Technology Support Challenges and Recommendations for Adapting an Evidence-Based Exercise Program for Remote Delivery to Older Adults: Exploratory Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Tele-exercise has emerged as a means for older adults to participate in group exercise during the COVID-19 pandemic. However, little is known about the technology support needs of older adults for accessing tele-exercise. OBJECTIVE: This study aims to examine the needs of older adults for transition to tele-exercise, identify barriers to and facilitators of tele-exercise uptake and continued participation, and describe technology support challenges and successes encountered by older adults starting tele-exercise. METHODS: We used an exploratory, sequential mixed methods study design. Participants were older adults with symptomatic knee osteoarthritis (N=44) who started participating in a remotely delivered program called Enhance Fitness. Before the start of the classes, a subsample of the participants (n=10) completed semistructured phone interviews about their technology support needs and the barriers to and facilitators for technology adoption. All of the participants completed the surveys including the Senior Technology Acceptance Model scale and a technology needs assessment. The study team recorded the technology challenges encountered and the attendance rates for 48 sessions delivered over 16 weeks. RESULTS: Four themes emerged from the interviews: participants desire features in a tele-exercise program that foster accountability, direct access to helpful people who can troubleshoot and provide guidance with technology is important, opportunities to participate in high-value activities motivate willingness to persevere through the technology concerns, and belief in the ability to learn new things supersedes technology-related frustration. Among the participants in the tele-exercise classes (mean age 74, SD 6.3 years; 38/44, 86% female; mean 2.5, SD 0.9 chronic conditions), 71% (31/44) had a computer with a webcam, but 41% (18/44) had little or no experience with videoconferencing. The initial technology orientation sessions lasted on average 19.3 (SD 10.3) minutes, and 24% (11/44) required a follow-up assistance call. During the first 2 weeks of tele-exercise, 47% of participants (21/44) required technical assistance, which decreased to 12% (5/44) during weeks 3 to 16. The median attendance was 100% for the first 6 sessions and 93% for the subsequent 42 sessions. CONCLUSIONS: With appropriate support, older adults can successfully participate in tele-exercise. Recommendations include individualized technology orientation sessions, experiential learning, and availability of standby technical assistance, particularly during the first 2 weeks of classes. Continued development of best practices in this area may allow previously hard-to-reach populations of older adults to participate in health-enhancing, evidence-based exercise programs.
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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 | 0.133 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".