research papers on college athletes

A Systematic Literature Review on the Academic and Athletic Identities of Student-Athletes

• Dr Andrea R. Steele Murdoch University https://orcid.org/0000-0001-5045-9520
• Dr Fleur E. C. A. van Rens Murdoch University https://orcid.org/0000-0002-6305-3359
• Ms Rebecca Ashley Murdoch University

Academic and athletic identities are related to performance and wellbeing indicators in both the educational and sport domains, respectively. This paper presents a systematic literature review examining empirical research into the academic and athletic identities of student-athletes in dual (education and sport) careers. The 42 records identified in this review suggest that research on the academic and athletic identities of student-athletes has focused on the themes of: identity development, role conflict, career development and motivation, and student-athlete stereotypes. Future research directions are considered, including the need for mixed-methods and longitudinal assessments of academic and athletic identities to assess to dynamic nature of identity development, and to ascertain how these relate to future performance and wellbeing outcomes.

Copyright (c) 2020 Dr Andrea R. Steele , Dr Fleur E. C. A. van Rens, Ms Rebecca Ashley

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Collegiate athletes' mental health services utilization: A systematic review of conceptualizations, operationalizations, facilitators, and barriers

Affiliations.

• 1 The Research Institute at Nationwide Children's Hospital, Columbus, OH 43205, USA.
• 2 Ohio Department of Mental Health & Addiction Services Office of Quality, Planning & Research, Columbus, OH 43215, USA.
• 3 Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
• PMID: 30356496
• PMCID: PMC6180550
• DOI: 10.1016/j.jshs.2017.04.009

Background: While mental health among collegiate athletes is receiving increased attention, research on factors surrounding collegiate athletes' decision to seek mental health services is limited. The goal of the present review was to analyze and synthesize the current literature concerning collegiate athletes' utilization of mental health services, including the facilitators of and barriers to use of these services.

Methods: The analysis was guided and organized using a socio-ecological framework, which considered the unique context in which collegiate athletes study and perform. A total of 21 articles, published between 2005 and 2016, which concern U.S. collegiate athletes' mental health services utilization (MHSU) were selected and included for the final analysis. Conceptualizations and operationalizations of MHSU were compared and contrasted. Facilitators of and barriers to athletes MHSU were examined and summarized while appropriately considering the proximity of each factor (facilitator or barrier) to the athletes.

Results: Results showed variations in conceptualizations and operationalizations of MHSU in the articles analyzed, which made interpretation and cross comparison difficult. Collegiate athletes are willing to utilize mental health services, but gender, perceived stigma, peer norms-for athletes and coaches-plus service availability impact their MHSU.

Conclusion: Key stakeholders, administrators, and public health officials should partner to eliminate MHSU barriers, support facilitators, and generally empower collegiate athletes to actively manage their mental health.

Keywords: College athlete; Mental health; Mental health services; NCAA; Psychology; Sport psychology; Systematic review.

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Mental Health in Elite Student Athletes: Exploring the Link Between Training Volume and Mental Health Problems in Norwegian College and University Students

Michael grasdalsmoen.

1 Department of Sport, Food and Natural Sciences, Western Norway University of Applied Sciences, Bergen, Norway

Benjamin Clarsen

2 Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway

3 Department of Disease Burden, Norwegian Institute of Public Health, Bergen, Norway

Børge Sivertsen

4 Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway

5 Department of Research & Innovation, Helse Fonna HF, Haugesund, Norway

6 Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway

Associated Data

The datasets presented in this article are not readily available because of privacy regulations from the Norwegian Regional Committees for Medical and Health Research Ethics (REC). Approval from REC ( https://helseforskning.etikkom.no ) is a pre-requirement. Guidelines for access to SHoT data are found at https://www.fhi.no/en/more/access-to-data . Requests to access the datasets should be directed to Børge Sivertsen, [email protected] .

To examine mental health problems among elite athletes in a student population, compared to the general student population, and to explore the association between weekly hours of training across mental health indicators.

Data are from a national study from 2018 of all college and university students in Norway. Participants indicated if they considered themselves to be an elite athlete, and how many hours per week they trained. Mental health problems were assessed using several well-validated questionnaires.

Among 50,054 students, 1.3% identified themselves as elite athletes. Both male and female elite athletes had generally better mental health across most health outcomes, reporting fewer mental health problems, less loneliness, higher satisfaction with life, more positive affect, and fewer alcohol problems. Elite athletes in team sports had slightly better mental health compared to athletes of individual sports. Increased hours of weekly exercise was associated with better mental health. However, there was generally little to be gained from increasing the amount of training from 7–10 hours/week to 14+ hours per week. Female athletes who trained 14 or more hours per week reported poorer mental health across most outcome measures.

This study showed that both male and female elite athletes generally had better mental health across a range of health outcomes, when compared to the general student population. The study also found a positive dose-response relationship between weekly hours of training and mental health, but also a worsening of mental health for females at the extreme end of exercise continuum. The self-report nature of this student sample means that care should be taken when generalizing to other studies of elite athletes.

Introduction

Physical exercise has unquestionable benefits for health, and taking part in regular exercise has been shown to prevent a host of non-communicable diseases (NCDs), including cardiovascular disease and type 2 diabetes (Lee et al., 2012 ). But despite overwhelming evidence of the many health benefits of physical exercise, it has been suggested that there might be a curvilinear relationship between the amount of physical exercise and exercise-induced improvements of somatic health (Mons et al., 2014 ; Williams and Thompson, 2014 ; Schnohr et al., 2015 ). Although such findings may lead to the speculation that physical exercise may be harmful at a certain dose, there is currently no known upper limit in terms of the somatic health benefits of physical exercise in healthy individuals (Eijsvogels and Thompson, 2015 ).

There is similar evidence showing that physical exercise also has large positive effects on mental health, especially in the case of depression (Kvam et al., 2016 ; Gordon et al., 2018 ). However, the research literature regarding the nature of this relationship also remains equivocal. While a large US study of 1.2 million individuals found a strong link between physical exercise and improved mental health (Chekroud et al., 2018 ), they also concluded that more exercise was not always better. Exercising 23 or more times per month, or longer than 90 min per session, was associated with worse mental health. In contrast, a recent national study of all college and university students in Norway found that the more physical exercise, the better; both in terms of exercise frequency and duration (Grasdalsmoen et al., 2020a ). However, none of these studies focused specifically on individuals at the extreme end of the exercise spectrum, elite athletes.

Despite the utmost importance of optimal physical and mental health when performing at a top international level of any sport, there are relatively few studies investigating in detail the mental health of elite athletes, and findings remain inconclusive (Reardon and Factor, 2010 ; Reardon et al., 2019 ). While some studies have reported prevalence rates of mental disorders and substance use disorder to be higher or comparable to the general population (Schaal et al., 2011 ; Rice et al., 2016 ; Gouttebarge et al., 2019 ; Akesdotter et al., 2020 ; Purcell et al., 2020 ), other studies have found lower suicide rates when compared to the general population (Maron et al., 2014 ; Rao et al., 2015 ; Lehman et al., 2016 ). Similarly, in one of the largest studies in this field, a recent study of US Olympians found a lower risk of mental health problems and suicide for this group, compared to the general population (Duncombe et al., 2020 ). In a consensus statement from IOC, it was concluded than more studies with large reference groups were needed to address specific domains of mental health outcomes in elite athletes, and also to examine whether there may by gender-specific patterns in the associations between the amount of exercise and mental health indicators (Reardon et al., 2019 ).

Based on these considerations, the aim of the current study was twofold; first, to investigate in detail the prevalence of mental health problems across a range of outcome measures in elite athletes compared to the general student population, and second, to explore the linear vs. curvilinear association between weekly hours of training across all mental health indicators.

The current paper used data from the SHoT2018 study ( Students' Health and Wellbeing Study) , a large national survey of students enrolled in higher education in Norway. The SHoT2018 is a comprehensive survey of several domains of mental health and lifestyle factors, distributed electronically through a web-based platform at the University of Oslo. Details of the study has been published elsewhere (Sivertsen et al., 2019a ), but in short, SHoT2018 was conducted between February 6 and April 5, 2018, and invited all full-time Norwegian students pursuing higher education, both in Norway and abroad. In all, 162,512 students fulfilled the inclusion criteria, of whom 50,054 students completed the online questionnaires (after being sent two reminders), yielding a response rate of 30.8%.

Elite Athlete and Exercise

The students were first presented with the following brief definition of physical exercise: “ With physical exercise, we mean that you, for example, go for a walk, go skiing, swim or take part in a sport .” Physical exercise was then assessed using three sets of questions, assessing the average number of times exercising each week, and the average intensity and average hours each time (Kurtze et al., 2007 ): (1) “ On an average week, how frequently do you perform physical exercise?” (Never, Less than once a week, Once a week, 2–3 times per week, Almost every day); (2) “ If you perform physical exercise as frequently as once or more times a week: How hard do you push yourself?” (I take it easy without breaking into a sweat or losing my breath, I push myself so hard that I lose my breath and break into a sweat, I push myself to near-exhaustion); and (3) “ How long does each session last?” (Less than 15 min, 15–29 min, 30 min to 1 h, More than 1 h.”) This 3-item questionnaire has previously been used in the large population-based Nord-Trøndelag Health Study (HUNT) (Kurtze et al., 2007 , 2008 ). Detailed information on the physical exercise items in the SHoT2018 study has been published elsewhere (Grasdalsmoen et al., 2019 , 2020a , b ).

If respondents answered that they exercised “almost every day” on the frequency item, they were then asked if they considered themselves to be an “ elite athlete” (yes/no), and if so, how many hours per week they trained (drop-down menu: 0 to 40 h). For the item of weekly hours of training, we categorized all responses into “0–2 h/wk, 3–6 h/wk, 7–10 h/wk, 11–13 h/wk, and 14+ h/wk (based on the distribution of the responses). Due to restrictions related to statistical power, we were unable to further explore in detail those training more than 14 h/wk. Finally, those self-categorized as an elite athlete were also asked (in free text) which sport they considered themselves as an elite athlete. For purposes of the present study, all responses were manually coded as either individual or team sport.

Mental Health Problems

Mental health problems were assessed by the widely used Hopkins Symptoms Checklist (HSCL-25) (Derogatis et al., 1974 ), derived from the 90-itemSymptom Checklist (SCL-90), a screening tool designed to detect symptoms of anxiety and depression. Several factor structures and cut-offs for clinical levels have been proposed for the HSCL-25 (Ventevogel et al., 2007 ; Glaesmer et al., 2014 ). An investigation of the factor structure based on the SHoT2014 dataset showed that a unidimensional model had the best psychometric properties in the student population and not the original subscales of anxiety and depression (Skogen et al., 2017 ). We have chosen to follow this recommendation in the present study. As recommended in previous publications (Derogatis et al., 1974 ), the average scores on the HSCL-25 of ≥ 1.75 and <2.00, and >2.00, were used as cut-off values for identifying moderate and high levels of mental health problems, respectively. Details on development of mental health problems in the SHoT waves were recently published by Knapstad et al. ( 2019 ).

Mental Disorders

Self-reported mental disorders were assessed by a pre-defined list adapted to fit this age-cohort. The list was based on a similar operationalisation used in previous large population-based studies [the HUNT study (Krokstad et al., 2013 )] and included several subcategories for most conditions/disorders (not listed here). For mental disorders, the list comprised the following specific disorders/group of disorders: attention-deficit/hyperactivity disorder (ADHD), anxiety disorder, autism/Asperger, bipolar disorder, depression, posttraumatic stress disorder (PTSD), schizophrenia, personality disorder, eating disorder, Tourette's syndrome, obsessive-compulsive disorder (OCD), and others. The list contained no definition of the included disorders/conditions. Due to statistical power limitations, we only included anxiety and depressive disorders, in addition to eating disorders.

Self-Harm and Suicidal Behavior

History of non-suicidal self-harm (NSSH) and suicide attempts were assessed with two items drawn from the Adult Psychiatric Morbidity Survey (APMS) (McManus et al., 2016 ); “ Have you ever made an attempt to take your life, by taking an overdose of tablets or in some other way?” , and “ Have you ever deliberately harmed yourself in any way but not with the intention of killing yourself? (i.e., self-harm)” If respondents answered yes to any item, the timing of the most recent episode was assessed, using the following response options: “last week”, “past year”, “more than a year ago, but after I started studying at the university”, and “before I started studying at university”. For purposes of the current study, we created a joint variable encompassing students that reported positive on any of these four items, and if they indicated the most recent episode to be after they started studying at university. More detailed information about self-harm and suicidal behavior in SHoT2018 has been published elsewhere (Sivertsen et al., 2019b ).

Life Satisfaction

The Satisfaction With Life Scale (SWLS) (Diener et al., 1985 ) is a 5-item scale designed to measure global cognitive judgments of one's life satisfaction (not a measure of either positive or negative affect). In the current study, the SWLS was analyzed in three ways: (1) as a continuous total score (range 5–35), (2) using pre-defined categories ( dissatisfied : total SWLS score 5–19; neutral : total SWLS score 20–25, and satisfied : total SWLS score 26–35); and (3) dichotomously, using a total SWLS total score of <19 as the cut-off value indicating poor life satisfaction. The Cronbach's alpha of the SWLS in the current study was 0.89.

Loneliness was assessed using an abbreviated version of the widely used UCLA Loneliness Scale, “The Three-Item Loneliness Scale (T-ILS)” (Hughes et al., 2004 ). The T-ILS has displayed satisfactory reliability and both concurrent and discriminant validity in two US nationally representative population-based studies (Hughes et al., 2004 ), and also performed well among US college students (Matthews-Ewald and Zullig, 2013 ). The three items were analyzed separately, and each item was dichotomized using “often” or “very often” as cut-off value. The Cronbach's alpha of the T-ILS in the current study was 0.88.

Perfectionism

Perfectionism was assessed by the short version of the Perfectionism subscale from the Eating Disorder Inventory (EDI) (Garner et al., 1985 ). The Perfectionism subscale (EDI-P) comprises two dimensions: socially prescribed perfectionism and self-oriented perfectionism, and this two-factor model has been supported in both clinical (Lampard et al., 2012 ) and non-clinical (Muro-Sans et al., 2006 ) adolescent samples. The Cronbach's alpha of the EDI in the current study was 0.81.

Disturbed Eating Patterns

Disturbed eating patterns were assessed by the Eating Disturbance Scale (EDS-5) (Rosenvinge et al., 2001 ), a brief screening instrument for problematic eating in normal populations. The EDS-5 has been shown to have good concurrent and construct validity, and a sensitivity and specificity of 0.90 and 0.88 with respect to DSM-IV eating disorders (Rosenvinge et al., 2001 ). The Cronbach's alpha of the EDS-5 in the current study was 0.83.

Positive Affect

The Positive and Negative Affect Schedule (PANAS) is a 20-item questionnaire which comprises two subscales, one that measures positive affect (positive affect) and the other which measures negative affect (NA). A sum score is calculated with higher scores representing greater positive affect. The Cronbach's alpha for the positive affect subscale in the current study was 0.91. The NA subscale was not included in the SHoT study.

Sleep Duration and Insomnia

Participants' self-reported usual bedtime and bed-rise time were indicated in hours and minutes, and data were reported separately for weekdays and weekends. Time in bed (TIB) was calculated as the difference between bedtime and rise time. Sleep onset latency (SOL: defined as the length of time that it takes to accomplish the transition from full wakefulness to sleep) and wake after sleep onset (WASO: defined as amount of time a person spends after sleep onset) were also indicated separately for weekdays and weekends in hours and minutes. Sleep duration was defined as TIB minus SOL and WASO.

All participants also indicated the average number of nights per week they experienced difficulties initiating sleep (DIS), difficulties maintaining sleep (DMS), and early morning awakenings (EMA), as well as daytime sleepiness and tiredness. Those suffering from sleep problems were asked about how long the problems had been present. The following 3 criteria were used as an operationalization for insomnia disorder, in line with the DSM-5 criteria: (1) the presence of either DIS, DMS, or EMA for at least 3 nights per week, (2) the presence of daytime sleepiness and tiredness for at least 3 days per week, and (3) a duration of the sleep problems for at least 3 months. More details about the sleep inventory used in SHoT2018 has been published elsewhere (Sivertsen et al., 2019c ).

Alcohol-Related Problems

Potential alcohol-related problems were measured by the Alcohol Use Disorders Identification Test (AUDIT), which is a widely used instrument developed by the World Health Organization to identify risky or harmful alcohol use (Saunders et al., 1993 ; Babor et al., 2001 ). The 10-item AUDIT includes items for measuring the frequency, typical amount and episodic heavy drinking frequency (items 1–3), alcohol dependence (items 4–6), and problems related to alcohol consumption (items 7–10) (Shevlin and Smith, 2007 ). The AUDIT score ranges from 0 to 40. More information about the AUDIT in the SHoT surveys has been published elsewhere (Heradstveit et al., 2019 ).

Sociodemographic Information

All participants reported their gender, age and relationship status (coded as single vs. married/partner or girlfriend/boyfriend). Annual income was coded dichotomously according to self-reported income last year (before tax and deductions, and not including loans and scholarships): annual income > 10,000 NOK vs. ≤ 10,000 NOK. Finally, participants were categorized as an immigrant if the student or one or both of the parents were born outside Norway.

IBM SPSS version 27 (SPSS Inc., Chicago, IL USA) for Windows was used for all analyses. Differences between elite athletes and the control group were examined across all continuous outcome measures (HSCL-25, SWLS, T-ILS, EDI-P, EDS-5, PANAS and AUDIT) separately for male and female athletes, by calculating estimated marginal means (EMM), adjusting for age. Differences between elite athletes in team sports and individual sports were also examined using age-adjusted EMM. Cohen's d effect-sizes were calculated in line with recognized guidelines (Carlson and Schmidt, 1999 ; Morris, 2008 ). As a benchmark for interpreting Cohen's d , 0.80 should be regarded as large, 0.50 as moderate, and 0.20 as small, respectively (Cohen, 1988 ). We also conducted log-link binomial regression analysis to calculate effect-sizes for the dichotomous outcomes (anxiety and depressive disorder, self-harm and suicidal ideation, insomnia and eating disorder), adjusting for age. Results are presented as risk ratios (RR) with 95% confidence intervals. The normality of the data was examined using skewness and kurtosis, and all continuous measures were well within the recommended ranges (+/– 2) (George and Mallery, 2016 ). P -values adjusted for multiple comparisons using the Benjamini-Hochberg's false discovery rate (FDR). There was generally little missing data ( n < 270 [0.5%]), and hence missing values were handled using listwise deletion. As the SHoT2018 study had several objectives and was not designed to be a study of elite athletes specifically, no a priori power calculations were conducted to ensure that the sample size had sufficient statistical power to detect differences in outcomes.

Sample Characteristics

In all, 634 of the 50 034 students (1.3%) reported being an elite athlete, of whom 366 (57.8%) were female. The elite athletes were significantly younger than the control group (mean age 22.0 vs. 23.3 years, P < 0.001), and the elite athlete group also included a larger proportion of males compared to the control group (42.2 vs. 30.8% [ P < 0.001], respectively). There were no significant group differences regarding income and ethnicity. More details of the sociodemographic and clinical characteristics among elite athletes vs. non-athletes are found in Table 1 .

Sociodemographic and clinical characteristics of the SHoT 2018 study.

§ p-values based on overall Chi-squared analyses (categorical variables) or independent samples t-test (continuous variables) .

Elite Athletes

In general, elite athletes reported better mental health across most continuous measures, a trend which was evident in both male and female athletes. As detailed in Figure 1 , compared to the control group, elite athletes reported significantly fewer mental health problems (HSCL-25), less loneliness (T-ILS), higher satisfaction with life (SWLS), less disturbed eating patterns (EDS-5), more positive affect (PANAS), as well as fewer alcohol problems (AUDIT). However, elite athletes also reported significantly higher levels of perfectionism compared to the control group. Effect-sizes ranged from small to moderate (Cohen's d : 0.2 to 0.6). Similar results were also observed across the dichotomous measures. As displayed in Figure 2 , male and female elite athletes had significantly lower prevalence (and significantly lower age-adjusted relative risks) of both anxiety and depressive disorder and self-harm and suicidal ideation, compared to the control group. The prevalence of self-reported anorexia or bulimia did not differ significantly between the two groups. There were no significant sex × group interactions for any of the health indicators.

Health indicators among elite athletes and control group in male and female college and university students (age-adjusted estimates represented in T-scores and Cohen's d effect size (in white text box). Error bars represent 95% confidence intervals. * P < 0.001.

Prevalence of anxiety and depressive disorders, self-harm and suicidal ideation, insomnia and eating disorders among elite athletes and control group, in male and female college and university students. Bars represent age-adjusted prevalence estimates, and error bars represent 95% confidence intervals. RR = age-adjusted relative risk. *** P < 0.001; ** P < 0.01; * P < 0.05.

Elite athletes had a significantly lower prevalence of insomnia than the control group. As displayed in Figure 2 , the prevalence of insomnia among male and female elite athletes were 11.4 and 22.6%, respectively, compared to 22.6 and 34.4% among male and female controls. Female elite athletes slept for similar duration to those in the control group (7:26 vs. 7:24 h), whereas male elite athletes slept 12 min longer than the control group (7:35 vs. 7:23 h). However, the sex × group interaction was not statistically significant ( P = 0.163).

Individual and Team Sport

Compared to individual sports ( n = 380), elite athletes in team sports ( n = 234) had significantly fewer mental health problems ( P = 0.045), less loneliness, but also more alcohol problems (male athletes only), and more disturbed eating patterns. No differences were observed for quality of life, positive affect, sleep duration or perfectionism.

Weekly Hours of Exercise

The associations between weekly hours of training and all health indicators are detailed in Figure 3 . Among male students (blue lines), we observed a positive association across most instruments: the more hours of training, the better the mental health and higher life satisfaction. However, for most outcomes, there was generally little to be gained from increasing the amount of training from 7–10 hours/week to 14+ h per week ( Figure 3 ). Some exceptions should be noted: for alcohol problems we observed a significant curvilinear relationship between hours of training and reported alcohol problems, with the least alcohol problems observed for those training the least and the most. In contrast, there were linear relationships throughout all categories of training hours and positive affect: the more training, the more positive affect.

Hours of training (X axis) and age-adjusted health indicators (Y axis) in male and female college and university students exercising “almost every day” (not just elite athletes). The unconnected point estimates (to the left on each panel) represent students training 2–3 days/wk or less. Error bars represent 95% confidence intervals.

Among female students, we generally found the same pattern of results, but with one gender-specific trend: For female athletes training the most (14+ h per week), there was generally a worsening of mental health across most outcome measures. As displayed in Figure 3 (red lines), we found evidence of significant U- shaped (curvilinear) associations between training hours and all outcome instruments, except insomnia. The correlations between hours of training per week and mental health problems are detailed in Table 2 .

Pearson correlation coefficient between hours of training and instruments (continuous) assessing mental health problems.

* p < 0.05;

This is the first survey of elite athletes' mental health among students containing a large control group. Both male and female elite athletes had generally better mental health across all examined health outcomes, and elite athletes in team sports had even slightly better mental health compared to athletes in individual sports. The overall pattern was that the more hours of physical exercise, the better the mental health and higher life satisfaction, although there was little to be gained from increasing the amount of training at the extreme end the exercise continuum. Importantly, this study also demonstrated that among female athletes training the most, there was generally a worsening of mental health across most outcome measures.

Despite being crucial for optimal performance, the mental health aspect of elite athletes was historically somewhat neglected for many years, both in the popular media and research literature. One possible reason for this may have been the tendency to idealize elite athletes (Doherty et al., 2016 ), leading both the general public and health care professionals to assume a low prevalence of mental health issues in sport. Also, athletes may have a negative perception of help-seeking behavior (Steinfeldt and Profile, 2012 ), and may often minimize any displays of weakness (Sinden, 2010 ). Similarly, there is also the possibility of stigma which may prevent reporting of a prior diagnosis, which in turn may lead to both underreporting of mental health problems, as well as lack of adequate mental support during their careers (Doherty et al., 2016 ). Fortunately, recent years have shown a rapid increased in high quality studies in this field, and with several high profile athletes reporting struggling with mental health issues, especially recently during the Tokyo Olympics in 2021 (Park, 2021 ; Peter, 2021 ), the times have changed in terms of how we regard the importance of mental health of elite athletes.

Findings from the few existing studies in this field have been mixed, and while a recent systematic review and meta-analyses suggested that the prevalence of mental health problems and disorders in elite athletes might be slightly higher than in the general population, the authors also stressed that the typical lack of control groups limited the generalizability of findings. Also, methodological differences both in how elite athletes are defined, and how mental health is operationalized, may explain some of these inconclusive findings, and there has clearly been a need for large studies with well-defined instruments to further shed light on this important issue. As such, the current study corroborates the findings from one of the largest register studies in this field, examining all US Olympians ( n = 8124) who participated in the Summer or Winter Games between 1912 and 2012. That study found that elite athletes have significantly lower risk of both mental health problems and suicide when compared to the general population (Duncombe et al., 2020 ). Extending on these findings of reduced risk of mental health problems and suicidality, the current study also found elite athletes reported significantly higher quality of life, more positive affect, less loneliness and insomnia, as well as fewer alcohol problems, when compared to the general student population. As such, when contrasted to the findings from the meta-analyses of Gouttebarge et al. ( 2019 ), one may conclude that more well-conducted and well-powered studies are needed to identify possible subgroups of athletes which may be more prone to developing mental health problems. The current study extends on previous research reports by showing that athletes in team sports have slightly better mental health and less loneliness (Elbe and Jensen, 2016 ; Sabiston et al., 2016 ; Pluhar et al., 2019 ), but also somewhat more alcohol problems (males only) (Denault and Poulin, 2018 ). As such, the mental health benefits of participation in team sports is somewhat nuanced by the increase risk of alcohol problems, when compared to participation in individual sports. However, effect-sizes were generally small, and non-significant for the other outcome measures.

While the current study did not aim to assess potential mechanisms as to why elite athletes may have better mental health than non- athletes, there may be several reasons for these findings. First, we cannot disregard the possibility of selection bias, as optimal mental health is essential when performing at a top international level in any sport. Second, several biological mechanisms have been shown to be involved in the association between physical exercise and mood, and a recent meta-analysis (Morres et al., 2019 ) showed that extensive physical exercise not only has an anti-depressive effect by boosting endorphin level in the short term, but it may also help to stimulate the functioning of the brain on a broader level. Still, there is clearly a need for both epidemiological and physiological studies that may help shed light on possible involved mechanisms.

A second aim of this study was to examine if there is a linear or curvilinear relationship between hours of exercise and mental health; is more always better? Our general pattern of findings suggests this to be the case, demonstrating that the more hours of physical exercise were associated with better mental health and higher life satisfaction. This is a somewhat different finding than a large study of 1.2 million individuals in the USA. While that study also found a strong link between physical exercise and improved mental health, they also concluded that more exercise was not always better: extreme ranges of more than 23 training sessions per month, or longer than 90 min per session, were associated with worse mental health. This curvilinear association was also partly replicated in the current study, with female (but not male) athletes training the most (14+ h per week), displaying a worsening of mental health across most outcome measures. There may be several reasons for this gender differences. On one hand, the response bias hypothesis suggests that gender differences in mental health may reflect a tendency for men to underreport their problems and symptoms (Sigmon et al., 2005 ). However, while this may explain the overall gender pattern in this study, this does not fully explain the worsening in mental health among female elite athletes training the most. As such, more studies are needed to further disentangle this important finding. The current findings can also be compared with a US study from 2008 of 7674 adults from the general population, who found a curvilinear association between physical activity and general mental health (Kim et al., 2012 ). Of interest, that study concluded with an optimal range of 2.5 to 7.5 h of physical activity per week.

However, despite the ongoing discussion regarding the existence of a dose-response relationship between physical exercise and mental health, most will agree that even a small increase in physical exercise from inactivity is beneficial. Although both educational institutions and student welfare organizations try hard to encourage and facilitate their students to take part in a wide range of sports, physical exercise and outdoor activities, the current results suggest that increased efforts are warranted.

Methodological Considerations

The current study has some important methodological considerations. First, an important limitation of the study is the cross-sectional design, which makes it difficult to evaluate the directionality between physical exercise and mental health. While there is much evidence showing that regular physical exercise has a positive impact on mood (Morres et al., 2019 ), there are also studies showing that the association is likely to be bidirectional. For example, prospective studies have found that symptoms of depression predict subsequent lower activity levels (Pinto Pereira et al., 2014 ), and there are also plausible mechanisms which may explain how symptoms of depression may lead to inactivity, including low energy levels or apathy (Goodwin, 2003 ), psychomotor retardation and anhedonia (Jerstad et al., 2010 ), and social isolation which in turn may reduce the motivation to be active (Kaplan et al., 1991 ). Also, our operationalization of “elite athlete” based on self-report among college and university students only, may differ from some other high quality studies, of which some have used more stringent definitions (e.g. competing in the Olympics etc.). Furthermore, the questionnaire we did not include any subcategories of elite athletes, and as such we were unable to consider and compare non-athletes vs. recreational athletes and elite athletes. Similarly, it should be stressed that parallel careers and college/university studies may not be common at the elite level in many large professional sports, which further limits the generalizability of this study. Also, this crude measure did not enable us to explore differences between various sports. In sum, care should be taken when comparing our findings with the existing literature base. Another limitation is the moderate response rate of 31%, and we also had limited information regarding non-responders. Furthermore, due to lack of statistical power, 14+ h per week was the highest category of exercise duration per week. As many world class athletes train 20+ h per week, we were unable to the extreme end of this continuum in detail. The strengths of the study include the large and heterogeneous sample, as most previous studies in this field have examined white, young and female undergraduates (Fedina et al., 2018 ). Other strengths include well-validated instruments of both physical exercise and mental health outcomes.

In conclusion, the current study showed that both male and female elite athletes generally had better mental health across a range of health outcomes, when compared to the general student population. The study also found a positive and graded relationship between weekly hours of training and mental health, but also a worsening of mental health for females at the extreme end of exercise continuum.

What We Already Know?

• Mental health problems seem prevalent among elite athletes, but findings remain inconclusive when compared to the general population.
• Findings are mixed regarding the presence of a linear vs. curvilinear association between weekly hours of training and mental health indicators.

What Are the New Findings?

• Both male and female elite athletes had generally better mental health across all examined health outcomes.
• Compared to individual sports, athletes in team sports reported better mental health and less loneliness, but also somewhat more alcohol problems (males only).
• The overall pattern was that the more hours of physical exercise, the better the mental health and higher life satisfaction, although there was little to be gained from increasing the amount of training at the extreme end.
• Among female athletes training the most, there was generally a worsening of mental health across most outcome measures.

Data Availability Statement

Ethics statement.

All procedures involving human subjects/patients were approved by the Regional Committee for Medical and Health Research Ethics in Western Norway (No. 2017/1176 [SHOT2018]). Electronic informed consent was obtained after the participants had received a detailed introduction to the study.

Author Contributions

BS contributed with planning and design of the survey data-collection. MG and BS planned, designed, and coordinated the present study. MG and BS conducted the statistical analyses on the survey data, conducted the literature review, and led the writing of the manuscript. BC contributed with input on design and analytical plan, interpretation of results, and critical revision of the manuscript and analyses. All authors approved the submission.

SHoT2018 has received funding from the Norwegian Ministry of Education and Research (2017) and the Norwegian Ministry of Health and Care Services (2016).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We wish to thank all participating students as well as the three largest student associations in Norway (SiO, Sammen and SiT), who initiated and designed SHoT studies.

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Review article, stress in academic and athletic performance in collegiate athletes: a narrative review of sources and monitoring strategies.

• 1 School of Kinesiology, Applied Health and Recreation, Oklahoma State University, Stillwater, OK, United States
• 2 Department of Kinesiology, California State University, Fullerton, CA, United States
• 3 Department of Kinesiology, Point Loma Nazarene University, San Diego, CA, United States
• 4 Department of Kinesiology and Sport Sciences, University of Miami, Miami, FL, United States

College students are required to manage a variety of stressors related to academic, social, and financial commitments. In addition to the burdens facing most college students, collegiate athletes must devote a substantial amount of time to improving their sporting abilities. The strength and conditioning professional sees the athlete on nearly a daily basis and is able to recognize the changes in performance and behavior an athlete may exhibit as a result of these stressors. As such, the strength and conditioning professional may serve an integral role in the monitoring of these stressors and may be able to alter training programs to improve both performance and wellness. The purpose of this paper is to discuss stressors experienced by collegiate athletes, developing an early detection system through monitoring techniques that identify the detrimental effects of stress, and discuss appropriate stress management strategies for this population.

Introduction

The college years are a period of time when young adults experience a significant amount of change and a variety of novel challenges. Academic performance, social demands, adjusting to life away from home, and financial challenges are just a few of the burdens college students must confront ( Humphrey et al., 2000 ; Paule and Gilson, 2010 ; Aquilina, 2013 ). In addition to these stressors, collegiate athletes are required to spend a substantial amount of time participating in activities related to their sport, such as attending practices and training sessions, team meetings, travel, and competitions ( Humphrey et al., 2000 ; López de Subijana et al., 2015 ; Davis et al., 2019 ; Hyatt and Kavazis, 2019 ). These commitments, in addition to the normal stress associated with college life, may increase a collegiate-athlete's risk of experiencing both physical and mental issues ( Li et al., 2017 ; Moreland et al., 2018 ) that may affect their overall health and wellness. For these reasons, it is essential that coaches understand the types of stressors collegiate athletes face in order to help them manage the potentially deleterious effects stress may have on athletic and academic performance.

Strength and conditioning coaches are allied health care professionals whose primary job is to enhance fitness of individuals for the purpose of improving athletic performance ( Massey et al., 2002 , 2004 , 2009 ). As such, many universities and colleges hire strength and conditioning coaches as part of their athletic staff to help athletes maximize their physical potential ( Massey et al., 2002 , 2004 , 2009 ). Strength and conditioning coaches strive to increase athletic performance by the systematic application of physical stress to the body via resistance training, and other forms of exercise, to yield a positive adaptation response ( Massey et al., 2002 , 2004 , 2009 ). For this reason, they need to understand and to learn how to manage athletes' stress. Additionally, based on the cumulative nature of stress, it is important that both mental and emotional stressors are also considered in programming. It is imperative that strength and conditioning coaches are aware of the multitude of stressors collegiate athletes encounter, in order to incorporate illness and injury risk management education into their training programs ( Radcliffe et al., 2015 ; Ivarsson et al., 2017 ).

Based on the large number of contact hours strength and conditioning coaches spend with their athletes, they are in an optimal position to assist athletes with developing effective coping strategies to manage stress. By doing so, strength and conditioning coaches may be able to help reach the overarching goal of improving the health, wellness, fitness, and performance of the athletes they coach. The purpose of this review article is to provide the strength and conditioning professional with a foundational understanding of the types of stressors collegiate athletes may experience, and how these stressors may impact mental health and athletic performance. Suggestions for assisting athletes with developing effective coping strategies to reduce potential physiological and psychological impacts of stress will also be provided.

Stress and the Stress Response

In its most simplistic definition, stress can be described as a state of physical and psychological activation in response to external demands that exceed one's ability to cope and requires a person to adapt or change behavior. As such, both cognitive or environmental events that trigger stress are called stressors ( Statler and DuBois, 2016 ). Stressors can be acute or chronic based on the duration of activation. Acute stressors may be defined as a stressful situation that occurs suddenly and results in physiological arousal (e.g., increase in hormonal levels, blood flow, cardiac output, blood sugar levels, pupil and airway dilation, etc.) ( Selye, 1976 ). Once the situation is normalized, a cascade of hormonal reactions occurs to help the body return to a resting state (i.e., homeostasis). However, when acute stressors become chronic in nature, they may increase an individual's risk of developing anxiety, depression, or metabolic disorders ( Selye, 1976 ). Moreover, the literature has shown that cumulative stress is correlated with an increased susceptibility to illness and injury ( Szivak and Kraemer, 2015 ; Mann et al., 2016 ; Hamlin et al., 2019 ). The impact of stress is individualistic and subjective by nature ( Williams and Andersen, 1998 ; Ivarsson et al., 2017 ). Additionally, the manner in which athletes respond to a situational or environmental stressor is often determined by their individual perception of the event ( Gould and Udry, 1994 ; Williams and Andersen, 1998 ; Ivarsson et al., 2017 ). In this regard, the athlete's perception can either be positive (eustress) or negative (distress). Even though they both cause physiological arousal, eustress also generates positive mental energy whereas distress generates anxiety ( Statler and DuBois, 2016 ). Therefore, it is essential that an athlete has the tools and ability to cope with these stressors in order to have the capacity to manage both acute and chronic stress. As such, it is important to understand the types of stressors collegiate athletes are confronted with and how these stressors impact an athlete's performance, both athletically and academically.

Literature Search/Data Collection

The articles included in this review were identified via online databases PubMed, MEDLINE, and ISI Web of Knowledge from October 15th 2019 through January 15th 2020. The search strategy combined the keywords “academic stress,” “athletic stress,” “stress,” “stressor,” “college athletes,” “student athletes,” “collegiate athletes,” “injury,” “training,” “monitoring.” Duplicated articles were then removed. After reading the titles and abstracts, all articles that met the inclusion criteria were considered eligible for inclusion in the review. Subsequently, all eligible articles were read in their entirety and were either included or removed from the present review.

Inclusion Criteria

The studies included met all the following criteria: (i) published in English-language journals; (ii) targeted college athletes; (iii) publication was either an original research paper or a literature review; (iv) allowed the extraction of data for analysis.

Data Analysis

Relevant data regarding participant characteristics (i.e., gender, academic status, sports) and study characteristics were extracted. Articles were analyzed and divided into two separate sections based on their specific topics: Academic Stress and Athletic Stress. Then, strategies for monitoring and workload management are discussed in the final section.

Academic Stress

Fundamentally, collegiate athletes have two major roles they must balance as part of their commitment to a university: being a college student and an athlete. Academic performance is a significant source of stress for most college students ( Aquilina, 2013 ; López de Subijana et al., 2015 ; de Brandt et al., 2018 ; Davis et al., 2019 ). This stress may be further compounded among collegiate athletes based on their need to be successful in the classroom, while simultaneously excelling in their respective sport ( Aquilina, 2013 ; López de Subijana et al., 2015 ; Huml et al., 2016 ; Hamlin et al., 2019 ). Davis et al. (2019) conducted surveys on 173 elite junior alpine skiers and reported significant moderate to strong correlations between perceived stress and several variables including depressed mood ( r = 0.591), sleep disturbance ( r = 0.459), fatigue ( r = 0.457), performance demands ( r = 0.523), and goals and development ( r = 0.544). Academic requirements were the highest scoring source of stress of all variables and was most strongly correlated with perceived stress ( r = 0.467). Interestingly, it was not academic rigor that was viewed by the athletes as the largest source of direct stress; rather, the athletes surveyed reported time management as being their biggest challenge related to academic performance ( Davis et al., 2019 ). This further corroborates the findings of Hamlin et al. (2019) . The investigators reported that during periods of the academic year in which levels of perceived academic stress were at their highest, students had trouble managing sport practices and studying. These stressors were also associated with a decrease in energy levels and overall sleep quality. These factors may significantly increase the collegiate athlete's susceptibility to illness and injury ( Hamlin et al., 2019 ). For this reason, coaches should be aware of and sensitive to the stressors athletes experience as part of the cyclical nature of the academic year and attempt to help athletes find solutions to balancing athletic and academic demands.

According to Aquilina (2013) , collegiate athletes tend to be more committed to sports development and may view their academic career as a contingency plan to their athletic career, rather than a source of personal development. As a result, collegiate athletes often, but certainly not always, prioritize athletic participation over their academic responsibilities ( Miller and Kerr, 2002 ; Cosh and Tully, 2014 , 2015 ). Nonetheless, scholarships are usually predicated on both athletic and academic performance. For instance, the National Collegiate Athletic Association (NCAA) requires collegiate athletes to achieve and maintain a certain grade point average (GPA). Furthermore, they are also often required to also uphold a certain GPA to maintain an athletic scholarship. The pressure to maintain both high levels of academic and athletic performance may increase the likelihood of triggering mental health issues (i.e., anxiety and depression) ( Li et al., 2017 ; Moreland et al., 2018 ).

Mental health issues are a significant concern among college students. There has been an increased emphasis placed on the mental health of collegiate athletes in recent years ( Petrie et al., 2014 ; Li et al., 2017 , 2019 ; Reardon et al., 2019 ). Based on the 2019 National College Health Assessment survey from the American College Health Association (ACHA) consisting of 67,972 participants, 27.8% of college students reported anxiety, and 20.2% reported experiencing depression which negatively affected their academic performance ( American College Health Association American College Health Association-National College Health Assessment II, 2019 ). Approximately 65.7% (50.7% males and 71.8% females) reported feeling overwhelming anxiety in the past 12 months, and 45.1% (37.1% males and 47.6% females) reported feeling so depressed that it was difficult for them to function. However, only 24.3% (13% males and 28.4% females) reported being diagnosed and treated by a professional in the past 12 months. Collegiate athletes are not immune to these types of issues. According to information presented by the NCAA, many certified athletic trainers anecdotally state that anxiety is an issue affecting the collegiate-athlete population ( NCAA, 2014 ). However, despite the fact that collegiate athletes are exposed to numerous stressors, they are less likely to seek help at a university counseling center than non-athletes ( NCAA, 2014 ), which could be related to stigmas that surround mental health services ( NCAA, 2014 ; Kaier et al., 2015 ; Egan, 2019 ). This not only has significant implications related to their psychological well-being, but also their physiological health, and consequently their performance. For instance, in a study by Li et al. (2017) it was found that NCAA Division I athletes who reported preseason anxiety symptoms had a 2.3 times greater injury incidence rate compared to athletes who did not report. This same study discovered that male athletes who reported preseason anxiety and depression had a 2.1 times greater injury incidence, compared to male athletes who did not report symptoms of anxiety and depression. ( Lavallée and Flint, 1996 ) also reported a correlation between anxiety and both injury frequency and severity among college football players ( r = 0.43 and r = 0.44, respectively). In their study, athletes reporting high tension/anxiety had a higher rate of injury. It has been suggested that the occurrence of stress and anxiety may cause physiological responses, such as an increase in muscle tension, physical fatigue, and a decrease in neurocognitive and perception processes that can lead to physical injuries ( Ivarsson et al., 2017 ). For this reason, it is reasonable to consider that academic stressors may potentiate effects of stress and result in injury and illness in collegiate athletes.

Periods of more intense academic stress increase the susceptibility to illness or injury ( Mann et al., 2016 ; Hamlin et al., 2019 ; Li et al., 2019 ). For example, Hamlin et al. (2019) investigated levels of perceived stress, training loads, injury, and illness incidence in 182 collegiate athletes for the period of one academic year. The highest levels of stress and incidence of illness arise during the examination weeks occurring within the competitive season. In addition, the authors also reported the odds ratio, which is the occurrence of the outcome of interest (i.e., injury), based off the given exposure to the variables of interest (i.e., perceived mood, sleep duration, increased academic stress, and energy levels). Based on a logistic regression, they found that each of the four variables (i.e., mood, energy, sleep duration, and academic stress) was related to the collegiate athletes' likelihood to incur injuries. In summary, decreased levels of perceived mood (odds ratio of 0.89, 0.85–0.0.94 CI) and sleep duration (odds ratio of 0.94, 0.91–0.97 CI), and increased academic stress (odds ratio of 0.91, 0.88–0.94 CI) and energy levels (odds ratio of 1.07, 1.01–1.14 CI), were able to predict injury in these athletes. This corroborates Mann et al. (2016) who found NCAA Division I football athletes at a Bowl Championship Subdivision university were more likely to become ill or injured during an academically stressful period (i.e., midterm exams or other common test weeks) than during a non-testing week (odds ratio of 1.78 for high academic stress). The athletes were also less likely to get injured during training camp (odds ratio of 3.65 for training camp). Freshmen collegiate athletes may be especially more susceptible to mental health issues than older students. Their transition includes not only the academic environment with its requirements and expectations, but also the adaptation to working with a new coach and teammates. In this regard, Yang et al. (2007) found an increase in the likelihood of depression that freshmen athletes experienced, as these freshmen were 3.27 times more likely to experience depression than their older teammates. While some stressors are recurrent and inherent in academic life (e.g., attending classes, homework, etc.), others are more situational (e.g., exams, midterms, projects) and may be anticipated by the strength and conditioning coach.

Athletic Stress

The domain of athletics can expose collegiate athletes to additional stressors that are specific to their cohort (e.g., sport-specific, team vs. individual sport) ( Aquilina, 2013 ). Time spent training (e.g., physical conditioning and sports practice), competition schedules (e.g., travel time, missing class), dealing with injuries (e.g., physical therapy/rehabilitation, etc.), sport-specific social support (e.g., teammates, coaches) and playing status (e.g., starting, non-starter, being benched, etc.) are just a few of the additional challenges collegiate athletes must confront relative to their dual role of being a student and an athlete ( Maloney and McCormick, 1993 ; Scott et al., 2008 ; Etzel, 2009 ; Fogaca, 2019 ). Collegiate athletes who view the demands of stressors from academics and sports as a positive challenge (i.e., an individual's self-confidence or belief in oneself to accomplish the task outweighs any anxiety or emotional worry that is felt) may potentially increase learning capacity and competency ( NCAA, 2014 ). However, when these demands are perceived as exceeding the athlete's capacity, this stress can be detrimental to the student's mental and physical health as well as to sport performance ( Ivarsson et al., 2017 ; Li et al., 2017 ).

As previously stated, time management has been shown to be a challenge to collegiate athletes. The NCAA rules state that collegiate athletes may only engage in required athletic activities for 4 h per day and 20 h/week during in-season and 8 h/week during off-season throughout the academic year. Although these rules have been clearly outlined, the most recent NCAA GOALS (2016) study reported alarming numbers regarding time commitment to athletic-related activities. Data from over 21,000 collegiate athletes from 600 schools across Divisions I, II, and III were included in this study. Although a breakdown of time commitments was not provided, collegiate athletes reported dedicating up to 34 h per week to athletics (e.g., practices, weight training, meetings with coaches, tactical training, competitions, etc.), in addition to spending between 38.5 and 40 h per week working on academic-related tasks. This report also showed a notable trend related to athletes spending an increase of ~2 more athletics-related hours per week compared to the 2010 GOALS study, along with a decrease of 2 h of personal time (from 19.5 h per week in 2010 to 17.1 in 2015). Furthermore, ~66% of Division I and II and 50% of Division III athletes reported spending as much or more time in their practices during the off-season as during the competitive season ( DTHOMAS, 2013 ). These numbers show how important it is for collegiate athletes to develop time management skills to be successful in both academics and athletics. Overall, most collegiate athletes have expressed a need to find time to enjoy their college experience outside of athletic obligations ( Paule and Gilson, 2010 ). Despite that, because of the increasing demand for excellence in academics and athletics, collegiate athletes' free time with family and friends is often scarce ( Paule and Gilson, 2010 ). Consequently, trainers, coaches, and teammates will likely be the primary source of their weekly social interactivity.

Social interactions within their sport have also been found to relate to factors that may impact an athlete's perceived stress. Interactions with coaches and trainers can be effective or deleterious to an athlete. Effective coaching includes a coaching style that allows for a boost of the athlete's motivation, self-esteem, and efficacy in addition to mitigating the effects of anxiety. On the other hand, poor coaching (i.e., the opposite of effective coaching) can have detrimental psychological effects on an athlete ( Gearity and Murray, 2011 ). In a closer examination of the concept of poor coaching practices, Gearity and Murray (2011) interviewed athletes about their experiences of receiving poor coaching. Following analysis of the interviews, the authors identified the main themes of the “coach being uncaring and unfair,” “practicing poor teaching inhibiting athlete's mental skills,” and “athlete coping.” They stated that inhibition of an athlete's mental skills and coping are associated with the psychological well-being of an athlete. Also, poor coaching may result in mental skills inhibition, distraction, insecurity, and ultimately team division ( Gearity and Murray, 2011 ). This combination of factors may compound the negative impacts of stress in athletes and might be especially important for in injured athletes.

Injured athletes have previously been reported to have elevated stress as a result of heightened worry about returning to pre-competition status ( Crossman, 1997 ), isolation from teammates if the injury is over a long period of time ( Podlog and Eklund, 2007 ) and/or reduced mood or depressive symptoms ( Daly et al., 1995 ). In addition, athletes who experience prolonged negative thoughts may be more likely to have decreased rehabilitation attendance or adherence, worse functional outcomes from rehabilitation (e.g., on measures of proprioception, muscular endurance, and agility), and worse post-injury performance ( Brewer, 2012 ).

Monitoring Considerations

In addition to poor coaching, insufficient workload management can hinder an athlete's ability to recover and adapt to training, leading to fatigue accumulation ( Gabbett et al., 2017 ). Excessive fatigue can impair decision-making ability, coordination and neuromuscular control, and ultimately result in overtraining and injury ( Soligard et al., 2016 ). For instance, central fatigue was found to be a direct contributor to anterior cruciate ligament injuries in soccer players ( Mclean and Samorezov, 2009 ). Introducing monitoring tools may serve as a means to reduce the detrimental effects of stress in collegiate athletes. Recent research on relationships between athlete workloads, injury, and performance has highlighted the benefits of athlete monitoring ( Drew and Finch, 2016 ; Jaspers et al., 2017 ).

Athlete monitoring is often assessed with the measuring and management of workload associated with a combination of sport-related and non-sport-related stressors ( Soligard et al., 2016 ). An effective workload management program should aim to detect excessive fatigue, identify its causes, and constantly adapt rest, recovery, training, and competition loads respectively ( Soligard et al., 2016 ). The workload for each athlete is based off their current levels of physical and psychological fatigue, wellness, fitness, health, and recovery ( Soligard et al., 2016 ). Accumulation of situational or physical stressors will likely result in day-to-day fluctuations in the ability to move external loads and strength train effectively ( Fry and Kraemer, 1997 ). Periods of increased academic stress may cause increased levels of fatigue, which can be identified by using these monitoring tools, thereby assisting the coaches with modulating the workload during these specific periods. Coaches who plan to incorporate monitoring and management strategies must have a clear understanding of what they want to achieve from athlete monitoring ( Gabbett et al., 2017 ; Thornton et al., 2019 ).

Monitoring External Loads

External load refers to the physical work (e.g., number of sprints, weight lifted, distance traveled, etc.) completed by the athlete during competition, training, and activities of daily living ( Soligard et al., 2016 ). This type of load is independent of the athlete's individual characteristics ( Wallace et al., 2009 ). Monitoring external loading can aid in the designing of training programs which mimic the external load demands of an athlete's sport, guide rehabilitation programs, and aid in the detection of spikes in external load that may increase the risk of injury ( Clubb and McGuigan, 2018 ).

The means of quantifying external load can involve metrics as simple as pitch counts in baseball and softball ( Fleisig and Andrews, 2012 ; Shanley et al., 2012 ) or quantifying lifting session training loads (e.g., sum value of weight lifted during an exercise x number of repetitions × the number of sets). Neuromuscular function testing is another more common way of analyzing external load. This is typically done using such measures such as the counter movement jump, squat jump, or drop jump. A force platform can be used to measure a myriad of outcomes (e.g., peak power, ground contact time, time to take-off, reactive strength index, and jump height), or simply measure jump height in a more traditional manner. Jumping protocols, such as the countermovement jump, have been adopted to examine the recovery of neuromuscular function after athletic competition with significant decreases for up to 72 h commonly reported ( Andersson et al., 2008 ; Magalhães et al., 2010 ; Twist and Highton, 2013 ). ( Gathercole et al., 2015 ) found reductions in 18 different neuromuscular variables in collegiate athletes following a fatiguing protocol. The variables of eccentric duration, concentric duration, total duration, time to peak force/power, and flight time:contraction time ratio, derived from a countermovement jump were deemed suitable for detecting neuromuscular fatigue with the rise in the use of technology for monitoring, certain sports have adopted specific software that can aid in the monitoring of stress. For example, power output can be measured using devices such as SRM™ or PowerTap™ in cycling ( Jobson et al., 2009 ). This data can be analyzed to provide information such as average power or normalized power. The power output can then be converted into a Training Stress Score™ via commercially available software ( Marino, 2011 ). More sophisticated measures of external load may involve the use of wearable technology devices such as Global Positioning System (GPS) devices, accelerometers, magnetometer, and gyroscope inertial sensors ( Akenhead and Nassis, 2016 ). These devices can quantify external load in several ways, such as duration of movement, total distance covered, speed of movement, acceleration, and decelerations, as well as sport specific movement such as number and height of jumps, number of tackles, or breakaways, etc. ( Akenhead and Nassis, 2016 ). The expansion of marketing of wearable devices has been substantial; however, there are questions of validity and reliability related to external load tracking limitations related to proprietary metrics, as well as the overall cost that should be considered when considering the adoption of such devices ( Aughey et al., 2016 ; Torres-Ronda and Schelling, 2017 ).

Monitoring Internal Loads

While external load may provide information about an athlete's performance capacity and work completed, it does not provide clear evidence of how athletes are coping with and adapting to the external load ( Halson, 2014 ). This type of information comes from the monitoring of internal loads. The term internal load refers to the individual physiological and psychological response to the external stress or load imposed ( Wallace et al., 2009 ). Internal load is influenced by a number of factors such as daily life stressors, the environment around the athlete, and coping ability ( Soligard et al., 2016 ). Indirect measures, such as the use of heart rate (HR) monitoring, and subjective measurements, such as perceived effort (i.e., ratings of perceived exertion), are examples of internal load monitoring. Using subjective measurement systems is a simple and practical method when dealing with large numbers of athletes ( Saw et al., 2016 ; Nässi et al., 2017 ). Subjective reporting of training load (Rating of Perceived Exertion—RPE) ( Coyne et al., 2018 ), Session Rating of Perceived Exertion—sRPE) ( Coyne et al., 2018 ), perceived stress and recovery (Recovery Stress Questionnaire for Athletes—RESTQ-S), and psychological mood states (Profile of Mood States—POMS) have all been found to be a reliable indicator of training load ( Robson-Ansley et al., 2009 ; Saw et al., 2016 ) and only take a few moments to complete. In addition, subjective measures can be more responsive to tracking changes or training responses in athletes than objective measures ( Saw et al., 2016 ).

Heart rate (HR) monitoring is a common intrinsic measure of how the body is responding to stress. With training, the reduction of resting HR is typically a clear indication of the heart becoming more efficient and not having to beat as frequently. Alternately, increases of resting HR over time with a continuation of training may be an indicator of too much stress. Improper nutrition, such as regular or ongoing suboptimal intakes of vitamins or minerals, may result in increased ventilation and/or increased heart rate ( Lukaski, 2004 ). It has been suggested that the additional stress may lead to parasympathetic hyperactivity, leading to an increase in resting HR ( Statler and DuBois, 2016 ). This largely stems from research examining the sensitivity of various HR derived metrics, such as resting HR, HR variability (HRV), and HR recovery (HRR) to fluctuations in training load ( Borresen and Ian Lambert, 2009 ). HRR in athlete monitoring is the rate of HR decline after the cessation of exercise. A common measure of HHR is the use of a 2 min step test followed by a 60 s HR measurement. The combination of the exercise (stress) on the cardiovascular system and then its subsequent return toward baseline has been used as an indicator of autonomic function and training status in athletes ( Daanen et al., 2012 ). In collegiate athletes it was found that hydration status impacted HRR following moderate to hard straining sessions ( Ayotte and Corcoran, 2018 ). Athletes who followed a prescription hydration plan performed better in the standing long jump, tracked objects faster, and showed faster HRR vs. athletes who followed their normal self-selected hydration plan ( Ayotte and Corcoran, 2018 ). To date, HR monitoring and the various derivatives have mainly been successful in detecting changes in training load and performance in endurance athletes ( Borresen and Ian Lambert, 2009 ; Lamberts et al., 2009 ; Thorpe et al., 2017 ). Although heart rate monitoring can provide additional physiological insight for aerobic sessions or events, it thus far has not been found to be an accurate measurement for quantifying internal load during many explosive, short duration anaerobic activities ( Bosquet et al., 2008 ).

A multitude of studies have reported the reliability and validity of using RPE and sRPE across a range of training modalities ( Foster, 1998 ; Impellizzeri et al., 2004 ; Sweet et al., 2004 ). This measure can be used to create a number of metrics such as session load (sRPE × duration in minutes), daily load (sum of all session loads for that day), weekly training load (sum of all daily training loads for entire week), monotony (standard deviation of weekly training load), and strain (daily or weekly training load × monotony) ( Foster, 1998 ). Qualitative questionnaires that monitor stress and fatigue have been well-established as tools to use with athletes (see Table 1 for examples of commonly used questionnaires in research). Using short daily wellness questionnaires may allow coaches to generate a wellness score which then can be adjusted based off of the stress the athlete may be feeling to meet the daily load target ( Foster, 1998 ; Robson-Ansley et al., 2009 ). However, strength and conditioning coaches need to be mindful that these questionnaires may require sports psychologist or other licensed professional to examine and provide the results. An alternative that may be better suited for strength and conditioning professionals to use could be to incorporate some of the themes of those questionnaires into programing.

Table 1 . Overview of common tool/measures used by researchers to monitor training load.

A Multifaceted Approach

Dissociation between external and internal load units may be indicative of the state of fatigue of an athlete. Utilizing a monitoring system in which the athlete is able to make adjustments to their training loads in accordance with how they are feeling in that moment can be a useful tool for assisting the athlete in managing stress. Auto-regulation is a method of programming that allows for adjustments based on the results of one or more readiness tests. When implemented properly, auto regulation enables the coach or athlete to optimize training based on the athlete's given readiness for training on a particular day, thereby aiming to avoid potential overtraining ( Kraemer and Fleck, 2018 ). Several studies have found that using movement velocity to designate resistance training intensities can result in significant improvements in maximal strength and athletic performance ( Pareja-Blanco et al., 2014 , 2017 ; Mann et al., 2015 ). Velocity based training allows the coach and athlete to view real time feedback for the given lifts, thereby allowing them to observe how the athlete is performing in that moment. If the athlete is failing to meet the prescribed velocity or the velocity drops greater than a predetermined amount between sets, then this should signal the coach to investigate. If there is a higher than normal amount of stress on that athlete for the day, that could be a potential reason. This type of combination style program of using a quantitative or objective measurement (s) and a subjective measure of wellness (qualitative questionnaire) has recently been reported to be an effective tool in monitoring individuals apart of a team ( Starling et al., 2019 ). The subjective measure in this study was the readiness to train questionnaire (RTT-Q) and the objective measures were the HRR 6min test (specifically the HRR 60s = recorded as decrease in HR in the 60 s after termination of the test) to assess autonomic function and the standing long jump (SLJ) to measure neuromuscular function. The findings found that, based on the absolute typical error of measurement, the HRR 60s and SLJ could detect medium and large changes in fatigue and readiness. The test took roughly 8 min for the entire team, which included a group consisting of 24 college-age athletes. There are many other combinations of monitoring variables and strategies that coaches and athletes may utilize.

Data Analysis – How to Utilize the Measures

Regardless of what type of monitoring tool a coach or athlete may incorporate, it is essential to understand how to analyze this data. There are excellent resources available which discuss this topic in great detail ( Gabbett et al., 2017 ; Clubb and McGuigan, 2018 ; Thornton et al., 2019 ). This section will highlight two main conclusions from these sources and briefly describe two of the main statistical practices and concepts discussed. The use of z-scores or modified z-scores has been proposed as a method of detecting meaningful change in athlete data ( Clubb and McGuigan, 2018 ; Thornton et al., 2019 ). For different monitoring tools listed in Table 1 , the following formula would be an example of how to assess changes: (Athlete daily score—Baseline score)/Standard deviation of baseline. The baseline would likely be based off an appropriate period such as the scores across 2 weeks during the preseason.

In sports and sports science, the use of a magnitude-based inference (MBI) has been suggested as more appropriate and easier to understand when examining meaningful changes in athletic data, than null-hypothesis significance testing (NHST) ( Buchheit, 2014 ). Additional methods to assess meaningful change that are similar to MBI are using standard deviation, typical error, effect sizes, smallest worthwhile change (SWC), and coefficient of variation ( Thornton et al., 2019 ). It should be noted that all of these methods have faced criticism from sources such as statisticians. It is important to understand that the testing methods, measurements, and analysis should be based on the resources and intended goals from use, which will differ from every group and individual. Once identified, it is up to the practitioner to keep this system the same, in order to collect data that can then be examined to understand meaningful information for each setting ( Thornton et al., 2019 ).

Managing and Coping Strategies

Once the collegiate-athlete has been able to identify the need to balance their stress levels, the athlete may then need to seek out options for managing their stress. Coaches are be able to assist them by sharing information on health and wellness resources available for the students, both on and off campus. Another way a coach can potentially support their athletes is by establishing an open-door policy, wherein the team members feel comfortable approaching a member of the strength and conditioning staff in order to seek out resources for coping with challenges related to stress.

There are some basic skills that strength and conditioning coaches can teach (while staying within their scope of practice). Coaches can introduce their athletes to basic lifestyle concepts, such as practicing deep breathing techniques, positive self-talk, and developing healthy sleep habits (i.e., turning off their mobile devices 1 h before bed and aiming for 8 h of sleep each night, etc.). A survey of strength and conditioning practitioners by Radcliffe et al. (2015) found that strategies used by practitioners included a mix of cognitive and behavioral strategies, which was used as justification for recommending practitioners find opportunities to guide professional development toward awareness strategies. Practitioners reported using a wide variety of psychological skills and strategies, which following survey analysis, highlighted a significant emphasis on strategies that may influence athlete self-confidence and goal setting. Themes identified by Radcliffe et al. (2015) included confidence building, arousal management, and skill acquisition. Additionally, similar lower level themes that are connected (i.e., goal setting, increasing, or decreasing arousal intensities, self-talk, mental imagery) are all discussed in the 4th edition of the NSCA Essentials of Strength and Conditioning book ( Haff et al., 2016 ). When the interventions aiming to improve mental health expand from basic concepts to mental training beyond a coach's scope, it would be pertinent for the coach to refer the collegiate-athlete to a sport psychology or other mental health consultant ( Fogaca, 2019 ). Moreover, strength and conditioning coaches may find themselves in a position to become key players in facilitating management strategies for collegiate athletes, thereby guiding the athlete in their quest to learn how to best manage the mental and physical energy levels required in the quest for overall optimal performance ( Statler and DuBois, 2016 ).

Conclusion and Future Directions

This review article has summarized some of the ways that strength and conditioning professionals may be able to gain a better understanding of the types of stressors encountered by collegiate athletes, the impact these stressors may have on athletic performance, and suggestions for assisting athletes with developing effective coping strategies to reduce the potential negative physiological and psychological impacts of stress. It has been suggested that strategies learned in the context of training may have a carry-over effect into other areas such as competition. More education is needed in order for strength and conditioning professionals to gain a greater understanding of how to support their athletes with stress-management techniques and resources. Some ways to disseminate further education on stress-management tools for coaches to share with their athletes may include professional development events, such as conferences and clinics.

Author Contributions

All of the authors have contributed to the development of the manuscript both in writing and conceptual development.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling editor declared a past collaboration with one of the authors RL.

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Keywords: stress, load management, academic stress, stress management, injury

Citation: Lopes Dos Santos M, Uftring M, Stahl CA, Lockie RG, Alvar B, Mann JB and Dawes JJ (2020) Stress in Academic and Athletic Performance in Collegiate Athletes: A Narrative Review of Sources and Monitoring Strategies. Front. Sports Act. Living 2:42. doi: 10.3389/fspor.2020.00042

Received: 05 October 2019; Accepted: 30 March 2020; Published: 08 May 2020.

Reviewed by:

Copyright © 2020 Lopes Dos Santos, Uftring, Stahl, Lockie, Alvar, Mann and Dawes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: J. Bryan Mann, Bmann@miami.edu

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How Marketers Choose College Athlete Influencers

• Kimberly A. Whitler
• Graham Twente

The authors’ research findings: Athletes’ image and quality of social media posts are more important than their follower counts, posts should feature sports more than personal content, and sexy imagery should be avoided.

Here are the characteristics that matter most.

Since 2021 McDonald’s, Microsoft, PepsiCo, Berkshire Hathaway, Amazon, Unilever, and other leading companies have done something that was never before possible: They have paid U.S. college athletes to act as product endorsers and influencers. Until a Supreme Court ruling that year, paying college athletes was forbidden under the rules of the National Collegiate Athletic Association (NCAA). In the aftermath of the Court’s ruling, the NCAA adopted a policy that enabled more than 520,000 student athletes to monetize their names, images, and likenesses by signing what have become known as NIL deals. Although no definitive count exists of athletes who have signed such deals, 278 students (40% of varsity athletes) at Texas Tech had been sponsored as of 2022. In just a few years marketers have already spent more than $1 billion on such endorsements. For individual athletes these deals can be lucrative. Consider Paige Bueckers, a University of Connecticut basketball player, whom Gatorade chose as its first sponsored college athlete. Bueckers is expected to earn more than $1 million while playing college basketball.

Over the past 20 years the social media influencer industry has completely rearranged the way information and culture are conceived, produced, marketed, and shared. This month’s Spotlight package looks at how brands are responding.

• Kimberly A. Whitler is the Frank M. Sands Sr. Associate Professor of Business Administration at the University of Virginia’s Darden School of Business and a coauthor of Athlete Brands: How to Benefit from Your Name, Image & Likeness .
• GT Graham Twente is a former senior research manager at the Darden School of Business and currently a member of the data and analytics technology consulting staff at EY in Washington, DC.

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Suicide Rates Have Doubled in 20 Years Among U.S. College Athletes

By Dennis Thompson HealthDay Reporter

FRIDAY, April 5, 2024 (HealthDay News) -- Suicides among U.S. college athletes have doubled over the past two years, according to data from the National Collegiate Athletic Association (NCAA).

Suicide is now the second most common cause of death for college athletes after accidents, results show.

“Athletes are generally thought of as one of the healthiest populations in our society, yet the pressures of school, internal and external performance expectations, time demands, injury, athletic identity and physical fatigue can lead to depression, mental health problems and suicide,” wrote the research team led by Bridget Whelan , a research coordinator with the University of Washington in Seattle.

For the study, Whelan and colleagues analyzed suicides among NCAA athletes from June 2002 to June 2022.

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During the two decades, 1,102 athletes died. Of those, 128 took their own lives, including 98 men and 30 women.

The suicide rate among college athletes doubled comparing the first decade and the second, rising from 7.6% to 15.3%. At the same time, the overall U.S. suicide rate rose just 36%.

Suicides among males increased each year throughout the study period, while suicides among females increased from 2010 onwards.

Male suicides increased from 31 during the first 10 years to 67 in the second decade, results show. Female suicides increased from 9 to 21 between the two decades.

There were nine deaths every two years in male athletes and three deaths every two years in female athletes.

The highest number of suicides was among male cross-country athletes and among the more competitive division I and II athletes, compared with division III athletes, results show.

In fact, there were two deaths every five years in cross-country athletes, researchers found.

Suicides also were more common during the school year, with an average of 12 per month compared with less than seven per month in the summer, results show.

The findings were published April 4 in the British Journal of Sports Medicine .

The data didn’t include any information that might explain why athletes might commit suicide, the researchers noted.

“Athletes may … experience harassment and abuse within their sport, including psychological abuse, physical abuse, sexual abuse, hazing and cyberbullying from the public and members of their team including peer athletes, coaches and members of the entourage,” the researchers speculated.

The researchers pointed out that the NCAA has renewed efforts in recent years to address mental health among college athletes.

“Despite recent increased focus on mental health in athletes, death by suicide is increasing,” they noted.

More information

The NCAA has more on college athlete mental health .

SOURCE: British Journal of Sports Medicine , news release, April 4, 2024

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Suicides among a surprising population have doubled over the past 20 years, study finds

Editor’s note:  If you or someone you know is struggling with suicidal thoughts or mental health matters, please call the 988 Suicide & Crisis Lifeline by dialing 988 to connect with a trained counselor, or visit the  988 Lifeline website .

Suicide rates among people of all ages in the United States have increased over the past two decades, making it a serious public health problem .

Among US college athletes, suicide is now the second leading cause of death after accidents — and rates have doubled from 7.6% to 15.3% over the past 20 years, according to a study published April 4 in the British Journal of Sports Medicine.

Suicide is the 11th leading cause of death in the US, but for Americans ages 10 to 14 and 25 to 34, it’s the second leading cause, and for those ages 15 to 24, it’s the third leading cause.

“Collegiate athletes are often thought to have protective factors like the sense of community with their team (and) support of coaches, trainers, doctors and others,” said the study’s first author, Bridget Whelan, a research scientist in the department of family medicine at the University of Washington’s School of Medicine, via email.

“Unfortunately, this study shows that collegiate athletes are just as susceptible to these mental health concerns,” Whelan said.

Suicide had previously been identified as a leading cause of death among athletes in the National Collegiate Athletic Association, which has put more emphasis on the well-being of athletes in the past decade by publishing a consensus document on best practices for mental health. Those recommendations include creating an environment that supports mental health by following a written plan that considers risk and protective factors in multiple areas of the students’ lives, and screening students for psychological distress using validated tools at least annually.

But past research on how these recommendations and participation in different sports affect suicide rates has been mixed, the authors of the new study said — so they looked into the rates of suicides by NCAA athletes from July 1, 2002, to June 30, 2022, by consulting NCAA memorial lists and insurance claims, the National Center for Catastrophic Sports Injury database, the Parent Heart Watch database and media reports.

The authors also studied how suicide rates were affected by factors such as age, sex, race, division, sport and time of year.

Of the 1,102 deaths that occurred during the 20-year time period, 128 were suicides among people who ranged in age from 17 to 24 and were predominantly male (77%) and White (59%). However, most suicides occurred at age 20, which is typically the middle of a collegiate athlete’s school career.

There weren’t significant differences when considering sex, race or sport, but there were contrasts among divisions — Division III athletes had a 59% and 66% lower rate than Division I and II athletes, respectively, Whelan said.

Male cross-country athletes had the highest rates compared with anyone else in the study — a finding that surprised the authors since previous research found higher rates in football, Whelan said.

“This trend highlights the need for better screening methods, prevention strategies and support systems for student-athletes, as well as continued training (and) education for those working with student-athletes,” said Dr. Jacob Kay, clinical research affiliate at the University of South Carolina’s Arnold School of Public Health, via email. Kay wasn’t involved in the study.

The study also found suicides were more common in months outside of June through August and on Mondays and Tuesdays.

“Understanding the timing of suicides may help inform targeted intervention efforts and support systems to better meet the needs of student-athletes, particularly during these windows of vulnerability,” Kay added.

Mental health risks among athletes

Both the authors and experts uninvolved in the study acknowledge some shortcomings of the research.

“The accuracy of determining the cause of death is particularly tricky with suicides (and) may vary depending on the availability of autopsy and even family reports surrounding circumstances,” said Dr. Urszula Klich, an Atlanta-based clinical psychologist who wasn’t involved in the study.

“Relying on media reports and public records to identify causes of athlete deaths will likely result in underreporting,” Klich added. “Not all suicides will be covered in media. Athletes from lower-profile sports will be less visible.”

However, experts have some ideas on the contextual factors that might explain the findings..

“Suicide is the tragic outcome of medical illness and multiple, often interacting, biological, psychological and social risk factors,” said Dr. Rebecca Bernert, a suicidologist and assistant professor of psychiatry and behavioral sciences at the Stanford University School of Medicine, via email. Bernert, who wasn’t involved in the study, is also founding director of the Stanford Suicide Prevention Research Laboratory.

There’s the pressure to perform both academically and athletically and balance the responsibilities of each, which can lead to anxiety and depression, experts said. This loaded schedule can leave less time for social connection, resulting in feelings of isolation even if they’re part of a sports team.

“Student-athletes routinely experience physical injuries or can wind up with chronic pain due to the physical requirements of their sport,” Klich said. This possibility is especially true for cross-country athletes.

“The psychological impact of injuries, including fear of losing scholarships or opportunities for advancement, can further amplify feelings of hopelessness and despair,” Klich added.

An athlete’s sense of value may also be affected by social media, according to the study.

“Athletes are competing during a time when levels of self-oriented perfectionism and socially prescribed perfectionism are at an all-time high,” the authors said. “Even though athletes are more likely to spend time on non-screen activities than their non-athletic peers, the perceptions or messaging around their performance on social media often leads to worse feelings of well-being.”

These issues are likely especially true for Division I and II athletes who, compared with Division III athletes, are more subject to media coverage, online criticism and stressors associated with the “recent emergence of name, image and likeness … deals in the NCAA, where athletes may earn payment for their personal brand,” the authors added.

Reducing the risk of suicide

If you’re a college athlete and concerned about your mental health, experts want you to know you’re not alone and that the biggest step you can take is to reach out.

“There are people willing and wanting to help, and some really good treatments for mental health concerns. There is hope that things can get better,” Whelan said. “You can reach out to someone close to you that you trust, or if you feel like you want something more anonymous, you can reach out to the ( 988 Suicide & Crisis Lifeline ) via call or text at 988.”

Make sure to also take time for self-care amid busy schedules, Kay said.

If you’re someone wanting to help, get to know these potential warning signs of suicide and how the way you talk about suicide can reduce stigma.

“Overall, it is important to remember that these individuals aren’t just athletes, but students as well,” Kay said. “A comprehensive approach that includes promoting mental health awareness, reducing stigma, providing accessible and culturally sensitive screening and support services, and fostering an inclusive athletic culture that prioritizes the well-being of all student-athletes is needed to address this critical issue.”

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