Physical activity patterns in independently mobile adult stroke survivors: an in-depth exploratory, observational study
Bibliographic record
Abstract
Purpose Social/community activities contribute to incidental physical activity, which may help prevent secondary stroke. This research aims to explore stroke survivor activity patterns, physical activity levels, and self-efficacy.Materials and methods An exploratory observational study in a cohort of community-dwelling stroke survivors was conducted. Data were collected with accelerometers, activity diaries, and the Self-Efficacy for Exercise Scale. Pre-specified categories were used to describe activity context. Pearson’s correlation and Kruskal–Wallis analyses were used to analyse physical activity relative to self-efficacy and time of day.Results Forty-seven stroke survivors were recruited (47% female, aged 76 years (IQR 65–83)). Most awake time (81%) was spent in the home. Structured exercise and community and/or social activities were efficient forms of activity and accounted for 17.7% and 23.2% of steps/day, and 2.5% and 14.1% of time, respectively. Participants were most active in the afternoon and morning compared with the evening (p = 0.005 and p = 0.045). Community ambulators had higher self-efficacy scores compared to household ambulators (p = 0.007).Conclusions Stroke survivors can be active via structured exercise, as well as engaging in outdoor social and community activities. Those who reported higher levels of self-efficacy were more active. Health professionals should consider these factors when promoting physical activity during rehabilitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".