Can a Website-Delivered Computer-Tailored Physical Activity Intervention Be Acceptable, Usable, and Effective for Older People?
Bibliographic record
Abstract
Despite the numerous health benefits, population physical activity levels are low and declining with age. A continued increase of Internet access allows for website-delivered interventions to be implemented across age-groups, though older people have typically not been considered for this type of intervention. Therefore, the purpose of this study was to evaluate a website-delivered computer-tailored physical activity intervention, with a specific focus on differences in tailored advice acceptability, website usability, and physical activity change between three age-groups. To mimic "real-life" conditions, the intervention, which provided personalized physical activity feedback delivered via the Internet, was implemented and evaluated without any personal contact for the entire duration of the study. Data were collected online at baseline, 1-week, and 1-month follow-up and analyzed for three age-groups (≤44, 45-59, and ≥60 years) using linear mixed models. Overall, 803 adults received the intervention and 288 completed all measures. The oldest age-group increased physical activity more than the other two groups, spent the most time on the website, though had significantly lower perceived Internet self-confidence scores when compared with the youngest age-group. No differences were found in terms of website usability and tailored advice acceptability. These results suggest that website-delivered physical activity interventions can be suitable and effective for older aged 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".