BackgroundThe number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available.ObjectiveTo guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps.MethodsA literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design.ResultsSixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp.ConclusionsRandomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems.

Kazantzis, Deane, and Ronan (2000) estimated the effect size (ES) for homework's causal effects on outcome, but did not (a) estimate ES for ''control'' therapy conditions, (b) incorporate data from correlational studies, or (c) test for outliers. The present analysis (46 studies, N = 1,072) replicated and extended Kazantzis and colleagues' review and obtained a pre-posttreatment ES of d = 0.63 for control conditions, and a larger d = 1.08 for therapy conditions with homework. A pooled ES of d = 0.48 favoring homework was obtained when the analysis was restricted to controlled studies contrasting the same therapy. No evidence was found for outlier or publication bias effects. Results supported the conclusion that homework assignments enhance therapy outcomes.

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