Integrated Virtual Exercise for Older Adults in Remote Patient Monitoring Program: A Feasibility Study
Bibliographic record
Abstract
Objectives: This study tested the feasibility of integrating a virtually facilitated exercise intervention into routine RPM program for older adults to establish its acceptability and patients’ satisfaction. Methods: We performed a retrospective analysis of data curated from a population-based virtual exercise intervention for older adults receiving RPM in Ontario, Canada. Results: A total of 16 patients participated in at least 1 exercise class, 64% were females, with mean (SD) age of 76 (±10) years. Overall, 100% of participants were “very satisfied” with the program, 81.3% (13) agreed/strongly agreed that the program “motivated them to move,” while 100% (16) agreed/strongly agreed that participating in the intervention has improved their physical endurance. Attendance to the virtual classes were relatively good with patients attending more than 60% of scheduled classes per session. Comorbidity had a strong effect on attendance, with the presence of each additional chronic condition associated with a 15.8% decline in attendance rate ( P = .005) over time Conclusion: Integrating a virtually facilitated exercise program into routine RPM program for older adults is feasible, acceptable, as well as safe. Larger studies are required to establish efficacy of the intervention in improve health outcomes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".