Exercise Telemonitoring and Telerehabilitation Compared with Traditional Cardiac and Pulmonary Rehabilitation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Despite exercise capacity and quality-of-life benefits, pulmonary rehabilitation (PR) and cardiac rehabilitation (CR) programmes are not easily accessed because of several barriers. A solution may be telerehabilitation (TR), in which patients exercise in their communities while they are monitored via teletechnology. However, the benefits of TR for the purposes of PR and CR have not been systematically reviewed. Objective: To determine whether the benefits of the exercise component of PR and CR using TR are comparable to usual-care (UC) programmes. Methods: A comprehensive literature search was performed of the Medline, Embase, and CINAHL databases up to July 13, 2015. Meta-analyses were performed for peak oxygen consumption, peak workload, exercise test duration, and 6-minute walk test (6MWT) distance using the I 2 statistic and forest plots displaying standardized mean difference (SMD). Results: Of 1,431 citations found, 8 CR studies met the inclusion criteria. No differences were found in exercise outcomes between UC and TR groups for CR studies, except in exercise test duration, which slightly favoured UC (SMD 0.268, 95% CI: 0.002, 0.534, p<0.05). Only 1 PR study was included, and it showed similar improvements on the 6MWT between the UC and TR groups. Conclusion: TR for patients with cardiac conditions provided benefits similar to UC with no adverse effects reported. Similar studies of TR for patients with pulmonary conditions need to be conducted.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| 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) | 0.005 | 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".