Effectiveness of mHealth Interventions to Improve Follow-Up and Management Among Solid Organ Transplant Recipients: Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
BACKGROUND: Effective follow-up and management after organ transplantation are crucial for transplant recipients. Mobile health (mHealth) interventions have emerged as a significant approach for facilitating follow-up and management. However, there is a lack of systematic reviews and meta-analyses of their effectiveness. OBJECTIVE: This study aimed to systematically review and synthesize evidence regarding the effectiveness of mHealth interventions in enhancing follow-up and management for transplant recipients. METHODS: This study included both randomized controlled trials (RCTs) and nonrandomized studies of interventions (NRSIs) that compared the effects of mHealth interventions with usual care in transplant recipients by searching PubMed, Web of Science, Scopus, Embase, CINAHL, and CENTRAL from database inception to June 2025. The primary outcomes included self-care ability, medical regimen adherence, self-monitoring, communication and counseling, medication adherence, physical activity, nutrition, all-cause mortality, complications, rehospitalization, and emergency and outpatient department visits. The risk of bias for each study was assessed using version 2 of the Cochrane risk-of-bias tool for RCTs and the Risk of Bias in Nonrandomized Studies of Interventions tool for NRSIs. Data extraction and quality assessment were conducted by 2 reviewers independently. Data synthesis was conducted using Review Manager. Both a meta-analysis and a narrative synthesis were carried out. RESULTS: A total of 23 studies (n=15, 65% RCTs and n=8, 35% NRSIs) with 2022 transplant recipients were included. Compared to the control group, mHealth interventions significantly improved self-care ability (mean difference 14.49, 95% CI 9.61-19.36; P<.001) and reduced rehospitalization (odds ratio [OR] 0.49, 95% CI 0.34-0.71; P<.001). The meta-analysis demonstrated no statistically significant difference in mortality rates (OR 0.73, 95% CI 0.39-1.35; P=.31), rejection (OR 0.55, 95% CI 0.25-1.19; P=.13), or infection (OR 0.33, 95% CI 0.06-1.82; P=.20) between the mHealth intervention and control groups. The narrative synthesis indicated that mHealth interventions could effectively promote adherence to medical regimens and medications, facilitate self-monitoring, and improve communication and consultation. CONCLUSIONS: mHealth interventions significantly improved self-care ability and reduced rehospitalization rates among organ transplant recipients. However, these interventions did not demonstrate a significant effect on all-cause mortality or complications. mHealth interventions showed potential benefits for various self-management behaviors in organ transplant recipients, but these findings need to be further verified. Future research should prioritize high-quality studies that investigate the impact of mHealth on physical activity, nutrition, and other patient-centered outcomes. TRIAL REGISTRATION: International Platform of Registered Systematic Review and Meta-Analysis Protocols INPLASY202480101; https://inplasy.com/inplasy-2024-8-0101/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.37766/inplasy2024.8.0101.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,022 | 0,038 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 source (Gemma direct ou Codex distillé), 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 ».