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Over the past decade, climate anxiety and eco-anxiety have rapidly been gaining attention. While there is no doubt that climate-related events can certainly lead to psychological distress, if the prevailing popular thinking is accurate, one would expect to see an observable, parallel increase in these issues being explicitly expressed in therapy. Natural language processing (NLP) models capture key moments and conversational topics occurring in therapy sessions, and can be trained to identify climate discussions to help inform training and provide feedback to therapists. This study analyzed 32,542 therapy sessions provided in the U.S. between July 2020 and December 2022. The transcriptions included 1,722,273 labeled therapist-client micro-dialogues. The results indicated that climate- and weather-related topics constituted only a small percentage of the total conversations, with climate change mentioned in only 0.3% of the sessions. Clients with greater depressive or anxiety symptoms mentioned weather and climate significantly less than those with mild or no symptoms. Findings suggest that despite the documented mental health concerns associated with global warming, this study shows that these issues have yet to be adequately represented in psychotherapy. This study highlights the need for increased training for therapists in understanding and addressing with their clients the psychological effects of climate change and eco-anxiety. The use of NLP models can provide valuable feedback to therapists and assist in identifying key moments and conversational topics that can inform training and improve the effectiveness of therapy sessions.

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.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.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.Greater and longer engagement in an ED app mediates its efficacy in terms of ED remission.

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 healthcare services by helping clinicians collect objective data on patients’ progress, streamline their workflow, and automate administrative tasks. OBJECTIVE 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 U.S. 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 compared to treatment-as-usual (TAU) during the first two months of therapy. This AI platform summarizes and transcribes the therapy session, provides feedback to therapists on the usage of evidence-based practices (EBPs), 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 intention-to-treat 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 Seventy-two patients were approached, of whom 47 (67%) agreed to participate. Participants were adults (34 [72.34%] women and 13 [27.65%] men; mean [SD] age was 30.64 [11.02] years old), 23 randomized to the AI platform group, and 24 to TAU. Participants in the AI group attended, on average, 67% more sessions (n = 5.24, SD = 2.31) than those in TAU (n = 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. CONCLUSIONS In this RCT, 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. CLINICALTRIAL ClinicalTrials.gov NCT05745103

Artificial intelligence (AI) offers unmet potential for mental health services but also presents new challenges for providers. As AI tools become more ubiquitous in our lives, there is a disconnect between clients’ interests and the adoption of AI in mental healthcare. AI algorithms are informed by expert knowledge, can process multiple data points quickly, and perform many automatic tasks. As such, they can complement the therapy provided by trained professionals and relieve much of the administrative burden healthcare workers experience. In this paper we offer a model for using AI in the daily practice of mental health providers. We discuss ways for clinicians to utilize the data and the services provided by AI to augment therapists’ decision making and maintain the interpersonal connection, while delegating other functions to automated services. We review how AI can augment and scale therapy in many parts of the clinician’s work, from initial screening and routine measurement-based care to digitally-enabled and blended interventions, as well as operational services. We review evidence related to responsible implementation of AI in clinical practice and suggest future research directions and implementation strategies for AI-augmentation.

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. 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 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.

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.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.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.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.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.

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. METHODS: Participants were 690 women from 28 U.S. 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 U.S. college women who screened positive for EDs. Findings can inform tailoring of ED screening to reduce current disparities in these underrepresented populations.

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.

Objective Eating disorders (EDs) compromise the health and functioning of affected individuals, but it can often take them several years to acknowledge their illness and seek treatment. Early identification of individuals with EDs is a public health priority, and innovative approaches are needed for such identification and ultimate linkage with evidence-based interventions. This study examined whether Internet activity data can predict ED risk/diagnostic status, potentially informing timely interventions. Method Participants were 936 women who completed a clinically validated online survey for EDs, and 231 of them (24.7%) contributed their Internet browsing history. A machine learning algorithm used key attributes from participants' Internet activity histories to predict their ED status: clinical/subclinical ED, high risk for an ED, or no ED. Results The algorithm reached an accuracy of 52.6% in predicting ED risk/diagnostic status, compared to random decision accuracy of 38.1%, a relative improvement of 38%. The most predictive Internet search history variables were the following: use of keywords related to ED symptoms and websites promoting ED content, participant age, median browsing events per day, and fraction of daily activity at noon. Discussion ED risk or clinical status can be predicted via machine learning with moderate accuracy using Internet activity variables. This model, if replicated in larger samples where it demonstrates stronger predictive value, could identify populations where further assessment is merited. Future iterations could also inform tailored digital interventions, timed to be provided when target online behaviors occur, thereby potentially improving the well-being of many individuals who may otherwise remain undetected.

The offspring of parents with obesity are at an increased risk of developing this condition themselves because of genetic and environmental factors. One subgroup that may be at particularly high risk of developing obesity is the offspring of parents who have undergone weight loss surgery (PWLS). To date, little research has focused on these offspring or their parents. This systematic review addresses this gap by integrating available literature and assessing the quality of the evidence. To be included, studies were required to have researched characteristics of the offspring of PWLS or parental feeding practices within this population. After review, 12 studies met inclusion criteria. Findings include evidence for heightened risk of obesity among children of PWLS. However, research suggests these children may experience positive, although time-limited, health outcomes after their parents' surgeries. Quality of the evidence was rated as low, primarily because of the lack of randomized controlled studies and information regarding available interventions specifically targeting this vulnerable population. This review underscores the need for research to improve understanding of PWLS families to better support them and capitalize on postbariatric surgery benefits.

Background: Although behavioral interventions have been found efficacious and effective in randomized clinical trials for most mental illnesses, the quality and efficacy of mental healthcare delivery remains inadequate in real-world settings, partly due 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 studies. 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 dataset for this study comprised 17,607 behavioral treatment sessions given by 322 therapists to 3,519 patients in 37 behavioral healthcare programs across the U.S. Sessions were captured by a therapy-specific artificial intelligence (AI) platform, and an automatic speech recognition system (ASR) 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 two psychologists validating the text that emerged.Results: We found that despite clinical recommendations, only 54 sessions (0.30%) 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 with 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 dataset of real-world treatment practices. Findings provide evidence that fidelity with the core components of empirically-based psychological interventions as designed is a challenge in real-world settings. 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 healthcare services, and facilitating better clinical outcomes in real-world settings.

Background: Although behavioral interventions have been found efficacious and effective in randomized clinical trials for most mental illnesses, the quality and efficacy of mental healthcare delivery remains inadequate in real-world settings, partly due to suboptimal treatment fidelity. This “therapist drift” is an ongoing issue that ultimately reduces the effectiveness of treatments, however until recently there was limited opportunity to assess adherence beyond controlled studies and at scale. 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 augmenting their understanding of the session and the treatment plan. Methods: The dataset for this study comprised 17,607 behavioral treatment sessions given by 322 therapists to 3,519 patients in 37 behavioral healthcare programs across the U.S. Sessions were captured by a therapy-specific artificial intelligence (AI) platform, and an automatic speech recognition system (ASR) 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 two psychologists validating the text that emerged.Results: We found that despite clinical recommendations, only 54 sessions (0.30%) 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 also included greater therapist use of validation, complex reflections, and proactive problem-solving techniques. Conclusions: Findings suggest that fidelity with the core components of evidence-based psychological interventions as designed is a challenge in real-life settings. 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 healthcare services, and facilitating better clinical outcomes in real-world settings.

BACKGROUND Eating disorders (EDs) are complex mental illnesses with debilitating, pervasive psychological and physiological consequences when left untreated. Unfortunately, patients may face barriers to receiving treatment, such as stereotypes surrounding EDs, denial of illness severity, lack of motivation for treatment, and lack of knowledge about treatment resources. Barriers such as these result in a large treatment gap: only 20% of those with EDs will ever receive treatment. Digital tools like chatbots show potential to disseminate mental health–related interventions to large populations while offering a user-friendly, cost-effective, accessible, and anonymous means of tackling patient concerns. OBJECTIVE 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 4 iterative cycles, with user feedback informing refinements to the next iteration. Postintervention, participants (N=21) completed the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), and a semistructured interview. This process aimed to create an optimized chatbot by the final cycle for use in a randomized trial. 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 SUS score. CONCLUSIONS Overall, participants responded well in 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. Alex is the first chatbot designed for pairing with an ED or other mental health–related online screen, with the goal of ultimately increasing service utilization.

OBJECTIVE: This study examined the associations between insomnia, anxiety, and depression in college women with eating disorders (EDs). PARTICIPANTS: Six hundred and ninety female students from 28 U.S. 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

The ways families approach eating, shape, and weight can result in stress for individual family members and challenge the overall functioning of the family. This is further complicated among families with a parent who has history of obesity or undergone weight loss surgery (WLS). Although WLS can positively impact other family members, it can also exacerbate conflicts regarding feeding and weight. Such conflicts can involve uncertainty regarding the extent to which the entire family should make the dietary changes recommended for the post-WLS parent. Conflict might also center on the appropriate level of concern regarding the children's risk of developing (or maintaining) obesity. This paper uses two case examples to describe the application of a specialized, time-limited intervention: Parent-Based Prevention following Bariatric Surgery (PBP-B). The program was developed to address the unique challenges and concerns that arise after, or are exacerbated by, WLS. Each detailed case example illustrates a common child-feeding challenge and the employment of key PBP-B strategies throughout the course of treatment. In the first case, the parent who had undergone WLS believed the family's current eating behaviors were the same as those that had led to her own overeating, obesity, and co-occurring psychiatric symptoms, while her husband disagreed. In the second case, both parents were concerned about their son's weight, yet due to their prior eating histories, they felt unable to construct boundaries around the feeding experience. Both cases follow families through the entire intervention and illustrate key points and challenges. These cases underscore the need for novel treatment modalities to support families following parental WLS.Las maneras en las que las familias abordan la alimentación, la figura y el peso pueden causar estrés en los integrantes individuales de la familia y poner a prueba el funcionamiento general de la familia. Esto es aun más complicado entre las familias con un padre que tiene antecedentes de obesidad o que se sometió a una cirugía para adelgazar. Aunque la cirugía para adelgazar puede repercutir de manera positiva en otros miembros de la familia, también puede exacerbar conflictos con respecto a la alimentación y al peso. Dichos conflictos pueden consistir en la incertidumbre con respecto al grado en el cual toda la familia debería hacer los cambios alimentarios recomendados para el padre que se ha operado para adelgazar. El conflicto también podría centrarse en el nivel adecuado de preocupación en relación con el riesgo de los niños de desarrollar (o mantener) la obesidad. Este artículo utiliza dos ejemplos de casos para describir la aplicación de una intervención especializada y limitada temporalmente: “La prevención basada en los padres después de una cirugía bariátrica” (Parent-Based Prevention following Bariatric Surgery, PBP-B). El programa se desarrolló para abordar los desafíos y las preocupaciones particulares que surgen después de la cirugía para adelgazar o que son exacerbados por esta. Cada ejemplo de un caso detallado ilustra un desafío común con respecto a la alimentación de los niños y al empleo de estrategias fundamentales de la PBP-B a lo largo del transcurso del tratamiento. En el primer caso, la madre que se había sometido a la cirugía para adelgazar creía que los comportamientos alimentarios actuales de la familia eran los mismos que los que la habían conducido a su propia sobreingesta, obesidad, y síntomas psiquiátricos concomitantes, mientras que su esposo no estaba de acuerdo. En el segundo caso, ambos padres estaban preocupados acerca del peso de su hijo, sin embargo, debido a sus antecedentes alimentarios previos, se sentían incapaces de establecer límites en torno a la experiencia alimentaria. Ambos casos siguen a las familias durante toda la intervención e ilustran puntos clave y desafíos. Estos casos subrayan la necesidad de incorporar modalidades innovadoras de tratamiento orientadas a apoyar a las familias después de la cirugía para adelgazar de uno de los padres.一个家庭对待饮食、体形和体重的方式会对家中某单个家庭成员带来一定的压力，并挑战该家庭的整体运作。对于父母中曾有肥胖病史或进行过减肥手术（WLS）的家庭而言，情况则更加复杂。尽管WLS可以对其他家庭成员产生积极的影响，但也可能加剧在喂养和体重方面的矛盾。此类冲突可能涉及为了遵循给WLS后的父母推荐的饮食，整个家庭应在多大程度上相应调整改变的不确定性。冲突也可能集中有关对儿童患肥胖症（或维持肥胖症）的风险的适当关注上。本文使用两个案例来描述专门的限时干预措施的应用：减肥手术后基于父母的预防（PBP-B）。该程序的开发主要是针对解决在进行WLS之后出现的或者加剧的独特挑战和特殊问题。每个详细的病例示例都说明了常见的儿童喂养方面的挑战以及在整个治疗过程中如何采用关键的PBP-B策略。在第一种情况下，经历过WLS的母亲认为家庭目前的饮食行为与导致自己暴饮暴食、肥胖和与此同时出现的精神病症状的行为相同，而她的丈夫则不同意。在第二种情况下，父母双方都担心自己儿子的体重，但是由于他们以前的饮食历史，他们感到无法在喂养经历周围建立边界。这两个案例跟踪了在整个干预过程中的家庭表现，并阐明一些关键点和难点。这些病例强调需要新的治疗手段来支持父母做过WLS后的家庭。.

The transition to parenthood is perceived as a stressful life event, when parents experience an immense change of their psychological focus and a reorientation of roles and responsibilities in the family system. This process may be even more challenging in the presence of a parental eating disorder history. This paper reviews the impact of parental eating disorders on the parents, the couple relationship, and their child during the perinatal period. A parental eating disorder is associated with more negative expectations of parental efficacy as well as specific difficulties in couple communication over the child's feeding, shape, and weight. Providers who better understand the effects of an eating disorder on parental functioning can more effectively intervene early on. We also present couple- or parent-based, empirically supported interventions for adults with eating disorders and their partners in the prenatal and postnatal periods: Uniting Couples in the treatment of Anorexia Nervosa (UCAN) and Uniting couples In the Treatment of Eating disorders (UNITE) both enhance recovery from the eating disorder through a couple-based intervention; the Maudsley Model of Treatment for Adults with Anorexia Nervosa (MANTRA) incorporates the support of partners, when appropriate; Parent-Based Prevention (PBP) focuses on improving parental functioning and reducing risk of negative parental and child outcomes. Finally, we discuss the clinical implications of addressing parental eating disorders and encourage more research on these families.La transición a la paternidad se percibe como una situación estresante de la vida, cuando los padres sufren un cambio inmenso en su foco de atención psicológico y una reorientación de los roles y las responsabilidades en el sistema familiar. Este proceso puede ser aun más difícil en presencia de antecedentes de trastornos alimentarios en los padres. Este artículo analiza el efecto de los trastornos alimentarios parentales en los padres, la relación de pareja y su hijo durante el periodo perinatal. Un trastorno alimentario parental está asociado con expectativas más negativas de la eficacia parental así como con dificultades específicas en la comunicación entre la pareja sobre la alimentación, la figura y el peso del niño. Los profesionales que mejor comprendan los efectos de un trastorno alimentario en el funcionamiento parental pueden intervenir más eficazmente en etapas tempranas. También presentamos intervenciones respaldadas empíricamente basadas en la pareja o en los padres para adultos con trastornos alimentarios y sus parejas en los periodos prenatales y posnatales: la “Unión de las parejas en el tratamiento de la anorexia nerviosa” (UCAN) y la “Unión de las parejas en el tratamiento de los trastornos alimentarios” (UNITE) mejoran la recuperación del trastorno alimentario mediante una intervención basada en la pareja; el modelo Maudsley de tratamiento para adultos con anorexia nerviosa (MANTRA) incorpora el apoyo de las parejas, cuando es adecuado; la prevención basada en los padres (PBP) se centra en mejorar el funcionamiento parental y en reducir el riesgo de consecuencias negativas para los padres y los niños. Finalmente, debatimos las implicancias clínicas de abordar los trastornos alimentarios parentales y de fomentar más investigaciones sobre estas familias.为人父母之后心理重心发生巨大变化，在家庭系统中的角色和责任也重新定位，身份的转变被认为是一个压力很大的生活事件。如果在亲历过自己父母的饮食失调史，该转换过程可能更具挑战性。本文回顾了围产期父母的进食障碍对父母，夫妻关系及其子女的影响。父母方面的的进食障碍与对父母效力的更多负面期望以及夫妻在孩子的喂养，体型和体重方面的交流中遇到的特殊困难有关。更好地了解饮食失调对父母功能的影响之后，提供者可以尽早更有效地进行干预。我们还针对有饮食障碍的成年人及其伴侣在产前和产后提供基于夫妇或父母的有实证支持的干预措施：联合夫妻治疗神经性厌食症（UCAN）和联合夫妻饮食失调（ UNITE）都通过基于夫妇的干预措施来增强从饮食失调中恢复的能力；Maudsley治疗成人神经性厌食症的模型（MANTRA）在适当时吸收了伴侣的支持；家长为基础的预防（PBP）的重点是改善父母的机能并降低父母和孩子的负面结果的风险。最后，我们讨论了解决父母饮食失调问题的临床意义，并鼓励针对这些家庭进行更多研究。.

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 Eating disorders (EDs) are complex mental illnesses with debilitating, pervasive psychological and physiological consequences when left untreated. Unfortunately, patients may face barriers to receiving treatment, such as stereotypes surrounding EDs, denial of illness severity, lack of motivation for treatment, and lack of knowledge about treatment resources. Barriers such as these result in a large treatment gap: only 20% of those with EDs will ever receive treatment. Digital tools like chatbots show potential to disseminate mental health–related interventions to large populations while offering a user-friendly, cost-effective, accessible, and anonymous means of tackling patient concerns. Objective 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 4 iterative cycles, with user feedback informing refinements to the next iteration. Postintervention, participants (N=21) completed the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), and a semistructured interview. This process aimed to create an optimized chatbot by the final cycle for use in a randomized trial. 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 SUS score. Conclusions Overall, participants responded well in 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. Alex is the first chatbot designed for pairing with an ED or other mental health–related online screen, with the goal of ultimately increasing service utilization. Acknowledgments This research was supported by K08 MH120341 from the National Institute of Mental Health. Availability of Data, Materials, and Code The data will be made available by reasonable request to the corresponding author. Authors’ Contributions EEFC conceptualized and designed the study. OL and BD conducted the investigation process. BD and JS assisted with data curation and conducted formal thematic analyses. JS conducted formal statistical analyses. JS wrote the original manuscript, with contribution from BD. EEFC, CBT, DEW, and SSS designed the data collection instruments, and coordinated and supervised data collection, in addition to reviewing and editing the manuscript with LS, LMF, LAF, and LD. Conflicts of Interest None declared.

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.

UNSTRUCTURED 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.

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 for 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 risk of EDs in the context of COVID-19 and offers suggestions for modifying intervention efforts to accommodate for the unique challenges individuals with EDs and providers may be experiencing in light of the ongoing public health crisis.

Objective This article uses three brief case reports to illustrate how family-based treatment (FBT) can be used to treat pre-adolescents with avoidant/restrictive food intake disorder (ARFID). Method We present case material illustrating how FBT can be used in three different clinical presentations of ARFID: (1) low appetite and lack of interest; (2) sensory sensitive eaters; and (3) fear of aversive consequences eaters-all without shape or weight concerns. Results This case material illustrates that the main principles of FBT-agnosticism as to the cause of the illness, externalization, emphasizing the seriousness of ARFID, parental empowerment, behavioral consultation, and practical behavioral focus-are applicable for a range of ARFID clinical presentations. Common challenges in this patient group include (1) promoting urgency; (2) challenging long term behavioral accommodation; (3) lack of parental alignment, parental fatigue, (4) and co-morbid psychiatric problems in the patients. Strategies to address these problems are described. Conclusion FBT can be adapted for children with ARFID using the main principles of the approach.

Objective To identify the correlates of suicidal ideation (SI) in a large sample of college women with eating disorders (EDs). Method A total of 690 female college students from 28 US colleges who screened positive for an ED, with the exception of anorexia nervosa, were assessed for SI. Univariate logistic regression analyses were performed to determine independent correlates of SI. Measures included: ED psychopathology, ED behaviors (i.e., binge eating, vomiting, laxatives, compulsive exercise), current co-morbid psychopathology (i.e., depression, anxiety, insomnia), weight/shape concerns, ED-related clinical impairment, and body mass index (BMI). All significant variables were included in a backward binary multivariate logistic regression model to determine which variables were most strongly associated with SI. Results A total of 25.6% of the sample reported SI. All variables examined were significantly independently associated with SI, with the exception of compulsive exercise. Depression, anxiety, and vomiting remained as significant correlates of SI in the multivariate logistic regression model. Discussion ED screening on college campuses should assess for suicidality, and prevention and treatment efforts should target vomiting and co-morbid depression and anxiety symptoms to reduce risk of SI for high-risk individuals.

Objective Smartphone applications (apps) for eating disorders are a promising approach to assist individuals who do not receive traditional treatment. This study examines usage characteristics, perceptions regarding the acceptability of a new self-help intervention developed for users with eating disorders, and associations between attitudes and use patterns. Methods 189 individuals pilot-tested a personalized app-based program, and 133 completed the required components of the pilot-test over an 8-day period. Of these, 64 individuals (49%) completed an exit survey pertaining to acceptability. Results Seventy percent of those who pilot-tested the app-based program completed the required components, i.e. a baseline review and then a 1-week assessment. Body mass index was associated with the total number of recorded meal logs. Study participants rated the app as highly suitable and acceptable, providing evidence of the feasibility and appropriateness of the program. Conclusion The app-based program demonstrated feasibility of deploying the app across user groups and high acceptability.

Objective Family-based treatment (FBT) for adolescent anorexia nervosa (AN) promotes faster weight restoration when compared to other treatments. However, the mechanisms through which this occurs are not clarified. This study explored the trajectories of parental self-efficacy and perceived family flexibility during FBT and systemic family therapy (SyFT). We also explored whether parental self-efficacy mediates the effects of treatment on weight gain early in treatment. Method 158 adolescents (12-18 years old; 89% girls) and their parents were randomized to FBT or SyFT. Parental self-efficacy as well as adolescents' and parental perceptions of the family's flexibility were collected at baseline and at sessions 2, 4, 6, and 8. Results Over time, only parents in FBT reported significantly greater self-efficacy. The change in maternal self-efficacy over the first 8 weeks of treatment was a significant mediator of session 10 weight gain. There were no significant group differences in perceived flexibility by session 8. Discussion Both parents in FBT and mothers in SyFT understand early the need to change their family's rules and roles. However, the specific strategies of FBT appear to mediate early weight gain in AN.

Objective Family-based treatment (FBT) is an evidence-based treatment for adolescent anorexia nervosa (AN), but many families cannot access it. This study evaluated feasibility, acceptability, and preliminary treatment effects of a parental guided self-help (GSH) version of FBT for adolescent AN. Method This was a case-series design. Parents of medically stable adolescents (11–18 years) with DSM-5 AN were recruited over 12 months. Parents received online training in parental GSH FBT and 12 20–30 min GSH sessions by phone or online over 6 months. Recruitment, dropout, changes in weight, and eating-related psychopathology were assessed. Analyses used mixed modeling that included all data for all participants. Results Of the 19 families that participated, most were white (94%) and from intact families (88%). Baseline median BMI (mBMI) percent was 85.01% (SD = 4.31). Participants’ mBMI percent increased to 97.31% (SD ± 7.48) at the end of treatment (EOT) (ES = 2.06; CI= 0.13–3.99). Eating-related psychopathology improved by EOT (ES = 0.58; CI=.04–1.21). Dropout rate was 21% during treatment and 33% during follow-up. Discussion Parental GSH-FBT is feasible and acceptable to families willing to undertake online treatment. Follow-up data was only available for nine families (47%); thus further systematic evaluation is required before reaching conclusions about the efficacy of this approach.

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. 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.

Digital technology, which includes the collection, analysis, and use of data from a variety of digital devices, has the potential to reduce the prevalence of disorders and improve mental health in populations. Among the many advantages of digital technology is that it allows preventive and clinical interventions, both of which are needed to reduce the prevalence of mental health disorders, to be feasibly integrated into health care and community delivery systems and delivered at scale. However, the use of digital technology also presents several challenges, including how systems can manage and implement interventions in a rapidly changing digital environment and handle critical issues that affect population-wide outcomes, including reaching the targeted population, obtaining meaningful levels of uptake and use of interventions, and achieving significant outcomes. We describe a possible solution, which is to have an outcome optimization team that focuses on the dynamic use of data to adapt interventions for populations, while at the same time, addressing the complex relationships among reach, uptake, use, and outcome. We use the example of eating disorders in young people to illustrate how this solution could be implemented at scale. We also discuss system, practitioner-related, and other issues related to the adaptation of such an approach Digital technology has great potential for facilitating the reduction of mental illness rates in populations. However, achieving this goal will require the implementation of new approaches. As a solution, we argue for the need to create outcome optimization teams, tasked with integrating data from various sources and using advanced data analytics and new designs to develop interventions/strategies to increase reach, uptake, use/engagement, and outcomes for both preventive and treatment interventions. Funding Agencies|National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 MH100455, T32 HL007456, T32 HL130357, K08 MH120341, K01 DK116925]; Australias National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [APP1057411]; Centre of Research Excellence from Australias National Health and Medical Research Council [APP1170937]

UNSTRUCTURED Digital technology, which includes the collection, analysis, and use of data from a variety of digital devices, has the potential to reduce the prevalence of disorders and improve mental health in populations. Among the many advantages of digital technology is that it allows preventive and clinical interventions, both of which are needed to reduce the prevalence of mental health disorders, to be feasibly integrated into health care and community delivery systems and delivered at scale. However, the use of digital technology also presents several challenges, including how systems can manage and implement interventions in a rapidly changing digital environment and handle critical issues that affect population-wide outcomes, including reaching the targeted population, obtaining meaningful levels of uptake and use of interventions, and achieving significant outcomes. We describe a possible solution, which is to have an outcome optimization team that focuses on the dynamic use of data to adapt interventions for populations, while at the same time, addressing the complex relationships among reach, uptake, use, and outcome. We use the example of eating disorders in young people to illustrate how this solution could be implemented at scale. We also discuss system, practitioner-related, and other issues related to the adaptation of such an approach. Digital technology has great potential for facilitating the reduction of mental illness rates in populations. However, achieving this goal will require the implementation of new approaches. As a solution, we argue for the need to create outcome optimization teams, tasked with integrating data from various sources and using advanced data analytics and new designs to develop interventions/strategies to increase reach, uptake, use/engagement, and outcomes for both preventive and treatment interventions. INTERNATIONAL REGISTERED REPORT RR2-10.2196/none

Background Eating disorders severely impact psychological, physical, and social functioning, and yet, the majority of individuals with eating disorders do not receive treatment. Mobile health apps have the potential to decrease access barriers to care and reach individuals who have been underserved by traditional treatment modalities. Objective The objective of this study was to evaluate the effectiveness of a tailored, fully automated self-help version of Recovery Record, an app developed for eating disorders management. We examined differences in eastin disorder symptom change in app users that were randomized to receive either a standard, cognitive behavioral therapy–based version of the app or a tailored version that included algorithmically determined clinical content aligned with baseline and evolving user eating disorder symptom profiles. Methods Participants were people with eating disorder symptoms who did not have access to traditional treatment options and were recruited via the open-access Recovery Record app to participate in this randomized controlled trial. We examined both continuous and categorical clinical improvement outcomes (measured with the self-report Eating Disorder Examination Questionnaire [EDE-Q]) in both intervention groups. Results Between December 2016 and August 2018, 3294 Recovery Record app users were recruited into the study, out of which 959 were considered engaged, completed follow-up assessments, and were included in the analyses. Both study groups achieved significant overall outcome improvement, with 61.6% (180/292) of the tailored group and 55.4% (158/285) of the standard group achieving a clinically meaningful change in the EDE-Q, on average. There were no statistically significant differences between randomized groups for continuous outcomes, but a pattern of improvement being greater in the tailored group was evident. The rate of remission on the EDE-Q at 8 weeks was significantly greater in the group receiving the tailored version (d=0.22; P≤.001). Conclusions This is the first report to compare the relative efficacy of two versions of a mobile app for eating disorders. The data suggest that underserved individuals with eating disorder symptoms may benefit clinically from a self-help app and that personalizing app content to specific clinical presentations may be more effective in promoting symptomatic remission on the EDE-Q than content that offers a generic approach. Trial Registration ClinicalTrials.gov NCT02503098; https://clinicaltrials.gov/ct2/show/NCT02503098.

For countless young people, technology plays an essential role in their lives. However, its many advantages have not yet been widely applied to the treatment of youth with eating disorders. This article looks at how smartphone applications, Web conferencing, and other developments could widen the range of care available in a field where suitable support can be hard to find. Various barriers to treatment exist, such as cost, access, and the stigma attached to eating disorders, but existing and new technologies could overcome those obstacles, if clinicians are willing and able to meet the requirements associated with digitally enhanced treatment.

BACKGROUND Eating disorders severely impact psychological, physical, and social functioning, and yet, the majority of individuals with eating disorders do not receive treatment. Mobile health apps have the potential to decrease access barriers to care and reach individuals who have been underserved by traditional treatment modalities. OBJECTIVE The objective of this study was to evaluate the effectiveness of a tailored, fully automated self-help version of Recovery Record, an app developed for eating disorders management. We examined differences in eastin disorder symptom change in app users that were randomized to receive either a standard, cognitive behavioral therapy–based version of the app or a tailored version that included algorithmically determined clinical content aligned with baseline and evolving user eating disorder symptom profiles. METHODS Participants were people with eating disorder symptoms who did not have access to traditional treatment options and were recruited via the open-access Recovery Record app to participate in this randomized controlled trial. We examined both continuous and categorical clinical improvement outcomes (measured with the self-report Eating Disorder Examination Questionnaire [EDE-Q]) in both intervention groups. RESULTS Between December 2016 and August 2018, 3294 Recovery Record app users were recruited into the study, out of which 959 were considered engaged, completed follow-up assessments, and were included in the analyses. Both study groups achieved significant overall outcome improvement, with 61.6% (180/292) of the tailored group and 55.4% (158/285) of the standard group achieving a clinically meaningful change in the EDE-Q, on average. There were no statistically significant differences between randomized groups for continuous outcomes, but a pattern of improvement being greater in the tailored group was evident. The rate of remission on the EDE-Q at 8 weeks was significantly greater in the group receiving the tailored version (d=0.22; P≤.001). CONCLUSIONS This is the first report to compare the relative efficacy of two versions of a mobile app for eating disorders. The data suggest that underserved individuals with eating disorder symptoms may benefit clinically from a self-help app and that personalizing app content to specific clinical presentations may be more effective in promoting symptomatic remission on the EDE-Q than content that offers a generic approach. CLINICALTRIAL ClinicalTrials.gov NCT02503098; https://clinicaltrials.gov/ct2/show/NCT02503098.

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 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.

The connection between maternal eating disorders and feeding and eating problems among their children has been substantially demonstrated. This pilot study focused on the interactions between mothers with eating disorders and their toddlers in non-feeding situations. Twenty-eight dyads of mothers with prenatal eating disorders and their toddlers were compared to a case-matched control group with no eating disorder. Maternal current eating and co-occurring psychopathology, children's symptoms and mother-child interactions were measured. Mothers with eating disorders were less sensitive to their children, tried to control their children's behaviors more, and were less happy during mother-child interactions. The children in the maternal eating disorder group were rated as less responsive to their mothers and their mothers also reported more behavioral problems than those in the control group. Findings imply that maternal eating disorders may be linked with a wide range of adverse maternal and child behaviors beyond those associated with eating.

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., This cluster randomized clinical trial examines whether a coached, digital, cognitive behavior therapy intervention improves outcomes for college women with eating disorders compared with referral to usual care., 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 (β [SE], −0.44 [0.10]; d = −0.40; t1387 = −4.23; P

In recent years, online screens have been commonly used to identify individuals who may have eating disorders (EDs), many of whom may be interested in treatment. We describe a new empirical approach that takes advantage of current evidence on empirically supported, effective treatments, while at the same time, uses modern statistical frameworks and experimental designs, data‐driven science, and user‐centered design methods to study ways to expand the reach of programs, enhance our understanding of what works for whom, and improve outcomes, overall and in subpopulations. The research would focus on individuals with EDs identified through screening and would use continuously monitored data, and interactions of interventions/approaches to optimize reach, uptake, engagement, and outcome. Outcome would be assessed at the population, rather than individual level. The idea worth researching is to determine if an optimization outcome model produces significantly higher rates of clinical improvement at a population level than do current approaches, in which traditional interventions are only offered to the few people who are interested in and able to access them. In recent years, online screens have been commonly used to identify individuals who may have eating disorders (EDs), many of whom may be interested in treatment. We describe a new empirical approach that takes advantage of current evidence on empirically supported, effective treatments, while at the same time, uses modern statistical frameworks and experimental designs, data‐driven science, and user‐centered design methods to study ways to expand the reach of programs, enhance our understanding of what works for whom, and improve outcomes, overall and in subpopulations. The research would focus on individuals with EDs identified through screening and would use continuously monitored data, and interactions of interventions/approaches to optimize reach, uptake, engagement, and outcome. Outcome would be assessed at the population, rather than individual level. The idea worth researching is to determine if an optimization outcome model produces significantly higher rates of clinical improvement at a population level than do current approaches, in which traditional interventions are only offered to the few people who are interested in and able to access them.