Effectiveness of commercial video gaming on fine motor control in chronic stroke within community-level rehabilitation
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the effectiveness of commercial gaming as an intervention for fine motor recovery in chronic stroke. METHODS: Ten chronic phase post-stroke participants (mean time since CVA = 39 mos; mean age = 72 yrs) completed a 16-session program using the Nintendo Wii for 15 min two times per week with their more affected hand (10 right handed). Functional recovery (Jebsen Hand Function Test (JHFT), Box and Block Test (BBT), Nine Hole Peg Test (NHPT)), and quality of life (QOL; Stroke Impact Scale (SIS)) were measured at baseline (pre-testing), after 8 sessions (mid-testing) and after 16 sessions (post-testing). RESULTS: Significant improvements were found with the JHFT, BBT and NHPT from pre-testing to post-testing (p = 0.03, p = 0.03, p = 0.01, respectively). As well, there was an increase in perceived QOL from pre-testing to post-testing, as determined by the SIS (p = 0.009). CONCLUSION: Commercial gaming may be a viable resource for those with chronic stroke. Future research should examine the feasibility of this as a rehabilitation tool for this population. IMPLICATIONS FOR REHABILITATION: Stroke survivors often live with lasting effects from their injury, however, those with chronic stroke generally receive little to no rehabilitation due to a perceived motor recovery plateau. Virtual reality in the form of commercial gaming is a novel and motivating way for clients to complete rehabilitation. The Nintendo Wii may be a feasible device to improve both functional ability and perceived quality of life in chronic stroke survivors.
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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 | 0.001 | 0.003 |
| 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".