Key Points Question Does a coached, digital, cognitive behavior therapy (CBT) intervention result in improved outcomes among college women with eating disorders (EDs) compared with referral to usual care? Findings In this cluster randomized clinical trial that included 690 women with binge-purge EDs from 27 US universities, the digital CBT intervention was superior to referral to usual care in decreasing ED psychopathology, compensatory behaviors, depression, and clinical impairment through long-term follow-up, as well as in realized treatment access. There was no difference in abstinence from all ED behaviors or academic impairment between groups. Meaning These results support the efficacy of a coached, digital, CBT intervention for college women with EDs, which has the potential to bridge the treatment gap for this problem.

The Internet-based Healthy Body Image (HBI) Program platform uses online screening to identify individuals at low risk for, high risk for, or with an eating disorder (ED) and then directs users to tailored, evidence-based online/mobile interventions or referral to in-person care to address individuals' risk/clinical status. We examined findings from the first state-wide deployment of HBI over the course of 3 years in Missouri public universities, sponsored by the Missouri Eating Disorders Council and the Missouri Mental Health Foundation. First, the screen was completed 2,454 times, with an average of 2.5% of the undergraduate student body on each campus taking the screen. Second, ED risk level in the participating students was high-over 56% of students screened were identified as being at high risk for ED onset or having a clinical/subclinical ED. Third, uptake for the HBI online/mobile interventions ranged from 44 -51%, with higher rates of uptake in the high-risk compared with low-risk group. Fourth, results showed that, for students with a clinical/subclinical ED, use of the clinical mobile application Student Bodies-Eating Disorders intervention resulted in significantly decreased restrictive eating and binge eating. Neither vomiting nor diet pill/laxative use was found to decrease, but reports of these behaviors were very low. This is the first deployment of a comprehensive online platform for screening and delivering tailored interventions to a population of individuals with varying ED risk and symptom profiles in an organized care setting. Implications for future research and sustaining and broadening the reach of HBI are discussed.

Objective The Internet‐based Healthy Body Image (HBI) Program, which uses online screening to identify individuals at low risk of, high risk of, or with an eating disorder (ED) and then directs users to tailored, evidence‐based online or in‐person interventions to address individuals' risk or clinical status, was deployed at 28 U.S. universities as part of a randomized controlled trial. The purpose of this study is to report on: (a) reach of HBI, (b) screen results, and (c) differences across ED status groups. Method All students on participating campuses ages 18 years or older were eligible, although recruitment primarily targeted undergraduate females. Results The screen was completed 4,894 times, with an average of 1.9% of the undergraduate female student body on each campus taking the screen. ED risk in participating students was high—nearly 60% of students screened were identified as being at high risk for ED onset or having an ED. Key differences emerged across ED status groups on demographics, recruitment method, ED pathology, psychiatric comorbidity, and ED risk factors, highlighting increasing pathology and impairment in the high‐risk group. Discussion Findings suggest efforts are needed to increase reach of programs like HBI. Results also highlight the increasing pathology and impairment in the high‐risk group and the importance of programs such as HBI, which provide access to timely screening and intervention to prevent onset of clinical EDs.

Background Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. Objective This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. Methods We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. Results We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. Conclusions The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts.

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