Effects of an Exercise Protocol for Improving Handgrip Strength and Walking Speed on Cognitive Function in Patients with Chronic Stroke
Bibliographic record
Abstract
BACKGROUND Handgrip strength and walking speed predict and influence cognitive function. We aimed to investigate an exercise protocol for improving handgrip strength and walking speed, applied to patients with chronic stroke who had cognitive function disorder. MATERIAL AND METHODS Twenty-nine patients with cognitive function disorder participated in this study, and were randomly divided into one of two groups: exercise group (n=14) and control group (n=15). Both groups underwent conventional physical therapy for 60 minutes per day. Additionally, the exercise group followed an exercise protocol for handgrip using the hand exerciser, power web exerciser, Digi-Flex (15 minutes); and treadmill-based weight loading training on their less-affected leg (15 minutes) using a sandbag for 30 minutes, three times per day, for six weeks. Outcomes, including cognitive function and gait ability, were measured before and after the training. RESULTS The Korean version of Montreal Cognitive Assessment (K-MoCA), Stroop test (both simple and interference), Trail Making-B, Timed Up and Go, and 10-Meter Walk tests (p<0.05) yielded improved results for the exercise group compared with the control group. Importantly, the K-MoCA, Timed Up and Go, and 10-Meter Walk test results were significantly different between the two groups (p<0.05). CONCLUSIONS The exercise protocol for improving handgrip strength and walking speed had positive effects on cognitive function in patients with chronic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".