The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials

Although impressive progress has been made toward developing empirically-supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app-supported smartphone interventions are touted as a possible solution, access to up-to-date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta-analysis of 66 randomized controlled trials of app-supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=–0.05, n=3), post-traumatic stress symptoms (g=0.18, n=4), and negative affect (g=–0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)-based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face-to-face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app-supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost-effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment

Interactions between different eating patterns on recurrent binge-eating behavior: a machine learning approach

OBJECTIVE: Previous research has shown that certain eating patterns (rigid restraint, flexible restraint, intuitive eating) are differentially related to binge eating. However, despite the distinctiveness of these eating patterns, evidence suggests that they are not mutually exclusive. Using a machine learning-based decision tree classification analysis, we examined the interactions between different eating patterns in distinguishing recurrent (defined as ≥4 episodes the past month) from nonrecurrent binge eating. METHOD: Data were analyzed from 1,341 participants. Participants were classified as either with (n = 512) or without (n = 829) recurrent binge eating. RESULTS: Approximately 70% of participants could be accurately classified as with or without recurrent binge eating. Intuitive eating emerged as the most important classifier of recurrent binge eating, with 75% of those with above-average intuitive eating scores being classified without recurrent binge eating. Those with concurrently low intuitive eating and high dichotomous thinking scores were the group most likely to be classified with recurrent binge eating (84% incidence). Low intuitive eating scores were associated with low binge-eating classification rates only if both dichotomous thinking and rigid restraint scores were low (33% incidence). Low flexible restraint scores amplified the relationship between high rigid restraint and recurrent binge eating (81% incidence), and both a higher and lower BMI further interacted with these variables to increase recurrent binge-eating rates. CONCLUSION: Findings suggest that the presence versus absence of recurrent binge eating may be distinguished by the interaction among multiple eating patterns. Confirmatory studies are needed to test the interactive hypotheses generated by these exploratory analyses