Effectiveness of mHealth Interventions to Improve Follow-Up and Management Among Solid Organ Transplant Recipients: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".