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Cited by 390Digital Mental Health InterventionsAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesMental Health Treatment and Access

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 access
Moshe, Isaac, Terhorst, Yannik, Philippi, Paula, Domhardt, Matthias, Cuijpers, Pim, Cristea, Ioana, Pulkki-Råback, Laura, Baumeister, Harald, Sander, Lasse B.·2021·Psychological Bulletin
DOI 10.1037/bul0000334
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Abstract

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
Publisher
American Psychological Association (APA)
Published
2021-08
Volume
147
Issue
8
Pages
749-786
ISSN
  • 0033-2909 (ISSN-L)
  • 1939-1455
DOI
10.1037/bul0000334
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
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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