Telehealth interventions for the secondary prevention of coronary heart disease: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease (CHD) is a major cause of death worldwide. Cardiac rehabilitation, an evidence-based CHD secondary prevention programme, remains underutilized. Telehealth may offer an innovative solution to overcome barriers to cardiac rehabilitation attendance. We aimed to determine whether contemporary telehealth interventions can provide effective secondary prevention as an alternative or adjunct care compared with cardiac rehabilitation and/or usual care for patients with CHD. METHODS: Relevant randomized controlled trials evaluating telehealth interventions in CHD patients with at least three months' follow-up compared with cardiac rehabilitation and/or usual care were identified by searching electronic databases. We checked reference lists, relevant conference lists, grey literature and keyword searching of the Internet. Main outcomes included all-cause mortality, rehospitalization/cardiac events and modifiable risk factors. (PROSPERO registration number 77507.). RESULTS: In total, 32 papers reporting 30 unique trials were identified. Telehealth was not significant associated with a lower all-cause mortality than cardiac rehabilitation and/or usual care (risk ratio (RR)=0.60, 95% confidence interval (CI)=0.86 to 1.24, p=0.42). Telehealth was significantly associated with lower rehospitalization or cardiac events (RR=0.56, 95% CI=0.39 to 0.81, p<0.0001) compared with non-intervention groups. There was a significantly lower weighted mean difference (WMD) at medium to long-term follow-up than comparison groups for total cholesterol (WMD= -0.26 mmol/l, 95% CI= -0.4 to -0.11, p <0.001), low-density lipoprotein (WMD= -0.28, 95% CI = -0.50 to -0.05, p=0.02) and smoking status (RR=0.77, 95% CI =0.59 to 0.99, p=0.04]. CONCLUSIONS: Telehealth interventions with a range of delivery modes could be offered to patients who cannot attend cardiac rehabilitation, or as an adjunct to cardiac rehabilitation for effective secondary prevention.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".