Digital therapeutics for depression in Germany: A systematic app review
TL;DR
DiGAs for depression differ substantially in their adherence to clinical guidelines, quality, and functional characteristics, highlighting the need for transparent and multidimensional evaluations to support informed clinical decision-making.
Abstract
Background and objective In Germany, digital health applications (DiGAs) approved for statutory health insurance are increasingly used in the treatment of depression, yet their quality, content, and adherence to evidence-based approaches and adherence to guidelines are poorly studied. Methods DiGAs for depression were identified through a structured selection process and independently evaluated using a predefined assessment framework. Analyses included guideline conformity and the presence of internet-based cognitive behavioral therapy (iCBT) components. App quality was assessed using the Mobile Application Rating Scale (MARS), and functional characteristics were evaluated using the Institute for Healthcare Informatics functionality scoring. Interrater reliability was calculated using Fleiss' kappa. Results Five DiGAs were included for the review. The overall degree of guideline conformity varied across applications (mean = 62.24%, SD = 26.74%, range = 22–72%), indicating marked differences in the implementation of guideline-recommended therapeutic content. Three DiGAs implemented all five iCBT components, whereas the remaining applications included only selected elements. Monitoring approaches ranged from comprehensive daily tracking to minimal symptom assessments without feedback. Individual therapeutic support via telemedicine varied from in-app coaching and crisis services to indirect integration via external healthcare professionals. Heterogeneity was also observed in structure and interactivity. App quality, as measured by the MARS, was moderate (mean = 3.8, SD = 0.4, range = 3.2–4.2), while functional design scores differed across applications (mean = 9, SD = 2, range = 7–11). Conclusion DiGAs for depression differ substantially in their adherence to clinical guidelines, quality, and functional characteristics. These findings highlight the need for transparent and multidimensional evaluations to support informed clinical decision-making.