Exercise-Based Real-time Telerehabilitation for Older Adult Patients Recently Discharged After Transcatheter Aortic Valve Implantation: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The use of telehealth technology to improve functional recovery following transcatheter aortic valve implantation (TAVI) has not been investigated. OBJECTIVE: In this study, we aimed to examine the feasibility of exercise-based cardiac telerehabilitation after TAVI. METHODS: This was a single-center, prospective, nonrandomized study using a mixed methods approach. Data collection included testing, researchers' observations, logbooks, and individual patient interviews, which were analyzed using a content analysis approach. The intervention lasted 3 weeks and consisted of home-based web-based exercise training, an activity tracker, a TAVI information website, and 1 web-based session with a nurse. RESULTS: Of the initially included 13 patients, 5 (40%) completed the study and were interviewed; the median age was 82 (range 74-84) years, and the sample comprised 3 men and 2 women. Easy access to supervised exercise training at home with real-time feedback and use of the activity tracker to count daily steps were emphasized by the patients who completed the intervention. Reasons for patients not completing the program included poor data coverage, participants' limited information technology skills, and a lack of functionality in the systems used. No adverse events were reported. CONCLUSIONS: Exercise-based telerehabilitation for older people after TAVI, in the population as included in this study, and delivered as a web-based intervention, does not seem feasible, as 60% (8/13) of patients did not complete the study. Those completing the intervention highly appreciated the real-time feedback during the web-based training sessions. Future studies should address aspects that support retention rates and enhance patients' information technology skills.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".