The DOI matches its Crossref record; 293 of the 293 references it cites were found in a registry or an index.
Digital interventions for the treatment of depression: a meta-analytic review
Open accessAbstract
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 (I2 = 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 interventions and no significant difference between human-guided digital interventions and face-to-face psychotherapy for depression, although the number of studies in both comparisons was low. Findings from the current meta-analysis provide evidence for the efficacy and effectiveness of digital interventions for the treatment of depression for a variety of populations. However, reported effect sizes may be exaggerated because of publication bias, and compliance with digital interventions outside of highly controlled settings remains a significant challenge. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Metadata
- Authors
- Moshe, I., Terhorst, Y., Philippi, P., Domhardt, M., Cuijpers, P., Cristea, I., Pulkki-Råback, L., Baumeister, H., & Sander, L. B.
- Affiliations
- University of Helsinki (FI)
- Universität Ulm (DE)
- Amsterdam Public Health (NL)
- University of Pavia (IT)
- Corresponding author
- Moshe, Isaac
- ORCID
- Venue
- Psychological Bulletin
- Published
- 2021-08
- Volume
- 147
- Issue
- 8
- Pages
- 749-786
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W4205983219
- Semantic Scholar
- 465098517e78d0cda5fff852518ff0e9bfb4e24b
- Cited by
- 390
- References
- 292
- Citations by year
- 2026: 66 · 2025: 109 · 2024: 85 · 2023: 88 · 2022: 39 · 2021: 2
- FWCI
- 47.92
- Type
- journal article
- Language
- en
- Open access
- green
- research.vu.nl
- Licence
- other-oa
- Locations
- Psychological Bulletin (publishedVersion)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- Padua Research Archive (University of Padova) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Suomen Kulttuurirahasto
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Keywords
- digital interventions, depression treatment, mobile application, computer-based intervention, online intervention, self-help intervention, remote care, COVID-19 pandemic, publication bias, intervention adherence
- MeSH terms
- COVID-19, SARS-CoV-2, Depression, Humans, Pandemics
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