Physical Activity Level and Perspectives of Participants Transitioning from Onsite to Virtual Cardiac Rehabilitation during the Early COVID-19 Pandemic: A Mixed-Method Study
Bibliographic record
Abstract
This mixed-method study aimed to compare physical activity (PA) patterns of a cross-over cardiac rehabilitation (CR) cohort with a center-based CR cohort and to explore barriers and facilitators of participants transitioning and engaging in virtual CR. It included the retrospective self-reported PA of a cross-over CR cohort (n = 75) and a matched center-based CR cohort (n = 75). Some of the participants included in the cross-over cohort (n = 12) attended semi-structured focus group sessions and results were interpreted in the context of the PRECEDE-PROCEED model. Differences between groups were not observed (p > 0.05). The center-based CR cohort increased exercise frequency (p = 0.002), duration (p = 0.007), and MET/minutes (p = 0.007) over time. The cross-over cohort increased exercise duration (p = 0.04) with no significant change in any other parameters. Analysis from focus groups revealed six overarching themes classified under predisposing factors (knowledge), enabling factors (external support, COVID-19 restrictions, mental health, personal reasons/preferences), and reinforcing factors (recommendations). These findings suggest an improvement of the PA levels of center-based CR cohort participants pre-pandemic and mitigated improvement in those who transitioned to a virtual CR early in the pandemic. Improving patients’ exercise-related knowledge, provider endorsements, and the implementation of group videoconferencing sessions could help overcome barriers to participation in virtual CR.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".