Introduction
Athletes often make significant social, psychological, and physical sacrifices, including adjustments to their eating behaviors in pursuit of optimal performance (Stoyel et al., 2021; Waldron & Kane, 2005). Eating behavior becomes a strategic tool in shaping the athletic body, which is often treated as a performance instrument (Giel et al., 2016). For example, athletes may increase protein intake to build muscle mass. When such behaviors support both physical and mental health, they are considered to be part of optimized nutrition practices (Wells et al., 2020). However, the intense pressures associated with competitive sport, such as performance expectations and sport-specific body ideals, can foster eating behaviors that compromise health and may evolve into disordered eating (DE) (Bratland-Sanda & Sundgot-Borgen, 2012; Giel et al., 2016). DE encompasses a range of problematic behaviors that do not meet the full diagnostic criteria for an eating disorder as defined in the DSM-5 (American Psychiatric Association [APA], 2022). These include weight control behaviors (e.g., restrictive dieting, binge eating, vomiting, or laxative use) and preoccupations with weight, shape, or body size (Reardon et al., 2019; Wells et al., 2020). In more severe cases, DE symptoms may change to eating disorders (ED) such as anorexia nervosa, bulimia nervosa, or binge eating disorder (APA, 2022). A longitudinal study indicated that adolescents who reported DE symptoms were at increased risk of meeting criteria for an ED five years later (Neumark-Sztainer et al., 2006).
Several reviews and meta-analyses have demonstrated that athletes are at heightened risk for DE symptoms and clinically relevant symptoms of EDs (e.g., Bratland-Sanda & Sundgot-Borgen, 2012; Byrne & McLean, 2001; Chapa et al., 2022; Fatt et al., 2024; Ghazzawi et al., 2024; López-Gil et al., 2023; Mancine et al., 2020). However, prevalence rates vary widely across studies, reflecting methodological differences (e.g., measures and study design) and differences in sample characteristics. A systematic review and meta-analysis of 32 studies across 16 countries indicated that 22.4% of children and adolescents (total N = 63,181) self-reported DE symptoms (López-Gil et al., 2023). Another recent meta-analysis of 177 studies across 27 countries (Ghazzawi et al., 2024) reported that 19.2% of athletes (total N = 70,957) self-reported DE symptoms. In Western countries, the prevalence was 18.5%, based primarily on data from the United States, but no studies have been conducted in Switzerland. In addition, most of the previous research used a cross-sectional design and relied on a variety of self-report measures, contributing to heterogeneity in prevalence estimates.
Beyond overall prevalence estimates, differences have been observed regarding DE frequency across gender, sport type, and sport level. Female athletes generally reported higher rates of DE than males (Fatt et al., 2024; Sundgot-Borgen et al., 2025) and DE frequency tended to be higher among athletes competing at national or international levels and especially among those in weight-sensitive sports (Byrne & McLean, 2001; Bonanséa et al., 2016; Chapa et al., 2022, Fatt et al., 2024; Mancine et al., 2020). Weight-sensitive sports include disciplines in which body weight, shape, or composition directly influence performance or evaluation, such as gravitational sports (e.g., ski jumping), weight-dependent sports (e.g., boxing), aesthetic sports (e.g., synchronized swimming), and endurance sports (e.g., rowing). In contrast, non-weight-sensitive sports include disciplines in which these factors are less central to performance, such as ball games (e.g., basketball), power sports (e.g., powerlifting), technical sports (e.g., fencing), or motor sports (e.g., motorcycling) (Ackland et al., 2012; Sundgot-Borgen et al., 2013).
Building on these findings, it is important to consider that the onset of DE frequently occurs during adolescence or early adulthood (Byrne & McLean, 2001). Thus, adolescent athletes represent a particularly vulnerable population and early assessment of DE would be needed. This heightened vulnerability in adolescent athletes may be attributed to developmental factors specific to this stage of life, such as puberty and the related rapid body transformation (Byrne & McLean, 2001; Klump, 2013; Littleton & Ollendick, 2003) but also to the expectations related to the athletes’ strength and muscularity needed for physical performance and early success. Recent data from elite adolescent athletes in Norway (Mage = 17.0 years) report 6.9% of male and 9.3% of female athletes with clinically relevant ED symptoms (Sundgot-Borden et al., 2025) which is higher than in children and adolescent non-athletes (Smink et al., 2012). However, because these findings are based on elite athletes, it remains unclear whether similar frequencies of ED symptoms are observed among non-elite adolescent athletes.
Given this elevated risk and the methodological limitations of existing tools (e.g., the use of general self-report measures not specifically adapted to sport contexts, and the lack of consensus regarding the interpretation of certain tools and thresholds), the need for athlete-specific screening instruments to identify potential DE symptoms already at an early age has become increasingly evident. Indeed, Hazzard and colleagues (2020) developed and validated a sport-specific questionnaire called Eating Disorders Screen for Athletes (EDSA), a promising brief screening tool designed to identify male and female athletes at risk for ED in sport contexts. So far, this questionnaire has been used in adults, but not in adolescent athletes.
Current study
To address this gap, the present study aims to investigate the frequencies of disordered eating among a broad range of adolescent athletes in Switzerland, considering different language regions, and to examine variations by sport sex category (female vs. male), sport type (weight-sensitive vs. non-weight-sensitive), and level of competition. Based on prior research, we hypothesize that: (1) adolescent female athletes report higher DE frequencies (i.e., eating disorder symptoms and weight control behaviors) than adolescent male athletes; (2) adolescent athletes competing at higher levels (e.g., national, international) will show higher DE frequencies (i.e., eating disorder symptoms and weight control behaviors); and (3) adolescent athletes in weight-sensitive sports will report higher DE frequencies (i.e., eating disorder symptoms and weight control behaviors) than those in non-weight-sensitive sports.
Assessment of DE symptoms should include both general eating disorder symptoms and sport-specific concerns, as this dual approach may provide a more comprehensive evaluation of DE risk by capturing symptomatology unique to sport contexts that may otherwise go undetected. For this reason, higher DE frequencies are expected to be detected by the sport-specific questionnaire than by the general one. By providing the first empirical data on disordered eating among young athletes in Switzerland, this study aims to inform evidence-based prevention and intervention efforts tailored to the Swiss Context.
Method
Study design
The data collection was part of a larger cross-sectional Sport & Eating study, a Swiss survey to investigate DE and influencing factors in adolescent athletes across the three sociocultural areas of Switzerland (i.e., German-, French-, and Italian-speaking areas). This project was approved by the institutional ethics committee of the University of Lausanne (E-SSP-072023_00001), and the study was conducted in accordance with the Declaration of Helsinki.
Recruitment and participants
Participants were recruited between September 2023 and March 2024 using a convenience sampling method. Recruitment was conducted through advertisements in sports clubs, medical centers, and on social media across different French-, German- and Italian-speaking areas of Switzerland. Participants were eligible if they: (1) were aged between 14 and 20 years, (2) were officially registered in a sports club, (3) participated in organized regional, national or international competitions and club training (i.e., with the presence of at least one coach), and (4) were fluent in one of the official languages (French, German, or Italian). The study focused on young athletes in middle to late adolescence (Hardin et al., 2017), a developmental period which is characterized by rapid physical maturation and heightened sensitivity to peer and environmental influences which may contribute to the development of body image concerns and disordered eating behavior. Individuals with a current injury (since more than one month), or with a severe mental or physical health condition requiring intensive treatment were excluded from the study.
Participants were able to complete the questionnaire in French, German, or Italian to ensure accessibility across Switzerland. However, no information regarding participants’ region of residence was collected, as language preference does not necessarily correspond to geographical location. Therefore, comparisons between language regions were not feasible.
The total sample consisted of 1,089 adolescent athletes aged between 14 and 20 years (M = 16.30, SD = 1.78), including 647 competing in female categories (59.4%) and 442 in male categories (40.6%) (see Table 1). Among male athletes, non-weight-sensitive sports were the most common, while among female athletes, weight-sensitive sports predominated. The most common level of competition reported by athletes was national. Most of the athletes reported being active for more than five years, the median weekly training duration was 8.73 hours, and the median Body Mass Index (BMI) was 20.6.
Table 1
Descriptive statistics of demographic variables.
| MALE SAMPLE | FEMALE SAMPLE | |
|---|---|---|
| n (%) | 442 (40.6) | 647 (59.4) |
| Age (years) n(%) | ||
| 14 years | 91(20.6) | 114(17.6) |
| 15 years | 91(20.6) | 123(19.0) |
| 16 years | 94(21.3) | 132(20.4) |
| 17 years | 62(14.0) | 98(15.1) |
| 18 years | 53(12.0) | 80(12.4) |
| 19 years | 31(7.0) | 53(8.2) |
| 20 years | 20(4.5) | 47(7.3) |
| Language group n(%) | ||
| German | 151(34.2) | 266(41.1) |
| French | 241(54.5) | 333(51.5) |
| Italian | 50(11.3) | 48(7.4) |
| Sport type n(%) | ||
| Weight-sensitive sports | 146(33.0) | 372(57.5) |
| Non-weight-sensitive | 296(67.0) | 275(42.5) |
| Sport level n(%) | ||
| International | 59(13.3) | 92(14.2) |
| National | 192(43.4) | 194(30.0) |
| Interregional | 82(18.6) | 151(23.3) |
| Regional | 90(20.4) | 176(27.2) |
| Other | 19(4.3) | 34(5.3) |
| Sport experience n(%) | ||
| <1 year | 26(5.9) | 27(4.2) |
| 1 to 5 years | 128(29.0) | 191(29.5) |
| >5 years | 288(65.2) | 429(66.3) |
| Training (hours) M(SD) | 8.67(5.06) | 8.77(5.76) |
| BMI (kg/m2) M(SD) | 21.00(2.91) | 21.00(2.76) |
| Study Variables M(SD) | ||
| EDE-Q global score | 0.75(0.90) | 1.72(1.40) |
| EDE-Q restraint | 0.79(1.15) | 1.29(1.44) |
| EDE-Q eating concern | 0.41(0.77) | 1.24(1.32) |
| EDE-Q weight concern | 0.82(1.13) | 2.04(1.67) |
| EDE-Q shape concern | 0.99(1.18) | 2.33(1.68) |
| EDSA global score | 1.85(0.83) | 2.85(1.07) |
[i] Note. BMI = body mass index measured by self-reported height and weight; EDE-Q = Eating Disorder Examination Questionnaire; EDSA = Eating Disorder Screen for Athletes.
Procedure
All participants individually accessed the online survey, hosted on the Qualtrics platform (Qualtrics, Provo, UT), through either a QR code or a link, and completed a set of questionnaires anonymously after providing informed consent. At the end of the survey, participants could take part in a raffle of shopping vouchers as reimbursement. Of note, the research plan of the present research article has not been pre-registered prior to the research being conducted.
Measures
Sociodemographics
Athletes self-reported information regarding their age, body weight and height (used to calculate BMI), as well as sport-specific characteristics, including sport sex category (“In which sex category do you compete? (male, female)”), sport level (international, national, interregional, regional, other), and their sport.
Based on previous classifications (Ackland et al., 2012; Sundgot-Borgen et al., 2013), the sports practiced by the participants were categorized as either weight-sensitive or non-weight-sensitive sports. Weight-sensitive sports included gravitational sports (e.g., ski jumping), weight-dependent sports (e.g., boxing), aesthetic sports (e.g., synchronized swimming), and endurance sport (e.g., cycling). Non-weight-sensitive sports included ball games (e.g., basketball), power sports (e.g., powerlifting), technical sports (e.g., fencing), and motor sports (e.g., motorcycling).
Frequencies of disordered eating
General eating disorder symptoms
The Eating Disorder Examination-Questionnaire (EDE-Q; original English version by Fairburn & Beglin, 1994; French version by Carrard et al., 2014; German version by Hilbert et al., 2007; Italian version by Calugi et al., 2016) is widely used in clinical and research contexts and assesses eating disorder symptoms over the past 28 days. The questionnaire consists of 28 items divided into two sections. The first section includes 22 items (items 1 to 12, and 19 to 28), grouped into four subscales: restraint (e.g., “Have you been deliberately trying to limit the amount of food you eat to influence your shape or weight (whether or not you have succeeded)?”), eating concern (e.g., “Have you had a definite fear of losing control over eating?”), weight concern (e.g., “Have you had a strong desire to lose weight?”), and shape concern (e.g., “Have you had a definite desire to have a totally flat stomach?”). Participants respond on a 7-point Likert scale, ranging from 0 to 6, with response options reflecting either the number of days over the past 28 days or the severity of symptoms, depending on the item. Subscale scores are calculated as the average of their respective items, with a global score reflecting the overall mean. Higher scores indicate greater levels of symptomatology. Participants were classified as being at risk for eating disorders if their global score exceeded the cut-off score of 2.5 (see Fischer et al., 2012; Munsch et al., 2021). Internal consistency for the global score in our sample has been demonstrated as excellent for the German version (α =.95, ω = .95), French version (α =.95, ω = .95), and Italian version (α =.96, ω = .96). Psychometric studies have also demonstrated good discriminative validity and test-retest reliability (Grilo et al., 2001; Reas et al., 2006; Hilbert et al., 2007).
The second section of the EDE-Q, which assesses specific eating behaviors symptoms (e.g., binge eating), is described separately below under “Weight control behaviors”, as these variables were analyzed as distinct outcomes in the present study. Although the two sections of the EDE-Q are conceptually distinct, the items assessing weight control behaviors (items 13–18) are interspersed within the questionnaire rather than presented as a separate block.
Sport-specific eating disorder symptoms
The Eating Disorders Screen for Athletes (EDSA; original English version by Hazzard et al., 2020) is a brief screening tool designed to identify athletes at risk for eating disorders. The EDSA was translated into German, French, and Italian following the standards of Corbière & Fraccaroli (2020) by the authors. The questionnaire is a unidimensional tool and consists of six items. Participants respond on a 5-point Likert scale ranging from 1 (never) to 5 (always) and a global score is computed as the average of the six items, with higher score indicating a greater level of symptomatology. A cut-off score of 3.3 (Hazzard et al., 2020) allows to classify athletes as being potentially at risk for developing an eating disorder. Internal consistency of the scale has been shown to be good (α =.86 for females, .80 for males; Hazzard et al., 2020), and the authors reported acceptable construct validity and excellent criterion validity. The translated versions (i.e., German, French, and Italian, see Supplementary file: Appendix A) were pilot-tested, and their psychometric properties – including construct validity, internal consistency, and measurement invariance across languages – were evaluated using the same sample as in the present study. Detailed results are reported in the Supplementary file: Appendix B.
Weight control behaviors
Weight control behaviors are assessed using six items from the second section of the EDE-Q (items 13 to 18), with participants reporting the frequency of specific behaviors over the past 28 days. These behaviors included the number of episodes of binge eating (item 13: “Over the past 28 days, how many times have you eaten what other people would regard as an unusually large amount of food (given the circumstances)?”), the number of episodes involving a sense of loss of control (item 14: “… On how many of these times did you have a sense of having lost control over your eating (at the time you were eating)?”), and the number of days on which such episodes occurred, combining both excessive intake and loss of control – defined as objective binge eating episodes (item 15: “Over the past 28 days, how many DAYS have such episodes of overeating occurred (i.e., you have eaten an unusually large amount of food and have had a sense of loss of control at the time)?”). It is important to distinguish between binge eating episodes and objective binge eating episodes as assessed by the EDE-Q. Binge eating episodes refer to episodes in which participants report consuming an unusually large amount of food, without necessarily considering the subjective experience of loss of control. In contrast, objective binge eating episodes are defined by the co-occurrence of both an objectively large amount of food intake and a sense of loss of control over eating. Thus, objective binge eating episodes represent a more stringent construct, requiring both behavioral and subjective components, whereas binge eating episodes may reflect quantity of food consumed irrespective of perceived control. Participants also reported the number of episodes of self-induced vomiting (item 16: “Over the past 28 days, how many times have you made yourself sick (vomit) as a means of controlling your shape or weight?”), the frequency of laxative use (item 17: “Over the past 28 days, how many times have you taken laxatives as a means of controlling your shape or weight?”), and the frequency of excessive exercise (item 18, “Over the past 28 days, how many times have you exercised in a “driven” or “compulsive” way as a means of controlling your weight, shape or amount of fat, or to burn off calories?”). For the purpose of the analyses, each weight control behavior was operationalized as a dichotomous variable (presence vs. absence), with a behavior considered present if the participant reported engaging in it at least once over the past 28 days. This approach was adopted to capture any occurrence of potentially relevant behaviors, in line with a screening perspective aimed at identifying individuals at risk rather than quantifying clinical severity.
Data analysis
Descriptive statistics were used to summarize the sample characteristics and the frequencies of disordered eating. Categorization of participants according to their risk profile for eating disorders was based on global scores from the EDE-Q and the EDSA. Two dichotomous variables (“at risk” vs. “not at risk”) were derived to reflect the risk status for general and sport-specific eating disorder symptoms, respectively. Participants scoring above the established cut-off score for each respective measure were classified as “at risk.” As the EDE-Q and EDSA are both screening instruments and do not allow clinical diagnoses, the reported frequencies should be interpreted as indicators of elevated symptom levels or risk rather than prevalence of clinically diagnosed eating disorders. For each specific weight control behavior assessed by the EDE-Q, additional dichotomous variables (“presence” vs. “absence”) were created. A behavior was considered present if the participant reported engaging in it at least once.
Group-level variables (sport sex (male vs. female), sport type (weight-sensitive vs. non-weight-sensitive), and sport level (international, national, interregional, regional)) were tested for associations with the frequency of disordered eating, including risk of eating disorders and engagement in weight control behaviors (e.g., binge eating, self-induced vomiting, excessive exercise). To assess associations between categorical predictors and binary outcomes, a series of chi-square (χ²) tests of independence were conducted. When a significant chi-square result was found, post-hoc analyses were performed using adjusted standardized residuals to identify which cells contributed most to the observed association. Residuals exceeding an absolute value of 1.96 were considered statistically significant.
Bonferroni correction was applied to control for multiple testing across the same dependent variables, adjusting the significance threshold to 0.017 (0.05/3) for each of the three primary comparisons per outcome variable. All statistical analyses were performed using Jamovi (version 2.3.19.0; The Jamovi Project, 2023), and the significance threshold was set at α = .05 (adjusted where appropriate).
Results
Frequencies of disordered eating
Results of the frequencies of DE symptoms, including general and sport-specific eating disorder symptoms and weight control behaviors, are presented in Table 2. In the total sample, 17.5% of participants scored above the cut-off score of general ED symptoms on the EDE-Q, and a total of 24.7% were above the threshold of sport-specific symptoms (EDSA). Among all athletes, 15.1% (n = 164) scored above the respective cut-off scores on both the EDE-Q and the EDSA. Despite some missing data on the EDSA, these findings indicate substantial overlap between general and sport-specific DE symptomatology, with most athletes identified as at risk on the EDE-Q also meeting the threshold on the sport-specific measure.
Table 2
Frequencies of disordered eating by sport sex category, including eating disorder symptoms and weight control behaviors.
| MALE ATHLETES (n = 442) n(%) | FEMALE ATHLETES (n = 647) n(%) | TOTAL (N = 1089) n(%) | |
|---|---|---|---|
| Eating disorder symptoms | |||
| Classified as at risk of eating disorder after the EDE-Q | 24(5.4) | 167(25.8) | 191(17.5) |
| Classified as at risk of eating disorder after the EDSA* | 29(6.7) | 233(37.1) | 262(24.7) |
| Weight control behaviors (EDE-Q) | |||
| Episode of binge eating | 283(64.0) | 427(67.0) | 710(65.2) |
| Episode with loss of control | 96(21.7) | 322(49.8) | 418(38.4) |
| Objective binge eating | 112(25.3) | 312(48.2) | 424(38.9) |
| Self-induced vomiting | 12(2.7) | 45(7.0) | 57(5.2) |
| Laxative use | 9(2.0) | 16(2.5) | 25(2.3) |
| Excessive exercise | 108(24.4) | 255(39.4) | 363(33.3) |
[i] Note. EDE-Q = Eating Disorder Examination Questionnaire (Fairburn & Beglin, 1994). EDSA = Eating Disorder Screening for Athletes (Hazzard et al., 2020).
*Data available for 1,059 participants due to 30 missing responses on the EDSA.
The most frequently reported weight control behaviors in the 28 days prior to study participation were binge eating episodes (65.2%), followed by objective binge eating (38.9%), loss of control eating episodes (38.4%), and excessive exercise (33.3%). Self-induced vomiting and laxative use were rare, reported by 5.2% and 2.3% of participants, respectively.
Sport sex category
The proportion of at-risk athletes differed significantly by sport sex categories, for both the EDE-Q (χ2(1, N = 1089) = 75.40, p < .001) and the EDSA (χ²(1, N = 1059) = 126.64, p < .001). Among female athletes, 25.8% were classified as at risk according to the EDE-Q and 37.1% according to the EDSA, compared to 5.4% and 6.7% of male athletes, respectively (see Table 2).
Female athletes also reported consistently higher rates of weight control behaviors than their male counterparts, except for binge eating episodes (χ²(1, N = 1076) = 0.20, p = .658) and laxative use (χ²(1, N = 1072) = 0.21, p = .651), where no significant differences were found. Loss of control eating was reported by 49.8% of female athletes versus 21.7% of male athletes, χ²(1, N = 1074) = 86.50, p < .001. Objective binge eating occurred in 48.2% of females versus 25.3% of males, χ²(1, N = 1072) = 56.90, p < .001. Self-induced vomiting was reported by 7.0% of females and 2.7% of males, χ²(1, N = 1070) = 9.42, p = .002. Excessive exercise was also more common among female athletes (39.4%) than males (24.4%), χ²(1, N = 1073) = 25.60, p < .001.
Sport level
No significant differences were found for the proportion of at-risk athletes across sport levels based on the EDE-Q (χ2(4, N = 1089) = 5.05, p = .282) (see Table 3). In contrast, results from the EDSA indicated a significant overall difference, χ²(4, N = 1059) = 12.20, p = .016. However, post-hoc comparisons based on standardized residuals did not reveal any statistically significant pairwise difference between sport levels (|z| < 1.96). Although national-level athletes were slightly underrepresented among those at risk (z = –1.91) and athletes in the “Other” sport level category were marginally overrepresented (z = 1.91), these effects did not reach conventional thresholds for statistical significance.
Table 3
Frequencies of disordered eating by sport level, including eating disorder symptoms and weight control behaviors.
| INTERNATIONAL (n = 151) n(%) | NATIONAL (n = 386) n(%) | INTERREGIONAL (N = 233) n(%) | REGIONAL (N = 266) n(%) | OTHER (N = 53) n(%) | |
|---|---|---|---|---|---|
| Eating disorder symptoms | |||||
| Classified as at risk of eating disorder after the EDE-Q | 30(19.9) | 57(14.8) | 38(16.3) | 55(20.7) | 11(20.8) |
| Classified as at risk of eating disorder after the EDSA* | 35(23.6) | 74(19.8) | 58(25.8) | 75(28.8) | 20(37.7) |
| Weight control behaviors (EDE-Q) | |||||
| Episode of binge eating | 97(65.5) | 253(66.2) | 151(65.9) | 180(67.9) | 29(55.8) |
| Episode with loss of control | 57(38.5) | 133(34.8) | 89(38.9) | 118(44.7) | 21(41.2) |
| Objective binge eating | 59(39.9) | 141(37.0) | 89(38.9) | 112(42.6) | 23(45.1) |
| Self-induced vomiting | 11(7.4) | 19(5.0) | 9(3.9) | 16(6.1) | 2(3.9) |
| Laxative use | 3(2.0) | 8(2.1) | 3(1.3) | 9(3.4) | 2(3.9) |
| Excessive exercise | 41(27.7) | 113(29.6) | 85(37.1) | 100(38.0) | 24(47.1) |
[i] Note. EDE-Q = Eating Disorder Examination Questionnaire (Fairburn & Beglin, 1994). EDSA = Eating Disorder Screening for Athletes (Hazzard et al., 2020).
*Data available for 1,059 participants due to 30 missing responses on the EDSA.
Regarding weight control behaviors, no significant differences were observed across sport levels for binge eating episodes (χ²(4, N = 1076) = 2.89, p = .577), loss of control eating (χ²(4, N = 1074) = 6.53, p = .163), objective binge eating (χ²(4, N = 1072) = 2.75, p = .600), self-induced vomiting (χ²(4, N = 1070) = 2.78, p = .595), and laxative use (χ²(4, N = 1072) = 3.17, p = .530). In contrast, results for excessive exercise indicated a significant overall difference, χ²(4, N = 1073) = 12.70, p = .013. Post-hoc comparisons based on standardized residuals indicated that no individual group contributed significantly to the overall effect (|z| < 1.96). Although athletes in the “Other” category were descriptively overrepresented (z = 1.62), while national- and international-level athletes were descriptively underrepresented (z = –1.27 and –1.44, respectively), these patterns did not reach conventional thresholds for statistical significance.
Sport type
No statistically significant differences were found for eating disorder symptoms based on the EDE-Q by sport type, χ2(1, N = 1089) = 3.16, p = .075 (see Table 4). However, EDSA results indicated a higher proportion of at-risk athletes in weight-sensitive sports (29.2%) compared to non-weight-sensitive sports (20.6%), χ²(1, N = 1059) = 10.60, p = .001.
Table 4
Frequencies of disordered eating by sport type, including eating disorder symptoms and weight control behaviors.
| WEIGHT-SENSITIVE SPORTS (n = 518) n(%) | NON-WEIGHT-SENSITIVE SPORTS (n = 571) n(%) | |
|---|---|---|
| Eating disorder symptoms | ||
| Classified as at risk of eating disorder after the EDE-Q | 102(19.7) | 89(15.6) |
| Classified as at risk of eating disorder after the EDSA* | 148(29.2) | 114(20.6) |
| Weight control behaviors (EDE-Q) | ||
| Episode of binge eating | 335(65.2) | 375(66.7) |
| Episode with loss of control | 224(43.7) | 194(34.6) |
| Objective binge eating | 223(43.5) | 201(36.0) |
| Self-induced vomiting | 27(5.3) | 30(5.4) |
| Laxative use | 11(2.1) | 14(2.5) |
| Excessive exercise | 189(36.8) | 174(31.1) |
[i] Note. EDE-Q = Eating Disorder Examination Questionnaire (Fairburn & Beglin, 1994). EDSA = Eating Disorder Screening for Athletes (Hazzard et al., 2020).
*Data available for 1,059 participants due to 30 missing responses on the EDSA.
Regarding weight control behaviors, loss of control eating (43.7% vs. 34.6%, χ²(1, N = 1074) = 9.30, p = .001) and objective binge eating (43.5% vs. 36.0%, χ²(1, N = 1072) = 6.32, p = .012) were more common in weight-sensitive sports compared to non-weight-sensitive ones. No significant differences were observed for binge eating episodes (χ2(1, N = 1076) = 0.29, p = .592), self-induced vomiting (χ2(1, N = 1070) = 0.01, p = .952), laxative use (χ2(1, N = 1072) = 0.15, p = .696), or excessive exercise (χ2(1, N = 1073) = 3.98, p = .046), which did not reach the adjusted significance threshold of .017.
Discussion
The primary aim of this study was to examine the frequencies of disordered eating symptoms among male and female adolescent athletes in Switzerland, across a range of sports and competition levels. To do so, we adopted a dual approach, capturing both general and sport-specific eating disorder symptoms, as well as self-reported engagement in weight control behaviors. A secondary objective was to examine variations in DE symptoms by sport sex category, sport type, and sport level.
The frequencies of athletes considered at risk of eating disorder symptoms in the overall sample (17.5% for general ED symptoms and 24.7% for sport-specific) closely align with findings from the recent meta-analysis of Ghazzawi et al. (2024), who reported an average of 19.2% DE frequencies in athletes worldwide and 18.5% in Western athlete samples. Other studies focused on adolescent athletes and using the EDE-Q also report elevated risk. For instance, Barrack et al. (2023) found DE/ED symptoms in 32.2% of adolescent athletes in the U.S., and Nichols et al. (2006) reported 18.2%.
It is important to note that frequency estimates of eating disorder risk based on the EDE-Q can vary considerably depending on the cut-off score applied. While a global score of 2.3 is frequently used in adult populations (Mond et al., 2004), several studies have applied different thresholds for males or for athlete population. For example, Schaefer et al. (2018) supports sex-specific thresholds and report a lower cut-off of 1.68 for males to account for differences in symptom reporting. Among athletes, other studies have used higher cut-offs; Barrack et al. (2023) applied a cutoff of 3.0, while Nichols et al. (2006) used 4.0. In this study, we used a global cut-off of 2.5, based on normative data for Swiss population (see Fisher et al., 2012; Munsch et al., 2021), to ensure contextual relevance. Nevertheless, it is important to acknowledge that variability in cut-off selection can significantly influence frequency estimates and limit comparability across studies.
Frequencies of athletes considered at risk for eating disorder symptoms based on the EDSA scores were lower than those reported in the study by Hazzard et al. (2020), for both female and male athletes. In their study, 31.7% of male athletes (versus 6.7% in the present study) and 52.9% of female athletes (versus 37.1% in our study) were at risk for eating disorder symptoms. As both studies relied on the same screening instrument (EDSA), these differences are less likely to be attributable to measurement variability and may instead reflect differences in sample characteristics. In this regard, the older age of participants in Hazzard et al. (2020), compared to the younger athletes included in the present study, may partly account for the higher frequencies observed in their sample. However, it is important to note that younger populations can also exhibit relatively high frequencies of disordered eating (e.g., López-Gil et al., 2023), suggesting that age alone is unlikely to fully explain the observed differences. Other factors, such as cultural differences (e.g., United States versus Switzerland), as well as variations in sport context or sample characteristics (e.g., level of competition), may also have contributed to these discrepancies.
Notably, the EDSA appeared to capture a broader range of sport-related concerns than the EDE-Q, suggesting greater sensitivity to symptoms specific to sport environments. This aligns with previous findings indicating that general screening tools may overlook behaviors or attitudes that are normalized or even encouraged in sports settings (Hazzard et al., 2020; Pope et al., 2015). For example, athletes may experience concerns about changes in body weight, shape, or composition in response to reduced training, reflecting the central role of training routines in body regulation within sport contexts. Such concerns are not explicitly captured by general measures like the EDE-Q. In addition, athletes may engage in restrictive eating patterns to optimize performance, feel pressure to maintain a specific body weight or composition, or normalize excessive exercise as part of training routines. These behaviors may not always be perceived as problematic within sport contexts and may therefore be underreported or insufficiently captured by general measures.
Furthermore, the EDSA may be more sensitive to certain signs of disordered eating in male athletes being designed to assess DE in female and male athletes. The EDE-Q has been widely validated in non-athlete populations, predominantly among females, and may therefore be less sensitive when used in an athletic context. For example, previous research has suggested that commonly used eating disorder measures may not sufficiently capture concerns that are more prevalent among male athletes, such as the desire to increase body size or muscularity (e.g., Pope et al., 2015). The higher frequencies observed with the EDSA highlight the importance of using sport-adapted assessments to identify at-risk individuals more accurately.
Interestingly, the discrepancy in frequency estimates between the EDSA and EDE-Q appeared more pronounced among female athletes. One possible explanation is that, within the present sample, female athletes exhibited a higher participation rate in weight-sensitive sports compared to male athletes. As concerns related to weight and pressures to achieve or maintain a lean physique for optimal performance are particularly salient in these types of sports (Sundgot-Borgen & Torstveit, 2004), these concerns may have been more readily captured by the EDSA.
In terms of weight control behaviors, our findings align with previous research showing that athletes are more likely to engage in compensatory behaviors such as binge eating and excessive exercise, rather than more severe or medically risky methods like self-induced vomiting or laxative use (Anderson & Petrie, 2012; Koppenburg et al., 2022; Turgeon et al., 2015).
Regarding group-level differences, sex emerged as the most robust factor associated with DE symptoms. Female athletes were significantly more likely than males to be classified as at-risk on both the EDE-Q (25.8% vs. 5.4%) and the EDSA (37.1% vs. 6.7%), and they reported higher frequencies of nearly all weight control behaviors. This pattern is well-documented in the literature and reflects broader sex-based disparities in eating disorder vulnerability among adolescents and athletes (e.g., Byrne & McLean, 2001; Fatt et al., 2024; Sundgot-Borgen et al., 2025). However, it is important to note that much of the existing literature on disordered eating has historically relied on female samples (Pope et al., 2015), which may have influenced how these symptoms are conceptualized and assessed. As a result, the magnitude of observed sex differences should be interpreted with caution.
Contrary to our expectations, sport level was not associated with increased risk of eating disorder symptoms. However, among weight control behaviors, only excessive exercise varied significantly by sport level, potentially reflecting how performance pressures or training norms may selectively influence behavior depending on the sport or context.
Sport type yielded clearer distinctions. While the EDE-Q did not detect differences based on sport type, the EDSA classified significantly more athletes in weight-sensitive sports as at-risk compared to those in non-weight-sensitive sports. This aligns with prior findings that athletes in sports emphasizing leanness or weight categories are at elevated risk for disordered eating (e.g., Byrne & McLean, 2001; Bonanséa et al., 2016; Chapa et al., 2022; Fatt et al., 2024; Mancine et al., 2020). Furthermore, behaviors such as loss of control eating and objective binge eating were more frequently reported in weight-sensitive disciplines, reinforcing the relevance of sport-specific vulnerability.
Limitations and future research
This study has several limitations that should be considered when interpreting the findings. First, the cross-sectional design does not allow for conclusions about causality or the temporal development of disordered eating. Second, all data were self-reported, which may have introduced reporting biases such as social desirability or underreporting, particularly among athletes who may normalize certain behaviors within sport culture. Third, although the sample was diverse in terms of sport type and competition level, it was not randomly selected and may not be representative of all adolescent athletes in Switzerland, limiting the generalizability of the findings. Additionally, while we used validated instruments, some constructs such as sport-specific eating symptoms are still evolving, and further validation of tools like the EDSA in adolescent populations is warranted. Lastly, the use of fixed cut-off scores, though grounded in prior research and normative data, remains somewhat arbitrary and may not fully capture the clinical significance of subthreshold eating behaviors. However, it is important to note that these reference studies were conducted in specific populations (e.g., predominantly female samples), which may limit the generalizability of this threshold to a mixed-sex adolescent athlete population. Importantly, these thresholds should be interpreted as screening indicators of elevated symptom levels rather than reflecting eating disorders.
Future studies should prioritize longitudinal designs to better understand the developmental trajectory of disordered eating in adolescent athletes, including how risk may change with age, training intensity, or transitions between sport levels. It would also be valuable to explore the impact of contextual factors such as coaching style, team culture, and sport-specific norms on the emergence and maintenance of DE. Finally, further validation of sport-specific screening instruments, including the EDSA, across diverse adolescent or clinical populations is warranted to enhance detection accuracy and comparability across studies. Additionally, further research should evaluate their content validity and age-appropriateness for younger populations.
Conclusion
This study contributes to the growing body of research on disordered eating in adolescent athletes by providing frequency estimates, with 17.5% of participants reporting elevated levels of general symptoms and 24.7% reporting sport-specific symptoms. The most frequently reported weight control behaviors were binge eating episodes. Using both general and sport-specific assessment tools, we observed higher levels of disordered eating symptoms among female athletes and those involved in weight-sensitive sports. Our findings underscore the value of using sport-adapted measures to better detect disordered eating symptoms that may be overlooked by general screening tools. These results highlight the need for early identification and context-specific prevention strategies, especially in sports where weight and appearance pressures are heightened.
Transparency Statement
We reported how we determined the sample size and reported all variables. We report all data exclusion criteria and whether these were determined before or during the data analysis. We did not exclude any data or outliers.
Additional File
The additional file for this article can be found as follows:
Data Accessibility Statement
The data used for this study are part of Franzoni’s Sport & Eating PhD thesis project. The data will not be available online as her thesis is still in progress, but can be requested, upon reasonable request, by email from the corresponding author.
Ethics and Consent
The present study protocol received ethical approval from the Research Ethics Commission of the Faculty of Social and Political Sciences of the University of Lausanne, Lausanne, Switzerland (E_SSP_072023_00001) prior to the research being conducted.
Author Contributions
Amandine Franzoni: conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing – original draft; writing – review and editing. Nadine Messerli-Bürgy: conceptualization; methodology; supervision; writing – review and editing.