Delivering Cardiac Rehabilitation Exercise Virtually Using a Digital Health Platform (ECME-CR): Protocol for a Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Exercise-based cardiac rehabilitation is recognized as a core component of cardiovascular disease management and has been shown to reduce all-cause and cardiovascular mortality and reduce the risk of hospital readmission following a cardiac event. However, despite this, the uptake of and long-term adherence to cardiac rehabilitation exercise is poor. Delivering cardiac rehabilitation exercise virtually (ie, allowing patients to participate from their own homes) may be an alternative approach that could enhance uptake and increase adherence. OBJECTIVE: The aim of this study is to assess the feasibility of delivering a virtual cardiac rehabilitation exercise program supported by the Eastern Corridor Medical Engineering - Cardiac Rehabilitation (ECME-CR) platform. METHODS: A convenience sample (n=20) of participants eligible to participate in community-based cardiac rehabilitation exercise will be recruited. Participants will be randomized to one of two study groups. Both study groups will perform the same exercise program, consisting of twice-weekly sessions of 60 minutes each, over an 8-week intervention period. Participants in the intervention group will partake in virtually delivered cardiac rehabilitation exercise classes in their own home. The virtual exercise classes will be delivered to participants using a videoconferencing platform. Participants in the control group will attend the research center for their cardiac rehabilitation exercise classes. Intervention group participants will receive the ECME-CR digital health platform for monitoring during the class and during the intervention period. Outcomes will be assessed at baseline and following the 8-week intervention period. The primary outcome will be exercise capacity as assessed using the 6-minute walk test. Other outcome measures will include heart rate, blood pressure, weight, percentage body fat, muscle strength, and self-reported quality of life. Semistructured interviews will also be conducted with a subset of participants to explore their experiences of using the digital platform. RESULTS: Participant recruitment and data collection will begin in July 2021, and it is anticipated that the study results will be available for dissemination in spring 2022. CONCLUSIONS: This pilot trial will inform the design of a randomized controlled trial that will assess the clinical effectiveness of the ECME-CR digital health platform. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31855.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".