Aerobic Exercise Improves Cognition and Motor Function Poststroke
Bibliographic record
Abstract
BACKGROUND: Cognitive deficits impede stroke recovery. Aerobic exercise (AEX) improves cognitive executive function (EF) processes in healthy individuals, although the learning benefits after stroke are unknown. OBJECTIVE: To understand AEX-induced improvements in EF, motor learning, and mobility poststroke. METHODS: Following cardiorespiratory testing, 38 chronic stroke survivors were randomized to 2 different groups that exercised 3 times a week (45-minute sessions) for 8 weeks. The AEX group (n = 19; 9 women; 10 men; 64.10 +/- 12.30 years) performed progressive resistive stationary bicycle training at 70% maximal heart rate, whereas the Stretching Exercise (SE) group (n = 19; 12 women; 7 men; 58.96 +/- 14.68 years) performed stretches at home. Between-group comparisons were performed on the change in performance at "Post" and "Retention" (8 weeks later) for neuropsychological and motor function measures. RESULTS: VO(2)max significantly improved at Post with AEX (P = .04). AEX also improved motor learning in the less-affected hand, with large effect sizes (Cohen's d calculation). Specifically, AEX significantly improved information processing speed on the serial reaction time task (SRTT; ie, "procedural motor learning") compared with the SE group at Post (P = .024), but not at Retention. Also, at Post (P = .038), AEX significantly improved predictive force accuracy for a precision grip task requiring attention and conditional motor learning of visual cues. Ambulation and sit-to-stand transfers were significantly faster in the AEX group at Post (P = .038), with balance control significantly improved at Retention (P = .041). EF measurements were not significantly different for the AEX group. CONCLUSION: AEX improved mobility and selected cognitive domains related to motor learning, which enhances sensorimotor control 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 | 0.000 | 0.000 |
| 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".