Wearable Devices for Exercise Prescription and Physical Activity Monitoring in Patients with Various Cardiovascular Conditions
Bibliographic record
Abstract
As wearable technologies have become increasingly affordable, accessible, and practical, an increasing number of people with cardiovascular disease (CVD) are beginning to use consumer-grade devices. Common health and wellness metrics reported by wearable devices include heart rate [HR], heart rhythm, and step count, which may afford opportunities to assess cardiovascular conditions, prescribe more personalized exercise for enhanced engagement, and monitor physical activity adherence in patients with CVD. This narrative review discusses the application of wearable devices in patients with coronary artery disease (CAD), heart failure (HF), atrial fibrillation (AF), cardiac implantable electric devices (CIEDs), and peripheral artery disease (PAD) in different cardiovascular rehabilitation settings (e.g., supervised and home-based). Available literature suggests that, when combined with telemonitoring, wearable devices can increase physical activity participation, thereby improving peak oxygen consumption (VO 2peak ) and quality of life (QoL) in patients with CAD, enhancing physical function and QoL in patients with HF, and increasing walking capacity and VO 2peak in patients with PAD. Wearable devices can also detect AF vs. sinus rhythm and guide exercise timing in patients with AF, and monitor safe exercise intensity in patients equipped with CIEDs. In conclusion, healthcare professionals can promote physical activity by incorporating wearable devices. Wearable devices can also help motivate device users by providing real-time feedback on their behaviours. Commercially available wearable devices have the potential to enhance engagement in physical activity, thereby augmenting the established effects of exercise programs on VO 2peak , functional capacity, and QoL in patients with various cardiovascular conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".