Daily physical activity and its contribution to the health-related quality of life of ambulatory individuals with chronic stroke
Bibliographic record
Abstract
BACKGROUND: Participation in daily physical activity (PA) post-stroke has not previously been investigated as a possible explanatory variable of health-related quality of life (HRQL). The aims were 1) to determine the contribution of daily PA to the HRQL of individuals with chronic stroke and 2) to assess the relationship between the functional ability of these individuals to the amount of daily PA. METHODS: The amount of daily PA of forty adults with chronic stroke (mean age 66.5 +/- 9.6 years) was monitored using two measures. Accelerometers (Actical) were worn on the hip for three consecutive days in conjunction with a self-report questionnaire [the PA Scale for Individuals with Physical Disabilities (PASIPD)]. The daily physical activity was measured as the mean total accelerometer activity counts/day and the PASIPD scores as the metabolic equivalent (MET) hr/day. HRQL was assessed by the Physical and Mental composite scores of the Medical Outcomes Study Short-Form 36 (SF-36) in addition to the functional ability of the participants. Correlation and regression analyses were performed. RESULTS: After controlling for the severity of the motor impairment, the amount of daily PA, as assessed by the PASIPD and accelerometers, was found to independently contribute to 10-12% of the variance of the Physical Composite Score of the SF-36. No significant relationship was found between PA and the Mental Composite Score of the SF-36.The functional ability of the participants was found to be correlated to the amount of daily PA (r = 0.33 - 0.67, p < 0.01). CONCLUSION: The results suggest that daily PA is associated with better HRQL (as assessed by the Physical composite score of the SF-36) for people living with stroke. Daily PA should be encouraged to potentially increase HRQL. Accelerometers in conjunction with a self-report questionnaire may provide important measures of PA which can be monitored and modified, and potentially influence HRQL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".