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. Author manuscript; available in PMC: 2022 Jan 18.
Published in final edited form as: Body Image. 2019 Dec 13;32:94–102. doi: 10.1016/j.bodyim.2019.11.006

Sexual Orientation Correlates with Baseline Characteristics but Shows no Moderating Effects of Dissonance-Based Eating Disorder Prevention Programs for Women

Heather Shaw 1, Paul Rohde 1, Christopher David Desjardins 1, Eric Stice 1
PMCID: PMC8765481  NIHMSID: NIHMS1569603  PMID: 31841780

Abstract

This study provided the first test of whether sexual orientation (categorized as heterosexual vs. sexual minority) is associated with baseline eating disorder risk factors and symptoms, moderated the intervention effects of variants of the dissonance-based Body Project, or moderated the relation of baseline risk factors to future change in eating disorder symptoms. A total of 680 women with body image concerns were randomized to clinician-or peer-led Body Project groups, the eBody Project, or educational video control and completed assessment of eating disorder risk factors and symptoms at pretest, posttest, and at six-, 12-, 24-, and 36-month follow-up. Results indicated that sexual minority women had significantly higher negative affect and impaired psychosocial functioning at baseline, but did not differ on other eating disorder risk factors or symptoms. We found no evidence that sexual orientation moderates the effects of the Body Project on risk factor or symptom change over follow-up or the relation of baseline risk factors to future change in eating disorder symptoms. Overall, sexual minority and heterosexual women differ on two, less specific eating disorder-related risk factors at baseline, but did not differ in response to different versions of the Body Project or the relations of risk factors to future symptom change.

Keywords: Eating Disorder, Prevention, Sexual Minority, Women, Risk Factors, Moderation

1. Introduction

Approximately 13% of women are affected by eating disorders, which are marked by chronicity, relapse, distress, functional impairment, and increased risk for obesity, depression, suicide, and mortality (Allen, Byrne, Oddy, & Crosby, 2013; Arcelus, Mitchell, Wales, & Nielsen, 2011; Stice, Marti, & Rohde, 2013). Broad implementation of effective prevention programs is important because 80% of individuals with eating disorders do not receive treatment (Swanson, Crow, Le Grange, Swendsen, & Merikangas, 2011). Between 7–19% of women aged 8–44 years of age identify as a sexual minority (Copen, Chandra, & Febo-Vazquez, 2016), but studies to date have not tested whether eating disorder prevention programs are differentially effective for sexual majority and minority women (the term “sexual minority” generally includes those who identify as lesbian, gay, bisexual, or transgender; engage in same-sex sexual behavior; or have same-sex attractions; Bostwick et al., 2014). Although one study compared an adapted version of the dissonance-based Body Project to waitlist control for sexual minority men (Brown & Keel, 2015) and found encouraging effects, and another study focusing on process and acceptability (but not efficacy) of an adapted Body Project found improved body satisfaction and dietary habits among HIV-positive gay/bisexual men (Feldman, Torino, & Swift, 2011), no study has examined how the Body Project or any other eating disorder prevention program might differentially affect sexual minority women. Assessing the needs of sexual minority populations has been identified as a public health priority (Institute of Medicine, 2011) and could reveal the need for tailored prevention programs.

Little research has examined the prevalence of eating disorders among sexual minority women (Calzo et al., 2017). Some studies estimate that approximately 8% to 9% of lesbian women and 11% of bisexual women report a lifetime eating disorder (Austin et al., 2009; Mason, Lewis, & Heron, 2017), and found that lesbian women are at similar or increased risk for eating disorders and disordered eating compared to heterosexual women, especially during adolescence (Austin et al., 2009; Bankoff, Marks, Swenson, & Pantalone, 2016; Feldman & Meyer, 2010; Frisell, Lichtenstein, Rahman, & Langstrom, 2010; Hadland, Austin, Goodenow, & Calzo, 2014). Other studies report conflicting results, some indicating no significant differences in eating disorder diagnoses (Feldman & Meyer, 2007) or symptoms (Moore & Keel, 2003, Share & Mintz, 2002) in lesbian/bisexual women compared to heterosexual women, while another found that lesbian/bisexual women had higher levels of disordered eating compared to heterosexual women (Wichstrom, 2006).

More research has compared eating disorder risk factors and symptoms of sexual minority to heterosexual females. Compared with heterosexual girls, lesbian/bisexual girls had greater body satisfaction and were less concerned with trying to resemble women in the media (Austin et al., 2004), which could be due to a greater acceptance of a range of body types within the lesbian community and might protect against thin-ideal internalization (Alvy, 2013). Links between peer appearance pressures, thin-ideal internalization, and body dissatisfaction were strongest for bisexual women compared to heterosexual and lesbian women (Hazzard et al., 2019). Other research found that shame, concealing sexual orientation, and experiencing discrimination are associated with eating disorder risk among sexual minority women (Bayer, Robert-McComb, Clopton, & Reich, 2017; Watson, Velez, Brownfield, & Flores, 2016). A recent theoretical model proposes that experiences associated with sexual orientation (e.g., heterosexism) and gender (e.g., gender roles) might interact to increase the risk for disordered eating (Mason, Lewis, & Heron, 2018). Further, three studies have found that, similar to studies using general samples (see Haedt-Matt & Keel, 2011), there was a strong relation between negative affect and disordered eating behaviors among lesbian women (Mason & Lewis, 2015, 2016; Mason, Lewis, & Heron, 2017), and one study (Mason, Lewis, & Heron, 2017) found that weight discrepancy was associated with disordered eating, consistent with the larger literature establishing body image as one of the main risk factors for disordered eating (e.g., see Stice & Shaw, 2002).

Research examining differences in eating disordered behaviors among sexual minority women compared to heterosexual women has generally found the former to be at greater risk. Compared to heterosexual women, women who defined themselves as “mostly heterosexual,” bisexual, or lesbian were more likely to report binge eating, and those defining themselves as “mostly heterosexual” and bisexual were more likely to report purging (Austin et al., 2009). Other studies have found that sexual minority women were more likely to binge eat (Laska, et al., 2015); diet (Matthews-Ewald et al., 2014); fast or use diet pills (Austin, et al., 2013; Watson et al., 2017), use laxatives or other purging behavior (Austin et al., 2013; Calzo, et al., 2015) than heterosexual females. Finally, Polimeni, Austin, and Kavanagh (2009) found that lesbians were less likely to have body dissatisfaction and cut down on fats and sugars than heterosexual women. This study also found that mainly heterosexual and bisexual women, compared with exclusively heterosexual women, were more likely engage in harmful weight control behaviors (e.g., smoke, cut out meals, vomit after eating, use laxatives). However, recent research has found similarities between heterosexual, bisexual, and lesbian women in disordered eating behaviors (Hazzard et al., 2019).

This paper addresses the significant gap in knowledge regarding whether sexual minority women respond differently to a widely implemented eating disorder prevention program compared to heterosexual women, as no study has examined this important question. If intervention effects for sexual minority women are different than for heterosexual women, the prevention program might need to be tailored; if they do not vary, adaptations may not be needed. Specifically, this study examined the impact of sexual orientation on the effects of completing three variants of the Body Project, an empirically supported selective eating disorder prevention program for women.

The second aim of this study was to extend knowledge of whether sexual minority women significantly differ from heterosexual females on eating disorder symptoms and risk factors. Relatively few studies have addressed this question and published findings have been mixed.

The third aim was to test whether baseline risk factors show differential relations to future change in eating disorder symptoms for heterosexual versus sexual minority women (i.e., whether sexual minority status moderated the impact of risk factors on symptom change). To our knowledge, no study has specifically addressed this important question. Given the lack of research on prevention intervention effects and the inconsistent findings related to potential differences in eating disorders risk factors and symptomology, we did not have specific hypotheses for the three aims.

2. Method

2.1. Participants and Procedure

Data were obtained from the largest randomized controlled trial of the Body Project, which evaluated the relative effectiveness of clinician-led Body Project groups, undergraduate peer educator-led Body Project groups, and an Internet-delivered version (eBody Project) compared to an educational video control condition with young college women with body image concerns through three-year follow-up. The sample included only female college students because eating disorders are much more common among women than men (Hudson, Hiripi, Pope, & Kessler, 2007), there are over 10 million female college students in the U.S. (US Department of Education, 2008), and colleges typically have an existing infrastructure for delivering prevention programs. A previous report with the entire sample (Stice, Rohde, Shaw, & Gau, 2017) found that participants in clinician-led and peer-led Body Project groups, relative to video controls, showed similar reductions in outcome measures at both post-intervention (d = 0.54 and 0.52, respectively) and six-month follow-up (d = 0.33 and 0.35, respectively); effects for the Internet-delivered intervention relative to video control were approximately 50% smaller (d = 0.33 at post and 0.16 at six-month follow-up).

The sample included 680 participants (M age = 22.2, SD = 7.1; M BMI [kg/m2] = 25.5, SD = 5.6) from three universities in Oregon and Texas. The sample was 60% Caucasian, 17% Latina, 14% Asian, 5% African American, 3% American Indian/Alaska Native, and 1% Native Hawaiian/Pacific Islander. Participants were recruited between March 2013 – April 2015 using mass emails and flyers. Interested women were directed to a webpage to confirm that they had body image concerns (inclusion criterion) and to complete the Eating Disorder Diagnostic Scale (EDDS) (Stice, Fisher, & Martinez, 2013); those with probable DSM-IV-TR (American Psychiatric Association, 2000) anorexia nervosa, bulimia nervosa, and binge eating disorder were excluded. Eligible participants were randomly assigned to one of the three Body Project variants [clinician-led groups, (n = 173); peer-led groups (n = 162), the eBody Project (n = 184)], or an educational video condition (n = 161). Participants completed assessments at pretest, posttest, and six-, 12-, 24-, and 36-month follow-up conducted by female assessors masked to condition.

The Body Project is a scripted intervention that consisted of four weekly one-hour group sessions with five-nine participants delivered by pairs of either clinicians or undergraduate peer educators. Participants voluntarily engaged in exercises critiquing the beauty ideal during sessions and in-home exercises (see Stice, Butryn, Rohde, Shaw, & Marti, 2013 for session content). The eBody Project is an Internet-based version of this intervention that includes six 40-minute modules involving user driven self-education activities and games that parallel the group intervention (see Stice, Rohde, Durant, & Shaw, 2012). Participants in the education video condition were asked to view Dying to Be Thin (WGBH Educational Foundation, 2000), a 55-minute documentary on eating disorders and body acceptance. Additional details regarding the study are provided in Stice et al. (2017).

2.2. Measures

2.2.1. Sexual orientation and gender identity.

Two questions assessed sexual orientation and gender identity: (1) “How do you describe yourself (circle one): Male, Female, Transgender, Do not identify as female, male, or transgender; (2) Do you think of yourself as: (please check all that apply): Straight/heterosexual; gay or lesbian; bisexual; queer; questioning; or another identity not specified (please specify).” Questions were adapted from the measure developed by the Network for LBGT Health Equity (Conron, Lombardi, & Reisner, 2014).

2.2.2. Thin-ideal internalization.

Participants assessed agreement with eight statements representing facets of the beauty ideal (“Slim women are more attractive) rated on a 5-point scale. The measure showed an average α = .75 across assessments in the present trial. An earlier version of the measure, which shared most items, had shown two-week test-retest reliability (r = .80), predictive validity for future onset of eating disorders, and sensitivity to detecting intervention effects (Stice, Gau, Rohde, & Shaw, 2017).

2.2.3. Body dissatisfaction.

Items from the Satisfaction and Dissatisfaction with Body Parts Scale (Berscheid, Walster, & Bohrnstedt, 1973) assessed satisfaction with nine body parts rated on a six-point scale. It has shown internal consistency (α = .94), three-week test-retest reliability (r = .90), predictive validity for future onset of eating disorder, and sensitivity to intervention effects (Stice, Gau et al., 2017); α = .86 at baseline.

2.2.4. Dieting.

The 10-item Dutch Restrained Eating Scale (DRES) (van Strien et al., 1986) assessed the frequency of dieting behaviors rated on a 5-point scale, and has shown internal consistency (α = .95), two-week test-retest reliability (r = .82), convergent validity with self-reported caloric intake, predictive validity for future onset of eating disorder, and sensitivity to intervention effects (Stice, Gau et al., 2017; van Strien et al., 1986); α = .91 at baseline.

2.2.5. Negative affect.

Negative affect was assessed with the sadness, guilt, and fear/anxiety subscales from the Positive Affect and Negative Affect Scale-Revised (PANAS-X) (Watson & Clark, 1992). Participants reported the extent to which they had felt 20 negative emotional states, rated on a 5-point scale. This scale has shown internal consistency (α = .95), three-week test-retest reliability (r = .78), convergent validity, and predictive validity for future onset of eating disorders (Stice, Gau et al., 2017; Stice, Shaw, Burton, & Wade, 2006; Watson & Clark, 1992), α = .94 at baseline.

2.2.6. Eating disorder symptoms.

The semi-structured Eating Disorder Diagnostic Interview (EDDI) (Stice, Rohde, Gau, & Shaw, 2009) assessed DSM-IV eating disorder symptoms. Items assessing symptoms in the past month at each assessment were summed to form a composite, which has shown internal consistency (α = .92), one-week test-retest reliability (ICC r = .95), sensitivity to prevention and treatment interventions, and predictive validity for future onset of depression (Stice, Rohde, Gau, & Shaw, 2009). The symptoms composite showed internal consistency (average α = .70), inter-rater agreement (ICC = .96), and one-week test-retest reliability (ICC = .96) in the present trial. All statistical models applied a natural-log transformation to the eating disorder symptoms, after adding a continuity-correction (.01) for participants with no eating disorder symptoms.

2.2.7. Psychosocial functioning.

Impaired psychosocial functioning in the family, peer, school, and work spheres, which has been found to be a risk factor for all eating disorders (Stice, Gau et al., 2017) was assessed with 17 items adapted from the Social Adjustment Scale (SAS) (Weissman & Bothwell, 1976) rated on a 5-point scale. The adapted items have shown internal consistency (α = .77), one-week test-retest reliability (r = .83), and sensitivity to intervention effects in prevention trials (e.g., Stice, Shaw, Burton, & Wade, 2006); average α = .74 at baseline.

2.3. Statistical Methods

All statistical models controlled for study site as random assignment occurred within site. Prior to modeling, we examined whether we needed to control for baseline BMI; because sexual orientation was not related to BMI at baseline, t(1) = 1.58, p = .114, or any follow-up, we did not control for it in our models.

To address whether group attendance varied by sexual orientation, we calculated the number of sessions that each group participant attended, and used logistic regression to assess whether orientation predicted attendance.

Receiving non-study mental health services was assessed to control for other factors that might reduce eating disorder symptoms. We first calculated whether a participant sought mental health treatment at a given time and examined whether initial non-study treatment seeking differed by sexual orientation using logistic regression. Next, a latent growth model (LGM) examined differences at posttest (intercept factor) and linear changes in treatment utilization across follow-ups controlling for initial non-study treatment status (slope factor) (Grimm & Ram, 2018). We allowed error terms of adjacent measurements to co-vary.

To assess whether sexual orientation was related to baseline risk factors or eating disorder symptoms, each variable was regressed onto sexual orientation separately using either multiple linear regression (for continuous variables) or logistic regression (for dichotomous variables).

To assess whether sexual orientation moderated the acute effects of interventions on change in risk factors or symptoms, we fit a LGM with both an intercept and a linear slope factor and allowed error terms of adjacent measurements to co-vary. The intercept corresponds to differences at posttest (i.e., end of intervention) and the linear slope factor corresponds to change in risk factors/symptoms from posttest to 36-month follow-up. We regressed each factor onto sexual orientation, condition, the interaction of orientation and condition, study site, and the risk factor/symptoms on the pre-assessment (i.e., beginning of intervention). A significant interaction of sexual orientation and condition for the intercept factor would indicate a differential number of posttest risk factors or symptoms for condition as a function of sexual orientation, while a significant interaction of orientation and condition on the linear slope factor would indicate differential trajectories over three-year follow-up for the risk factor or symptom composite for condition as a function of orientation.

To assess whether sexual orientation moderated the effects of baseline risk factors on future change in symptoms, we fit a LGM with both an intercept and a linear slope factor and correlated adjacent residuals. Each factor was regressed onto the baseline risk factor, orientation, and the interaction of the risk factor and orientation, while controlling for baseline symptoms. We used z-tests to test the significance of moderation.

Given that data were missing from follow-up assessments, we used full information maximum likelihood (FIML) because it gives unbiased parameter estimates when data are missing at random (Schafer & Graham, 2002) and is asymptotically equivalent to multiple imputation. Auxiliary variables were included in the models if they correlated at least .50 with the outcome (Graham, 2009); this applied only to negative affect and psychosocial functioning. All analyses were performed in R (Version 3.5.1; R Core Team, 2018) except the LGCMs which were run in Mplus (Version 8.1; Muthén & Muthén, 2017).

We controlled for an inflated type I error rate by applying the Benjamini-Hochberg (BH) correction (Benjamini & Hochberg, 1995). The BH correction controls the false discovery rate, and relative to the Bonferroni’s correction is more powerful, while still adequately protecting against Type I errors (Williams, Jones, & Tukey, 1999) and is the adopted correction of the Institute of Education Science’s What Works (WWC, 2017). We applied the BH method in two ways. First, we corrected for the 20 comparisons associated with testing whether sexual orientation was related to baseline factors and second, we corrected for the 22 comparisons related to testing sexual orientation as a moderator.

Sensitivity analyses were performed comparing bisexual participants (n = 80) to heterosexual participants and bisexual participants to other sexual minority participants (n = 34). For the former comparison, we reran all analyses, while for the latter comparison all analyses except the LGCMs were rerun (because of the small sample size). After adjusting for multiple comparisons, the analyses comparing bisexual participants to heterosexual participants were qualitatively identical to the main findings. Additionally, bisexual participants did not differ from other sexual minority participants on any of the outcomes. Both analyses supported the decision to combine bisexual with other sexual minority participants. As a final sensitivity analysis, heterosexual participants who also identified as sexual minority (n = 9) were included in the heterosexual group and this did not affect the findings. Therefore, the findings were robust to which group these participants were placed in.

3. Results

3.1. Descriptive Statistics

Seventy-seven percent of participants provided sexual orientation data. Of these, 417 (78%) identified as heterosexual, while 114 (22%) identified as either sexual minority or heterosexual and another category (henceforth referred to as the sexual minority group). Of the 24 participants who identified as more than one category, 11 identified as bisexual and queer, six as heterosexual and questioning; two as bisexual and questioning; two as heterosexual, bisexual, and questioning; one as gay and queer; one as heterosexual and bisexual; and one as gay, bisexual, and queer; all were included in the sexual minority group. Regarding gender identity, 99% of the sample identified as female (one identified as male, three as transgender, and three as neither female, male, or transgender), which precluded any analyses based on this variable. Table 1 provides a breakdown of sexual orientation by condition and site. Chi-square tests found no association between sexual orientation and condition, χ2(3) = 3.67, p = .299, or site, χ2(2) = 5.02, p = .082.

Table 1.

Study participants’ sexual orientation and gender identity by study condition and site.

Condition
Video control Clinician-delivered Peer-delivered Internet-based
Sexual Orientation N % N % N % N %
Straight/Heterosexual 99 81.8 100 77.5 93 72.7 115 80.4
Gay/Lesbian 2 1.7 2 1.6 3 2.3 0 0.0
Bisexual 10 8.3 17 13.2 21 16.4 15 10.5
Queer 4 3.3 3 2.3 4 3.1 5 3.5
Question 1 0.8 2 1.6 0 0.0 1 0.7
Multiple categories 5 4.1 5 3.9 7 5.5 7 4.9
Data not available 40 44 34 41
Gender Identity N % N % N % N %
Female 120 99.2 133 100.0 126 97.7 144 98.0
Male 0 0.0 0 0.0 1 0.8 0 0.0
Transgender 0 0.0 0 0.0 0 0.0 3 2.0
Do not identify as female, male, or transgender 1 0.8 0 0.0 2 1.5 0 0.0
Data not available 40 40 33 37
Site Study
Texas Oregon School 1 Oregon School 2
Sexual Orientation N % N % N %
Straight/Heterosexual 210 78.4 93 72.1 103 83.7
Gay/Lesbian 3 1.1 1 0.8 3 2.4
Bisexual 32 11.9 22 17.1 9 7.3
Queer 7 2.6 6 4.7 3 2.4
Question 2 0.7 1 0.8 1 0.8
Multiple categories 14 5.2 6 4.7 4 3.3
Data not available 66 42 51
Gender Identity N % N % N %
Female 270 98.9 127 97.7 125 99.2
Male 1 0.4 0 0.0 0 0.0
Transgender 1 0.5 2 1.5 0 0.0
Do not identify as female, male, or transgender 1 0.4 1 0.8 1 0.8
Data not available 61 41 48
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Missing sexual orientation data were not significantly associated with condition, χ2(3) = 1.25, p = .742, or site, χ2(2) = 5.91, p = .052. At pretest, missing data on sexual orientation were not associated with any risk factors but were associated with total symptoms, χ2(1) = 4.11, p = .042, and several specific symptoms (8 out of 14). In all cases, as the number of symptoms increased, the odds of missing data on sexual orientation increased (OR ranged from 1.01 to 1.92). Statistical analyses focused on comparisons between heterosexual and sexual minority participants, as we did not have enough cases to explore more refined categories.

3.2. Preliminary Analyses

3.2.1. Group attendance by sexual orientation.

There were no differences in the odds of attending group sessions by sexual orientation after controlling for study site. Seventy-five percent of heterosexual participants and 78% of sexual minority participants attended all four sessions, while 12% of heterosexual participants and 9% of sexual minority participants attended no sessions. The average number of sessions attended by heterosexual and sexual minority participants were M = 3.31 (SD = 1.37) and M = 3.44 (SD = 1.23), respectively, a non-significant difference, OR = 1.20, χ2(1) = 0.80, p = .372.

3.2.2. Rates of non-study mental health treatment by sexual orientation.

The percent of sexual minority participants who received out-of-study mental health treatment at baseline (35%) was greater than the heterosexual participants (18%); OR = 2.61, χ2(1) = 15.52, p< .001.

Differences in rates of non-study treatment utilization at posttest by sexual orientation were smaller and not significantly different (OR = 1.55, z = 1.91, p = .056) but sexual orientation was a significant predictor of the non-study treatment slope factor (OR = 1.03, z = 2.51, p = .012). The effect was such that sexual minority women sought a greater number of treatments as the follow-up time increased relative to heterosexual women, who remained relatively constant in rate of treatment seeking.

3.3. Relation between Sexual Orientation and Baseline Risk Factors and Symptoms

Table 2 summarizes the results examining whether sexual orientation was associated with differences in baseline risk factors. Sexual minority women had higher negative affect, t(1) = 3.91, p < .001, adj. p = .002, d = 0.40, and psychosocial impairment, t(1) = 3.33, p = .001, adj. p = .009, d = 0.34, which were small to medium effects per Cohen (1992). There were no baseline differences in thin-ideal internalization, body dissatisfaction, or dieting associated with sexual orientation.

Table 2.

Comparison of participants of identifying as heterosexual and participants identifying as sexual minority on baseline risk factors and eating symptom types.

Outcome Heterosexual Sexual Minority Test statistics Effect size
Risk factor (continuous) Mean SD Mean SD t p adj. p d
Thin ideal internalization 3.85 0.43 3.86 0.40 0.33 .738 .796 0.02
Body dissatisfaction 3.62 0.64 3.72 0.61 1.63 .104 .189 0.15
Dieting 3.13 0.83 3.16 0.76 0.57 .566 .666 0.05
Negative affect 2.25 0.77 2.56 0.83 3.91 < .001 .002 0.40
Psychosocial functioning 2.25 0.49 2.42 0.53 3.33 .001 .009 0.34
Eating symptoms(continuous) Mean SD Mean SD t p adj. p d
Composite 2.56 0.75 2.65 0.67 1.37 .171 .285 0.12
Weight or shape influenced how feel about self 3.95 1.17 3.97 1.08 0.31 .756 .796 0.02
Fear of gaining weight 2.29 2.46 2.18 2.35 −0.23 .815 .815 −0.05
Felt fat 3.4 2.17 3.79 1.94 1.84 .067 .149 0.19
Eating symptoms (dichotomized) N % N % z p adj. p OR
Binge episodes 90 22.17 29 25.44 0.91 .362 .518 1.25
Eat rapidly 101 24.94 34 29.82 1.19 .233 .359 1.33
Eat until uncomfortably full 115 28.40 45 39.47 2.43 .015 .086 1.72
Eat large amounts food when not hungry 119 29.38 43 37.72 1.90 .058 .146 1.53
Eat alone b/c embarrassed how much you were eating 73 18.02 30 26.32 1.89 .059 .146 1.61
Feel depressed or guilty after overeating 124 30.62 46 40.35 2.11 .035 .117 1.60
Feel upset that you could not control eating 117 28.89 44 38.60 2.15 .031 .117 1.62
Times made yourself sick to control shape or weight 19 4.68 12 10.53 2.38 .017 .086 2.53
Times take laxatives/diuretics to control weight 12 2.96 2 1.75 −0.65 .514 .666 0.60
Times fasted to control shape or weight 119 29.31 29 25.44 −0.62 .538 .666 0.86
Times exercised to compensate for overconsumption 143 35.31 31 27.19 −1.67 .098 .189 0.67
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Note. adj. p refers to a Benjamini-Hochberg adjusted p-value based on 20 comparisons and d and OR refer to Cohen’s d and odds ratio, respectively.

Table 2 also summarizes the results examining whether sexual orientation was associated with baseline differences in total eating disorder symptoms or individual eating disorder symptoms. Differences on these variables were nonsignificant, but a few differences in specific behaviors were noted in the unadjusted analyses. Namely, sexual minority women were more likely than heterosexual women to say they ate until uncomfortably full (z = 2.43, p = .015, adj. p = .086, OR = 1.72), felt depressed or guilty after overeating (z = 2.11, p = .035, adj. p = .117, OR = 1.60), felt upset that they could not control eating (z = 2.16, p = .031, adj. p = .117, OR = 1.62), and vomited to control their shape or weight (z = 2.38, p = .018, adj. p = .086, OR = 2.53).

3.4. Sexual Orientation as a Moderator of the Relation between Condition and Change in Eating Disorder Risk Factors and Symptoms

Table 3 shows the fit of the LGMs for the eating disorder risk factors and symptoms. Because of differences in non-study treatment seeking, we controlled for this variable at baseline, which is reflected in Table 3. Three of the risk factors (thin-ideal internalization, body dissatisfaction, and negative affect) had good fit based on the chi-square test of model fit (p > .10 for all outcomes), RMSEA (< .025 for all outcomes), CFI (> .990 for all outcomes), and SRMR (< .04). Dieting and eating disorder symptoms had good fit based on the fit statistics but not the chi-square test of model fit (p < .02 for both outcomes) and psychosocial functioning had acceptable fit based on the fit statistics but again not the chi-square test of model fit. For all models, correlations residuals and standardized mean residuals were examined to assess local misfit. For all outcomes, the maximum correlation residual (in absolute value) was .087, suggesting that the model-implied correlation matrices adequately captured the sample-covariance matrices. For the standardized mean residuals, the thin-ideal internalization, dieting, and eating disorder symptoms models each had one large residual (z = −2.038 at the third follow-up, z = −2.913 at the second follow-up and z = 2.373 at posttest follow-up, respectively), while psychosocial functioning models had two large residuals (z = 10.576 and −6.808 at posttest and 6-month follow-up, respectively), implying that the model-implied means did not adequately capture these observed means and that the linear model, for these outcomes, did not capture all mean change over time. These large standardized mean residuals were likely responsible for the poorer fit of these models relative to the other models considered. On the whole, all the eating disorder risk factors and symptom models appeared to adequately capture the sample covariance matrices and the majority of mean changes for all outcomes and were retained.

Table 3.

Fit of latent growth curve models for the risk factors and eating disorder symptoms.

Chi-Square Test of Model Fit Fit Statistics
Risk factor χ2 df p RMSEA (90% CI) CFI SRMR
Thin-ideal internalization 42.43 36 .325 .013 (.000 – .034) .997 .035
Body dissatisfaction 42.52 36 .322 .013 (.000 – .034) .997 .034
Dieting 60.24 36 .016 .032 (.014 – .048) .986 .029
Negative affect 34.05 36 .695 .000 (.000 – .002) 1.00 .021
Psychosocial functioning 139.33 36 <.001 .070 (.058 – .083) .931 .062
Eating disorder symptoms 71.86 36 .001 .040 (.025 – .055) .961 .033
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Table 4 summarizes the results examining whether sexual orientation moderated the effectiveness of condition, in terms of changes in risk factors or eating disorder symptoms; condition by sexual orientation interactions were non-significant in both unadjusted and adjusted analyses, indicating a lack of support for sexual orientation as a moderator of these outcomes at posttest. Similarly, all condition by sexual orientation by time interactions were nonsignificant, indicating a lack of support for sexual orientation as a moderator of the impact of conditions on changes in both risk factors and symptoms over 36-month follow-up. The average change in R2 for these models was .011 indicating that the interactions, on average, explained about 1.1% additional variation in either initial values or change during the 36-month follow-up. This value is quite small and unlikely to be clinically meaningful. The average effect sizes across outcomes for clinician-led groups at posttest were −0.445 for sexual minority women and - 0.522 for heterosexual women; for peer-led groups the average effect size across outcomes at posttest was −0.612 for sexual minority women and −0.387 for heterosexual women; and for Internet-led groups the effect size across outcomes was −0.385 for sexual minority women and - 0.292 for heterosexual women, with negative values all indicating that the intervention groups were producing superior effects to the control group and all represented medium effect sizes.

Table 4.

Tests of sexual orientation as a moderator of posttest condition differences (intercept factor) and of changes in condition trajectories across the 36-month follow-up for eating disorder risk factors and symptom composite (slope factor).

Intercept factor Slope factor
Risk factor χ2 p adj. p ΔR2 χ2 p adj. p ΔR2
Thin-ideal internalization 2.50 .475 .651 .005 0.69 .876 .918 .006
Body dissatisfaction 0.35 .951 .951 < .001 1.38 .711 .823 .003
Dieting 3.75 .290 .624 .005 3.57 .312 .624 .008
Negative affect 4.53 .209 .511 .010 3.13 .372 .636 .011
Psychosocial functioning 2.69 .123 .451 .001 5.25 .155 .451 .026
Eating disorder symptoms 1.46 .692 .823 .004 1.06 .786 .865 .058
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Note. χ2 corresponds to the chi-square test of difference, degrees of freedom were 3 for all tests and adj. p refers to a Benjamini-Hochberg adjusted p-value based on 22 comparisons in Tables 4 and 5. Finally, ΔR2 corresponds to the change in R2 associated with adding the interaction to the intercept and slope factors, respectively.

3.5. Sexual Orientation as a Moderator of the Relation between Baseline Risk Factors and Change in Eating Disorder Symptoms

The third aim was to test whether sexual orientation moderated the relation between baseline risk factors and change in eating disorder symptoms over 3-year follow-up. These analyses controlled for non-study treatment. Results of both the unadjusted and adjusted analyses revealed that sexual orientation did not significantly moderate any of the relations between any of the baseline risk factors and future change in eating disorder symptoms (Table 5). The average change in R2for these models was .012 or approximately 1.2% additional variation, on average, was explained by the interactions.

Table 5.

Tests of sexual orientation as a moderator of baseline risk factor (intercept factor) and changes in the trajectory of the eating disorder symptom composite across 36-month follow-up (slope factor).

Intercept factor Slope factor
Risk factor Est. z p adj. p ΔR2 Est. z p adj. p ΔR2
Thin-ideal internalization −0.41 −1.76 .078 .451 .011 0.01 0.67 .503 .651 .002
Body dissatisfaction 0.13 0.90 .503 .651 .002 −0.01 −1.39 .164 .451 .004
Dieting 0.11 0.88 .378 .636 .001 0.01 1.85 .064 .451 .013
Negative affect −0.10 −0.83 .405 .636 .005 0.01 1.49 .136 .451 .046
Psychosocial functioning −0.32 −1.75 .080 .451 .015 0.01 1.51 .132 .451 .025
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Note. Est. corresponds to the unstandardized parameter and adj. p refers to a Benjamini-Hochberg adjusted p-value based on 22 comparisons in Tables 4 and 5. Finally, ΔR2 corresponds to the change in R2 associated with adding the interaction to the intercept and slope factors, respectively.

4. Discussion

This study examined whether sexual orientation was associated with baseline eating disorder risk factors or symptoms, whether sexual orientation moderated prevention intervention effects on changes in both risk factors and symptoms, and whether the relation of baseline risk factors and future increases in eating disorder symptoms varied by sexual orientation. First, results indicated that sexual minority women had significantly higher levels of negative affect and psychosocial impairment at baseline than heterosexual women, but that they did not differ on thin-ideal internalization, body dissatisfaction, or dieting. It was noteworthy that both risk factors that were higher for sexual minority versus heterosexual women are general indices of impairment and might be associated with other psychiatric disorders, such as depression and anxiety, whereas the two groups did not differ on the three risk factors that are more specifically related to eating disorder etiology. Regarding the question of whether sexual orientation was associated with different baseline eating disorder symptoms, the difference in the composite scores was non-significant but rates of a few specific eating disorder behaviors appeared higher among the sexual minority women; namely eating until uncomfortably full, making themselves sick in order to control shape or weight, feeling upset or depressed or guilty because they could not control their eating. However, sexual orientation was not significantly associated with eating disorder symptoms in the adjusted analyses, suggesting that these differences may have occurred by chance or that we were underpowered to detect them given low base rates or small magnitude differences.

Two theoretical models have been used to interpret the relation of sexual orientation to eating pathology: the sociocultural model suggests that gender and sexual minority-based community norms regarding beauty ideals drive any differences in eating disorders (Feldman & Meyer, 2007; McClain & Peebles, 2016) and potentially reduce risk, whereas the minority stress model posits that responses to victimization, discrimination and stigma related to sexual orientation might increase eating disorder risk (Katz-Wise et al. 2015; Meyer, 2003). Similar to prior research, our findings provide mixed support for these models. That sexual minority women in this study had higher negative affect and psychosocial impairment provides some support for the minority stress model; however, the lack of significant differences observed in the other eating disorder risk factors and the symptom composite measure does not support this model. Further, contrary to what the sociocultural model would predict, we did not find evidence that sexual minority women had lower thin-ideal internalization or body dissatisfaction, suggesting that protective factors resulting from sexual minority status were not present. In contrast, one study found that younger lesbian/bisexual girls (ages 9–14) had greater body satisfaction and were less concerned with trying to resemble women in the media (Austin et al., 2004). However, our conclusions regarding support for these models should be considered within the limitation that factors related to minority stress experiences, such as openness regarding sexual orientation, or sociocultural experiences specific to sexual minority individuals, such as perceived norms within certain subgroups of sexual minority individuals, were not assessed in this study, and it could be that these experiences affect future disordered eating in women. A recently proposed model of disordered eating and body image among sexual minority women integrates the sociocultural and minority stress models and posits that gender- and sexual orientation-related experiences are both interactively and directly related to internalization of sociocultural norms, social resources, and maladaptive emotion regulation, which then are related to negative affect and body image concerns, which then relate to disordered eating (Mason, Lewis, & Heron, 2018). Incorporating measures of these experiences unique to sexual minority women in future research would help ascertain which theoretical model is most appropriate for this group, as would including individuals identifying across the full spectrum of genders.

There was no evidence that sexual orientation moderated intervention effects, suggesting that the intervention may work equally well for heterosexual and sexual minority women. The medium intervention effect sizes across outcomes for both sexual minority and heterosexual participants indicate that the Body Project in all of its three delivery modalities is working well for many women in the study. It was also interesting that although the difference was not statistically significant, the peer-led Body Project groups appeared to work slightly better for sexual minority participants. Though we did not directly test for non-inferiority, the consistent lack of moderation effects across all variables (including attendance) is reassuring and increases confidence in the findings. Thus, the results suggest that adaptations for this population may not be necessary. A qualitative study of participant feedback to the three delivery modalities of the Body Project (Shaw, Rohde, & Stice, 2016) corroborates these findings. Although eating disorder prevention interventions specifically designed for gay men have been developed and appear to be effective, producing larger effects (d = 0.55) than a version for straight males (d = 0.31) (Brown, Forney, Pinner, & Keel, 2017; Brown & Keel, 2015), separate interventions for sexual minority women are not suggested by the current data. Nonetheless, it is possible that a version of the Body Project that was tailored to sexual minority females might have higher acceptability and be more effective than the standard Body Project. The slightly higher effect size for sexual minority women in peer-led groups might also suggest that future research test whether peer-led groups composed exclusively of sexual minority individuals might produce better results for this subgroup. Indeed, the recent call for evidence-based affirmative practice for sexual and gender minority mental health treatments (Pachankis, 2018), including recognizing how minority-related stigma impacts mental health and the cultivation of community pride, could also be applied to prevention.

Finally, we found no evidence that sexual orientation moderated the relation of baseline risk factors to future change in eating disorder symptoms, which had not been previously examined and provides support for the notion that baseline risk factors predict change in eating disorder symptoms similarly for heterosexual and sexual minority women. The evidence that risk factors for future increases in eating disorder symptoms are similar for heterosexual and sexual minority women appears consistent with the evidence that eating disorder prevention programs that seek to reduce psychosocial pressures for thinness and related constructs are similarly effective for these groups.

Important study limitations should be noted. First, the sample size was not adequate to examine lesbian or bisexual individuals separately, which would have provided more detailed information about these groups than was provided by combining them. Second, we were not able to examine gender identity as a moderator as the vast majority identified as female, and no one identified as transgender in our sample. Further, although 32 participants identified as bisexual, this did not provide adequate power to examine potential differences between study conditions. One prior study found that transgender individuals of all sexual orientations have elevated prevalence of self-reported eating disorders in the past year compared to cisgender heterosexual women (15.8%) (Diemer et al., 2015), suggesting that this is an important focus of future research. Third, we were missing 23% of the sexual orientation data, presumably because this is sensitive personal information, limiting the generalization of our findings. It should also be noted that all participants were recruited from college campuses, which are presumed to have more inclusive and accepting attitudes regarding sexual orientation than society in general. Fourth, sexual orientation was also assessed at 36-month follow-up rather than baseline, which could have affected the missing data. However, it is important to note that the percentage of missing data for sexual orientation is only 8–9% higher than for the other outcomes measured at 36 months (missing data during the post-assessment and follow-ups [six-, 12-, 24-, and 36-months] ranged from 1.9% to 8.1% with an average amount of data missing per variable at any given assessment of 4.6%; the average amount of missingness at the post-assessment was 4.6% and at the six-, 12-, 24-, and 36-month follow-up were 4.8%, 4.6%, 6.9%, and 2.2%).

Fifth, participants were recruited for study participation on the basis of body image concerns and sexual minority women who engage in disordered eating behaviors due to other factors, such as minority stress, might have been unlikely to enroll in project; thus, study results are not assumed to reflect the entire population of sexual minority (or heterosexual) women. It is important to note, however, that although we did not specifically over-recruit sexual minority groups, we felt a responsibility to conduct large studies and conduct boundary condition analyses to ensure that the intervention works well for different subgroups of participants in our samples. Lastly, although there was no evidence of moderation, it is always possible that the study was under-powered and that we committed a Type II error. However, the observed effect sizes were generally quite small (ΔR2) and given our large sample size, we were unlikely to miss a clinically meaningful effect. Nonetheless, clinicians delivering the Body Project should not simply ignore sexual orientation without future studies replicating our findings and focused directly on this population.

This is the first study to examine whether sexual orientation moderated intervention effects for an eating disorder prevention program targeting women with body image disturbances. We were reassured that our findings suggest that the three delivery modalities of the Body Project appear to work equally well for sexual minority as heterosexual women, although there were baseline differences in the broad risk factors of negative affect and impaired psychosocial functioning, which generally dovetails with the lack of differences in baseline risk factors and symptoms and change in risk factors and symptoms across sexual orientation groups. An important direction for future research would also be to examine whether certain outcomes related to sexual orientation and gender identity (e.g., self-acceptance of gender identity and sexual orientation, degree of concealment, stigma consciousness, minority stress) might be impacted by the Body Project. It would also be interesting to assess whether the effectiveness of the Body Project might vary for sexual minority members in a group of mixed-sexual orientation versus a group of solely sexual minority members. Future research in this area should strive to address how sexual orientation can best be measured so that more young women feel comfortable providing this information so that we can better address the questions outlined in this paper.

Highlights.

  • Higher non-specific eating disorder risk factors at baseline for sexual minority women

  • No evidence that sexual orientation moderates Body Project intervention effects

  • No difference in relations of eating disorder risk factors to future symptom change

  • No difference on key eating disorder risk factors or symptoms by sexual orientation

Footnotes

Author statement

My co-authors and I appreciate the opportunity to revise our manuscript “Sexual Orientation Correlates with Baseline Characteristics but Shows no Moderating Effects of Dissonance-Based Eating Disorder Prevention Programs for Women” for publication in Body Image. We appreciated the additional feedback by Reviewer 1, and believe we have addressed all of their concerns (in red font in the revised manuscript; please see specific responses to Reviewer 1 on next page).

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Allen K, Byrne S, Oddy W, & Crosby R. (2013). DSM–IV–TR and DSM-5 eating disorders in adolescents: Prevalence, stability, and psychosocial correlates in a population-based sample of male and female adolescents. Journal of Abnormal Psychology, 122, 720–732. doi: 10.1037/a0034004 [DOI] [PubMed] [Google Scholar]
  2. Alvy L. (2013). Do lesbian women have a better body image? Comparisons with heterosexual women and model of lesbian-specific factors. Body Image, 10, 524–534. doi: 10.1016/j.bodyim.2013.06.002 [DOI] [PubMed] [Google Scholar]
  3. American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. [Google Scholar]
  4. Arcelus J, Mitchell AJ, Wales J, & Nielsen S. (2011). Mortality rates in patients with anorexia nervosa and other eating disorders. A meta-analysis of 36 studies. Archives of General Psychiatry, 68, 724–31. [DOI] [PubMed] [Google Scholar]
  5. Austin SB, Ziyadeh N, Kahn J, Camargo C, Colditz G, & Field A. (2004). Sexual orientation, weight concerns, and eating-disordered behaviors in adolescent girls andboys. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 1115–1123. doi: 10.1097/01.chi.0000131139.93862.10 [DOI] [PubMed] [Google Scholar]
  6. Austin SB, Ziyadeh N, Corliss H, Rosario M, Wypij D, & Haines J. et al. (2009). Sexual orientation disparities in purging and binge eating from early to late adolescence. Journal of Adolescent Health, 45, 238–245. doi: 10.1016/j.jadohealth.2009.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Austin SB, Nelson L, Birkett M, Calzo J, & Everett B. (2013). Eating disorder symptoms and obesity at the intersections of gender, ethnicity, and sexual orientation in US high school students. American Journal of Public Health, 103, e16–e22. doi: 10.2105/ajph.2012.301150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bankoff SM, Marks AK, Swenson LP, & Pantalone DW (2016). Examining associations of sexual attraction and attitudes on women’s disordered eating behavior. Journal of Clinical Psychology, 72, 350–64. 10.1002/jclp.22244 [DOI] [PubMed] [Google Scholar]
  9. Bayer V, Robert-McComb J, Clopton J, & Reich D. (2017). Investigating the influence of shame, depression, and distress tolerance on the relationship between internalized homophobia and binge eating in lesbian and bisexual women. Eating Behaviors, 24, 39–44. doi: 10.1016/j.eatbeh.2016.12.001 [DOI] [PubMed] [Google Scholar]
  10. Benjamini Y, & Hochberg Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 289–300. [Google Scholar]
  11. Berscheid E, Walster E, & Bohrnstedt G. (1973). The happy American body: A survey report. Psychology Today, 7, 119–131. [Google Scholar]
  12. Bostwick WB, Meyer I, Aranda F, Russell S, Hughes T, Birkett M, & Mustanski B. (2014). Mental health and suicidality among racially/ethnically diverse sexual minority youths. American Journal of Public Health, 104, 1129–1136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brown T, Forney K, Pinner D, & Keel P. (2017). A randomized controlled trial of The Body Project: More Than Muscles for men with body dissatisfaction. International Journal of Eating Disorders, 50, 873–883. 10.1002/eat.22724 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Brown T, & Keel P. (2015). A randomized controlled trial of a peer co-led dissonance-based eating disorder prevention program for gay men. Behaviour Research and Therapy, 74, 1–10. doi: 10.1016/j.brat.2015.08.008 [DOI] [PubMed] [Google Scholar]
  15. Calzo J, Blashill A, Brown T, & Argenal R. (2017). Eating disorders and disordered weight and shape control behaviors in sexual minority populations. Current Psychiatry Report, 19, 49. 10.1007/s11920-017-0801-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cohen J. (1992). Statistical power analysis. Current Directions in Psychological Science, 3, 98–101. 10.1111/252F1467-8721.ep10768783 [DOI] [Google Scholar]
  17. Conron K, Lombardi E, & Reisner S. (2014). Identifying transgender and other gender minority respondents in population-based surveys: Approaches. In Herman JL (Ed.), Best practices for asking questions to identify transgender and other gender minority respondents on population-based surveys. Los Angeles, CA: The Williams Institute. [Google Scholar]
  18. Copen C, Chandra A, & Febo-Vazquez I. (2016). Sexual behavior, sexual attraction, and sexual orientation among adults aged 18–44 in the United States: data from the 2011–2013 National Survey of Family Growth. National Health Statistics Report, 88, 1–14. [PubMed] [Google Scholar]
  19. Diemer EW, Grant JD, Munn-Chemoff MA, Patterson DA, & Duncan AE (2015). Gender identity, sexual orientation, and eating related pathology in a national sample of college students. Journal of Adolescent Health, 57, 144–149. 10.1016/j.jadohealth.2015.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Feldman M. & Meyer I. (2007). Eating disorders in diverse lesbian, gay, and bisexual populations. International Journal of Eating Disorders, 40, 218–226. doi: 10.1002/eat.20360 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Feldman M. & Meyer I. (2010). Comorbidity and age of onset of eating disorders in gay men, lesbians and bisexuals. Psychiatry Research, 180, 126–131. 10.1016/j.psychres.2009.10.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Feldman MB, Torino JA, & Swift M. (2011). A group intervention to improve body image satisfaction and dietary habits in gay and bisexual men living with HIV/AIDS. Eating Disorders, 19, 377–391. [DOI] [PubMed] [Google Scholar]
  23. Frisell T, Lichtenstein P, Rahman Q, & Langstrom N. (2010). Psychiatric morbidity associated with same-sex behavior: Influence of minority stress and familial factors. Psychological Medicine, 40, 315–324. 10.1080/10640266.2011.609084 [DOI] [PubMed] [Google Scholar]
  24. Graham J. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. 10.1146/annurev.psych.58.110405.085530 [DOI] [PubMed] [Google Scholar]
  25. Grimm K, & Ram N. (2018). Latent growth and dynamic structural equation models. Annual Review of Clinical Psychology, 14, 55–89. doi: 10.1146/annurev-clinpsy-050817-084840 [DOI] [PubMed] [Google Scholar]
  26. Haedt-Matt AA & Keel PK (2011). Revisiting the affect regulation model of binge eating: A meta-analysis of studies using ecological momentary assessment. Psychological Bulletin, 137, 660–681. 10.1037/a0023660 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hadland SE, Austin SB, Goodenow CS, & Calzo JP (2014). Weight misperception and unhealthy weight control behaviors among sexual minorities in the general adolescent population. Journal of Adolescent Health, 54, 296–303. 10.1016/j.jadohealth.2013.08.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hazzard VM, Schaefer LM, Schaumberg K, Bardone-Cone AM, Frederick DA, . . . Thompson JK (2019). Testing the Tripartite Influence Model among heterosexual, bisexual, and lesbian women. Body Image, 30, 145–149. 10.1016/j.bodyim.2019.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hudson J, Hiripi E, Pope H, & Kessler R. (2007). The prevalence and correlates of eating disorders in the National Comorbidity Survey Replication. Biological Psychiatry, 61, 348–358. doi: 10.1016/j.biopsych.2006.03.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Institute of Medicine (2011). The health of lesbian, gay, bisexual, and transgender people: Building a foundation for better understanding. Washington, DC: The National Academies Press. [PubMed] [Google Scholar]
  31. Institute of Education Science’s What Works Clearinghouse (WWC, 2017). https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_handbook_v4.pdf [Google Scholar]
  32. Katz-Wise S, Scherer E, Calzo J, Sarda V, Jackson B, Haines J, & Austin SB (2015). Sexual minority stressors, internalizing symptoms, and unhealthy eating behaviors in sexual minority youth. Annals of Behavioral Medicine, 49, 839–852. doi: 10.1007/s12160-015-9718-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Laska M, Van Kim N, Erickson D, Lust K, Eisenberg ME, & Rosser B. (2015). Disparities in weight and weight behaviors by sexual orientation in college students. American Journal of Public Health, 105, 111–121. 10.2105/AJPH.2014.302094 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mason TB, & Lewis RJ (2015). Minority stress and binge eating among lesbian and bisexual women. Journal of Homosexuality, 62, 971–992. 10.1080/00918369.2015.1008285 [DOI] [PubMed] [Google Scholar]
  35. Mason TB, & Lewis RJ (2016). Minority stress, body shame, and binge eating among lesbian women: Social anxiety as a linking mechanism. Psychology of Women Quarterly, 40, 428–440. 10.1177/252F0361684316635529 [DOI] [Google Scholar]
  36. Mason TB, Lewis RJ, & Heron KE (2017). Indirect pathways connecting sexual orientation and weight discrimination to disordered eating among young adult lesbians. Psychology of Sexual Orientation and Gender Diversity, 4, 193–204. [Google Scholar]
  37. Mason TB, Lewis RJ, & Heron KE (2018). Disordered eating and body image concerns among sexual minority women: A systematic review and testable model. Psychology of Sexual Orientation and Gender Diversity, 5, 397–422. [Google Scholar]
  38. Matthews-Ewald M, Zullig K, & Ward R. (2014). Sexual orientation and disordered eating behaviors among self-identified male and female college students. Eating Behaviors, 15, 441–444. 10.1016/j.eatbeh.2014.05.002 [DOI] [PubMed] [Google Scholar]
  39. McClain Z. & Peebles R. (2016). Body image and eating disorders among lesbian, gay, bisexual, and transgender youth. Pediatric Clinics of North America, 63, 1079–1090. 10.1016/j.pcl.2016.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Meyer I. (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychological Bulletin, 129, 674–697. 10.1016/j.pcl.2016.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Moore F, & Keel P. (2003). Influence of sexual orientation and age on disordered eating attitudes and behaviors in women. International Journal of Eating Disorders, 34, 370–374. 10.1002/eat.10198 [DOI] [PubMed] [Google Scholar]
  42. Muthén L. & Muthén B. (2017). Mplus user’s guide. Eighth edition. Los Angeles, CA: Muthén & Muthén. [Google Scholar]
  43. Pachankis JE (2018). The scientific pursuit of sexual and gender minority mental health treatments: Toward evidence-based affirmative practice. American Psychologist, 73, 1207–1219. 10.1037/amp0000357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Polimeni A, Austin SB, & Kavanagh A. (2009). Sexual orientation and weight, body image, and weight control practices among young Australian women. Journal of Women’s Health, 18, 355–362. doi: 10.1089/jwh.2007.0765 [DOI] [PubMed] [Google Scholar]
  45. R Core Team. (2018). R: A language and environment for statistical computing. (Version 3.5.0). Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ [Google Scholar]
  46. Schafer J, & Graham J. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177. 10.1037//1082-989X.7.2.147 [DOI] [PubMed] [Google Scholar]
  47. Share T, & Mintz L. (2002). Differences between lesbians and heterosexual women in disordered eating and related attitudes. Journal of Homosexuality, 42, 89–106. 10.1300/J082v42n04_06 [DOI] [PubMed] [Google Scholar]
  48. Shaw H, Rohde P, & Stice E. (2016). Participant feedback from peer-led, clinician-led, and Internet-delivered eating disorder prevention interventions. International Journal of Eating Disorders, 12, 1087–1092. 10.1002/eat.22605 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Stice E, Butryn M, Rohde P, Shaw H, & Marti N. (2013). An effectiveness trial of a new enhanced dissonance eating disorder prevention program among female college students. Behavior Research and Therapy, 51, 862–871. 10.1016/252Fj.brat.2013.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Stice E, Fisher M, & Martinez E. (2004). Eating Disorder Diagnostic Scale: Additional evidence of reliability and validity. Psychological Assessment, 16, 60–71. 10.1037/1040-3590.16.1.60 [DOI] [PubMed] [Google Scholar]
  51. Stice E, Gau J, Rohde P, & Shaw H. (2017). Risk factors that predict future onset of each DSM-5 eating disorder: Predictive specificity in high-risk adolescent females. Journal of Abnormal Psychology, 126, 38–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Stice E, Marti C, & Rohde P. (2013). Prevalence, incidence, impairment, and course of the proposed DSM-5 eating disorder diagnoses in an 8-year prospective community study of young women. Journal of Abnormal Psychology, 122, 445–457. 10.1037/abn0000219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Stice E, Rohde P, Durant S, & Shaw H. (2012). A preliminary trial of a prototype Internet dissonance-based eating disorder prevention program for young women with body image concerns. Journal of Consulting and Clinical Psychology, 80, 907–916. 10.1037/a0028016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Stice E, Rohde P, Gau J, & Shaw H. (2009). An effectiveness trial of a dissonance-based eating disorder prevention program for high-risk adolescent girls. Journal of Consulting and Clinical Psychology, 77, 825–834. 10.1037/a0016132 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Stice E, Rohde P, Shaw H, & Gau J. (2017). Implementation trial of clinician-led, peer-led, and Internet-delivered dissonance-based eating disorder prevention programs. Journal of Consulting and Clinical Psychology, 85, 883–895. 10.1037/ccp0000211 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Stice E, & Shaw H. (2002). Role of body dissatisfaction in the onset and maintenance of eating pathology: A synthesis of research findings. Journal of Psychosomatic Research, 53, 985–993. 10.1016/s0022-3999(02)00488-9 [DOI] [PubMed] [Google Scholar]
  57. Stice E, Shaw H, Burton E, & Wade E. (2006). Dissonance and healthy weight eating disorder prevention programs: A randomized efficacy trial. Journal of Consulting and Clinical Psychology, 74, 263–275. 10.1037/252F0022-006X.74.2.263 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Swanson S, Crow S, Le Grange D, Swendsen J, & Merikangas K. (2011). Prevalence and correlates of eating disorders in adolescents. Results from the national comorbidity survey replication adolescent supplement. Archives of General Psychiatry, 68, 714–723. 10.1001/archgenpsychiatry.2011.22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. U.S. Department of Education (2008). Digest of Educational Statistics, 2007. National Center for Educational Statistics. [Google Scholar]
  60. Van Strien T, Frijters J, Van Staveren W, Defares P, & Deurenberg P. (1986). The predictive validity of the Dutch Restrained Eating Scale. International Journal of Eating Disorders, 5, 747–755. [DOI] [Google Scholar]
  61. WGBH Video. Dying to be thin [DVD]. (2000) Twin Cities Public Television. Saint Paul, MN: WGBH Educational Foundation. [Google Scholar]
  62. Watson D, & Clark L. (1992). Affects separable and inseparable: On the hierarchical arrangement of the negative affects. Journal of Personality and Social Psychology, 62, 489–505. DOI: 10.1037/0022-3514.62.3.489 [DOI] [Google Scholar]
  63. Watson L, Velez B, Brownfield J, & Flores M. (2016). Minority stress and bisexual women’s disordered eating: The role of maladaptive coping. The Counseling Psychologist, 44, 1158–1186. [Google Scholar]
  64. Watson R, Adjei J, Saewyc E, Homma Y, & Goodenow C. (2017). Trends and disparities in disordered eating among heterosexual and sexual minority adolescents. International Journal of Eating Disorders, 50, 22–31. 10.1177/252F0011000016669233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Weissman M,& Bothwell S. (1976). Assessment of social adjustment by patient self-report. Archives of General Psychiatry, 33, 1111–1115. 10.1001/archpsyc.1976.01770090101010 [DOI] [PubMed] [Google Scholar]
  66. Wichstrom L. (2006). Sexual orientation as a risk factor for bulimic symptoms. International Journal of Eating Disorders, 39, 448–453. 10.1002/eat.20286 [DOI] [PubMed] [Google Scholar]

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