Sitting time and physical activity after stroke: physical ability is only part of the story
Bibliographic record
Abstract
BACKGROUND: Understanding factors that influence the amount of time people with stroke spend sitting and being active is important to inform the development of targeted interventions. OBJECTIVE: To explore the physical, cognitive, and psychosocial factors associated with daily sitting time and physical activity in people with stroke. METHOD: Secondary analysis of an observational study (n = 50, mean age 67.2 ± 11.6 years, 33 men) of adults at least 6 months post-stroke. Activity monitor data were collected via a 7-day, continuous wear (24 hours/day) protocol. Sitting time [total, and prolonged (time in bouts of ≥ 30 minutes)] was measured with an activPAL3 activity monitor. A hip-worn Actigraph GT3X+ accelerometer was used to measure moderate-to-vigorous-intensity physical activity (MVPA) time. Univariate analyses examined relationships of stroke severity (National Institutes of Health Stroke Scale), physical [walking speed, Stroke Impact Scale (SIS) physical domain score], cognitive (Montreal Cognitive Assessment), and psychosocial factors (living arrangement, SIS emotional domain score) with sitting time, prolonged sitting time, and MVPA. RESULTS: Self-reported physical function and walking speed were negatively associated with total sitting time (r = - 0.354, P = 0.022 and r = - 0.361, P = 0.011, respectively) and prolonged sitting time (r = - 0.5, P = 0.001 and - 0.45, P = 0.001, respectively), and positively associated with MVPA (r = 0.469, P = 0.002 and 0.431, P = 0.003, respectively). CONCLUSIONS: Physical factors, such as walking ability, may influence sitting and activity time in people with stroke, yet much of the variance in daily sitting time remains unexplained. Large prospective studies are required to understand the drivers of activity and sitting time.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".