The high global prevalence of depression, together with the recent acceleration of remote care owing to the COVID-19 pandemic, has prompted increased interest in the efficacy of digital interventions for the treatment of depression. We provide a summary of the latest evidence base for digital interventions in the treatment of depression based on the largest study sample to date. A systematic literature search identified 83 studies (N = 15,530) that randomly allocated participants to a digital intervention for depression versus an active or inactive control condition. Overall heterogeneity was very high (I 2 = 84%). Using a random-effects multilevel metaregression model, we found a significant medium overall effect size of digital interventions compared with all control conditions (g = .52). Subgroup analyses revealed significant differences between interventions and different control conditions (WLC: g = .70; attention: g = .36; TAU: g = .31), significantly higher effect sizes in interventions that involved human therapeutic guidance (g = .63) compared with self-help interventions (g = .34), and significantly lower effect sizes for effectiveness trials (g = .30) compared with efficacy trials (g = .59). We found no significant difference in outcomes between smartphone-based apps and computer-and Internet-based intervenproofreading the article and Arne Lutsch and Franziska Wegner for their support with the updated literature search and data extraction.A file containing all extracted data used in the current meta-analysis can be downloaded from the following link: https://osf.io/us4f5/?view_only= 58dc3441f27f44f283df5f8602af82f9.

Background Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Instruments for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA. Only few validation studies investigated its metric quality. No study has evaluated the construct validity and concurrent validity. Objective This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS. Methods Data was pooled from 15 international app quality reviews to evaluate the metric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. Construct validity was evaluated by assessing related competing confirmatory models by confirmatory factor analysis (CFA). Non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices were used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations to another quality assessment tool (ENLIGHT) were investigated. Reliability was determined using Omega. Objectivity was assessed by intra-class correlation. Results In total, MARS ratings from 1,299 MHA covering 15 different health domains were included. Confirmatory factor analysis confirmed a bifactor model with a general factor and a factor for each dimension (RMSEA = 0.074, TLI = 0.922, CFI = 0.940, SRMR = 0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC = 0.82). MARS correlated with ENLIGHT (ps<.05). Conclusion The metric evaluation of the MARS demonstrated its suitability for the quality assessment. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.

Background Although the efficacy of Internet‐ and mobile‐based interventions (IMIs) for anxiety is established, little is known about the intervention components responsible for therapeutic change. We conducted the first comprehensive meta‐analytic review of intervention components of IMIs for adult anxiety disorders. Methods Randomized controlled trials (RCTs) comparing IMIs for anxiety disorders to active online control groups, or IMIs to dismantled variations of the same intervention (± specific components) were identified by a systematic literature search in six databases. Outcomes were validated observer‐rated or self‐report measures for anxiety symptom severity and treatment adherence (number of completed modules and completer rate). This meta‐analytic review is registered with PROSPERO (CRD42017068268). Results We extracted the data of 34 RCTs (with 3,724 participants) and rated the risk of bias independently by two reviewers. Random‐effects meta‐analyses were performed on 19 comparisons of intervention components (i.a., different psychotherapeutic orientations, disorder‐specific vs. transdiagnostic approaches, guidance factors). IMIs had a large effect when compared to active online controls on symptom severity (standardized mean difference [SMD] of −1.67 [95% CI: −2.93, −0.42]; P = 0.009). Thereby, guided IMIs were superior to unguided interventions on symptom severity (SMD of −0.39 [95% CI: −0.59, −0.18]; P = 0.0002) and adherence (SMD of 0.38 [95% CI: 0.10, 0.66]; P = 0.007). Conclusions Overall, the results of this meta‐analysis lend further support to the efficacy of IMIs for anxiety, pointing to their potential to augment service supplies. Still, future research is needed to determine which ingredients are essential, as this meta‐analytic review found no evidence for incremental effects of several single intervention components apart from guidance.

Preventive and clinical interventions for survivors of CSA should utilize psychoeducation and cognitive strategies that are adapted to the developmental level of the victim and that seek to enhance social support from significant others. Future research should focus on longitudinal research designs considering resilience rather as a dynamic process with multiple dimensions in a social and developmental context.

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