Abstract
Context
Lean sports, such as endurance running, have been at the forefront of disordered eating and body image research, particularly in female populations. Yet little is known about how athletic men and women differ in body-checking behaviors, a known risk factor for body dissatisfaction and disordered eating, across sport type and athletic status.
Objective
The purpose of this study was to examine gender differences on measures of eating behavior and body checking between full-time collegiate student-athletes and nonathletes.
Design
Cross-sectional study.
Setting
National Collegiate Athletic Association Division I collegiate athletics.
Patients or Other Participants
Two hundred fifty-nine full-time college students (n = 174 student-athletes, 85 nonathletes).
Main Outcome Measure(s)
Primary outcomes included self-reported disordered eating behavior and body-checking behavior through the Eating Attitudes Test-26 and the Body Checking Questionnaire (BCQ) and the Male BCQ (MBCQ). We explored differences based on sport type, team, individual, or nonathlete, and gender identity.
Results
There was a statistically significant multivariate main effect of gender, F10,464 = 9.219, P < .001, ηp2 = 0.166, and a significant multivariate interaction of gender and sport type, F15,699 = 2.806, P = .001, ηp2 = 0.057. Follow-up comparisons for team-sport athletes showed that women scored significantly higher (P < .001) on the MBCQ compared with men. Women team-sport athletes also scored significantly higher on the MBCQ than women nonathletes (P < .001). Conversely, nonathlete men scored significantly higher on the MBCQ than men team- and individual-sport athletes (P = .003 and P = .048, respectively).
Conclusions
Findings suggest that body-checking behaviors traditionally studied as masculine occur more frequently in women than men. This effect seems to be driven by women team-sport athletes, who reported engaging in more body-checking behaviors on the MBCQ than nonathletes. Therefore, assessments based on traditional views of maleness and femaleness may overlook significant risk factors for eating disorders in college athletes.
Keywords: body image, eating disorders, student-athletes
Key Points
Body-checking behaviors are associated with body dissatisfaction, a known cause of eating disorder development.
Women team-sport athletes reported greater body checking on the Male Body Checking Questionnaire than men team-sport athletes.
Gender-specific measures may fail to identify athletes at risk of body image dissatisfaction.
The National Collegiate Athletic Association (NCAA) is the governing body for intercollegiate athletics, encompassing 526 084 collegiate student-athletes participating in sanctioned sports in the year 2023.1 Disordered eating (DE) is characterized as a spectrum of behaviors that range from DE to diagnosed eating disorders (EDs). Behaviors within the spectrum of DE include restriction, binging, purging, use of laxatives, excessive exercising, and other compensatory patterns that prevent adequate or healthful consumption of foods.2 Body image, or one’s perception of one’s appearance, is a significant risk factor in the development of body dissatisfaction, a causal factor contributing to DE and the potential for an ED.3
Factors shown to correlate with negative body image appraisal include being teased in youth, previous traumatic experience(s), previous and current dieting, peer pressure, cultural beauty standards, dating history, and experiences during puberty, among other factors.4 Not only are collegiate student-athletes prone to the aforementioned factors, but adding athletic body image ideals and expectations for performance places them at higher risk for heightened detrimental outcomes associated with insufficient and improper eating habits compared with nonathletes.5
According to data from the National Institute of Mental Health, the lifetime prevalence of clinically diagnosed EDs, such as anorexia nervosa (AN), bulimia nervosa, and binge-ED, ranges from 0.3% to 1.2% of the adult population in the United States.6 In the student-athlete population, clinically diagnosed EDs like AN, bulimia nervosa, and binge-EDs are estimated to affect 10 in every 1000 student-athletes in the United States.5 Not only is AN the second most fatal psychiatric disorder behind opioid use disorder, but individuals who are diagnosed with AN are at considerably high risk for suicide and lifetime relapse.7
Disordered eating and EDs can have major implications for outcomes related to one’s ability to optimally compete in sports. In addition to the negative health consequences of DE for athletes, such as electrolyte imbalances, nutritional insufficiencies, gastrointestinal issues, mental health concerns, and hormone dysregulation, to name a few, there are performance detriments as well. Some performance consequences of DE include less effective training and fewer overall training days (eg, from increased illness, increased injury, and recovery insufficiency), resulting in reduced training capacity and adaptation, as well as short-term impairments during practice or competition, such as reduced coordination, focus, emotional/mood regulation, strength, and endurance.8,9 It is important to be able to recognize and identify risks and symptoms of DE/EDs before the problem reaches a critical point, with primary prevention strategies pointing to identifying body image disturbance.
Within the athlete-specific population, previous work has shown that upward of 13.5% of athletes meet the criteria for clinical or subclinical EDs, indicating a nearly 3-fold greater prevalence than nonathletes.10 Furthermore, the prevalence of clinically diagnosed EDs has been shown to be as high as 6 times greater in female athletes compared with the average adult population.5 Not only was a higher prevalence for clinical EDs in athletes reported compared with the general adult population, but subclinical ED rates were 5 times greater in both female and male athletes compared with diagnosed EDs.5
Exploring the etiology of body image disturbance is a key factor in understanding the progressive pathology into DE and EDs. One proposed mechanism is body checking, which consists of recurring evaluative behaviors assessing one’s weight or shape, as well as body-avoidance behaviors, such as intentionally not looking in a mirror or stepping on a scale. Strategies of evaluating body weight, shape, and composition are normalized in the sporting culture. Rates of mandatory weigh-ins range from 22% to 41% of athletes, with female collegiate athletes reporting greater weighing frequency.11,12 Understanding the cultural and evaluative pressures in athletics may provide insight into how greater engagement in body-checking behavior has been strongly associated with increased body dissatisfaction and ED pathology.13 Gender differences have also been observed in body-checking behaviors and body image perception, with adult women reporting greater overall body-checking behaviors and body image disturbance than adult men.14,15 However, differences in gender identity relating to body-checking behaviors in the collegiate student athlete population remain to be studied in further depth.
Therefore, the purpose of the current study was to examine differences across gender identity and sport status on self-reported body-checking and DE behaviors. The findings may provide greater insight into how to better identify athletes at risk for body dissatisfaction, and thus at risk for DE or EDs, while accounting for performance and health. The primary research aim was to explore differences between current NCAA individual- (eg, track and field, swimming, and diving) and team-sport student-athletes (eg, soccer, baseball, basketball) and nonathletes across gender identity (ie, man, woman, or other gender identity) on reported eating behavior and body-checking behavior. The purpose in grouping athletes based on team or individual sport type is to account for and mitigate the effects of heterogeneity in specific sport participation and representation on the statistical methods we used. We hypothesized that DE and body checking would differ between men and women athletes. Specifically, we expected women to report higher engagement in DE and body-checking behaviors compared with men, regardless of sport type. Additionally, we hypothesized eating behavior and body checking would differ across sport types (ie, individual, team, and nonathlete) with individual-sport athletes, regardless of gender, reporting significantly more engagement in body-checking behaviors and DE, as there is a greater focus on the performance of a single person competing rather than a group.
METHODS
Participants
Sample size calculations were based on an anticipated medium effect size (f = 0.13) for a 3-group multivariate analyses of variance with α = .05 and statistical power (ie, 1 − β) = 0.80.16,17 Therefore, a sample size of 126 was required to yield sufficient statistical power. To be eligible for the study, individuals had to be at least 18 years of age at the time of completing the survey and currently participating in NCAA sports if they identified as an athlete or full-time students if they were nonathletes. Email correspondence was the main method of survey dissemination, and included the institutional review board approval number, informed consent, and contact information for participant questions. Eligible participants were provided online informed consent before completion of the online survey via Qualtrics. We reached out to approximately 12 colleges’ or universities’ athletic training departments, coaches, and compliance offices in the northeast region of the United States.
Measures
The online survey comprised an eating behavior assessment, an integrated body-checking behaviors questionnaire, and a demographics section (Table). Internal consistency coefficients for each measure used in the current study are reported in the results.
Table.
Participant Categorical Demographics
| Characteristic | No. (% of Total Sample) |
|---|---|
| Gender | |
| Woman | 156 (60.2) |
| Man | 91 (35.1) |
| Other gender identitya | 12 (4.6) |
| Academic year | |
| Freshman | 68 (26.3) |
| Sophomore | 64 (24.7) |
| Junior | 60 (23.2) |
| Senior | 44 (17.0) |
| Fifth-year undergraduate | 8 (3.1) |
| Graduate Student | 15 (5.8) |
| Athlete status | |
| Team-sport athlete | 99 (38.2) |
| Individual-sport athlete | 75 (29.0) |
| Not an athlete | 85 (32.8) |
| National Collegiate Athletic Association division | |
| I | 77 (29.7) |
| III | 97 (37.5) |
| No affiliation | 85 (32.8) |
| Ethnicity/racial identity | |
| Black or African American | 18 (6.9) |
| White or Caucasian | 236 (91.1) |
| Hispanic or Latino | 14 (5.4) |
| Asian | 4 (1.5) |
| Native Hawaiian or Pacific Islander | 1 (0.4) |
| American Indian or Alaskan Native | 3 (1.2) |
| Other not listed | 1 (0.4) |
Individuals who identified as other than man or woman were provided the opportunity to self-identify; given the small sample size for each gender identity alone, they were grouped collectively as “other gender identity.”
Eating Attitudes Test
The Eating Attitudes Test-26 (EAT-26) is a 26-item survey that assesses an individual’s eating habits, behaviors, and body image and is empirically supported as a validated scale for use in clinical and nonclinical female adult populations.18,19 The EAT-26 remained highly correlated with the original EAT-40 (r = 0.98) while also being found to have a 90% accuracy rate in identifying those with an ED.18 Other studies examining eating behavior in athletic populations have also used the EAT-26.20,21 The measure uses a 5-point Likert scale ranging from 0 for never, rarely, and sometimes to scores of 1, 2, and 3 for often, usually, and always, respectively. Scores range from 0 to 78, with higher scores reflecting greater engagement in DE behaviors; additionally, a clinical cutoff score of 20 is used as a parameter for high risk and warrants medical attention.18
Body Checking Questionnaire
The Body Checking Questionnaire (BCQ) is a 23-item scale measuring idiosyncratic body-checking behaviors, focusing on thin idealization and body-fat vigilance.22 The BCQ was originally validated in clinical and nonclinical adult female populations but has been used in mixed-gender populations including male adolescents and adults.23,24 Likert scale responses range from a score of 1 being never engaging in that specific behavior to a score of 5 denoting engaging in that specific behavior very often, with higher scores reflecting greater checking. The total BCQ had good overall internal consistency of r = 0.94 along with the subscales of overall appearance (r = 0.88), specific body parts (r = 0.92), and idiosyncratic checking (r = 0.83) in addition to being correlated with scores on the Body Shape Questionnaire, the EAT-26, and the Body Image Avoidance Questionnaire.22
Male BCQ
The Male BCQ (MBCQ) is a 4-factor modified form of the BCQ that emphasizes muscularity-related body-checking behaviors across 19 questions.25 Divergent validity of the MBCQ was correlated with the BCQ in a cross-gender sample, whereas convergent validity showed good associations with body image and eating behavior scales including the Eating Disorder Examination Questionnaire, the Eating Disorder Inventory perfectionistic subscale, and the Muscle Dysmorphic Disorder Inventory.25 The MBCQ uses a Likert scale that ranges from 1, never engaging in that behavior, to 5, engaging in that body-checking behavior very often, with higher scores reflecting greater checking. Internal consistency of the MBCQ was high (α = .93 and .94 across 2 time points) and exhibited good test-retest reliability with the global score (r = 0.84) and on each of the 4 subscales of global muscle checking (r = 0.79), chest and shoulder checking (r = 0.69), other-comparative checking (r = 0.71), and body testing (r = 0.84).25
RESULTS
Participant Demographics
A total of 267 participants completed the survey. The final sample included 259 NCAA collegiate athletes (n = 174, 67.2%) and nonathletes (n = 85, 32.8%) after exclusion of participants who were <18 years of age (n = 1) and who were missing 50% or more of the data (n = 7). The average age ± SD of the sample was 20.00 ± 1.96 years, including 156 (60.2%) identifying as women and 91 (35.1%) identifying as men. Twelve athletes (4.6%) identified as other than male or female gender, including gender-fluid and demigirl. As a result of the low number and heterogeneity of those who identified as another gender identity across athletes and nonathletes, we collectively grouped them as “other gender identity” throughout the remainder of the study. A majority of the total sample of athletes and nonathletes identified as White (91.1%), followed by those who identified as Black/African American and Hispanic/Latino (representing 6.9% and 5.4%, respectively). More participant demographic information is presented in the Table.
Internal Consistency
Internal consistency was assessed using Cronbach ɑ and McDonald ω for each of the psychometric evaluations used in the survey to observe if any similarities or differences emerged with the 2 coefficients. Cronbach ɑ has been a reputable measure of internal consistency, but the current movement in behavioral science research is leaning toward McDonald ω, as it accounts for differences in error from variables’ true scores, whereas Cronbach ɑ assumes τ equivalence.26 The internal consistency coefficients were included for each of the primary measures of eating behavior and body checking, including the EAT-26 (ɑ = 0.908, ω = 0.931), the BCQ (ɑ = 0.961, ω = 0.962), and the MBCQ (ɑ = 0.930, ω = 0.935), which upheld very good internal consistency within this sample. Bivariate correlation table on items from the BCQ and MBCQ can be found in the Supplemental Table.
Group Differences Across Sport Type and Gender Identity
A 3 × 3 multivariate analysis of variance was conducted using SPSS (version 28) via general multivariate linear model analysis. The fixed factors included the sport type (k = 3), of nonathlete (n = 85, 32.8%), team-sport athlete (n = 99, 38.2%), and individual-sport athlete (n = 75, 29.0%), along with the gender identity (k = 3) of woman (n = 156, 60.2%), man (n = 91, 35.1%), and those grouped as identifying as other gender identity (n = 12, 4.6%).
There was a nonsignificant main effect of sport type on the scores from the dependent variables of EAT-26, BCQ, and MBCQ (P = .576). However, there was a statistically significant multivariate main effect of gender, F10,464 = 9.219, P < .001, ηp2 = 0.166, and a statistically significant interaction of sport type and gender, F15,699 = 2.806, P = .001, ηp2 = 0.057. Univariate tests for the effect of sport type on the EAT-26 and the BCQ revealed significant differences, F2,239 = 5.436, P = .005, ηp2 = 0.044, and F2,239 = 3.065, P = .048, ηp2 = 0.025, respectively.
Eating Behaviors
Women reported significantly higher scores (P < .001) on the EAT-26 than men, regardless of sport type, indicating greater engagement in DE. However, individuals who identified as other gender identity reported significantly higher scores (P = .016) on the EAT-26 compared with women (P < .041) and men (P < .001), indicating greater reporting of DE behaviors (Figure 1).
Figure 1.
Bar chart of mean differences on the Eating Attitudes Test-26.
Nonathletes reported significantly higher scores (P < .001) on the EAT-26 than team-sport and individual-sport athletes, meaning greater self-reported DE in the nonathlete sample compared with the athletes. No significant differences were observed between team- and individual-sport athletes on the EAT-26 (P = .840).
There was a significant multivariate effect of gender identity on the dependent variables, F10,472 = 9.387, P < .001, ηp2 = 0.166. Specifically, this effect revealed that nonathlete women reported significantly higher (P < .001) scores than individual-sport athletes on the EAT-26, indicating greater DE patterns in the nonathlete sample; however, no significant differences emerged when comparing team-sport and nonathlete women (P = .055).
Men who participated in team sports reported significantly (P < .001) lower scores on the EAT-26 compared with women’s team-sport athletes, whereas no significant difference was observed with athletes who identified as other gender identity. Men appear to report less engagement in DE than women in team sports, with individuals identifying as other gender identity reporting similar behaviors as women. For individual-sport athletes, those who identified as other gender identity reported significantly higher (P < .001 and P = .012) scores on the EAT-26 compared with men and women, respectively, suggesting greater reporting of DE.
Body-Checking Behaviors
Regarding the BCQ, women reported significantly (P < .001) more engagement in body checking compared with men, regardless of sport type. Additionally, individuals who identified as other gender identity reported significantly greater (P < .001) scores on the BCQ compared with men, indicating more frequent body checking, but not significantly different from women (P = .960).
No significant differences were observed between reported body-checking behaviors in women and in those who identified as other gender identity who participated in team sports. Women individual-sport athletes, along with individual-sport athletes who identified as other gender identity, scored significantly higher (P = .001 and P = .013, respectively) than men on the BCQ, suggesting more frequent engagement in body-checking behaviors. However, no significant differences were observed between individual-sport athlete women and those who identified as other gender identity (P = .649).
Male Body Checking
On the MBCQ, nonathlete men reported significantly greater engagement in body checking (P = .008) than men from team sports, but not significantly different from men who participated in individual sports (P = .126). Women team-sport athletes reported significantly higher (P = .001) scores on the MBCQ compared with nonathlete women, suggesting greater reporting of muscularity-related body-checking behaviors; however, team- and individual-sport women did not convey significant differences in reporting such behaviors (P = .733) (Figure 2).
Figure 2.
Bar chart of mean differences on the Male Body Checking Questionnaire.
Nonathlete men reported significantly higher (P = .011) MBCQ scores than nonathlete women, suggesting greater engagement in muscularity-related body-checking behaviors. There was no difference observed between men and those who identified as other gender identity who were not athletes (P = .303). Among team-sport athletes, women scored significantly higher (P < .001) on the MBCQ compared with men, suggesting that women reported more frequent muscularity-related body-checking behaviors compared with men of the same sport type. No significant differences were observed between women and individuals identifying as other gender identity.
DISCUSSION
This study explored the differences in eating behavior and body-checking behavior in collegiate athletes and nonathletes across gender identity and sport type. Most research investigating the prevalence of EDs and DE within the collegiate athlete population has focused on at-risk sporting populations, including endurance sports, aesthetic sports, and weight-classification sports.27 Results from the current study supported our hypothesis that overall, women, regardless of sport type, would tend to report more DE and body-checking behaviors compared with men. Understanding the role of gender as a risk factor is relevant in the athletic population, especially when women appear to report more frequent body-checking behavior and DE, regardless of sport type, than their male counterparts.28
An emergent finding was that individuals who did not identify as either a man or a woman reported significantly greater DE behaviors than those who identified as men and women, regardless of sport type. Furthermore, individuals who identified as other gender identity reported significantly more frequent body-checking behaviors compared with men. This finding is in alignment with results from Kalash et al with eating behavior and body image disturbances being reported more frequently in the lesbian, gay, bisexual, transgender, queer, intersexual, asexual, and other (LGBTQIA+) community compared with cisgender individuals.29 Based on these emergent findings, it may be relevant to account for gender and sexual identity when assessing collegiate athletes for DE/ED risk as well as body-checking engagement.
Nonathletes, regardless of gender, reported more DE compared with individual and team athletes, with no significant differences between individual- and team-sport athletes. This finding does not support our hypothesis that individual-sport athletes, regardless of gender, would report greater DE and body checking than nonathletes and team-sport athletes. Despite this finding hinting at athlete status as a potential “protective” factor against DE, it is important to consider that most of the nonathlete population identified as women, skewing the representativeness of gender in this specific sample who reported greater DE regardless of sport type. Although evidence does not offer a clear distinction of sport participation as a risk or protective factor for DE/EDs, caution should be practiced when conducting health assessments for collegiate athletes to monitor risk of and engagement in DE/EDs throughout the season, particularly in female athletes and in lean and aesthetic sports.30
Men in our sample who participated in team sports reported significantly less DE compared with women team-sport athletes, supporting our initial hypothesis. Interestingly, individual-sport athletes identifying as other gender identity reported significantly greater DE compared with men and women in the same sports category. Previous studies are limited by a lack of assessment of athletes who identify as transgender or nonbinary, as well as athletes who are a part of the LGBTQIA+ community, for eating behaviors, body checking, and body image. Our low recruitment numbers of this population, however, prevent us from statistically analyzing the results of those individuals.
Nonathlete women reported significantly greater DE compared with individual-sport women; however, the nonathlete women did not report significantly different DE when compared with team-sport women. These findings did not support our hypothesis that individual-sport athletes would report greater engagement in DE. Future research can strive to consider the potential role that sport type (eg, team or individual) may play in problematic eating and body-checking behaviors in collegiate athletes.
Women reported greater engagement in body-checking behaviors compared with men regardless of sport type, supporting our hypothesis. Additionally, individuals who identified as other gender identity reported significantly greater body-checking behaviors compared with men, regardless of sport type. This finding is in alignment with previous literature on adults who identify as transgender or nonbinary outside of the sports setting who report increased body image disturbance and evaluation behaviors.29
Women and individuals identifying as other gender identity who participated in individual sports reported significantly greater body-checking behaviors compared with men of the same sport. This aligned with both hypotheses suggesting that women and individual-sport athletes would report more engagement of body-checking behavior compared with men, team-sport athletes, and nonathletes, despite not having much previous research to support this proposition. Individual-sport athletes who were grouped as other gender identity reported significantly more frequent body checking compared with men, potentially placing them at greater risk for body image dissatisfaction and risk for DE or EDs; this finding highlights gender identity and sexuality as a potentially pertinent factor for student-athlete health support staff (eg, athletic trainers, team physicians) to attend to when assessing athlete health.29
Both the BCQ and MBCQ exhibited small and medium interaction effects, respectively. Women who competed in team sports reported significantly greater engagement in body checking on the MBCQ compared with nonathletes and individual-sport women, potentially indicating body-checking behavior historically associated with men/masculinity such as muscle size and definition.31 The findings from our study must be taken with some consideration, particularly regarding the lack of psychometric validity of implementing the MBCQ in a female sample. Because the MBCQ was validated and has since been repeatedly administered in adolescent and adult male populations, generalizability of our findings is limited. Despite this limitation, our findings may suggest that women who are a part of team sports at the collegiate level may be at risk for body-centric behaviors related to muscularity idealization and internalization, which may be missed using current batteries of body checking. Moving forward, the current study can open the opportunity for future researchers to examine body checking and body image from a more inclusive and dimensional perspective rather than heavily focusing on historical discrepancies between men and women regarding body checking and image. Additionally, future validation efforts should ensure representation for individuals who do not identify as male or female, which will permit a more inclusive and generalizable measure.
Limitations and Future Directions
There are a few limitations of the study to address. The first is that the study design was a self-report, cross-sectional design with data being collected at 1 time point. There may have been changes in eating behavior and body checking if we were to assess at follow-up intervals, which would provide greater insight into the temporal changes associated with eating behavior and body checking in the athletic population.
From a generalizability perspective, the MBCQ has not been previously validated in the female population. The reason for its use in our study was to see if women athletes engaged in more muscularity-focused body-checking behaviors and how that compared with male athletes and nonathlete men. With that said, interpretation of the results should be seen as exploratory in nature and future endeavors should examine gender differences in body checking and body image perspective, particularly in the athletic population. Additionally, future research can develop more gender-inclusive measures of body checking to increase generalizability of comparing gender differences as well as expanding the breadth of sign and symptom identification in at risk athletes.
Representation of gender, sport type, and ethnicity/race is important to address. Only 5 men were in the nonathlete sample, compared with 68 women and 8 who identified as other gender identity. Additionally, athletes and nonathletes who identified as other gender identity had very low representation, with 0 team-sport athletes and 4 individual-sport athletes, respectively, making the results difficult to generalize in the current study. Moving forward, acquiring a more representative sample across gender identity and recruiting more homogenous subgroups of the sample population will facilitate methodologically sound statistical approaches and may increase generalizability. Additionally, athlete comparisons were limited to team- and individual-sport athletes. A sport-by-sport comparative analysis was not warranted as the sample was heterogenous based on sport type, with baseball players being the most represented athletes (n = 49, 28.2%) and fencing, gymnastics, lacrosse, and tennis being the least represented athletes with 1 each.
It is also necessary to acknowledge the lack of ethnic/racial diversity in the current sample of collegiate athletes and nonathletes. Overall, 91% of the total sample identified as White, with the second highest represented group being Black/African American (6.9%) individuals. In the student-athlete sample, 88.4% identified as White and 7.18% identified as Black/African American. According to the NCAA Demographics Database, White individuals account for approximately 62% of collegiate student-athletes across all divisions of competition, followed by Black/African American athletes at about 16%.1 Future researchers should focus on recruiting a more representative population of collegiate athletes, with intentions of increasing the generalizability of the findings to a greater breadth of individuals from various cultural and ethnic backgrounds and identities.
Despite some limitations, athletes who identified as women and other gender identity in our sample reported more frequent body checking, potentially placing them at risk for body image dissatisfaction and the development of DE/EDs. Future researchers should focus on body checking and body image satisfaction in athletes who are a part of the LGBTQIA+ community, as well as athletes who identify with a gender other than man or woman. In the nonathlete populations, those who were grouped as identifying as other gender identity did report greater body dissatisfaction and greater prevalence of DE compared with heterosexual and cisgender people, but we are unsure if this finding generalizes to the athletic population at the time of this study.29
Future researchers can continue to push for validated screening tools for body checking, body image, and eating behavior, specifically for use in the athlete population. Our findings suggest that having separate-gender evaluation tools for body checking might leave athletes at risk of not being identified early, which may lead to body image disturbance and risk of DE/EDs. This is particularly relevant for the MBCQ, which has yet to be administered and psychometrically analyzed in adult female populations. Additionally, a call for revalidation of older psychometrically validated tools can also enhance adaptation and generalizability to account for temporal changes socially and culturally (eg, the advent of social media). Psychometric efforts can enhance primary prevention in identifying those at risk for body image disturbance through continued pursuits in developing inclusive and encompassing inventories of body checking and body image questions, particularly for use in the athletic environment (eg, athletic training facility, psychological counseling center).
Supplementary Material
ACKNOWLEDGMENTS
This manuscript was based on a master’s thesis conducted by Stephen Cirella Jr under the direction of Dr Lisa Harlow and Dr Kathleen Webster. Sincere appreciation is extended to Dr Mark Hartman for continuing this project’s efforts as well as Dr Hector Lopez-Vergara and Dr Christie-Ward Ritacco for their input on the thesis.
Data Availability Statement
Due to the nature of this research, participants did not agree for their data to be shared publicly, so supporting data are not available.
SUPPLEMENTAL MATERIAL
Supplemental Table. Bivariate correlation of items on the BCQ and MBCQ.
REFERENCES
- 1.NCAA demographics database . National Collegiate Athletic Association. October 2024. Accessed February 9, 2025. https://www.ncaa.org/sports/2018/12/13/ncaa-demographics-database.aspx
- 2.Wilson D, O’Connor EL. Mindfulness, personality and disordered eating. Pers Individ Differ. 2017;119:7–12. 10.1016/j.paid.2017.06.033 [DOI] [Google Scholar]
- 3.Shafran R, Fairburn CG, Robinson P, Lask B. Body checking and its avoidance in eating disorders. Int J Eat Disord. 2004;35(1):93–101. 10.1002/eat.10228 [DOI] [PubMed] [Google Scholar]
- 4.Rosen JC, Orosan-Weine P, Tang T. Critical experiences in the development of body image. Eat Disord. 1997;5(3):191–204. 10.1080/10640269708249225 [DOI] [Google Scholar]
- 5.DiPasquale LD, Petrie TA. Prevalence of disordered eating: a comparison of male and female collegiate athletes and nonathletes. J Clin Sport Psychol. 2013;7(3):186–197. 10.1123/jcsp.7.3.186 [DOI] [Google Scholar]
- 6.Eating disorders . National Institute of Mental Health. Accessed February 9, 2025. https://www.nimh.nih.gov/health/statistics/eating-disorders.shtml
- 7.Chesney E, Goodwin GM, Fazel S. Risks of all-cause and suicide mortality in mental disorders: a meta-review. World Psychiatry. 2014;13(2):153–160. 10.1002/wps.20128 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Mountjoy M, Sundgot-Borgen J, Burke L, et al. The IOC consensus statement: beyond the female athlete triad—relative energy deficiency in sport (RED-S). Br J Sports Med. 2014;48(7):491–497. 10.1136/bjsports-2014-093502 [DOI] [PubMed] [Google Scholar]
- 9.Loucks AB, Kiens B, Wright HH. Energy availability in athletes. J Sports Sci. 2011;29(suppl 1):S7–S15. 10.1080/02640414.2011.588958 [DOI] [PubMed] [Google Scholar]
- 10.Bratland-Sanda S, Sundgot-Borgen J. Eating disorders in athletes: overview of prevalence, risk factors and recommendations for prevention and treatment. Eur J Sport Sci. 2013;13(5):499–508. 10.1080/17461391.2012.740504 [DOI] [PubMed] [Google Scholar]
- 11.Walsh JA, Petrie TA, Chatterton J. The frequency of weigh-ins, weight intentionality and management, and eating among male collegiate athletes. Eat Behav. 2020;39:101432. 10.1016/j.eatbeh.2020.101432 [DOI] [PubMed] [Google Scholar]
- 12.Tackett BP, Petrie TA, Anderson CM. The frequency of weigh-ins, weight intentionality and management, and eating among female collegiate athletes. Eat Behav. 2016;23:82–85. 10.1016/j.eatbeh.2016.08.007 [DOI] [PubMed] [Google Scholar]
- 13.Bennett BL, Wagner AF, Latner JD. Body checking and body image avoidance as partial mediators of the relationship between internalized weight bias and body dissatisfaction. Int J Environ Res Public Health. 2022;19(16):9785. 10.3390/ijerph19169785 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Walker DC, White EK, Srinivasan VJ. A meta-analysis of the relationships between body checking, body image avoidance, body image dissatisfaction, mood, and disordered eating. Int J Eat Disord. 2018;51(8):745–770. 10.1002/eat.22867 [DOI] [PubMed] [Google Scholar]
- 15.Zaccagni L, Gualdi-Russo E. The impact of sports involvement on body image perception and ideals: a systematic review and meta-analysis. Int J Environ Res Public Health. 2023;20(6):5228. 10.3390/ijerph20065228 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Routledge; 1988. 10.4324/9780203771587 [DOI] [Google Scholar]
- 17.Harlow LL. The Essence of Multivariate Thinking. 2nd ed. Routledge; 2014. 10.4324/9781315832746 [DOI] [Google Scholar]
- 18.Garner DM, Olmsted MP, Bohr Y, Garfinkel PE. The Eating Attitudes Test: psychometric features and clinical correlates. Psychol Med. 1982;12(4):871–878. 10.1017/S0033291700049163 [DOI] [PubMed] [Google Scholar]
- 19.Mintz LB, O’Halloran MS. The Eating Attitudes Test: validation with DSM-IV eating disorder criteria. J Pers Assess. 2000;74(3):489–503. 10.1207/S15327752JPA7403_11 [DOI] [PubMed] [Google Scholar]
- 20.Kong P, Harris LM. The sporting body: body image and eating disorder symptomatology among female athletes from leanness focused and nonleanness focused sports. J Psychol. 2015;149(1–2):141–160. 10.1080/00223980.2013.846291 [DOI] [PubMed] [Google Scholar]
- 21.Krentz EM, Warschburger P. Sports-related correlates of disordered eating in aesthetic sports. Psychol Sport Exerc. 2011;12(4):375–382. 10.1016/j.psychsport.2011.03.004 [DOI] [Google Scholar]
- 22.Reas DL, Whisenhunt BL, Netemeyer R, Williamson DA. Development of the Body Checking Questionnaire: a self-report measure of body checking behaviors. Int J Eat Disord. 2002;31(3):324–333. 10.1002/eat.10012 [DOI] [PubMed] [Google Scholar]
- 23.Alfano L, Hildebrandt T, Bannon K, Walker C, Walton KE. The impact of gender on the assessment of body checking behavior. Body Image. 2011;8(1):20–25. 10.1016/j.bodyim.2010.09.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zaitsoff SL, Pullmer R, Coelho JS. A longitudinal examination of body-checking behaviors and eating disorder pathology in a community sample of adolescent males and females. Int J Eat Disord. 2020;53(11):1836–1843. 10.1002/eat.23364 [DOI] [PubMed] [Google Scholar]
- 25.Hildebrandt T, Walker DC, Alfano L, Delinsky S, Bannon K. Development and validation of a male specific Body Checking Questionnaire. Int J Eat Disord. 2010;43(1):77–87. 10.1002/eat.20669 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.McNeish D. Thanks coefficient alpha, we’ll take it from here. Psychol Methods. 2018;23(3):412–433. 10.1037/met0000144 [DOI] [PubMed] [Google Scholar]
- 27.Chapman J, Woodman T. Disordered eating in male athletes: a meta-analysis. J Sports Sci. 2016;34(2):101–109. 10.1080/02640414.2015.1040824 [DOI] [PubMed] [Google Scholar]
- 28.Harris J, Peterson JT. Eating disorders in male runners. Strength Cond J. 2020;42(1):53–59. 10.1519/SSC.0000000000000509 [DOI] [Google Scholar]
- 29.Kalash N, Harb H, Zeeni N, El Khoury M, Mattar L. Determinants of body image disturbance and disordered eating behaviors among self-identified LGBTQ individuals. J Eat Disord. 2023;11(1):87. 10.1186/s40337-023-00810-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chapa DAN, Johnson SN, Richson BN, et al. Eating-disorder psychopathology in female athletes and non-athletes: a meta-analysis. Int J Eating Disord. 2022;55(7):861–885. 10.1002/eat.23748 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Muller SM, Gorrow TR, Schneider SR. Enhancing appearance and sports performance: are female collegiate athletes behaving more like males? J Am Coll Health. 2009;57(5):513–520. 10.3200/JACH.57.5.513-520 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Due to the nature of this research, participants did not agree for their data to be shared publicly, so supporting data are not available.