Telerehabilitation with live-feed biomedical sensor signals for patients with heart failure: a pilot study
Bibliographic record
Abstract
BACKGROUND: Integrating biomedical sensors to a telerehabilitation platform allowed clinicians to receive real-time transmissions of the electrocardiogram (ECG) signal, oxygen saturation, and heart rate during an exercise program. These clinical data could be helpful to adjust and personalize the intensity of exercises to each patient's condition. The purpose of this study was to evaluate the feasibility and usefulness of biomedical sensors in telerehabilitation in patients with heart failure (HF). METHODS: Four participants with HF (mean age 66 years) followed the 12-week cardiac program using telerehabilitation, including sensors to monitor real-time vital signs during sessions. The exercise program included cardiovascular, strengthening and flexibility exercises. Participants were evaluated before the intervention and one month after the end of the program. Functional capacity was measured with Cardiopulmonary Exercise Testing (CPX), the 6-minute walk test (6MWT), and the sit to stand test (STST). Quality of life was objectified using the Kansas City Cardiomyopathy Questionnaire (KCCQ). RESULTS: Our main objective demonstrated that real-time biomedical sensors can be safely used by clinicians during a telerehabilitation session. Most participants showed a tendency to improve their physical capacities such as walking distance and lower limb muscular strength. As a main outcome of cardiac rehabilitation (CR), quality of life seems to improve after the 12-week intervention. CONCLUSIONS: This study proved the feasibility of using telerehabilitation with real-time biomedical sensors as an alternative or a complement to the conventional CR program. Use of sensors allowed a safe environment for the patient and an adequate and personalized exercise prescription. Limitation in one-to-one supervision must be challenged in future clinical trials to demonstrate that telerehabilitation could be efficient for cardiac patients requiring more individual supervision than group sessions in a gymnasium.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".