Identifying factors associated with sedentary time after stroke. Secondary analysis of pooled data from nine primary studies.
Bibliographic record
Abstract
Background: High levels of sedentary time increases the risk of cardiovascular disease, including recurrent stroke. Objective: This study aimed to identify factors associated with high sedentary time in community-dwelling people with stroke. Methods: For this data pooling study, authors of published and ongoing trials that collected sedentary time data, using the activPAL monitor, in community-dwelling people with stroke were invited to contribute their raw data. The data was reprocessed, algorithms were created to identify sleep-wake time and determine the percentage of waking hours spent sedentary. We explored demographic and stroke-related factors associated with total sedentary time and time in uninterrupted sedentary bouts using unique, both univariable and multivariable, regression analyses. Results: The 274 included participants were from Australia, Canada, and the United Kingdom, and spent, on average, 69% (SD 12.4) of their waking hours sedentary. Of the demographic and stroke-related factors, slower walking speeds were significantly and independently associated with a higher percentage of waking hours spent sedentary (p = 0.001) and uninterrupted sedentary bouts of >30 and >60 min (p = 0.001 and p = 0.004, respectively). Regression models explained 11–19% of the variance in total sedentary time and time in prolonged sedentary bouts. Conclusion: We found that variability in sedentary time of people with stroke was largely unaccounted for by demographic and stroke-related variables. Behavioral and environmental factors are likely to play an important role in sedentary behavior after stroke. Further work is required to develop and test effective interventions to address sedentary behavior after stroke.
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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 | 0.033 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.027 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".