OBJECTIVE:Less than 20% of individuals with eating disorders (EDs) ever receive treatment, highlighting a need for scalable, innovative methods of identifying and providing support to individuals with ED symptoms. At the same time, ED-related content on social media (SM) platforms is pervasive, offering an opportunity to detect signals of ED symptoms from SM data. This paper examines how artificial intelligence (AI) and computational methods can be leveraged to detect ED symptoms from SM content and provide timely intervention. METHOD:We review SM-based ED detection methods researched to date, including content tags, topic modeling, and natural language processing. We also discuss critical next directions for this area, including opportunities to pair detection with digital interventions, and examine challenges in developing, evaluating, and implementing these tools. Finally, we offer recommendations for ED experts for guiding the development, evaluation, and deployment of robust detection systems. RESULTS:Research supports the feasibility of harnessing SM data to identify individuals with ED symptoms and has begun exploring methods of pairing SM-based ED detection with interventions. Although SM platforms already use automated methods of detecting and moderating harmful content, these systems are not transparent and show room for improvement, highlighting the importance of ED experts' involvement in developing detection methods. DISCUSSION:Leveraging SM data presents an unprecedented opportunity to identify and provide support to millions of individuals with ED symptoms. Research, interdisciplinary collaborations, and ethical safeguards can transform SM into a supportive resource for individuals with EDs.

Objective: This study explored the perspectives of key campus stakeholders on the status of college mental health services and digital mental health interventions (DMHIs) as a strategy to address mental health disorders among college students. Methods: A nationwide online survey (N = 615) was conducted, including clinicians (n = 87) and campus leadership representatives (n = 49) from 27 U.S. colleges and universities during 3 periods of the COVID-19 pandemic (January 2020 to December 2021). Responses from students from a parallel, related sub-study (n = 479) were collated to allow for potential comparisons. The survey assessed the uptake and perspectives of on-campus mental health services and 13 DMHIs. Chi-squared tests and one-way ANOVAs, along with Bonferroni-adjusted post hoc pairwise comparisons, were run to tease apart group differences. Linear regression controlling for institutional location was run to examine changes over time on 1) usage of DMHIs, (2) perceptions of implementation interest for DMHIs, and (3) perceptions of delivery feasibility of on-campus mental health services. Results: Leadership representatives, to a higher degree, expressed being ‘Uncertain’ about the presence of training/guidelines than clinicians (75.6% versus 35.5%). Clinicians reported the highest relative degree of use for all DMHIs (M = 6.32, SD = 2.30). Telehealth therapy was viewed among the highest priorities to make available on campus by students (66.0%), clinicians (83.1%), and leadership representatives (82.9%). Students endorsed the lowest perceived delivery feasibility of on-campus mental health services. No significant interactions were found within early to mid to late pandemic periods. Conclusions: Overall stakeholders endorsed marked heterogeneity in uptake and perspectives and were observed not to change over time during the COVID-19 pandemic.

Publicly-insured and uninsured individuals—many of whom are marginalized because of race/ethnicity, ability status, and/or other social identities—experience barriers to accessing evidence-based interventions (EBIs) for eating disorders (EDs). Additionally, EBIs have not been developed with or for diverse populations, exacerbating poor treatment uptake. Mobile technology is well-positioned to bridge this gap and increase access to low-cost, culturally-sensitive EBIs. This study leverages a user-centered design approach to adapt an existing coached cognitive-behavioral therapy-based digital program and evaluate its usability in a sample of 11 participants with (sub)clinical binge type EDs who are publicly-insured (n = 10) or uninsured (n = 1). Participants were primarily non-Latinx White women (n = 8). Two semi-structured interviews occurred with participants: one to assess treatment needs and the other to obtain program-specific feedback. Interviews were coded using inductive thematic analysis. Interview 1 feedback converged on three themes: Recovery Journey, Treatment Experiences, and Engagement with and Expectations for Online Programs. Participants endorsed facing barriers to healthcare, such as poor insurance coverage and a lack of trained providers, and interest in a coach to increase treatment accountability. Interview 2 feedback converged on three themes: Content Development, Participant Experiences with Mental Health, and Real-World Use. Participants liked the content but emphasized the need to improve diverse representation (e.g., gender, body size). Overall, user feedback is critical to informing adaptations to the original EBI so that the intervention can be appropriately tailored to the needs of this underserved population, which ultimately has high potential to address critical barriers to ED treatment. This study was reviewed and approved by the Institutional Review Board (IRB) at the University California, San Francisco (IRB #22-35936) and the IRB at Washington University in St. Louis (IRB ID 202304167).

The pandemic forced virtually all mental health professionals to use some form of digital technology, yet few receive any training in digital mental health tools (DMHT). Therapists and students in all mental health treatment settings should be trained to routinely include DMHT in their practice. In this article, we describe why DMHT will play an increasingly important part in clinical mental health practice and discuss how we train psychologists and counselors in our eClinic to learn the basics of incorporating digital technologies into the care they provide. More specifically, we describe the three basic components of the training: (a) an asynchronous onboarding program; (b) a didactic curriculum, implemented via monthly core skill development seminars led by experts in digital mental health practice; and (c) ongoing weekly supervision by licensed supervisors. The eClinic training approach described is a work in progress, and we expect to adjust it to follow the evolution of digital tools for mental health assessment and treatment.

Background While the positive impact of homework completion on symptom alleviation is well-established, the pivotal role of therapists in reviewing these assignments has been under-investigated. This study examined therapists' practice of assigning and reviewing action recommendations in therapy sessions, and how it correlates with patients’ depression and anxiety outcomes. Methods We analyzed 2,444 therapy sessions from community-based behavioral health programs. Machine learning models and natural language processing techniques were deployed to discern action recommendations and their subsequent reviews. The extent of the review was quantified by measuring the proportion of session dialogues reviewing action recommendations, a metric we refer to as “review percentage”. Using Generalized Estimating Equations modeling, we evaluated the correlation between this metric and changes in clients' depression and anxiety scores. Results Our models achieved 76% precision in capturing action recommendations and 71.1% in reviewing them. Using these models, we found that therapists typically provided clients with one to eight action recommendations per session to engage in outside therapy. However, only half of the sessions included a review of previously assigned action recommendations. We identified a significant interaction between the initial depression score and the review percentage ( p = 0.045). When adjusting for this relationship, the review percentage was positively and significantly associated with a reduction in depression score ( p = 0.032). This suggests that more frequent review of action recommendations in therapy relates to greater improvement in depression symptoms. Further analyses highlighted this association for mild depression ( p = 0.024), but not for anxiety or moderate to severe depression. Conclusions An observed positive association exists between therapists’ review of previous sessions’ action recommendations and improved treatment outcomes among clients with mild depression, highlighting the possible advantages of consistently revisiting therapeutic homework in real-world therapy settings. Results underscore the importance of developing effective strategies to help therapists maintain continuity between therapy sessions, potentially enhancing the impact of therapy.

OBJECTIVE:Anorexia nervosa (AN) is often treated in the acute setting, but relapse after treatment is common. Cognitive-behavioral therapy (CBT) is useful in the post-acute period, but access to trained providers is limited. Social support is also critical during this period. This study utilized a user-centered design approach to develop and evaluate the usability of a CBT-based mobile app and social networking component for post-acute AN support. METHOD:Participants (N = 19) were recently discharged from acute treatment for AN. Usability testing of the intervention was conducted over three cycles; assessments included the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), the Mobile Application Rating Scale (MARS), a social media questionnaire, and a semi-structured interview. RESULTS:Interview feedback detailed aspects of the app that participants enjoyed and those needing improvement. Feedback converged on three themes: Logistical App Feedback, boosting recovery, and Real-World App/Social Media Use. USE and MARS scores were above average and SUS scores were "good" to "excellent" across cycles. CONCLUSION:This study provides evidence of feasibility and acceptability of an app and social networking feature for post-acute care of AN. The intervention has potential for offering scalable support for individuals with AN in the high-risk period following discharge from acute care.

BACKGROUND:Recent evidence suggests that multiple emotional disorders may be better assessed using dimensional models of psychopathology. The current study utilized a cross-sectional population survey of college students (N = 8613 participants) to examine the extent to which broad psychopathology factors accounted for specific associations between emotional problems and clinical and behavioral validators: suicidality, dysfunctional attitudes, and treatment seeking. METHODS:Confirmatory factor models were estimated to identify the best structure of psychopathology. Models were then estimated to examine the broad and specific associations between each psychopathology indicator and the clinical and behavioral validators. RESULTS:The hierarchical model of psychopathology with internalizing problems at the top, fear, and distress at the second level, and five specific symptom dimensions at the third level evidenced the best fit. The associations between symptom indicators of psychopathology and clinical and behavioral validators were relatively small and inconsistent. Instead, much of the association between clinical and behavioral validators and emotional problems operated at a higher-order level. LIMITATIONS:The cross-sectional nature of the survey precludes the ability to make conclusions regarding causality. CONCLUSIONS:Researchers should focus on investigating the shared or common components across emotional disorders, particularly concerning individuals presenting with higher rates of suicidal ideation dysfunctional attitudes, and help-seeking behavior. Using higher-order dimensions of psychopathology could simplify the complex presentation of multiple co-occurring disorders and suggest valid constructs for future investigations.

ABSTRACTObjectiveFew individuals with eating disorders (EDs) receive treatment. Innovations are needed to identify individuals with EDs and address care barriers. We developed a chatbot for promoting services uptake that could be paired with online screening. However, it is not yet known which components drive effects. This study estimated individual and combined contributions of four chatbot components on mental health services use (primary), chatbot helpfulness, and attitudes toward changing eating/shape/weight concerns (“change attitudes,” with higher scores indicating greater importance/readiness).Methods Two hundred five individuals screening with an ED but not in treatment were randomized in an optimization randomized controlled trial to receive up to four chatbot components: psychoeducation, motivational interviewing, personalized service recommendations, and repeated administration (follow‐up check‐ins/reminders). Assessments were at baseline and 2, 6, and 14 weeks.ResultsParticipants who received repeated administration were more likely to report mental health services use, with no significant effects of other components on services use. Repeated administration slowed the decline in change attitudes participants experienced over time. Participants who received motivational interviewing found the chatbot more helpful, but this component was also associated with larger declines in change attitudes. Participants who received personalized recommendations found the chatbot more helpful, and receiving this component on its own was associated with the most favorable change attitude time trend. Psychoeducation showed no effects.DiscussionResults indicated important effects of components on outcomes; findings will be used to finalize decision making about the optimized intervention package. The chatbot shows high potential for addressing the treatment gap for EDs.

Sexual and gender minority (SGM) teens may experience body image concerns and eating disorders (EDs) at higher rates than their non-SGM peers. The current investigation examined differences in baseline survey responses by SGM and non-SGM youth who participated in a pilot randomized controlled trial of a digital intervention for EDs. Eligible teens (N = 147) aged 14-17 years old who screened positive or at high risk for an ED completed a baseline survey to assess current ED symptoms, mental health comorbidities, and ED treatment history. The majority of participants (mean age 16.021 years) screened positive for symptoms of a clinical/subclinical ED (n = 98, 66.7%), 123 endorsed severe anxiety (83.7%), and 81 endorsed severe depression (55.1%). A total of 72.1% (n = 106) identified as SGM and were more likely to report severe symptoms of depression (p = 0.01), any symptoms of anxiety (p = 0.03), social anxiety disorder (p = 0.02), and lifetime suicide attempts (p<.001), compared with non-SGM peers. Qualitative feedback on the intersection of SGM identities and body image concerns were also reported among SGM teens. Future eating disorder interventions provided to SGM youth should include content on comorbidities like depression and anxiety, which may be barriers to ED recovery among SGM teens.

Background: Previous studies showed that comorbidity and demographic factors added to burden on healthrelated quality of life (HRQoL). Only one study explored the relationship between HRQoL and comorbidity in college students with mental disorders, leaving generalizability of findings uncertain. Less is known about the association of demographics on HRQoL. This study investigated HRQoL based on demographics and comorbidity among college students with mental disorders. Methods: Participants were students (N = 5535) across 26 U.S. colleges and universities who met criteria for depression, generalized anxiety, panic, social anxiety, post-traumatic stress, or eating disorders based on selfreport measures. ANOVA and linear regressions were conducted. Results: Overall, female, minoritized (gender, sexual orientation, race, or ethnicity), and lower socioeconomic status students reported lower HRQoL than male, heterosexual, White, non-Hispanic, and higher socioeconomic status peers. After accounting for comorbidity, differences in physical HRQoL based on sex assigned at birth and gender were no longer significant. For mental HRQoL, only gender and sexual orientation remained significant. A greater number of comorbidities was associated with lower HRQoL regardless of demographic group. Limitations: The non-experimental design limits causal inference. The study focused on univariable associations without examining potential interactions between demographic factors. Future research should explore structural factors like discrimination. Conclusion: Results suggested that increased comorbidities placed an additional burden on HRQoL and that certain demographic groups were more vulnerable to HRQoL impairment among students with mental disorders. Findings suggest the need for prevention of disorders and their comorbidity and implementing tailored interventions for specific student subgroups with increased vulnerability.

Mental health disparities experienced by sexual and gender diverse (SGD) young adults are well documented. Yet, few studies have examined mental health disparities between SGD subgroups. Even fewer have investigated disparities that may exist for individuals whose SGD identities are nonmonosexual (i.e., diverse sexual orientations besides gay/lesbian) or gender nonbinary, who may experience exacerbated marginalization and disparities. The present study examines differences in weight and shape concerns and symptoms of depression, anxiety (general, panic, social, and posttraumatic stress), alcohol use disorder, and insomnia among sexually diverse (SD) subgroups (lesbian or gay, bisexual, queer, asexual, pansexual, multiple identities, and questioning), and gender diverse (GD) subgroups (trans man, trans woman, and nonbinary) of college students. We hypothesized that nonmonosexual students would have a greater mental health symptom burden than their monosexual peers and we explored additional subgroup differences among SD and GD subgroups separately. Kruskal-Wallis tests with Mann-Whitney U post hoc tests were conducted to examine associations between mental health symptoms and sexual orientation and gender identity separately. Results show high mental health symptom levels among most subgroups. Some nonmonosexual SD subgroups were at particularly high risk; namely, pansexual students. Questioning and asexual SD subgroups had similar and lower symptom levels than their monosexual peers, respectively. SD subgroup disparities varied by mental health symptom type. No significant differences by GD subgroups were found. Clinicians and institutions should consider these disparities and future research should aim to better understand them.

Importance: Given the rising prevalence of anxiety, depression, and eating disorders (EDs), optimizing population treatment outcomes for the college population is needed. Objective: To develop a machine learning model using baseline socio-demographic and clinical information to predict which students would show long-term benefit from transdiagnostic digital cognitive-behavioral guided self-help (D-CBTgsh). Design: In this secondary analysis, we used baseline variables to predict those who did (n=1380) or did not (n=1723) remit from any anxiety, depression, or eating disorder at a two-year follow-up from D-CBTgsh. Setting: 26 U.S. colleges and universities. Participants: Between October 2019 and December 2023, full populations of college students were screened, and those 18 or older with or at risk of DSM-5 diagnosis of panic disorder (PD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), depression, and/or EDs (excluding anorexia nervosa) were enrolled. Of 3,103 participants within D-CBTgsh (Mage = 20.2; SD ± 4.0), 29.6% were male, and 64.6% were White and non-Hispanic. Intervention: D-CBTgsh via SilverCloud Health. Main Outcome(s) and Measure(s): Discriminatory accuracy of Super Learner trained for binary classification of two-year prevention and remission of all PD, GAD, SAD, depression, and EDs using 10 baseline variables. Results: The model achieved good discriminatory accuracy with an AUC of .77 (95% CI = .74-.79) with a large effect (d = 1.06) to distinguish the probability of prevention and remission from depression, anxiety, and eating disorders at a two-year follow-up (threshold = .485; accuracy = .721, sensitivity = .712; specificity = .728; NPV = .756; Precision = .682). Higher baseline severity in depression, SAD, GAD, anxiety-related avoidance, insomnia, chronicity of anxiety, and binge eating were associated with an increased probability of nonremission, whereas those who identified as male and with better mental health quality of life were associated with an increased probability of prevention and remission of all clinical disorders. Super Learner demonstrated clinical utility having identified 855 cases of two-year nonremission without increasing false positives per 1000 individuals. Conclusion and relevance: Routinely collected pre-treatment data may identify two-year outcome remission from D-CBTgsh, thereby potentially facilitating population-level mental health care delivery on college campuses. Trial Registration: ClinicalTrials.gov: NCT04162847.

Background:A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated. Methods:The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks. Results:Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia. Conclusion:These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder. Trial registration:Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

Objective: Using a sample of U.S. college students, the authors evaluated whether barriers to mental health treatment varied by race and ethnicity. Methods: Data were drawn from a large multicampus study conducted across 26 U.S. colleges and universities. The sample (N=5,841) included students who screened positive for at least one mental disorder and who were not currently receiving psychotherapy. Results: The most prevalent barriers to treatment across the sample were a preference to deal with issues on one's own, lack of time, and financial difficulties. Black and Hispanic/Latine students reported a greater willingness to seek treatment than did White students. However, Black and Hispanic/Latine students faced more financial barriers to treatment, and Hispanic/Latine students also reported lower perceived importance of mental health. Asian American students also reported financial barriers and preferred to handle their issues on their own or with support from family or friends and had lower readiness, willingness, and intentionality to seek help than did White students. Conclusions: Disparities in unmet treatment needs may arise from both distinct and common barriers and point to the potential benefits of tailored interventions to address the specific needs of students of color from various racial and ethnic backgrounds. The findings further underscore the pressing need for low-cost and brief treatment models that can be used or accessed independently to address the most prevalent barriers for students.

The current pilot study examines engagement with and preliminary effectiveness of an mHealth intervention designed for teens with eating disorders (EDs) to delineate specific user characteristics associated with intervention engagement and the impact of this engagement on ED symptoms. Teens 14–17 years old with or at high-risk for an ED were recruited from social media (n=29) and provided access to an mHealth intervention for 2 months. At baseline, participants were surveyed on ED and other mental health symptoms and demographics. Bivariate analyses were used to examine associations between baseline characteristics and time spent in the app (<10 vs. ≥ 10 minutes). Qualitative feedback from participants on intervention content and usability was also collected and reported. Out of the 29 participants, 22 (76

INTRODUCTION:It is crucial to identify and evaluate feasible, proactive ways to reach teens with eating disorders (EDs) who may not otherwise have access to screening or treatment. This study aimed to explore the feasibility of recruiting teens with EDs to a digital intervention study via social media and a publicly available online ED screen, and to compare the characteristics of teens recruited by each approach in an exploratory fashion. METHOD:Teens aged 14-17 years old who screened positive for a clinical/subclinical ED or at risk for an ED and who were not currently in ED treatment completed a baseline survey to assess current ED symptoms, mental health comorbidities, and barriers to treatment. Bivariate analyses were conducted to examine differences between participants recruited via social media and those recruited after completion of a widely available online EDs screen (i.e., National Eating Disorders Association [NEDA] screen). RESULTS:Recruitment of teens with EDs using the two online approaches was found to be feasible, with 934 screens completed and a total of 134 teens enrolled over 6 months: 77% (n = 103) via social media 23% (n = 31) via the NEDA screen. Mean age of participants (N = 134) was 16 years old, with 49% (n = 66) identifying as non-White, and 70% (n = 94) identifying as a gender and/or sexual minority. Teens from NEDA reported higher ED psychopathology scores (medium effect size) and more frequent self-induced vomiting and driven exercise (small effect sizes). Teens from NEDA also endorsed more barriers to treatment, including not feeling ready for treatment and not knowing where to find a counselor or other resources (small effect sizes). DISCUSSION:Online recruitment approaches in this study reached a large number of teens with an interest in a digital intervention to support ED recovery, demonstrating the feasibility of these outreach methods. Both approaches reached teens with similar demographic characteristics; however, teens recruited from NEDA reported higher ED symptom severity and barriers to treatment. Findings suggest that proactive assessment and intervention methods should be developed and tailored to meet the needs of each of these groups. PUBLIC SIGNIFICANCE:This study examined the feasibility of recruiting teens with EDs to a digital intervention research study via social media and NEDA's online screen, and demonstrated differences in ED symptoms among participants by recruitment approach.

OBJECTIVE:To examine the mental health problems that college students with eating disorders (EDs) and comorbid depression and/or anxiety disorders preferred to target first in a digital treatment program and explore correlates of preferred treatment focus. METHODS:Four hundred and eighty nine college student users of a digital cognitive-behavioral guided self-help program targeting common mental health problems (76.7% female, Mage  = 20.4 ± 4.4, 64.8% White) screened positive for an ED and ≥one other clinical mental health problem (i.e., depression, generalized anxiety disorder, social phobia, and/or panic disorder). Students also reported on insomnia, post-traumatic stress, alcohol use, and suicide risk. Before treatment, they indicated the mental health problem that they preferred to target first in treatment. Preferred treatment focus was characterized by diagnostic profile (i.e., ED + Depression, ED + Anxiety, ED + Depression + Anxiety), symptom severity, and demographics. RESULTS:58% of students with ED + Anxiety, 47% of those with ED + Depression, and 27% of those with ED + Depression + Anxiety chose to target EDs first. Across diagnostic profiles, those who chose to target EDs first had more severe ED symptoms than those who chose to target anxiety or depression (ps < .05). Among students with ED + Depression + Anxiety, those who chose to target EDs first had lower depression symptoms than those who chose to target depression, lower generalized anxiety than those who chose to target anxiety, and lower suicidality than those who chose to target anxiety or depression (ps < .01). CONCLUSIONS:Students with EDs and comorbid depression and/or anxiety disorders showed variable preferred treatment focus across diagnostic profiles. Research should explore specific symptom presentations associated with preferred treatment focus. PUBLIC SIGNIFICANCE:Findings indicate that a sizable percentage of college students with depression/anxiety who also have EDs prefer to target EDs first in treatment, highlighting the importance of increasing availability of ED interventions to college students. Students with EDs and comorbid depression and/or anxiety disorders showed variable preferred treatment focus across diagnostic profiles, and preference to target EDs was associated with greater ED psychopathology across diagnostic profiles.

Digital guided self-help for eating disorders (GSH-ED) can reduce treatment disparities. Understanding program participants' interests throughout the program can help adapt programs to the service users' needs. Participants were 383 college students receiving a digital GSH-ED, who were each assigned a coach to help them better utilize the intervention through text correspondence. A thematic and affective analysis of the texts participants had sent found they primarily focused on: strategies for changing their ED-related cognitions, behaviors, and relationships; describing symptoms without expressing an active endeavor to change; and participants' relationship with their coach. Most texts also expressed affect, demonstrating emotional engagement with the intervention. Findings suggest that participants in GSH-ED demonstrate high involvement with the intervention, and discuss topics that are similar to those reported in clinician-facilitated interventions. The themes discussed by digital program participants can inform future iterations of GSH-ED, thereby increasing scalability and accessibility of digital evidence-based ED interventions.

BACKGROUND:Food insecurity (FI), characterized by limited or uncertain access to adequate food, has been associated with eating disorders (EDs). This study explored whether FI was associated with ED behaviors, ED diagnosis, current treatment status, and treatment-seeking intentions among adults who completed an online ED screen. METHODS:Respondents to the National Eating Disorders Association online screening tool self-reported demographics, FI, height and weight, past 3-month ED behaviors, and current treatment status. Respondents were also asked an optional question about treatment-seeking intentions. Hierarchical regressions evaluated relations between FI and ED behaviors, treatment status, and treatment-seeking intentions. Logistic regressions explored differences in probable ED diagnosis by FI status. RESULTS:Of 8714 respondents, 25 % screened at risk for FI. FI was associated with greater binge eating (R2Change = 0.006), laxative use (R2Change = 0.001), and presence of dietary restriction (R2Change = 0.001, OR: 1.32) (ps < .05). Having FI was associated with greater odds of screening positive for a probable ED or as high risk for an ED (ps < .05). FI was not associated with current treatment status or treatment-seeking intentions (ps > .05). CONCLUSIONS:Findings add to existing literature supporting a relation between FI and EDs. Implications include a need to disseminate EDs screening and treatment resources to populations affected by FI and to tailor treatments to account for barriers caused by FI.

OBJECTIVE:A comprehensive understanding of the relationship between depressive symptoms and eating disorder (ED) symptoms requires consideration of additional variables that may influence this relationship. Health-related quality of life (HRQOL) has been associated with both depression and EDs; however, there is limited evidence to demonstrate how all three variables interact over time. This study sought to explore the bi-directional relationships between depressive symptoms, ED symptoms and HRQOL in a large community sample of young adolescents METHOD: Adolescents (N = 1393) aged between 11 and 14 years (M = 12.50, SD = 0.38) completed an online survey measuring depressive symptoms, ED symptoms and HRQOL. Two-level autoregressive cross-lagged models with three variables (i.e., depressive symptoms, HRQOL and ED) assessed across two time points (T1 and T2) were created to address the study aims. RESULTS:HRQOL was found to predict depressive symptoms and depressive symptoms were found to predict ED symptoms. Components of HRQOL (i.e., social relationships and ability to cope) were found to share a differential relationship with depressive symptoms. Inability to cope predicted depressive symptoms and depressive symptoms predicted negative social relationships. EDs were found to predict reduced HRQOL and negative social relationships. DISCUSSION:Findings suggest prevention and early intervention programs designed to address adolescent depression should focus on improving HRQOL. Future research should assess the relationship between HRQOL and individual ED symptoms (e.g., body-related ED symptoms, restrictive symptoms) as a means of exploring relationships that may have been masked by assessing ED symptoms using a total score. PUBLIC SIGNIFICANCE:This study sought to explore how eating disorders, depressive symptoms, and health-related quality of life (HRQOL) relate over time in a sample of young adolescents. Findings indicate adolescents who self-reported lower levels of HRQOL, including a reduced ability to cope, are at risk of experiencing depressive symptoms. Adolescents should be provided with the tools to develop problem-focused coping strategies as a means of reducing depressive symptoms.