Effects of Fast Functional Exercise on Muscle Activity After Stroke
Bibliographic record
Abstract
BACKGROUND: In stroke rehabilitation, considerable emphasis is placed on improving muscle strength with less focus on the speed of movement. Muscle power (product of force and velocity) is essential for balance and mobility but velocity of movement is impaired after stroke. OBJECTIVE: The purpose of this efficacy study is to determine if a single session of fast functional movements can increase muscle activation and the speed of movement in participants with a subacute stroke. METHODS: In total, 32 individuals poststroke and 32 age- and sex-matched controls performed a single session of 50 fast squats and steps. Electromyographic (EMG) activity was measured bilaterally in the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and soleus muscles. The average EMG area and the movement speed were calculated over 10 trials. The effect of exercise was determined as the change from the second set (Start) to the last set (End) of 10 trials. RESULTS: The stroke group had significant increases in EMG area of the TA, BF, and RF during the squatting exercise. There was an increase in EMG area of the RF and BF when the paretic leg was stepping. Improvements in EMG area of the soleus and RF when the paretic leg was in stance accompanied increases in EMG area when the nonparetic leg was stepping. There was a trend for improved movement speed for both exercises. CONCLUSION: A single session of exercises emphasizing speed of movement can be used to improve muscle activation in persons with mild to moderately severe strokes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".