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. Author manuscript; available in PMC: 2025 Sep 7.
Published in final edited form as: Eat Behav. 2025 Aug 15;59:102022. doi: 10.1016/j.eatbeh.2025.102022

Evaluating the muscularity-oriented eating test: Factor structure and psychometric properties among university athletes and across sexes

Kaitlin Hanss a, Dominic M Denning b,c, Rachael E Flatt d, Christina M Sanzari e, Stuart B Murray f,g, Jason M Lavender h,i, Emilio J Compte j,k, Aaron J Blashill l,m, Jason M Nagata n, Pamela K Keel o, Jennifer Harriger p, Riley Nickols q, Jonathan Mond r,s,t,1, Tiffany A Brown u, Sasha Gorrell a,*,1
PMCID: PMC12413867  NIHMSID: NIHMS2108372  PMID: 40876385

Abstract

Objective:

The Muscularity-Oriented Eating Test (MOET) was developed to measure muscularity-oriented eating pathology among young men; however, this construct is relevant across sexes and may be particularly applicable to athletes. Thus, this study sought to validate the MOET among male and female athletes and non-athletes.

Methods:

Students (N = 2189) from three U.S. universities completed an online survey. Confirmatory factor analyses (CFA) testing two MOET versions (12-items, 15-items) were conducted in male and female athletes and non-athletes. Chi-squared tests and invariance analyses were used to evaluate the fit of each model across groups. Convergent validity of the MOET was assessed with Spearman correlation tests.

Results:

A one-factor CFA displayed adequate fit for 12-item and 15-item versions across all groups, except the 15-item version in the full sample. Chi-squared tests indicated superior fit of 12-item MOET CFAs among male and female athletes, female non-athletes, and all participants compared to 15-item models (adjusted p < .050); no difference in fit was evidenced between 12-item and 15-item CFAs for male non-athletes (adjusted p = .062). Both versions showed similar convergent validity.

Conclusions:

A 12-item MOET may provide improved screening for muscularity-oriented eating pathology in male and female athletes and female non-athletes.

Keywords: Drive for muscularity, Eating disorders, Athletes, Muscularity-oriented disordered eating

1. Introduction and aims

Across samples of individuals with eating disorders, consistent evidence supports the role of body dissatisfaction in driving disorder-related behaviors (Prnjak et al., 2022) and negatively impacting longer-term clinical outcomes (Gorrell et al., 2023). Of note, traditional measures of body dissatisfaction and disordered eating were developed using primarily female samples, with a particular focus on assessing preoccupation and eating/weight-control behaviors consistent with pursuit of the ‘thin ideal’ (Austen & Griffiths, 2019; Brown & Keel, 2023; Smith et al., 2017). Although thinness-oriented features can apply broadly across samples of individuals with eating disorders, many assessment tools do not address forms of eating pathology that may emerge due to dissatisfaction with other body characteristics, such as muscularity or leanness. For some individuals, a drive for muscularity and desires for increased muscle size, leanness, and/or definition may exist alongside or supersede desires to be ‘thin’ (Eschrich et al., 2025; Jürgensen et al., 2025; Lavender et al., 2017; Smith et al., 2017; Murray et al., 2016). This may be the case particularly for athletes who retain sport-specific body ideals (e.g., leanness; muscle bulk) and performance goals that may motivate engagement in disordered eating (Galli et al., 2015; Homan, 2010). Perhaps because muscularity-oriented attitudes and behaviors were thought to present more commonly among boys and men (Brown & Keel, 2023; Gorrell & Murray, 2019; Lavender et al., 2017; Zaiser et al., 2024), such symptoms were comparatively neglected within standard eating disorder nosologies. Instead, measures of this construct, such as the Drive for Muscularity Scale (DMS; McCreary et al., 2004), traditionally focused on attitudes and behaviors related to muscle-building (e.g., drive for size, weight training, appearance/performance-enhancing substance misuse) more closely aligned with muscle dysmorphia than eating pathology.

The Muscularity-Oriented Eating Test (MOET; (Murray et al., 2019)) was developed to expand and improve upon earlier measures to better assess eating-related attitudes and behaviors associated with the pursuit of muscularity. The 15-item questionnaire was originally validated in samples of undergraduate men in the United States (U.S.) (Murray et al., 2019), and has since been validated in a community sample of gay men in the U.S. (Donahue et al., 2022). The measure has also been translated into multiple languages and subsequently validated in undergraduate men in Argentina (Compte et al., 2021) and Turkey (Caliskan & Alim, 2021); men and women in Brazil (De Carvalho et al., 2023); and men (He et al., 2021) and women (He et al., 2023) in China (please see Appendix A for a brief overview of this work). To date, the only validation of the MOET in a sample of women using the original English-language version of the measure (Cunningham et al., 2021) found initial support for a 12-item (vs. 15-item) version in Australian undergraduates. These findings have not yet been replicated. For women, drive for muscularity has historically been understudied compared to drive for thinness, particularly in the context of eating pathology (Jürgensen et al., 2025; Rodgers et al., 2018). The feminine body ideal has evolved over time, but in the last several decades, an emphasis on tone and/or leanness has prevailed, suggesting that screening and assessment of this component of body (dis)satisfaction warrants attention for both men and women (Laskowski et al., 2023; Wu et al., 2022).

Further, no studies have specifically examined the factor structure and psychometric properties of the MOET among university athletes. Previous work has indicated that undergraduate students, especially those engaged in athletics, are at heightened risk for disordered eating behaviors focused on muscularity and leanness (Gorrell et al., 2021; Laskowski et al., 2023; McDonald et al., 2020; Palermo et al., 2024; Zaiser et al., 2024). University years can be a critical period for the development of maladaptive eating patterns due to athletic, social, and appearance-related pressures. Accordingly, it has been shown that first-year collegiate athletes subjected to uniforms that emphasize body concerns and coaches who promote thinness display increases in both weight/shape concern and restrictive eating behaviors (Palermo et al., 2024). Athletes who report eating pathology also report more disorder-related compulsive exercise compared to non-athlete peers (Flatt et al., 2021; Turton et al., 2017), as well as high levels of body dissatisfaction (de Bruin & Oudejans, 2018; Flatt et al., 2021).

However, athlete status should not be considered in isolation. Instead, we hypothesize that both athletic status and sex may jointly play important roles in shaping muscularity- and leanness-focused behaviors. In the aforementioned study among Australian undergraduates, there were three items in the original 15-item MOET that showed low factor loadings among women. Notably, these items referred to over-eating in order to achieve muscularity or using supplements, which may be more typical of efforts to add bulk or volume rather than tone (Cunningham et al., 2021), and are behaviors that are more commonly evidenced among men (Nagata et al., 2021; Nagata, Ganson, et al., 2022). However, this gender difference in non-athletes may not extend to athletes – a hypothesis that warrants testing. Should a shorter measure prove appropriate for use across groups, the current study would inform the potential for integrating a more parsimonious MOET version in future screening batteries. To investigate this possibility, it is important to ensure that the MOET is valid for use in these populations and invariant across athletes and non-athletes to allow for mean comparisons. More generally, confirming that the MOET is suitable for use among athletes across sex is critical to inform future screening and eating disorder prevention efforts in this higher-risk population.

Toward this end, the current study examined the factor structure and psychometric properties of the MOET among male and female university athletes and their respective non-athlete peers. We had three main aims: (1) First, to evaluate the factor structure of both the 12- and 15-item versions in the full sample as well as in each of four subgroups (i.e., male and female athletes and non-athletes); (2) to examine potential invariance across all models; and, (3) to evaluate the convergent validity of each version with measures of theoretically related constructs (e.g., traditional eating pathology, drive for muscularity). Based on prior work, we expected our respective male and female non-athlete samples to replicate the 15-item single-factor structure among young men, and 12-item single-factor structure among young women. For athletes, we expected a more comparable model fit of 15-items across both male and female samples. As noted above, determining that a more parsimonious, 12-item measure is suitable for use across groups has potential to inform future best-practice screening efforts for muscularity-oriented eating in at-risk populations.

2. Methods

2.1. Participants and procedures

Study data (n = 1766) were drawn from a parent study of exercise and eating behavior among undergraduates enrolled in Psychology courses from three U.S. universities; we chose this approach over and above recruiting exclusively from athletic departments, with the aim of capturing reports across a broad range of athletic performance levels. These institutions encompass a range of characteristics, including size (small to large), funding types (public and private), and U.S. geography locations (Northeast [n = 539], West [n = 383], and Southeast [n = 844]). As expected with institutions of different populations, sizes, and location, participant demographics and MOET scores varied across study sites (Table 1). Described below, invariance of the MOET across sites was examined to further explore these differences.

Table 1.

Participant characteristics (N = 1766).

Individual level variables All participants Athletes Non-athletes Site
Male Female Male Female Northeast Southeast West
Mean (SD) or n (%)
Sample N 1766 (100 %) 281 (100 %) 381 (100 %) 195 (100 %) 909 (100 %) 844 (100 %) 539 (100 %) 383 (100 %)
Age 19.1 (±1.6) 19.1 (±1.9) 18.9 (±1.3) 19.6 (±2.4) 19.0 (±1.5) 19.3 (±1.8) 18.8 (±1.7) 18.9 (±1.1)
BMI 23.4 (±4.5) 23.9 (±3.5) 22.7 (±3.8) 24.5 (±4.7) 23.4 (±4.8) 23.2 (±4.1) 24.4 (±5.4) 22.6 (±3.5)
Gender
 Female 1290 (73.0 %) 381 (100.0 %) 909 (100.0 %) 679 (80.5 %) 352 (65.3 %) 259 (67.6 %)
 Male 476 (27.0 %) 281 (100.0 %) 195 (100.0 %) 165 (19.5 %) 187 (34.7 %) 124 (32.4 %)
Race/ethnicity
 White 1229 (69.6 %) 195 (69.4 %) 295 (77.4 %) 111 (56.9 %) 628 (69.1 %) 763 (90.4 %) 230 (42.7 %) 236 (61.6 %)
 Black or African American 215 (12.2 %) 41 (14.6 %) 42 (11.0 %) 23 (11.8 %) 109 (12.0 %) 29 (3.4 %) 167 (31.0 %) 19 (5.0 %)
 Hispanic or Latino 18 (1.0 %) 1 (0.4 %) 9 (2.4 %) 1 (0.5 %) 7 (0.8 %) 5 (0.6 %) 5 (0.9 %) 8 (2.1 %)
 Asian 184 (10.4 %) 23 (8.2 %) 24 (6.3 %) 42 (21.5 %) 95 (10.5 %) 40 (4.7 %) 66 (12.2 %) 78 (20.4 %)
 American Indian or Alaskan Native 15 (0.8 %) 4 (1.4 %) 3 (0.8 %) 1 (0.5 %) 7 (0.8 %) 3 (0.4 %) 1 (0.2 %) 11 (2.9 %)
 Native Hawaiian or Other Pacific Islander 215 (12.2 %) 28 (10.0 %) 40 (10.5 %) 30 (15.4 %) 117 (12.9 %) 33 (3.9 %) 114 (21.2 %) 68 (17.8 %)
 Other 12 (0.7 %) 5 (1.8 %) 2 (0.5 %) 2 (1.0 %) 3 (0.3 %) 1 (0.1 %) 4 (0.7 %) 7 (1.8 %)
Participation level
 Olympic 8 (0.5 %) 3 (1.1 %) 5 (1.3 %) 0 (0.0 %) 0 (0.0 %) 5 (0.6 %) 2 (0.4 %) 1 (0.3 %)
 Paralympic 1 (0.1 %) 0 (0.0 %) 1 (0.3 %) 0 (0.0 %) 0 (0.0 %) 0 (0.0 %) 0 (0.0 %) 1 (0.3 %)
 Professional 23 (1.3 %) 11 (3.9 %) 12 (3.1 %) 0 (0.0 %) 0 (0.0 %) 7 (0.8 %) 11 (2.0 %) 5 (1.3 %)
 Competitive 929 (52.6 %) 182 (64.8 %) 220 (57.7 %) 104 (53.3 %) 423 (46.5 %) 508 (60.2 %) 233 (43.2 %) 188 (49.1 %)
 Club level 246 (13.9 %) 94 (33.5 %) 104 (27.3 %) 13 (6.7 %) 35 (3.9 %) 77 (9.1 %) 105 (19.5 %) 64 (16.7 %)
 Recreational 260 (14.7 %) 76 (27.0 %) 101 (26.5 %) 25 (12.8 %) 58 (6.4 %) 94 (11.1 %) 115 (21.3 %) 51 (13.3 %)
 None 554 (31.4 %) 11 (3.9 %) 24 (6.3 %) 74 (37.9 %) 445 (49.0 %) 263 (31.2 %) 163 (30.2 %) 128 (33.4 %)
MOET scores (mean, SD)
 15-Item 0.8 (±0.8) 0.9 (±0.8) 0.9 (±0.8) 0.8 (±0.8) 0.8 (±0.8) 0.8 (±0.7) 0.7 (±0.7) 1.0 (±0.9)
 12-Item 0.9 (±0.8) 0.9 (±0.9) 0.9 (±0.9) 0.9 (±0.9) 0.9 (±0.8) 0.9 (±0.8) 0.8 (±0.8) 1.0 (±0.9)
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Surveys were administered online via participant pool systems at one university in fall 2022 and fall 2022/spring 2023 at the other two universities. Students received course credit for starting the survey and were restricted from completing surveys more than once. Surveys included attention checks to ensure adherence. All study procedures were approved by two respective Institutional Review Boards (Northeast and Southeast, the latter of which included IRB approval for the West).

Among N = 2189 participants who started the survey, 265 (12 %) did not submit responses (Fig. 1); 39 (1.8 %) were excluded for insufficient attention (i.e., spending <2 s per item or answering >2 infrequency items incorrectly; see description of infrequency items in Measures section below). Given the MOET was the survey of interest in this study, participants with incomplete responses on this measure (77 [3.5 %]) were excluded. Six participants were excluded due to data inconsistencies (i.e., they indicated no current involvement in any sport while still reporting current Olympic, Paralympic, or professional levels of participation). In addition, due to small sample size of transgender and/or gender diverse participants and those who preferred not to provide gender (n = 36), only cisgender participants (n = 1766) were included in analysis. There were no statistically significant differences in demographics, athletic participation, or MOET scores between those who identified as cisgender, those who identified as transgender, and those who chose ‘other’ or not to disclose gender (please see Appendix B). Athletic identity appeared generally lower among transgender participants, with only 11.1 % of transgender participants identifying as an “athlete” compared to 37.4 % of cisgender participants. This likely reflects complex psychosocial, cultural, and identity-related factors—including access to sports, experiences within sports culture, and broader societal influences on self-identification—that shape how individuals relate to and define themselves within athletic contexts. Due to substantial data sparsity among participants excluded based on other criteria, we were unable to determine whether these excluded cases differed systematically from those that were retained.

Fig. 1.

Fig. 1.

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Inclusion and exclusion criteria for the data sample.

2.2. Measures

2.2.1. Infrequency scale

Eight verifiably false, previously validated infrequency items were embedded throughout the survey, e.g., “I am interested in pursuing a degree in parbanjology” (Billman Miller et al., 2024; Huang et al., 2015). A participant’s effort was considered insufficient if they spent an average of <2 s per item across all items answered or if they answered >2 infrequency items incorrectly (Huang et al., 2015; Meade & Craig, 2012).

2.2.2. Demographics

Participants reported demographic information including gender, race, ethnicity, and self-reported weight and height (Table 1). In this study, we use ‘male’ and ‘female’ (typically referring to sex) to refer to gender categories for cis-gender men and women, reflecting the wording from our questionnaires; we did not collect sexual orientation data.

2.2.3. Athletic status

Participants were queried about their athletic participation. For the purposes of analysis, and in line with prior work (Gorrell et al., 2021), participants who indicated “yes” to the question “Do you consider yourself an athlete?” were considered “athletes” and those who responded “no” were considered “non-athletes.” As per the survey design, participants were allowed to provide detailed information about their athletic involvement in up to two sports (primary and secondary). Sport level (ranging recreational to Olympic/Paralympic) and primary sport type are provided in Tables 1 and 2.

Table 2.

Participant characteristics of the subsample self-identifying as athletes.

Individual level variables Athletes MOET scores (mean)
All Male Female 15-Item 12-Item
Mean (SD) or n (%) Mean (SD) or n (%)
Sample N 662 (100 %) 281 (100 %) 381 (100 %) – –
Age 19.0 (±1.6) 19.1 (±1.9) 18.9 (±1.3) – –
BMI 23.2 (±3.7) 23.9 (±3.5) 22.7 (±3.8) – –
Participation level
 Olympic 8 (1.2 %) 3 (1.1 %) 5 (1.3 %) 1.0 (±0.8) 1.0 (±0.9)
 Paralympic 1 (0.2 %) 0 (0.0 %) 1 (0.3 %) – –
 Professional 23 (3.5 %) 11 (3.9 %) 12 (3.1 %) 1.1 (±0.9) 1.1 (±0.9)
 Competitive 402 (60.7 %) 182 (64.8 %) 220 (57.7 %) 0.8 (±0.8) 0.9 (±0.9)
 Club level 198 (29.9 %) 94 (33.5 %) 104 (27.3 %) 1.0 (±0.8) 1.0 (±0.9)
 Recreational 177 (26.7 %) 76 (27.0 %) 101 (26.5 %) 0.9 (±0.8) 0.9 (±0.9)
 None 35 (5.3 %) 11 (3.9 %) 24 (6.3 %) 0.7 (±0.7) 0.8 (±0.8)
Primary sport
 Baseball 20 (3.0 %) 20 (7.1 %) 0 (0 %) 1.1 (±1.0) 1.1 (±1.0)
 Basketball 83 (12.5 %) 59 (21.0 %) 24 (6.3 %) 0.7 (±0.7) 0.8 (±0.8)
 Cheerleading 16 (2.4 %) 0 (0 %) 16 (4.2 %) 1.0 (±0.9) 1.2 (±1.0)
 Cross country/track & field 44 (6.6 %) 16 (5.7 %) 28 (7.3 %) 0.8 (±0.8) 0.9 (±0.9)
 Dance 35 (5.3 %) 0 (0 %) 35 (9.2 %) 0.8 (±0.7) 0.9 (±0.8)
 Lacrosse 14 (2.1 %) 14 (5.0 %) 0 (0 %) 1.2 (±1.2) 1.1 (±1.2)
 Running 44 (6.6 %) 19 (6.8 %) 25 (6.6 %) 0.9 (±0.8) 1.0 (±0.9)
 Soccer 31 (4.7 %) 4 (1.4 %) 27 (7.1 %) 0.8 (±0.8) 0.9 (±0.9)
 Softball 70 (10.6 %) 33 (11.7 %) 37 (9.7 %) 0.8 (±0.7) 0.9 (±0.8)
 Swimming 22 (3.3 %) 10 (3.6 %) 12 (3.1 %) 1.0 (±1.0) 1.1 (±1.0)
 Tennis 52 (7.9 %) 12 (4.3 %) 40 (10.5 %) 0.9 (±0.9) 0.9 (±1.0)
 Volleyball 20 (3.0 %) 15 (5.3 %) 5 (1.3 %) 1.1 (±0.8) 1.2 (±0.9)
 Weightlifting 20 (3.0 %) 20 (7.1 %) 0 (0 %) 1.1 (±1.0) 1.1 (±1.0)
Site
 Southeast 288 (43.5 %) 94 (33.5 %) 194 (50.9 %) 0.7 (±0.7) 0.8 (±0.8)
 Northeast 230 (34.7 %) 118 (42.0 %) 112 (29.4 %) 0.8 (±0.7) 0.9 (±0.8)
 West 144 (21.8 %) 69 (24.6 %) 75 (19.7 %) 1.0 (±0.9) 1.0 (±0.9)
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2.2.4. Muscularity-Oriented Eating Test (MOET)

The MOET is a 15-item questionnaire assessing eating attitudes, behaviors, and affect related to the pursuit of muscularity. Items are rated on a 5-point scale ranging from 0 (never true) to 4 (always true) (Murray et al., 2019) and reference experiences in the past four weeks. The global score used in the current study is calculated by summing items to create a composite score; higher scores represent more muscularity-oriented eating attitudes, behaviors, and affect (Murray et al., 2019). Prior studies validating the 15-item MOET are presented in Appendix A and item-level descriptions of both the 15- and 12-item versions can be found in Appendix C.

2.2.5. Drive for Muscularity Scale (DMS)

The DMS is a 15-item instrument measuring attitudes and behaviors related to pursuit of increased muscularity. Items are rated on a 6-point scale ranging from 1 (always) to 6 (never) (McCreary & Sasse, 2000). The instrument sums items for a total score, and includes two subscales, Muscle-Oriented Body Image (i.e., Attitudes) and Muscle-Oriented Behaviors (i.e., Behaviors), calculated as sums of their corresponding items; higher values indicate greater muscular-oriented body image and behaviors. In prior work, the DMS has demonstrated consistently adequate psychometric properties among young adults, across gender (De Carvalho et al., 2019; Tod et al., 2012). Cronbach’s alpha for our full sample was 0.939 (± 0.003), indicating strong internal consistency. In addition, the minimum and maximum 95 % confidence intervals for Cronbach’s alphas across all EDE-Q subscales and gender-athlete permutations ranged from 0.847 to 0.944, indicating adequate internal consistency across subscales and subgroups of interest.

2.2.6. Eating Disorder Examination-Questionnaire version 6 (EDE-Q 6.0)

The EDE-Q is a 28-item, self-reported instrument designed to assess behaviors and concerns regarding eating, weight, and shape over the past 28 days (Fairburn & Beglin, 1994). The instrument includes four subscales: Restraint, Eating concerns, Weight concerns, and Shape concerns (Peterson et al., 2007; Reas et al., 2006). In addition, a global score is calculated via the mean of subscales (Gideon et al., 2016); this global score demonstrates good psychometric properties across clinical and non-clinical samples, as well as athletes (Aardoom et al., 2012; Lavender et al., 2010; Mond et al., 2004; Pope et al., 2015). Notably, the EDE-Q may not assess body image adequately for young men (Laskowski et al., 2023), which likely contributes to lower norms for the global score in this population (Lavender et al., 2010). Cronbach’s alpha for our sample was 0.914 (± 0.032), indicating strong internal consistency. In addition, the minimum and maximum 95 % confidence intervals for Cronbach’s alphas across all EDE-Q subscales and gender-athlete permutations ranged from 0.724 to 0.948, indicating adequate internal consistency across subscales and subgroups of interest.

2.2.7. Eating Pathology Symptoms Inventory (EPSI)

The EPSI is a 45-item, self-report instrument that captures a range of eating disorder pathology (Forbush et al., 2014) within eight domains including Body dissatisfaction, Binge eating, Cognitive restraint, Purging, Restricting, Excessive exercise, Negative attitudes toward obesity, and Muscle building (Forbush et al., 2014, Forbush et al., 2020; Richson et al., 2021). The EPSI demonstrates convergent and discriminant validity, internal consistency, and test–retest reliability across samples (Forbush et al., 2013, Forbush et al., 2014) and has demonstrated invariance across men and women (Forbush et al., 2014). Cronbach’s alpha for our sample was 0.934 (± 0.004), indicating strong internal consistency. The cognitive restraint subscale displayed somewhat less internal consistency among male athletes (0.704 [0.641–0.757]) and male non-athletes (0.735 [0.668, 0.790]). Further, the muscle building subscale displayed somewhat less internal consistency among female athletes (0.741 [0.699, 0.779]) and female non-athletes (0.690 [0.658, 0.720]). These metrics may reflect underlying sociocultural factors and variation in how these constructs are experienced. For example, men might engage in dietary restraint for more diverse or less traditionally measured reasons, while women’s muscle-building goals may not align as closely with items emphasizing size or bulk; these possibilities warrant further investigation. For all other subscales and gender-athlete permutations, minimum and maximum 95 % confidence intervals for Cronbach’s alphas ranged from 0.761 to 0.961, indicating adequate internal consistency.

2.2.8. Muscle Dysmorphic Disorder Inventory (MDDI)

The MDDI is a 13-item, self-report instrument designed to assess symptoms of muscle dysmorphia (Hildebrandt et al., 2004). Items are rated on a 5-point scale ranging from 1 (never) to 5 (always). The instrument captures three subscales with sums of corresponding items: Drive for size, Appearance intolerance, and Functional impairment; higher scores indicate greater levels of muscle dysmorphia. The MDDI has demonstrated strong psychometric properties, including internal consistency and construct validity across various samples, including men, women, athlete and non-clinical populations (Cerea et al., 2022; Compte et al., 2022; Hildebrandt et al., 2004; Nagata, Compte, et al., 2022; Nagata, Junqueira, et al., 2022). Cronbach’s alpha for our sample was 0.803 (± 0.014), indicating good internal consistency. In addition, the minimum and maximum 95 % confidence intervals for Cronbach’s alphas across all EDE-Q subscales and gender-athlete permutations ranged from 0.74 to 0.917, indicating adequate internal consistency across subscales and subgroups of interest.

2.3. Analytic plan

2.3.1. Confirmatory factor analysis (CFA)

Given the extensive work broadly confirming a single factor structure for the MOET (Appendix A), we opted to not conduct an exploratory factor analysis and instead moved directly to a CFA approach. Based on previous work suggesting CFA with 174 participants will yield 80 % for a root mean square error (RMSEA) value of 0.05, and α of 0.05 (Flora & Curran, 2004; MacCallum et al., 1996; Murray et al., 2019), the current study aimed to recruit roughly 180 participants per study subgroup of interest. These sample size parameters were met, and all samples exceeded the 10:1 participant to item ratio (Table 1).

All analyses were conducted in R version 4.3.1. Using the lavaan R package version .6–16 (Rosseel, 2012), we conducted CFAs with both the 15- and 12-item MOET versions across five groups: (1) all participants, (2) female athletes, (3) female non-athletes, (4) male athletes, (5) male non-athletes. CFA parameters were estimated using robust weighted least squares (i.e., WLSMV estimator) given the ordinal nature of the data. Model fit was evaluated based on Hu and Bentler’s (1999) criteria. Models were determined to have good fit if the comparative fit index (CFI) ≥ 0.95 and standard root mean squared residual (SRMR) ≤ 0.08 or if RMSEA ≤0.06, and SRMR ≤0.08. Items were considered poor indicators of the latent factor if their standardized loadings were < 0.60 or if their bivariate associations were <0.30 (Chen, 2007).

Chi-squared differences tests compared the relative fit of the 15- and 12-item MOET models for each of the five participant groups. Degrees of freedom were 90 and 54 for the 15-item and 12-item models respectively. To adjust for multiple hypothesis testing, the Bonferroni correction was applied with the significance level set at α = 0.05.

2.3.2. Invariance analysis

Invariance of the MOET was examined across study sites to validate that despite differences in mean MOET scores observed across sites (Table 1), the measure demonstrated equivalent factor structure, factor loadings, and item intercepts in all geographic regions. In addition, a series of invariance analyses were used to compare model fits between (1) male and female participants, (2) athlete and non-athlete identifying participants, (3) male athletes and female athletes, (4) male non-athletes and female non-athletes, (5) male athletes and male non-athletes, and (6) female athletes and female non-athletes. Configural invariance was assumed when factor structure showed adequate fit across groups of interest. Metric invariance was assumed when comparison of configural and metric models showed ΔCFI ≤0.010, and either ΔRMSEA ≤0.015 or ΔSRMR ≤0.030. Scalar invariance was assumed when metric invariance criteria were met and comparison of metric and scalar models showed ΔCFI ≤0.010, and either ΔRMSEA ≤0.015 or ΔSRMR ≤0.010 (Chen, 2007).

2.3.3. Convergent validity

Convergent validity of the 15- and 12- item MOET with DMS, EDE-Q, EPSI, and MDDI were assessed via Spearman correlation tests. Correlation was considered weak for coefficients ≤0.3, moderate 0.3–0.7, and strong ≥0.7 (Dancey & Reidy, 2007). Fisher z-transformation was used to compare Spearman correlations between the 15-item MOET and corresponding scales and subscales, and the 12-item MOET with those same scales and subscales. For all of these analyses, the Benjamini–Hochberg correction was applied with a false discovery rate of 0.050 to adjust for multiple hypothesis testing, treating any adjusted p ≤ .05 as statistically significant.

3. Results

In the aggregate sample, the majority of participants were female (72.9 %), White (68.9 %), and identified as non-athletes (62.0 %). Female athlete participants were less represented (29.8 %) compared to their male counterparts (60.0 %) (Table 1). The majority of participants identifying as athletes participated at the recreational level (52.7 %). Basketball (21.1 %), soccer (12.1 %), and baseball (7.3 %) were the most common activities among male participants, while volleyball (10.6 %), soccer (9.6 %), and dance (9.0 %) were the most common activities among female participants (Table 2).

The MOET showed strong internal consistency with a Cronbach’s alpha of 0.923 (± 0.005). While MOET average scores differed across sites (Table 1), analysis confirmed configural, metric, and scalar invariance across sites (Appendix D). In addition, the 15-item and 12-item MOETs demonstrated strong correlation among all participants, men, women, athletes, and non-athletes (Spearman correlation coefficients >0.98, p-values < .001; Appendix E).

3.1. Aim 1: CFA results

A one-factor CFA displayed adequate fit indices for the 15- and 12-item versions across all groups of interest, except for the 15-item MOET in the combined sample of all participants (Table 3). Chi-squared differences test comparing CFAs using data from both versions indicated superior fit of the 12-item MOET among male athletes, female athletes, female non-athletes, and all participants compared to 15-item model (adjusted p < .050). There was no significant difference in fit between the 15-item and 12-item versions for male non-athletes (adjusted p = .062).

Table 3.

Confirmatory factor analysis fit on 15-item and 12-item Muscularity-Oriented Eating Test data.

Population Version CFI (>0.95) RMSEA (<0.06) SRMR (<0.08) Factor loadings (<0.60) Correlation (<0.30)
Male athletes 15-Item 0.966 0.084a 0.057 – Q4, Q9, Q13
12-Item 0.971 0.088a 0.055 –
Female athletes 15-Item 0.963 0.103a 0.073 – Q3, Q4, Q9, Q10
12-Item 0.971 0.112a 0.064 –
Male non-athletes 15-Item 0.973 0.073a 0.062 Q9a Q4, Q8, Q9a
12-Item 0.986 0.062a 0.049 –
Female non-athletes 15-Item 0.963 0.093a 0.070 Q9a Q9a, Q10
12-Item 0.977 0.091a 0.049 –
All participants 15-Item 0.946a 0.108a 0.072 Q9a Q1, Q3, Q4, Q5, Q8, Q9a, Q10, Q13
12-Item 0.971 0.097a 0.050 –
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Note: CFI = Scaled Comparative Fit Index; RMSEA = Scaled Root Mean Square Error of Approximation; SRMR = Scaled Standardized Root Mean Square Residual; Factor Loadings = MOET item(s) for which the standardized factor loadings(s) are <0.60. Correlation = instrument items for which the correlation with another item was <0.30.

a

Indices that do not meet column-specified criteria for relative model fit.

3.2. Aim 2: invariance analysis

Configural and metric invariance were supported for all 15- and 12-item CFA models across groups, suggesting the factor structure and loadings are the same between (1) male and female participants, (2) athlete and non-athlete identifying participants, (3) male athletes and female athletes, (4) male non-athletes and female non-athletes, (5) male athletes and male non-athletes, and (6) female athletes and female non-athletes (Table 4). In the 12-item CFA models, scalar invariance was established for two group comparisons: (1) athletes vs. non-athletes, and (2) female athletes vs. female non-athletes. This suggests that 12-item MOET scores can be validly compared between these groups. In contrast, for the 15-item CFA models, scalar invariance was established only for female athletes vs. female non-athletes, suggesting 15-item MOET scores can be compared only between these groups (Table 4).

Table 4.

Measurement invariance of the 12-item and 15-item Muscularity-Oriented Eating Test across subgroups.

Subset Group Items Invariance type CFI Δ CFI ≤0.010 Δ RMSEA ≤0.015 Δ SRMR ≤0.010|≤0.030
All participants Gender (male vs female) 15-Item Configural 0.958
Metric 0.97 −0.012 0.018a −0.006
Scalara 0.944 0.026a −0.019 0.002
12-Item Configural 0.973
Metric 0.981 −0.008 0.02a −0.005
Scalara 0.968 0.013a −0.011 0.003
All participants Athlete (athlete vs nonathlete) 15-Item Configural 0.953
Metric 0.967 −0.014 0.02a −0.006
Scalara 0.952 0.015a −0.008 0.004
12-Item Configural 0.973
Metric 0.982 −0.009 0.02a −0.003
Scalar 0.972 0.01 −0.006 0.002
Athletes Gender (male vs female) 15-Item Configural 0.956
Metric 0.968 −0.012 0.018a −0.006
Scalara 0.946 0.022a −0.015 0.003
12-Item Configural 0.973
Metric 0.981 −0.008 0.019a −0.004
Scalara 0.97 0.011a −0.009 0.003
Non-athletes Gender (male vs female) 15-Item Configural 0.958
Metric 0.97 −0.012 0.019a −0.011
Scalara 0.952 0.018a −0.009 0.006
12-Item Configural 0.975
Metric 0.982 −0.007 0.019a −0.007
Scalar 0.972 0.01 −0.005 0.006
Males Athlete (athlete vs nonathlete) 15-Item Configural 0.956
Metric 0.969 −0.013 0.018a −0.006
Scalara 0.946 0.023a −0.015 0.003
12-Item Configural 0.973
Metric 0.982 −0.009 0.02a −0.003
Scalara 0.97 0.012a −.01 0.002
Females Athlete (athlete vs nonathlete) 15-Item Configural 0.95
Metric 0.972 −0.022 0.029a 0
Scalar 0.964 0.008 −0.002 0
12-Item Configural 0.972
Metric 0.984 −0.012 0.027a −0.001
Scalar 0.979 0.005 0 0.001
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Note: Bold invariance types meet criteria for model fit across all three invariance types. N = 1766 participants; n = 281 male athletes, n = 195 male non-athletes, n = 381 female athletes, n = 909 female non-athletes.

a

Indices that do not meet column-specified criteria for relative model fit.

3.3. Aim 3: convergent validity

MDDI, DMS, EPSI, and EDE-Q scores and subscales all displayed significant, positive correlations with 15- and 12-item MOET scores (Table 5, adjusted p < .010). The MDDI Drive for size subscale and DMS Attitudes subscale were weakly correlated with 15- and 12-item MOET scores. All other scores and subscales were moderately to strongly correlated with both versions. After Benjamin-Hochberg correction for multiple hypothesis testing, there were no significant differences in the correlations between the 15-item MOET and instruments measuring theoretically similar constructs compared to the correlations between the 12-item MOET and these same instruments (adjusted p > .050).

Table 5.

Convergence between Muscularity-Oriented Eating Test scores with other relevant instruments.

15-item MOET 12-item MOET Fisher Z-transformation
Spearman correlation Correlation strength Adjusted p-value Spearman correlation Correlation strength Adjusted p-value Adjusted p-value
MDDI
 Drive for size 0.11 Weak <.001 0.06 Weak <.001 .291
 Appearance intolerance 0.48 Moderate <.001 0.5 Moderate <.001 .291
 Functional impairment 0.54 Moderate <.001 0.52 Moderate <.001 .291
DMS
 Attitudes 0.25 Weak <.001 0.21 Weak <.001 .291
 Behaviors 0.35 Moderate <.001 0.3 Moderate <.001 .291
EPSI
 Body dissatisfaction 0.5 Moderate <.001 0.52 Moderate <.001 .291
 Binge eating 0.46 Moderate <.001 0.44 Moderate <.001 .291
 Cognitive restraint 0.73 Strong <.001 0.75 Strong <.001 .291
 Purging 0.48 Moderate <.001 0.49 Moderate <.001 .389
 Restricting 0.39 Moderate <.001 0.39 Moderate <.001 .500
 Excessive exercise 0.56 Moderate <.001 0.54 Moderate <.001 .291
 Negative attitudes toward obesity 0.41 Moderate <.001 0.4 Moderate <.001 .389
 Muscle building 0.38 Moderate <.001 0.34 Moderate <.001 .291
EDE-Q
 Global score 0.66 Moderate <.001 0.68 Moderate <.001 .291
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Note: Spearman correlations between MOET scores and scores and subscales of other eating disorder instruments across all participants (N = 1766). P-values are adjusted using the Benjamini–Hochberg correction for multiple hypothesis testing. MDDI = Muscle Dysmorphic Disorder Inventory; DMS = Drive for Muscularity Scale; EPSI = Eating Pathology Symptoms Inventory; EDE-Q = Eating Disorder Examination – Questionnaire.

4. Discussion

This study was the first to validate the MOET specifically among university athletes and to extend prior work suggesting a modified, shorter version of the measure might be indicated to capture the construct more parsimoniously, particularly among females. The current study was motivated by the importance of optimizing the assessment of muscularity-oriented eating pathology, a clinical phenomenon particularly relevant to university students, especially athletes, who are at heightened risk of disordered eating behaviors focused on muscularity and leanness (Gorrell et al., 2021; McDonald et al., 2020; Palermo et al., 2024). Collectively, our findings support the psychometric properties of the abbreviated 12-item MOET among male athletes, and female athletes and non-athletes. The 12-item version also performed comparably to the original version in non-athlete men, suggesting non-inferiority of its model fit and suitability for use across all groups. In addition, the 12-item version displayed strong correlation with the original, 15-item version suggesting that the exclusion of three items did not result in significant loss of information. The nature of the three items removed to render the 12-item version may partially explain this finding. Specifically, these items refer to (i) eating past the point of fullness, (ii) using meal-replacement supplements when feeling full, and (iii) experiencing anxiety related to running out of protein supplements. Moreover, the item corresponding to eating past the point of fullness was the only item to demonstrate a factor loading <0.6 in the full sample. Together, these items align with a desire to increase muscle mass, often described among young men (Brown & Keel, 2023; Lavender et al., 2017), and also reflect supplement use, a behavioral feature that is often reported among young men in the presence of eating pathology (Nagata et al., 2021; Nagata, Ganson, et al., 2022). It is notable that these items were supported in the 15-item model for male non-athletes but not male athletes in the current sample. This may be because the sport types most frequently represented in the current study (basketball, baseball, soccer) are activities where success may depend just as much on skill deployment as it does on high muscle mass. These findings suggest the 15-item version might be more relevant in athletes participating in sport types where muscle bulk is prioritized (e.g., men’s US football defense; women’s rugby), as well as in clinical samples with more severe eating pathology where some items may be differentially endorsed. Future research to confirm these possibilities is warranted, along with further investigation of constructs specifically capturing muscle bulk and muscle-building behaviors.

Of eight prior MOET validation studies, only one reported examined invariance; this study found support for equivalence at the configural level, across gender (De Carvalho et al., 2023). Our findings also supported invariance, with both MOET versions demonstrating a unidimensional factor structure and invariance across all athlete-sex subgroups tested in this study. These findings support the utility of the 12-item MOET for comparing groups across different types of community-based samples.

Notably, while the MOET was invariant across sites, indicating the instrument captures the same underlying construct and supports comparisons of means between regions, we did find site differences in mean MOET scores. This might reflect differences in the spread of athletes vs. non-athletes across institutions (only ~22 % of students from the West were athletes whereas twice that number, 43 %, were athletes within the Southeast sample). Moreover, within the athlete sample, there were differences across sex relative to schools, (e.g., compared to non-athlete counterparts, there were more female athlete (50 %) than male athlete participants (33 %) in the Southeast sample). Taken together, results from our tests of invariance support the MOET factor structure (both versions) across samples, with a caveat that there may be other confounds that drive scores - and impact their interpretability - that have yet to be directly tested. Finally, in line with all prior work (Appendix A), both the 15- and 12-item versions of the MOET generally demonstrated expected moderate to strong positive correlations with measures of eating pathology. Correlations between both versions of the MOET with measures of drive for muscularity were weak (one subscale each on the MDDI and DMS, respectively) to moderate (the other MDDI and DMS subscales and EPSI ‘muscle building’). Although weak correlations were unexpected, it is notable that the two weakly correlated subscales corresponded to attitudes, with stronger correlations found for subscales corresponding to behavior. To further interpret our findings, future research might include other measures of drive for muscularity that are specifically designed for females (Rodgers et al., 2018).

Taken together, given the improved model fit of the 12-item MOET demonstrated via CFA, invariance testing, and convergent validity, the current study makes a case for employing this more parsimonious version of the MOET both among community-based samples of non-athletes, and among athletes. In particular, our findings suggest the 12-item MOET may serve to improve the screening and assessment of muscularity-oriented body image dissatisfaction in women, a domain that warrants more attention (Rodgers et al., 2018). For athletes, individuals who are often at heightened risk for eating disorder pathology (Flatt et al., 2021), a drive for muscularity may be mistakenly assumed to derive from sport-achievement orientation rather than eating pathology. However, evidence suggests that screening for muscularity-oriented disordered eating may be indicated among young adult athletes, given that overtraining and eating behaviors that limit energy availability may ultimately come at a cost of compromising athletic performance (Stellingwerff et al., 2021). Although screening for eating disorders across athlete populations who participate in all types and levels of sport is typically not comprehensive, a briefer, 12-item version of the MOET may be a useful tool for efficient assessment across athlete settings.

4.1. Strengths and limitations

Strengths of the current study include its large and geographically diverse sample, its potential value in informing improved screening efforts for muscularity-oriented disordered eating among women and athletes, and its support for a more parsimonious version of an existing evidence-based measure. Athlete categorization was defined broadly via self-report yielding heterogenous participation levels (i.e., ranging from recreational to professional/Olympic) which increases the generalizability of our study findings. Future research should include athletes who are specifically recruited from athletic organizations or departments to support the validity of self-reported athletic status. Limitations of note include that our survey did not query sexual orientation, along with the relative lack of racial, ethnic, and gender diversity in our sample, suggesting a need for future work in additional samples to increase generalizability of findings beyond those included in current analyses. In particular, we excluded anyone who did not identify within binary gender; given consistent evidence of increased vulnerability for eating pathology among those with minoritized gender identities (Nagata et al., 2020; Rasmussen et al., 2023), this is an important focus for future work. In addition, our findings may not generalize to more competitive athletes who forgo university—an important group to engage in future research. Finally, when comparing fit between the 12- and 15-item versions of the MOET, we adopted a conservative threshold for significance due to the body of evidence supporting the 15-item MOET version. Future research could consider exploring whether the 12-item version offers similar fit – with naturally improved parsimony - in other populations.

5. Conclusions

In summary, the current study provides important evidence to inform improved screening and evaluation of muscularity-oriented eating pathology. Findings support the use of a shorter, 12-item version of the MOET in community-based samples of young men and women, including those who identify as athletes.

Supplementary Material

Supp

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.eatbeh.2025.102022.

Funding statement

This work received no specific grant from any funding agency, commercial or not-for-profit sectors. Dr. Gorrell is supported by the National Institute of Mental Health (K23MH126201; R21MH131787) and the Brain & Behavior Research Foundation. Dr. Hanss is supported by the National Institute of Mental Health (R25MH060482). Ms. Sanzari is supported by the National Science Foundation Graduate Research Fellowship (Grant No. 1645421). Dr. Brown is supported by the National Institute of Mental Health (R61MH133710).

Footnotes

CRediT authorship contribution statement

Kaitlin Hanss: Writing – original draft, Formal analysis. Dominic M. Denning: Writing – review & editing, Formal analysis. Rachael E. Flatt: Writing – review & editing, Conceptualization. Christina M. Sanzari: Writing – review & editing, Data curation. Stuart B. Murray: Writing – review & editing. Jason M. Lavender: Writing – review & editing. Emilio J. Compte: Writing – review & editing. Aaron J. Blashill: Writing – review & editing. Jason M. Nagata: Writing – review & editing. Pamela K. Keel: Writing – review & editing. Jennifer Harriger: Writing – review & editing, Data curation. Riley Nickols: Writing – review & editing. Jonathan Mond: Writing – review & editing. Tiffany A. Brown: Writing – review & editing, Resources, Project administration, Methodology, Data curation, Conceptualization. Sasha Gorrell: Writing – original draft, Resources, Project administration, Methodology, Data curation, Conceptualization.

Declaration of competing interest

All authors have no conflicts of interest to declare.

Data availability

Data can be made available upon reasonable request submitted to Dr. Tiffany Brown, tiffanybrown@auburn.edu.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp
NIHMS2108372-supplement-Supp.pdf (292.4KB, pdf)

Data Availability Statement

Data can be made available upon reasonable request submitted to Dr. Tiffany Brown, tiffanybrown@auburn.edu.

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