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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Self-citationA systematic review of digital and face-to-face cognitive behavioral therapy for depression
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Evaluation of the Use of Digital Mental Health Platforms and Interventions: Scoping Review
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Home-Use Transcranial Direct Current Stimulation for the Treatment of a Major Depressive Episode
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Self-citationSingle-session interventions embedded within Tumblr: acceptability, feasibility, and utility study
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Self-citationDigital mental health interventions for the treatment of depression: A multiverse meta-analysis
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Self-citationPersonalised depression forecasting using mobile sensor data and ecological momentary assessment
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Updated taxonomy of digital mental health interventions: a conceptual framework
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The Economics of Treatment for Depression
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Self-citationInternet-Based Behavioral Activation for Depression: Systematic Review and Meta-Analysis
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A Mental Health and Well-Being Chatbot: User Event Log Analysis
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Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression
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“There is now a large body of evidence supporting the efficacy of digital interventions for the prevention and treatment of depression [16-20], with recent studies providing promising evidence for the treatment of depression in patients with comorbid physical conditions [21-25].”
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Toward a Theory of Lifespan Personality Trait Development
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Predictors of adherence to a publicly available self-guided digital mental health intervention
Woolley, M. G., Klimczak, K. S., Davis, C. H., & Levin, M. E. (2024) · Cognitive Behaviour Therapy · doi:10.1080/16506073.2024.2341807 · Cited by 25 · see citing works
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Transforming mental health research and care through artificial intelligence
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Personalization in Digital Psychological Interventions for Young Adults
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Exploring the efficacy of psychotherapies for depression: a multiverse meta-analysis
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Self-citationArtificial intelligence and psychotherapy: A counterpoint
Richards, D. (2024) · Counselling and Psychotherapy Research · doi:10.1002/capr.12758 · Cited by 25 · see citing works
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Sextl-Plötz, T., Steinhoff, M., Baumeister, H., Cuijpers, P. J., Ebert, D. D., & Zarski, A. (2024) · Internet Interventions · doi:10.1016/j.invent.2024.100760 · Cited by 20 · see citing works
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An Integrative Engagement Model of Digital Psychotherapy: Exploratory Focus Group Findings
Zech, J. M., Johnson, M., Pullmann, M. D., Hull, T. D., Althoff, T., Munson, S. A., Fridling, N., Litvin, B., Wu, J., & Areán, P. A. (2023) · JMIR Formative Research · doi:10.2196/41428 · Cited by 18 · see citing works
Digitization and its Discontents: The Promise and Limitations of Digital Mental Health Interventions
Conrad, J. A. (2024) · Journal of Contemporary Psychotherapy · doi:10.1007/s10879-024-09620-2 · Cited by 19 · see citing works
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“That approach reduced the nuanced description of the concepts but allowed for a comprehensive depiction of the themes and their key changes across the set time-points.”
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Digital Wellness Programs in the Workplace: Meta-Review
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“Although preliminary, this is broadly in line with several recent meta-analyses that evaluated digital interventions for symptoms of depression [17,20,61].”
InfluentialBraun, P., Atik, E., Guthardt, L., Apolinário-Hagen, J., & Schückes, M. (2023) · JMIR Formative Research · doi:10.2196/45970 · Cited by 17 · see citing works
“Internet-based CBT (iCBT) is widely acknowledged as a useful and effective resource for increasing access to mental health care [15,16], especially for digital natives such as students [17].”
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Effects of game-based digital interventions for mental disorders: A meta-analysis
Zhan, J., Liu, C., Wang, Z., Cai, Z., & He, J. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.07.095 · Cited by 15 · see citing works
Digital body image interventions for adult women: A meta-analytic review
Conboy, L., Mingoia, J., Hutchinson, A. D., Reisinger, B. A. A., & Gleaves, D. H. (2024) · Body Image · doi:10.1016/j.bodyim.2024.101776 · Cited by 15 · see citing works
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Bendig, E., Küchler, A.-M., Baumeister, H., & Becker, T. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18189433 · Cited by 14 · see citing works
“This is promising, especially since adherence problems are repeatedly reported in stand-alone online interventions [35,36].”
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Self-citationAngerer, F., Mennel, V., Grund, S., Mayer, A., Büscher, R., Sander, L., Cuijpers, P. J., Terhorst, Y., Baumeister, H., & Domhardt, M. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.09.055 · Cited by 15 · see citing works
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Bostrøm, K., Børøsund, E., Eide, H., Varsi, C., Kristjansdottir, Ó. B., Schreurs, K. M. G., Waxenberg, L. B., Weiss, K. E., Morrison, E. J., Stavenes Støle, H., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/47284 · Cited by 12 · see citing works
“Recently, digital CBT-based self-management programs have also shown promise in positively impacting depression [60,61].”
Väkevä, J., Hämäläinen, P., & Lindqvist, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714075 · Cited by 13 · see citing works
Lillelien, K., & Jensen, M. T. (2025) · Education Sciences · doi:10.3390/educsci15070799 · Cited by 17 · see citing works
- 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 · 2013: 1
- 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
- Social Sciences › Psychology › Social Psychology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-08.
Statistics and arithmetic are recomputed, not judged.
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