Effects of muscle strength training combined with aerobic training versus aerobic training alone on cardiovascular disease risk indicators in patients with coronary artery disease: a systematic review and meta-analysis of randomised clinical trials
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of aerobic training combined with muscle strength training (hereafter referred to as combined training) to aerobic training alone on cardiovascular disease risk indicators in patients with coronary artery disease (CAD). DESIGN: Systematic review with meta-analysis. DATA SOURCES: MEDLINE, Embase, CINAHL, SPORTDiscus, Scopus, trial registries and grey literature sources were searched in February 2024. ELIGIBILITY CRITERIA: Randomised clinical trials comparing the effects of ≥4 weeks of combined training and aerobic training alone on at least one of the following outcomes: cardiorespiratory fitness (CRF), anthropometric and haemodynamic measures and cardiometabolic blood biomarkers in patients with CAD. RESULTS: Of 13 246 studies screened, 23 were included (N=916). Combined training was more effective in increasing CRF (standard mean difference (SMD) 0.26, 95% CI 0.02 to 0.49, p=0.03) and lean body mass (mean difference (MD) 0.78 kg, 95% CI 0.39 kg to 1.17 kg, p<0.001), and reducing per cent body fat (MD -2.2%, 95% CI -3.5% to -0.9%, p=0.001) compared with aerobic training alone. There were no differences in the cardiometabolic biomarkers between the groups. Our subgroup analyses showed that combined training increases CRF more than aerobic training alone when muscle strength training was added to aerobic training without compromising aerobic training volume (SMD 0.36, 95% CI 0.05 to 0.68, p=0.02). CONCLUSION: Combined training had greater effects on CRF and body composition than aerobic training alone in patients with CAD. To promote an increase in CRF in patients with CAD, muscle strength training should be added to aerobic training without reducing aerobic exercise volume.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".