“If I want to be able to keep going, I must be active.” Supporting Remote Physical Activity Programming for Older Adults during COVID-19 and Beyond: a mixed-methods study
Bibliographic record
Abstract
Abstract Background: Pandemic-related public health restrictions limited older adults’ physical activity programs and opportunities. Supports for older adults' physical activity shifted to remote options, including virtual programming; however, information regarding the adoption and effectiveness of these supports is limited. Thus, the purpose of this study was to investigate i) changes in physical activity of older adults during the pandemic, and ii) the uptake, perceived effectiveness, facilitators of and barriers to remote supports for physical activity among older adults during the pandemic. Methods: Community-dwelling older adults (60+) were recruited to a cross-sectional online survey and an optional semi-structured follow-up interview. Survey questions addressed demographics, physical activity behaviors, and perceived effectiveness of, and facilitators and barriers for remote supports for physical activity. Interview questions were guided by the Behaviour Change Wheel and data was analyzed via inductive and deductive thematic analysis. Results: 57 older adults (68.3±7.1 years, 43 Female) completed the survey and 15 of these (67.4±5.8 years, 12 Female) completed interviews. Most participants were Caucasian, highly educated, and lived in Canada. There was no change in older adults' total physical activity from before to during the pandemic (p=0.74); however, at-home exercise participation increased as did technology usage and adoption of new technology. Participants perceived real-time virtual exercise, recorded exercise videos, and phone/webchat check-ins to be the most effective remote supports. The greatest barriers to physical activity were lack of contact with exercise professionals, limited access to exercise equipment or space, and decreased mental wellness. Thematic analysis identified four main themes: i) Knowledge, access to equipment, and space enhance or constrain physical activity opportunities, ii) Individual and environmental factors motivate physical activity uptake, iii) Social connection and real-time support encourage physical activity engagement, and iv) Current and future considerations to support technology usage for exercise. Conclusion: Use of remote supports for physical activity increased during the pandemic, with video-based programming being most favored. Live virtual programming may be best suited to encouraging physical activity among older adults as it may provide greater motivation for exercise, increase social and mental wellness, and alleviate safety concerns.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".