Digital Interventions and Mental Health Outcomes in Patients With Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Rising cancer rates have amplified psychiatric and psychosocial burdens, with 35-40% of patients exhibiting diagnosable psychiatric disorders. While Digital Mental Health Interventions (DMHIs) present potential solutions for improving emotional well-being in this population, evidence remains fragmented and lacks clarity regarding optimal implementation strategies. This study evaluates the efficacy of digital interventions on mental health outcomes in cancer patients, with particular focus on intervention duration and stakeholder involvement as moderating factors. OBJECTIVE: This study aims to (1) characterize digital interventions targeting mental health outcomes in cancer patients; (2) quantify their effectiveness in reducing anxiety and depression; and (3) examine whether intervention duration and stakeholder involvement moderate treatment outcomes. METHODS: This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guidelines and was retrospectively registered in PROSPERO on May 25th (no. CRD420251058005). Eight databases (Cochrane Central Trials Registry, Web of Science, Scopus, PubMed, PsycINFO, Global Health, Embase and Medline) were searched from inception to 2024. Eligible randomized controlled trials (RCTs) evaluated digital interventions for mental health in cancer patients. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias Tool 2.0. Random-effects meta-analyses calculated standardized mean differences (SMDs). Pooled results were reported as the odds ratio and 95% confidence interval (CI). The heterogeneity was assessed with the I² test (%). Subgroup analyses explored the potential effects of intervention duration and stakeholder involvement. Sensitivity analyses and publication bias assessments were performed to ensure robustness of findings. RESULTS: Twenty-two RCTs were included in the review. The geolocation involves four continents worldwide: Asia (n=9), Europe (n=5), North America (n=6), and Oceania (n=2). Interventions comprised meditation/mindfulness (n=3), education (n=8), self-management (n=11), physical exercise (n=4), and patient community communication (n=8). Twelve studies were included in the meta-analysis. Overall, digital interventions showed non-significant effects on depression (SMD -0.48, 95% CI [-1.00, 0.03], p=0.07; 9 studies) or anxiety (SMD -0.61, 95% CI [-1.29, 0.06], p=0.08; 8 studies) with substantial heterogeneity (I2>90%). Subgroup analyses revealed interventions (<1 month) significantly reduced anxiety (SMD -0.73, 95% CI [-1.42, -0.04], p=0.04), while interventions (1-2 months) reduced depression (SMD -0.18, 95% CI [-0.35, -0.01], p=0.04). Interventions showed no statistically significant differences when stratified by stakeholder involvement. Sensitivity analyses excluding one outlier yielded significantly lower heterogeneity but preserved unchanged overall and subgroup patterns. CONCLUSIONS: While DMHIs overall showed no effect on anxiety or depression interventions, exploratory analyses suggest potential benefits of duration-tailored approaches. High heterogeneity and methodological limitations indicate that DMHIs may be most effective when integrated into personalized care models rather than standalone treatments. Future research should employ standardized outcomes and investigate mechanisms underlying potential duration-dependent efficacy. CLINICALTRIAL: PROSPERO 2025 CRD420251058005; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251058005.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".