Effectiveness of home-based cardiac rehabilitation interventions delivered via mHealth technologies: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Centre-based cardiac rehabilitation (CBCR) is underused due to low referral rates, accessibility barriers, and socioeconomic constraints. mHealth technologies have the potential to address some of these challenges through remote delivery of home-based cardiac rehabilitation (HBCR). This study aims to assess the effects of mHealth HBCR interventions compared with usual care and CBCR in patients with heart disease. Methods We conducted a systematic review and meta-analysis of randomised controlled trials of mHealth HBCR interventions. Four electronic databases (MEDLINE, CENTRAL, CINAHL, and Embase) were searched from inception to March 31, 2023, with no restrictions on language or publication type. Eligible studies were randomised controlled trials of adult patients (age ≥18 years) with heart disease, comparing mHealth interventions with usual care or CBCR. The primary outcome of interest was aerobic exercise capacity, assessed with VO 2 peak or 6-min walk test (6MWT). Quality of evidence was assessed using the GRADE system. This review was registered with PROSPERO, CRD42024544087. Findings Our search yielded 9164 references, of which 135 were retained for full-text review. 13 randomised controlled trials met eligibility criteria and were included in the systematic review, involving 1508 adults with myocardial infarction, angina pectoris, or heart failure, or who had undergone revascularisation. Intervention duration ranged from 6 weeks to 24 weeks. Random-effects meta-analysis showed that, compared with usual care, mHealth HBCR significantly improved 6MWT (mean difference 24·74, 95% CI 9·88–39·60; 532 patients) and VO 2 peak (1·77, 1·19–2·35; 359 patients). No significant differences were found between mHealth HBCR and CBCR. Quality of evidence ranged from low to very low across outcomes due to risk of bias and imprecision (small sample size). Interpretation mHealth HBCR could improve access and health outcomes in patients who are unable to attend CBCR. Further research is needed to build a robust evidence base on the clinical effectiveness and cost-effectiveness of mHealth HBCR, particularly in comparison with CBCR, to inform clinical practice and policy. Funding None.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".