Are collegiate athletes as healthy as we think they are?

Are collegiate athletes as healthy as we think they are? Abstract The current study compared multiple health-risk behaviors, self-efficacy, and temptation between young adult athletes and nonathletes. Cross-sectional data were collected via a web-based survey. Participants included nonathletes and Division I National Collegiate Athletic Association (NCAA) athletes attending a midwestern university. Multiple health-risk behaviors existed at a high prevalence among participating athletes and nonathletes, with a slightly higher proportion of nonathletes reporting more risk. Twenty-two percent of participating athletes were at risk for excessive screen time, whereas 36% of nonathletes were at risk for the same. A higher proportion of athletes were at risk for inadequate sleep (58%) compared with nonathletes (33%). Ninety-eight percent of nonathletes were at risk for inadequate vegetable intake, whereas 96% of athletes were at risk for the same. Risk for low fruit intake was highest among nonathletes (81%) compared with athletes (77%). Future research targeting collegiate athletes’ multiple health behavior is warranted. Implications Research: Research efforts are needed to better understand the factors influencing collegiate athletes’ multiple health behavior. Practice: Collegiate athletes are young adults who require practitioner support to develop healthy behaviors that extend beyond fitness and athletic performance. Policy: Policymakers should develop lifestyle programs to holistically support collegiate athletes’ current and future health. Most U.S. adults practice multiple health-risk behaviors, which is associated with inflated health concerns [1]. Similarly, health-risk behaviors co-occur among young adult athletes [2], and many former athletes suffer from the same chronic illnesses plaguing most Americans to date [3]. Compared with their nonathlete counterparts, college athletes are at a greater risk for health concerns related to motor vehicle safety, substance abuse, unsafe sexual encounters, and unhealthy dietary practices [4–8]. Given 460,000 young adults compete in National Collegiate Athletic Association (NCAA) collegiate sport [9], multiple health-risk behavior among athletes is a critical public health concern. The transtheoretical model (TTM) provides a framework for the conceptualization of health behavior change [10]. Two TTM motivational constructs are self-efficacy and temptation, which have demonstrated strong association to adults’ health behavior [11–13]. Collegiate athletes’ experiences differ from typical college students, which may uniquely influence their health behaviors. The current study objective was to compare health-risk behavior, self-efficacy, and temptation between collegiate athletes and nonathletes. The multiple health behaviors observed included screen time, physical activity, sleep, and fruit and vegetable consumption. Compared with nonathletes, collegiate athletes were expected to report proportionally more healthy behaviors and higher self-efficacy to support these behaviors. METHODS Participants All participants were young adult students (18–21 years old) attending a midwestern university and were either NCAA Division I competitive athletes or nonathletes. Athletes were recruited via partnership with the university athletic directors and college coaches. Coaches provided the link to the survey and encouraged their athletes to participate. Nonathletes were recruited from an invitation email to complete an online survey. At the time of the study, nonathletes were not participating in collegiate sport or university-level club/intramural sport. Informed consent preceded all study procedures, which were approved by the university institutional review board and the university’s athletic office. Measurement Data were cross-sectional and collected anonymously with a web-based survey (approximately 20 min). Following consent, participants reported their gender, age, and academic class. Athlete participants specified their collegiate sport. All participants reported their perceived health using a 5-point Likert scale (1 = poor, 2 = fair, 3 = good, 4 = very good, or 5 = excellent). Participants were then asked to report their multiple health behavior and relative self-efficacy and temptation. Multiple health behaviors Physical activity was assessed with the International Physical Activity Questionnaire [14]. Participants’ weekly minutes of moderate and vigorous physical activity were also weighted by the associated metabolic equivalent (MET). Participants who reported <150 weekly minutes of moderate and/or vigorous physical activity were categorized as at risk for inactivity. Participants reported how many hours they typically sleep each night, and those reporting <8 hr were characterized as being at risk for inadequate sleep. Sedentary time was reported as nonacademic screen time (TV and computer), and >2 hr was considered as at risk. Fruit and vegetable consumption was operationalized according to previous recommendations for daily intake [15]. Participants at risk reported eating <4 daily fruit servings and/or <6 daily vegetable servings. TTM constructs Physical activity barrier self-efficacy was measured with seven questionnaire items targeting participants’ confidence to overcome common barriers to regular physical activity [16]. This scale has demonstrated strong factorial validity among distinct populations [17]. Self-efficacy for fruit and vegetable consumption was measured with six items, using a previously validated assessment [12]. Participants responded to all efficacy items on a 5-point Likert scale, ranging from not at all confident [1] to completely confident [5]. One self-efficacy example question was, “How confident would you say you are to exercise when you feel like you don’t have time?” The TTM temptations scale to measure factors that may persuade someone to be physically inactive (busy, angry, stressed) was assessed with 10 items. One temptation example questions was, “How tempted are you not to exercise when you are stressed?” Participants responded on a percentage scale, ranging from 0 to 100% tempted [17]. Data analysis Data analyses were performed using IBM SPSS Statistics 23 (2015, Chicago, IL). There were no missing data. Descriptive statistics were calculated for the total sample and athlete/nonathlete subgroups. Athletes and nonathletes were first compared based on the proportion of multiple health behavior risk. Analysis of variance (ANOVA) was used to test potential differences in perceived health, multiple health behavior, self-efficacy, and temptation between athletes and nonathletes. Confounding effects (gender, age, and ethnicity) were accounted for by calculating both unadjusted and adjusted means. RESULTS The total sample of athletes and nonathletes (N = 308) was predominantly female (n = 233, 76%) with a mean age of 20.15 years and standard deviation of 1.47. The total sample represented all academic classes (freshman n = 89, 29%; sophomore n = 73, 24%; junior n = 78, 25%; and senior n = 68, 22%). The majority of participants self-identified as Caucasian, non-Hispanic (n = 279, 91%). Participating NCAA Division I athletes (n = 111) represented 12 different sports, which included 72% (n = 80) individual sport athletes, more specifically, track and field (n = 32), synchronized skating (n = 22), cross-country (n = 17), tennis (n = 5), and swimming/diving (n = 4) athletes. Twenty-eight percent were team sport athletes, including soccer (n = 16), field hockey (n = 6), ice hockey (n = 3), softball (n = 2), football (n = 1), volleyball (n = 1), and baseball (n = 1) athletes. The proportion of athletes and nonathletes reporting multiple health behavior risk is provided in Table 1. No athletes were at risk for inactivity, whereas 21% of nonathletes were at risk. Twenty-two percent of athletes were at risk for excessive screen time, whereas 36% of nonathletes were at risk for the same. Relative to sleep, a higher proportion of athletes were at risk (58%) compared with nonathletes (33%). Ninety-eight percent of nonathletes were at risk for inadequate vegetable intake, whereas 96% of athletes were at risk for the same. Risk for low fruit intake was highest among nonathletes (81%) compared with athletes (77%). Table 2 includes differences in health and health behavior between participating athletes and nonathletes. There were no significant covariates. Table 1 | Multiple health behavior risk among athletes and nonathletes Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% MHB multiple health behavior; MHB Risk number of health behavior risks reported. View Large Table 1 | Multiple health behavior risk among athletes and nonathletes Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% MHB multiple health behavior; MHB Risk number of health behavior risks reported. View Large Table 2 | Differences in college-age athletes and nonathletes health and health behavior Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 The adjusted model included participants’ self-reported gender, age, and ethnicity as covariates. Statistically significant differences are in bold (p < .05). MET metabolic equivalents; PA physical activity; SEM standard error of the mean. View Large Table 2 | Differences in college-age athletes and nonathletes health and health behavior Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 The adjusted model included participants’ self-reported gender, age, and ethnicity as covariates. Statistically significant differences are in bold (p < .05). MET metabolic equivalents; PA physical activity; SEM standard error of the mean. View Large DISCUSSION The current study compared multiple health behavior, self-efficacy, and temptation between NCAA Division I college athletes and similarly aged nonathletes. Not surprisingly, participating athletes reported more physical activity than nonathletes. Athletes also reported more daily intake of fruit than nonathletes; however, nonathletes reported more weekly sleep. Self-efficacy and temptation also differed between athletes and nonathletes. Current outcomes are discussed and compared with previous research below. Parallel to previous research, participating athletes and nonathletes did not differ in their sedentary time [18]. Contradictory to evidence supporting healthier sleep patterns among collegiate athletes [19], current athletes reported less sleep than participating nonathletes. However, both athletes and nonathletes reported a concerning number of health-risk behaviors. Fifty-one percent and 36% of participating athletes and nonathletes reported habitual practice of three co-occurring health-risk behaviors, respectively. Participating athletes reported higher physical activity self-efficacy and lower temptation for inactivity than nonathletes. However, there is no comparable research to report. Finally, parallel to previous research, athletes and nonathletes reported similar fruit and vegetable self-efficacy [5, 6, 20]. Certain study limitations/strengths are noteworthy. The current cross-sectional study design should be expanded to longitudinal examinations and/or holistic statistical analyses to more clearly understand the most salient influences on lifestyle behaviors. The athlete sample included multiple athletic sport types; future research should examine multiple health behavior between individual athletes and team athletes. The current sample also lacked ethnic diversity. Similar examinations among more ethnically diverse samples are needed. Study strengths included an appropriately large sample size and the comprehensive focus on multiple lifestyle behaviors. The contractual obligation of NCAA collegiate athletes should be capitalized on by the university’s athletic administration to promote healthy behaviors beyond their physical activity performance, including adequate sleep and healthy eating habits. Current outcomes provide novel insights; however, future focus on the differences between these two groups is warranted. Compliance with Ethical Standards Primary Data: The findings from this brief report have not been published in previous journals, and this manuscript is not being submitted to any other journal at this time. These data have not been previously reported. Authors of this manuscript have full control over the data, and if needed, the journal may gain access to review the data. Conflicts of Interest: None to report. Ethical Approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed Consent: Informed consent was obtained from all individual participants included in the study. Acknowledgments This research was funded by the Committee on Faculty Research, Office of Advancement of Research & Scholarship (OARS) at Miami University, Ohio. References 1. Berrigan D , Dodd K , Troiano RP , Krebs-Smith SM , Barbash RB . Patterns of health behavior in U.S. adults . Prev Med . 2003 ; 36 ( 5 ): 615 – 623 . Google Scholar CrossRef Search ADS PubMed 2. Friery KB . Incidence of injury and disease among former athletes: a review . J Exerc Physiol . 2008 ; 11 ( 2 ): 26 – 45 . 3. Geller KS , Herbert MA . Identifying and measuring multilevel influences on college-aged athletes’ multiple health behavior: a pilot study . Health . 2014 ; 6 ( 7 ): 576 – 586 . Google Scholar CrossRef Search ADS 4. Nattiv A , Puffer JC , Green GA . Lifestyles and health risks of collegiate athletes: a multi-center study . Clin J Sport Med . 1997 ; 7 ( 4 ): 262 – 272 . Google Scholar CrossRef Search ADS PubMed 5. Dunn D , Turner LW , Denny G . Nutrition knowledge and attitudes of college athletes . Sport J . 2007 ; 10 ( 4 ): 1 – 8 . 6. Hudd SS , Dumlao J , Erdmann-Sager D et al. Stress at college: effects on health habits, health status and self-esteem . Coll Stud J . 2000 ; 34 ( 2 ): 217 – 228 . 7. Jonnalagadda SS , Rosenbloom CA , Skinner R . Dietary practices, attitudes, and physiological status of collegiate freshman football players . J Strength Cond Res . 2001 ; 15 ( 4 ): 507 – 513 . Google Scholar PubMed 8. Martinsen M , Bratland-Sanda S , Eriksson AK , Sundgot-Borgen J . Dieting to win or to be thin? A study of dieting and disordered eating among adolescent elite athletes and non-athlete controls . Br J Sports Med . 2010 ; 44 ( 1 ): 70 – 76 . Google Scholar CrossRef Search ADS PubMed 9. NCAA . Current Student-Athletes . 2015 . Available at http://www.ncaa.org/. Accessed 1 October 2017. 10. Prochaska JO , DiClemente CC . Stages and processes of self-change of smoking: toward an integrative model of change . J Consult Clin Psychol . 1983 ; 51 ( 3 ): 390 – 395 . Google Scholar CrossRef Search ADS PubMed 11. Hendricks PS , Thompson JK . An integration of cognitive-behavioral therapy and interpersonal psychotherapy for bulimia nervosa: a case study using the case formulation method . Int J Eat Disord . 2005 ; 37 ( 2 ): 171 – 174 . Google Scholar CrossRef Search ADS PubMed 12. Horwath CC , Nigg CR , Motl RW , Wong KT , Dishman RK . Investigating fruit and vegetable consumption using the transtheoretical model . Am J Health Promot . 2010 ; 24 ( 5 ): 324 – 333 . Google Scholar CrossRef Search ADS PubMed 13. Nigg CR , McCurdy DK , McGee KA et al. Relations among temptations, self‐efficacy, and physical activity . Int J Sport Exerc Psychol . 2009 ; 7 ( 2 ): 230 – 243 . Google Scholar CrossRef Search ADS 14. Craig CL , Marshall AL , Sjöström M et al. International physical activity questionnaire: 12-country reliability and validity . Med Sci Sports Exerc . 2003 ; 35 ( 8 ): 1381 – 1395 . Google Scholar CrossRef Search ADS PubMed 15. US Department of Health and Human Services . Dietary Guidelines for Americans . 6th ed . Washington, DC : US Government Printing Office ; 2005 . 16. Benisovich SV , Rossi JS , Norman GJ , Nigg CR . Development of a multidimensional measure of self-efficacy . Ann Behav Med . 1998 ; 20 : 190 . Google Scholar CrossRef Search ADS PubMed 17. Geller KS , Nigg CR , Motl RW , Horwath C , Dishman RK . Transtheoretical model constructs for physical activity behavior are invariant across time among ethnically diverse adults in Hawaii . Psychol Sport Exerc . 2012 ; 13 ( 5 ): 606 – 613 . Google Scholar CrossRef Search ADS PubMed 18. Aries E , McCarthy D , Salovey P , Banaji MR . A comparison of athletes and non-athletes at highly selective colleges: academic performance and personal development . Res High Educ . 2004 ; 45 ( 6 ): 577 – 602 . Google Scholar CrossRef Search ADS 19. Armstrong S , Oomen-Early J . Social connectedness, self-esteem, and depression symptomatology among collegiate athletes versus nonathletes . J Am Coll Health . 2009 ; 57 ( 5 ): 521 – 526 . Google Scholar CrossRef Search ADS PubMed 20. Hutchins M , Drolet JC , Ogletree RJ . Physical activity patterns and self-efficacy of selected college students . Health Educator . 2010 ; 42 ( 2 ): 84 – 88 . © Society of Behavioral Medicine 2018. All rights reserved. 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Are collegiate athletes as healthy as we think they are?

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Abstract

Abstract The current study compared multiple health-risk behaviors, self-efficacy, and temptation between young adult athletes and nonathletes. Cross-sectional data were collected via a web-based survey. Participants included nonathletes and Division I National Collegiate Athletic Association (NCAA) athletes attending a midwestern university. Multiple health-risk behaviors existed at a high prevalence among participating athletes and nonathletes, with a slightly higher proportion of nonathletes reporting more risk. Twenty-two percent of participating athletes were at risk for excessive screen time, whereas 36% of nonathletes were at risk for the same. A higher proportion of athletes were at risk for inadequate sleep (58%) compared with nonathletes (33%). Ninety-eight percent of nonathletes were at risk for inadequate vegetable intake, whereas 96% of athletes were at risk for the same. Risk for low fruit intake was highest among nonathletes (81%) compared with athletes (77%). Future research targeting collegiate athletes’ multiple health behavior is warranted. Implications Research: Research efforts are needed to better understand the factors influencing collegiate athletes’ multiple health behavior. Practice: Collegiate athletes are young adults who require practitioner support to develop healthy behaviors that extend beyond fitness and athletic performance. Policy: Policymakers should develop lifestyle programs to holistically support collegiate athletes’ current and future health. Most U.S. adults practice multiple health-risk behaviors, which is associated with inflated health concerns [1]. Similarly, health-risk behaviors co-occur among young adult athletes [2], and many former athletes suffer from the same chronic illnesses plaguing most Americans to date [3]. Compared with their nonathlete counterparts, college athletes are at a greater risk for health concerns related to motor vehicle safety, substance abuse, unsafe sexual encounters, and unhealthy dietary practices [4–8]. Given 460,000 young adults compete in National Collegiate Athletic Association (NCAA) collegiate sport [9], multiple health-risk behavior among athletes is a critical public health concern. The transtheoretical model (TTM) provides a framework for the conceptualization of health behavior change [10]. Two TTM motivational constructs are self-efficacy and temptation, which have demonstrated strong association to adults’ health behavior [11–13]. Collegiate athletes’ experiences differ from typical college students, which may uniquely influence their health behaviors. The current study objective was to compare health-risk behavior, self-efficacy, and temptation between collegiate athletes and nonathletes. The multiple health behaviors observed included screen time, physical activity, sleep, and fruit and vegetable consumption. Compared with nonathletes, collegiate athletes were expected to report proportionally more healthy behaviors and higher self-efficacy to support these behaviors. METHODS Participants All participants were young adult students (18–21 years old) attending a midwestern university and were either NCAA Division I competitive athletes or nonathletes. Athletes were recruited via partnership with the university athletic directors and college coaches. Coaches provided the link to the survey and encouraged their athletes to participate. Nonathletes were recruited from an invitation email to complete an online survey. At the time of the study, nonathletes were not participating in collegiate sport or university-level club/intramural sport. Informed consent preceded all study procedures, which were approved by the university institutional review board and the university’s athletic office. Measurement Data were cross-sectional and collected anonymously with a web-based survey (approximately 20 min). Following consent, participants reported their gender, age, and academic class. Athlete participants specified their collegiate sport. All participants reported their perceived health using a 5-point Likert scale (1 = poor, 2 = fair, 3 = good, 4 = very good, or 5 = excellent). Participants were then asked to report their multiple health behavior and relative self-efficacy and temptation. Multiple health behaviors Physical activity was assessed with the International Physical Activity Questionnaire [14]. Participants’ weekly minutes of moderate and vigorous physical activity were also weighted by the associated metabolic equivalent (MET). Participants who reported <150 weekly minutes of moderate and/or vigorous physical activity were categorized as at risk for inactivity. Participants reported how many hours they typically sleep each night, and those reporting <8 hr were characterized as being at risk for inadequate sleep. Sedentary time was reported as nonacademic screen time (TV and computer), and >2 hr was considered as at risk. Fruit and vegetable consumption was operationalized according to previous recommendations for daily intake [15]. Participants at risk reported eating <4 daily fruit servings and/or <6 daily vegetable servings. TTM constructs Physical activity barrier self-efficacy was measured with seven questionnaire items targeting participants’ confidence to overcome common barriers to regular physical activity [16]. This scale has demonstrated strong factorial validity among distinct populations [17]. Self-efficacy for fruit and vegetable consumption was measured with six items, using a previously validated assessment [12]. Participants responded to all efficacy items on a 5-point Likert scale, ranging from not at all confident [1] to completely confident [5]. One self-efficacy example question was, “How confident would you say you are to exercise when you feel like you don’t have time?” The TTM temptations scale to measure factors that may persuade someone to be physically inactive (busy, angry, stressed) was assessed with 10 items. One temptation example questions was, “How tempted are you not to exercise when you are stressed?” Participants responded on a percentage scale, ranging from 0 to 100% tempted [17]. Data analysis Data analyses were performed using IBM SPSS Statistics 23 (2015, Chicago, IL). There were no missing data. Descriptive statistics were calculated for the total sample and athlete/nonathlete subgroups. Athletes and nonathletes were first compared based on the proportion of multiple health behavior risk. Analysis of variance (ANOVA) was used to test potential differences in perceived health, multiple health behavior, self-efficacy, and temptation between athletes and nonathletes. Confounding effects (gender, age, and ethnicity) were accounted for by calculating both unadjusted and adjusted means. RESULTS The total sample of athletes and nonathletes (N = 308) was predominantly female (n = 233, 76%) with a mean age of 20.15 years and standard deviation of 1.47. The total sample represented all academic classes (freshman n = 89, 29%; sophomore n = 73, 24%; junior n = 78, 25%; and senior n = 68, 22%). The majority of participants self-identified as Caucasian, non-Hispanic (n = 279, 91%). Participating NCAA Division I athletes (n = 111) represented 12 different sports, which included 72% (n = 80) individual sport athletes, more specifically, track and field (n = 32), synchronized skating (n = 22), cross-country (n = 17), tennis (n = 5), and swimming/diving (n = 4) athletes. Twenty-eight percent were team sport athletes, including soccer (n = 16), field hockey (n = 6), ice hockey (n = 3), softball (n = 2), football (n = 1), volleyball (n = 1), and baseball (n = 1) athletes. The proportion of athletes and nonathletes reporting multiple health behavior risk is provided in Table 1. No athletes were at risk for inactivity, whereas 21% of nonathletes were at risk. Twenty-two percent of athletes were at risk for excessive screen time, whereas 36% of nonathletes were at risk for the same. Relative to sleep, a higher proportion of athletes were at risk (58%) compared with nonathletes (33%). Ninety-eight percent of nonathletes were at risk for inadequate vegetable intake, whereas 96% of athletes were at risk for the same. Risk for low fruit intake was highest among nonathletes (81%) compared with athletes (77%). Table 2 includes differences in health and health behavior between participating athletes and nonathletes. There were no significant covariates. Table 1 | Multiple health behavior risk among athletes and nonathletes Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% MHB multiple health behavior; MHB Risk number of health behavior risks reported. View Large Table 1 | Multiple health behavior risk among athletes and nonathletes Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% Total sample (N = 308) Athletes (n = 111) Nonathletes (n = 195) MHB Risk n % n % n % 0 2 0.6% 2 1.8% 0 0.0% 1 21 7.5% 9 8.1% 12 6.1% 2 114 37.0% 36 32.4% 78 39.6% 3 127 41.2% 57 51.4% 70 35.5% 4 39 12.7% 7 6.3% 32 16.2% 5 5 1.6% 0 0.0% 5 2.5% MHB multiple health behavior; MHB Risk number of health behavior risks reported. View Large Table 2 | Differences in college-age athletes and nonathletes health and health behavior Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 The adjusted model included participants’ self-reported gender, age, and ethnicity as covariates. Statistically significant differences are in bold (p < .05). MET metabolic equivalents; PA physical activity; SEM standard error of the mean. View Large Table 2 | Differences in college-age athletes and nonathletes health and health behavior Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 Variables Unadjusted Adjusted Athletes (n = 111) Nonathletes (n = 195) p Athletes (n = 111) Nonathletes (n = 195) p Mean SEM Mean SEM Mean SEM Mean SEM Perceived health 4.24 0.07 3.78 0.05 .00 4.26 0.07 3.76 0.06 .00 Moderate MET 1104.18 89.79 635.61 67.74 .00 1160.77 91.81 603.40 68.22 .00 Vigorous MET 5094.41 197.21 1544.99 148.79 .00 5168.65 204.72 1502.73 152.11 .00 Screen time 100.93 9.74 138.53 7.35 .00 111.96 9.75 132.26 7.24 .10 Sleep 7.32 0.11 8.15 0.08 .00 7.30 0.12 8.16 0.09 .00 Fruit servings 2.87 0.13 2.51 0.10 .02 2.94 0.13 2.47 0.10 .01 Vegetable servings 2.57 0.14 2.47 0.10 .57 2.61 0.14 2.45 0.10 .38 PA self-efficacy 3.47 0.08 2.93 0.06 .00 3.50 0.08 2.92 0.06 .00 PA temptation 34.09 1.92 46.37 1.45 .00 34.27 1.99 46.27 1.48 .00 Fruit/vegetable self-efficacy 3.49 0.09 3.36 0.07 .23 3.51 0.09 3.34 0.07 .15 The adjusted model included participants’ self-reported gender, age, and ethnicity as covariates. Statistically significant differences are in bold (p < .05). MET metabolic equivalents; PA physical activity; SEM standard error of the mean. View Large DISCUSSION The current study compared multiple health behavior, self-efficacy, and temptation between NCAA Division I college athletes and similarly aged nonathletes. Not surprisingly, participating athletes reported more physical activity than nonathletes. Athletes also reported more daily intake of fruit than nonathletes; however, nonathletes reported more weekly sleep. Self-efficacy and temptation also differed between athletes and nonathletes. Current outcomes are discussed and compared with previous research below. Parallel to previous research, participating athletes and nonathletes did not differ in their sedentary time [18]. Contradictory to evidence supporting healthier sleep patterns among collegiate athletes [19], current athletes reported less sleep than participating nonathletes. However, both athletes and nonathletes reported a concerning number of health-risk behaviors. Fifty-one percent and 36% of participating athletes and nonathletes reported habitual practice of three co-occurring health-risk behaviors, respectively. Participating athletes reported higher physical activity self-efficacy and lower temptation for inactivity than nonathletes. However, there is no comparable research to report. Finally, parallel to previous research, athletes and nonathletes reported similar fruit and vegetable self-efficacy [5, 6, 20]. Certain study limitations/strengths are noteworthy. The current cross-sectional study design should be expanded to longitudinal examinations and/or holistic statistical analyses to more clearly understand the most salient influences on lifestyle behaviors. The athlete sample included multiple athletic sport types; future research should examine multiple health behavior between individual athletes and team athletes. The current sample also lacked ethnic diversity. Similar examinations among more ethnically diverse samples are needed. Study strengths included an appropriately large sample size and the comprehensive focus on multiple lifestyle behaviors. The contractual obligation of NCAA collegiate athletes should be capitalized on by the university’s athletic administration to promote healthy behaviors beyond their physical activity performance, including adequate sleep and healthy eating habits. Current outcomes provide novel insights; however, future focus on the differences between these two groups is warranted. Compliance with Ethical Standards Primary Data: The findings from this brief report have not been published in previous journals, and this manuscript is not being submitted to any other journal at this time. These data have not been previously reported. Authors of this manuscript have full control over the data, and if needed, the journal may gain access to review the data. Conflicts of Interest: None to report. Ethical Approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed Consent: Informed consent was obtained from all individual participants included in the study. Acknowledgments This research was funded by the Committee on Faculty Research, Office of Advancement of Research & Scholarship (OARS) at Miami University, Ohio. References 1. Berrigan D , Dodd K , Troiano RP , Krebs-Smith SM , Barbash RB . Patterns of health behavior in U.S. adults . Prev Med . 2003 ; 36 ( 5 ): 615 – 623 . Google Scholar CrossRef Search ADS PubMed 2. Friery KB . Incidence of injury and disease among former athletes: a review . J Exerc Physiol . 2008 ; 11 ( 2 ): 26 – 45 . 3. Geller KS , Herbert MA . Identifying and measuring multilevel influences on college-aged athletes’ multiple health behavior: a pilot study . Health . 2014 ; 6 ( 7 ): 576 – 586 . Google Scholar CrossRef Search ADS 4. Nattiv A , Puffer JC , Green GA . Lifestyles and health risks of collegiate athletes: a multi-center study . Clin J Sport Med . 1997 ; 7 ( 4 ): 262 – 272 . Google Scholar CrossRef Search ADS PubMed 5. Dunn D , Turner LW , Denny G . Nutrition knowledge and attitudes of college athletes . Sport J . 2007 ; 10 ( 4 ): 1 – 8 . 6. Hudd SS , Dumlao J , Erdmann-Sager D et al. Stress at college: effects on health habits, health status and self-esteem . Coll Stud J . 2000 ; 34 ( 2 ): 217 – 228 . 7. Jonnalagadda SS , Rosenbloom CA , Skinner R . Dietary practices, attitudes, and physiological status of collegiate freshman football players . J Strength Cond Res . 2001 ; 15 ( 4 ): 507 – 513 . Google Scholar PubMed 8. Martinsen M , Bratland-Sanda S , Eriksson AK , Sundgot-Borgen J . Dieting to win or to be thin? A study of dieting and disordered eating among adolescent elite athletes and non-athlete controls . Br J Sports Med . 2010 ; 44 ( 1 ): 70 – 76 . Google Scholar CrossRef Search ADS PubMed 9. NCAA . Current Student-Athletes . 2015 . Available at http://www.ncaa.org/. Accessed 1 October 2017. 10. Prochaska JO , DiClemente CC . Stages and processes of self-change of smoking: toward an integrative model of change . J Consult Clin Psychol . 1983 ; 51 ( 3 ): 390 – 395 . Google Scholar CrossRef Search ADS PubMed 11. Hendricks PS , Thompson JK . An integration of cognitive-behavioral therapy and interpersonal psychotherapy for bulimia nervosa: a case study using the case formulation method . Int J Eat Disord . 2005 ; 37 ( 2 ): 171 – 174 . Google Scholar CrossRef Search ADS PubMed 12. Horwath CC , Nigg CR , Motl RW , Wong KT , Dishman RK . Investigating fruit and vegetable consumption using the transtheoretical model . Am J Health Promot . 2010 ; 24 ( 5 ): 324 – 333 . Google Scholar CrossRef Search ADS PubMed 13. Nigg CR , McCurdy DK , McGee KA et al. Relations among temptations, self‐efficacy, and physical activity . Int J Sport Exerc Psychol . 2009 ; 7 ( 2 ): 230 – 243 . Google Scholar CrossRef Search ADS 14. Craig CL , Marshall AL , Sjöström M et al. International physical activity questionnaire: 12-country reliability and validity . Med Sci Sports Exerc . 2003 ; 35 ( 8 ): 1381 – 1395 . Google Scholar CrossRef Search ADS PubMed 15. US Department of Health and Human Services . Dietary Guidelines for Americans . 6th ed . Washington, DC : US Government Printing Office ; 2005 . 16. Benisovich SV , Rossi JS , Norman GJ , Nigg CR . Development of a multidimensional measure of self-efficacy . Ann Behav Med . 1998 ; 20 : 190 . Google Scholar CrossRef Search ADS PubMed 17. 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For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Translational Behavioral MedicineOxford University Press

Published: Apr 13, 2018

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