Nurturing innovation, catalyzing change: Honoring the legacy of Dr. C. Barr Taylor in the eating disorders field.

This Virtual Issue of the International Journal of Eating Disorders honors the legacy of the late Dr. C. Barr Taylor in the eating disorders (EDs) field. For decades, Dr. Taylor led the way in not only conducting the research needed to achieve the ultimate goal of making affordable, accessible, and evidence-based care for EDs available to all, but also nurturing the next generation of scientific leaders and innovators. Articles included in this Virtual Issue are a selection of Dr. Taylor's published works in the Journal in the past decade, spanning original research, ideas worth researching, commentaries, and a systematic review. We hope this Virtual Issue will inspire the next generation of research in EDs, and equally, if not more importantly, the next generation of young investigators in the field. We urge the field to continue and build upon Dr. Taylor's vision-to increase access to targeted prevention and intervention for EDs in innovative and forward-thinking ways-while embracing his unique and powerful mentorship style to lift up early career investigators and create a community of leaders to address and solve our field's biggest challenges.

Action recommendations review in community-based therapy and depression and anxiety outcomes: a machine learning approach.

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.

Effects of an Artificial Intelligence Platform for Behavioral Interventions on Depression and Anxiety Symptoms: Randomized Clinical Trial.

BACKGROUND: The need for scalable delivery of mental health care services that are efficient and effective is now a major public health priority. Artificial intelligence (AI) tools have the potential to improve behavioral health care services by helping clinicians collect objective data on patients' progress, streamline their workflow, and automate administrative tasks. OBJECTIVE: The aim of this study was to determine the feasibility, acceptability, and preliminary efficacy of an AI platform for behavioral health in facilitating better clinical outcomes for patients receiving outpatient therapy. METHODS: The study was conducted at a community-based clinic in the United States. Participants were 47 adults referred for outpatient, individual cognitive behavioral therapy for a main diagnosis of a depressive or anxiety disorder. The platform provided by Eleos Health was compared to a treatment-as-usual (TAU) approach during the first 2 months of therapy. This AI platform summarizes and transcribes the therapy session, provides feedback to therapists on the use of evidence-based practices, and integrates these data with routine standardized questionnaires completed by patients. The information is also used to draft the session's progress note. Patients were randomized to receive either therapy provided with the support of an AI platform developed by Eleos Health or TAU at the same clinic. Data analysis was carried out based on an intention-to-treat approach from December 2022 to January 2023. The primary outcomes included the feasibility and acceptability of the AI platform. Secondary outcomes included changes in depression (Patient Health Questionnaire-9) and anxiety (Generalized Anxiety Disorder-7) scores as well as treatment attendance, satisfaction, and perceived helpfulness. RESULTS: A total of 72 patients were approached, of whom 47 (67%) agreed to participate. Participants were adults (34/47, 72% women and 13/47, 28% men; mean age 30.64, SD 11.02 years), with 23 randomized to the AI platform group, and 24 to TAU. Participants in the AI group attended, on average, 67% (mean 5.24, SD 2.31) more sessions compared to those in TAU (mean 3.14, SD 1.99). Depression and anxiety symptoms were reduced by 34% and 29% in the AI platform group versus 20% and 8% for TAU, respectively, with large effect sizes for the therapy delivered with the support of the AI platform. No group difference was found in 2-month treatment satisfaction and perceived helpfulness. Further, therapists using the AI platform submitted their progress notes, on average, 55 hours earlier than therapists in the TAU group (t=-0.73; P<.001). CONCLUSIONS: In this randomized controlled trial, therapy provided with the support of Eleos Health demonstrated superior depression and anxiety outcomes as well as patient retention, compared with TAU. These findings suggest that complementing the mental health services provided in community-based clinics with an AI platform specializing in behavioral treatment was more effective in reducing key symptoms than standard therapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT05745103; https://classic.clinicaltrials.gov/ct2/show/NCT05745103.

Machine Learning Model to Predict Assignment of Therapy Homework in Behavioral Treatments: Algorithm Development and Validation.

BACKGROUND: Therapeutic homework is a core element of cognitive and behavioral interventions, and greater homework compliance predicts improved treatment outcomes. To date, research in this area has relied mostly on therapists' and clients' self-reports or studies carried out in academic settings, and there is little knowledge on how homework is used as a treatment intervention in routine clinical care. OBJECTIVE: This study tested whether a machine learning (ML) model using natural language processing could identify homework assignments in behavioral health sessions. By leveraging this technology, we sought to develop a more objective and accurate method for detecting the presence of homework in therapy sessions. METHODS: We analyzed 34,497 audio-recorded treatment sessions provided in 8 behavioral health care programs via an artificial intelligence (AI) platform designed for therapy provided by Eleos Health. Therapist and client utterances were captured and analyzed via the AI platform. Experts reviewed the homework assigned in 100 sessions to create classifications. Next, we sampled 4000 sessions and labeled therapist-client microdialogues that suggested homework to train an unsupervised sentence embedding model. This model was trained on 2.83 million therapist-client microdialogues. RESULTS: An analysis of 100 random sessions found that homework was assigned in 61% (n=61) of sessions, and in 34% (n=21) of these cases, more than one homework assignment was provided. Homework addressed practicing skills (n=34, 37%), taking action (n=26, 28.5%), journaling (n=17, 19%), and learning new skills (n=14, 15%). Our classifier reached a 72% F1-score, outperforming state-of-the-art ML models. The therapists reviewing the microdialogues agreed in 90% (n=90) of cases on whether or not homework was assigned. CONCLUSIONS: The findings of this study demonstrate the potential of ML and natural language processing to improve the detection of therapeutic homework assignments in behavioral health sessions. Our findings highlight the importance of accurately capturing homework in real-world settings and the potential for AI to support therapists in providing evidence-based care and increasing fidelity with science-backed interventions. By identifying areas where AI can facilitate homework assignments and tracking, such as reminding therapists to prescribe homework and reducing the charting associated with homework, we can ultimately improve the overall quality of behavioral health care. Additionally, our approach can be extended to investigate the impact of homework assignments on therapeutic outcomes, providing insights into the effectiveness of specific types of homework.

Moderators and mediators of a digital cognitive behavior therapy-guided self-help intervention for eating disorders: Informing future design efforts.

OBJECTIVE: The Student Bodies-Eating Disorders intervention (SB-ED), a digital cognitive behavior therapy-guided self-help intervention for college women with an eating disorder, is effective for reducing eating disorder psychopathology. The purpose of this study was to evaluate moderators and mediators of the SB-ED intervention. To our knowledge, this is the first evaluation of clinical mediators of a digital intervention for women with eating disorders. METHOD: This is an exploratory secondary analysis of a cluster randomized trial comparing the SB-ED intervention to referral to usual care among 690 women at 27 United States colleges. Moderators included body mass index (BMI), race, ethnicity, weight/shape concerns, eating disorder impairment, thin ideal internalization, depression, anxiety, and motivation for treatment, assessed at baseline. Thin ideal internalization and depressive symptoms were tested as predictors at postintervention and mediators at 2-year follow-up. Outcome was change in global eating disorder psychopathology. RESULTS: BMI moderated the effect of the intervention at follow-up (but not posttreatment), with individuals with a lower BMI experiencing more continued improvements in eating disorder psychopathology following the intervention than individuals with a higher BMI. Thin ideal internalization mediated the effect of the intervention at follow-up, and depression partially mediated the effect of the intervention at follow-up. CONCLUSIONS: Results of the mediator analyses suggest that helping college women reduce inflated internalization of the thin ideal and improve depressive symptoms leads to improvements in eating disorder psychopathology. Results also suggest opportunities to optimize the intervention so individuals across the BMI spectrum experience ongoing improvements over time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Preventing eating disorders and disordered eating in genetically vulnerable, high-risk families.

OBJECTIVE: To close the chasm between theory about families containing a parent with an eating disorders (EDs) history and lack of selective or indicated prevention programming for such families with an older child or adolescent who is, genetically, at high risk. METHOD: A search of four major databases for January 2000 through September 2022 yielded no publications that (a) identified genetically high-risk families with offspring ages 10 through 18; (b) devised a prevention program for the family; and (c) evaluated program effects on risk/protective factors. To rectify this gap, research on three lines of family-based prevention is reviewed: (1) programs for adolescents at genetic risk for depression or anxiety; (2) the Stanford-Dresden project for adolescents at high risk for anorexia nervosa; and (3) Sadeh-Sharvit et al.'s work concerning the Parent-Based Prevention program for mothers with an EDs history and a child under age 5. RESULTS: The significant challenges for innovative prevention programming should be addressed by experts in effective EDs, depression, and anxiety prevention, and in family-based treatment (FBT) for EDs, collaborating with people from genetically vulnerable families. Innovative programming should focus on robust risk factors for EDs, adaptive expression of non-specific risk factors (e.g., temperament), and strengthening family functioning. DISCUSSION: The field is overdue for development of prevention programs designed for older children or adolescents who are at risk because a parent has an ED. Evidence-based prevention programs for EDs and for depression and anxiety, as well as parent-based prevention informed by FBT, provide a springboard for addressing this gap. PUBLIC SIGNIFICANCE: The foundation of theory and research is available for stakeholders to develop prevention programming that closes the huge gap between theory and research about families that are genetically vulnerable for eating disorders versus the complete lack of prevention programming for such families that have an older child or adolescent at high risk.

Digital guided self-help for eating disorders: thematic analysis of participant text messages to coaches.

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.

Development and usability testing of a chatbot to promote mental health services use among individuals with eating disorders following screening.

OBJECTIVE: A significant gap exists between those who need and those who receive care for eating disorders (EDs). Novel solutions are needed to encourage service use and address treatment barriers. This study developed and evaluated the usability of a chatbot designed for pairing with online ED screening. The tool aimed to promote mental health service utilization by improving motivation for treatment and self-efficacy among individuals with EDs. METHODS: A chatbot prototype, Alex, was designed using decision trees and theoretically-informed components: psychoeducation, motivational interviewing, personalized recommendations, and repeated administration. Usability testing was conducted over four iterative cycles, with user feedback informing refinements to the next iteration. Post-testing, participants (N= 21) completed the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), and a semi-structured interview. RESULTS: Interview feedback detailed chatbot aspects participants enjoyed and aspects necessitating improvement. Feedback converged on four themes: user experience, chatbot qualities, chatbot content, and ease of use. Following refinements, users described Alex as humanlike, supportive, and encouraging. Content was perceived as novel and personally relevant. USE scores across domains were generally above average (~5 out of 7), and SUS scores indicated "good" to "excellent" usability across cycles, with the final iteration receiving the highest average score. DISCUSSION: Overall, participants generally reflected positively on interactions with Alex, including the initial version. Refinements between cycles further improved user experiences. This study provides preliminary evidence of the feasibility and acceptance of a chatbot designed to promote motivation for and use of services among individuals with EDs. PUBLIC SIGNIFICANCE: Low rates of service utilization and treatment have been observed among individuals following online eating disorder screening. Tools are needed, including scalable, digital options, that can be easily paired with screening, to improve motivation for addressing eating disorders and promote service utilization.

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians.

The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for "pragmatic AI-augmentation" that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice-automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.

A Comparison Between Clinical Guidelines and Real-World Treatment Data in Examining the Use of Session Summaries: Retrospective Study.

BACKGROUND: Although behavioral interventions have been found to be efficacious and effective in randomized clinical trials for most mental illnesses, the quality and efficacy of mental health care delivery remains inadequate in real-world settings, partly owing to suboptimal treatment fidelity. This "therapist drift" is an ongoing issue that ultimately reduces the effectiveness of treatments; however, until recently, there have been limited opportunities to assess adherence beyond large randomized controlled trials. OBJECTIVE: This study explored therapists' use of a standard component that is pertinent across most behavioral treatments-prompting clients to summarize their treatment session as a means for consolidating and augmenting their understanding of the session and the treatment plan. METHODS: The data set for this study comprised 17,607 behavioral treatment sessions administered by 322 therapists to 3519 patients in 37 behavioral health care programs across the United States. Sessions were captured by a therapy-specific artificial intelligence (AI) platform, and an automatic speech recognition system transcribed the treatment meeting and separated the data to the therapist and client utterances. A search for possible session summary prompts was then conducted, with 2 psychologists validating the text that emerged. RESULTS: We found that despite clinical recommendations, only 54 (0.30%) sessions included a summary. Exploratory analyses indicated that session summaries mostly addressed relationships (n=27), work (n=20), change (n=6), and alcohol (n=5). Sessions with meeting summaries were also characterized by greater therapist interventions and included greater use of validation, complex reflections, and proactive problem-solving techniques. CONCLUSIONS: To the best of our knowledge, this is the first study to assess a large, diverse data set of real-world treatment practices. Our findings provide evidence that fidelity with the core components of empirically designed psychological interventions is a challenge in real-world settings. The results of this study can inform the development of machine learning and AI algorithms and offer nuanced, timely feedback to providers, thereby improving the delivery of evidence-based practices and quality of mental health care services and facilitating better clinical outcomes in real-world settings.

Eating disorders during the COVID-19 pandemic and quarantine: an overview of risks and recommendations for treatment and early intervention.

Individuals with eating disorders (EDs) are at significant risk for increases in symptomatology and diminished treatment access during the COVID-19 pandemic. Environmental precautions to limit coronavirus spread have affected food availability and access to healthy coping mechanisms, and have contributed to weight-stigmatizing social media messages that may be uniquely harmful to those experiencing EDs. Additionally, changes in socialization and routine, stress, and experiences of trauma that are being experienced globally may be particularly deleterious to ED risk and recovery. This paper presents a brief review of the pertinent literature related to the risk of EDs in the context of COVID-19 and offers suggestions for modifying intervention efforts to accommodate the unique challenges individuals with EDs and providers may be experiencing in light of the ongoing public health crisis.

Psychometric properties of the Perceived Benefits of Thinness Scale in college-aged women.

Thin ideal internalization is a risk factor for disordered eating behaviors, poor body image, and eating disorders (EDs). This paper evaluated the psychometric properties of a novel measure, the Perceived Benefits of Thinness Scale (PBTS), which assesses how individuals feel being thinner would affect various aspects of their lives. Three separate studies with unique samples of college-aged women over 18 years were conducted to assess reliability and validity. In Study 1, exploratory and confirmatory factor analyses suggested all PBTS items loaded onto one factor that was distinct from a measure of weight and shape concerns. A large correlation between changes in PTBS scores and changes in ED psychopathology scores over 8 months (r = .57, p < .01) suggested sensitivity to change. Greater severity in ED pathology was also associated with higher scores on the PBTS. In Study 2, the PBTS showed good test-retest reliability (r = .84, p < .001) and, in Study 3, expected correlations with existing measures of thin ideal internalization (rs = .38-.60, ps < .001). Overall, the PBTS displayed good factor structure, reliability, concurrent validity, and sensitivity to change. By emphasizing social, emotional, and quality of life benefits, the PBTS may serve clinicians, researchers, and patients in understanding thin ideal internalization and associated ED risk.

Eating disorders early app use mediates treatment effect on clinical improvement.

OBJECTIVE: Eating disorders (EDs) contribute considerably to the global burden of disease. However, most affected individuals do not receive treatment. Mobile apps present an enormous opportunity to increase access to mental healthcare services. This study examined whether the degree of usage of a self-help app for EDs mediated the app's effects on the clinical response by individuals with EDs. METHOD: App usage measures included the total number of cognitive-behavioral meal logs, total number of days spent using the app, and the last day the app was used during the study period. Mediation analysis was performed using the MacArthur framework. RESULTS: All usage variables met the analytic requirements for testing mediation (group means (sd) for app and standard app, respectively: logs = 74 (108) vs. 51.4 (88.1), days spent = 14.3 (17.5) vs. 10.6 (15.0), p-values from Wilcox rank sum tests p < .01). Regression coefficients indicated mediation effects. The mediation effects demonstrated support that increased engagement (as measured by logs and time spent on the app) was related to an increased likelihood of achieving a significant clinical change by the end of the trial. DISCUSSION: Greater and longer engagement in an ED app mediates its efficacy in terms of ED remission.

Overweight and obesity are associated with increased eating disorder correlates and general psychopathology in university women with eating disorders.

OBJECTIVE: Examine how eating disorder (ED) correlates, ED-related clinical impairment, general psychopathology, and ED diagnoses differ across weight statuses in a sample of university women with EDs. METHOD: Participants were 690 women from 28 U.S. universities who screened positive for an ED (with the exception of anorexia nervosa [AN]) and participated in the Healthy Body Image Program study. ED correlates, ED-related clinical impairment, general psychopathology (i.e., depression and anxiety), and ED diagnoses were compared across weight statuses (i.e., healthy weight, overweight, obesity) using analyses of variance and chi-square tests. RESULTS: Women with EDs and overweight or obesity had higher levels of, perceived benefit of thinness, depressive symptoms, anxiety, and weight/shape concerns (obesity only) than those with healthy weight (ps ≤ .017). Compared to those with healthy weight, those with obesity had higher rates of clinical and sub-clinical binge eating disorder and lower rates of bulimia nervosa (p < .001). DISCUSSION: Overweight and obesity in individuals with EDs, excluding AN, are associated with greater severity of ED correlates, ED-related clinical impairment, and co-morbid general psychopathology. The current study highlights the need to consider weight status in ED treatment and for optimization of ED treatments to address shared risk factors between EDs and overweight and obesity.

Depression and anxiety mediate the relationship between insomnia and eating disorders in college women.

Objective: This study examined the associations between insomnia, anxiety, and depression in college women with eating disorders (EDs). Participants: Six hundred and ninety women from 28 US colleges who screened positive for an ED were assessed for psychiatric comorbidities. Women were, on average, 22.12 years old, mostly White (60.1%) and undergraduates (74.3%). Methods: Two mediation models were tested to determine if depression and/or anxiety mediated the relationship between insomnia and ED symptomatology. Results: One-fifth of the sample (21.7%) reported clinically moderate and severe levels of insomnia. Both depression (B = .13, p <. 001) and anxiety (B = .13, p <. 001) significantly mediated the relationship between insomnia and ED psychopathology. Conclusions: Insomnia is relatively common in college-age women with EDs. Findings suggest that this association between ED and sleep disturbances can be explained, in part, by changes in depression and anxiety. Clinicians should consider incorporating mental health assessments for insomnia, depression, and anxiety into current ED prevention, intervention, and screening efforts on college campuses.

Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment: Case Study.

Regular assessment of the effectiveness of behavioral interventions is a potent tool for improving their relevance to patients. However, poor provider and patient adherence characterize most measurement-based care tools. Therefore, a new approach for measuring intervention effects and communicating them to providers in a seamless manner is warranted. This paper provides a brief overview of the available research evidence on novel ways to measure the effects of behavioral treatments, integrating both objective and subjective data. We highlight the importance of analyzing therapeutic conversations through natural language processing. We then suggest a conceptual framework for capitalizing on data captured through directly collected and nondisruptive methodologies to describe the client's characteristics and needs and inform clinical decision-making. We then apply this context in exploring a new tool to integrate the content of therapeutic conversations and patients' self-reports. We present a case study of how both subjective and objective measures of treatment effects were implemented in cognitive-behavioral treatment for depression and anxiety and then utilized in treatment planning, delivery, and termination. In this tool, called Eleos, the patient completes standardized measures of depression and anxiety. The content of the treatment sessions was evaluated using nondisruptive, independent measures of conversation content, fidelity to the treatment model, and the back-and-forth of client-therapist dialogue. Innovative applications of advances in digital health are needed to disseminate empirically supported interventions and measure them in a noncumbersome way. Eleos appears to be a feasible, sustainable, and effective way to assess behavioral health care.

Eating disorder symptomatology, clinical impairment, and comorbid psychopathology in racially and ethnically diverse college women with eating disorders.

OBJECTIVE: To examine eating disorder (ED) symptomatology, related clinical impairment, and comorbid psychopathology in college women with EDs across five racial and two ethnic groups. METHOD: Participants were 690 women from 28 US universities who screened positive for an ED. Thirteen variables assessing ED symptoms, related clinical impairment, and comorbid psychopathology were compared across racial and ethnic groups using analyses of variance (ANOVAs) and independent samples t-tests. RESULTS: Across racial groups, significant differences emerged in binge eating and laxative use. Asian women reported significantly more binge eating than White women (p < .01). Individuals self-identified as the "Other" racial group reported greater laxative use than Asian and White women (ps ≤ .01). No other significant differences emerged across all other variables (ps ≥ .13). Across ethnic groups, Hispanic women reported significantly more laxative use (p < .01), and more comorbid insomnia symptoms (p = .03) than non-Hispanic women. No other significant differences were observed (ps ≥ .24). DISCUSSION: Findings suggest that binge eating, laxative use, and insomnia symptoms differ across racial and ethnic groups in US college women who screened positive for EDs. Findings can inform tailoring of ED screening to reduce current disparities in these underrepresented populations.

Effectiveness of a Digital Cognitive Behavior Therapy-Guided Self-Help Intervention for Eating Disorders in College Women: A Cluster Randomized Clinical Trial.

Importance: Eating disorders (EDs) are common, serious psychiatric disorders on college campuses, yet most affected individuals do not receive treatment. Digital interventions have the potential to bridge this gap. Objective: To determine whether a coached, digital, cognitive behavior therapy (CBT) intervention improves outcomes for college women with EDs compared with referral to usual care. Design, Setting, and Participants: This cluster randomized trial was conducted from 2014 to 2018 at 27 US universities. Women with binge-purge EDs (with both threshold and subthreshold presentations) were recruited from enrolled universities. The 690 participants were followed up for up to 2 years after the intervention. Data analysis was performed from February to September 2019. Interventions: Universities were randomized to the intervention, Student Bodies-Eating Disorders, a digital CBT-guided self-help program, or to referral to usual care. Main Outcomes and Measures: The main outcome was change in overall ED psychopathology. Secondary outcomes were abstinence from binge eating and compensatory behaviors, as well as ED behavior frequencies, depression, anxiety, clinical impairment, academic impairment, and realized treatment access. Results: A total of 690 women with EDs (mean [SD] age, 22.12 [4.85] years; 414 [60.0%] White; 120 [17.4%] Hispanic; 512 [74.2%] undergraduates) were included in the analyses. For ED psychopathology, there was a significantly greater reduction in the intervention group compared with the control group at the postintervention assessment (ß [SE], -0.44 [0.10]; d = -0.40; t1387 = -4.23; P < .001), as well as over the follow-up period (ß [SE], -0.39 [0.12]; d = -0.35; t1387 = -3.30; P < .001). There was not a significant difference in abstinence from any ED behaviors at the postintervention assessment (odds ratio, 1.48; 95% CI, 0.48-4.62; P = .50) or at follow-up (odds ratio, 1.51; 95% CI, 0.63-3.58; P = .36). Compared with the control group, the intervention group had significantly greater reductions in binge eating (rate ratio, 0.82; 95% CI, 0.70-0.96; P = .02), compensatory behaviors (rate ratio, 0.68; 95% CI, 0.54-0.86; P < .001), depression (ß [SE], -1.34 [0.53]; d = -0.22; t1387 = -2.52; P = .01), and clinical impairment (ß [SE], -2.33 [0.94]; d = -0.21; t1387 = -2.49; P = .01) at the postintervention assessment, with these gains sustained through follow-up for all outcomes except binge eating. Groups did not differ in terms of academic impairment. The majority of intervention participants (318 of 385 participants [83%]) began the intervention, whereas only 28% of control participants (76 of 271 participants with follow-up data available) sought treatment for their ED (odds ratio, 12.36; 95% CI, 8.73-17.51; P < .001). Conclusions and Relevance: In this cluster randomized clinical trial comparing a coached, digital CBT intervention with referral to usual care, the intervention was effective in reducing ED psychopathology, compensatory behaviors, depression, and clinical impairment through long-term follow-up, as well as realizing treatment access. No difference was found between the intervention and control groups for abstinence for all ED behaviors or academic impairment. Given its scalability, a coached, digital, CBT intervention for college women with EDs has the potential to address the wide treatment gap for these disorders. Trial Registration: ClinicalTrials.gov Identifier: NCT02076464.

Parent-based prevention after parental weight loss surgery: a pilot case-series trial.

BACKGROUND: Overeating and obesity are elevated in children of parents who have undergone weight loss surgery. Parents who have undergone weight loss surgery often report their personal history of obesity interferes with their knowledge, skills, and self-efficacy in developing their children's healthy habits, thus reducing the likelihood of addressing obesogenic environmental factors. OBJECTIVES: This study examines whether a 6-session parent-based prevention after bariatric surgery online intervention is feasible and acceptable for parents. The study also explores the program's signal of efficacy in improving short-term outcomes related to decreased long-term risks for obesity by examining short-term impact on targeted parental cognitions, feeding practices, and child eating behaviors and physical activity habits. SETTING: University Hospital, United States. METHODS: Parents were recruited using flyers, clinician referrals, and social media. Measures assessed parental feeding practices, children's eating behaviors, daily hours of screen time, and outdoor play. RESULTS: Ten families enrolled and 7 completed the study. Parents found the intervention relevant and suitable for addressing their parenting concerns. Parental feeding behaviors, such as restriction and pressure to eat, reduced while tracking of sweets and high-fat snacks increased. Children reduced both emotional overeating and undereating. Children's daily hours of screen time reduced as well as their outdoor play time. CONCLUSIONS: Parent-based prevention after bariatric surgery aimed at helping parents who have undergone weight loss surgery engineer healthier family lifestyles is feasible, acceptable, and associated with reduced obesogenic risk factors.