Efficacy of Task-Specific Training on Physical Activity Levels of People With Stroke: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The majority of people after stroke demonstrate mobility limitations, which may reduce their physical activity levels. Task-specific training has been shown to be an effective intervention to improve mobility in individuals with stroke, however, little is known about the impact of this intervention on levels of physical activity. OBJECTIVES: The main objective is to investigate the efficacy of task-specific training, focused on both upper and lower limbs, in improving physical activity levels and mobility in individuals with stroke. The secondary objective is to investigate the effects of the training on muscle strength, exercise capacity, and quality of life. DESIGN: This is a randomized controlled trial. SETTING: The setting is public health centers. PARTICIPANTS: Community-dwelling people with chronic stroke. INTERVENTIONS: Participants will be randomized to either an experimental or control group, who will receive group interventions 3 times per week over 12 weeks. The experimental group will undertake task-specific training, while the control group will undertake global stretching, memory exercises, and health education sessions. MEASUREMENTS: Primary outcomes include measures of physical activity levels and mobility, whereas secondary outcomes are muscle strength, exercise capacity, and quality of life. The outcomes will be measured at baseline, postintervention, and at the 4- and 12-week follow-ups. CONCLUSIONS: The findings of this trial have the potential to provide important insights regarding the effects of task-specific training, focused on both upper and lower limbs, in preventing secondary poststroke complications and improving the participants' general health through changes in physical activity levels.
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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 | 0.048 | 0.048 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.010 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.071 | 0.014 |
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".