“If I want to be able to keep going, I must be active.” Exploring older adults’ perspectives of remote physical activity supports: a mixed-methods study
Bibliographic record
Abstract
Introduction: Pandemic-related public health restrictions limited older adults' physical activity programs and opportunities. Physical activity supports shifted to remote options, however, information on their adoption and effectiveness is limited. This study aims to describe the remote supports received by older adults and their perceived effectiveness. Additionally, it aims to describe facilitators and barriers to remote supports for physical activity among older adults, particularly those reliant on technology. Methods: This study used an explanatory, sequential, mixed-methods design. Community-dwelling older adults (≥ 60 years) were recruited to partake in a web-based survey and an optional semi-structured follow-up interview informed by the COM-B model. Participant characteristics, perceived effectiveness of remote supports, and the presence and severity of barriers were described. Changes in physical activity levels before and during the pandemic were analyzed using the Wilcoxon signed-rank test. Qualitative data underwent inductive thematic analysis. Results: = 0.74); however, at-home exercise participation and technology usage increased. Pre-recorded and real-time virtual exercise supports were perceived as most effective. Main barriers included limited contact with exercise professionals, limited access to exercise equipment or space, and decreased mental wellness. Thematic analysis identified five main themes: (i) Enabled by knowledge and resources; (ii) Diverse motivations for physical activity; (iii) Fostering participation through social connection; (iv) Supervision and safety: enabling adherence; and (v) Virtual exercise: a sustainable option with technological considerations. Conclusion: Virtual platforms show promise in supporting older adults' physical activity at home, especially for those with limited in-person access. Our study suggests that both real-time and pre-recorded virtual exercise supports are feasible, depending on technological capacity and support. While interactive real-time virtual programs allow interaction with professionals and peers, pre-recorded programs provide timing flexibility. Further research is needed to establish best practices for safe and effective virtual exercise programming, promoting its long-term adoption for supporting a wider range of older adults.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| 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".