Defying distance: exercise providers’ perspectives on remote physical activity supports for older adults: a mixed-methods study
Bibliographic record
Abstract
Introduction Many older adults encounter barriers to participating in physical activity programs, often due to cost, accessibility, and transportation challenges. Implementing feasible and effective remote support strategies may enhance their physical activity participation. This mixed-methods study examines exercise providers’ use of remote supports for physical activity among older adults, their perceived effectiveness, and barriers and facilitators to adoption during the COVID-19 pandemic and beyond. Methods Exercise providers (≥18 years) completed a web-based survey (June–September 2020), and optional semi-structured interviews (September–December 2020), guided by the COM-B model. Participant characteristics, uptake and perceived effectiveness of remote supports, and presence and severity of barriers were explored and analyzed with inductive thematic analysis. Results Fifty-one exercise providers (age 36.3 ± 12.3 years, 38 female) completed the survey; 86% provided remote support for physical activity, including provision of copy materials (63%) and delivery of real-time virtual programs (59%), with the latter rated the most effective (88%). Key barriers included older adults’ limited technical skills (78%) and access to technology (82%). Interviews (n = 12, age 40.5 ± 15 years, 11 female), yielded five themes: (1) Capacity, Collaboration, and Adaptability Supported Successful Transition to Remote Supports; (2) Tailoring Remote Supports to Needs and Abilities Promoted Safety; (3) Real-time Virtual Programs Fostered Social Support and Engagement; (4) Accessible Technology and Ongoing Support Facilitated Virtual Delivery; and (5) A Hybrid Approach Balances Convenience and Social Benefits. Conclusion During the transition to virtual exercise programming during the COVID-19 pandemic, exercise providers widely used remote supports, favoring real-time virtual programs for socialization and supervision. While there were challenges including safety concerns, technological barriers, and engagement, these challenges were met with innovative solutions. A hybrid approach may be the most sustainable model, balancing the accessibility of virtual programs with the social and motivational benefits of in-person exercise.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".