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. 2024 Aug 11;17(4):732–743. doi: 10.1177/19417381241264491

Sex Differences in Mental Health Symptoms in Elite Athletes: A Systematic Review and Meta-analysis

Michelle E Kew 1, Udit Dave 2, William Marmor 3, Reena Olsen 4, Bridget Jivanelli 5, Sung Huang Laurent Tsai 6, Liang-Tseng Kuo 7, Daphne I Ling 8,*
PMCID: PMC11569523  PMID: 39129353

Abstract

Context:

Mental health is a growing area of concern for elite athletes.

Objective:

To determine the sex differences in mental health symptoms in elite athletes.

Data Sources:

PubMed, EMBASE, and Cochrane Library databases were used.

Study Selection:

Included studies included comparisons of mental health symptoms of athletes by sex.

Study Design:

Systematic review and meta-analysis were conducted following the PRISMA guidelines.

Level of Evidence:

Level 2a.

Data Extraction:

The rate ratio (RR) was calculated as the rates in female and male athletes. Data were pooled using a random-effects model.

Results:

Of 1945 articles identified, 60 articles were included. Male athletes reported higher alcohol misuse (RR, 0.74; CI, 0.68-0.80), illicit drug abuse (RR, 0.82; CI, 0.75-0.89), and gambling problems (RR, 0.14; CI, 0.08-0.25). Female athletes reported higher overall anxiety (RR, 1.17; CI, 1.08-1.27), depression (RR, 1.42; CI, 1.31-1.54), distress (RR, 1.98; CI, 1.40-2.81), and disordered eating (RR, 2.19; CI, 1.58-3.02). Sleep disturbances were reported at similar rates in male and female athletes (RR, 1.13; CI, 0.98-1.30).

Conclusion:

Female and male athletes have significant differences in reported mental health symptoms. Female athletes are more likely to report anxiety, depression, distress, and disordered eating, while male athletes report more alcohol misuse, illicit drug abuse, and gambling. Monitoring and evaluation of mental health is a necessary part of any sport, including access to resources. Longitudinal studies following athletes over time to determine the development and causation for mental health symptoms should be included in future research directions.

Keywords: anxiety, athletes, depression, eating disorders, sleep disorders

Sex-based disparities in mental health symptoms are well documented, with women often experiencing more severe anxiety and depression than men.7,77,89,93 Risk factors for poor mental health are increased for women suffering from eating disorders,2,96 which include disorders such as anorexia and bulimia that do not have a precisely known etiology or definitively delineated treatment.69,82 Conversely, men experience poorer mental health outcomes through mechanisms such as pathological gambling and alcohol abuse.27,38,92 Developing an understanding of sex-based disparities in mental health issues, including a spectrum of symptoms, from depression and anxiety to disordered eating and illicit drug abuse, is crucial to tailoring effective therapeutic measures.

Athletes comprise a subset of the population in whom mental health sequelae are being increasingly studied. 34 Mental health can be affected by several factors during an athlete’s career, including injury, surgery, and poor performance. 74 Proper maintenance of mental health is crucial not only for athlete and team performance on the playing field but also for maintenance of an athlete’s quality of life. 95 In addition, elite athletes face barriers in accessing mental health care due to factors such as fear of missing playing time, stigma against mental health care, and poor health literacy. 71 Female athletes are more likely subjected to psychological stressors and diagnosed with a mental health disorder compared with male athletes,4,51,70,78 underscoring the need for sex-based analysis of the mental health of elite-level athletes. 66 The purpose of this systematic review and meta-analysis is to compare mental health disorders in elite female and male athletes.

Methods

Literature Search Methodology

The search strategy was initially developed in MEDLINE (PubMed) by a medical librarian. It was then adapted for the other databases searched—Embase and the Cochrane Library (including the Cochrane Central Register of Controlled Trials, Health Technology Assessment database, and National Health Service Economic Evaluation Database). According to the PICO (patient, intervention, comparison, outcome) format, the population consisted of elite athletes defined as college level or beyond. The comparison groups were female athletes compared with male athletes (control group), and the outcome was the presence of mental health symptoms. No limits were placed on the study type.

Search terms included a combination of keywords and controlled vocabularies: “Sex,” “gender,” “male,” “men,” “female,” “woman,” “college,” “professional,” “elite,” “athlete,” “sport,” “mental health,” “sleep disturbance,” “anxiety,” “depression,” and “alcohol/drug/substance use, misuse, or abuse.” The full search strategy can be found in Appendix 1 (available in the online version of this article).

The inclusion criteria for this meta-analysis were studies that included male-to-female comparisons of mental health symptoms (alcohol misuse, anxiety, depression, distress, eating disorders, gambling, sleep disorders, illicit drug abuse) of athletes by sex and were published between January 1, 2010 and October 31, 2021. This timeframe was based on a recent meta-analysis on mental health in athletes (without taking into account sex differences) in which all studies were published after 2010. 25 We included both current and retired athletes. Conference abstracts, studies that did not present sex comparisons, studies that presented mental health data from nonelite athletes (eg, youths or adolescents), and studies written in a language other than English were excluded from this meta-analysis. Two authors used the online software program Covidence (Veritas Health Innovation Ltd) to independently screen titles, abstracts, and then full-article texts. Systematic reviews and meta-analyses (8 total manuscripts) were searched manually for additional studies to include. Any disagreements were resolved through consensus with the senior author.

Data Extraction

The following data were extracted independently from full-text articles by the co-authors: study design, country, number of athletes, age of athletes, current or former athlete, team or individual sport, competitive level, and mental health symptom. Detailed data were extracted for subgroup analyses when possible (eg, mild, moderate, severe levels).

Data Synthesis and Meta-analysis

Each study in the meta-analysis contributed at least 2 pairs of numbers: the total number of male and female athletes and the number of male and female athletes with the mental health symptom. A rate ratio (RR) was calculated by dividing the prevalence for female athletes by that for male athletes. Therefore, RRs >1 indicated a greater symptom burden among women, and RRs <1 indicated a greater symptom burden among men.

Data from the included studies were pooled using the DerSimonian-Laird random-effects model. This model provides more conservative estimates with wider confidence intervals compared with fixed-effects models, since it assumes that the studies included in the meta-analysis are only a sample of all possible studies. 50 In addition, the random-effects model accounts for both between-study variability (heterogeneity) and within-study variability (random error). A separate meta-analysis was performed for each of mental health symptoms. The forest plots display RRs and 95% CIs for each sex comparison. Some studies provided data for >1 comparison, and they are presented as a separate “study” (indicated by lower case letters after the study date in Figures 2-4, 7, and 9).

Figure 2.

Figure 2.

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Forest plot for studies evaluating sex differences in alcohol misuse. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Figure 3.

Figure 3.

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Forest plot for studies evaluating sex differences in anxiety. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Figure 4.

Figure 4.

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Forest plot for studies evaluating sex differences in depression. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Figure 7.

Figure 7.

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Forest plot for studies evaluating sex differences in eating disorders. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Figure 9.

Figure 9.

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Forest plot for studies evaluating sex differences in sleep disturbances. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Risk of Bias Assessment

A 9-item tool was used to describe the risk of bias for the included studies. 37 Each of the 9 potential biases was rated as low (0) or high (1). Scores were then summed for the determination of quality. The total score of the study was rated as follows: low risk of bias with a score 0 to 3; moderate risk with a score 4 to 6, and high risk with a score 7 to 9. The risk of bias was assessed by 2 authors, with any discrepancies resolved through consensus. Risk of bias presented in Appendix 2 (available online).

Results

Literature Search

The PRISMA search process is presented in Figure 1. The initial search returned 1945 articles, with 712 duplicates identified and 1233 articles screened. A total of 92 full-text articles were found to meet our inclusion criteria and 32 were excluded. A list of the 60 included articles is found in Appendix 3 (available online). The majority of the studies were cross-sectional studies, with 2 retrospective cohort studies,15,88 2 prospective cohort studies,55,83 and 1 case-control study. 87 We did not identify any studies that explicitly mentioned gender-based analysis, as opposed to sex-based analysis.

Figure 1.

Figure 1.

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PRISMA search process. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-analyses.

Meta-analysis

Alcohol Misuse

Alcohol use was evaluated as overall alcohol misuse and 2 separate subgroups of alcohol binging and alcohol intoxication. A total of 13 studies evaluated alcohol use in athletes (Figure 2).3,6,17,28,30,43,45,47,67,78,83,91,98 Male athletes reported higher overall alcohol misuse compared with female athletes (RR, 0.83; CI, 0.74-0.84). Schaal et al 78 evaluated current (<6 months) and lifetime alcohol use across male and female athletes, with male athletes reporting higher risk for alcohol misuse at both timepoints. Similar findings were noted for alcohol binging (RR, 0.75; CI, 0.69-0.81) and alcohol intoxication (RR, 0.42; CI, 0.27-0.64). Kroshus et al 47 followed heterosexual athletes and athletes who identified as sexual minorities and found that male athletes in both groups had increased alcohol binging behavior. Peretti-Watel et al 67 followed a cohort of athletes in 2002 and from 2013 to 2016, with the latter cohort further divided based on the level of play. The study noted that male athletes had higher alcohol intoxication in both cohorts, as well as binging behavior at the regional and national level. However, male and female athletes at the international level of competition had similar rates of alcohol binging behavior (RR, 1.03; CI, 0.82-1.29).

Anxiety

Anxiety was evaluated as overall anxiety and further subdivided into mild, moderate, severe, and anxiety codiagnosed with depression. A total of 12 studies were included (Figure 3).3,19,22,31,42,43,47,55,63,78,79,88 Female athletes reported higher overall risk of anxiety (RR, 1.42; CI, 1.02-1.98), as well as mild (RR, 1.45; CI, 1.13-1.87) and moderate (RR, 2.32; CI, 1.40-3.84) anxiety. No sex difference was found for severe anxiety (RR, 1.61; CI, 0.63-4.13). Schall et al 78 evaluated athletes for current symptoms or symptoms present over the athlete’s lifetime and found increased anxiety in female athletes in both groups. In addition, no significant differences were found between male and female athletes in those diagnosed with both anxiety and depression (RR, 1.01; CI, 0.92-1.11). However, Kroshus et al 47 noted increased codiagnosis of anxiety and depression in heterosexual female athletes and in male athletes who identified as a sexual minority.

Depression

Depression was evaluated in 24 studies (Figure 4).3,8,13,19,22,23,31,33,36,42,43,47,55,61,65,75,78,79,83,85,88,94,97 Female athletes had an increased risk of depression, regardless of subgroup (RR, 1.88; CI, 1.65-2.15). Female athletes were found to have a higher risk of moderate depression (RR, 1.58; CI, 1.12-2.24), severe depression (RR, 1.85; CI, 1.05-3.28), as well as moderate to severe depression (RR, 1.39; CI, 1.18-1.64). Male and female athletes were found to have a similar risk of mild depression (RR, 1.34; CI, 0.97-1.87). Palmer et al 65 followed 3000 Olympic athletes and noted increased depression in female athletes both during their career and after retirement. Finally, Schaal et al 78 noted increased depression in female athletes during the most recent 6 months and during the athlete’s lifetime when compared with male athletes during the same timepoints.

Distress

Distress was evaluated in 3 studies.22,31,43 Female athletes were found to be twice as likely as male athletes to report distress (RR, 1.98; CI, 1.40-2.81). Figure 5 shows individual study analyses.

Figure 5.

Figure 5.

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Forest plot for studies evaluating sex differences in distress. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Illicit Drug Abuse

Illicit drug abuse was evaluated in 8 studies and was divided by type of illicit drug use.6,17,29,45,47,57,78 Male athletes were found to have a higher risk of overall drug use (RR, 0.82; CI, 0.75-0.89). Use of crack/cocaine (RR, 0.64; CI, 0.43-0.96) was reported by 2 studies and use of cannabis was reported by 5 studies (RR, 0.75; CI, 0.67-0.84), with both analyses showing increased use in male athletes. Schaal et al 78 evaluated behaviors during the most recent 6 months and across the athlete’s lifetime and noted no difference in cannabis use in female and male athletes. Kroshus et al 47 found increased use of illicit drugs in male athletes who identified as sexual minorities, as well as heterosexual male athletes. Figure 6 shows individual study analyses.

Figure 6.

Figure 6.

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Forest plot for studies evaluating sex differences in substance abuse. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Eating Disorders

Six studies examined athletes with eating disorders,21,31,43,46,61,78 with subgroup analysis on 4 studies that focused on disordered eating, bulimia and binging behavior, vomiting, and intense exercise.1,14,21,46 Female athletes were found to be twice as likely to report an eating disorder compared with male athletes (RR, 2.19; CI, 1.58-3.02). Schaal et al 78 showed similar data during the athletes’ lifetime and during the last 6 months. Binging/bulimia and vomiting were increased in female athletes; however, only 2 studies were found for these subgroups, limiting the analysis (Figure 7).

Gambling

Gambling was reported in 3 studies.32,43,91 Male athletes were found to have a higher risk of reporting a gambling problem than female athletes (RR, 0.14; CI, 0.08-0.25). Figure 8 shows individual study analyses.

Figure 8.

Figure 8.

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Forest plot for studies evaluating sex differences in gambling problems. Numbers to the right of the solid vertical line indicate a greater rate in female athletes.

Sleep Disturbance

Sleep disturbance was evaluated in 14 studies (Figure 9).9,15,19,35,36,40,43,44,58,63,73,78 -80 Sleep disturbance and poor sleep quality were found to be similar across genders, but female athletes were more prone to severe sleep disturbance (RR, 1.68; CI, 1.09-2.58) and daytime sleepiness (RR, 1.54; CI, 1.17-2.03). Schaal et al 78 reported increased sleep disturbance in female athletes during the last 6 months and across the athletes’ lifetime.

Postconcussion Mental Health

Several studies evaluated the effect of concussion on sleep disturbance. Davis-Hayes et al 15 followed collegiate athletes after concussion and noted a significant increase in sleep disturbance in female athletes than male athletes (42.0% vs 29.3%; P = 0.04). Hoffman et al 88 followed athletes after concussion and noted that male athletes were twice as likely to sleep longer than female athletes. One study found that male athletes had higher prevalence of depression after concussions, though this difference was not significant. 72

Risk of Bias

Almost all of the 60 original studies included in our systematic review had an overall low risk of bias, with only 1 study showing moderate risk of bias (included in Appendix 2, available online).

Discussion

The purpose of this study was to compare mental health symptoms between elite male and female athletes. The results of the meta-analysis show that female athletes have an increased risk of reporting depression, anxiety, distress, sleep disturbances, and eating disorders, while male athletes are at increased risk of reporting alcohol misuse, illicit drug abuse, and gambling problems. In addition, a few studies noted that both male and female athletes experience sleep disturbances after concussion.

Depression was the mental health disorder studied most commonly in this meta-analysis. Female athletes had a 1.4 times higher rate of depressive symptoms, across the spectrum of severity, compared with male athletes, which is consistent with rates of depression in the general population. 81 Wolanin et al 94 noted a difference between depressive symptoms varying by gender and sport. Female athletes participating in track and field, softball, and soccer had the highest rates of depression across a sample of collegiate athletes. Individualized data can assist clinicians in screening for and identifying athlete at risk for depression and other mental health symptoms. In addition, female athletes were found to have a 1.2 higher risk of anxiety. Several studies have shown that female athletes have a higher rate of anxiety symptoms across youth, 48 collegiate, and elite levels of play.4,5,42,78 In addition, Schaal et al 78 found that generalized anxiety disorder was the most common mental health disorder in a cohort of over 2000 athletes. This is not surprising, as previous research has shown increased prevalence of anxiety in patients aged 18 to 34 years, 51 which correlates to the age of the majority of elite athletes. Contrary to depression, Li et al 54 did not find a correlation between specific sports and anxiety; however, individual sports and poor sports performance have been highlighted as a risk factor for depression and distress.

Female athletes have been found to report a higher level of psychological distress, which is linked to an overall increased rate of anxiety and depression. 76 Our study included 3 studies that investigated psychological distress in athletes, with female athletes demonstrating a 2 times higher rate of reporting this mental health symptom. Overall, psychological distress can be augmented by multiple mental health symptoms and external sport-related factors, including injury, highlighting the need for continued assessment and mental health support for athletes.

Illicit drug abuse among athletes can vary by substance, sport, age, sex, and many other factors. 74 Overall, athletes use alcohol and illicit substances at a lower rate than their nonathlete peers 64 ; however, sex differences do exist with specific substances. This meta-analysis showed that male athletes have a higher rate of alcohol misuse, binging behavior, and alcohol intoxication compared with female athletes. Lewis et al 53 investigated the causes of athlete drinking habits in a sample of collegiate athletes. Female athletes were found to binge drink because of a perceived benefit of a positive experience, while both male and female athletes were influenced by perceiving their friends as heavy drinkers or by experiencing fewer alcohol-related consequences. 53 Zamboanga and colleagues56,98 showed a relationship between positive experiences and alcohol use in female athletes. Alcohol use in athletes has been linked to the presence of mental health symptoms, 60 suggesting use of alcohol as a coping mechanism for other disorders.

Illicit drug use is relatively rare among athletes, but marijuana has recently become the second most common drug among collegiate athletes. 64 In addition, cocaine appears to be a growing problem among collegiate athletes. 64 The present study evaluated overall drug use, in addition to specific illicit drug use, and found increased use across all subgroups in male athletes. Similar results have been noted in the general population, in addition to male collegiate athletes.20,49,86 Brisola-Santos et al 11 performed a systematic review of marijuana use in athletes and also noted increased use in male athletes, with a large number of athletes reporting use for performance enhancement. Studies have noted that athletes overperceive drug use among fellow athletes, suggesting this perception as the reason for drug and alcohol use in this population. 49 This finding is particularly notable among male athletes, with 1 study showing that male athletes perceived that 88% of their peers used marijuana, with only 46.9% of male athletes reporting actual use. 49 Furthermore, male athletes tend to tie social identity to their status as athletes, so athlete norms are more influential for male athletes. 59

Another addictive behavior found in male athletes is gambling disorders. 74 Gambling has become socially acceptable as the legality of sports gambling has changed in the last few years. Relatively few studies focused on gambling disorder in athletes, with 3 articles included in this meta-analysis. Both current and former athletes have been found to report increased rates of gambling, at 13% and 7% respectively, 90 while 8.2% of professional athletes have reported a gambling problem. 26 Studies have linked gambling disorders with the inherent desire for competition and impulsive and risk taking behaviors in athletes, 16 suggesting that male athletes display these traits more frequently than female athletes.

Athletes have an increased risk of eating disorders compared with the general population. 12 Female athletes have an increased risk of eating disorders or disordered eating (subclinical diagnosis), with studies noting up to 45% of female athletes reporting disordered eating, 10 with higher risk in elite athletes. 84 This meta-analysis noted that female athletes report an eating disorder twice as often as male athletes, with 3 times higher risk of binging/bulimia behavior and 6 times higher risk of reporting vomiting behavior based on limited data. Prevention programs are critical to provide educational resources to athletes and coaching staff for the diagnosis and management of eating disorders, particularly for female athletes.

Sleep disorders are prevalent in collegiate and elite athletes, with up to 83% of athletes reporting daytime sleepiness and poor sleep quality. 18 Previous studies have also shown increased nocturnal waking and difficulty falling asleep in female athletes. 78 This meta-analysis noted an increased rate of daytime sleepiness in female athletes compared with male athletes, with similar rates of sleep disturbance and poor sleep quality between both sexes. Sleep can also be affected by injury, particularly concussions. Hoffman et al 35 and Davis-Hayes et al 15 noted increased sleep disturbances in athletes after head injury. Common sleep disturbances after concussion include daytime sleepiness, insomnia, and unusual behaviors during sleep, and treatment guidelines for concussions have been developed to target restoration of the sleep cycle in this patient population. 39

Strengths and Limitations

This study has several strengths and provides a comprehensive summary of the sex-based differences in mental health symptoms between female and male athletes. The International Olympic Committee’s recent development of 2 mental health tools was used to determine the 8 mental health symptoms that were analyzed in this study. 74 We extracted data that allowed for direct head-to-head comparisons between male and female athletes across a wide range of mental health symptoms. Thus, our comparative findings are not pooled based on data that come from disparate populations, settings, or competitive levels.

A major limitation of our study is that the included studies used many different scales to assess each mental health symptom, which could explain the significant heterogeneity in our results. In addition, for some mental health symptoms, some subgroups contained a small number of studies, limiting our analysis. Furthermore, mental health symptoms are related and may exacerbate other mental health symptoms. We evaluated the symptoms in isolation, which likely did not accurately represent symptoms clinically. For example, increased risk of depression and anxiety related to weigh-ins and personal performance in weight-sensitive sports may be risk factors for the development of an eating disorder. 74 De Souza et al 17 noted a link between sleepiness and fatigue and increased distress symptoms in both male and female athletes, further cementing the link between sleep disturbance and other mental health symptoms.

The studies included in this meta-analysis were comprised of cross-sectional data, which limits the quality of the data included. We also had limitations due to lack of data for specific comparisons. There were no data to allow for sex-based comparisons in mental health after injury other than concussions, and only 1 study provided data for this comparison in current versus former athletes. In addition, we could not stratify our analysis based on team versus individual sports, as the majority of studies included both. Studies have noted that individual sports have different effects on mental health than team sports.24,41,52,78,94 Individual-sport female athletes are at increased risk for depression,52,68 as authors have hypothesized that the support of teammates may overcome psychological obstacles. Schaal et al 78 also found that similar to athletes with depression, there was an increased rate of anxiety in athletes competing in individual sports.

Conclusion

Female and male athletes have significant differences in reported mental health symptoms. Female athletes are more likely to report anxiety, depression, distress, and eating disorders, while male athletes report more alcohol misuse, illicit drug abuse, and gambling. Sleep disturbances are generally similar. Careful monitoring and evaluation of the mental health of athletes is a necessary part of any sport, with inclusion of mental health resources such as mindfulness-based programs. 62

Supplemental Material

sj-docx-1-sph-10.1177_19417381241264491 – Supplemental material for Sex Differences in Mental Health Symptoms in Elite Athletes: A Systematic Review and Meta-analysis
sj-docx-1-sph-10.1177_19417381241264491.docx (13.5KB, docx)

Supplemental material, sj-docx-1-sph-10.1177_19417381241264491 for Sex Differences in Mental Health Symptoms in Elite Athletes: A Systematic Review and Meta-analysis by Michelle E. Kew, Udit Dave, William Marmor, Reena Olsen, Sung Huang Laurent Tsai, Liang-Tseng Kuo and Daphne I. Ling in Sports Health

sj-docx-2-sph-10.1177_19417381241264491 – Supplemental material for Sex Differences in Mental Health Symptoms in Elite Athletes: A Systematic Review and Meta-analysis
sj-docx-2-sph-10.1177_19417381241264491.docx (34.6KB, docx)

Supplemental material, sj-docx-2-sph-10.1177_19417381241264491 for Sex Differences in Mental Health Symptoms in Elite Athletes: A Systematic Review and Meta-analysis by Michelle E. Kew, Udit Dave, William Marmor, Reena Olsen, Sung Huang Laurent Tsai, Liang-Tseng Kuo and Daphne I. Ling in Sports Health

sj-docx-3-sph-10.1177_19417381241264491 – Supplemental material for Sex Differences in Mental Health Symptoms in Elite Athletes: A Systematic Review and Meta-analysis
sj-docx-3-sph-10.1177_19417381241264491.docx (32.9KB, docx)

Supplemental material, sj-docx-3-sph-10.1177_19417381241264491 for Sex Differences in Mental Health Symptoms in Elite Athletes: A Systematic Review and Meta-analysis by Michelle E. Kew, Udit Dave, William Marmor, Reena Olsen, Sung Huang Laurent Tsai, Liang-Tseng Kuo and Daphne I. Ling in Sports Health

Footnotes

The following author declared potential conflicts of interest: M.E.K. has received education payments from Stryker.

Contributor Information

Michelle E. Kew, Sports Medicine Institute, Hospital for Special Surgery, New York, New York.

Udit Dave, Tulane University School of Medicine, New Orleans, Louisiana.

William Marmor, University of Miami, Department of Orthopaedics, Miami, Florida.

Reena Olsen, Sports Medicine Institute, Hospital for Special Surgery, New York, New York.

Bridget Jivanelli, Kim Barrett Memorial Library, HSS Education Institute, Hospital for Special Surgery, New York, New York.

Sung Huang Laurent Tsai, Division of Sports Medicine, Department of Orthopaedic Surgery, Chang Gung Memorial Hospital, Chiayi, Chiayi County, Taiwan and School of Medicine, Chang Gung University, Taoyuan, Taiwan.

Liang-Tseng Kuo, School of Medicine, Chang Gung University, Taoyuan, Taiwan and Department of Orthopaedic Surgery, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan.

Daphne I. Ling, Sports Medicine Institute, Hospital for Special Surgery, New York, New York, Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan, and Department of Population Health Sciences, Weill Cornell Medical College, New York, New York.

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