The effect of a virtual reality exergame on handgrip strength and cognitive function in post-stroke patients
Bibliographic record
Abstract
Background: More than 60% of stroke patients have chronic neurological impairments that impair hand strength and cognitive function, lowering their quality of life. While virtual reality (VR) exergames have been extensively researched as adjuvant therapies, evidence of their simultaneous effects on motor and cognitive skills is scarce. This study aimed to evaluate the impact of VR exergames on handgrip strength and cognitive function in post-stroke patients. Methods: The study used a randomized controlled trial design with 60 subacute post-stroke patients who were randomly assigned to either the control group, which received conventional care (occupational therapy and physiotherapy), or the intervention group (conventional care plus VR exergame therapy) for eight weeks. This study included first-time stroke patients with onset ≥1 month, mild to moderate hemiparesis (MMT ≥ 3), and hemodynamic stability. Participants with significant spasticity (MAS > 3), aphasia, or uncontrolled comorbidities were excluded. Before, during, and after the intervention, handgrip strength and cognitive function were tested using the Indonesian version of the Montreal Cognitive Assessment (MoCA-Ina). Results: After 8 weeks of therapy, the intervention group showed a significant increase in handgrip strength (+3.9 points, p-value= 0.040) and MoCA-Ina scores (+5 points, p-value= 0.007) compared to the control group. Conclusion: Integrating VR exergames with conventional rehabilitation significantly improves handgrip strength and cognitive function in post-stroke patients compared to traditional therapy alone.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".