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Purpose Evidence shows that dialectical behavioural therapy (DBT) is efficacious for eating disorders (ED), yet few people have access to specialized treatments like DBT. Translating key DBT skills for delivery via a smartphone application may broaden the dissemination of evidence-based interventions. However, prior to developing a DBT-based app, it is crucial to gather information on target-user needs and preferences. Assessing overall acceptance and identifying predictors of acceptance, informed by the UTAUT framework, is also important. This process ensures not only a demand for such an app, but also that users receive content and features tailored to their needs. Method This study aimed to understand target-user preferences of DBT-based apps for EDs by assessing willingness to engage, overall acceptance levels, and preferred functionality/content delivery modes ( n  = 326 symptomatic participants). Results Eighty-eight percent indicated they would be willing to use a DBT-based ED app if it were available. Acceptance levels of a DBT app were high (64%), which was uniquely predicted by performance expectancy (perceptions of how beneficial an intervention is) and facilitating conditions (expectations of technological infrastructure and support in interventions) in path analysis. Content perceived as important to contain were emotion regulation techniques, tailored intervention strategies, and psychoeducation. Conclusion Findings generate important information about target-user preferences of a DBT-based app for EDs, highlighting necessary design principles for apps of this kind. Level of evidence Level V, cross-sectional descriptive study.

Mental health apps that adopt a transdiagnostic approach to addressing depression and anxiety are emerging, yet a synthesis of their evidence-base is missing. This meta-analysis evaluated the efficacy of transdiagnostic-focused apps for depression and anxiety, and aimed to understand how they compare to diagnostic-specific apps. Nineteen randomized controlled trials ( N  = 5165) were included. Transdiagnostic-focused apps produced small post-intervention effects relative to controls on pooled outcomes related depression, anxiety and distress ( N  = 23 comparisons; g = 0.29; 95% CI = 0.17–0.40). Effects remained significant across various sensitivity analyses. CBT apps and apps that were compared with a waitlist produced larger effects. Significant effects were found at follow-up (g = 0.25; 95% CI = 0.10, 0.41). Effects were comparable to disorder-specific app estimates. Findings highlight the potential of transdiagnostic apps to provide accessible support for managing depression and anxiety. Their broad applicability highlights their public health relevance, especially when combined with in-person transdiagnostic therapies to create new hybrid care models.

Mental health apps are efficacious, yet they may pose risks in some. This review (CRD42024506486) examined adverse events (AEs) from mental health apps. We searched (May 2024) the Medline, PsycINFO, Web of Science, and ProQuest databases to identify clinical trials of mental health apps. The risk of bias was assessed using the Cochrane Risk of Bias tool. Only 55 of 171 identified clinical trials reported AEs. AEs were more likely to be reported in trials sampling schizophrenia and delivering apps with symptom monitoring technology. The meta-analytic deterioration rate from 13 app conditions was 6.7% (95% CI = 4.3, 10.1, I 2  = 75%). Deterioration rates did not differ between app and control groups (OR = 0.79, 95% CI = 0.62–1.01, I 2  = 0%). Reporting of AEs was heterogeneous, in terms of assessments used, events recorded, and detail provided. Overall, few clinical trials of mental health apps report AEs. Those that do often provide insufficient information to properly judge risks related to app use.

Mental health researchers are increasingly using large language models (LLMs) to improve efficiency, yet these tools can generate fabricated but plausible-sounding content (hallucinations). A notable form of hallucination involves fabricated bibliographic citations that cannot be traced to real publications. Although previous studies have explored citation fabrication across disciplines, it remains unclear whether citation accuracy in LLM output systematically varies across topics within the same field that differ in public visibility, scientific maturity, and specialization. This study aims to examine the frequency and nature of citation fabrication and bibliographic errors in GPT-4o (Omni) outputs when generating literature reviews on mental health topics that varied in public familiarity and scientific maturity. We also tested whether prompt specificity (general vs specialized) influenced fabrication or accuracy rates. In June 2025, GPT-4o was prompted to generate 6 literature reviews (~2000 words; ≥20 citations) on 3 disorders representing different levels of public awareness and research coverage: major depressive disorder (high), binge eating disorder (moderate), and body dysmorphic disorder (low). Each disorder was reviewed at 2 levels of specificity: a general overview (symptoms, impacts, and treatments) and a specialized review (evidence for digital interventions). All citations were extracted (N=176) and systematically verified using Google Scholar, Scopus, PubMed, WorldCat, and publisher databases. Citations were classified as fabricated (no identifiable source), real with errors, or fully accurate. Fabrication and accuracy rates were compared by disorder and review type by using chi-square tests. Across the 6 reviews, GPT-4o generated 176 citations; 35 (19.9%) were fabricated. Among the 141 real citations, 64 (45.4%) contained errors, most frequently incorrect or invalid digital object identifiers. Fabrication rates differed significantly by disorder (χ =13.7; P=.001), with higher rates for binge eating disorder (17/60, 28%) and body dysmorphic disorder (14/48, 29%) than for major depressive disorder (4/68, 6%). While fabrication did not differ overall by review type, stratified analyses showed higher fabrication for specialized versus general reviews of binge eating disorder (11/24, 46% vs 6/36, 17%; P=.01). Accuracy rates also varied by disorder (χ =11.6; P=.003), being lowest for body dysmorphic disorder (20/34, 59%) and highest for major depressive disorder (41/64, 64%). Accuracy rates differed by review type within some disorders, including higher accuracy for general reviews of major depressive disorder (26/34, 77% vs 15/30, 50%; P=.03). Citation fabrication and bibliographic errors remain common in GPT-4o outputs, with nearly two-thirds of citations being fabricated or inaccurate. Reliability systematically varied by disorder familiarity and prompt specificity, with greater risks in less visible or specialized mental health topics. These findings highlight the need for careful prompt design, rigorous human verification of all model-generated references, and stronger journal and institutional safeguards to protect research integrity as LLMs are integrated into academic practice.

Dialectical behavior therapy (DBT) is a specialized treatment that has a growing evidence base for binge-spectrum eating disorders. However, cost and workforce capacity limit wide-scale uptake of DBT since it involves over 20 in-person sessions with a trained professional (and six sessions for guided self-help format). Interventions translated for delivery through modern technology offer a solution to increase the accessibility of evidence-based treatments. We developed the first DBT-specific skills training smartphone application ( : ) for binge-spectrum eating disorders and evaluated its efficacy in a randomized clinical trial. Participants reporting recurrent binge eating were randomized to ( = 287) or a waitlist ( = 289). Primary outcomes were objective binge eating episodes and global levels of eating disorder psychopathology. Secondary outcomes were behavioral and cognitive symptoms, psychological distress, and the hypothesized processes of change (mindfulness, emotion regulation, and distress tolerance). Intention-to-treat analyses showed that the intervention group reported greater reductions in objective binge eating episodes (incidence rate ratio = 0.69) and eating disorder psychopathology ( = -0.68) than the waitlist at 6 weeks. Significant group differences favoring the intervention group were also observed on secondary outcomes, except for subjective binge eating, psychological distress, and distress tolerance. Primary symptoms showed further improvements from 6 to 12 weeks. However, dropout rate was high (48%) among the intervention group, and engagement decreased over the study period. A novel, low-intensity DBT skills training app can effectively reduce symptoms of eating disorders. Scalable apps like these may increase the accessibility of evidence-based treatments.

The minute lheard Diana Ross was supposed to play Billie Holiday I knew it was another cultural rip-off in addition to being an outright abomination.

To The Editor: