Background Depression is a major cause for disability worldwide, and digital health interventions are expected to be an augmentative and effective treatment. According to the fast-growing field of information and communication technologies and its dissemination, there is a need for mapping the technological landscape and its benefits for users. Objective The purpose of this scoping review was to give an overview of the digital health interventions used for depression. The main goal of this review was to provide a comprehensive review of the system landscape and its technological state and functions, as well as its evidence and benefits for users. Methods A scoping review was conducted to provide a comprehensive overview of the field of digital health interventions for the treatment of depression. PubMed, PSYNDEX, and the Cochrane Library were searched by two independent researchers in October 2020 to identify relevant publications of the last 10 years, which were examined using the inclusion and exclusion criteria. To conduct the review, we used Rayyan, a freely available web tool. Results In total, 65 studies were included in the qualitative synthesis. After categorizing the studies into the areas of prevention, early detection, therapy, and relapse prevention, we found dominant numbers of studies in the area of therapy (n=52). There was only one study for prevention, 5 studies for early detection, and 7 studies for relapse prevention. The most dominant therapy approaches were cognitive behavioral therapy, acceptance and commitment therapy, and problem-solving therapy. Most of the studies revealed significant effects of digital health interventions when cognitive behavioral therapy was applied. Cognitive behavioral therapy as the most dominant form was often provided through web-based systems. Combined interventions consisting of web-based and smartphone-based approaches are increasingly found. Conclusions Digital health interventions for treating depression are quite comprehensive. There are different interventions focusing on different fields of care. While most interventions can be beneficial to achieve a better depression treatment, it can be difficult to determine which approaches are suitable. Cognitive behavioral therapy through digital health interventions has shown good effects in the treatment of depression, but treatment for depression still stays very individualistic.

Zusammenfassung Ziel der Studie Die Nutzer*innenakzeptanz von digitalen Gesundheitstechnologien bei leicht- bis mittelgradiger Depression ist bislang rudimentär erforscht und wird in vorliegender Studie untersucht. Methodik Es wurden problemzentrierte Einzelinterviews mit 3 Betroffenen, 3 Angehörigen und 13 Leistungserbringern (v. a. Medizin, Psychologie, Pflege) durchgeführt und qualitativ ausgewertet. Ergebnisse Entlang der Unified Theory of Acceptance and Use of Technology wurden Haltungen von Betroffenen, Angehörigen und Leistungserbringern dargestellt. Die Leistungserwartung und unterstützenden Rahmenbedingungen stellen bedeutsame Prädiktoren für die Nutzungsintention dar. Schlussfolgerung Die Ergebnisse bieten eine Basis, um im Folgeschritt die Kernbedürfnisse und Haltungen zu priorisieren. Im Sinne eines ethischen, nachhaltigen und ökonomischen Einsatzes ist weitere Forschung zur Nutzer*innenakzeptanz notwendig.

Digital health interventions may contribute to closing the treatment gap for depression by reaching large populations at relatively low costs. This article presents the results of a broad, multisided German survey in 2020 on the acceptance and use of digital health interventions in depression care from the perspective of patients, their relatives, and health professionals. A total of 97 patients and relatives and 229 health professionals participated. Survey participants reported openness towards the use of digital health interventions in depression care but little knowledge and experience in the field. Digital health interventions appear to be a promising opportunity for reducing depressive symptoms and shortening waiting time for depression treatment, especially in rural areas. Providing information and technical competencies may increase awareness and knowledge about digital health interventions and the benefits of depression care.

BACKGROUND Depression is a major cause for disability worldwide and digital health interventions are expected to be a more augmentative and effective treatment. According to the fast-growing field of information and communication technologies and its dissemination, there is a need of mapping the technological landscape, as well as its benefits for users. OBJECTIVE The purpose of this scoping review was to give an overview of the used DHI for depression. The main goal of this review was then to provide a comprehensive review of the system landscape and its technological state and functions, as well as its evidence and benefits for users. METHODS A scoping review was conducted to provide a comprehensive overview in the field of digital health interventions for the treatment of depression. PubMed, Psyndex and the Cochrane Library were searched by two independent researchers in October 2020 to identify relevant publications of the last ten years and were examined due to inclusion and exclusion criteria. For conducting the systematic review, ‘Rayyan’, a free web-tool, was used. RESULTS In total, 65 studies were included in the qualitative synthesis. After categorizing the field of application in prevention, early detection, therapy and relapse prevention, the search showed dominant numbers of studies in the field of therapy (N= 52). There was only one study for prevention, five studies for early detection and seven studies for relapse prevention. The most dominant therapy approaches were cognitive behavior therapy, acceptance and commitment therapy and problem-solving therapy. Most of the studies revealed significant effects of digital health interventions when cognitive behavior therapy applied. Cognitive behavior therapy as the most dominant form was often provided by web-based systems. Combined approaches consisting of web-based and smartphone-based approaches are constantly rising. CONCLUSIONS Digital health interventions for treating depression are quite comprehensive. There are different interventions focusing on different fields of care. While most interventions can be beneficial to achieve a better depression treatment, it can be hindering in determining which approaches are suitable. Cognitive behavior therapy that has been realized with digital health interventions has shown good effects in the treatment of depression, but treatment for depression still stays very individualistic.

Zusammenfassung Hintergrund Entscheidungsunterstützungssysteme auf Basis künstlicher Intelligenz können dazu beitragen, den Antibiotikaeinsatz im Krankenhaus zu optimieren und die Entstehung von Resistenzen vorzubeugen. Das Ziel der vorliegenden Untersuchung ist es, hemmende und fördernde Faktoren für eine erfolgreiche Implementierung aus Perspektive von ärztlichem Personal herauszuarbeiten. Methode Es wurden 14 problemzentrierte Interviews mit ärztlichem Personal aus der stationären Versorgung durchgeführt und anhand der strukturierenden Inhaltsanalyse nach Kuckartz qualitativ ausgewertet. Ergebnisse Entlang des Human-Organization-Technology-fit-Modells wurden Haltungen aus der Perspektive des ärztlichen Personals dargestellt. Technologie- und organisationsbezogene Themen stellen bedeutende Faktoren für die Implementierung dar. Vor allem die Kompatibilität mit bestehenden Systemen sowie die Benutzerfreundlichkeit des Systems nehmen einen hohen Stellenwert bei einer erfolgreichen Implementierung ein. Zusätzlich wird die Einarbeitung von potenziellen Nutzergruppen und die technische Ausstattung der Organisation als zentral erachtet. Nicht zuletzt gilt es die Technikkompetenzen potenzieller Nutzergruppen nachhaltig zu fördern und Vertrauen für das System zu schaffen. Schlussfolgerungen Die Ergebnisse bieten eine Basis, um im Folgeschritt die identifizierten Faktoren quantitativ priorisieren zu können. Es wird deutlich, dass beim Einsatz von Entscheidungsunterstützungssystemen neben Systemeigenschaften auch kontextspezifischen und nutzerbezogenen Gegebenheiten eine zentrale Bedeutung zukommt, um Systemvertrauen und eine langfristige Implementierung zu gewährleisten.