Providing Human Support for the Use of Digital Mental Health Interventions: Systematic Meta-review
Open accessAbstract
BACKGROUND: Digital mental health interventions (DMHIs) have been increasingly deployed to bridge gaps in mental health care, particularly given their promising efficacy. Nevertheless, attrition among DMHI users remains high. In response, human support has been studied as a means of improving retention to and outcomes of DMHIs. Although a growing number of studies and meta-analyses have investigated the effects of human support for DMHIs on mental health outcomes, systematic empirical evidence of its effectiveness across mental health domains remains scant. OBJECTIVE: We aimed to summarize the results of meta-analyses of human support versus no support for DMHI use across various outcome domains, participant samples, and support providers. METHODS: We conducted a systematic meta-review of meta-analyses, comparing the effects of human support with those of no support for DMHI use, with the goal of qualitatively summarizing data across various outcome domains, participant samples, and support providers. We used MEDLINE, PubMed, and PsycINFO electronic databases. Articles were included if the study had a quantitative meta-analysis study design; the intervention targeted mental health symptoms and was delivered via a technology platform (excluding person-delivered interventions mediated through telehealth, text messages, or social media); the outcome variables included mental health symptoms such as anxiety, depression, stress, posttraumatic stress disorder symptoms, or a number of these symptoms together; and the study included quantitative comparisons of outcomes in which human support versus those when no or minimal human support was provided. RESULTS: The results of 31 meta-analyses (505 unique primary studies) were analyzed. The meta-analyses reported 45 effect sizes; almost half (n=22, 48%) of them showed that human-supported DMHIs were significantly more effective than unsupported DMHIs. A total of 9% (4/45) of effect sizes showed that unsupported DMHIs were significantly more effective. No clear patterns of results emerged regarding the efficacy of human support for the outcomes assessed (including anxiety, depression, posttraumatic stress disorder, stress, and multiple outcomes). Human-supported DMHIs may be more effective than unsupported DMHIs for individuals with elevated mental health symptoms. There were no clear results regarding the type of training for those providing support. CONCLUSIONS: Our findings highlight the potential of human support in improving the effects of DMHIs. Specifically, evidence emerged for stronger effects of human support for individuals with greater symptom severity. There was considerable heterogeneity across meta-analyses in the level of detail regarding the nature of the interventions, population served, and support delivered, making it difficult to draw strong conclusions regarding the circumstances under which human support is most effective. Future research should emphasize reporting detailed descriptions of sample and intervention characteristics and describe the mechanism through which they believe the coach will be most useful for the DMHI.
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Real-time or synchronous human support from a trained mental health professional improves engagement rates.
“Real-time or ‘synchronous’ human support from a trained mental health professional improves engagement rates as mental health professionals can provide supportive accountability and motivational support.18 19”
From A mindfulness-based, cognitive, social, digital relapse-prevention intervention for youth with depression in Australia: study protocol for a randomised controlled trial of Rebound (2024) · cites this paper as [19]
The Introduction (Added Support for DMHIs; page unavailable) says human support can increase engagement through encouragement and supportive accountability, delivered synchronously or asynchronously. It does not establish that synchronous support specifically improves engagement rates or that a trained mental health professional is necessary. The Discussion (Principal Findings; page unavailable) reports no clear pattern favoring highly trained providers and describes the proposed engagement mechanisms as needing further study. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA recent review of 31 meta-analyses confirmed that just under half of reported studies showed that guidance was significantly better than treatment with no human support or minimal such support.
“a recent review of 31 meta-analyses confirmed that just under half of reported studies showed that guidance was significantly better than treatment with no human support or minimal such support.”
From Transdiagnostic and tailored internet intervention to improve mental health among university students: Research protocol for a randomized controlled trial (2024) · cites this paper as [10]
The Discussion (Principal Findings) reports 31 meta-analyses and says that 22 of 45 effect sizes (48%) favored human-supported over unsupported digital mental health interventions. The claim incorrectly describes that fraction as a fraction of reported studies; the paper counts effect sizes, not studies. (LLM, conf=0.90)
Experimental — accuracy not yet measuredIn the same review, 9% of studies showed that unguided interventions had better outcomes than supported ones.
“At the same time, 9% showed that unguided interventions had better outcomes than supported ones.”
From Transdiagnostic and tailored internet intervention to improve mental health among university students: Research protocol for a randomized controlled trial (2024) · cites this paper as [10]
The Discussion (Principal Findings; page unavailable) reports that 9% (4/45) of effect sizes favored unsupported interventions significantly. The claim correctly conveys the direction and percentage, but calls these effect sizes “studies”; the review does not say that 9% of studies found better outcomes for unguided interventions. (LLM, conf=0.91)
Experimental — accuracy not yet measuredIt is still not clear what factors indicate an obvious need for supported interventions, although severity of mental health problems might be one indicator.
“It is still not clear what factors indicate an obvious need for supported interventions, although the severity of mental health problems might be one indicator [10].”
From Transdiagnostic and tailored internet intervention to improve mental health among university students: Research protocol for a randomized controlled trial (2024) · cites this paper as [10]
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Optimizing Workplace Digital Mental Health Interventions: Systematic Review and Meta-Analysis
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“It is still not clear what factors indicate an obvious need for supported interventions, although the severity of mental health problems might be one indicator [10].”
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A systematic review on personalization of treatment components in IBIs for mental disorders
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Digitale Technologien zur Verbesserung der psychischen Gesundheit
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The Vanguard of Psychiatry: Artificial Intelligence as a Catalyst for Change
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Digital Therapies for Substance Use Disorders: Recent Advances and Engagement Strategies
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“18 19 Furthermore, to provide an effective intervention, human support needs to lead to ‘effective’ engagement, which includes behaviour change.”
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Youth Interventions With and Without Supervised Practice: A Second-Order Meta-Analysis
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Is AI-supported therapy the answer to the growth of mental health problems or snake oil?
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Increasing Provider Utilization of Apps for Anxiety and Depression Management
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The Effect of Chatbot Interventions on Gambling Disorder: A Systematic Review
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Digital interventions for mental health promotion among individuals with disabilities
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Self-Directed vs Clinician-Delivered Cognitive Behavioral Therapy for Chronic Pain
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Blended Care für Jugendliche mit depressiven Erkrankungen aus der Sicht von Behandelnden
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A pilot study of a scalable training programme for coaches delivering webSTAIR
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- Authors
- Werntz, A., Amado, S., Jasman, M., Ervin, A., & Rhodes, J. E.
- Affiliations
- University of Massachusetts Boston (US)
- Corresponding author
- Rhodes, Jean E
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-02-06
- Volume
- 25
- Pages
- e42864
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC9941905
- OpenAlex
- W4315695090
- Cited by
- 158
- References
- 78
- Citations by year
- 2026: 63 · 2025: 42 · 2024: 39 · 2023: 14
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (1r41mh126795-01a1)
- National Institute of Mental Health (1R41MH126795-01A1, R41 MH126795)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- digital mental health interventions, human support, human-technology interaction, attrition, retention, anxiety, depression, stress, post-traumatic stress disorder, symptom severity, mental health treatment gap, telemedicine, mobile health apps
- MeSH terms
- Anxiety, Humans, Mental Health, Stress Disorders, Post-Traumatic, Telemedicine
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.
Statistics and arithmetic are recomputed, not judged.
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