute care. Didactic Education for Acute Care Participants spoke about the acute care-focused education that they would have liked to have been included, which would have helped their preparedness. Participants noted having courses that prepared them for their level II acute care fieldwork including neuro, anatomy, and courses that reviewed various levels of care and adult conditions. For instance, participant 010 stated: I learned about every setting in one semester. But I feel like if there was more time, maybe we learn about a different population and then about the setting that goes with that population. That would be really nice. Additionally, some participants stated that their didactic education included lectures and courses that were specific to acute care. Participant 008 noted: We have pretty much like a good chunk of a full semester that was acute and inpatient rehab. So that was super helpful just by having that time to spend on it. But I think within that class one of the best things that we did was -- and I think we're lucky. Participant 004 gave an example of a guest lecturer that helped to increase their understanding of the acute care setting: We also had someone who works in acute care come and give a lecture during one of our adult conditions classes. She would tell us about some of the patients that she saw but a lot of her experience was about working in the ICU. PERCEPTION OF READINESS 26 Participants expressed how they believe that there could have been more acute care-specific education as a part of their didactic education. Participant 001 stated: We really only did like one lecture focused on acute care. Everything else was just kind of adults in generalwe didn't spend a ton of time on it. Participant 004 provided the following example: I would have known what to expect in a skilled nursing unit or long-term unit or even home health. Acute care sort of feels like this unknown setting that wasnt talked about in a lot of detail. So, more about acute care in our programs would have at least helped me out. Participant 002 provided a similar example: I feel like a lot of my schooling was definitely more, like, towards outpatient and, like, having time with patients. And, like, having access to supplies. And so, if you think that's what acute care is going to look like, that's definitely going to impact, like, your ability. Participants also noted feelings of uncertainty of the acute care setting due to the lack of exposure to this setting. Participant 008 stated, who had some prior experience in acute care during their time as a rehabilitation aide stated: There never really was an opportunity once I was in grad school to be in acute care. So, I was really glad I had done it before and had seen it. Additionally, participant 002 stated: I wish I would have already had, an intro to acute care. I feel like maybe -- I didn't have a class specifically geared towards acute care. So just maybe if we just would have had like a little bit more of, like, compare contrast about, like, what a session looks like in acute care versus outpatient. Participant 004 provided insight into the gap in understanding of acute care level II fieldwork stating, PERCEPTION OF READINESS 27 I guess I would say that I think even understanding the hospital setting like the different units, the patients you may see, and even more about OTPs role in the hospital. I have only been in a hospital once and it was when my sister had a baby. There werent any OTPs on that unit. So, just my experience and knowledge of a hospital didnt help me prepare. Participant 004 also iterated the role of occupational therapy in the acute setting by stating, We spent so much time talking about advocacy for occupational therapy but if I didnt know how to be an OTP in that setting how am I supposed to advocate for it? Hands-On Practice As a part of the preparation for fieldwork, participants spoke about how hands-on practice both facilitated and hindered their learning and preparation for level II fieldwork in acute care. Participants provided examples of how the ability to participate in practice hands-on skills helped prepare them for their level II fieldwork. Participant 009 spoke about the acute carespecific hands-on practice that helped them: We had a whole class, a whole semester that was like an adult inpatient class. So, it was like acute care and inpatient rehab. And, what you would see there. Skills you would need, like, transfers, skills you would need for there. We have a whole room in our school -- one of our school buildings. Like hospital simulation rooms. We are able to be familiarized with the beds, with certain kinds of lines. Participants noted the differences in classmates helping each other during their hands-on scenarios. Participant 003 provided an example of needing to decrease classmates acting as patients, I really just wish there was, like, more hands-on transferring, line management scenarios during the coursework. More actors maybe during our labs, not classmates. PERCEPTION OF READINESS 28 Specifically, Participants 004 and 005 provided examples of how hands-on practice was impacted due to classmates helping; Participant 005 stated, But when your classmates are helping with a transfer, it takes some of the real-life out of it. Participant 004 gave the following example: The practicals with the teachers were a little more realistic than the ones with other students. My partner in those practicals did a good job with pretending to have the impairment she was given but I know my other classmates were trying to help each other out by making it easier. Despite these hands-on scenarios helping to prepare students, there should be an emphasis on hands-on practice related to acute care level II fieldwork. Participant 010 noted that: Its very hard to mimic acute and what it is like in a hospital room. Like we went and did a lab one day with there's like a dummy manikin that has some lines running from him. But I think it was just -- I feel it was hindering. Participant 005 added that, The practicums and the simulation that I talked about felt so fake. And like, I knew that my school was trying to help us prepare but it was such a controlled scenario for how uncontrolled acute care can be. Theme Two: Acute Care Occupational Therapy The second theme that emerged during the coding process was the distinctive process of acute care occupational therapy. Participants noted that there were gaps in the basic knowledge of acute care, medical knowledge such as diagnoses and precautions, and other acute care occupational therapy knowledge including awareness of medical equipment and the process of chart reviews. Additionally, the participants expressed the unique skills needed for working as a part of the interdisciplinary care team. PERCEPTION OF READINESS 29 Medical Knowledge Students in acute care fieldwork must have a baseline understanding of medical knowledge and medical conditions and how these medical conditions impact a patients ability to participate in rehabilitation. Participants mentioned how their gap in medical knowledge impacted their readiness for level II fieldwork in acute care. Several participants expressed a need to include more medical knowledge in their didactic education. Participant 005 provided the following: I think that having the foundational medical knowledge definitely makes an impact. Like knowing surgical precautions or what certain lab values mean and how that can affect a patient, like if we need to hold therapy for a certain reason. Or what do certain medications do and how that can affect a patient. We, of course, went over these things in school, and I know that we apply them during level II fieldwork, but acute care seems like such a specialized setting that more knowledge or maybe practice with the medical side of things would have helped. Participant 010 added how they felt during their fieldwork by not feeling that they had enough medical knowledge, I would say I was overall nervous knowing that the nature of a hospital and how patients can be very ill. I was nervous in my ability to like safely interact with the patients. Some participants provided more details on how medical knowledge was covered during their didactic education. Participant 001 stated: At least in our program when I was there was so focused on, you know, like, you don't need to know the diagnosis. Or you know, you need to know it, but you don't need to know every single thing about it. But in the hospital, it's really just about like safety. But PERCEPTION OF READINESS 30 I don't think the focus was enough on what the progress and the course of a diagnosis or common disease would be. And I think that would have helped me a lot. Participant 007 added, there were things that we reviewed in the classroom but then seeing it and knowing how it impacts a patient and them being able to do therapy is totally different. The participants discussed how additional education on medical diagnosis and precautions would have helped with their readiness for level II fieldwork. Participant 005 spoke about the gaps in not having this additional education by stating, I feel like we should have just gone into more detail with diagnoses or conditions that you would see working with adults. Participant 008 also stated: I feel, like, we didn't get a lot of information about other diagnoses and conditions and organs. And, like, all the things that I feel, like, are really relevant to the understanding of medical acuity of patients in acute care. So, I think that we had a great anatomy class, but it definitely could have been a lot more. It just didn't necessarily, like, prepare me well for this setting. I think I would have been a lot more prepared had we spent more time on specific conditions. Participant 011 provided an example of the differences with another allied health student also completing a clinical rotation at the same time and in the same setting, questioning if other students academic coursework was more acute-care specific: And like if a patient has congestive heart failure you know theyre going to get fatigued during their sessions or you know they may have swollen legs but what does that mean for the rest of their body systems and structures? Or if a patient has elevated troponins what does that mean and how is it being managed for them to be able to participate in therapy and how should that guide me during my time with a patient? And again, I know PERCEPTION OF READINESS 31 Im not going to know everything walking into fieldwork, but I felt like the physical therapy student I started with wasnt having these same conversations with their instructor. Chart Review Throughout the coding process, one of the subthemes that emerged quickly was the challenges the participants experienced with the chart review process during their level II acute care fieldwork. Participants expressed the difficulty they experienced with understating the chart review process while on their level II fieldwork. Participants also noted how this was not covered in enough detail in their didactic education. Participant 008 stated: Its the ability to synthesize information. I feel like with like chart reviews and then reading other discipline's notes like I constantly feel like I'm on information overload. And still being able to like parse out what's important and combine all the information. And to take the pieces that I need and leave the rest. Because there are millions of words that I read on Epic a day, and knowing which ones to grab is helpful. Participant 011 added: I think it was the combination of not having all of the medical knowledge, how this is all going to impact the patient, and like the ranges for the lab values. In our electronic medical record system, the lab values may turn red if theyre out of a normal range. For instance, we had a patient with low hemoglobin, but this was normal for them, and they werent going to get a transfusion because of their religious beliefs. So, even though this lab value was red because it was out of the normal range, this was normal for them. Several participants discussed what was covered in their educational programs. Participant 008 stated: PERCEPTION OF READINESS 32 I feel like it was the kind of thing [chart reviews] that we would get, like, one slide and a whole power point on. And that was helpful, because then I can, like, go back and look at it. But I don't think it gave me a lot of understanding. It served more like a resource. So, it was more just -- it was just addressed. But more in a sense of, like, this exists, it's something to look at. Not like here's why this is the way it is. And here how it really affects things. I wish we could have gotten deeper on. Participant 011 provided the following on what was covered during their didactic education and suggestions of what should be included: I really wish that we would have had more practice and just some more information on chart reviews. In one of our lab practicums we had like it was just like printed off maybe copy and pasted from an electronical medical record at some point in time but it was very vague and very basic it was one physician note with really just medical abbreviations. There needs to be some way that the school can be able to like really to simulate what that chart review is going to look like. Like opening up the chart, navigating through all of those notes, looking at the lab values and I know I mentioned this earlier too, I know that we're not going to know everything we need to know and thats what fieldwork is for. But like having that chart review knowledge would help me better understand what's happening with the patient and then how that impacts like my job and how that impacts the patient's ability to perform occupational therapy. Several participants stated that they received education on chart reviewing during their level II fieldwork, Participant 010 stated: I got none of it [education on chart reviews] during my classroom time which is frustrating looking back. Like, I think about at the beginning how I was writing down PERCEPTION OF READINESS 33 everything from a chart and my fieldwork educator was like what are you doing? I didnt know how to take the information from other members of the care team and make it make sense to what I was doing. Participant 001 also provided: I wish I would have been taught how to chart review. I have never really done that before. I think we did it once, but during fieldwork I had no idea what I was even looking for. So being, you know, kind of taught how to pull out relevant informationand what to look for I think would have been helpful. Medical Equipment The participants provided examples on various types of medical equipment they encountered during their level II fieldwork and stated they would have liked exposure to these prior to their level II fieldwork. Participants provided examples on the lack of education on medical equipment and the impact on level II fieldwork in acute care. Participant 009 noted this by stating: I think just familiarity with hospital equipment. Yeah, equipment I think is a big thing if you're not understanding what patients are hooked up to or what precautions, that's another thing. Like if you don't know what those precautions are. And what mobility and ADLs look like on people or for people to have certain precautions. Participant 011 provided insight into the lack of knowledge of medical equipment by stating, Like the first time I saw a wound vac I knew nothing about it. Participants also mentioned that there are opportunities for medical equipment into incorporate didactic education and provide hands-on practice. Participant 001 stated: PERCEPTION OF READINESS 34 I would have loved a -- even just a day to practice with lines and tubes and drains. And I know, you know, those things are equally available. But coming up with ideas on how to make it simulate that would have been nice. Participant 007 added that: I would have liked more practice on how to manage a wound vac or multiple IVs and oxygen. Participant 003 mentioned how their program provided these opportunities and how it helped their perception of readiness, we had a lecture like on lines. And, respiratory, like, pulse oximeters, the high flow. So that kind of helped me kind of identify all of the O2 machines that patients had to use. Basic Acute Care Knowledge The participants discussed how there are certain skills and expectations for completing level II fieldwork in acute care, including understanding the acute care setting and occupational therapys role in this setting. Participant P006 stated: I just generally wasnt prepared for the medical side of itbeing super prepared for things to go sideways, like syncope, like, drop of blood pressure, seizures, things like that. I think to be prepared for acute care you should be really prepared and informed, educated, on the medical side of things, which I think kind of lacks a little bit. Participant 007 added: Not having an idea of what my role as an OTP [in this setting] didnt help my readiness. Participant 011 provided insight into their preparedness for level II acute care level II fieldwork: I feel like Im on like a 10 out of 10 all of the time. Like bed alarms are going off, or the IV is beeping, and there are people always talking. There is just so much going on in the PERCEPTION OF READINESS 35 environment at all times that its overwhelming. I dont think there was a way to prepare me for acute care. Participant 009 added: I think just familiarity with hospital equipment. The way the whole system works. What care looks like there. Like, if youre expecting it to be, like, outpatient when you walk in then youre in for a surprise. Interdisciplinary Care Team Participants recognized the skills needed and the importance of collaborating and communicating with the interdisciplinary team. Participants also noted their exposure to the interdisciplinary care team as a part of their didactic education. Participant 004 provided an example of how working with an interdisciplinary team was covered during their didactic education: We also spent some time talking about the team we would work with in a hospital like the physician, nurse, nursing assistant, physical therapy, speech therapy. Maybe once a social worker or case manager was mentioned. I feel like we spend more time talking with social work about our discharge recommendations than what I spend talking with the doctor. Most of our time is spent with the nurse or like I said the social worker or case manager. Participants spoke about the importance of communication within the interdisciplinary team. Participant 008 provided that, communication is huge. But I think also interdisciplinary care and communication with nursing staff and all of that in acute care is huge. Participant 010 provided the following: Understanding like, where occupational therapy falls, like, in the roles of an entire medical team. That's definitely kind of gotten a better understanding of that as time has PERCEPTION OF READINESS 36 gone onand, how those interactions go, and the kind of communication that is there. Like, I was definitely anxious about being able to hold up my end of the bargain in terms of being collaborate with other parts of the medical team because that's just how it is when a patient has so many people working on their case. Participant 007 spoke about the role of occupational therapy and communication: I think it was the practice of effectively talking about occupational therapy and who needs occupational therapy in the hospital. Since Im almost at the end of my fieldwork, my fieldwork educator has had do all of the hand offs. She also has me communicate with doctors. I still get really nervous and maybe its because Im the student in the room but yeah. I just dont feel totally confident speaking up. Theme Three: Fieldwork The final theme that emerged during the coding process were the concepts of fieldwork. Participants described the skills needed for successful completion of their level II fieldwork in acute care. Participants expressed the enjoyment of completing level II acute care fieldwork. Participant 010 said the following about their level II acute care fieldwork: It has been great. It has been extremely knowledgeable. Like, one thing I really love about the hospital setting is that you just learn so much because of the pace and the nature of it. And I feel like it has really helped me understand the quote, unquote, spectrum of settings that one can work in. Participant 005 added: I love it so much. I went into my occupational therapy program knowing that I wanted acute care and being in this fieldwork only makes me want it more. I feel like theres a good variety and if I wanted to get a special certification and just work with neuro PERCEPTION OF READINESS 37 patients, I could do that too. This is my second level II fieldwork, my first one was in outpatient. It was good but I definitely feel like acute care is my place. Participants also noted the learning that has occurred throughout the level II fieldwork experience in acute care. Participant 008 stated: It's been fun. It's been -- I feel like I've learned a lot. Especially having to float in a huge hospital, like, every single thing is different, every person is different. It's just fun to meet people. I think that being out in the real world for a change has been really nice to see things come together. I will say it's been overstimulating in a way that other settings that I've been in aren't. Just all the hospital sounds. And, like, just all the things I feel, like, come together in acute care in a way that they don't other places. Additionally, Participant 007 provided: Its been great, I really... I feel like Ive learned so much. Its been tough because of there being such a learning curve but it makes me excited to be an OTP. I love the variety of patients we see, and I feel like I make a difference with the patients that Im seeing. I would tell every student to ask for a fieldwork in acute care. Fieldwork Educator Participants discussed the relationship with their fieldwork educator and how this relationship further facilitated their learning. Participants provided examples of how fieldwork educators provided a positive learning environment, asked about learning styles, and ensured an open line of communication. Participant 005 provided an example of this by stating: I have a really good relationship with my fieldwork educator. On day one we sat down and talked about learning styles and giving feedback. She also made sure to tell me that PERCEPTION OF READINESS 38 she wants feedback on how shes doing too. We talked about the tentative schedule again and she also asked me if there was something or like a certain unit I wanted to go to. Participant 006 provided a similar experience: The very first day she asked me she was like is there a way you prefer feedback. She's like anything that you tell me like that I can improve on for you is not going to hurt my feelings or anything. She was just very accommodating about things because she knows that everyone learns differently. Participants also provided examples of how their fieldwork educator provided support to continue to facilitate learning. Participant 004 stated: Yeah, my fieldwork educator told me on day one that I should be asking questions or be looking up my questions. I really appreciated her telling me that and she knew that I wasnt going to know everything. Participant 008 provided that, [my fieldwork educator] is extremely supportive. And I think she challenges me when she knows that I can do somethingBut my fieldwork educator is really good at recognizing, like, when I can push beyond a comfort zone. Participant 006 provided an example of the supportive learning environment created by their fieldwork educator by stating, and so, she's really good at being like, oh, hey, I noticed this, let's try this. And so, I perform better because I don't feel like down on myself. Fieldwork Sites Participants described how their fieldwork sites helped prepare them for their acute care fieldwork by providing information on policies and procedures and setting expectations. Several participants noted feelings of nervousness and anxiousness and the communication and expectations from their fieldwork sites help to relieve these feelings. Several participants also PERCEPTION OF READINESS 39 noted the orientation process on the first day. Participant 009 provided details of the first day orientation: Day one I did not see any patients; we just had a whole day of orientation. It was all about, like, facility procedures and policies, and equipment. It was about they have their own way of, like, grading ADLs. So, it was all about that. It was a very quick tour that they give us at the end too, because this hospital is humongous. They also gave their expectation for students, and we were given lots of information that first day. Participant 006 added that, The site sent videos I was required to watch and complete quizzes before I was even allowed to step foot in the building. They were like you need to be aware of all these lines and leadsthat helped so much. Participant 008 had a similar experience and stated, We had an orientation day, so that was nice. We had like worksheets kind of to do beforehand that I think exposed us at least to like lab values and supplemental oxygenand medical abbreviations. Therapeutic Use of Self Within the scope of therapeutic use of self, the therapeutic modes are interpersonal communication styles that include the OTPs verbal and nonverbal communication with a client (Taylor, 2020). Participants provided examples of how they utilized their therapeutic use of self and the therapeutic modes throughout their level II fieldwork in acute care. Participant 010 mentioned: I feel like it's definitely helped me with patient interactions and therapeutic use of self because some patients are extremely ill. And so just being able to connect with them. I've always been like an empathetic person. But I just feel like it's just kind of enhancing PERCEPTION OF READINESS 40 those empathizing skills like kind of crucial when you're a therapist working in the medical setting. Participant 007 also noted the importance of therapeutic use of self and implementing the various modes based on the patient: I also think knowing how to use your therapeutic use of self with patients. Some of these patients are really sick and theyre going through a lot because theyre in the hospital. There are some patients that need encouragement and some that need motivation and being able to read that situation and be able to provide that to your patient is an important skill. Participant 003 added how therapeutic use of self could be incorporated into didactic education: So having to exercise, like, more therapeutic use of self during patient encounters. And also, maybe how you would respond to, you know, a patient that starts throwing up during your session or, you know, that has an accident when they stand up, like, they just pee all over the place, what would you do, you know. So, kind of maybe questions depicting situations like that. And taking like the best choice of action. Participant 007 provided an example of not being prepared to use their therapeutic use of self: I know I mentioned this too, but I would have loved to have some type of information on how to better use my therapeutic use of self. It was week two and I remember a patient crying and I was not leading the session, but I remember sort of just freezing, you know. I didnt know what to do and I feel like Im a pretty empathetic person. It just caught me so off guard and my fieldwork educator was so good with the patient and comforted her. After that we talked, and I asked her about using therapeutic use of self with patients and we talked about how building an occupational profile and a rapport with patients is so PERCEPTION OF READINESS 41 important to be able to better read the situation. I mean I have one more week left and now I feel like I can better match what a patient needs like empathy or encouragement or even a little tough love. I dont really like the tough love situation, but my fieldwork educator reminds me that some patients need that, and I feel like it is my responsibility to be able to give the patients what they need. Skills for Success Several participants described examples of skills they needed to display to be successful in their level II fieldwork in acute care, such as confidence, flexibility, and time management. They found these skills to help in their readiness and to be successful in completing their level II acute care fieldwork. Participant 004 stated, Confidence, flexibility, good time management, and also being humble. I have asked so many questions since I started, and my fieldwork educator told me that she appreciated my questions and didnt pretend to know everything. Participant 010 also spoke about these skills, Being able to multitask. Being flexible. You know, your day could change in a matter of seconds. Participants also noted how having the readiness to learn and the intrinsic values needed to be an OTP in the acute care setting are a part of these skills of success. Participant 008 noted: Just like the readiness to learn. I think that this is a hard setting to simulate in education. And so, I think that a lot of the readiness pieces come with like the personal skills and the adaptability and those types of things that the support that students have as learners in that setting. Participant 006 added that: Being super prepared with the things that you have control ofyou control your attitude when you walk in there. You control how much [knowledge] you prepare with. Just PERCEPTION OF READINESS 42 taking criticism with a grain of salt, becauseeverybody does occupational therapy differently. And that's what's really nice about it. Discussion and Conclusion This study highlights the experiences of students completing their level II fieldwork in an acute care setting. By gaining an understanding of these students, their perceptions of readiness can be taken into consideration for making improvements for occupational therapy students. The findings of this study will impact academic and fieldwork educators. The coding and data analysis found the following themes: coursework and preparation, acute care occupational therapy, and fieldwork which assisted in answering the research question. There is little documented evidence outside of ACOTE standards of what the occupational therapy curriculum offers to prepare students for the acute care setting even though there is strong evidence about the importance of occupational therapy services in the acute care setting. Based on the available literature, it was unsurprising that the participants noted variances in their classroom education on simulated patient experiences, learning how to be an occupational therapist in the acute care setting, and the heavy reliance on fieldwork educators. Theme One: Coursework and Preparation The standards of Doctor of Occupational Therapy education are outlined in the various ACOTE standards. These standards maintain that this education should provide comprehensive exposure to a variety of practice settings and populations to allow students to become a generalist and the standards are not specific to practice settings (ACOTE, 2018). With the objective of creating generalists, students are being placed in specific practice settings that require certain skills to provide safe patient care, like the acute care setting. It is documented in the ACOTE standards and within the occupational therapy scope of practice that OTPs provide PERCEPTION OF READINESS 43 occupational therapy services within a hospital setting (AOTA, 2013). ACOTE standards that could be specific to acute care are the foundational content on curriculum that students should demonstrate the knowledge of body structures that include anatomical parts of the body that support function and the educational content must have a basis in the sciences that encourages an understanding of occupation such as biological, physical, social, and behavior (ACOTE, 2018). Despite the ACOTE standards and acknowledgment that acute care is an important practice setting, many participants noted the gap in the general knowledge of the acute care setting. Overall, participants noted how they experienced challenges preparing for acute care fieldwork due to the limitations in simulating the acute care environment within a classroom setting. Participants noted that they would have benefited from acute care focused education and classes and cited examples of how increasing their familiarity with acute care would have increased their readiness for level II fieldwork in acute care. Findings from this research are consistent with a 2011 study that found that an in-person human anatomy course can aide in building a foundation for clinical education for occupational therapy students (Thomas et al., 2011). Participants also acknowledged that anatomy classes were one of the courses that supported their readiness for level II fieldwork in acute care. Outside of this specific set of course work, students generally felt unprepared for level II fieldwork due to the lack in education and acknowledgement of this practice setting which may be an area for improvement in future curriculum changes. The purpose of fieldwork is to create entry level OTPs and to aide in bridging the gap between the classroom and clinical settings. Participants recognized that there was a lack of exposure to this setting within their didactic program including simulation activities. Simulation PERCEPTION OF READINESS 44 can provide a realistic learning experience in a low risk setting to aide in skill development (Santie, 2016). There is an abundance of research on the use of simulation in healthcare education. Through the use of simulation, opportunities for receiving feedback, using clinical reasoning skills, and practicing communication skills are created for students. Kuhl et al. (2022) state that clinical reasoning skills can be further developed by applying didactic knowledge to clinical situations by engaging students in complex simulation activities. Simulation activities also provide students with the opportunity to practice their communication skills during their simulated experience through their interactions with patients (Grant et al., 2021). Despite the development of hands-on and clinical reasoning skills that participants admitted was helpful in their learning, participants still noted that it was difficult to create a true acute care situation for students. Grant et al. (2021) found that when engaging in simulated patient experiences, the role as the simulated patient can provide students with a patient point of view and assist in improving skills. Increasing the realism of the simulation by creating an environment where OTPs may address clients can help readiness for clinical situations (Grant et al., 2021). Participants expressed that the limitations of realism of the acute care environment impacted their readiness for level II fieldwork. To affect readiness for acute care fieldwork, additional opportunities to replicate an acute care setting to help decrease to unknown nature of this setting as described by participants of this study. Theme Two: Acute Care Occupational Therapy Occupational therapists must be able to balance the priorities of physical medicine and the holistic nature of the patient (Dove et al., 2022). To provide this high quality and unique care PERCEPTION OF READINESS 45 to patients, students should have access and an understanding of what the role of occupational therapy is within the acute care setting. Based on participant statements, there appears to be a lack of awareness for students entering into their level II acute care fieldwork rotations and their understanding of the importance, value, and the role of OTPs. A strategy for learning and exposure are opportunities for observation of acute care clinicians (Hayward et al., 2015). Acute care OTPs help to drive discharge recommendations and teach skills to patients after a new injury or illness so patients can safely and independently live their lives. Additionally, OTPs in acute care impact the clinical and social risk factors of patients to help facilitate a safe discharge home. These OTPs are skilled at preventing hospital readmissions, As evidenced by patients receiving occupational therapy services within their first day of intubation were more independent in ADLs and mobility than patients with delayed therapy services (Costigan et al., 2019; Roberts et al., 2020; Rogers et al., 2017). Despite the literature supporting the importance of occupational therapy in the acute care setting, participants felt unprepared to begin their fieldwork rotations in this setting. Having this baseline knowledge of OTPs in the acute care setting can help to shape how Doctor of Occupational Therapy Students enter into their level II fieldwork in acute care. The role of acute care occupational therapy and the importance that OTPs have in this setting should be included in didactic education to prepare students for fieldwork in this setting and to ensure students have understand of this practice setting. This serves as another example of creating generalists, yet students are required to understand the intimate details of their practice settings to be entry level practitioners. To aide in creating OTPs that can excel in these specific practice settings, additional didactic education, time, and training should be provided to students including the acute care setting. PERCEPTION OF READINESS 46 Throughout the interview process, participants noted the limitations of medical knowledge in their didactic education. Students spoke to not having the baseline knowledge needed to treat a variety of critically ill patients due to this not being available to them in their didactic coursework. While providing rehabilitation the acute care setting, clinicians must balance the medical needs of the patient and engaging these patients in rehabilitation (Gorman et al., 2010). While occupational therapy specific research is limited, physical therapy students at the beginning of their clinical experiences felt least prepared to treat patients with cardiac and pulmonary conditions as well as decreased readiness in responding to unexpected medical events and providing patient education (Fairburn et al., 2019). Participants of this study also provided general comments on how to account for the medical conditions and how this can impact the appropriateness of when a patient can participate in occupational therapy in the acute care setting. Fairburn et al. (2019) report that physical therapy clinical instructors found that the skills important to patient care were medical history, safety and positioning, communication, and understanding a patients physiologic response to rehabilitation. Physical therapy coursework is comparative to the occupational therapy coursework (McCombie et al. (2015), yet one participant from this study noted that it was her perspective that the physical therapy students felt much more prepared for their acute care educational experience. In a study by Mason et al. (2020), fieldwork educators recognized that there are nonoccupational therapy specific skills needed by students to be successful in their fieldwork including the knowledge of general medical information, how lab parameters impact patients, and performing vitals. These same skills were acknowledged by participants in this study and may be cause for re-evaluation of standards required in occupational therapy didactic education. PERCEPTION OF READINESS 47 The focused education and preparation needed to aid in student success in the acute care setting must be considered with occupational therapy academic educators as students are stating that they are not ready to enter into this setting which could lead to a reconsideration of the standards to better prepare students. It has been well established that fieldwork is meant for students to apply their didactic knowledge to real life patient situations. There is a discrepancy in what can be applied when the information was not provided in the classroom, and this discrepancy can be seen in the statements participants made about learning the chart review process. In addition to not being prepared to handle the medical complexities seen in the acute care setting, participants also spoke at length about the lack of preparedness for completing chart reviews. Burrows et al. (2022) state the importance of interactive learning with the use of electronic health records by incorporating reviewing test results, completing chart reviews, and clinical documentation to increase critical thinking skills, decision-making skills, and improved understanding of the interdisciplinary team. In a survey of occupational therapy and occupational therapy assistant educational programs, it was found that electronic health record education and practice were not embedded within the didactic education for students suggesting that occupational therapy students are at a disadvantage when they complete their fieldwork (Dmytryk & DeAngelis, 2017). Incorporating electronic health records into didactic education provides students with the knowledge base to enter into entry-level practice (Burrows et al., 2022) which is the goal for successful completion of level II fieldwork. Dmytryk and DeAngelis (2017) found that educational programs may be relying on fieldwork experiences to provide the education on electronic health records and documentation and placing more responsibility to teach this onto PERCEPTION OF READINESS 48 fieldwork educators. This is consistent with participant statements that their fieldwork educator provided this education and training. Additionally, from the study completed by Dmytryk and DeAngelis (2017), it is suspected that access and education of electronic health records in occupational therapy education may be lacking as a result of the lack of knowledge of the academic educators and the lack of overall technology utilized in these didactic programs. The ACOTE standards reference electronic documentation systems as a standard for Doctor of Occupational Therapy programs. Unfortunately, there is no literature on the length of time that is needed for the knowledge translation of learning electronic documentation systems. Students suggested that they had a disadvantage due to not having enough education and experience with electronic documentation systems. Based on the findings from this study, it would be recommended that more exposure and training with electronic documentation systems records be included into occupational therapy didactic education to help prepare students for understanding and synthesizing information to safely provide occupational therapy services in the acute care setting. While students are on their fieldwork, they are learning the importance of collaboration and understanding the roles of other health professionals to improve patient resources and outcomes. (Yu et al., 2018). Participants in this study spoke about their experiences in the classroom and on their fieldwork with the communication and collaboration needed for the interdisciplinary care team. Students provided insight into the classroom practice they had with interdisciplinary communication and how these classroom experiences did not always match the clinical setting due to the variety of interdisciplinary care team that is seen in the acute care setting. Participants also noted that the understanding of other roles as a part of the patients care team was information learned and appreciated while on their fieldwork as during their didactic PERCEPTION OF READINESS 49 education, they typically only had access to other allied health professionals. Maharajan et al. (2017) found that in medical practice settings, students prefer working with others from their same profession; however, working with students from a variety of health professions can increase understanding and respect of these other health professionals. Yu et al. (2018) found that occupational therapy students on their fieldwork rotations gain the experiences needed for communicating with sensitivity and respect with the patient, their families, and the patient care team. Participants in this study noted the importance of effective communication with the interdisciplinary team as this was a new experience for them, with one participant specifically spoke about the confidence needed to talk to these other members. Yu et al. (2018) acknowledged the importance of communication in a clinical setting and the struggle that students experience in their communication from the classroom to the clinical environment. Participants acknowledged this statement as well and found that overall, in the acute care setting, they experienced challenges translating experiences from the classroom to the clinical environment. Theme Three: Fieldwork The goal of fieldwork is for students to bridge the gap of didactic education to the reality of clinical practice by utilizing clinical skills, clinical reasoning, and professional behaviors to become competent entry level clinicians (Horwitz et al., 2023; Wang et al., 2023). A successful fieldwork experience is a shared responsibility of students and fieldwork educators, as students must be able to bridge knowledge to clinical reasoning skills, and educators acknowledge factors that could impact fieldwork performance (Wang et al., 2023). Fieldwork educators anticipate that students have this baseline knowledge and be able to apply this knowledge to patient encounters (Mason et al., 2020). Participants recognized that fieldwork was the opportunity to apply their PERCEPTION OF READINESS 50 didactic knowledge to the acute care setting. The real environment of fieldwork forces students to apply their didactic knowledge to the needs of the patients (Occupational Therapy Fieldwork Education: Value and Purpose, 2022). Participants described how their fieldwork sites helped prepare them for their acute care fieldwork by providing logistical instructions, information on policies and procedures, and setting expectations. The participants of this study provided details of their first day on their fieldwork which included review of hospital policies, review of lab values and medical equipment, an overview of the available patient education handouts and home exercise programs, and education on review of documentation standards. One participant was required to complete education on medical equipment and patient precautions. This participant provides an example of when the acute care didactic knowledge and simulation experiences are not available, this education must be provided by the fieldwork sites and fieldwork educators. Participants provided insight into how they utilized their therapeutic use of self during fieldwork. Participants of this study provided examples of providing patients with empathy or motivation based on how the patient presented during their session due to sudden and unexpected nature of patients injuries or illnesses. One student spoke about having to read the room with patients to be able to provide effective occupational therapy services. Therapeutic use of self requires an occupational therapy practitioner to understand clients as human beings at an interpersonal level and the ability to apply empathy and use of interpersonal knowledge and related skills to thoughtfully resolve evocative interpersonal events in practice (Taylor, 2020, p. 2). The Intentional Relationship Model (IRM) was developed to aid the occupational therapy profession with an approach to understanding therapeutic communication and therapeutic relationships within the occupational therapy process (Taylor, 2020). Even though the concepts PERCEPTION OF READINESS 51 of therapeutic use of self directly were developed to be utilized by OTPs and are referenced in the ACOTE referral, screening, and intervention standards, participants expressed a need for practicing their therapeutic use of self skills before level II fieldwork in acute care attributed to needing to manage the patients physical and emotional needs. Andonian (2017b) found that therapeutic use of self and emotional intelligence are shared theories due to the overlap of common skills and the focus on relating to others; however, these theories are separate through their interpretation and implementation. Increasing awareness of emotional reasoning and emotional management through academic education and training can supports students communication, collaboration, and the ability to accept feedback on their performance (Wang et al., 2023). It was found that participants would need to utilize the various modes of therapeutic use of self and implement these based on the patient or situation that they were in throughout their fieldwork. While on fieldwork, students will collaborate with patients and the interdisciplinary team; having emotional reasoning and management can assist with this collaboration (Wang et al., 2023). The use of therapeutic use of self in acute care may possibly accelerated due to needing to build rapport quickly with multiples patients per day. Several participants described examples of skills they needed to display to be successful in their level II fieldwork in acute care, such as confidence, flexibility, and time management. Brown et al. (2020a) found that professional skills needed by students for successful completion of fieldwork include time management skills, taking initiative, having accountability, and being receptive to constructive feedback to modify patient care. Additionally, it was found that communication with the interdisciplinary team is also a necessary professional skill for student success (Brown et al., 2020a). Participants agreed that these skills helped in their readiness and successful completion of their level II acute care fieldwork. These skills were needed when PERCEPTION OF READINESS 52 completing chart reviews, planning for patients that would be seen that day, and the time management needed for seeing patients and documenting results of that patient session. Brown et al., (2020a) found that fieldwork experiences have an influence on the professional and clinical skills of occupational therapy students. Participants recognized that having the readiness to learn also assisted in their success during level II fieldwork in the acute care setting given the amount of the learning that occurred while in this setting. Within the occupational therapy didactic education, there is a concentration on the clinical reasoning and professionalism required to be an effective practitioner within a practice setting (St. Peters & Short, 2018). Participants admitted that having these intrinsic traits provided the motivation to continue to improve on their motivation for willingness to learn and to continue to improve upon their clinical skills and critical thinking skills. A students clinical reasoning is developed during their supervised practice during the experiences of time management, communication, and with patient interactions (Rodger et al., 2016). Participants expressed feelings of being overwhelmed in their fieldwork. These feelings were due to the wide variety of patients, the fast-paced nature, and the limited didactic knowledge provided to students. With students missing this foundational knowledge for acute care, students lean into their fieldwork educator to serve as this facilitator of education. Outside of the clinical skills needed for readiness of level II fieldwork in an acute care setting, participants stated that their fieldwork educators made them excited about their future as an OTP and helped them become competent in this setting. When speaking on their fieldwork experiences, participants stated they felt excited for fieldwork and enjoyed being in the acute care setting. Fieldwork educators responsible for supervising level II fieldwork occupational therapy students shall meet state and federal regulations governing practice, have a minimum one PERCEPTION OF READINESS 53 year of practice experience following initial certification, and be adequately prepared to serve as a fieldwork educator. (AOTA, n.d.b). Participants recognized that their fieldwork educator helped to facilitate their ongoing learning. It is an expectation of fieldwork educators that students have a blend of professional and technical skills upon entering their level II fieldwork, including communication and being able to provide treatment interventions (Mason et al., 2020). In a study of fieldwork educators, it was found that students that have a focus on aspects of patient care such as development of clinical skills, clinical reasoning, and therapeutic use of self are all needed for positive fieldwork experiences (Drynan at al., 2022). Fieldwork educators have identified that providing emotional support to students throughout their fieldwork placement was important to student development and an unexpected role as a fieldwork educator (Drynan at al., 2022). Throughout their fieldwork experiences, participants acknowledged the importance of their relationship with their fieldwork educator. Participants expressed feelings of anxiousness and that the relationship established with their fieldwork educator assisted in their ability to participate in fieldwork and aided in their ability to apply didactic knowledge. The participants provided insight into the expectations and communication styles that were established by their fieldwork educators. The fieldwork educator and student relationship aids in the students learning while on their fieldwork placement due to the close nature of this relationship (Brown et al., 2020b). Based on the information provided from the participants, it appears as if there is heavy reliance on fieldwork educators to fill the gaps of foundational didactic knowledge. There is no formal training for OTPs prior to taking on a level II fieldwork student. The American Occupational Therapy Association offers a fieldwork educator certificate workshop. This workshop offers a greater understanding of the role as a fieldwork educator and how to support PERCEPTION OF READINESS 54 best practice in fieldwork education and is recommended, not required, for fieldwork educators (AOTA, n.d.b). Limitations At the time of this study, the Accreditation Council for Occupational Therapy Education allows entry level occupational therapy education to be completed at the Masters and Doctoral degree level. Although participants provided detailed experiences on their perception of readiness for level II fieldwork in acute care, the sample exclusively consisted of Doctor of Occupational Therapy students. This limitation of students could limit the generalizability to both Masters and Doctoral entry level educational programs. Another limitation is biases on behalf of the researcher as the researcher is an occupational therapist with experience providing occupational therapy services in the acute care setting. To ensure no biases, the practices previously outlined were followed including memoing and keeping a journal for an outlet to express feelings and capture biases as a part of reflexivity to address credibility (Henderson & Rheault, 2004). Additionally, a secondary coder completed independent coding of all interviews, and member checking was completed to ensure the study's credibility. Implications for future research and practice The findings from this study have implications on current education, practice, and ongoing research in the field of occupational therapy. There is available evidence on the important role that acute care OTPs play in the acute care setting. The OTPs providing occupational therapy services to patients in the acute care setting have the knowledge and skill set to provide meaningful rehabilitation to patients experiencing a life altering illness or injury. PERCEPTION OF READINESS 55 The ACOTE standards state that the hospital setting is a practice setting that students need to be prepared for, however; students are stating that they are not prepared for this setting. Within the study, participants noted that their academic programs provided the opportunities within their control to prepare them for a level II fieldwork in acute care. The participants recognized that the unknown nature of this setting and the challenges of simulating an acute care patient made it challenging in their preparedness. Although there appears to be fewer opportunities in the classroom to create a realistic acute care setting, creating this environment should be an objective of didactic programs due to the referenced hospital setting in the ACOTE standards. Embedding resources to bridge the gap of didactic education and the realistic acute care setting should be included for student preparedness for an acute care fieldwork. It is vital for student success to have an open line of communication and set expectations for certain fieldwork sites. This would involve the didactic programs, academic educators, and fieldwork educators collaborating on skills that need to be addressed and fostered in the classroom then applied during fieldwork (Mason et al., 2020). Although this study is centered on the acute care setting, this collaborating between academic settings and fieldwork settings could have an impact on all fieldwork and future practice settings. Mason et al. (2020) surveyed fieldwork educators in a physical dysfunction setting and found that although there are certain skills students need in this setting to have a positive fieldwork experience, these skills were not more important that skills needed for another setting. The attention on the substantial dependence on the fieldwork educator to provide acute care knowledge to students must also be considered in future research. Although it was only mentioned by a few participants, these select participants agreed that academic programs should also examine student success on fieldwork to incorporate a PERCEPTION OF READINESS 56 holistic approach with focusing on professional behaviors such as confidence, self-efficacy and professionalism and have less focus on grades (Brothertonet al., 2021; Horwitz et al, 2023). There should also be a focus on the multiple skills needed for successful fieldwork completion and how these skills can be learned and practiced throughout academic programs and the variety of fieldwork settings students may experience. There should also be an understanding of how students apply their didactic knowledge to the clinical setting. Additional details on knowledge translation approaches and the methods of these approaches should be discussed in future research (Perkins et al., 2020). The lack of clarity and different definitions of critical thinking is a challenge in the literature as critical thinking and clinical reasoning are used interchangeably and inconsistently (Berg et al., 2021) A consistent definition of critical thinking will be helpful to healthcare educators; without educators defining critical thinking, students will be unable to define or engage in critical thinking (Berg et al., 2021). Implications for future research could be focused on the preparation for acute care fieldwork and the opportunity to grow occupational therapy students for a career in the acute care practice setting. Student fieldwork placements are valuable to the student, the school, and the fieldwork educator as these student fieldwork placements bring evidence and current practice trends into the clinical sites (Drynan at al., 2022). Mason et al. (2020) highlight the need for the occupational therapy profession to consider how the intersection of didactic and clinical education will impact the profession. There is also the consideration for fieldwork placement options for students. Despite the documented benefits of providing fieldwork placements, occupational therapy academic fieldwork coordinators face challenges associated with ensuring an adequate number of PERCEPTION OF READINESS 57 placement offerings each academic year (Drynan et al., 2022). Mackenzie and OToole (2017) state that fieldwork placements can be unpredictable depending on the time of placements and the availability of fieldwork supervision. There have been several sites that successfully implemented year-round fieldwork sites. Increasing the amount of level II fieldwork options provides additional opportunities for occupational therapy students. Finally, based on the findings from this study, it would be recommended that electronic health records be included into occupational therapy didactic to help prepare students for understanding and synthesizing information to safely provide occupational therapy services in the acute care setting. In addition to the availability of fieldwork educators, this research highlights the demands placed on the fieldwork educators to provide didactic knowledge to this acute care practice setting. If the occupational therapy profession is placing this additional demand for didactic education, then more should be done to align expectations from fieldwork educators to create equity in producing entry level acute care OTPs. Conclusion This study aimed to understand Doctor of Occupational Therapy students perceptions of readiness for level II fieldwork in an acute care setting. The research question also focused on the educational activities that facilitated student learning and perception of readiness. Three themes emerged that provided insight into the experiences of these students who were completing their level II fieldwork in acute care that included coursework and preparation, acute care occupational therapy, and fieldwork. These interviews provided detailed information on how didactic education impacted their readiness, that there are specific skills and requirements of the acute care setting, and the undertaking of completing level II fieldwork in acute care. As evidence has been found, OTPs have a valuable role in the acute care setting; however, students PERCEPTION OF READINESS 58 are not being prepared to perform in this practice area. 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Cross-cultural service learning as pedagogy for character development in occupational therapy doctoral students. The Open Journal of Occupational Therapy 6(4). https://doi.org/10.15453/2168-6408.1493 Taylor, R. R. (2020). The intentional relationship: Occupational therapy and use of self (2nd ed.). F. A. Davis. Thomas, K. J., Denham, B. E., & Dinolfo, J. D. (2011). Perceptions among occupational and physical therapy students of a nontraditional methodology for teaching laboratory gross anatomy. Anatomical Sciences Education, 4(2), 71-77. https://doi.org/10.1002/ase.208 PERCEPTION OF READINESS 68 Timmerberg, J. F., Dole, R., Silberman, N., Goffar, S. L., Mathur, D., Miller, A., Murray, L., Pelletier, D., Simpson, M. S., Stolfi, A., Thompson, A., & Utzman, R. (2019). Physical therapist student readiness for entrance into the first full-time clinical experience: A delphi study. Physical Therapy, 99(2), 131146. https://doi.org/10.1093/ptj/pzy134 van Belle, E., Giesen, J., Conroy, T., van Mierlo, M., Vermeulen, H., Huismande Waal, G., & Heinen, M. (2020). Exploring personcentered fundamental nursing care in hospital wards: A multisite ethnography. Journal of Clinical Nursing, 29(11-12), 1933-1944. Wang, Y., Chung, L.-H., Cheng, C.-Y., Wang, W.-J., Chang, L.-C., Huang, Y.-M., Tso, S.-Y., Chen, Y.-L., & Wu, C.-Y. (2023). Predictors of academic and fieldwork performance in occupational therapy students: A systematic review. Occupational Therapy International, 2023, 72815057281505. https://doi.org/10.1155/2023/7281505 Wimpenny, K. (2013). Using participatory action research to support knowledge translation in practice settings. International Journal of Practice-based Learning in Health and Social Care, 1(1), 3-14. Yu, M. L., Brown, T., White, C., Marston, C., & Thyer, L. (2018). The impact of undergraduate occupational therapy students interpersonal skills on their practice education performance: A pilot study. Australian Occupational Therapy Journal, 65(2), 115-125. https://doi.org/10.1111/1440-1630.12444 PERCEPTION OF READINESS 69 Table 1 Participants Participant ID P001 P002 P003 P004 P005 P006 P007 P008 P009 P010 P011 Age 26 25 25 26 25 26 28 26 27 25 28 Sex Female Female Female Female Female Female Female Female Female Female Female Week of Fieldwork 11 7 7 8 10 6 11 7 7 9 10 PERCEPTION OF READINESS 70 Table 2 Interview Themes and Subthemes Theme Coursework and Preparation Acute Care Occupational Therapy Fieldwork Subtheme Didactic Education for Acute Care Hands-on Practice Medical Knowledge Chart Review Medical Equipment Basic Acute Care Knowledge Interdisciplinary Care Team Fieldwork Educator Fieldwork Sites Therapeutic Use of Self Skills for Success PERCEPTION OF READINESS 71 Appendix A Recruitment Email to Academic Fieldwork Coordinator Hello! I am currently completing a qualitative study is to better understand Doctor of Occupational Therapy students perception of readiness for level II fieldwork in an acute care setting, including the educational activities that facilitated their learning and perception of readiness. Can you please pass the following information onto your Doctor of Occupational Therapy Students: I am looking for Doctor of Occupational Therapy Students participants to participate in a qualitative research study. The purpose of this study is to better understand Doctor of Occupational Therapy students perception of readiness for level II fieldwork in an acute care setting, including the educational activities that facilitated their learning and perception of readiness. Data will be collected via individual semi-structured interviews using a videoconferencing platform (Microsoft Teams or Zoom). The interviews are expected to take 30 to 45 minutes and will include broad questions to explore perception of readiness for fieldwork placement in acute care. Additional study information: o The right of participants to voluntarily withdraw from the study at any time o There are no known risks associated with participation in the study o The expected benefits of participating in this study include: Assisting occupational therapy faculty and educators in better preparing students for this setting, as well as potentially enhancing the occupational therapy program curriculum to facilitate effective knowledge to clinical skills transition. If you are interested in participating in this study, please contact Jackie Dusing at dusingj@uindy.edu Feel free to pass along this research information onto fellow Doctor of Occupational Therapy students who may also provide helpful insight into this topic. University of Indianapolis IRB approved study # 01849; approval date 4/14/2023 PERCEPTION OF READINESS 72 Appendix B Recruitment Posting to Online Forums and Occupational Therapy Association Email Listservs Who: Doctor of Occupational Therapy Students Research topic: Readiness for level II fieldwork in an acute care setting Background: The aim of this qualitative study is to better understand Doctor of Occupational Therapy students perception of readiness for level II fieldwork in an acute care setting, including the educational activities that facilitated their learning and perception of readiness. Inclusion criteria: Inclusion criteria for this study will comprise Doctor of Occupational Therapy students from any school in the United States who are completing level II fieldwork at an acute care hospital. Data will be collected via individual semi-structured interviews using a videoconferencing platform (Microsoft Teams or Zoom). The interviews are expected to take 30 to 45 minutes and will include broad questions to explore perception of readiness for fieldwork placement in acute care. Additional study information: o The right of participants to voluntarily withdraw from the study at any time o There are no known risks associated with participation in the study o The expected benefits of participating in this study include: Assisting occupational therapy faculty and educators in better preparing students for this setting, as well as potentially enhancing the occupational therapy program curriculum to facilitate effective knowledge to clinical skills transition. If you are interested in participating in this study, please contact Jackie Dusing at dusingj@uindy.edu Feel free to pass along this research information onto fellow Doctor of Occupational Therapy students who may also provide helpful insight into this topic. University of Indianapolis IRB approved study # 01849; approval date 4/14/2023 PERCEPTION OF READINESS 73 Appendix C Follow-up Recruitment Email Hello! Thank you for your interest in this research study. My name is Jackie, I am conducting a qualitative research study to better understand Doctor of Occupational Therapy students perception of readiness for level II fieldwork in an acute care setting. I would like to schedule an interview date and time. Interviews will be scheduled between weeks six and 12 of your fieldwork, please let me know of days and times you would be available to complete this interview. As a reminder, participation will take approximately 30 to 45 minutes and will occur via a videoconferencing platform. Also, there are a few pieces of information I would like to collect. In your response with the options for days and times of your interview, can you please provide information on the following: Gender Age College or university you attend Please let me know if you have any questions you would like answered now. Also, attached to this email is the research study sheet which has additional information on the study. You may contact me via email (dusingj@uindy.edu) or if you prefer to speak over the phone, I can be reached at 708-738-2554. Thank you for participating in this study! University of Indianapolis IRB approved study # 01849; approval date 4/14/2023 THE HRP OFFICE WILL ADD THE STUDY IDENTIFICATION INFORMATION AFTER THE STUDY IS APPROVED, AND WILL REMOVE THIS NOTE. THE STUDY IDENTIFICATION INFO MUST APPEAR ON ALL HARD COPIES AND ONLINE. Minimal Risk UIndy Study # Study Version: Study Version Date: Informed Consent Form (ICF) Version: ICF Version Date: Appendix D Study Information Sheet Interprofessional Health and Aging Studies, University of Indianapolis KEY INFORMATION FOR POTENTIAL RESEARCH PARTICIPANTS Consent is being sought for participation in a study to understand Doctor of Occupational Therapy students perception of readiness for level II fieldwork in an acute care setting. Interviews conducted virtually will be scheduled with potential participants which are expected to take approximately 30-45 minutes and will be audio recorded. Participation is voluntary. CONSENT TO PARTICIPATE IN RESEARCH STUDY Perceptions of Occupational Therapy Student Readiness in the Acute Care Setting Study Principal Investigator (PI): Dr. Lisa Borrero UIndy Email: borrerol@uindy.edu UIndy Telephone: 800-232-8634 x5944 Student Researcher: Jaclyn Dusing UIndy Email: dusingj@uindy.edu UIndy Telephone: 800-232-8634 x5944 Lisa Borrero, PhD, FAGHE, and Jaclyn Dusing OTR/L, from the Department of Interprofessional Health and Aging Studies at the University of Indianapolis (UIndy) are conducting a research study. You were selected as a possible participant in this study because you are a Doctor of Occupational Therapy student completing level II fieldwork in acute care setting. Your participation in this research study is voluntary. Why is this study being done? This study is being to better understand Doctor of Occupational Therapy students perception of readiness for level II fieldwork in an acute care setting, including the educational activities that facilitated their learning and perception of readiness. What will happen if I take part in this research study? INFORMED CONSENT LETTERHEAD AUGUST 2022 THE HRP OFFICE WILL ADD THE STUDY IDENTIFICATION INFORMATION AFTER THE STUDY IS APPROVED, AND WILL REMOVE THIS NOTE. THE STUDY IDENTIFICATION INFO MUST APPEAR ON ALL HARD COPIES AND ONLINE. Minimal Risk UIndy Study # Study Version: Study Version Date: Informed Consent Form (ICF) Version: ICF Version Date: Participation will take a total of about 30-45 minutes. Additionally, follow up emails will be sent within two weeks of the interview to confirm ideas and themes derived from the interview. Are there any potential risks or discomforts that I can expect from this study? There are no anticipated risks or discomforts associated with participating in this research study. Are there any potential benefits if I participate? You will not directly benefit from your participation in this research. Will information about me and my participation be kept confidential? The results of this study may be published in a scholarly book or journal, presented at professional conferences or used for teaching purposes. However, only aggregate data will be used. Personal identifiers will not be used in any publication, presentation or teaching materials. Will the data from my study be used in the future for other studies? It is possible that de-identified data from this study could be used for future research or shared with other researchers for use in studies, without additional informed consent. De-identified means that any codes and personal information that could identify you will be removed before the data is shared. What are my rights if I take part in this study? You can choose whether or not you want to be in this study, and you may withdraw your consent and discontinue participation at any time. Whatever decision you make, there will be no penalty to you, and no loss of benefits to which you were otherwise entitled. You may refuse to answer any question/s that you do not want to answer and still remain in the study. Who can I contact if I have questions about this study? The Research Team: If you have any questions, comments or concerns about the research, you can talk to the one of the researchers. Please contact: Lisa Borrero Email: borrerol@uindy.edu INFORMED CONSENT LETTERHEAD AUGUST 2022 THE HRP OFFICE WILL ADD THE STUDY IDENTIFICATION INFORMATION AFTER THE STUDY IS APPROVED, AND WILL REMOVE THIS NOTE. THE STUDY IDENTIFICATION INFO MUST APPEAR ON ALL HARD COPIES AND ONLINE. Minimal Risk UIndy Study # Study Version: Study Version Date: Informed Consent Form (ICF) Version: ICF Version Date: Telephone: 800-232-8634 x5944 Jaclyn Dusing Email: dusingj@uindy.edu Telephone: 708-738-2554 The Director of the Human Research Protections Program (HRPP): If you have questions about your rights as a research participant, or you have concerns or suggestions and you want to talk to someone other than the researchers, you may contact the Director of the Human Research Protections Program, by either emailing hrpp@uindy.edu or calling 1 (317) 781-5774 or 1 (800) 232-8634 ext. 5774. How do I indicate my informed consent to participate in this study? At the beginning of the interview you will be asked to verbally indicate your consent to participate. INFORMED CONSENT LETTERHEAD AUGUST 2022 PERCEPTION OF READINESS 78 Appendix E Interview Guide Introductory Paragraph Hello! Id like to start by letting you know that I am recording. Thank you for agreeing to be interviewed today. This study is being completed as a part of a Doctor of Health Sciences doctoral research project. The aim of this study is to better understand occupational therapy students perception of readiness for level II fieldwork in acute care and the impact the educational methods have on this perception. The information you share with me will be completely confidential and will not be linked to any identifying information. The data collected will be analyzed and will be included in a final research presentation. I will be asking you questions regarding your feelings and thoughts pertaining to readiness for level II fieldwork in an acute care setting. In addition, I hope to gain insight into your thoughts on educational methods that best prepared you for this level II fieldwork. You will also have an opportunity to share anything else you would like pertaining to the subject. This interview is expected to take 30-45 minutes and will be audio recorded to ensure that I capture all your thoughts and feelings as accurately as possible. Participation is voluntary, and you may skip questions or stop at any time. Do you have any questions on how this interview will be conducted? Interview Questions 1. What does readiness for level II fieldwork in acute care look like to you? a. What qualities would a student who is ready for level II fieldwork in acute care display? PERCEPTION OF READINESS 79 b. What barriers may impact a students readiness for level II fieldwork in acute care? 2. What do you believe helped you prepare, or feel ready, for your level II fieldwork in acute care? a. What factors do you believe negatively impacted your preparedness or readiness for level II fieldwork in acute care? 3. Think back on your education, can you describe the educational experiences or activities that provided the most meaningful helpful learning experiences in preparation for acute care for you? a. What was it about those experiences that made them so uniquely helpful? 4. Can you describe the educational experiences that were least meaningful in preparing you for your level II fieldwork in acute care? a. What was it about those experiences that made them least helpful? 5. What education experiences do you feel wouldve better prepared you for level II fieldwork in acute care that werent available? 6. What has it been like for you to be a level II fieldwork student in this acute care setting? a. How did you feel the day before beginning your first day in this acute care fieldwork placement? 7. How did your site help with your readiness for this fieldwork placement in acute care? a. After you began your fieldwork placement, what did you do to help your readiness for treating patients in acute care? 8. What advice would you have to other students preparing for a level II fieldwork placement in acute care?  ...