Digital Mental Health Interventions for University Students With Mental Health Difficulties: A Systematic Review and Meta‐Analysis
Open accessAbstract
Background: While third‐level educational institutions have long provided counselling, a sharp rise in demand has led to limited access to mental health supports for many students, including those with ongoing difficulties. Digital mental health interventions represent one response to this unmet need, given the potential low cost and scalability associated with no‐to‐low human resources involved. Objective: The aim of this study was to conduct a systematic review and meta‐analysis of the literature examining effectiveness of digital mental health interventions for university students with ongoing mental health difficulties. Methods: The following databases were searched: PubMed, EBSCOhost (CINHAHL/PsycINFO/PsycArticles) and Web of Science. Two‐armed randomised‐control trials were included in the meta‐analysis. A random‐effects meta‐analysis was conducted and standardised mean differences were calculated. Effect sizes were then compared in terms of therapeutic approach, and whether interventions were fully automated or guided interventions. This study was registered with PROSPERO, CRD42024504265. Results: Thirty four eligible studies were included in this narrative synthesis, of which 21 randomised‐controlled trials were included in the meta‐analysis. Random‐effects meta‐analysis indicated an overall medium effect size in favour of digital interventions for both depression (Cohen's d = 0.55), and anxiety (Cohen's d = 0. 46). Of note, for anxiety outcomes, fully automated interventions appeared more effective (d = 0.55) than guided interventions (d = 0.35). Conclusions: Digital mental health interventions are associated with beneficial effects for college students when measured in terms of anxiety and depression symptom severity. For anxiety, fully automated interventions may be more effective than guided interventions to reduce symptom severity.
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The adherence figures in this study are notably higher than completion rates typically observed in digital mental health interventions.
“These figures are notably higher than completion rates typically observed in digital mental health interventions, where dropout rates commonly range from 23% to 64% and completion rates vary widely from 26% to 100% depending on intervention characteristics and population [29,32,33].”
From Schema-Informed Digital Mental Health Intervention for Maladaptive Cognitive-Emotional Patterns: Randomized Controlled Trial (2025) · cites this paper as [33]
The Introduction, under “Guided Versus Fully Automated Digital Interventions,” reports engagement rates of 26%–100% and study attrition rates of 23%–64%. Those ranges match the citing sentence, but the paper distinguishes engagement from intervention completion and defines study attrition as failure to complete the research protocol. It therefore does not establish the quoted ranges as completion and intervention-dropout rates, respectively. (LLM, conf=0.86)
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- Authors
- Madrid‐Cagigal, A., Kealy, C., Potts, C., Mulvenna, M. D., Byrne, M., Barry, M. M., & Donohoe, G.
- Affiliations
- Ollscoil na Gaillimhe – University of Galway (IE)
- University of Ulster (GB)
- Corresponding author
- Donohoe, Gary
- ORCID
- Venue
- Early Intervention in Psychiatry
- Publisher
- Wiley
- Published
- 2025-03
- Volume
- 19
- Issue
- 3
- Pages
- e70017-e70017
- ISSN
- 1751-7885 (ISSN-L)
- 1751-7893
- PMC
- PMC11876723
- OpenAlex
- W4408162062
- Cited by
- 78
- References
- 116
- Citations by year
- 2026: 62 · 2025: 16
- FWCI
- 9.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Early Intervention in Psychiatry (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway) (acceptedVersion, cc-by-nc-nd)
- Ulster University Research Portal (Ulster University) (submittedVersion, cc-by-nc-nd)
- Ulster University Research Portal (Ulster University) (submittedVersion, other-oa)
- ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway) (submittedVersion, cc-by)
- Research Repository UCD (University College Dublin) (cc-by)
- Funders
- Higher Education Authority (422560)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- digital mental health interventions, university students, student mental health, depression, anxiety, automated intervention, guided intervention, access to mental health care, symptom severity
- MeSH terms
- Mental Disorders, Humans, Students, Universities, 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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