Systematic review and meta-analysis of adverse events in clinical trials of mental health apps.

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.

A dialectical behavior therapy skills training smartphone app for recurrent binge eating: a randomized clinical trial.

Background: 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.

Autism spectrum disorder and anorexia nervosa: Investigating the behavioural and neurocognitive overlap.

Autism spectrum disorder (autism) and anorexia nervosa (AN) share many clinical features. Two key neurocognitive correlates of the autistic dyad, specifically, mentalising (social impairment) and set-shifting (restricted and repetitive behaviours/interests [RRBI]) were investigated in a sample of 327 adult participants with autism (n = 100; 50 females, 50 male), AN (n = 82; 54 females, 28 male), autism and AN (n = 45; 36 females, 9 male), and 100 (50 female, 50 male) control participants from the general population. A battery of self-report (Autism Spectrum Quotient, Eating Disorder Examination Questionnaire, Reflective Function Questionnaire, and Repetitive Behaviour Questionnaire 2 - Adult version) and performance-based (Wisconsin Card Sort Task [WCST] and Penn Emotion Recognition Test [ER-40]) measures were administered online.

Application of Machine Learning Techniques to the Prediction of Onset and Persistence of Binge Eating: A Prospective Study.

Objective: Machine learning (ML) techniques have shown promise for enhancing prediction of clinical outcomes; however, its application to predicting binge eating has been scarcely explored. We applied ML techniques to predict binge eating onset (vs. continued absence) and persistence (vs.