Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people.

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making.

Personalised app-based relapse prevention of depressive and anxiety disorders in remitted adolescents and young adults: a protocol of the StayFine RCT.

Introduction: Youth in remission of depression or anxiety have high risks of relapse. Relapse prevention interventions may prevent chronicity. Aim of the study is therefore to (1) examine efficacy of the personalised StayFine app for remitted youth and (2) identify high-risk groups for relapse and resilience.

Meta-Analysis: Relapse Prevention Strategies for Depression and Anxiety in Remitted Adolescents and Young Adults.

Objective: Depression and anxiety cause a high burden of disease and have high relapse rates (39%-72%). This meta-analysis systematically examined effectiveness of relapse prevention strategies on risk of and time to relapse in youth who remitted.

Method: PubMed, PsycInfo, Embase, Cochrane, and ERIC databases were searched up to June 15, 2021.