Longer-Term Effects of Cardiac Telerehabilitation on Patients With Coronary Artery Disease: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cardiac telerehabilitation offers a flexible and accessible model for patients with coronary artery disease (CAD), effectively transforming the traditional cardiac rehabilitation (CR) approach. OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the long-term effectiveness of cardiac telerehabilitation. METHODS: We searched randomized controlled trials (RCTs) in 7 electronic databases: PubMed, Web of Science, EMBASE, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, the China National Knowledge Infrastructure, and WANFANG. The primary outcome focused on cardiopulmonary fitness. For secondary outcomes, we examined cardiovascular risk factors (blood pressure, BMI, and serum lipids), psychological scales of depression and anxiety, quality of life (QoL), cardiac telerehabilitation adherence, and adverse events. RESULTS: In total, 10 RCTs fulfilled the predefined criteria, which were reviewed in our meta-analysis. The results showed that after cardiac telerehabilitation, there was a significant difference in the improvement in long-term peak oxygen uptake compared to center-based CR (mean difference [MD] 1.61, 95% CI 0.38-2.85, P=.01), particularly after 6-month rehabilitation training (MD 1.87, 95% CI 0.34-3.39, P=.02). The pooled effect size of the meta-analysis indicated that there were no significant differences in the reduction in cardiovascular risk factor control. There was also no practical demonstration of anxiety scores or depression scores. However, cardiac telerehabilitation demonstrated an improvement in the long-term QoL of patients (MD 0.92, 95% CI 0.06-1.78, P=.04). In addition, the study reported a high completion rate (80%) for cardiac telerehabilitation interventions. The incidence of adverse events was also low during long-term follow-up. CONCLUSIONS: Cardiac telerehabilitation proves to be more effective in improving cardiopulmonary fitness and QoL during the long-term follow-up for patients with CAD. Our study highlights monitoring-enabled and patient-centered telerehabilitation programs, which play a vital role in the recovery and development of CAD and in the long-term prognosis of patients.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".