Efficacy of task-specific circuit training on physical activity levels and mobility of stroke patients: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Impaired mobility is related to low physical activity (PA) levels observed after stroke. Therapeutic approaches, such as task-specific circuit training (TSCT), used to improve mobility in individuals with stroke, could also improve PA levels. OBJECTIVE: To investigate the efficacy of TSCT, focused on both upper (UL) and lower (LL) limbs, in improving PA levels and mobility (primary outcomes), as well as muscle strength, exercise capacity, and quality of life (secondary outcomes) in subjects with stroke. METHODS: A randomized controlled trial with 36 subjects with chronic stroke was conducted. Experimental group: TSCT, involving both UL and LL. CONTROL GROUP: global stretching, memory exercises, and education sessions. Both groups received 60 minute sessions/week over 12 weeks. Outcomes were measured at baseline, post-intervention and 16 week follow-up. RESULTS: No changes were found for primary and secondary outcomes (0.11≤p≤0.99), except for quality of life, which improved in the experimental group post-intervention and 16 week follow-up (p = 0.02). CONCLUSION: TSCT focused on both UL and LL was not effective on PA levels and mobility of individuals with chronic stroke, however, improvements in quality of life were observed. Since this is the first study to investigate this combined training aimed at improving PA levels, future studies are necessary to better understand the impact of this type of intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".