The Acceptability of Digital Technology and Tele-Exercise in the Age of COVID-19: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: With the COVID-19 pandemic, telehealth has been increasingly used to offset the negative outcomes of social isolation and functional decline in older adults. Crucial to the success of telehealth is end user adoption. OBJECTIVE: This study aims to investigate perception and acceptability of digital technology among Asian older adults. METHODS: The Healthy Ageing Promotion Program for You (HAPPY) dual-task exercise was conducted virtually to participants aged ≥60 years. Questionnaires were administered digitally and collected data on demographics, perceptions of digital technology and evaluation of HAPPY, the 6-item Lubben Social Network Scale, intrinsic capacity using the Integrated Care for Older People tool, and a functional screening with the FRAIL scale and five chair rises. Descriptive analysis was used. RESULTS: A total of 42 participants were digitally interviewed. The mean age was 69.1 (4.7) years. Hearing, vision, and 3-item recall difficulty were present in 14% (n=6), 12% (n=5), and 24% (n=10) of participants, respectively. Of the participants, 29% (n=12) had possible sarcopenia and 14% (n=6) were prefrail. Around 24% (n=10) were at risk of social isolation. Most of the participants (n=38, 91%) agreed that technology is good, and 79% (n=33) agreed that technology would allow them to be independent for longer. Over three-quarters of participants (n=33, 79%) agreed that they have the necessary knowledge, and 91% (n=38) had technological assistance available. However, 57% (n=24) were still apprehensive about using technology. Despite 71% (n=30) of older adults owning their devices, 36% (n=15) felt finances were limiting. Through digital HAPPY, 45% (n=19) of participants reported feeling stronger, 48% (n=20) had improved spirits, and 40% (n=17) and 38% (n=16) had improved mood and memory, respectively. CONCLUSIONS: The majority of older adults in this study believed in digital technology and had the necessary knowledge and help, but almost half still felt apprehensive and had financial barriers to adopting technology. A digitally administered exercise program especially in a group setting is a feasible option to enhance intrinsic capacity in older adults. However, more work is needed in elucidating sources of apprehension and financial barriers to adopting technology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".