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Purpose Digital interventions that consider end-user needs, preferences, and concerns may address suboptimal rates of e-health uptake, usage, and engagement. We explored target-user perspectives of e-health treatment and prevention programs for eating disorders (EDs), with a focus on investigating (1) perceived advantages and barriers of e-health; (2) help-seeking intentions; and (3) preferences for different digital functionality, device types, and content-delivery formats. Methods Survey data were analysed from 722 community-based participants. Participants were categorized into one of four groups based on symptom presentation and severity, ranging from low risk to probable bulimia nervosa or binge-eating disorder. Results e-health advantages that received the highest endorsement (~ 84%) were “always there in times of need” and “travel not required”. e-health barriers that received the highest endorsement (~ 50%) were concerns about data privacy and the accuracy of content presented. Nearly three-quarters reported an intention to use an e-health platform for preventing or treating EDs. Preference ratings were highest for programs to be available on all digital devices (relative to restricting the program to one type of device) and for content to be presented via graphics and video tutorials (rather than audio-based). e-health functionality that received highest preference ratings (~ 80%) were added clinician support, tailored feedback, strategies to change unhelpful ED thoughts, screening scales to assess symptoms, ED psychoeducation, and just-in-time intervention prompts. Preference and intention ratings were strikingly similar across all subgroups. Conclusion Findings may inform the development and design of e-health platforms that meet the needs of people at different stages of an ED. Level of evidence Level V, cross-sectional descriptive study.

Purpose Despite featuring in prominent theoretical models, the role of “feeling fat” in certain eating and weight disorder presentations remains poorly understood. This study compared levels of feeling fat between people categorized with referable bulimia nervosa (BN) symptoms, binge-eating disorder (BED) symptoms, and overweight/obesity, and examined the unique associations of feeling fat on measures of eating pathology and functional impairment within each of these subgroups. Methods Data were analyzed from 977 participants who met criteria referable to BN symptoms ( n  = 419), BED symptoms ( n  = 346), or overweight/obesity without ED psychopathology ( n  = 212) based on self-report symptom frequency. Results Analysis of variance revealed that feeling fat levels were highest in the referable BN group, followed by the referable BED group, and then the overweight/obese subgroup. Multiple regressions revealed that feeling fat contributed additional variance to functional impairment and key cognitive (e.g., eating concerns) and behavioural (e.g., dietary restraint) symptoms only among those who met criteria referable to BN. Conclusion Overall, findings suggest that the experience of feeling fat may be an important component of body image particularly among individuals with BN-type symptoms. Present findings may also have implications for the assessment and treatment of feeling fat among different eating and weight disorder presentations. Level of evidence Cross-sectional descriptive study, Level V.

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.

IntroductionOrthorexia nervosa (ON) is a newly recognised condition characterised by an excessive fixation with healthy eating, yet the prevalence of ON is poorly understood. This protocol presents the methodology to undertake a systematic review and meta-analysis on the prevalence of ON in a wide range of populations (including general population and ‘high-risk’). To the authors’ knowledge, the proposed review will be the first systematic review to critically appraise the quality and quantity of evidence on this topic.MethodsThe protocol has been developed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines. Eligible studies will be identified through a systematic search of electronic databases (eg, Medline Complete, PsycInfo and CINAHL complete via the EBSCOHost platform and Embase). Two reviewers will independently screen and review the full text of records, extract the data and critically appraise the evidence using the Joanna Briggs Institute critical appraisal checklist for prevalence studies. A descriptive synthesis will present the characteristics of the included studies and key findings in text and tables. Where appropriate, meta-analysis will be conducted to determine the proportion of individuals with ON (yes/no) according to population groups of interest (ie, general and ‘high-risk’ populations) and/or ON tools.Ethics and disseminationThis review will include published data only; thus, ethical permission will not be necessary. Results of this review will be published in a relevant peer-reviewed scientific journal and presented at conferences in related fields.PROSPERO registration numberCRD42024576557.

Digital health interventions (DHIs) are often burdened by poor user engagement and high drop-out rates, diminishing their potential public health impact. Identifying user-related factors predictive of engagement has therefore drawn significant research attention in recent years. Absent from this literature—yet implied by DHI design—is the notion that individuals who use DHIs have well-regulated learning capabilities that facilitate engagement with unguided intervention content. In this narrative review, we make the case that learning capacity can differ markedly across individuals, and that the requirements of self-guided learning for many DHIs do not guarantee that those who sign up for these interventions have good learning capabilities at the time of uptake. Drawing upon a rich body of theoretical work on self-regulated learning (SRL) in education research, we propose a user-as-learner perspective to delineate parameters and drivers of variable engagement with DHIs. Five prominent theoretical models of SRL were wholistically evaluated according to their relevance for digital health. Three key themes were drawn and applied to extend our current understanding of engagement with DHIs: (a) common drivers of engagement in SRL, (b) the temporal nature of engagement and its drivers, and (c) individuals may differ in learning capability. Integrating new perspectives from SRL models offered useful theoretical insights that could be leveraged to enhance engagement with intervention content throughout the DHI user journey. In an attempt to consolidate these differing—albeit complementary—perspectives, we develop an integrated model of engagement and provide an outline of future directions for research to extend the current understanding of engagement issues in self-guided DHIs.

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.

Objectives Accumulated evidence from longitudinal studies indicated that higher self-compassion levels are associated with greater reductions in eating disorder symptoms over time. However, the nature of these associations is not fully understood. It is possible that these constructs are related in a reciprocal—rather than a unidirectional—fashion, serving to influence each other over time. This study tested for possible longitudinal reciprocal relations between self-compassion and eating disorder symptoms over the course of 8 months. Method Participants were community-based adult women who were invited to complete study measures at baseline (T1; n  = 3039), 4-month follow-up (T2; n  = 1483), and 8-month follow-up (T3; n  = 1361). Cross-lagged panel analyses were computed to test for the hypothesized reciprocal relationships. Results Evidence for reciprocal, negative associations was found between these constructs. Higher (or lower) self-compassion scores at T1 predicted greater decreases (or increases) in global levels of eating pathology at T2 ( β  =  − 0.06), which in turn predicted further increases (or decreases) in self-compassion at T3 ( β  =  − 0.05). The same pattern emerged when modeling overvaluation with weight and shape as a construct of eating disorder symptoms. Conclusions This study established reciprocal longitudinal associations between self-compassion and indices of eating pathology. Incorporating strategies designed to cultivate compassionate ways of responding within established eating disorder prevention programs may help to improve their potency. Preregistration This study was not preregistered.

Existing internet-based prevention and treatment programmes for binge eating are composed of multiple distinct modules that are designed to target a broad range of risk or maintaining factors. Such multi-modular programmes (1) may be unnecessarily long for those who do not require a full course of intervention and (2) make it difficult to distinguish those techniques that are effective from those that are redundant. Since dietary restraint is a well-replicated risk and maintaining factor for binge eating, we developed an internet- and app-based intervention composed solely of cognitive-behavioural techniques designed to modify dietary restraint as a mechanism to target binge eating. We tested the efficacy of this combined selective and indicated prevention programme in 403 participants, most of whom were highly symptomatic (90% reported binge eating once per week). Participants were randomly assigned to the internet intervention ( = 201) or an informational control group ( = 202). The primary outcome was objective binge-eating frequency. Secondary outcomes were indices of dietary restraint, shape, weight, and eating concerns, subjective binge eating, disinhibition, and psychological distress. Analyses were intention-to-treat. Intervention participants reported greater reductions in objective binge-eating episodes compared to the control group at post-test (small effect size). Significant effects were also observed on each of the secondary outcomes (small to large effect sizes). Improvements were sustained at 8 week follow-up. Highly focused digital interventions that target one central risk/maintaining factor may be sufficient to induce meaningful change in core eating disorder symptoms.

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.