SPORT NUTRITION KNOWLEDGE AND DIETARY HABITS IN COLLEGE ATHLETES By Emily Nicole Werner A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Kinesiology—Doctor of Philosophy 2021 ABSTRACT SPORT NUTRITION KNOWLEDGE AND DIETARY HABITS IN COLLEGE ATHLETES By Emily Nicole Werner Adequate nutrition is vital for the health, wellness, and performance of college athletes1,2. Previous literature suggests that college athletes have poor dietary practices3- 8. A possible explanation for this is lack of nutrition knowledge. Previous knowledge surveys used have limitations that make them inappropriate for use in this population. In order to assess the nutrition knowledge of college athletes reliably, a new tool must be developed that has characteristics that promote the athletes to respond in completion, and practitioners must believe in its usefulness and practicality. Therefore, the purpose of this dissertation was to examine the relationship between sport nutrition knowledge and dietary habits of college athletes through the development and validation of a sport nutrition knowledge assessment tool made specifically for this population. Three studies were conducted. The first was a nutrition knowledge assessment in college athletes (n=125) using a tool previously validated in the general population9. The average score was 58%, with females (average 67%) and athletes of non-revenue sports (i.e., sports other than football, basketball, or ice hockey; average 70%) scoring significantly better than males (average 46%) or athletes of revenue sports (average 46%), respectively. In general, the athletes had poor nutrition knowledge. The second study was the development and validation of the 25-question Sport Nutrition Assessment of Knowledge (SNAK) screener. First, the SNAK was developed using position stands and reviews on nutrition for sport in conjunction with feedback from experts. Next, a sample of college athletes and dietetic students (n=116 total) completed the SNAK. Results showed high knowledge scores (average 88%), which suggests that either the pilot version may have been too easy, or the athletes truly have high knowledge. A revised, 22-question SNAK was then developed based on statistical and qualitative feedback. The third study was a deeper investigation into the dietary habits of college athletes (n=94). This was done using the Automated Self-Administered 24-hour (ASA24) Dietary Assessment Tool10 and analyzed using the Healthy Eating Index (HEI)11. Results showed an average score of 59 out of 100 possible for diet quality, which is equivalent to a grade of F based on the recommended grading scheme12. In general, the athletes displayed poor diet quality. Overall, this dissertation indicates that having high nutrition knowledge does not lead to high diet quality in college athletes. Future research should be done to investigate the barriers between knowledge and behavior, as well as the best intervention strategies to improve diet quality in this specific population. Copyright by EMILY NICOLE WERNER 2021 This dissertation is dedicated to my parents, Carl and Sharon, for always supporting me, teaching me, and encouraging me. v ACKNOWLEDGEMENTS First, I would like to acknowledge Dr. Jim Pivarnik, my advisor and committee chair. From our first phone call when you told me I was accepted into the program because my height was needed on the intramural basketball team, I knew I would love working with you. Thank you for teaching me, putting up with my constant grammar mistakes, and always keeping your promise to “serve and protect”. My appreciation for you is unfailing. I also would like to acknowledge my previous advisors, Dr. Heather Betz and Dr. Stella Volpe. You two were the catalysts that ignited my love of exercise physiology and nutrition. You each taught me countless lessons about both academia and life, and I am so grateful to have been taught by such strong, inspiring women. You will forever be my favorite other “moms”. Next, I would like to acknowledge my committee members, Dr. Jean Kerver, Dr. Serena Miller, and Dr. Karl Erickson. Each of our expertise, enthusiasms and willingness to teach me have been invaluable to my education and this dissertation journey. Lastly, I want to acknowledge my friends, family, and the Michigan State University Department of Intercollegiate Athletics for all the continued support and encouragement. I would not be where I am today without your backing. vi PREFACE This dissertation is organized into six chapters. Chapter One includes a brief introduction, along with specific aims and associated hypotheses. Chapter Two is a review of the literature related to the specific aims. Chapters Three, Four, and Five correspond to Specific Aims 1, 2, and 3, respectively, and each are organized as individual manuscripts (including abstract, introduction, methods, results, discussion, and references). Only one chapter (Chapter Three) has been previously published in the Journal of American College Health. Findings for Chapters Three, Four, and Five are summarized in Chapter Six, accompanied by strengths and limitations of this project and a discussion of future directions. vii TABLE OF CONTENTS LIST OF TABLES ......................................................................................................................... xi LIST OF FIGURES ...................................................................................................................... xii CHAPTER ONE: INTRODUCTION ..........................................................................................1 Research Aims & Hypotheses ..............................................................................................4 CHAPTER TWO: REVIEW OF THE LITERATURE ................................................................6 College Athletics & the College Athlete ...............................................................................6 The Role of Sport Nutrition in College Athletics ................................................................9 Conceptually Defining Sport Nutrition ................................................................10 The Importance of Sport Nutrition for the College Athlete ...................................13 Sport Nutrition Knowledge & its Relationship with Dietary Behaviors ..........................15 Conceptually Defining Sport Nutrition Knowledge .............................................15 Sport Nutrition Knowledge and Dietary Behaviors in College Athletes ..............18 The Present State of Sport Nutrition Knowledge in College Athletes ..................19 Assessing Sport Nutrition Knowledge ..............................................................................21 Characteristics of an Ideal Measure for use with College Athletes........................21 Previously Published Measures .............................................................................23 Assessing Sport Nutrition Knowledge: Evaluation of Validated Tools.............................29 Measure Developed by Shifflett, Timm, & Kahanov, 2002 ...................................29 Measure Developed by Trakman et al., 2017 and 2018.........................................31 Measure Developed by Karpinski, Dolins & Bachman, 2019 ...............................35 Conclusion .........................................................................................................................36 CHAPTER THREE: MANUSCRIPT ONE ...............................................................................38 Abstract ..............................................................................................................................39 Introduction .......................................................................................................................39 Methods..............................................................................................................................41 Recruitment ...........................................................................................................42 Assessment .............................................................................................................42 Scoring and Statistical Analyses ...........................................................................44 Results................................................................................................................................46 Participant Descriptives ........................................................................................46 Total Sample Analyses ...........................................................................................46 Sex Differences .......................................................................................................50 viii Nutrition Resource Access Differences .................................................................51 Discussion ..........................................................................................................................52 Total Sample Analyses ...........................................................................................52 Sex Differences .......................................................................................................55 Nutrition Resource Access Differences .................................................................56 Strengths ................................................................................................................58 Limitations .............................................................................................................59 Future Directions ..................................................................................................60 Conclusions ............................................................................................................62 CHAPTER FOUR: MANUSCRIPT TWO ...............................................................................63 Abstract ..............................................................................................................................64 Introduction .......................................................................................................................65 Sport Nutrition in College Athletics .....................................................................66 Best Practices in Survey Development ..................................................................68 Purpose and Aims ..................................................................................................70 Methods..............................................................................................................................71 Approval and Consent to Participate ....................................................................71 Development and Validation Procedures...............................................................72 Phase I: SNAK Development .................................................................................73 Phase II: Pilot Test .................................................................................................74 Statistical Analyses ................................................................................................75 Results................................................................................................................................77 Phase I: SNAK Development .................................................................................77 Step 1. Define the Construct......................................................................77 Step 2. Determine Dimensions ..................................................................77 Step 3. Generate Item Pool.........................................................................79 Step 4. Choose Response and Scoring Formats .........................................80 Step 5. Assess Content Validity by Panel of Experts ................................81 Step 6. Finalize Initial SNAK Version ......................................................82 Phase II: Pilot Test .................................................................................................82 Step 7. Administer SNAK with ASA24 Simultaneously .........................82 Step 7a. Construct Validity .......................................................................85 Step 7b. External Validity..........................................................................86 Step 8a. Indicator Collinearity ...................................................................87 Step 8b. Item Difficulty .............................................................................87 Step 8c. Item Discrimination .....................................................................87 Step 9a. Test-Retest Reliability ..................................................................88 Step 9b. Face Validity ................................................................................89 Step 10. Finalize SNAK .............................................................................89 ix Discussion ..........................................................................................................................90 Pilot SNAK Screener .............................................................................................90 Reliability and Validity ..........................................................................................92 Subsequent Revisions to the Pilot SNAK Screener ...............................................95 Limitations .............................................................................................................96 Strengths ................................................................................................................97 Future Directions and Conclusions .......................................................................98 APPENDICES ................................................................................................................100 APPENDIX A: Original SNAK Screener ..........................................................101 APPENDIX B: Revised SNAK Screener ............................................................103 CHAPTER FIVE: MANUSCRIPT THREE .............................................................................105 Abstract ............................................................................................................................106 Introduction .....................................................................................................................107 Methods............................................................................................................................111 Approval and Consent to Participate ..................................................................111 Participants ..........................................................................................................111 Study Design .......................................................................................................112 Statistical Analyses ..............................................................................................114 Results..............................................................................................................................116 Discussion ........................................................................................................................121 Limitations ...........................................................................................................126 Strengths ..............................................................................................................127 Future Directions and Conclusions .....................................................................127 CHAPTER SIX: SUMMARY AND CONCLUSIONS ...........................................................130 Strengths and Limitations ...............................................................................................133 Future Research Directions .............................................................................................135 REFERENCES ............................................................................................................................138 x LIST OF TABLES Table 1. Chronological List of Studies Assessing Nutrition Knowledge of College Athletes .......24 Table 2. Nutrition Knowledge Surveys that Were Developed using Athletes .............................27 Table 3. Quality Rating Scores of Surveys Developed using College Athletes ............................29 Table 4. Score Cutoffs for Knowledge Categories .........................................................................45 Table 5. Participant Scores ...........................................................................................................47 Table 6. Athlete Characteristics ....................................................................................................83 Table 7. Average SNAK Scores by Dimension .............................................................................84 Table 8. Average SNAK Scores by Characteristic for the Total Sample ......................................85 Table 9. HEI Minimums, Maximums, and Averages for the Athlete Sample .............................86 Table 10. HEI Component Score Standards ...............................................................................113 Table 11. Grading Scheme of HEI Scores Based on Adherence to Dietary Guidelines for Americans .......................................................................................................................114 Table 12. Participant Characteristics .........................................................................................116 Table 13. Average Consumption Reported in ASA24 ................................................................117 Table 14. HEI Minimums, Maximums, Average and Grades ...................................................118 Table 15. Frequency of HEI Grades ............................................................................................119 Table 16. HEI Component Scores for the Total Sample .............................................................120 xi LIST OF FIGURES Figure 1. Knowledge Categories for the Total Survey ..................................................................49 Figure 2. Knowledge Categories for the General Nutrition Section .............................................49 Figure 3. Knowledge Categories for the Sport Nutrition Section ................................................50 Figure 4. Flow Chart of Development and Validation of the SNAK Screener .............................72 xii CHAPTER ONE: INTRODUCTION It has been well established that nutrition is important for health, wellness and maximizing exercise performance1,2. For college athletes, in particular, adequate and appropriate nutrition is vital for sustained vigor throughout the school year, prevention of or recovery from illness/injury, as well as being an ergogenic aid for sport performance. Unfortunately, current evidence indicates that college athletes have sub- optimal nutrition practices, which can result in low energy availability and other detrimental physiologic effects13. There are many potential explanations for these sub- optimal nutrition practices; among these is a lack of nutrition knowledge, specifically knowledge related to proper nutrition for sport performance. Many researchers have assessed the nutrition knowledge of different athlete populations, from recreational to elite athletes. Systematic reviews of such research show that average knowledge scores of college athletes ranged from 33% to 84% correct when scores were converted to percentages for comparison purposes4,14. The wide range of scores has led to disagreement in the field of sport nutrition as to whether or not college athletes have adequate nutrition knowledge. Some authors suggest that athletes have acceptable knowledge15-17 while others suggest the knowledge levels are inadequate18,19. Although it is likely that nutrition knowledge truly varies across 1 different athlete populations, the aforementioned wide range in scores and subsequent discourse in the literature may be caused, in part, by differences in tools used to assess nutrition knowledge. Many different survey tools have been used by researchers to assess the nutrition knowledge of athletes. Unfortunately, all have limitations that make them inappropriate for use in a college athletics setting. Some surveys used have not been validated20-23, some are sport-specific23,24 and thus not appropriate for athletes of other sports, some have been validated using a non-athlete (i.e., general) populations9,25, some were created outside of the United States (U.S.) and thus use alternate nutrition terminologies9,24-27, and even an unpublished survey has been commonly used by researchers to assess the nutrition knowledge of athletes28. More recently, a survey validated on a U.S.-based athlete population has been published29; however, it is too lengthy and would be difficult to administer quickly and reliably in the fast-paced environment of college athletics. To date, there has not been a nutrition knowledge assessment tool created for college athletes that focuses on sport nutrition, is appropriately validated in a college athlete population, and is brief enough to use in the college athletics setting. College athletes are a diverse group that exist in a unique environment. Within the National Collegiate Athletic Association (NCAA), there are almost 500,000 male and female students30, ages 17-24 years, who balance full-time academic workloads with 2 rigid training schedules. These student-athletes contribute directly to the generation of significant income for their respective institutions annually30,31. For example, in 2015, the median total revenue generated by a school in the Football Bowl Subdivision (FBS) of the NCAA was $63,659,000, while the highest was $192,609,00031. With such large sums of money involved, these student-athletes are often subject to an implicit expectation for athletic excellence by coaches, peers, and the institutions they represent. During their years of athletic eligibility, athletes may have an athletic scholarship that includes money for food, but many do not have access to a sports dietitian or qualified nutrition professional32. When a qualified nutrition professional is unavailable, athletes turn to coaches, athletic trainers, or strength and conditioning staff as sources of nutrition information, who have been shown to have inadequate nutrition knowledge20. The nutritional needs of athletes are constantly changing based on the current training period (i.e., in- versus off-season), year of eligibility (e.g., senior/junior/sophomore/freshman), and body composition goals, among other reasons. Subsequently, an athlete’s pressure to succeed, intense training schedules, potential lack of nutrition resources, evolving nutritional needs, and seemingly low nutrition knowledge make it unsurprising that college athletes are at high risk to make poor nutrition choices. Without an adequate nutrition knowledge base, it cannot be expected that student-athletes will practice appropriate nutritional habits to keep them healthy and performing optimally. 3 Previous authors have shown that individuals with higher nutrition knowledge are more likely to meet nutrition recommendations3,15,17,33; although there is no guarantee that knowledge leads to behavior, assessing the knowledge of a population is a substantial forward-step toward positive behavior change. The hope is that adequate sport nutrition knowledge would eventually translate into dietary behaviors that optimize the college athlete’s health, wellness, and sport performance. In order to appropriately and reproducibly assess this knowledge, there must first be a tool created that is evidence-based, properly validated, and functionally practical for a college athletics environment. Research Aims and Hypotheses Specific Aim 1: To assess the state of nutrition knowledge in an NCAA Division I athlete sample using a previously validated nutrition knowledge assessment tool that includes a sport nutrition focus. H 1.1: Nutrition knowledge in this sample will be low. H 1.2: Females will have higher nutrition knowledge than males. H 1.3: Athletes participating in non-flagship (i.e., non-revenue) sports will have higher knowledge than athletes participating in flagship sports (e.g., football). 4 Specific Aim 2: To develop and validate a sport nutrition knowledge questionnaire specifically for use in a collegiate athlete population, then use this questionnaire to assess the sport nutrition knowledge of college athletes at an NCAA Division I university. H 2.1: Sport nutrition knowledge in this population will be low. H 2.2: Females will have higher nutrition knowledge than males. H 2.3: Athletes participating in non-flagship (i.e., non-revenue) sports will have higher knowledge than athletes participating in flagship sports (e.g., football). H 2.4: Athletes of greater years of eligibility (e.g., seniors) will have higher knowledge than athletes of lesser years of eligibility (e.g., freshmen). Specific Aim 3: To assess the dietary habits and quality of college athletes using the United States Department of Health and Human Services (USDHHS) Automated Self- Administered 24-Hour Dietary Assessment Tool (ASA24). H 3.1: Athletes will exhibit dietary quality scores of 50 or less as determined by the Healthy Eating Index (HEI) developed by the United States Department of Agriculture (USDA). H 3.2: Female athletes will have a higher diet quality than male athletes. 5 CHAPTER TWO: REVIEW OF THE LITERATURE The purpose of this literature review is to provide a background on the importance of assessing sport nutrition knowledge in college athletes and justification for the significance of the project. In this review, the author will very briefly explore the world of college athletics and consider some unique characteristics of the population in question: college athletes. The author will define sport nutrition and discuss its role in college athletics. Additionally, the author will define sport nutrition knowledge and discuss its relationship with dietary behaviors. Finally, the author will evaluate currently available sport nutrition knowledge assessment tools found in the literature. This will include an analysis of the development and validation theories and techniques utilized in the creation of each individual tool. Additionally, the evaluation will include a discussion on the appropriateness of using any of the currently available tools in a college athletics setting. College Athletics & the College Athlete Founded in 1906, the National Collegiate Athletic Association (NCAA) includes 1,098 colleges and universities that compete in over 100 different athletic conferences34. There are approximately 500,000 male and female student athletes (i.e., students enrolled at a college/university who participate in a varsity sport for that 6 college/university) split between three divisions (Division I, II, III) within the NCAA30. The average number of sport teams and student athletes per school varies between Divisions. According to 2019-20 data from the NCAA, Division I, II, and III schools have on average 19.2, 15.9 and 18.8 teams per school, respectively35. Division I schools have approximately 179,200 student athletes spread between 351 colleges and universities, while Division II includes almost 122,000 student athletes across 308 institutions and Division III include almost 191,000 student athletes within 443 colleges/universities36. These numbers exemplify that collegiate athletic programs encompass a large number of student athletes. Male and female student athletes are diverse in terms of race, ethnicity, culture, religion, socioeconomic status, and familial backgrounds30. With few exceptions, they are 17 to 24 years of age, which classifies them as “emerging” or “post-adolescent young” adults who are still developing both mentally and physically37. Research shows that this age range encompasses a time period where individuals have a subjective feeling beyond that of adolescence but prior to adulthood, which includes a developing sense of accepting responsibility and making independent decisions for oneself37. With those evolving senses of responsibility and independence also come increases in certain risk behaviors, such as substance abuse37. Additionally, these student athletes have the pressure of joining a sports team with new teammates, in a new environment, and with new public exposure that they likely did not encounter in high school. These factors 7 play a direct role in how these emerging adults adjust to the life changes that accompany attending a university/college. In addition to the common pressures of being a college student, collegiate athletes face a unique set of pressures and expectations unlike those of any other elite athlete population. These students participate in a sport for the institution which they attend while maintaining a full-time academic schedule. In 2015, Division I student- athletes spent 34 and 38.5 hours per week devoted to athletics and academics, respectively, totaling over 70 hours per week of responsibility to their institutions alone38. These student athletes are expected to train almost daily to improve their athletic performance while maintaining at least a 2.3 (on a 4.0 point scale) Grade Point Average (GPA) to be eligible to compete in their sports (per NCAA rules) and maintain the scholarships that pay for their education and livelihood39. Additionally, college/university athletics are a multi-million dollar industry; some institutions have revenues up to $193,000,000 per year for athletics alone31. Given that these revenues are heavily influenced by the success of the university’s sport teams, there is a strong emphasis and expectation placed on the athletes to excel in their training while maintaining NCAA eligibility. 8 The Role of Sport Nutrition in College Athletics Given the expectation on student athletes to perform successfully in competition and the classroom, universities provide resources to help them. Examples include coaches [e.g., head/assistant coach(es), strength and conditioning coach(es)], athletic administrators (e.g., athletic director), support services (e.g., academic support) and medical personnel (e.g., team physicians, athletic trainers). One additional professional that student athletes may have access to is a Registered Dietitian (RD). The RD is the qualified nutrition professional responsible for a myriad of different tasks related to athletes’ food and drink intake. Unfortunately, the presence of an RD on an institution’s athletic staff is often dependent upon the size of the institution and the athletic revenue generated by the school. Although not every institution has an athletics-specific RD on staff, the field of collegiate sport nutrition has been growing exponentially over the past two decades. In the year 2005, only eight collegiate institutions employed a full-time sport dietitian, the first of whom was hired only in 199440. This number grew to 20 institutions by 2010 and to over 70 by the year 2017, with some programs hiring a second full-time RD40. The exponential growth in hiring nutrition professionals correlates directly with the timeline of the NCAA’s deregulation of college athlete feeding. Sport nutrition has been a topic of importance for college athletics since at least the 1986 version of the NCAA Sports Medicine Handbook was published (most recently updated in 201441). 9 Athletic departments at the time were restricted by the NCAA regarding how much food they were allowed to provide in order to avoid gross differences in provisions between schools with disparate budgets42. This restriction meant athletic departments could provide the athletes no more than one meal per day and only fruits, nuts, or bagels (without spreads) as snacks42. Nutrition professionals were critical of this policy, speaking out in multiple newspaper/magazine articles published between 2009 and 2014 that brought attention to the issue and sparked national outrage43-46. Finally in April of 2014, after years of criticism from professionals and student athletes, the NCAA voted to deregulate and allow for unlimited food provision42. This deregulation has resulted in a vast increase in both nutrition-related staffing and budgets. Many intercollegiate athlete food budgets currently exceed $500,000 annually47. The collegiate sport nutrition-related policy change timeline has coincided with an increased awareness and utilization of sport nutrition to optimize health and athletic performance of college athletes. However, in order to aptly discuss the relationship between sport nutrition and health or performance and clarify what is meant or not meant by use of the term, “sport nutrition” must first be defined48. Conceptually Defining Sport Nutrition Surprisingly, few explicit definitions of sport nutrition can be found in academic literature. In fact, many of the primary sport nutrition texts touted by professionals do 10 not offer an explicit definition of the construct2, just implications such as “All athletes should adopt specific nutritional strategies before, during and after training and competition to maximise their mental and physical performance”49 and “the performance of, and recovery from, sporting activities are enhanced by well-chosen nutrition strategies”1. This ambiguity and lack of an explicit definition allows for open interpretation of what sport nutrition does and does not refer to, which could lead to researchers and practitioners defining and understanding sport nutrition differently. This could then cause issues when the concept is attempting to be measured or taught; therefore, it is important to have interdisciplinary agreement for the betterment of the population in question. In general, sport nutrition has been interpreted as the use of nutrition to improve an athlete’s sport performance; however, this interpretation may be problematic when used in different populations for multiple reasons. First, a lay-person may not know what ‘nutrition’ is or may be unaware that it refers simply to food and drink intake. Additionally, this interpretation implies that only athletes can utilize sport nutrition and that sport nutrition is exclusively used for enhancing sport performance. The term ‘athlete’ can be used very broadly to describe anyone that exercises recreationally to those who play a sport professionally, thus adding ambiguity to the interpretation. Also, the interpretation implies that sport nutrition only relates to sports, whereas many 11 of the lessons to be learned from understanding sport nutrition topics could be applied to promote general health and wellness. Despite a lack of consensus that leads to different interpretations, one notable definition of sport nutrition written by Fink, Burgoon, and Mikesky50 addresses some of the aforementioned issues. They define sport nutrition as “the application of nutrition knowledge to a practical daily eating plan focused on providing the fuel for activity, facilitating the repair and rebuilding process following hard physical work, and optimizing athletic performance in competitive events, while also promoting overall health and wellness.”50 Although the authors also uses the term ‘nutrition knowledge’ without specifically describing it, this definition has many valuable elements. First, use of the word “practical” allows individual tailoring of the definition to the readers for fitting the practicality of their daily lives. Secondly, “daily eating plan” gives implication of actual dietary intake in a habitual pattern. The definition then uses the word “fuel” to remind the reader that sport nutrition is about more than just food but using that food as fuel for the “activity” and “repair and rebuilding” that follows activity. The authors also remind the reader that sport nutrition isn’t specific to sports, but inclusive of nutrition strategies for “hard physical work” and “overall health and wellness”. Overall, this definition appears to be an appropriate, specific, and simplistic definition for the term sport nutrition. This definition will help guide the development 12 and identification of items incorporated in a tool designed to measure sport nutrition knowledge. The Importance of Sport Nutrition for the College Athlete The health, wellness, and athletic performance of college athletes has been shown to be improved with the application and habitual use of sport nutrition strategies1,2,49. Unfortunately, previous researchers have shown that the nutrition intakes of college and other elite athletes are typically suboptimal, particularly with respect to fruits and vegetables, and overall caloric intake3-8. Such inadequate dietary habits could lead to an athlete experiencing low energy availability, which is coincidental with low energy consumption (i.e., calories) to support normal physiologic function13. Low energy availability negatively affects cognition, cardiovascular health, bone health, gastrointestinal function, reproductive health, among other physiologic functions13. Additionally, low energy availability decreases athletic performance and puts athletes at great risk for injury13, which directly relates to their time spent training and competing in their sport. Another reason sport nutrition is important for college athletes is because their nutritional needs are constantly evolving throughout their time in school32. For example, as freshmen they are transitioning into college life, which includes experiences of acclimating into a new physical environment, learning to navigate a dining hall, and 13 embracing time management strategies. As sophomores or juniors, they may transition to off-campus housing, which comes with the new responsibilities of meal planning and grocery shopping. By senior year they are faced with the impending end of their collegiate athletics careers and inevitable transition by a select few to becoming professional athletes, or more likely, joining the rest of the young adult world. In most cases, nutritional focus may shift towards eating for longevity rather than athletic performance. In addition to these between-year changes, student athletes are faced with the within-year transitions from pre-season to off-season, which is accompanied by its own set of changes in nutritional needs, such as changes in the quantity of nutrients and calories needed. Student athletes are often tasked with navigating these changes on their own, which can create confusion and likely contribute to suboptimal dietary habits. The combination of evolving nutritional needs, the pressure to succeed, and intense training and academic schedules puts student-athletes at high risk to make poor nutrition choices. This probability is amplified if they are not guided by a qualified nutrition professional, or at the very least taught how to navigate these changes on their own. This risk of poor nutrition habits combined with the associated consequences of low energy availability make it imperative to find ways to optimize the dietary intake of college athletes. 14 Sport Nutrition Knowledge & its Relationship with Dietary Behaviors In order to focus on the dietary intakes of college athletes, we must first understand potential reasons behind the suboptimal food practices that previous researchers have described3-8. Although there are multiple explanations for these inadequate nutrition habits, one plausible explanation is the athlete’s sport nutrition knowledge. This is vital for college athletes because without an adequate nutrition knowledge base, it cannot be expected that they will maintain food habits that maximize health and performance. Before athletes’ “sport nutrition knowledge” can be researched optimally, the term must be defined. This is a critical step in construct measurement, as an operational definition must exist in order to observationally measure it51. Conceptually Defining Sport Nutrition Knowledge The construct “sport nutrition knowledge” is particularly difficult to define due to the many different dimensions (i.e., topics) that exist within sport nutrition (e.g., carbohydrates, hydration, nutrient timing, etc.). With so many different dimensions, it is difficult to narrow down a working definition that would allow someone to objectively confirm an athlete has adequate sport nutrition knowledge. This may be one explanation for why very few definitions for sport nutrition knowledge can be found in the literature. 15 The only explicit definition found was proposed by Trakman and colleagues (2017) who defined sport nutrition knowledge as the “knowledge of concepts and processes related to nutrition for optimal athletic performance including knowledge of weight management; hydration and fueling strategies for before, during and after training/performance; supplementation and alcohol use.”26 This definition poses the same issue as previously discussed for the term sport nutrition; it implies that sport nutrition is exclusively used for athletic performance. Additionally, this definition includes having knowledge of specific dimensions, thus implying that a subject who does not have knowledge in those dimensions does not have adequate sport nutrition knowledge. This implication is controversial. A recent systematic review about the nutrition knowledge of athletes identified 11 separate dimensions that have been included previously in different nutrition knowledge questionnaires14, and an argument could even be made that this list is not inclusive of all possible dimensions that could represent the construct. Given these points, it would be inappropriate to include mention of any specific dimensions in the working definition for sport nutrition knowledge because there are too many possible options. Without a clearly articulated definition in the literature, a new, and hopefully concise one must be developed. This definition should avoid using any of the concept words (e.g., “sport”, “nutrition”, or “knowledge”) within the definition itself, as it is inappropriate to use a word of the construct within its definition, i.e., a circular 16 definition52. For example, the definition of “sport nutrition knowledge” cannot simply be “knowledge about nutrition for sport” because that does not provide the reader with any explanation, nor does it provide the researcher with any guidance for how the construct could be measured. Circular definitions provide no clarification as to what the concept actually means or does not mean; it just rewords the concept. The definition should also avoid including potential predictor variables or themes (i.e., dimensions) within the wording of the definition (e.g., carbohydrates, nutrient timing). The construct of sport nutrition is too broad to include words that narrow its breadth. Considering the population in question (college athletes), the definition should also be simple and require little additional explanation from an expert in case one is not available to give explanation. Additionally, the definition should reflect that sport nutrition is meant to be used habitually, strategically and should be all-inclusive of differing motivations, i.e., not only for enhancing sport performance. Considering these points, I propose that sport nutrition knowledge be defined as the understanding of information related to purposeful daily food, drink, and supplement intake strategies to enhance health and performance during, and recovery from, exercise or physical competition. The words “food” and “drink” were used because they are better descriptors of what is involved rather than just using “nutrition”. Additionally, “supplement” was added to the definition because it is an important aspect of sport nutrition knowledge, however it cannot be classified as either a food or a 17 drink; therefore, it needed to be separated. The use of the words “daily”, “purposeful” and “strategies” were used to reflect the habitual and decisive elements of sport nutrition. Lastly, the second half of the definition encompasses both health and performance, reminding the reader that sport nutrition is not exclusive to athletes alone. Sport Nutrition Knowledge and Dietary Behaviors in College Athletes There are many factors that influence what college athletes eat, including but not limited to food environment or availability, taste, convenience, economic factors, and nutrition knowledge53. Some athletes may be able to eat healthfully because their team has the monetary resources to consistently provide healthful food/drink options; however, most teams at a given university are not revenue sports (i.e., a sports program that reports a positive net generated revenue after the university-paid expenses have been subtracted54) and likely do not have the means to provide much nutrition support for their athletes. In this case, the athletes must use their own money and make dietary decisions for themselves which will be influenced by their sport nutrition knowledge. It could be argued that nutrition knowledge is the most malleable of the aforementioned factors that affect dietary intake. An institution may not be able to provide significant monetary resources to a given sports or individual athlete, but it might be able to provide or encourage nutrition education opportunities. A review of nutrition education interventions in athletes by Tam and colleagues showed that most 18 nutrition education interventions result in significant knowledge improvement55, and other authors have shown that individuals with higher levels of nutrition knowledge are more likely to meet nutrition recommendations3,15,17,33. Unfortunately, the authors of the intervention studies used many different knowledge assessment tools and methodologies, which leaves room for questions regarding the true state of sport nutrition knowledge in this population. Although it can be theorized that improving the nutrition knowledge of college athletes could result in improved dietary behaviors, before we can begin any education interventions, we must first gain an understanding of the current nutrition knowledge level in college athletes. The Present State of Sport Nutrition Knowledge in College Athletes Researchers have been assessing the sport nutrition knowledge of college athletes for decades, with studies dating back to the 1980’s. Since that time there has been a steady increase in the number of studies published on this topic, thus exemplifying it is a growing area of interest. Unfortunately, although the frequency in publications about the nutrition knowledge of athletes is increasing, disagreement between authors has not lessened. There is much discourse on whether or not athletes have a sufficient level of nutrition knowledge. Two systematic reviews of studies on this subject have a shown that knowledge scores found in college athletes vary greatly4,14. Some authors have shown that college athletes have average nutrition knowledge scores as low as 33% 19 correct, whereas other authors have shown average scores as high as 84%4,14. Although it is likely that nutrition knowledge varies somewhat in college athletes across and within different college campuses, this large range in scores has contributed to the disagreement by researchers on whether or not college athletes truly have a satisfactory level of nutrition knowledge. One probable reason for this wide variation in scores is the differing measures utilized. Authors have used many different tools to assess the nutrition knowledge of college athletes, many of which the authors created for that study alone. Each survey differs in the nutrition topics assessed, as well as the styles in which the questions asked. Additionally, many surveys utilized have not been properly validated for use in a college athlete population, nor were they created with the college athletic environment in mind. For example, many surveys are lengthy (e.g., > 50 questions) and would require too much time to administer to large groups of athletes, as well as too much time to be analyzed by the practitioner later. At this time, there is not a singular, well-accepted, widely-used sport nutrition knowledge survey tool that can be found in the literature. This is problematic when comparing across studies because results cannot be appropriately evaluated when the methodologies differ so greatly. With authors using different measures, the true state of nutrition knowledge in college athletes is unknown. In order to truly grasp an understanding of the sport nutrition knowledge of college athletes, there needs to be a 20 survey tool developed and validated specifically for college athletes. Before creating a new survey, it is important to first analyze the available sport nutrition knowledge assessment tools to determine both their strengths and shortcomings. Assessing Sport Nutrition Knowledge Characteristics of an Ideal Measure for use with College Athletes There are several factors to consider when creating a measurement tool, including the traits of the population being measured and the controllable factors that can be improved to optimize response rates and practicality of that tool for future use. A measure designed to assess the sport nutrition knowledge of college athletes needs to be written in the language of college athletes, it needs to have characteristics that promote such athletes to respond in completion, and it needs to be written in a way that practitioners believe in its usefulness and realistic ease of use. First, the measure should be written simply, concisely, and in terminology that college athletes would understand. With a population that is made up of racially, ethnically, and socially diverse students, it is important to use straightforward, easy language that a student of any educational background or class level would understand. Second, the measure should be brief, both for length of individual items and the entire measure. Questions should be closed- or short-ended when possible to reduce 21 answer entry time and improve item response rates56. College students, in general, have short attention spans57; therefore, a measure with too many questions will likely lead to them losing interest in the beginning and either skipping or guessing on questions towards the end. Shorter measures (less than 1000 words58) have better response rates and less skipping questions compared to longer measures56. Additionally, data from the National Center for Education Statistics suggests that one in five U.S. adults have low literacy skills59; therefore, it is important that any testing or education materials are as short as possible and written clearly and simply to promote comprehension and measure completion. Third, abstaining from using specific foods as examples could help promote global usage of the measure. This would eliminate the potential issue of translating the tool to reflect the food culture of the region in which it is being administered. Additionally, the tool should not be tailored towards one specific sport; rather, it should incorporate broad concepts that an athlete of any sport could understand. For example, tailoring the tool towards gymnasts would make it inappropriate to use with football players due to the differing nutritional needs between the two sports. Lastly, it is vital that the practitioner administering the tool believe in the practicality and usefulness of the measure. If it is to be used in the fast-paced environment of college athletics, this tool must be able to be administered easily, quickly, and require little explanation to the participant so that the practitioner can 22 administer it to individuals or groups in a timely fashion. Additionally, the tool must be able to be analyzed quickly so that the practitioner feels it could be appropriate to use in either a one-on-one consult for immediate analysis or with a large group (e.g., a football team) for bulk analysis later. This tool should be used as a starting point for a conversation or nutrition education lesson between the practitioner and the athlete; therefore, the less time that must be taken to analyze it, the more time that can be spent on discussions about nutrition. Previously Published Measures Researchers have attempted to characterize the nutrition knowledge of college athletes since at least the 1980’s. Unfortunately, most authors have used unvalidated measurement tools as their survey instrument. Four recently published systematic reviews were used to summarize publications regarding the nutrition knowledge of college athletes (Table 1)3,4,14,55. Rather than using a previously validated instrument, some authors developed their surveys from scratch20,21,60-68, others modified a survey used by previous authors17,18,22,69-75, and others still used an unaltered version of a previously published survey76-83, regardless of whether or not that survey went through proper development techniques or validation analyses for the population of interest. Some authors have even utilized an “unpublished manuscript”28 as their knowledge measurement tool18,79. The most common logic that authors presented as to why they 23 selected a specific scale was because that scale had been used in previous literature. Most authors did not explain their scale selection decisions, nor discuss the validity of that scale for their population. Table 1. Chronological List of Studies Assessing Nutrition Knowledge of College Athletes Authors, Year Country Sport N, Sex USA USA USA Mixed Mixed 75 M 70 F Mixed 27 F Study Type CS CS CS Tool Used AM AD AD Average Score 52.3% + 11.2% 34% 77.9% Shoaf et al. 198669 Barr 198760 Frederick & Hawkins 199261 Guinard et al. 199562 USA Swim 20 M CS AD 47.3% Wiita et al. 199570 USA Distance runners 60 F CS AM 57.2% Collison et al. 199671 Abood et al. 200063 Kunkel et al. 200176 Rosenbloom et al. 200221 Zawila et al. 200322 Abood et al. 200464 USA Mixed 60 F INT AM 68.4 + 9.4% USA Mixed 70 F INT AD 68.5 + 42.1% USA NST 32 F INT E 66.7% + 8.3% USA Mixed USA Cross- country 91 F, 237 M F: 51.8% + 17.3% CS AD M: 53.6% + 60 F CS AM 16.4% 57.2% USA Mixed 30 F INT AD 68.5% + 1% Dunn et al. 200877 USA Mixed 92 F, 98 M 51.5% + 13.6% *Females scored CS E higher than males; scores not given. 24 Table 1 (cont’d) Rash et al. 200872 USA Track & Field 52 F, 61 M CS AM F: 57.8% + 1.8% M: 58.7% + 1.6% Hoogenboom et al. 200973 USA Swim 85 F CS AM 72% Jessri et al. 201074 Iran Mixed Azizi et al. 201065 Iran Mixed 98 F, 109 M 297 F, 298 M CS AM CS AD *F: 38.7% + 14.2% M: 28.2% + 12.7% *F: 60.4% M: 57.4% Hornstrom et al. 201166 USA Softball 185 F Davar 201275 India Hockey Iran NST CS CS CS AD 57.1% AM E 38.8% F: 56.5% M: 54.8% 30 F 58 F, 63 M USA Mixed 185 T CS AD 54.9% + 13.5% USA Volleyball 11 F INT E 44.9 + 10.7% USA Mixed USA Mixed Nigeria Mixed USA Mixed 4 F, 3 M 88 F, 86 M 47 F, 63 M 47 F, 76 M INT NST 18.4 + 12.0% F: 55.5% + 13.4% CS AD M: 57.3% + CS AM 13.4% 64.3% + 18.1% Sex differences not reported F: 56.5% + 13.2% CS E M: 47.1% + 14.9% USA Mixed 153 F INT AD 63.1% + 12.6% USA Football 88 M CS E 55.2% + 16.3% Arazi & Hosseini 201278 Torres-McGehee et al. 201220 Valliant et al. 201279 Martinelli et al. 201384 Weeden et al. 201467 Folasire et al. 201517 Andrews et al. 201680 Buffington et al. 201668 Abbey et al. 201781 Rossi et al. 201718 USA Baseball 18 M INT AM 56.7 + 11.4% Holden et al. 201882 USA Mixed 49 F, 31 M 48 + 8% CS E Sex differences not reported 25 Table 1 (cont’d) Werner et al. 202083 USA Mixed 70 F, 55 M *F: 64.5% + 16% CS E M: 44.8% + 14.3% *scored significantly higher than other sex Mixed = more than one sport type ; NST = not stated ; F = female, M = male, T = total ; CS = cross-sectional, INT = education intervention (only pre-intervention scores listed) ; AD = author developed, AM = author modified, E = Existing questionnaire  Byrd-Bredbenner (1984) ;  Werblow (1978) ;  Barr (1986) ;  Parmenter & Wardle (1999) ;  Zawila (2003) ;  Jonnalagadda (2001) ;  Bailey (2004) ;  Zinn (2005) ;  Reilly & Maughan (2000) ;  Supriya (2013) ;  Torres-McGehee (2012) ;  Calella (2017) Unsurprisingly, inconsistencies in study methods have led to a wide variation in knowledge scores. Regardless of the study participant’s sex or sport type, authors have published average nutrition knowledge scores as low as 18.5%84, while others have presented scores as high as 77.9%61. Of the nine studies in which sex differences were assessed, only four reported significant differences between males and females, with females scoring higher in each65,74,77,83. These wide ranges and disagreement among the literature leaves doubt as to what the true average nutrition knowledge is in college athletes. Although the variation in scores cannot be solely attributed to the differing methodologies, a lack of a consistent survey makes it difficult to compare across athlete populations. At this time, there have only been seven sport nutrition knowledge questionnaires developed and validated using athletes of any age (rather than general population)19,23,24,26,27,29,85. A summary of these can be found in Table 2. There are an 26 additional six nutrition knowledge questionnaires found in the literature that have been used by previous authors to assess the nutrition knowledge of college athletes specifically9,25,86-89; however, these six were not developed using athletes as the sample population. Statistical results during survey development can be influenced by factors specific to the sample population used; therefore, it is important that the sample used during tool development matches the population in which the tool is subsequently utilized90. Table 2. Nutrition Knowledge Surveys that Were Developed Using Athletes Authors, Year Country Level, Sport Shifflett et al., 200219 USA Collegiate, Mixed Furber et al., 201724 UK Elite, Track & Field Trakman et al., 201726 Trakman et al., 201827 (revision of 2017 survey) Australia Collegiate, Mixed No. Of Items, Format 37, Multiple Choice Nutrition Duration Topics Covered to Complete Validation Theories General + Sport NST NST 62, Mixed Formats General + Sport 15:20  2:45 min Kline (2007) 89, Mixed Formats 37, Mixed Formats General + Sport Approx. 25 min Approx. 12 min Classical Test Theory (CTT) & Rasch Analysis Heikkila et al., 201885 Finland Youth, Cross- Country Skiing 79, True/False Sport NST NST 27 Collegiate & Recreational, Mixed 49, True/False General + Sport NST NST Elite, Ultra- endurance 76, Mixed Formats General + Sport NST NST Table 2 (cont’d) Karpinski et al., 201929 Blennerhasset et al., 201923 *NST = not stated USA UK Only four of the aforementioned seven tools were developed using college athletes as the population of interest19,26,27,29. Now that these surveys have been identified, the next step is to critically evaluate their development and validation theories/techniques, as well as their appropriateness for use in the setting of college athletics. These topics will be discussed in the subsequent section in chronological order of publication. For evaluation of their development/validation, a quality rating was assigned using criteria from two widely-regarded manuscripts about key considerations for nutrition knowledge measure design and validation87,91. For each category (e.g., did the authors define the construct, conduct a pilot test, etc.), the article was assigned a “1” or a “0” to reflect that the article did or did not, respectively, meet the criterion of that category. Each article was given a total quality rating score based on the summation of “1” ratings received. The results of this quality rating can be found in Table 3. 28 Table 3. Quality Rating Scores of Surveys Developed using College Athletes Authors, Year Define the Construct Description of Item Generation Description of Scoring System & Response Format Pilot Test Item Difficulty Item Discrimination Homogeneity (Internal Reliability) Face Validity Test-Retest Reliability Content Validity Construct Validity Concurrent Validity Exploratory Factor Analysis (EFA) Total Shifflett et al., 200219 Trakman et al., 201726, 201827 Karpinski et al., 201929 0 0 0 1 1 0 0 0 0 1 1 0 0 4 1 1 1 1 1 1 1 1 1 1 1 0 0 11 0 1 1 1 1 0 1 1 1 1 1 0 0 9 0 = did not describe ; 1 = described in publication Assessing Sport Nutrition Knowledge: Evaluation of Validated Tools Measure Developed by Shifflett, Timm, & Kahanov, 200219 The earliest of the four tools validated using college athletes was published by Shifflett, Timm, and Kahanov almost twenty years ago19. As shown in Table 3, this 29 survey received four of 13 possible points for a quality rating. The authors did not define the construct they were attempting to measure, which is a critical first step of measuring any construct in order to distinguish between what is and is not being assessed87,91. Additionally, the authors did not describe how they generated their items, nor did they provide a justification for their response format or scoring system. As validity is partially determined by the choice of items that are used to assess a person’s knowledge about a topic, it is important for authors to explain why they chose the questions87,91. The authors noted the specific areas (i.e., dimensions) within nutrition that their questions were based upon; however, they did not explain why they chose those specific dimensions. Prior to pilot testing, the authors of the scale outsourced for two expert reviews (one nutrition professor, one exercise physiology professor) of the first version of the survey to serve as an initial evaluation of content validity. Although no registered dietitians working with college athletes were included as experts, the authors obtained useful qualitative feedback that was used to make revisions to the survey. The scale authors also did not conduct face validity analyses with a subset of the target population, which is an important step to determine how applicable and understandable the items are to the respondents87,91. The authors conducted a pilot test from which they were able to conduct item difficulty analyses and adjust the survey accordingly. The final version of the survey 30 contained 20 questions and required only wording and grammatical changes from previous versions. Although the authors did not report the average time required to complete their survey, it can be assumed that only 20 questions of a singular format could be completed in a reasonable amount of time for administration in a college athletics setting. However, this survey is not ideal for use in today’s college athletics because it is outdated and did not endure the most optimal development and validation methodologies. Measure Developed by Trakman et al., 201726and 201827 From a development and validation viewpoint, the Trakman et al.91 tool included almost every recommended technique, scoring an 11 out of 13 for quality rating (Table 3). The only aspects of validation quality that they did not address were conducting an exploratory factor analysis (EFA) nor addressing concurrent validity87,91. Exploratory factor analyses allow a mathematical assessment of the number of variables/factors within a survey, meaning it is a quantitative way of grouping the items together into dimensions based on how much the items correlate with48. This process is important because it can help reduce the amount of items related to each dimension, thus improving survey interpretation48. Additionally, it is important to identify the best sample of items to represent the construct. The authors also did not assess concurrent validity, which can be done by having participants complete the proposed survey along 31 with a previously validated survey that is supposed to measure the same construct, and then comparing the results. Although this technique can provide further support for the validation of a tool, it is not viewed among the most important validation techniques87, thus is often overlooked. Although the authors completed a rigorous validation process in the development of this survey, they chose to retain many items that their analyses suggested being removed, claiming that the items addressed important topics that they did not want removed. This resulted in an 89-item survey that the authors reported taking “around 25 minutes” to complete while giving no specific results regarding average duration. Approximately one year after publishing the initial tool, the authors published an abridged version that they had created using the same data as the original version, but adhering to the more strict statistical analyses27. The abridged version contained 37 of the previous 89 items (no item wordings were changed), and the authors report a median duration time of 12 minutes. However, no average time to completion was reported. There are multiple concerns to address regarding the practicality of using either Trakman et al. tool in a college athletics setting. First, only the face validity analysis included college athletes; the later validation analyses were conducted using a sample of community and recreational athletes whose ages ranged from 17 to over 55 years. Second, although the 37-item abridged version is more practical than the 89-item 32 version, the authors do not report an average time to completion, nor do they give a reason for not reporting the mean. This leaves the reader to suspect that the median 12 minutes is misleading with regards to a true average duration. Considering that a prominent factor when surveying college athletes is number of questions and subsequent time to completion, this must be considered before using the Trakman et al. abridged version. The survey utilizes multiple response formats, including “High/Low/Not Sure” questions without a criterion of what is considered “high” or “low”. Standards of psychometric testing recommend a rationale for response format decisions be provided90; however, the authors do not offer an explanation. This response format could lead to confusion from the participant, thus calling the validity of the test scores into question90. Additionally, there are concerns about the survey items themselves. Both surveys were created using Australian athletes and contained vernacular not typically used in the United States (e.g., “mincemeat”). Additionally, some items list a “protein shake” as part of questions or possible answers to questions, which is too vague and could be confusing to a reader, as a protein shake could contain any number of different ingredients. Lastly, the authors utilize specific measurement volumes (e.g., gram per kilogram of body weight, milliliters, etc.) in many of their questions. Although the nutrient recommendations set forth by the governing bodies of sport nutrition utilize 33 such measurements1,2, it is impractical to use them to assess the knowledge of college athletes because not all athletes may be familiar with those units. For example, using body weight measurements in kilograms is impractical because the United States relies on the English system (i.e., pounds). Additionally, given the newness of collegiate sport nutrition in the United States and the disengagement often seen in college athletes, it is unlikely the athletes would be able to correctly answer specific, in-depth questions that utilize exact measurements. In summary, the original, 89-item Trakman et al. tool is unrealistic for use in a college athletics setting due to its length. The 37-item abridged tool is more realistic; however, some items would likely require explanation by whoever is administering the survey, which could cause potential interviewer bias if the administrator were to phrase things differently between subjects or unintentionally prompt a participant to answer in a specific way. Additionally, the true average time to completion for either the original or abridged version is unknown. Not only do lengthy surveys put the athlete at risk of survey burden, but it also affects the time needed to evaluate a participant’s survey answers. Any person seeking to use either tool should consider these points prior to administration. 34 Measure Developed by Karpinski, Dolins & Bachman, 201929 The most recent tool developed using college athletes, by Karpinski, Dolins and Bachman29, is the only one created with the most recent sport nutrition guidelines set forth by the ACSM, AND, and DoC1, which is a positive attribute for potential use in a United States college athletics setting. Although the survey authors did not cite any psychological theories used in its development, they did achieve nine of the possible 13 quality ratings for nutrition survey development and validation87,91. Missing items included a definition of the construct being measured, item discrimination analyses, concurrent validity and an exploratory factor analysis. The importance of each of those techniques was described previously. This survey has multiple positive traits that support its use in college athletics. First, input from collegiate sport dietitians was included at multiple development stages. Second, it has a simple True/False/Don’t Know response format and includes minimal specific units of measurement in its questions, thus maximizing the ease of completion. Third, the survey includes only sport nutrition items that were developed using both previous literature and current recommendations. Despite the positives associated with this survey’s development process, there are limitations in using it in a college athletics setting. The survey instrument consists of 49 items, which is longer than the abridged Trakman et al. tool27. No average time to completion is given, thus leading the reader to assume both participant completion and 35 administrator analyses may be time-consuming processes. Additionally, the authors relied primarily on adult athletes during survey development; college athletes were only included in testing the final version. Therefore, new face validity analyses should be conducted in a sample of college athletes to give insight regarding this survey’s practicality for use in a college athlete population. Conclusion Although there are current nutrition knowledge assessment tools available that have been developed using college athletes, none of them are ideal for quick, efficient, reliable use in the fast-paced, hectic setting of college athletics. A major limitation of previous research has been the absence of surveys that were created using only college athletes in all development and validation processes. Additionally, there has been a lack of consideration for applied, in-the-field use of these sport nutrition knowledge surveys within the context of college athletics. Without a trustworthy methodology that can produce credible results, resources (i.e., time, money) may be unnecessarily wasted on nutrition education efforts; or in contrast, athletes in need of education interventions may go unnoticed. A new, brief sport nutrition knowledge survey must be developed that is based in sound psychometric theories, utilizes best practices, and employs comprehensive validation strategies. Such a survey could be invaluable to collegiate sport dietitians as a 36 starting point for conversations with athletes about their fueling strategies and other dietary practices, thus putting the student athlete in a better position to optimize health and performance. 37 CHAPTER THREE: MANUSCRIPT ONE Chapter Three addresses Specific Aim 1 and the manuscript titled Assessment of nutrition knowledge in division I college athletes. This manuscript was published on April 2, 2020. Werner, E. N., Guadagni, A.J., & Pivarnik, J.M. (2020). Assessment of nutrition knowledge in division I college athletes. Journal of American College Health. https://doi.org/10.1080/07448481.2020.1740234. Specific Aim 1: To assess the state of nutrition knowledge in an NCAA Division I athlete sample using a previously validated nutrition knowledge assessment tool that included a sport nutrition focus. H 1.1: Nutrition knowledge in this sample will be low. H 1.2: Females will have higher nutrition knowledge than males. H 1.3: Athletes participating in non-flagship (i.e., non-revenue) sports will have higher knowledge than athletes participating in flagship sports (e.g., football). 38 Abstract OBJECTIVE: Assess nutrition knowledge of Division I college athletes. PARTICIPANTS: 128 student-athletes (n=70 female) from eight sports completed the survey in June 2018. METHODS: The survey by Calella et al (2017) was used to assess both general and sport nutrition knowledge. RESULTS: Cases with more than 20% of responses missing were excluded (n=3). Overall average score was 57.6%±18.6%. Females scored significantly (p<0.001) better than the males (66.5%±16.4% versus 46.2%±14.7%). Participants were divided into revenue (football, ice hockey, men’s basketball, women’s basketball; n= 63) and non-revenue sports (field hockey, golf, rowing, soccer; n=62) to address differences in knowledge between sports with greater versus lesser nutrition resource access. Revenue sports scored significantly (p<0.001) worse than non-revenue sports (45.7%±15.2% versus 69.7%±13.1%). CONCLUSIONS: Athletes appear to have low nutrition knowledge, putting them at risk for inappropriate dietary choices that could decrease ability to optimally perform and increase risk of injury. Introduction There is a wealth of scientific evidence supporting the notion that proper nutrition practices are vital for an athlete’s health and wellness.2 In a joint position statement by the Academy of Nutrition and Dietetics (AND), Dietitians of Canada (DC), 39 and the American College of Sports Medicine (ACSM), the authors stated that “performance of, and recovery from, sporting activities are enhanced by well-chosen nutrition strategies.”1 Although it is clear that athletes would benefit from following optimal nutrition practices, evidence appears to indicate that athletes typically do not follow such optimal practices, often resulting in low energy availability, among other issues.13 One potential barrier to athletes following optimal nutrition practices is a lack of nutrition knowledge, particularly knowledge about nutrition for sport. There have been many studies published on the nutrition knowledge status of athletes. Systematic reviews on the subject show that knowledge test scores range anywhere from 33% to 84% correct4,14, with some authors suggesting the nutrition knowledge of athletes is inadequate18,19, while others suggest the nutrition knowledge of athletes is acceptable.15-17 One reason for discourse in the literature can be attributed to the absence of an agreed upon knowledge assessment tool that has been validated for an athlete population, particularly college athletes. The practice of using non-validated, often self-created tools has saturated the literature with mixed results, thus making it difficult to ascertain the true status of athletes’ nutrition knowledge. Without consensus on this issue, resources could be wasted on efforts to educate athletes who already have adequate nutrition knowledge. In contrast, much of the athletic population could be at risk for chronic, inappropriate nutrition choices in part due to a lack of knowledge on the subject. Although there are other potential variables that might determine why an 40 athlete may exhibit poor dietary choices (e.g., environmental influences), a feasible first step is to examine the current state of nutrition knowledge in an athlete population. Without a knowledge base, it is unlikely that athletes would make healthful choices on their own. It is especially important to understand this knowledge base in collegiate athletes because this population is at high nutritional risk due to the simultaneously demanding athletic and academic schedules, as well as their predisposition to fatigue, rigid training, and frequent poor nutrition choices.32 Previous literature has shown that individuals with high nutrition knowledge are more likely to meet nutrition recommendations;3,15,17,33 therefore, in order to promote optimal nutrition practices in college, it must first be evident that athletes have the appropriate knowledge base. The purpose of this study was to assess the state of nutrition knowledge in an NCAA Division I athlete sample using a previously validated nutrition knowledge assessment tool that included a sport nutrition focus. Methods This study was approved by the university’s Institutional Review Board and all athletes signed a consent form prior to participating. The researchers answered any questions participants asked about the study. The original purpose of this project was to conduct a nutrition education intervention. Baseline knowledge assessments were completed at the end of the 2018 41 Spring semester and voluntary attendance for the education intervention held on campus (and via online resources) was encouraged throughout Summer 2018; however, none of the collegiate athletes opted into the education sessions, therefore follow-up testing was unnecessary as the intervention portion of the project did not occur. Recruitment Participants were recruited from a large NCAA Division I university in the Midwest. In an attempt to include as many participants as possible, any athlete within the university’s Department of Intercollegiate Athletics whom the researchers could contact was invited to participate. Letters encouraging participation were sent to coaches, and encouragement to participate was additionally spread via word of mouth, as well as through the researchers having direct contact with various athletic teams. Recruitment took place in April and May 2018 while all athletes were still on campus prior to the end of the academic school year. This assessment was not a part of the normal training evaluation for the athletes, thus was completely voluntarily on the part of the athlete. Assessment The nutrition knowledge assessment tool chosen for this study was developed by Calella et al.9 At the time of data collection, this was one of few validated tools for the 42 assessment of sport nutrition knowledge, none of which were validated in collegiate athletes in the United States. This survey was validated in Italian adolescents and young adults (age range 14-19 years). Although the Calella et al.9 tool was not validated in an athlete population, it was chosen because it contained both general and sport nutrition sections, was formatted in a way that was easily understood and took minimal explanation to a participant, and it included fewer questions than other available surveys. Overall, we believed it to be the most appropriate choice for the purpose of this study at the time of data collection. The tool consisted of a total 62 questions, 29 of which were general nutrition- focused, the remaining 33 questions being sport nutrition focused. Described in their methodology, Calella et al.9report that the aim of the general nutrition section was to assess knowledge about macro- and micronutrient content of specific foods, “practical food choices”, and the participant’s “awareness of diet-health associations”. The foci of the sport nutrition section were fluid replacement, supplement intake, and “food and timing choices in recovery meals”. The survey was administered via paper copy with all questions being check-box format (i.e., no written answers or open-ended questions). The general nutrition knowledge section consisted of questions that asked the participants to rate the aforementioned aims as “High”, “Low”, or “I do not know”. For example, a question asked, “The Carbohydrate content of such foods is:”, and a list of foods (Boiled ham, White bread, Tomato, Apple, Ricotta, Breakfast cereals) were given 43 for which the participant could respond with “High”, “Low”, or “I do not know”. The sport nutrition section gave statements on the aforementioned sports nutrition-related topics and asked the participants to judge the statement as “True”, “False”, or “I do not know”. The survey was administered at different settings convenient to the participants (e.g., at team meetings, training table meals, or post-practice). This was done because it was the most efficient way to access entire teams at a single time. Additionally, individual athletes were encouraged to complete the survey during one-on-one consults with their team Dietitian. The survey was always proctored, and participants were asked not to share answers or use any outside resources (e.g., internet or teammates). Scoring and Statistical Analyses The 62 survey questions could yield a maximum score of 97. The survey was split into a general nutrition knowledge section, which consisted of 64 points possible, and a sport nutrition knowledge section that consisted of 33 points possible. Per instructions from the original survey9, the surveys were scored as +1 point for each correct answer and +0 for no answer, an incorrect answer, a double-answer, or the “I do not know” response. Additionally, based on the original survey cutpoints, knowledge categories were defined as low, medium, and high (Table 4). 44 Table 4. Score Cutoffs for Knowledge Categories Total Survey (out of 97 possible) General Nutrition (out of 64 possible) Sport Nutrition (out of 33 possible) Low Knowledge Medium Knowledge High Knowledge < 46 46 – 58 > 58 < 32 32 – 40 > 40 < 14 14 – 18 > 18 Statistics were conducted using IMB SPSS v.24.92 Descriptive statistics, including average scores with standard deviations, were calculated. Frequencies of knowledge categories were tallied for the total survey and for the general and sport nutrition sections separately. Independent t-tests were performed to determine differences in nutrition knowledge between males and females, as well as differences between athletes of revenue versus non-revenue sports, each for the total survey and for the general and sport nutrition sections, separately. Effect sizes for differences between the sexes were calculated by dividing the mean difference in average scores between the two groups by the pooled standard deviation. A Pearson-product moment correlation was conducted to determine the relationship between the ratio of “I do not know” responses to incorrect answers, and the total score per participant. Because the scoring strategy posed by the original authors of the survey did not score an incorrect answer differently than an “I do not know” answer, this analysis was done to examine whether or not low scores on the survey were influenced more by participants answering incorrectly or selecting the “I do not know” option. 45 Results Participant Descriptives A total of 128 student-athletes completed the survey. Cases with more than 20% of responses missing were excluded (n=3), leaving 125 surveys analyzed. Seventy respondents were female (56% of the sample) and most were non-kinesiology or nutrition majors (n=101). Respondents were from eight different varsity sports: women’s rowing (n=47), field hockey (n=13), basketball (n=8), soccer (n=1), and golf (n=1); and men’s football (n=46), basketball (n=8) and ice hockey (n=1). Additionally, participants were divided into revenue (football, ice hockey, men’s basketball, women’s basketball; n= 63) and non-revenue sports (field hockey, golf, rowing, soccer; n=62) to address differences in knowledge between sports with greater (revenue) versus lesser (non-revenue) amounts of nutrition resource access, respectively. Total Sample Analyses Participant scores can be seen in Table 5. Scores on the total survey ranged from 9 to 85 out of 97 possible points, with the average score being 57.6% ± 18.6%. Scores for the general nutrition section ranged from 7 to 57 out of 64 possible, with the average score being 56.7% ± 19.9%. Scores for the sport nutrition section ranged from 1 to 30 out of 33 possible, with the average score being 58.4% ± 19.5%. 46 Table 5. Participant Scores Total Sample (n=125) Females (n=70) Males (n=55) Table 5. (continued) Revenue Sports (n=63) Non-Revenue Sports (n=62) Total Survey (out of 97 possible) 55.8 ± 18.0 64.5 ± 15.9* 44.8 ± 14.3 General Nutrition (out of 64 possible) Sport Nutrition (out of 33 possible) 36.6 ± 12.7 43.1 ± 11.0* 28.4 ± 9.7 19.3 ± 6.4 21.4 ± 5.9* 16.5 ± 6.1 44.3 ± 14.7 28.2 ± 10.0 16.2 ± 6.1 67.6 ± 12.8** 45.2 ± 8.9** 22.4 ± 5.1** * Significantly higher than male score (p<0.001) ** Significantly higher than revenue sport score (p<0.001) The frequency in which each participant answered “I do not know” was also calculated. On average for the total survey, participants answered “I do now know” approximately 20 times out of the 97 possible answers, with a range of some participants never answering “I do not know” to up to others using that answer up to 70 times (out of 97 possible). The average number of “I do not know” options for the General Nutrition section was approximately 13 (out of 64 possible) with a range of zero to 50. The average number of “I do not know” options for the Sport Nutrition section was approximately 7 (out of 33 possible) with a range of zero to 31. When comparing the number of “I do not know” responses to total scores per participant (i.e., a ratio of “I do not know” to correct responses), small-to-moderate correlations93 were seen for the 47 total survey (r=0.225, p<0.05) and for the general nutrition section (r=0.222, p<0.05); there was not a significant correlation for the sport nutrition section. Participants were also divided into low, medium, or high nutrition knowledge based on scores for the total survey, as well as the general and sport nutrition sections separately (Figures 1-3). These cut-offs were based on the validation paper by the authors of the knowledge assessment tool used (Table 4)9. For the total survey, the largest number of participants fell into the high knowledge category (n=61; 48.8% of the sample), while the next highest frequency was in the low knowledge category (n=37; 29.6% of the sample). Both the general nutrition section and sport nutrition sections followed a similar trend. Specifically, for general nutrition, the largest number of participants fell into the high knowledge category (n=58; 46.4% of the sample), while the next highest frequency was in the low knowledge category (n=48; 38.4% of the sample). For sport nutrition, the largest number of participants fell into the high knowledge category (n=81; 64.8% of the sample), while the next highest frequency was in the low knowledge category (n=26; 20.8% of the sample). 48 y c n e u q e r F 90 80 70 60 50 40 30 20 10 0 Low Knowledge Medium Knowledge High Knowledge Total Sample Females Males Revenue Sports Non-Revenue Sports Figure 1. Knowledge Categories for the Total Survey Females and non-revenue sports comprised most of the high knowledge category. y c n e u q e r F 90 80 70 60 50 40 30 20 10 0 Low Knowledge Medium Knowledge High Knowledge Total Sample Females Males Revenue Sports Non-Revenue Sports Figure 2. Knowledge Categories for the General Nutrition Section Females and non-revenue sports comprised most of the high knowledge category. 49 y c n e u q e r F 90 80 70 60 50 40 30 20 10 0 Low Knowledge Medium Knowledge High Knowledge Total Sample Females Males Revenue Sports Non-Revenue Sports Figure 3. Knowledge Categories for the Sport Nutrition Section Most of the total sample fell into the high knowledge category, with majority being females and/or non-revenue sport players. Sex Differences Differences in scores between the sexes can be seen in Table 5. Females scored significantly (p<0.001) better than the males for the total survey (average 66.5% ± 16.4% versus 46.2% ± 14.7%) with a Cohen’s effect size value (d = 1.30) that suggests a high practical significance. Similarly, females outscored males on the general nutrition section (average 67.3% ± 17.1% versus 44.3% ± 15.2%) with a high Cohen’s effect size value (d = 1.42). Lastly, females outscored males on the sport nutrition section (average 65.0% ± 17.8% versus 50.0% ± 18.5%), which also showed a high significance based on Cohen’s effect size (d = 0.80). 50 Sex differences in nutrition knowledge categories can be seen in Figures 1-3. For the total survey, most participants who fell into the high nutrition knowledge category (total n=61) were female (n=52), while most participants in the low knowledge category were males (n=27 males of 37 total). This trend held true for both general and sport nutrition sections, with females comprising the majority of participants in the high knowledge categories. Nutrition Resource Access Differences Differences in nutrition knowledge scores between revenue versus non-revenue sports can be seen in Table 5. Revenue sports scored significantly (p<0.001) worse than non-revenue sports for the total survey (average 45.7% ± 15.2% versus 69.7% ± 13.1%), general nutrition section (average 44.0% ± 15.7% versus 70.6% ± 13.9%), and sport nutrition section (average 49.0% ± 18.5% versus 67.9% ± 15.5%). Differences in nutrition knowledge categories between revenue and non-revenue sports can be seen in Figures 1-3 For the total survey, most participants who fell into the low knowledge category were revenue sport athletes (n=31 revenue sport athletes of 37 total), whereas the high nutrition knowledge category was comprised primarily of non- revenue sport athletes (n=50 of 61 total). This trend held true for both the general and sport nutrition sections, with revenue comprising the majority of participants in the low 51 knowledge categories and non-revenue sports making up majority of the high knowledge categories. Discussion Total Sample Analyses The purpose of this study was to evaluate the nutrition knowledge of college athletes utilizing a validated assessment tool. We found that the average score for the total survey, general nutrition, and sport nutrition sections were each approximately 58%, with individual scores ranging from 9% to 88% correct. Although it is impossible to compare meaningfully across the literature due to widespread use of different assessment tools, based on multiple reviews, our scores were similar to scores found by many other authors for an athlete population.4,14 The specific validated tool we used allowed a participant the option to select “I do not know” as an answer. Although it is not a direct measure of the construct, the number of times a person selects the “I do not know” option speaks to his/her self- efficacy, or confidence, regarding nutrition knowledge. In other words, a person with low confidence in nutrition knowledge may be more likely to choose the “I do not know” option than someone with higher confidence in nutrition knowledge. Frequency of selecting this response showed a wide range across study participants. Some never selected this option, whereas one individual selected it 70 times (out of 97 possible). For 52 all participants combined, the average amount of times “I do not know” was selected was approximately 20% of answers for the total survey, as well as for each separate section. This frequency of selecting an “I do not know” option could indicate either a disinterest in the survey itself or a true lack of knowledge on the subject; however, it is impossible to discern which, if either, is the case. We found a significant, small-to-moderate correlation93 between the amount of “I do not know” answers chosen and the total score for the entire survey and general nutrition section, but not for the sport nutrition section. Athletes who score poorly on a nutrition knowledge survey by primarily incorrect answers will likely need to be handled differently than those who score poorly because they chose primarily the “I do not know” option. The fact that there was a weak, yet significant relationship between the ratio of I don’t know/wrong responses with total score and general nutrition questions suggests that the participants with higher scores were less willing to hazard a guess to questions when they were not confident in their responses. This result should remind the nutrition educator that teaching strategies might need to be varied, depending on the recipient. In order to assess confidence in the nutrition knowledge of their sample, previous researchers have used multiple strategies. For example, Rosenbloom et al21 assessed the nutrition knowledge of Division I athletes and found that the average amount of participants who chose “Don’t know” on any given question was 53 approximately 19% of the sample. Torres-McGehee and colleagues20 utilized a different approach to assess the confidence athletes felt when answering nutrition questions. With each question of their survey, the authors included a 4-point Likert scale for participants to specify their confidence in the correctness of their answers (1=not confident at all, 2=not very confident, 3=somewhat confident, 4=very confident). Athletes in this study exhibited an average knowledge score of 54.9% ± 13.5% with confidence scores of 2.8 ± 0.34 on the Likert scale for correct answers and 2.4 ± 0.37 on the Likert scale for incorrect answers. This shows that athletes were between “not very confident” and “somewhat confident” in whether their answers were correct. These studies, in concordance with the present study, may have had different analyses of results, yet all exemplify that athletes do not have a high amount of confidence in their nutrition knowledge. Addressing the confidence that athletes feels about their nutrition knowledge may prove to be an important step in helping them make healthful food decisions. Results such as these should encourage the inclusion of a knowledge confidence component in future surveys that are created. A final analysis of this study was dividing participants into low, medium, or high nutrition knowledge based on the criteria set by the assessment tool authors. When done, the highest proportion of our sample fell into the high knowledge category for the total survey and each individual section. Despite this, average scores for the total survey and each section individually were approximately 58%. Although there are 54 currently no nutrition knowledge standards set forth by the Academy of Nutrition and Dietetics, it is the authors’ opinion that a 58% on a nutrition knowledge survey should be considered suboptimal, rather than the high knowledge the survey authors consider it to be. We believe this discrepancy to be related to the cutoff criteria for the knowledge categories, which were created by the original authors based on separating their sample into tertiles based on knowledge scores (Table 3.1);9 therefore, those in the original validation study who fell into the high knowledge category had subjectively high knowledge only compared to others in that specific sample. It is plausible that the current sample would elicit different knowledge tertiles if they had been part of the original validation. This is similar to the issue within the survey questions themselves of using such terms as “high” and “low” as answer options without establishing a basis of comparison. Future surveys should avoid such subjective language because it does not paint a true picture of the nutrition knowledge level of the sample, nor does it allow comparison to other samples and therefore hinders the generalizability of results. Sex Differences In our sample, females scored significantly higher than males for both the total survey and each individual section. There is current discourse in the literature about whether there are significant differences in nutrition knowledge between sexes. In athlete populations, some studies have reported that female athletes have higher 55 nutrition knowledge than males65,94,95, while others have reported no significant differences between the sexes.4,21,96 These conflicting results could be attributed to both the lack of a consistent knowledge assessment tool being used, as well as the specifics of the sports being tested. In our study, males were primarily football or basketball players, two sports often made up of athletes who may come from lower socioeconomic backgrounds which could contribute to having fewer previous educational opportunities in which to learn about nutrition.97 Nutrition Resource Access Differences We also analyzed differences in nutrition knowledge between revenue (football, men’s and women’s basketball, ice hockey) and non-revenue sports (field hockey, golf, soccer, rowing). This analysis was done to discern whether sport teams who typically have greater access to food provision and often receive more attention from nutrition professionals (i.e., revenue teams) have a subsequent higher (or lower) nutrition knowledge than teams who would have to seek out the information of their own volition (i.e., non-revenue teams). Although previous researchers have examined nutrition knowledge specific to certain sports, to our knowledge, this is the first study to conduct a specific revenue versus non-revenue comparison. Our results showed that athletes from revenue sports scored significantly lower than those from non-revenue sports for the total survey and each individual section. These results align similarly 56 with differences in this sample between males and females because a large portion of the revenue group were males, specifically football players, and the non-revenue group were primarily females, specifically rowers. Thus, we cannot discern whether the difference is truly due to the difference in revenue versus non-revenue sports or whether it is because of the sex differences. As previously stated, our current study is the only one to specifically analyze revenue versus non-revenue sports. However, our findings are comparable to the results found by Gilis and colleagues98 who showed that football and men’s basketball had significantly lower nutrition knowledge scores than the other teams surveyed at a Division I university. Additionally, Hull et al99 showed that athletes who reported the sport dietitian at their collegiate institution to be the person “in charge of implementing/directing their sport dietary plan” were more likely to have post- workout nutrition options available, school-provided boxed meals on team trips, less likely to consume fast food, have better awareness of nutrition periodization, and a purposeful, appropriate decreased caloric intake during the off-season as compared to the group who reported choosing not to meet with the sport dietitian. While all athletes in this current study had access to a nutrition professional assigned to their team, only revenue sport teams have contact with these professionals on a regular basis. Additionally, revenue sport athletes have most of their meals/snacks provided to them during the school year, especially while in-season. These athletes do not need to 57 grocery shop/cook on their own, so they do not have to use significant time to learn how to accomplish such tasks. Athletes of non-revenue sports are typically provided much less, as those teams do not have the budget to provide as much food to their athletes. Therefore, they must provide for themselves much more than athletes of revenue sports. These findings could indicate that athletes of revenue sports who receive extra attention may be at a disadvantage for gaining nutrition knowledge because they are simply being provided to, rather than having to learn on their own. As previously stated, differences seen in knowledge between revenue and non- revenue sports could be related to many variables, including sex, the socioeconomic status of the athletes involved, even food availability. These differences should be noted by nutrition and health professionals within college and university athletic departments because revenue sport athletes with lower nutrition knowledge are at an increased risk for making inappropriate dietary choices. This is an important issue that should be addressed within each institution. Strengths One strength of this study was the use of a validated assessment tool, which is not often done. A second strength was the reasonably large sample of college athletes sampled, which is typically a difficult population to access. 58 Limitations This study is not without limitations. First, the survey itself had multiple issues. It was not validated in a college athlete population, thus allowing the question of whether or not the scores found are truly indicative of this population; however, the survey was the best option of the available options at the time of data collection and contained both general and sport nutrition sections. Additionally, the similarity of results in this study compared to the results of other tools validated in athlete populations4,14,65,94,95 suggests that this survey may be valid with the collegiate athlete population. An additional issue was that the survey was created in Italian, resulting in grammatical and content issues when the authors translated it to English. For example, the use of the word “courgette” is common in Europe and thus appeared on the survey; however, participants in the United States would only know that as “zucchini”, therefore that question caused confusion. Additionally, the survey format had elements of subjectivity. A majority of questions in the general nutrition section asked about the macronutrient content of certain foods, with answer options allowing the participant to check “High” or “Low”, which are subjective words with no comparison value. With all survey research, survey bias and overall effort on the survey must be considered. We did not assess some demographic information, such as ethnicity or socioeconomic status, both of which have been shown to have effects on nutrition knowledge in an adolescent population;100 therefore, it’s possible that this relationship exists in a young 59 adult population as well. Lastly, many sport teams did not participate in the survey. Most study participants were female rowers and male football players, thus limiting the generalizability to an entire college athlete population. Future Directions Future directions of this research should begin with the creation of a nutrition knowledge assessment tool that includes a sport nutrition section that is validated in a collegiate athlete population and appropriate for use in the collegiate athletics environment. A recently published tool by Karpinski et al29 shows promise, as it was created using the most recent joint-position paper about nutrition and athletic performance1, and additionally has been validated in an adult athlete population. However, this tool was not validated in collegiate athletes, specifically, and is perhaps too long for practical use with collegiate athletes. Once a college-athlete-specific tool has been validated, it can be used by practicing sport dietitians to assess the sport nutrition knowledge of college athletes, ideally a more representative sample that includes males and females from many different sports. There are many factors that play a role in dietary behavior. There is a recurrent relationship between environmental factors, personal factors, and subsequent dietary choices 101; therefore, it is unwise to expect dietary behavior change when influencing either the environment or the individual. Assessing the knowledge base of this specific 60 population is a viable first step in encouraging meaningful, healthful changes to a college athlete’s dietary lifestyle. However, it is necessary to exact a nutrition education intervention that could be used to both teach the athletes about nutrition for sport, as well as promote their nutrition self-efficacy. If we assess at collegiate performance nutrition programs from the perspective of the Social Ecological Model (SEM)102, many institutions will attempt to influence dietary behavior change from the outer levels of the SEM (Policy/Enabling Environment or Organizational) out of necessity, accessibility, and feasibility. For example, it is not always feasible that a collegiate sport coach would allow a Registered Dietitian to have a meaningful amount of time with the team to educate them about nutrition (i.e., working from the Individual level of the SEM). However, it is feasible for a coach to allow the Dietitian to choose their pre/post- game or training table meal menus (i.e., create an appropriate food environment). This is not necessarily a bad thing, as previous literature has shown that people’s dietary choices are often times more influenced by their environment than by anything else102. In order to promote sustainable dietary improvements, it is imperative that these athletes be given the opportunity to obtain a sufficient nutrition knowledge baseline that they can then take with them throughout their athletic careers and rest of their lives. 61 Conclusions Intercollegiate sports nutrition research is important because an athlete with poor knowledge cannot be expected to make optimal dietary choices. The high stress of being both a full-time student and college athlete, coupled with low nutrition knowledge, could lead to poor dietary choices, thus inhibiting proper fueling for and recovery from sport. Subsequently, the athlete could be at higher risk for in-sport injury and/or delayed recovery from injury, which could result in a detriment to the athlete’s psychosocial state and overall quality of life. Having an adequate base of nutrition knowledge can allow the athlete to effectively follow nutrition practices that will optimize performance and minimize risk of injury. 62 CHAPTER FOUR: MANUSCRIPT TWO Chapter Four addresses Specific Aim 2 and the manuscript titled Development and Validation of the Sport Nutrition Assessment of Knowledge (SNAK) Screener for College Athletes. Specific Aim 2: To develop and validate a sport nutrition knowledge questionnaire specifically for use in a collegiate athlete population, then use this questionnaire to assess the sport nutrition knowledge of college athletes at an NCAA Division I university. H 2.1: Sport nutrition knowledge in this population will on average be inadequate, defined as scores less than 75% correct. H 2.2: Females will have higher sport nutrition knowledge than males. H 2.3: Athletes participating in non-flagship (i.e., non-revenue) sports will have higher sport nutrition knowledge than athletes participating in flagship sports (e.g., football). H 2.4: Athletes of greater years of eligibility (e.g., seniors) will have higher sport nutrition knowledge than athletes of lesser years of eligibility (e.g., freshmen). 63 Abstract OBJECTIVE: To develop and validate a brief sport nutrition knowledge screener for use in a college athletics setting. PARTICIPANTS: 116 college students (n=94 varsity athletes, n=22 dietetic students) participated. METHODS: A 25-question screener titled the Sport Nutrition Assessment of Knowledge (SNAK) tool was developed using reviews of the literature and qualitative feedback from experts. Participants then completed the SNAK alongside the Automated Self-Administered Recall System (ASA24) Dietary Assessment tool10, which was then analyzed using the Healthy Eating Index (HEI)103 as an external validity comparison between knowledge and diet quality. The pilot SNAK version was revised based on statistical recommendations and qualitative feedback from the athletes. RESULTS: The average score on the pilot SNAK was 88% correct and average time to completion was 5.12 minutes. There were no differences in knowledge scores between sexes or sports. The average HEI score for the total sample was 59.2 ± 16.6, which is equivalent to a grade of F based on recommended grading standards12. Revisions to the pilot tool were made based on statistical and qualitative feedback, resulting in a final SNAK version with 22-questions. CONCLUSIONS: Results suggest that either the pilot screener was too simple, or the athletes truly had high knowledge. Regardless, the athletes exhibited poor diet quality, which suggests that nutrition knowledge does not lead to diet behavior. 64 Introduction There are almost half a million total collegiate student athletes (i.e., students enrolled at a college/university that participates in a varsity sport for that college/university) within the National Collegiate Athletic Association (NCAA), the governing entity of most college sports30. This population of 17 to 24 year old males and females is diverse in terms of race, ethnicity, culture, religion, socioeconomic status, and familial backgrounds, among other things30; however, one commonality that all college athletes share is a unique set of pressures and expectations unlike those of any other athlete population. College athletes are expected to train almost daily to improve their athletic performance while maintaining at least a 2.3 (on a 4.0 point scale) Grade Point Average (GPA) to be eligible to compete in their sports (per NCAA rules) and retain the scholarships that pay for their education and livelihood39. Additionally, college athletics is a multi-million dollar industry, with some larger institutions having athletic department revenues approaching $193,000,000 per year31. Given that this income is influenced heavily by the success of the university’s sport teams, there is a strong emphasis and expectation placed on the athletes to optimize their sport training while maintaining NCAA eligibility and overall health. One critical component of the optimization of athlete performance and health is sport nutrition. 65 Sport Nutrition in College Athletics The health, wellness, and athletic performance of college athletes can be improved with the application and habitual use of optimal sport nutrition strategies1,2,49. Unfortunately, previous researchers have shown that the nutrition intakes of college athletes are often suboptimal, particularly with respect to fruits, vegetables, and overall caloric intake3-8. Such inadequate dietary habits could lead to an athlete experiencing low energy availability (LEA), which is typically caused by chronic lower energy (i.e., calorie) consumption than needed to support normal physiologic function13. LEA can negatively affect cognition, cardiovascular health, bone health, gastrointestinal function, reproductive health, among other physiologic functions13. Subsequently, LEA decreases athletic performance and puts athletes at great risk for injury13, which directly relates to their time spent training and competing in their sport. In order to optimize dietary intakes of college athletes, we must first understand potential reasons behind the suboptimal food practices described by previous researchers3-8. Although there are many factors that influence what college athletes eat, such as food environment or availability, taste, convenience, or economic factors, one plausible explanation for their observed inadequate dietary habits could be inadequate nutrition knowledge53. Researchers have been assessing the sport nutrition knowledge of college athletes for decades, with studies dating back to the 1980’s; yet, there is much disagreement on whether or not athletes have a sufficient level of nutrition knowledge. Two systematic 66 reviews have a shown that knowledge survey scores in athletes vary from as low as 33% and up to 84% correct4,14. Additionally, some authors have shown significant differences in nutrition knowledge between males versus females and revenue (e.g., football) versus a non-revenue (e.g., rowing) sports83. Although it is feasible that nutrition knowledge truly varies among athletes of different sexes, sports, and colleges, this large range in scores has contributed to discourse in the literature on whether or not college athletes, in general, have a satisfactory level of nutrition knowledge. One possible reason for a wide range in knowledge scores is variation in methods utilized to assess knowledge, specifically the differing, and often unvalidated, knowledge questionnaires. Many authors used a survey they developed20,21,60-68 while others modified a survey created by previous authors17,18,22,69-75. Some authors have even utilized an “unpublished manuscript”28 as their knowledge measurement tool18,79, and others still have used an unaltered version of a previously published survey regardless of whether or not that survey went through proper development techniques or validation analyses for the population of interest76-83. At this time, there have only been seven sport nutrition knowledge questionnaires developed and validated using athletes of any age (rather than general population)19,23,24,26,27,29,85, and only four were developed using specifically college athletes as the population of interest19,26,27,29. Unfortunately, these four are outdated19 (i.e., nutrition recommendations for athletes have been updated since their conception) or 67 lengthy49,50,52, both of which are not ideal traits of a tool to be used in this population. College students have been shown to have very short attention spans57, therefore a measure with too many questions would likely lead to them losing interest in the beginning and either skipping or guessing on questions towards the end. Shorter measures (less than 1000 words58) have better response rates and less skipping questions compared to longer measures56. It is vital that the practitioner (e.g., sport dietitian) administering the tool believe in its practicality and usefulness. If it is to be used in the fast-paced environment of college athletics, a nutrition knowledge assessment tool must be short so it can be administered easily, quickly, and with little explanation. Additionally, the tool must be able to be analyzed quickly so that the practitioner believes it could be appropriate to use in either a one-on-one consult for immediate analysis or with a large group (e.g., a football team) for bulk analysis later. Such a tool should be used as a starting point for a conversation or nutrition education lesson between the practitioner and the athlete; therefore, the less time needed to administer and analyze it, the more time that can be spent on discussions about nutrition. Best Practices in Survey Development There are two types of measurement tools that can be utilized when measuring a construct: reflective or formative measures. Reflective (i.e., effect) indicators are 68 dependent on the construct; or put in other terms, the construct determines the indicators. A theoretical example of a reflective construct is satisfaction with a product (construct) determines someone’s likelihood to use that product/company again or recommend that product/company to a friend (indicators). Reflective scales are typically developed using classical test theory (CTT) that emphasizes reliability, item difficulty, item discrimination, and emphasizes uni-dimensionality and internal consistency104,105. It is important that reflective indicators have high collinearity (i.e., intercorrelation) and are essentially interchangeable within the scale105. Formative (i.e., causal) measures are the opposite – formative indicators influence the construct; or in other words, the construct is determined by the indicators106,107. In the theoretical example of sport nutrition knowledge, knowledge (the construct) does not indicate whether they know the answer to specific questions about carbohydrates (an indicator), it is the other way around; their knowledge about carbohydrates defines their level of sport nutrition knowledge, thus making this relationship formative. At present, all previously developed sport nutrition knowledge questionnaires have been created as reflective scales rather than formative indices. The creation of a formative model recommends the development of an index, which is a different process than developing a reflective scale106,107. There are four necessary steps to formative index development: content specification (thoroughly defining the construct which you are measuring), indicator specification (compiling a 69 list of questions that cover the breadth of the construct), indicator collinearity (a measure of the extent to which the items in a measure exhibit linear dependency with other items), and external validity (an examination of how well the index relates to what it should predict)107. The validation of formative indices does not involve the same statistical rigor as a reflective scale; therefore, formative index development must be strongly rooted in theory to best explain and support the relationship between the construct, the items meant to measure it, and the relationship between the index and what it is meant to predict107. Purpose and Aims A new sport nutrition knowledge survey should be developed for college athletes, specifically, that is based in sound psychometric theories using formative indicators, utilizes thoughtful and thorough development techniques, and that employs comprehensive validation strategies rooted in best practices. Such a tool would be of great value to collegiate sport dietitians because it could be administered quickly, understood easily by the population of interest, and analyzed expeditiously. This tool could be used as a starting point for conversations with athletes about their fueling strategies and other dietary practices, thus putting the student athlete in a better position to optimize health and performance. 70 The purpose of this research is to develop and validate a brief sport nutrition knowledge screener for use in a college athletics setting. This screener will be developed and validated as an index with formative indicators. The aim of this project is for the Sport Nutrition Assessment of Knowledge (SNAK) screener to allow for quick and accurate evaluation of the sport nutrition knowledge level of collegiate athletes, thus allowing practitioners to quickly analyze and initiate necessary education steps tailored to the individual athlete or team. An additional aim is that the SNAK screener will be sufficiently brief to be incorporated into annual physical evaluations that the NCAA requires every collegiate athlete complete prior to sport participation. These longitudinal results could then be used to track the efficacy of nutrition knowledge interventions by athletics staff and serve as evidence for the promotion of a sport nutrition program at the college or university. Methods Approval and Consent to Participate This research was given exempt approval status by the Institutional Review Board at Michigan State University. All data collection occurred via an online forum (Qualtrics, Provo, UT). Each survey began with a disclaimer statement regarding participation, data safety and implied consent through completion of the survey. 71 Development and Validation Procedures The creation and validation of the SNAK screener was guided using both scale48 and index107 development best practices. The steps taken can be separated into two phases (Figure 4): PHASE I: SNAK Development 1. Define the construct “sport nutrition knowledge” 2. Literature review & qualitative interviews with experts (n=2) to determine dimensions (n=7) 3. Generate initial item pool 4. Choose response and scoring formats 5. Assess content validity by panel of experts (n=8) Draft 1, n=101 items Draft 2, n=60 items 6. Finalize initial SNAK version Draft 3, n=25 items PHASE II: Pilot Test 7. Administer SNAK with ASA24 simultaneously a. Construct validity - compare nutrition knowledge of college athletes versus nutrition students b. External validity - compare nutrition knowledge (SNAK) with diet quality (ASA-24) 8. Item refinement a. Indicator collinearity b. Item difficulty c. Item discrimination 9. SNAK re-test with qualitative feedback a. Test-retest reliability b. Face validity from qualitative feedback 10. Finalize SNAK Draft 4, n = 22 items Figure 4. Flow Chart of Development and Validation of The SNAK Screener. 72 Phase I: SNAK Development The development phase of the SNAK screener began with defining the construct (sport nutrition knowledge), i.e., content specification (Step 1)107. Next, reviews of the literature and qualitative interviews with sport nutrition experts (n=2 collegiate sport dietitians) were conducted in order to identify dimensions that represent the construct (Step 2). Once the dimensions were identified, an initial pool of items was generated (i.e., item specification107) based on a systematic review of previous literature that included assessments of athlete’s nutrition knowledge and current sport nutrition recommendations (Step 3). Step 4 involved deciding the response and scoring formats for the screener. Next, a panel of experts were recruited via direct invitation in January 2020 to establish content validity (i.e., whether or not you are measuring the construct you are intending to measure108; Step 5). The inclusion criterion was that the expert needed to have worked as a dietitian in collegiate athletics for at least one year and was working as a collegiate dietitian at the time of participation. The experts were asked to complete an online survey that prompted them to rank-order a refined list of items (n=60) in order of most to least important to include in a brief nutrition knowledge screener for college athletes. Experts ranked items within their respective dimensions. The experts were also asked what they believe to be the maximum number of questions allowable for this brief screener to be incorporated into their institution’s annual pre-participation 73 physicals. Results from this survey were used to finalize the first version of the SNAK screener (Step 6). Phase II: Pilot Test College athletes were recruited between March and June 2020 through direct email invitation (Step 7). Dietetic students were recruited to participate in March 2020 through convenience sampling within the intern pool for the sport nutrition program of the University’s athletic department. Construct validity was established by comparing the athletes’ results versus those of the dietetic students (Step 7a). In conjunction with the SNAK, participants completed a 24-hour dietary recall. Dietary intake data for the 24-hour recalls were collected and analyzed using the Automated Self-Administered Recall System (ASA24) Dietary Assessment tool, version 24, developed by the National Cancer Institute (Bethesda, MD)10. This system was chosen because it can be administered online, has been well-validated, and can be analyzed using the Healthy Eating Index (HEI)103, a measure of diet quality that aligns with the Dietary Guidelines for Americans. Ideally, an athlete with higher nutrition knowledge should have a higher diet quality; in other words, higher nutrition knowledge could be viewed as predictive of diet quality. Therefore, comparing the nutrition knowledge of college athletes with their diet quality served as the external validation that is vital for validating an index (Step 7b)107. 74 Item refinement statistics were conducted to evaluate the results for indicator collinearity, item difficulty, and item discrimination (Step 8). Indicator collinearity is the extent to which the items in a measure exhibit linear dependency with other items. Collinearity is not desirable in a formative measure, as all items of an index should be distinct and not theoretically overlap105,107. Item difficulty is the extent which respondents answer an item in the same way. It is not desirable to have items be either too easy (i.e., everyone answers it correctly) or too difficult (i.e., everyone gets it incorrect). Lastly, item discrimination is the ability of an item to discriminate between those who did well on the screener from those who did not. A reliable item is one that can distinguish between examinees that do well versus poorly on the exam109. All participants who completed the initial round of testing were contacted to retake the SNAK within one to three months after their first test for test-retest reliability analysis (Step 9). During the retest, participants were also prompted to give qualitative feedback on the readability and understandability of individual items, which served as the measure of face validity. These statistical and qualitative results were used to edit items as necessary and finalize the SNAK screener (Step 10). Statistical analyses Statistics were conducted using SPSS v.26 (IBM Corp., Armonk, NY) and SAS® software (SAS Institute Inc., Cary, NC) packages. Construct validity was assessed by 75 performing independent t-tests between knowledge scores of college athletes and nutrition students. SAS software code provided by the National Cancer Institute was utilized to calculate HEI scores based on the ASA24 dietary recalls11. External validity was assessed by conducting Pearson-product moment correlations between SNAK and HEI (i.e., diet quality) scores. Test-retest reliability was evaluated by conducting Pearson-product moment correlations on the scores within individuals between first and second rounds of testing. Although index development best practices suggests that elimination of formative items be theoretically justified rather than based solely on statistical properties105,107, there were specific statistical procedures conducted to further justify validity of the SNAK screener. Regression statistics were used to evaluate collinearity between items by conducting a variance inflation factor (VIF) analysis. Although the traditional item inclusion cut-off is 10110, the cutoff criterion for consideration of rejection was set at 3.3 based on best practices105. Item difficulty was assessed by determining the frequency with which items were answered correctly. Cutoffs of <20% correct (too difficult) or >80% (too easy) were used to evaluate items for rejection87,91,111. Item discrimination was assessed by correlating the score of each item with the overall test score. A Pearson correlation of <0.2 was considered the cut-point of consideration for elimination of that item87,91. 76 Descriptive statistics, including average scores with standard deviations, were calculated. Independent t-tests were performed to determine differences in nutrition knowledge between participant characteristics such as sex and sport type. Effect sizes for these differences were calculated by dividing the mean difference in average scores between the two groups by the pooled standard deviation. Results Figure 4 outlines the steps taken during each phase of the development and validation of the SNAK screener. Phase I: SNAK Development Step 1. Define the construct The construct of interest, sport nutrition knowledge, is defined by the author as “the understanding of information related to purposeful daily food, drink, and supplement intake strategies to enhance health and performance during, and recovery from, exercise or physical competition.” Step 2. Determine dimensions A thematic analysis was conducted to identify dimensions that best represent the breadth of sport nutrition knowledge. First, a thorough review of the literature was 77 conducted to determine what dimensions previous authors believe make up the breadth of sport nutrition knowledge. This included evaluating multiple reviews on sport nutrition knowledge3,4,14, previously published nutrition knowledge assessment tools that have been used with athletes9,24,29,91, and the most recent position statement regarding nutrition for sport from the Academy of Nutrition and Dietetics, the American College of Sports Medicine and Dietitians of Canada as the standard for evidence-based recommendations1. Second, qualitative interviews with two collegiate sport dietitians (individually) were conducted, transcribed, and analyzed to identify themes that the dietitians believed should be included in a brief sport nutrition screener for use in a college athlete population. This process revealed over a dozen topics that could be included as dimensions of sport nutrition knowledge14. Given that the purpose of the present screener is to be brief enough for quick administration and analysis, it was not practical to include every dimension found in previous literature. Energy availability, the macronutrients (carbohydrates, fat, protein), and supplements were necessary to be included in the screener because those are the foundational topics that must be understood in order to build further nutrition knowledge. With these six dimensions, a practitioner could assess the baseline of an athlete’s nutrition knowledge. Including more dimensions would increase the length of the survey, thus decreasing practicality of use. However, 78 interviews with the collegiate sport dietitians revealed a common seventh dimensions agreed between the two: nutrient timing. This dimension had been mentioned in previous literature, and the agreement between interviewees justified a relevance for inclusion. In total, seven dimensions that were deemed most important for inclusion: energy availability, carbohydrates, protein, dietary fat, hydration, supplements, and nutrient timing. These dimensions encompass vital introductory topics within sport nutrition and will allow the practitioner to establish a starting point with the athlete whom they are consulting at the time. Step 3. Generate item pool A first draft of an item pool was developed using much of the same literature that were reviewed for the thematic analysis1,3,4,14,9,24,29,91. The first draft contained a bank of 101 items. This bank was then combed by the lead author for redundancies and irrelevance, narrowing the pool to 60 items in the second version. Items were worded strategically so that the message of the question was simple and not easily misunderstood, thus requiring minimal instruction/clarification from the administrator. Items were grouped within-dimensions (i.e., not random order) to optimize ease of scoring for the practitioner and improve consistency of administration. 79 Step 4. Choose Response and Scoring Formats. Format decisions must be guided by considerations for survey burden, practicality, and validity concerns90. The response format for the SNAK screener was chosen to be ‘Agree’ or ‘Disagree’ options only. This format was chosen because it would likely be familiar to any respondent and thus would not require explanation, thus optimizing simplicity. Additionally, it would ease both responding and scoring and by shortening response and analysis duration. An ‘I do not know’ option was not included because current best practices recommend not including it56. Previous authors have often demonstrated that respondents are more likely to choose such an option because they do not want to do the mental work required to truthfully answer, or they fear incorrectness and would opt for the relief of an ‘I do not know’ option56. However, to provide participants with a similar, less obvious option to express unknowing, the instructions at the top of the survey instruct the respondent to skip any question they do not wish to answer or do not know the answer to. The scoring format for the screener was determined to be +1 for correct answers and +0 for anything else (i.e., incorrect or missing). This scoring format was chosen to ease analysis burden and optimize expediency. This format should allow a practitioner to easily administer the screener to an athlete, score it, then go over the results with the athlete all in a timely manner during a single consult session. There is currently no 80 standard threshold for what is considered “adequate” nutrition knowledge; therefore, an athlete’s results should be analyzed subjectively by the practitioner and used as a starting point for conversations with that athlete about nutrition education. This will allow the practitioner more freedom to address the athlete individually, rather than diagnostically address their knowledge as high or low. Step 5. Assess content validity with panel of experts To assess content validity, registered dietitians with at least one year of experience working in collegiate athletics were invited directly to participate in a content validity survey. A total 12 experts were contacted, including at least one collegiate sport dietitian working at four of the five NCAA Power conferences [Big 10, Big 12, Atlantic Coast Conference (ACC), Southeastern Conference (SEC), Pacific-12 Conference]. Three sport dietitians from non-Power 5 institutions were also invited to participate. A total eight sport dietitians (seven from Division I universities, one from a Division II university) completed the survey. The items within each dimension that received either a first-place rank (i.e., any dietitian viewed it as most important for inclusion) or at least 5 votes (majority) for a second-place rank during the survey was included in version 3 of the SNAK screener. This resulted in a total 25 items included, which was below the threshold (30 items suggested as maximum allowable) identified by the group of dietitians as the maximum 81 number of items the screener should contain for practicality of use within a college athletics setting. Step 6. Finalize initial SNAK version The pilot test version of the SNAK screener contained 25 items divided into seven dimensions: energy availability, carbohydrates, protein, dietary fat, hydration, supplements, and nutrient timing. This version can be seen in Appendix A. Phase II: Pilot Test Step 7. Administer SNAK with ASA24 simultaneously A total 116 participants (n=94 athletes, n=22 dietetic students) responded to the invitation to complete the SNAK screener and ASA24 simultaneously. No data were excluded due to missing responses because participants were instructed to skip questions they did not know or declined to answer. The highest frequency of missing responses on a single question was two (out of 116 possible) and the highest frequency of missing responses from a single participant was six (out of 25 possible). Characteristics of the population of interest (athletes; n=94) can be found in Table 6. The athlete sample was primarily female (n=73) underclassmen [n=56 red shirt (RS) sophomore or younger]. The majority of the athletes were non-health majors (n=61), meaning they did not report their majors to be Kinesiology, Dietetics or Nutritional 82 Sciences, or Pre-Medical or Nursing. Most athletes reported having taken prior nutrition coursework (n=55) in either high school and/or college. A majority of the athletes reported participating in non-revenue sports (n=84 reported any sport other than football, men’s or women’s basketball, or ice hockey). The dietetic student sample (n=22) included only one male participant, and all were health majors, had previous nutrition coursework, and did not play a varsity sport. Table 6. Athlete Characteristics Males (n=21) Females (n=73) Age (years) 20.7 ± 1.3 19.7 ± 1.2 Total Sample (n=94) 19.9 ± 1.2 Class Underclassmen Upperclassmen Major Health Non-Health n=10 n=11 n=5 n=16 Previous Nutrition Coursework n=13 n=8 n=46 n=27 n=28 n=45 n=42 n=31 Yes No Sports Revenue Non-Revenue Football (n=4) Ice Hockey (n=2) Basketball (n=4) Cross Country (n=3) Cross Country (n=15) Golf (n=1) Soccer (n=1) Golf (n=2) Soccer (n=9) Swim and Dive (n=2) Track and Field (n=3) Swim and Dive (n=8) Track and Field (n=5) 83 n=56 n=38 n=33 n=61 n=55 n=39 n=10 n=84 Baseball (n=1) Tennis (n=1) Wrestling (n=3) Rowing (n=24) Field Hockey (n=3) Gymnastics (n=3) Table 6. (cont’d) Non-Revenue (continued) Average SNAK scores separated by dimension can be found in Table 7. The average score for the total sample was 88% correct (22.0 ± 1.9 out of 25 possible) and the average time to completion was 5.12 minutes. The only dimension in which either athletes or dietetic students scored below the cut-point determined as inadequate sport nutrition knowledge (75%) was the athletes scoring 70% correct for the protein dimension. The athletes scored at least an 84% on every other dimension and an 87% correct overall. The dietetic students scored at least 82% on every dimension and a 93% overall. Table 7. Average SNAK Scores by Dimension Athletes Dietetic Students Avg Score (%) Avg Score (%) Total Sample Avg Score (%) Dimension Energy Availability (out of 3) Carbohydrates (out of 3) Protein (out of 4) Dietary Fat (out of 4) Hydration (out of 3) Supplements (out of 4) Nutrient Timing (out of 4) Total Score (out of 25) 2.8 ± 0.5 (94%) 2.7 ± 0.6 (89%) 2.8 ± 0.8 (70%) 3.8 ± 0.5 (95%) 2.9 ± 0.3 (97%) 3.3 ± 0.8 (84%) 3.5 ± 0.9 (86%) 21.8 ± 1.9 (87%) 2.9 ± 0.3 (97%) 2.9 ± 0.4 (95%) 3.3 ± 0.6 (82%) 4.0 (100%) 2.9 ± 0.4 (95%) 3.6 ± 0.6 (91%) 3.7 ± 0.6 (92%) 23.2 ± 1.0 (93%) 2.8 ± 0.4 (95%) 2.3 ± 0.5 (91%) 2.9 ± 0.8 (72%) 3.8 ± 0.5 (95%) 2.9 ± 0.3 (97%) 3.4 ± 0.8 (85%) 3.5 ± 0.9 (87%) 22.1 ± 1.9 (88%) 84 Average SNAK scores separated by participant characteristics can be found in Table 8. There were no significant differences between males and females (p=0.158; Cohen’s d = 0.32), those who did and did not have previous nutrition coursework (p=0.207; Cohen’s d = 0.27), nor between revenue and non-revenue sports (p=0.177; Cohen’s d = 0.57). Health majors scored significantly better than non-health majors (90% versus 86%; p=0.005; Cohen’s d = 0.66). Table 8. Average SNAK Scores by Characteristic for the Total Sample Average Overall Score ± SD (%) Sex Major Male (n=22) Female (n=94) Health (n=55) Non-health (n=61) Previous Nutrition Coursework Yes (n=77) No (n=39) Sport (Athletes Only) Revenue (n=10) Non-Revenue (n=84) Step 7a. Construct validity 21.6 ± 2.0 (86%) 22.2 ± 1.8 (89%) 22.6 ± 0.8 (90%) 21.6 ± 1.9 (86%) 22.2 ± 1.8 (89%) 21.7 ± 1.9 (87%) 21.0 ± 1.1 (84%) 21.9 ± 2.0 (87%) Athletes scored significantly less than dietetic students for the protein dimension (70% versus 82%; p = 0.008; Cohen’s d = 0.65), fat dimension (95% versus 100%; p = 0.35; Cohen’s d = 0.44), and the overall SNAK screener (87% versus 93%; p = 0.001; Cohen’s d = 0.96). These results confirm acceptable construct validity for the SNAK screener. 85 Step 7b. External validity The HEI scores for the athlete sample can be found in Table 9. The average HEI score for all athletes was 59.2 ± 16.6 out of 100 points possible. This score corresponds to a grade “F” according to the recommended interpretation scale suggested by the authors of the HEI12. A Pearson correlation between SNAK and HEI scores for the total sample (athletes and dietetic students combined) revealed no significant relationship between sport nutrition knowledge and diet quality [r(114) = 0.063, p=0.5)]. This result does not meet acceptability standards for external validity of the SNAK screener. Table 9. HEI Minimums, Maximums, and Averages for the Athlete Sample All Athletes Sex Major Male (n=21) Female (n=73) Health (n=33) Non-Health (n=61) Previous Nutrition Coursework Yes (n=55) No (n=39) Revenue (n=10) Non-Revenue (n=84) Sport Min , Max 27.0 , 93.7 30.6 , 88.4 27.0 , 93.7 27.0 , 93.7 29.2 , 90.6 27.0 , 93.7 29.2 , 88.3 27.0 , 90.9 29.2 , 93.7 Average 59.2 ± 16.6 59.4 ± 19.0 59.2 ± 16.0 60.3 ± 17.1 58.7 ± 16.5 59.8 ± 16.2 58.4 ± 17.4 52.2 ± 19.5 60.1 ± 16.2 86 Step 8a. Indicator collinearity The analysis of multicollinearity within the model revealed variance inflation factors for all items ranging from 1.1 to 2.0. These results fall below the criterion for rejection of 3.3 based on best practices105, thus indicating acceptable item collinearity for a formative measure. Step 8b. Item difficulty Only three of the 25 items on the SNAK screener were within the 20% to 80% difficulty range considered acceptable for keeping in the screener87,91,111. If the lenient cut-points of <10% or >90% correct were used, 8 of the 25 items could be retained. All other items exceeded the 80% or 90% cutoff, meaning that many participants answered those specific items correctly. These item difficulty results indicate 17 to 22 of the 25 total questions are too easy for inclusion in this screener. Step 8c. Item discrimination Item discrimination is typically determined by correlating the scores of each individual item with the overall score. However, the response format of the SNAK (agree or disagree) is binary, therefore a cumulative score per item does not exist. Additionally, the items of formative measures are meant to be distinct from one another and cover the entire breadth of the construct105; therefore, it is inappropriate to assume 87 that correctness on any singular item should be able to discriminate between high and low scores of the overall construct. For example, whether or not a participant knows that protein is not the most important macronutrient to consume immediately after exercise would likely not indicate whether or not they scored adequately (>75% correct) or inadequately on the total nutrition knowledge screener. Although a single item on a formative scale should not be used to discriminate between adequate or inadequate scores, an entire dimension (e.g., carbohydrates) can give more insight. For example, a participant’s score on all protein-related questions should be more discriminatory between participants with adequate versus inadequate nutrition knowledge. Therefore, scores per dimension were correlated with total scores. Six of the seven dimensions showed significant correlations (p<0.001) with total scores and were above the 0.2 cut-point of consideration for elimination of that dimension87,91. The only dimension below the cut-point was hydration (r=0.147, p=0.116), thus suggesting elimination of the hydration dimension. Step 9a. Test-retest reliability A total 34 participants responded to the retest invite (29.3%). The average score for this sub-sample on the pilot test was 22.6 ± 1.7 (out of 25) and the average score on the retest was 23.0 ± 1.7. There was a significant, positive correlation between test and 88 retest scores [r(32) = 0.468, p=0.005]. This result confirms acceptable test-retest reliability for the SNAK screener. Step 9b. Face validity Participants who participated in the retest were asked the same 25 questions from the original SNAK screener. Additionally, after each individual question, the participants were asked “Is the wording of [Question 1] clear and easy to understand” and “If ‘no’, please explain why. How would you change the wording to improve clarity?” At least one of the 34 participants gave qualitative feedback on 12 of the 25 items. Step 10. Finalize SNAK Revisions to survey measures are necessary after pilot data are collected to determine if the condition of a test is still optimal or appropriate for use90. Item review analyses are important for evaluating item quality, clarity, and potentially irrelevant language within an item that may influence a participant’s response90. Best practices in formative index development suggest that elimination or revision of items be theoretically justified rather than based solely on statistical analyses105,107. The original 25-question SNAK screener was revised based primarily on qualitative feedback from participants collected during the retest phase and author 89 discretion regarding item clarity, redundancy, relevance and applicability. Results of the item collinearity, difficulty, discrimination analyses were also used as guidelines for item rejection. Three items were deleted due to redundancy with other items or advisement from item refinement analyses. The wording of 13 other items were revised for clarity and relevance based on qualitative feedback. Lastly, two items were re- worded to alter their correct answer from “agree” to “disagree” for more back-and-forth in response correctness. The revised version of the SNAK screener contains 22 questions with the same seven dimensions as the original version (Appendix B). Discussion Pilot SNAK Screener The purpose of this project was to develop and validate a brief sport nutrition knowledge screener for use in a college athletics setting. The aim was for this screener to facilitate quick and accurate evaluation of the sport nutrition knowledge of college athletes, thus allowing practitioners the ability to assess their knowledge and offer efficient sport nutrition consult. The final Sport Nutrition Assessment of Knowledge (SNAK) screener has 22 questions covering seven different topics: energy availability, carbohydrates, protein, dietary fats, hydration, supplements, and nutrient timing. These dimensions were chosen after a thorough thematic analysis of consensus papers and interviews with 90 experts in collegiate sport nutrition. These seven dimensions encompass important introductory topics for the practitioner to begin conversations with athletes about their sport nutrition knowledge. Questions within the SNAK were developed utilizing the most updated consensus guidelines for sport nutrition, as well as with the guidance and feedback from both nutrition experts working in collegiate athletics and college athletes themselves. Additionally, all items were written generically and simply to optimize understandability and usefulness with a variety of sport types within college athletics. Further evaluation would be required to assess functionality of the screener in populations other than college athletes; however, given the generic nature of the items and simplicity of the wording, the screener could be used with non-collegiate athlete populations. The screener takes approximately five minutes to complete and is below the maximum number of questions that an expert panel of college sport dietitians believes such a questionnaire should include. Compared to other questionnaires created for college athletes, the present screener is approximately the same length as the tool developed by Shifflett et al. in 200219 (20 items), and is shorter than the newer questionnaires developed by Trakman et al.27,91 and Karpinski et al.29, both of which took longer than five minutes to complete (if duration to completion was reported). 91 Reliability and Validity The screener demonstrated acceptable construct validity based on the significant difference in scores between the college athletes and dietetic students, as well as significant differences between those who are majoring in health-focused studies (e.g., nutrition, kinesiology, pre-medical) versus non-health studies. There were no significant differences found between males and females, which is in contrast to previous literature that has shown females have significantly higher knowledge than males65,74,77,83. Similarly, this study found no significant differences in knowledge between revenue (e.g., football) and non-revenue (e.g., rowing) sports, which does not support previous research83. Although there is a likelihood that knowledge varies among different samples of college athletes, the difference in results between studies is more likely due to the different survey methods utilized. The pilot 25-question version of the SNAK screener did not meet external validity standards when compared to diet quality measured by the HEI, which is problematic for the validation of this screener. The athletes displayed high nutrition knowledge but poor diet quality. Both of these results have been seen in previous literature on athletes, albeit within studies when diet habits and knowledge were measured sepearately3-8,14. Although previous authors have shown that individuals with higher nutrition knowledge are more likely to meet nutrition recommendations3,15,17,33, the results of the present study exemplify that there is no guarantee that nutrition 92 knowledge leads to better eating behaviors. Other factors should be considered when evaluating the diet habits of college athletes, such as food environment or availability, taste, convenience, and economic factors53. Item refinement analyses showed acceptable item collinearity and discrimination results. First, the satisfactory collinearity indicates that the questions of the SNAK are distinct and do not overlap theoretically, which is an essential element of a formative measure105,107. This means that the items are not redundant or asking about the same thing. Second, the discrimination analyses were conducted as dimensional groupings of the items because the individual item responses were binary (e.g., agree/disagree) and a cumulative score per item does not exist. Additionally, the individual questions within a formative measure are meant to be distinct from one another and cover the entire breadth of the construct105; therefore, it is inappropriate to assume that correctness on any singular question should be able to discriminate between high and low scores of the overall construct. Discrimination analyses were thus conducted per dimension (e.g., carbohydrates), which meant analyzing whether or not a participant’s score on a single dimension could discriminate between those who achieved high and low scores on the SNAK screener. Only one of the seven dimensions (hydration) resulted in a correlation coefficient below the cutoff for consideration to eliminate from the screener. This indicates that scores on six of the seven dimensions should allow the practitioner to discriminate between athletes with higher or lower 93 scores; however, given the overall high total scores on the screener, these discrimination results should be used with caution to make such distinctions. The pilot 25-question SNAK screener displayed poor item difficulty. Of the total 25 items, 22 exceeded the cutoff of 80% of the sample answering it correctly, meaning those 22 items were too easy. When examining the hydration dimension alone (the only dimension that did not pass the discrimination analyses), only 9 of the 94 athletes scored under 100% correct for that section. This indicates that either the hydration questions were too easy, which was also indicated in the qualitative feedback provided during the retest phase, or hydration is an easy topic for this sample of college athletes. These item difficulty results suggest that easy questions should be altered or removed from the screener. The SNAK screener exhibited acceptable test-retest reliability results between the pilot and retest for a subset of the population. Additionally, valuable qualitative feedback was received during the retest. Participants indicated that many questions were put too simply, or they suggested meaningful wording changes. For example, one respondent noted that “Adaptation to training could be clarified; Maybe just [change] to ability to adapt to training needs”. Overall, the qualitative feedback aligned with the item difficulty results that many of the items were too easy. Additional feedback included suggested further defining of terms (e.g., energy availability) or clarifying statements. 94 No respondents noted that any questions were above their reading level or incomprehensible. Subsequent Revisions to the Pilot SNAK Screener Best practices in formative index development suggest that elimination or revision of items must be theoretically justified rather than based solely on statistical analyses105,107. Considering the statistical results combined with the face validity feedback from the retest sample, the wordings of many items were revised, and three items were removed. This was done to improve the item clarity, redundancy, relevance and acceptability of the screener. The revised version of the SNAK screener contains 22 questions in agree/disagree response format and covers the same seven dimensions as the pilot version (Appendix 2). Given that the pilot screener took an average five minutes to complete, the revised version should take less time. Although time-to-score analyses were not conducted, it is plausible that a practitioner could administer and score the SNAK screener for an athlete in approximately five minutes during a one-on-one consult, thus leaving plenty of time to discuss results and offer nutrition advice. Additionally, a practitioner could easily administer the SNAK to a large group of athletes (e.g., an entire football team) in approximately five to ten minutes and analyze 95 them efficiently at a later time. Expediency in assessing nutrition knowledge of an athlete population is novel and could prove extremely useful for use in college athletics. The SNAK screener could be incorporated into the operations of an athletic department either on a large scale (e.g., during annual pre-participation physicals that all NCAA athletes must complete), or on an individual basis (e.g., during a sport dietitian’s initial consultation with a freshman athlete). This is a beneficial tool for practitioners for easy assessment of athletes or teams who need nutrition education more than others, thus saving time and resources that may otherwise be wasted on athletes/teams that would not benefit from the education. Additionally, a practitioner could use SNAK results from a team to choose which topics are more important to begin nutrition presentations. Limitations A limitation of this project is that the sample was primarily female, a majority were athletes of non-revenue sports, and the SNAK has only been validated in a population of college athletes of a single institution in the Midwest United States; therefore, we cannot comment on its reliability or validity in other regions, institutions or athlete populations. Second, with an agree/disagree format, there is a higher probability of respondents being able to guess the correct answer than if the response format included more options. Third, only 29% of the pilot sample responded to the 96 request for retesting, which included some dietetic students. It would have been beneficial for face validity purposes to receive more feedback from a higher percentage of the target population. Lastly, external validity comparisons of nutrition knowledge to dietary habits yielded poor results. However, the sample was somewhat homogenous (mostly female non-revenue athletes) and included college athletes who showed some level of interest in nutrition, otherwise they would not have been likely to participate. Additionally, although knowledge about nutrition should theoretically lead to healthy dietary habits, this sample exemplifies that there is not a definitively causal relationship between knowledge and behavior. Strengths The SNAK screener was developed with a sample of college athletes and based on best practices for formative measure development. This included thorough concept explication and utilizing content validity feedback from expert practitioners (sport dietitians) working in the environment of interest (college athletics), which improves its practicality of use. Additionally, the questionnaire is within the max number of questions as indicated by the experts and includes nutrient timing as a dimension, which makes it specific to athletes. It was written using consensus papers in sport nutrition and in the language of the target population (college athletes). The SNAK is worded with general food terms, thus making it easily understandable and usable 97 across different regions and sport types. Its short duration to completion increases both ease of administration and analysis, and it could be administered via online format. Lastly, the qualitative feedback from the target population was used to inform revisions to the pilot version, thus improving face validity of the survey tool. Future Directions and Conclusions The nutrition needs of college athletes will vary between sport type, sexes, age, and other individual characteristics. For example, a football player’s nutrition needs will differ greatly from a cross-country runner’s needs, both during competition and in everyday life.; therefore, their nutrition knowledge will eventually need to differ. While not being able to address every potential variation in nutrition knowledge needs, the SNAK screener can be administered to any type of athlete and then used to give the practitioner an idea of where to begin with that athlete’s tailored planning. It is then up to the professional to determine what avenue of intervention to take based on the athlete’s knowledge and individual characteristics. As a future direction, the 22-question revised SNAK should be administered to a larger, diverse college athlete population with the goal of gaining additional qualitative feedback from the athletes to make any necessary changes based on face validity. Once the screener is finalized, it can be utilized in other regions to help give more insight into the true state of sport nutrition knowledge across college athletes. Additionally, future 98 researchers should investigate the barriers between application of nutrition knowledge and diet quality in college athletes. In conclusion, the SNAK is a short screening tool that can be used efficiently in a college athletics setting. Researchers, college athletics administration, and collegiate sport dietitians can all benefit from using this screener with singular or large groups of athletes as it can be analyzed quickly, thus making this an efficient way to identify athletes who may need nutrition education and support. 99 APPENDICES 100 APPENDIX A: Original SNAK Screener Please answer each question to the best of your ability. If you are unsure, you may leave it blank. Sport Nutrition Assessment of Knowledge (SNAK) Screener 1. Having low “energy availability” (the energy available for my body to use after cost of exercise is subtracted) can impair my health and performance. 2. It is vital for athletes to have an adequate “energy availability”, which means they must consume more energy (calories) than their energy (calorie) cost of exercise. 3. Having low “energy availability” will not affect my performance. 4. Carbohydrates are found in four different food groups: fruits, vegetables, starches/grains (such as bread, pasta, and rice), and dairy. 5. Carbohydrates are the primary fuel for my brain and muscles. 6. Carbohydrates stored in my muscles play an important role in my muscle’s adaptation to training. 7. The only role of protein in the body is for muscle building. 8. Only animal-based products contain protein (such as meat, seafood, dairy). 9. An athlete should eat as much protein as possible in order to build muscle. 10. The primary nutrient to eat after weightlifting is protein. 11. An athlete should remove fat out of the diet completely in order to lose weight. 12. Dietary fat intake is required for energy, structural support for cell membranes, and absorption of vitamins. 13. An athlete who eats very little dietary fat may have difficulty absorbing certain vitamins. 14. Eating a high-fat, low-carbohydrate diet will result in performance improvements. 15. Sports drinks are better for hydration than water during exercise, regardless of exercise duration. 16. Dehydration has no effect on athletic performance. 17. Signs of dehydration include muscle cramps, headaches, and/or not being hungry immediately after practice. 18. Athletes can get all the nutrients they need through whole food and do not need supplements. AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE 101 APPENDIX A (cont’d) 19. Like whole foods, supplements are regulated by a government entity for safety and efficacy, therefore are as safe to consume as whole food. AGREE DISAGREE 20. Taking supplements does not risk my athletic eligibility. 21. Consuming alcohol has little to no effect on my athletic performance. 22. A large meal consisting of primarily carbohydrates should be eaten 3-4 hours before practice/competition. 23. A snack consisting of primarily carbohydrates should be eaten 30-60 minutes before practice/competition. 24. Carbohydrate is the most important nutrient in the hours/minutes leading up to practice/competition. 25. For my best athletic performance, it does not matter at what time(s) I eat, as long as I eat enough food for the total day. AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE 102 APPENDIX B: Revised SNAK Screener Please answer each question to the best of your ability. If you are unsure, you may leave it blank. Sport Nutrition Assessment of Knowledge (SNAK) Screener 1. “Energy availability” is the energy (calories) eaten that is available for my body to use after calories burned during exercise is subtracted. Having LOW energy availability (i.e., eating less calories than I burn) can impair my health and performance. 2. It is vital for athletes to have an adequate energy availability, which means I must consume more energy (calories) than I burn. 3. Carbohydrates are only found in starches/grains such as bread, pasta, and rice. 4. Carbohydrates, as opposed to protein or fat, are the primary fuel for my muscles. 5. Carbohydrates play an important role in my muscle’s ability to adapt to my training. 6. Athletes need to eat animal-based products (such as meat, seafood, dairy) to get the protein they need. 7. The more protein I eat, the more muscle I will build. 8. The most important macronutrient to eat after weightlifting is protein. 9. If I want to lose weight, I should remove fat from my diet. 10. An athlete who eats very little dietary fat may have difficulty absorbing certain vitamins. 11. Eating a high-fat, low-carbohydrate (i.e., “keto”) diet will result in performance improvements. 12. Sports drinks (e.g., Gatorade) are better for hydration than water during exercise, regardless of exercise duration. 13. Dehydration has no effect on my athletic performance. 14. A headache, muscle cramps, or not being hungry after practice might mean I am dehydrated. 15. Assuming no deficiency is present, athletes do not need to take supplements. 16. Supplements are regulated by a government entity for safety and efficacy, therefore are as safe to consume as whole food. 17. Taking any supplement does not risk my athletic (NCAA) eligibility. AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE 103 APPENDIX B (cont’d) 18. Drinking alcohol has little to no effect on my athletic performance. 19. I should eat a full meal that is primarily made up of carbohydrates 3 to 4 hours before training/competition. 20. I should eat a snack that is primarily made up of protein 30 to 60 minutes before training/competition. 21. Carbohydrate is the most important nutrient in the hours/minutes leading up to training/competition. 22. For my best athletic performance, it does not matter at what time(s) I eat, as long as I eat enough food for the total day. AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE AGREE DISAGREE 104 CHAPTER FIVE: MANUSCRIPT THREE Chapter Five addresses Specific Aim 3 and the manuscript titled Diet Quality of NCAA Division I Athletes. Specific Aim 3: To assess the dietary habits and quality of college athletes using the United States Department of Health and Human Services (USDHHS) Automated Self- Administered 24-Hour Dietary Assessment Tool (ASA24). H 3.1: Athletes will exhibit dietary quality scores of 50 or less as determined by the Healthy Eating Index (HEI) developed by the United States Department of Agriculture (USDA). H 3.2: Female athletes will have a higher diet quality than male athletes. 105 Abstract OBJECTIVE: Evaluate the dietary quality of NCAA Division I college athletes. PARTICIPANTS: 94 college athletes (n= 21 male, 73 female) from 19 different varsity teams at an NCAA Division I university. METHODS: Athletes completed the Automated Self-Administered 24-hour (ASA24) Dietary Assessment Tool10 that was then analyzed for diet quality using the Healthy Eating Index (HEI)11. RESULTS: Average intakes for kilocalories, carbohydrates, protein, and dietary fat were assessed, with males reported significantly higher intakes for each nutrient. The average HEI score for the total sample was 59.2 ± 16.6, which is equivalent to a grade of F based on recommended grading standards12. Only nine athletes achieved an HEI score of 80 or better, which corresponds to grades A or B. There were no significant differences in HEI scores between sexes, class, majors, sport played, or those who did or did not report taking previous nutrition coursework. CONCLUSIONS: The dietary quality of college athletes is poor based on USDA recommendations for the general population. These inadequate intakes could potentially have negative effects on the athletes’ health and performance; therefore, college athletes should be encouraged to work with a qualified nutrition professional in order to improve their dietary habits. 106 Introduction There are approximately 500,000 student athletes within the National Collegiate Athletic Association (NCAA)30 who face a unique set of pressures and expectations unlike those of any other athlete population. Multi-million dollar revenues31 that many Division I athletic departments see annually are influenced strongly by the success of student athletes; therefore, an emphasis should be placed on keeping the athletes healthy while also optimizing their sport training. One avenue for the optimization of athlete health and performance is sport nutrition. Proper application and habitual use of nutrition strategies have been shown to improve the health, wellness, and athletic performance of college athletes1,2,49. Conversely, inadequate dietary habits could lead to an athlete experiencing low energy availability (LEA), typically caused by lower energy (i.e., calorie) consumption than needed to support normal physiologic function13. Low energy availability negatively affects cognition, cardiovascular health, bone health, gastrointestinal function, and reproductive health, among other physiologic functions13. In addition to negative effects on overall health, low energy availability decreases athletic performance and puts athletes at increased risk for injury13, which directly relates to their time spent training and competing. For this reason, it is vital that the dietary intake of college athletes be examined. 107 Dietary intake assessment is a foundational element of the nutrition care process. There are different methodologies that can be employed, including prospective techniques (i.e., direct observation by the researcher/practitioner) or retrospective techniques (i.e., an athlete recalling what foods/drinks were consumed over a specific period of time). Within these two categories are a myriad of tools that can be used to assess the dietary intake of college athletes. For example, researchers have directly observed college athletes at a meal to determine food selection and intake of carbohydrates, proteins, and vegetables7. Other researchers have employed retrospective tools such as 3- or 7-day diet records112,113, food frequency questionnaires5,81, or even a combination of multiple retrospective methods6 to assess the dietary intakes of college athletes. Although each method has its pros and cons, a benefit to having multiple options is the ability to tailor an approach to the specific situation114. When assessing a unique population like college athletes, it is important to optimize ease of use and accessibility, as well as use a tool that offers a comprehensive look at typical energy and specific nutrient intakes. One tool that could be used in this population is the Automated Self- Administered 24-hour (ASA24) Dietary Assessment Tool10. Developed by the National Cancer Institute (Bethesda, MD), the ASA24 has been shown previously to be a valid tool for measuring dietary intake115. Although the 24-hour recall is the least frequently used protocol in athlete populations because of its only single-day analysis114 and there 108 is only one other publication in the literature in which authors utilized the ASA24 with college athletes116, it has many benefits for use with this population. The ASA24 is an online platform that can be accessed from a desktop/laptop or internet-enabled phone, it uses digital imaging of foods with multiple portion size options, prompts the participant for clarifications/additions to the recall, uses the United States Department of Agriculture’s (USDA) most current survey database, and allows for automated processing of nutrient and food group intakes117. Previous authors have shown that the ASA24 compares well against interviewer-administered recalls118, observed intakes115, and the USDA’s Automated Multiple-Pass Method (AMPM) that is used in the United States National Health and Nutrition Examination Survey (NHANES)119. An additional benefit of using the ASA24 is the ability to analyze diet results using the Healthy Eating Index (HEI)11. The HEI analyzes 13 specific components of a person’s diet (total fruit whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acids, refined grains, sodium, added sugars, saturated fats)120. The scoring metric assigns each component a score between 5 and 10 points with specific standards per component12. The total possible score is 100, which indicates a person meets USDA recommendations for all 13 components. This metric is designed to align with the USDA’s 2015-2020 Dietary Guidelines for Americans121, thus allowing the evaluator to determine diet quality by comparing to nutrition guidelines for the general population. 109 Athletes have their own set of nutrition recommendations for optimizing performance, as discussed in a joint position statement from the Academy of Nutrition and Dietetics (AND), Dietitians of Canada (DoC), and the American College of Sports Medicine (ACSM)1. However, the vast majority of college athletes will not continue onto professional sport teams122 and therefore must re-integrate into the general population after college. Comparing their dietary intakes during college against the recommendations set forth by the USDA can be beneficial for beginning the nutrition education process for how athletes may need to alter their diets to optimize overall health, longevity, and disease prevention. Thoroughly evaluating diet quality is an important first step in determining the need to improve nutrition efforts for optimizing the health of college athletes. The purpose of this study is to evaluate the dietary quality of NCAA Division I college athletes using the ASA24 and HEI. Because previous researchers have shown that the nutrition intakes of college athletes are often suboptimal regardless of the tool used to assess intakes3-7, it is hypothesized that college athletes will exhibit dietary quality scores of 50 or less as determined by the HEI. 110 Methods Approval and Consent to Participate This research was given exempt approval status by the Institutional Review Board at Michigan State University. All data collection occurred online and included a disclaimer statement regarding participation, data safety and implied consent through completion of the survey. Participants Data for this paper were taken from the pilot test of another study that involved comparing the dietary quality of college athletes with their nutrition knowledge in order to validate a nutrition knowledge questionnaire. Given the qualitative nature of the pilot study, there is no simple rule of thumb that satisfies all study methodologies; therefore, sample size was determined subjectively for the pilot test in order to adequately satisfy the statistical procedures123. A minimum sample size of 50 college athletes was chosen48, but given the minimal participant risk of completing a survey, more than 50 were recruited to accommodate nonresponses, dropouts, or missing/unusable data. A total of 94 (n= 21 male, 73 female) student athletes from 19 different varsity teams at an NCAA Division I university participated in this study. Permission was received from all coaching staff prior to contacting the athletes. Inclusion criteria were 111 participation in a varsity sport and having at least one more year of athletic eligibility with the intention of returning to their sport for the 2020-2021 season. Student athletes were recruited directly via email. Study Design This present paper is a cross-sectional analysis of student athletes’ dietary quality. Data collection occurred between March and June 2020 at a time when no student athletes were on campus or participating in team activities due to the COVID- 19 outbreak. Student athletes completed an automated, self-administered 24-hour dietary recall. Data for 24-hour recalls were collected and analyzed using the ASA24, version 201810. The ASA assesses dietary intake information in four steps: 1) participant submits foods consumed at each meal/snack, 2) participant is prompted regarding the possibility of omitted meals/snacks, 3) details about cooking methods, portions, ingredients are questioned, and 4) participant is prompted to review commonly forgotten items124,125. It concludes with a question regarding whether this was a day of usual intake, less than usual, or more than usual125. ASA24 results were then analyzed using the HEI-2015 (the latest version of the HEI). The HEI scoring metric is organized into 13 components to assess overall diet quality, including nine “Adequacy” components (total fruit whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant 112 proteins, fatty acids) and four “Moderation” components (refined grains, sodium, added sugars, saturated fats)120. Each component is worth between 5 and 10 points with specific standards of scoring per component (Table 10)12. Total possible score is 100. There is currently no recommended cut-point for a specific score; however, a grading scheme has been recommended to help qualitatively describe adherence to the Dietary Guidelines for Americans (Table 11)12. Table 10. HEI Component Score Standards12 HEI Component (Score Standard for Maximum Standard for Minimum range) Score Score Adequacy Components (higher score indicates higher consumption) Total Fruits (0-5) Whole Fruits (0-5) Total Vegetables (0-5)  0.8 cup equiv./1000 kcal  0.4 cup equiv./1000 kcal  1.1 cup equiv./1000 kcal No fruit No whole fruit No vegetables Greens and Beans (0-5)  0.2 cup equiv./1000 kcal Whole Grains (0-10) Dairy (0-10) Total Protein Foods (0-5)  1.5 ounce equiv./1000 kcal 1.3 cup equiv./1000 kcal  2.5 ounce equiv./1000 kcal No dark-green vegetables, beans or peas No whole grains No dairy No protein foods Seafood and Plant Proteins  0.8 ounce equiv./1000 No seafood or plant (0-5) kcal proteins Fatty Acids (0-10) Ratio of poly- + mono- Ratio of poly- + mono- unsaturated fats to saturated fats  2.5 unsaturated fats to saturated fats  1.2 113 Table 10. (cont’d) Moderation Components (higher scores indicates lower consumption) Refined Grains (0-10) Sodium (0-10) Added Sugars (0-10) Saturated Fats (0-10)  1.8 ounce equiv./1000  4.3 ounce equiv./1000 kcal  1.1 gram/1000 kcal  6.5% of total energy  8% of total energy kcal  2 grams/1000 kcal  26% of energy  16% of energy *equiv. = equivalents; kcal = kilocalories Table 11. Grading Scheme of HEI Scores Based on Adherence to Dietary Guidelines for Americans Overall Score Grade 90 to 100 80 to 89 70 to 79 60 to 69 0 to 59 A B C D F Statistical Analyses Statistics were conducted using IBM SPSS v.26 (Armonk, NY). Diet quality scores and sub-scores based on the ASA24 dietary recalls were calculated using the SASⓇ software (SAS Institute, Inc.) code provided by the National Cancer Institute11. Individual HEI scores were estimated using the per-person HEI scoring algorithm 114 which allows a single HEI score to be calculated per individual using only one recall day11. Component HEI scores are density-based and independent of overall energy intake, however information on total energy intake is also provided120. Descriptives, including participant characteristics (sex, class, major, previous nutrition coursework, and sport), average nutrient intakes and average diet quality (HEI) scores were calculated. Independent t-tests were performed to determine differences in diet quality between males and females, under- and upperclassmen [underclassmen include freshmen, redshirt (RS) freshmen, sophomores, and RS sophomores; upperclassmen include all others], health and non-health majors (health majors include kinesiology, dietetics or nutritional sciences, and nursing or pre-medical; non-health includes all others), whether or not the participant has taken previous nutrition coursework in high school and/or college, and sport [revenue (football, men’s and women’s basketball, and ice hockey) versus non-revenue (all others)]. Effect sizes between differences in participant characteristics were calculated by dividing the mean difference in average diet quality scores between the two groups by the pooled standard deviation. Cohen’s criteria were used to evaluate effect size as small (0.2), medium (0.5) and large (0.8)126. Linear regressions were conducted to assess each participant characteristic individually as a predictor of overall HEI score. Lastly, all participant characteristics (sex, class, major, previous nutrition coursework, and 115 sport) were run in a linear regression together to evaluate each characteristic as a predictor of overall HEI score while controlling for other characteristics. Results Table 12 contains participant characteristics of the 94 athletes who completed the ASA24 diet recall. The sample was primarily female (n=73) underclassmen [n=56 red shirt (RS) sophomore or younger]. Most of the sample were non-health majors (n=61), meaning they did not report their major to be Kinesiology, Dietetics or Nutritional Sciences, or Pre-Medical or Nursing. Most reported having taken prior nutrition coursework (n=55) in either high school and/or college. A vast majority of the sample were non-revenue sport athletes (n=84 reported any sport other than football, men’s or women’s basketball, or ice hockey). Table 12. Participant Characteristics Males (n=21) Females (n=73) Age (years) 20.7 ± 1.3 19.7 ± 1.2 Total Sample (n=94) 19.9 ± 1.2 Class Underclassmen Upperclassmen Major Health Non-Health n=10 n=11 n=5 n=16 Previous Nutrition Coursework Yes No n=13 n=8 116 n=46 n=27 n=28 n=45 n=42 n=31 n=56 n=38 n=33 n=61 n=55 n=39 Table 12. (cont’d) Sports Revenue Football (n=4) Ice Hockey (n=2) Basketball (n=4) Cross Country (n=3) Cross Country (n=15) Golf (n=1) Soccer (n=1) Golf (n=2) Soccer (n=9) n=10 Non-Revenue Swim and Dive (n=2) Track and Field (n=3) Swim and Dive (n=8) Track and Field (n=5) n=84 Baseball (n=1) Tennis (n=1) Wrestling (n=3) Rowing (n=24) Field Hockey (n=3) Gymnastics (n=3) Average intakes for kilocalories, carbohydrates, protein, and dietary fat can be found in Table 13. Males reported significantly higher intakes for each nutrient. Table 13. Average Consumption Reported in ASA24 Nutrient Males (n=21) Females (n=73) Kilocalories Protein (g) Carbohydrates (g) Fat (g) 3299 ± 1513** 170 ± 82** 336 ± 161* 145 ± 70** 2224 ± 727 97 ± 36 264 ± 105 91 ± 38 *significantly higher than opposite sex, p<0.05 **significantly higher than opposite sex, p<0.001 Total Sample (n=94) 2464 ± 1051 113 ± 58 280 ± 122 91 ± 38 Minimum, maximum, and average HEI scores and grades can be found in Table 14. The average HEI score for the total sample was 59.2 ± 16.6, which is equivalent to a grade of F. There were no significant differences in HEI scores between males and females (p=0.209; Cohen’s d = 0.01), under- and upperclassmen (p=0.343; Cohen’s d 117 =0.2), health and non-health majors (p=0.701; Cohen’s d = 0.1), revenue and non-revenue sports (p=0.295; Cohen’s d = 0.4), or between those who did or did not have previous nutrition coursework (p=0.412; Cohen’s d = 0.08). Table 14. HEI Minimums, Maximums, Averages and Grades Min , Max Scored Average Grade Based on Average Total Sample Sex 27.0 , 93.7 59.2 ± 16.6 Male (n=21) Female (n=73) 30.6 , 88.4 27.0 , 93.7 Class Underclassmen (n=56) Upperclassmen (n=38) 27.0 , 93.7 32.9 , 88.3 Major Health (n=33) Non-Health (n=61) 27.0 , 93.7 29.2 , 90.6 Previous Nutrition Coursework Yes (n=55) No (n=39) 27.0 , 93.7 29.2 , 88.3 Revenue (n=10) Non-Revenue (n=84) 27.0 , 90.9 29.2 , 93.7 Sport 59.4 ± 19.0 59.2 ± 16.0 57.9 ± 17.3 59.2 ± 16.6 60.3 ± 17.1 58.7 ± 16.5 59.8 ± 16.2 58.4 ± 17.4 52.2 ± 19.5 60.1 ± 16.2 F F F F F D F D F F D Table 15 shows the frequency of participants who received each HEI grade. Only nine total athletes achieved an HEI score of 80 or better, which corresponds to grades A or B. Neither males nor females were more likely to achieve any specific HEI grade (likelihood ratio 0.05). 118 Table 15. Frequency of HEI Grades HEI Grade Males (n) Females (n) Total Sample (n) Frequency of… 0 4 4 1 12 n = 21 2 3 15 18 35 2 7 19 19 47 n = 73 N = 94 A B C D F TOTAL Linear regressions were conducted to assess each participant characteristic individually and together in a single model. Neither sex ( = -0.178, t(1) = -0.043, p = 0.966), class ( = 3.331, t(1) = 0.953, p = 0.343), major ( = 1.616, t(1) = 0.448, p = 0.655), previous nutrition coursework ( = 0.972, t(1) = 0.605, p = 0.547), nor sport ( = 7.812, t(1) = 1.412, p = 0.161) were predictors of overall HEI scores individually. In a single model that controlled for all other characteristics, no single characteristic stood out as a significant predictor of overall HEI scores. Table 16 shows HEI component scores for the total sample. The only component for which a score of zero was not achieved by any athlete was protein (i.e., no athletes reported eating zero protein). All other component results showed a minimum score of zero, meaning at least one athlete did not meet the minimum requirement for that component (e.g., at least one athlete did not eat whole fruit). The Adequacy component 119 for which the athletes achieved an average score closest to the maximum possible was total protein foods (4.3 out of 5). The Adequacy component for which the athletes achieved a score furthest from the maximum was whole grains (4.2 out of 10 possible). The highest score for a Moderation component (indicating lower consumption, which is considered more healthful) was added sugars (8.2 out of 10 possible). The lowest score for a Moderation component (indicating higher consumption) was sodium (2.9 out of 10 possible). Table 16. HEI Component Scores for the Total Sample Min , Max Scored Average ± SD Adequacy Components (higher score indicates higher consumption) Total Fruits (0-5) Whole Fruits (0-5) Total Vegetables (0-5) Greens and Beans (0-5) Whole Grains (0-10) Dairy (0-10) Total Protein Foods (0-5) Seafood and Plant Proteins (0-5) Fatty Acids (0-10) 0 , 5 0 , 5 0 , 5 0 , 5 0 , 10 0 , 10 0.3 , 5 0 , 5 0 , 10 3.1 ± 2.0 3.4 ± 2.1 3.7 ± 1.6 2.9 ± 2.3 4.2 ± 3.5 6.2 ± 3.2 4.3 ± 1.3 3.3 ± 2.1 5.3 ± 3.8 Moderation Components (higher scores indicates lower consumption) Refined Grains (0-10) Sodium (0-10) Added Sugars (0-10) Saturated Fats (0-10) 0 , 10 0 , 10 0 , 10 0 , 10 6.7 ± 3.6 2.9 ± 3.2 8.2 ± 2.4 5.2 ± 3.7 There were no significant differences between males versus females nor between those who have versus have not taken previous nutrition coursework for scores in any 120 component. Athletes of revenue sports scored significantly lower on the total vegetable (p=0.012) and greens and beans (p=0.007) than athletes of non-revenue sports, meaning they reported eating less of those food groups. Underclassmen (p=0.039) and health majors (p=.047) each showed significantly lower scores for added sugar intake in the Moderation category, meaning they reported eating more added sugar than upperclassmen and non-health majors, respectively. Discussion The purpose of this study was to evaluate the diet quality of NCAA Division I college athletes using the ASA24 food recall tool and HEI, developed by the NIH120. It was hypothesized that college athletes would exhibit dietary quality scores of 50 or less out of 100 possible. The average HEI score of this college athlete sample was a 59, with scores ranging from 27 to 94. Based on interpretation guidelines for the HEI12, these results correspond to an average grade of F for the total sample, with grades ranging from F to A. Only nine of the total 94 athletes scored above a C grade, and half of all athletes scored an F grade. Although we reject our hypothesis that athletes would score 50 or less on average, our estimate was not far off. It is clear that the college athletes surveyed are not meeting dietary quality standards set forth by the Dietary Guidelines for Americans121. 121 This is one of the few studies that have used the HEI to evaluate diet quality in college athletes. Although the HEI does not take athlete-specific considerations into account, it still provides valuable information by comparing the athlete’s dietary choices to USDA recommendations. One of the other few studies, previously conducted by Lawson et al116, involved surveying the diet quality of NCAA Division I football players, during which the authors found a mean HEI of 48 (considered an F grade), slightly below that of over half of athletes in the current study. A third study by Jontony et al127 resulted in a mean HEI score of 71.0, which corresponds to a C grade, in a sample of NCAA Division I rowing, swimming, gymnastics and wrestling athletes. The present study found no significant differences in HEI scores between males and females. This is in opposition to Jontony et al.127 who found significant differences between sexes, with women’s rowing scoring significantly higher than men’s rowing (74 versus 57 respectively; p=0.002)127. The sample of college athletes that participated in the Jontony et al. study was also from a Division I university also in the Midwest region of the United States; therefore, it is surprising the results of that study and the present study do not align. These results each give evidence to believe the diet quality of college athletes widely varies, but that the overall dietary intake of athletes is suboptimal. Interestingly, the present study also showed no significant differences in HEI scores between revenue and non-revenue sports. During the regular season, revenue sport athletes would likely report higher diet quality because they are typically being 122 provided one, or multiple, meals per day that have been tailored by a team dietitian (if the institution employs one). However, the present data were collected at a time when no athletes were on campus training, therefore the revenue sport athletes did not have the same nutrition access they would have had normally. Additionally, previous authors have shown non-revenue sport athletes display higher nutrition knowledge than revenue athletes83 and that nutrition knowledge is one of many factors that influence what college athletes eat53, it could be theorized that higher nutrition knowledge would result in better diet quality. Yet, results of this study show that might not be the case. The aforementioned studies by Lawson116 and Jontony127 used only revenue or non-revenue athletes, respectively, as their entire sample. In other words, their samples were homogenous with respect to revenue sport status, so the authors could not make revenue versus non-revenue sport comparisons. The present study sample included athletes from both revenue and non-revenue sports that showed no differences in diet quality; therefore, assumptions cannot yet be made as to whether or not participating in a revenue versus non-revenue sport is related to diet quality in college athletes. Although total HEI scores may vary, there is much more consistency among athlete populations when comparing scores on specific dietary components (i.e., food groups). Athletes in both the current study and the study by Jontony et al.127 demonstrated the highest diet quality in the total protein component scores and lowest 123 diet quality in whole grain scores. Although the athletes in the Jontony et al.127 study also scored well in the whole and total fruit components, the diets of the athletes in the present study aligned much more with previous literature that shows athlete diets are typically suboptimal in fruits and vegetables3-8. Other authors have utilized the HEI to evaluate diet quality of non-collegiate athlete or highly active populations. Tsoufi et al.128 reported all players on an elite EuroLeague men’s basketball team scored over 80 on the HEI, which is a B grade and much higher of an average than the current athlete sample. This result could be rooted in the differences in experience and/or utilization of nutrition strategies between college and elite athletes. Additionally, elite athlete data were obtained during their competitive seasons, whereas the present study assessed the athletes during the off- season when they were without on-campus food resources, which could help explain the lower HEI scores. Zanella et al.129 showed the HEI in a population of adolescent (approximately 16 years of age) volleyball players to be an average score of 43, which corresponds to an F grade. This score is lower than the athletes of the present study. Similarly to the aforementioned study with elite basketball players, the college athletes participating in the present study could have scored better than adolescent athletes because they are older and have more experience in athletics than adolescent athletes, which could mean more exposure to nutrition recommendations over time. This idea of age/experience 124 differences in HEI scores is also exemplified in a study by Farina et al.130 who found that younger soldiers (18-24 years of age) scored significantly less than older (>25 years) soldiers (HEI scores of 63 versus 65, respectively). The current study population showed found no differences in HEI scores between age groups (i.e., under- versus upperclassmen); therefore, it is clear that there is a lack of available data to make conclusions about whether or not age/experience plays a role in dietary quality. The HEI was originally created as a means to address whether Americans were meeting dietary recommendations131. The HEI was designed for use in groups of any age, income status, race, or any other demographic characteristic131. Since its development in 1995, it has been used primarily to examine diet quality and its relationship with chronic disease or food security in the general population12. The HEI was recently utilized within a National Health and Nutrition Examination Survey (NHANES) 2015-16, where data show that the average score for American adults 20 to 64 years is 59 out of 100132, which is the same score as the present sample of athletes. When separated by sex, the NHANES data show that males score slightly lower than females in the general population (57 versus 61, respectively121); however, we did not see any differences between males and females in the present study, both of whom scored approximately 59 out of 100. There also appears to be a difference in HEI scores between age groups in the general population, with adults 20 to 64 years of age scoring better than children and adolescents age 2 to 19 years (59 vs 53, respectively)132. This 125 aligns with our comparison between the present sample and the study about adolescent volleyball athletes by Zanella and colleagues129 where the college participants scored better than adolescents (59 versus 43, respectively). Limitations This project is not without limitations. First, the sample of college athletes surveyed was primarily females (n=73 of 94) and athletes of non-revenue sports (n=84 of 94); therefore, it cannot be said that these results are generalizable to all college athletes even at the university involved in the study. Second, there were not enough athletes of any single sport to make meaningful comparisons among sports. Third, we did not collect body weight nor training load information at the time of survey. Because a majority of nutrition recommendations for athletes are based on body weight and training load2, we cannot make assumptions about whether the athletes are meeting athlete-specific nutrition recommendations. Fourth, reporting bias may have affected the diet data, as they are based on self-reported dietary intakes. Fifth, because diet intake data were collected over only a 24-hour period and at a time during which no athletes were training on campus and therefore had no access to on-campus resources, we cannot make assumptions about their true dietary patterns or how their intake may differ during their competitive seasons. Finally, these HEI scores were derived from a 126 single 24-hour recall completed by the athletes, therefore assumptions about long-term diet patterns cannot be made. Strengths One strength of this project was the participation of similar amounts of under- and upperclassmen, health and non-health majors, and those who have and have not taken previous nutrition coursework. This variety helps improve the generalizability of the results. Second, these athletes were surveyed soon after being sent away from campus during the beginning stages of the COVID-19 global pandemic in 2020. During this time, the athletes were not on campus nor training with their teams; therefore, theoretically their diet recalls could be considered indicative of their everyday lives outside of being college athletes. Considering that most college athletes will not play professional sports after college122, obtaining diet quality information during a time that simulates their lives as non-athletes could help inform them how to structure dietary habits as future members of the general population. Future Directions and Conclusion To our knowledge, this is only the third study to utilize the HEI to measure diet quality in college athletes. Our results show that college athletes exhibit a diet quality that is similar to that of the general American population, which is an overall poor diet 127 quality. Future research in this area should include using HEI to assess the diet quality of athletes both in- and outside-of their sport seasons. This could help to address potential inadequacies during times of lesser and also increased training. Second, anthropometric measurements should be assessed at the same time as the diet survey in order to compare dietary intake with nutrition recommendations for athletes that are based on body weight and training load. Third, studies should be conducted that compare the general-population-specific HEI scores with emerging athlete-specific measures of diet quality, such as the Athlete Diet Index (ADI)133. Lastly, more intervention studies should be conducted to determine if diet quality can be increased through nutrition education or other strategies, as has been shown by previous literature to be possible116. In conclusion, this study provides evidence that the dietary quality of college athletes is poor based on USDA recommendations for the general population, reflecting inadequate intakes of important food groups such as fruits, vegetables, and whole grains, among others. These inadequate intakes could potentially have negative effects on the athletes’ health and performance; therefore, college athletes should be encouraged to work with a qualified nutrition professional in order to improve their dietary habits. Additionally, this study supports the need for universities to hire enough nutrition professionals (i.e., Registered Dietitians) to provide nutrition 128 counseling to the entire student athlete population and help these individuals optimize their dietary quality in the present and preemptively for their futures. 129 CHAPTER SIX: SUMMARY AND CONCLUSIONS Proper nutrition is important for health, wellness and maximizing exercise performance, particularly for college athletes who need enough energy and nutrients for sustained vigor throughout the school year, prevention of or recovery from illness/injury in addition to fueling for sport performance1,2. Current evidence suggests that college athletes have sub-optimal nutrition practices3-8, which can result in detrimental physiologic effects such as low energy availability that could lead to impaired cognitive, cardiovascular, and gastrointestinal function13. There are many potential explanations for these sub-optimal dietary practices, and among these is a lack of nutrition knowledge, specifically knowledge related to fueling sport performance. In an attempt to address this potential lack of knowledge, many different survey tools have been used by researchers to assess the nutrition knowledge of athletes. Unfortunately, all have limitations that make them inappropriate for use in a college athletics setting, such as not having been validated prior to use20-23, being validated using only the general population (i.e., not athletes)9,25, or being validated using athletes of specific sports (e.g., track and field)23,24. Additional factors such as lengthiness or international food terms (e.g., “courgettes” used in European countries, known as “zucchini” in the United States) have made other assessment tools impractical for using 130 with an American college athlete sample9,24-27,29. A measure designed to assess the sport nutrition knowledge of all college athletes needs to be written simply with generic food terms. It needs to have characteristics that promote such athletes to respond in completion (such as a simple response format), and it needs to be written in a way that practitioners believe in its usefulness and realistic ease of use (such as being short in length for easy distribution and scoring). Without a valid assessment tool for this population, it cannot be determined whether or not a lack of knowledge is a relevant cause for the poor nutrition habits. Therefore, the purpose of this dissertation was to examine the relationships between sport nutrition knowledge and dietary habits of college athletes through the development and validation of a sport nutrition knowledge assessment tool made specifically for college athletes. Through three chapters, we investigated the state of nutrition knowledge and diet quality in college athletes. We 1) used a previously published tool9, 2) developed and validated a new screener for assessing the sport nutrition knowledge of college athletes, and also 3) gained insight into the diet quality of college athletes based on USDA standards. Our findings indicate that the nutrition knowledge of college athletes varies with the type of assessment tool utilized, which could be related to whether or not the tool was validated in college athletes. Using a previously published tool that was validated in the general population9, the nutrition knowledge of a college athlete sample was low (Chapter Three). When a tool developed in college athletes was 131 utilized, the sport nutrition knowledge of the sample appeared to be high; however, poor item difficulty results coupled with qualitative feedback from the athletes suggest the survey was too easy, which could be one reason for the high knowledge results (Chapter Four). Lastly, Chapter Five of this dissertation shows that the diet quality of college athletes is poor by USDA standards, yet matches diet quality scores in the general American population. Overall, this dissertation indicates that having high nutrition knowledge does not relate to high diet quality in college athletes, i.e., knowledge does not lead to behavior. This result has many implications for practitioners working in college athletics. Coaches, athletic trainers, registered dietitians, and other health providers working with college athletes should be made aware of the likelihood that their athletes have a poor diet quality. Over time, poor dietary practices could lead to impairments to health, performance, recovery, as well as an increased injury risk13. Having an optimal diet cannot guarantee the athlete will never be sick, injured, or perform poorly. However, it can reduce the risk of illness134 and optimize training efforts2 that can help protect them from potential injury and improve their skill. Therefore, it is imperative that coaches and the athlete care team prioritize optimizing the dietary habits of their athletes. Given the results of this dissertation, it does not appear that increasing nutrition knowledge would be an effective solo strategy for achieving that goal. However, 132 assessing an athlete’s knowledge is still a necessary first step in any intervention process. Existence of the knowledge must be confirmed/denied before taking further steps to improve the behavior, if not just to rule it out as a causal factor. The updated Sport Nutrition Assessment of Knowledge (SNAK) screener is 22-questions in length and should take less than five minutes to administer and score, thus making it efficient and realistic for use by practitioners in college athletics. Once the level of nutrition knowledge is established for an athlete (or group of athletes), the level of intervention necessary can be determined. This could include a full dietary analysis and one-on-one nutrition counseling to address any disparities within the athlete’s diet pattern. Ideally, an institution would employ nutrition experts who could work directly with the athletes, as other athlete care practitioners (e.g., strength coaches) would not be qualified to manage the task. Based on the results of this dissertation, it should not be expected that improving nutrition knowledge alone will result improved dietary choices, so it is important that qualified professionals are available to explore different avenues of intervention with the athletes. Strengths and Limitations This dissertation has many strengths. First, the diet recall and quality measures utilized were created by well-recognized, reputable organizations10,12, thus providing confidence in the validity of dietary data collected. An additional strength was the use 133 of best practices in formative index development to create the SNAK screener. This included obtaining qualitative feedback from many experts working in collegiate sport nutrition, as well as feedback from college athletes themselves. The screener was also developed as an efficient method for assessing sport nutrition knowledge quickly, thus giving it optimal practicality for use in the college athletics setting. One limitation of this project is the reliance on a sample of college athletes from a single NCAA Division I university in the Midwest, thus limiting the generalizability of results to the entire college athlete population. Although the data do not yet exist in college athletes, a previous study has shown significant regional differences in diet quality (measured by the HEI), with the highest scores achieved by a sample of adults (average age 50 years) in the western United States (U.S.) and lowest scores in the southern U.S.135 Based on these findings, it is possible that this occurrence would also be seen in the college athlete population. A second limitation is that the athlete diet data were taken cross-sectionally and at time when the athletes were not in-season, thus limiting our ability to make assumptions about their habitual or during-season intake patterns. Previous authors have shown that athletes have better dietary habits on competition days compared to training days128; therefore, it is possible the athletes would have had different (perhaps better) dietary habits if we had assessed diet while they were training. The ideal method would have been to obtain at least seven days of diet data at multiple times throughout 134 a given year (i.e., pre-, during- and post-season) in order to gain a better understanding of an athlete’s diet patterns and habits over time. This would allow for a comparison of diet quality between training periods and provide further insight into how serious the issue of poor dietary habits in college athletes might be. Future Research Directions The next step in this research process would be to administer the updated SNAK to a large sample of college athletes of mixed sports and reevaluate the item difficulty and dimensional discrimination statistics. Ideally the SNAK would also be administered with a diet recall as a measure of external validity for the updated SNAK. Once the validity of the updated SNAK has been established, it could be administered to samples of college athletes at other institutions to improve our current understanding of the nutrition knowledge across the entire college athlete population. Although the item difficulty and face validity results for the original SNAK suggest the screener was too easy, that is not the only feasible explanation for the high knowledge scores found in Chapter Four. It is possible that this sample of athletes truly did have high nutrition knowledge and there were other reasons for their poor diet quality. Many factors influence athletes’ food choices, including motivation to eat healthy or perform in their sport, taste preferences, food environment/availability, cost, and body image/weight control, among others53. Athletes’ food choices are complex and 135 continually changing53, so practitioners must be aware of not only the factors themselves but the way in which intake patterns vary over time. There are almost 100 behavior change techniques identified in the literature that practitioners could use to improve dietary habits in college athletes136; however, with so many food decision factors involved, it is unlikely that there is a single, copy-and-paste behavior change formula that will work to improve the diet of all college athletes. The approach to improving an athlete’s diet behavior will need to be individualized (either to the athlete or the sport team), and practitioners must make theory- or experience-informed decisions to tailor intervention efforts. One way to guide the sport nutrition field on how this could happen is to conduct a study that would foster discussion in the field about the best methods to promote behavior change in this specific population. For example, dietitians across the country working in collegiate sports could be commissioned to write two case studies of their experiences: one that describes strategies utilized for improving diet quality that resulted in a successful outcome, and one that describes strategies utilized that were unsuccessful. Then, a thematic analysis of these experiences could be conducted to identify similarities in strategies that work or do not work. This analysis could then be used to create a model for nutrition behavior change that is specific to the college athlete and collegiate sport environment. Having a college-athlete-specific behavior change model 136 could revolutionize the nutrition care process for registered dietitians working in collegiate sports. In summary, there appears to be a gap between nutrition knowledge and adequate dietary behaviors in college athletes. Poor diet quality coupled with the unique set of pressures placed on college athletes can increase their vulnerability for poor health outcomes. There is much to still be understood, but these nutrition knowledge and inadequate diet quality issues must be addressed in order to promote the health, longevity, and wellness of this population. 137 REFERENCES 138 REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. Academy of Nutrition and Dietetics DoC, American College of Sports Medicine. Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance. Journal of the Academy of Nutrition and Dietetics. 2016;116:501-528. Sports Nutrition: A Handbook for Professionals. Chicago, IL: Academy of Nutrition and Dietetics; 2017. Spronk I, Kullen C, Burdon C, O’Connor H. Systematic Review: Relationship between nutrition knowledge and dietary intake. British Journal of Nutrition. 2014;111(1713-1726). Heaney S, O’Connor H, Michael S, Gifford J, Naughton G. Nutrition knowledge in athletes: a systematic review. International journal of sport nutrition and exercise metabolism. 2011;21:248-261. Webber K, Stoess AI, Forsythe H, Kurzynske J, Vaught JA, Adams B. Diet quality of collegiate athletes. College Student Journal. 2015;49:251+. Shriver LH, Betts NM, Wollenberg G. Dietary Intakes and Eating Habits of College Athletes: Are Female College Athletes Following the Current Sports Nutrition Standards? Journal of American College Health. 2013;61(1):10-16. Brown K, Ellis J, Brooks S, Brown L, Krick R, Anderson A. Selection and intake of carbohydrate, protein, and vegetables among NCAA division I athletes. Academy of Nutrition and Dietetics: Sports, Cardiovascular and Wellness Nutrition Pulse2018. Burkhart SJ, Pelly FE. Dietary intake of athletes seeking nutrition advice at a major international competition. Nutrients. 2016;8(10). Calella P, Iacullo VM, Valerio G. Validation of a general and sport nutrition knowledge questionnaire in adolescents and young adults: GeSNK. Nutrients. 2017;9. 10. Automated Self-Administered 24-Hour (ASA24) Dietary Assessment Tool. National Cancer Institute. https://epi.grants.cancer.gov/asa24. Published 2018. Accessed. 139 11. 12. 13. 14. The Healthy Eating Index - Scores for Describing Dietary Intake. National Cancer Institute. https://epi.grants.cancer.gov/hei/hei-scores-for-describing-dietary- intake.html. Published 2017. Accessed February 14, 2021. Krebs-Smith SM, Pannucci TE, Subar AF, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591-1602. Logue D, Madigan SM, Delahunt E, Heinen M, Donnell S-JM, Corish CA. Low energy availability in athletes: A review of prevalence, dietary patterns, physiological health, and sports performance. Sports Medicine. 2018;48:73-96. Trakman GL, Forsyth A, Devlin BL, Belski R. A systematic review of athletes’ and coaches’ nutrition knowledge and reflections on the quality of current nutrition knowledge measures. Nutrients. 2016;8:570. 15. Alauntye I, Perry JL, Aubrey T. Nutritional knowledge and eating habits of professional rugby league players: does knowledge translate into practice? Journal of the International Society of Sports Nutrition. 2015;12. 16. 17. 18. 19. 20. Cupisti A, D’Alessandro C, Castrogiovanni S, Barale A, Morelli E. Nutrition knowledge and dietary composition in Italian adolescent female athletes. International journal of sport nutrition and exercise metabolism. 2002;12:207-219. Folasire OF, Akomolafe AA, Sanusi RA. Does nutrition knowledge and practice of athletes translate to enhanced athletic performance? Cross-sectional study amongst Nigerian undergraduate athletes. Global Journal of Health Science. 2015;7:215-225. Rossi FE, Landreth A, Beam S, Jones T, Norton L, Cholewa JM. The effects of a sports nutrition education intervention on nutritional status, sport nutrition knowledge, body composition, and peformance during off season training in NCAA Division I baseball players. Journal of Sports Science and Medicine. 2017;16:60-68. Shifflett B, Timm C, Kahanov L. Understanding of athletes’ nutritional needs among athletes, coaches, and athletic trainers. Research Quarterly for Exercise and Sport. 2002;73(3):357-362. Torres-McGehee TM, Pritcherr KL, Zippel D, Minton DM, Cellamare A, Sibilia M. Sports nutrition knowledge among collegiate athletes, coaches, athletic trainers, and strength and conditioning specialists. Journal of Athletic Training. 2012;47(2):205-211. 140 21. 22. 23. 24. 25. 26. 27. 28. 29. Rosenbloom CA, Jonnalagadda SS, Skinner R. Nutrition knowledge of collegiate athletes in a Division I National Collegiate Athletic Association Institution. Reserach and Professional Briefs. 2002;102(3):418-420. Zawila LG, Steib C-SM, Hoogenboom B. The female collegiate cross-country runner: nutritional knowledge and attitudes. Journal of Athletic Training. 2003;38(1):67-74. Blennerhasset C, McNaughton L, Cronin L, Sparks S. Development and implementation of a nutrition knowledge questionnaire for ultraendurance athletes. International journal of sport nutrition and exercise metabolism. 2019;29:39- 45. Furber MJW, Roberts JD, Roberts MG. A valid and reliable nutrition knowledge questionnaire for track and field athletes. BMC Nutrition. 2017;3. Zinn C, Schofield G, Wall C. Development of a psychometrically valid and reliable sports nutrition knowledge questionnaire. Journal of Science and Medicine in Sport. 2005;8(3):346-351. Trakman GL, Forsyth A, Hoye R, Belski R. The nutrition for sport knowledge questionnaire (NSKQ): development and validation using classical test theory and Rasch analysis. Journal of the International Society of Sports Nutrition. 2017;14. Trakman GL, Forsyth A, Hoye R, Belski R. Development and validation of a brief general and sports nutrition knowledge questionnaire and assessment of athletes’ nutrition knowledge. Journal of the International Society of Sports Nutrition. 2018;15. Reilly C, Maughan R. The development of a reliable and validated questionnaire to assess sports nutrition knowledge. Unpublished manuscript. 2007. Karpinski CA, Dolins KR, Bachman J. Development and Validation of a 49-item sports nutrition knowledge instrument (49-SNKI) for adult athletes. Top Clin Nutr. 2019;34(3):174-185. 30. Irick E. 1981-82 - 2015-16 NCAA sports sponsorship and participation rates report. Indianapolis, IN: National Collegiate Athletic Association;2016. 31. DL F. NCAA revenues and expenses of Division I intercollegiate athletics program report: fiscal years 2004 through 2015. Indianapolis, IN2016. 141 32. Ketterly J, Mandel C. College Athletes. In: Karpinski C, Rosenbloom C, eds. Sports Nutrition: A Handbook for Professionals. 6 ed. Chicago, IL: Academy of Nutrition and Dietetics; 2016:266-295. 33. Wardle J, Parmenter K, Waller J. Nutrition knowledge and food intake. Appetitie. 2000;34:269-275. 34. NCAA. What is the NCAA? http://www.ncaa.org/about/resources/media- center/ncaa-101/what-ncaa. Published 2020. Accessed. 35. NCAA. Our Three Divisions. http://www.ncaa.org/about/resources/media- center/ncaa-101/our-three-divisions. Published 2019. Accessed. 36. NCAA. NCAA Recruiting Facts. 2018. 37. Arnett JJ. Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist. 2000;55(5):469-480. 38. NCAA. NCAA GOALS Study of the Student-Athlete Experience - Initial Summary of Findings January 2016. 2016. 39. NCAA. Play Division I Sports. Division I Academic Eligibility Web site. http://www.ncaa.org/student-athletes/play-division-i-sports. Published 2020. Accessed July 14, 2020. 40. 41. 42. Growth of the Sports RD: Collegiate, Professional, and Military. Collegiate and Professional Sport Dietitian Association (CPSDA);2017. Parsons JT. 2014-15 NCAA Sports Medicine Handbook. In: NCAA, ed. 25 ed. Indianapolis, IN: NCAA; 2014. (CPSDA) CaPSDA. Timeline of CPSDA Advocacy Efforts Leading to the NCAA’s Deregulation of Athlete Feeding. https://www.sportsrd.org/ncaa-deregulation- of-feeding/. Published 2015. Accessed. 43. Olson E. Sports dietitians fueling top football programs. In. Associated Press: ESPN; 2011:http://www.espn.com/espn/wire?id=6913432. 44. Clark K. College football’s last frontier: better food. In. The Wall Street Journal2011:https://www.wsj.com/articles/SB10001424052970204138204576599220 823556108. 142 45. 46. 47. 48. 49. 50. 51. Eder S. Some dietitians say college athletes are underfed. In. The New York Times2012:https://www.nytimes.com/2012/2010/2026/sports/ncaafootball/dietitia ns-press-ncaa-to-allow-more-meals-for-athletes.html. Rosenfeld V. NCAA nutrition regulations hamper student-athletes on the field and in the classroom. In. Stack.com2013:https://www.stack.com/a/ncaa-nutrition. (CPSDA) CaPSDA. 2017 CPSDA Collegiate Fueling Station Management & Staffing Survey. https://www.sportsrd.org/educational-resources-2/research- library/. Published 2017. Accessed. Carpenter S. Ten Steps in Scale Development and Reporting: A Guide for Researchers. Communication Methods and Measures. 2018;12(1):25-44. IOC consensus statement on sports nutrition 2010. Journal of sports sciences. 2011;29(sup1):S3-S4. Fink HH, Burgoon LA, Mikesky AE. Practical Applications in Sports Nutrition. Mississauga, Ontario: Jones and Bartlett Publishers Canada; 2006. Karsay K. Construct. In: Matthes J, ed. The International Encyclopedia of Communication Research Methods: John Wiley & Sons, Inc.; 2017. 52. Gratton C. Circular definitions, circular explanations, and infinite regresses. Argumentation. 1994;8(3):295-308. 53. Birkenhead KL, Slater G. A Review of Factors Influencing Athletes’ Food Choices. Sports Medicine. 2015;45(11):1511-1522. 54. Adamek J. Academic Fraud in Revenue and Nonrevenue Sports. The Sport Journal. https://thesportjournal.org/article/academic-fraud-in-revenue-and- nonrevenue- sports/#:~:text=Revenue%20sport.,a%20negative%20net%20generated%20revenu e. Published 2017. Accessed July 17, 2020. 55. 56. Tam R, Beck KL, Manore MM, Gifford J, Flood VM, O’Connor H. Effectiveness of Education Interventions Designed to Improve Nutrition Knowledge in Athletes: A Systematic Review. Sports Medicine. 2019;49(11):1769-1786. Phillips AW, Reddy S, Durning SJ. Improving response rates and evaluating nonresponse bias in surveys: AMEE Guide No. 102. Medical Teacher. 2016;38(3):217-228. 143 57. Bunce DM, Flens EA, Neiles KY. How Long Can Students Pay Attention in Class? A Study of Student Attention Decline Using Clickers. Journal of Chemical Education. 2010;87(12):1438-1443. 58. Edwards P, Roberts I, Clarke M, et al. Increasing response rates to postal questionnaires: systematic review. Bmj. 2002;324(7347):1183. 59. Adult Literacy in the United States. National Center for Education Statistics. https://nces.ed.gov/datapoints/2019179.asp. Published 2019. Accessed January 26, 2021. 60. 61. Barr SI. Nutrition knowledge of female varsity athletes and university students. Journal of the American Dietetic Association. 1987;87(12):1660-1664. Frederick L, Hawkins ST. A comparison of nutrition knowledge and attitudes, dietary practices, and bone densities of postmenopausal women, female college athletes, and nonathletic college women. Journal of the American Dietetic Association. 1992;92(3):299-305. 62. Guinard JX, Seador K, Beard JL, Brown PL. Sensory acceptability of meat and dairy products and dietary fat in male collegiate swimmers. International journal of sport nutrition. 1995;5(4):315-328. 63. Abood DA, Black DR. Health education prevention for eating disorders among college female athletes. American Journal of Health Behavior. 2000;24(3):209-219. 64. Abood DA, Black DR, Birnbaum RD. Nutrition Education Intervention for College Female Athletes. Journal of nutrition education and behavior. 2004;36(3):135- 139. 65. Azizi M, Rahmani-Nia F, Malaee M, Malaee M, Khosravi N. A study of nutritional knowledge and attitudes of elite college athletes in Iran. Brazilian Journal of Biomotricity. 2010;4(2):105-112. 66. Hornstrom GR, Friesen CA, Ellery J, Pike K. Nutrition Knowledge, Practices, Attitudes, and Information Sources of Mid-American Conference College Softball Players. Food and Nutrition Sciences. 2007;2:109-117. 67. Weeden AM, Olsen Jr, Batacan JM, Peterson T. Differences in collegiate athlete nutrition knowledge as determined by athlete characteristics. The sport journal. 2014;17. 144 68. 69. Buffington BC, Melnyk BM, Morales S, Lords A, Zupan MR. Effects of an energy balance educational intervention and the COPE cognitive behavioral therapy intervention for Division I U.S. Air Force Academy female athletes. J Am Assoc Nurse Pract. 2016;28(4):181-187. Shoaf LR, McClellan PD, Birskovich KA. Nutrition knowledge, interests, and information sources of male athletes. Journal of Nutrition Education. 1986;18(6):243-245. 70. Wiita BG, Stombaugh IA. Nutrition Knowledge, Eating Practices, and Health of Adolescent Female Runners: A 3-Year Longitudinal Study. 1996;6(4):414. 71. 72. Collison SB, Kuczmarski MF, Vickery CE. Impact of nutrition education on female athletes. American Journal of Health Behavior. 1996;20:14-23. Rash CL, Malinauskas BM, Duffrin MW, Barber-Heidal K, Overton RF. Nutrition-related knowledge, attitude, and dietary intake of college track athletes. The Sport Journal. 2008:10. 73. Hoogenboom BJ, Morris J, Morris C, Schaefer K. Nutritional knowledge and eating behaviors of female, collegiate swimmers. N Am J Sports Phys Ther. 2009;4(3):139-148. 74. Jessri M, Jessri M, RashidKhani B, Zinn C. Evaluation of Iranian college athletes' sport nutrition knowledge. International journal of sport nutrition and exercise metabolism. 2010;20(3):257-263. 75. Davar V. Nutritional Knowledge and Attitudes Towards Healthy Eating of College-going Women Hockey Players. Journal of Human Ecology. 2012;37(2):119- 124. 76. Kunkel ME, Bell LB, Luccia BHD. Peer nutrition education program to improve nutrition knowledge of female collegiate athletes. Journal of Nutrition Education. 2001;33(2):114-115. 77. Dunn D, Turner LW, Denny G. Nutrition knowledge and attitudes of college athletes. The Sport Journal. 2007;10. 78. Arazi H, Hosseini R. A comparison of nutritional knowledge and food habits of collegiate and non-collegiate athletes. SportLogia. 2012;8:100-107. 145 79. Valliant MW, Emplaincourt HP, Wenzel RK, Garner BH. Nutrition education by a registered dietitian improves dietary intake and nutrition knowledge of a NCAA female volleyball team. Nutrients. 2012;4(6):506-516. 80. Andrews A, Wojcik JR, Boyd JM, Bowers CJ. Sports nutrition knowledge among mid-major division I university student-athletes. Journal of Nutrition and Metabolism. 2016;2016. 81. Abbey EL, Wright CJ, Kirkpatrick CM. Nutrition practices and knowledge among NCAA Division III football players. Journal of the International Society of Sports Nutrition. 2017;14(1):13. 82. Holden SL, Forester BE, Smith AL, Keshock CM, Williford HN. Nutritional knowledge of collegiate athletes. Applied Research in Coaching and Athletics Annual. 2018;33:65-77. 83. Werner EN, Guadagni AJ, Pivarnik JM. Assessment of nutrition knowledge in division I college athletes. Journal of American college health.1-8. 84. Martinelli L. The implementation and evaluation of a nutrition education programme for university elite athletes. Progress in Nutrition. 2013;15(2):71-80. 85. Heikkilä M, Valve R, Lehtovirta M, Fogelholm M. Development of a nutrition knowledge questionnaire for young endurance athletes and their coaches. Scandinavian Journal of Medicine & Science in Sports. 2018;28(3):873-880. 86. 87. 88. 89. Towler G, Shepherd R. Development of a nutriitonal knowledge questionnaire. Journal of Human Nutrition and Dietetics. 1990;3:255-264. Parmenter K, Wardle J. Evaluation and design of nutrition knowledge measures. Journal of Nutrition Education. 2000;32(5). Kliemann N, Wardle J, Johnson F, Croker H. Reliability and validity of a revised version of the General Nutrition Knowledge Questionnaire. European journal of clinical nutrition. 2016;70:1174-1180. Tam R, Beck KL, Gifford JA, Flood VM, O’Connor HT. Development of an Electronic Questionnaire to Assess Sports Nutrition Knowledge in Athletes. Journal of the American College of Nutrition. 2020:1-9. 90. Standards for educational and psychological testing. American Educational Research Association, American Psychological Assocation, National Council on 146 Measurement in Education, & Joint Committee on Standards for Educational and Psychological Testing; 2014. Trakman GL, Forsyth A, Hoye R, Belski R. Developing and validating a nutrition knowledge questionnaire: key methods and considerations. Public health nutrition. 2017;20(15):2670-2679. IBM SPSS Statistics for Windows [computer program]. Version 24. Armonk, NY2016. Fields A. Discovering Statistics Using IBM SPSS Statistics. 4 ed: SAGE Publications; 2016. 91. 92. 93. 94. Worme JD, Doubt TJ, Singh A, Ryan CJ, Moses FM, Deuster PA. Dietary patterns, gastrointestinal complaints, and nutrition knowledge of recreational triathletes. The American journal of clinical nutrition. 1990;51:690-670. 95. Douglas PD, Douglas JG. Nutrition knowledge and food practices of high school athletes. Journal of the American Dietetic Association. 1984;84(10):1198-1202. 96. Hamilton G, Thomson C, Hopkins W. Nutrition knowledge of elite distance runners. NZ J Sports Med. 1994;22. 97. Kelly B. NCAA - An overview of scioeconomic status’s impact on college athletes, and the regulations and impact that can revolutionize the amateurism world. 6 Pace Intell Prop Sports & Ent LF. 2016;212. 98. Gilis JT, Anderson DL, Morgan AL, Hamady CM. Nutrition knowledge and interest of collegiate athletes at a Division I university. J Food Nutr. 2014;1(203). 99. Hull MV, Jagim AR, Oliver JM, Greenwood M, Busteed DR, Jones MT. Gender differences and access to a sports dietitian influence dietary habits of collegiate athletes. JISSN. 2016;13(38). 100. Manore MM, Patton-Lopez MM, Meng Y, Wong SS. Sport nutrition knowledge, behaviors, and beliefs of high school soccer players. Nutrients. 2017;9(350). 101. Harnack L, Block, G., & Lane, S. Influence of Selected Environmental and Personal Factors on Dietary Behavior for Chronic Disease Prevention: A Review of the Literature. Journal of Nutrition Education. 1997;29(6):306-312. 147 102. The Social Ecological Model: A Framework for Prevention. https://www.cdc.gov/violenceprevention/publichealthissue/social- ecologicalmodel.html. Accessed. 103. Institute NC. The Healthy Eating Index - Population Ratio Method. https://epi.grants.cancer.gov/hei/population-ratio-method.html. Published 2017. Updated August 29, 2017. Accessed. 104. Magno C. Demonstrating the difference between classical test theory and item response theory using derived test data. The International Journal of Educational and Psychological Assessment. 2009;1(1):1-11. 105. Diamantopoulos A, Siguaw JA. Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. British Journal of Management. 2006;17:263-282. 106. Bollen K, Lennox R. Conventional wisdom on measurement: a structural equation perspective. Psychological Bulletin. 1991;110(2):305-314. 107. Diamantopoulos A, Winklhofer HM. Index construction with formative indicators: an alternative to scale development. Journal of Marketing Research. 2001;38:269-277. 108. Rusticus S. Content Validity. In: Michalos AC, ed. Encyclopedia of Quality of Life and Well-Being Research. Dordrecht: Springer Netherlands; 2014:1261-1262. 109. James R. Morrow Jr. AWJ, James G. Disch, Dale P. Mood. Measurement and evaluation in human performance. Champaign, IL: Human Kinetics; 1995. 110. Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariable methods. Boston, MA: PWS-Kent Publication Co.; 1988. 111. Kline P. The Handbook of Psychological Testing. London: New York: Routledge; 2000. 112. Kirwan RD, Kirwan RD, Kordick LK, McFarland S, Lancaster D. Dietary, Anthropometric, Blood-Lipid, and Performance Patterns of American College Football Players during 8 Weeks of Training. International journal of sport nutrition and exercise metabolism.22(6):444-451. 148 113. Fox EA, McDaniel JL, Breitbach AP, Weiss EP. Perceived protein needs and measured protein intake in collegiate male athletes: an observational study. Journal of the International Society of Sports Nutrition. 2011;8(1):9. 114. Burke LM. Dietary assessment methods for the athlete: Pros and cons of different methods. Gatorade Sports Science Exchange. 2015;28(150):1-6. 115. Kirkpatrick SI, Subar AF, Douglass D, et al. Performance of the Automated Self- Administered 24-hour Recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. The American journal of clinical nutrition. 2014;100(1):233-240. 116. Lawson ST, Gardner JC, Carnot MJ, Lackey SS, Lopez NV, Sutliffe JT. Assessing the outcomes of a brief nutrition education intervention among Division 1 football student-athletes at moderate altitude. The Sport Journal. 2020. 117. Thompson FE. Dietary Assessment Methodology. In: Nutrition in the Prevention and Treatment of Disease. London: Elsevier; Amy F. Subar. 118. Vereecken CA, Covents M, Sichert-Hellert W, et al. Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe. International Journal of Obesity. 2008;32(5):S26-S34. 119. Thompson FE, Dixit-Joshi S, Potischman N, et al. Comparison of Interviewer- Administered and Automated Self-Administered 24-Hour Dietary Recalls in 3 Diverse Integrated Health Systems. American journal of epidemiology. 2015;181(12):970-978. 120. Overview & Background of the Healthy Eating Index. National Institutes of Health. https://epi.grants.cancer.gov/hei/. Accessed. 121. 2015-2020 Dietary Guidelines for Americans. U.S. Department of Health and Human Services and the U.S. Department of Agriculture. https://health.gov/our- work/food-and-nutrition/2015-2020-dietary-guidelines/. Published December 2015. Accessed. 122. NCAA. Estimated probability of competing in professional athletics. http://www.ncaa.org/about/resources/research/estimated-probability-competing- professional-athletics. Published 2020. Accessed October 8, 2020. 123. Kline RB. Principles and Practice of Structural Equation Modeling. New York, NY: The Guilford Press; 2016. 149 124. Kirkpatrick SI, Subar AF, Douglass D, et al. Performance of the Automated Self- Administered 24-hour Recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. The American journal of clinical nutrition. 2014;100(1):233-240. 125. ASA24 Frequently Asked Questions. National Institutes of Health. https://epi.grants.cancer.gov/asa24/resources/faq.html. Accessed. 126. Cohen J. Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic; 1988. 127. Jontony N, Hill EB, Taylor CA, et al. Diet Quality, Carotenoid Status, and Body Composition in NCAA Division I Athletes. Am J Health Behav. 2020;44(4):432-443. 128. Tsoufi A, Maraki MI, Dimitrakopoulos L, Famisis K, Grammatikopoulou MG. The effect of professional dietary counseling: elite basketball players eat healthier during competition days. The Journal of sports medicine and physical fitness. 2017;57(10):1305-1310. 129. Zanella PB, August PM, Alves FD, Matté C, de Souza CG. Association of Healthy Eating Index and oxidative stress in adolescent volleyball athletes and non- athletes. Nutrition (Burbank, Los Angeles County, Calif). 2019;60:230-234. 130. Farina EK, Thompson LA, Knapik JJ, Pasiakos SM, Lieberman HR, McClung JP. Diet Quality Is Associated with Physical Performance and Special Forces Selection. Medicine and science in sports and exercise. 2020;52(1):178-186. 131. Kennedy ET, Ohls J, Carlson S, Fleming K. The Healthy Eating Index: Design and Applications. Journal of the American Dietetic Association. 1995;95(10):1103-1108. 132. U.S. Department of Agriculture FaNS, Center for Nutrition Policy and Promotion. Average Healthy Eating Index-2015 Scores for Americans by Age Groups, What We Eat in America. NHANES 2015-2016;2020. 133. Capling L, Tam R, Beck KL, et al. Diet Quality of Elite Australian Athletes Evaluated Using the Athlete Diet Index. Nutrients. 2020;13(1). 134. Walsh NP. Nutrition and Athlete Immune Health: New Perspectives on an Old Paradigm. Sports Medicine. 2019;49(2):153-168. 135. Vadiveloo M, Perraud E, Parker HW, Juul F, Parekh N. Geographic Differences in the Dietary Quality of Food Purchases among Participants in the Nationally 150 Representative Food Acquisition and Purchase Survey (FoodAPS). Nutrients. 2019;11(6):1233. 136. Michie S, Richardson M, Johnston M, et al. The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions. Annals of Behavioral Medicine. 2013;46(1):81-95. 151