Digital Interventions and Mental Health Outcomes in Patients With Cancer: Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,009 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».