Adapted Virtual Reality Exergaming Using Off-the-Shelf Supplies for Poststroke Hemiparetic Arm Rehabilitation: Case Study
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) can supplement exercise therapy for poststroke upper-arm hemiparesis, but treatments have been largely limited by specialized or costly equipment, hindering replicability and generalizability. OBJECTIVE: This study examined the feasibility of using a commercially available bundle of VR supplies to improve hemiparetic arm function before and after an exergaming program in an individual post stroke. METHODS: We conducted a pre-post case study (male, aged 72 years, chronic stroke) of a 20-day VR exergaming program (1-hour session per day) using a head-mounted display (Meta Quest 2), with adaptive software (WalkinVR) to boost and adjust in-game movements. Measures of upper-arm function were performed at preintervention (day 0), midintervention (day 10), and postintervention (day 21) and included the Wolf Motor Function Test (WMFT) and Disabilities of the Arm, Shoulder, and Hand Questionnaire (DASH). Data were descriptively analyzed. RESULTS: The participant demonstrated improvement in the mean time to complete tasks of the WMFT by 70.5% (δ=11.73 s; preintervention mean time 16.63, SD 31 s; effect size=0.54) from preintervention to midintervention and 78% (δ=12.96 s; effect size=0.59) from preintervention to postintervention. WMFT mean functional ability score demonstrated an improvement of 18% (δ=0.46 points; preintervention mean score 2.67, SD 0.87 points; effect size=0.59) from preintervention to midintervention and 23% (δ=0.6 points; effect size=0.79) from preintervention to postintervention. Range of motion improved in all joints by an average of 35.64% (SD 20%) from preintervention to postintervention. DASH scores demonstrated minimal improvements across the intervention. CONCLUSIONS: VR exergaming with adaptive software could be an easy-to-adopt method for improving the functional ability of the hemiparetic arm among people post stroke. Improvements were potentially meaningful but warrant confirmation in more rigorous study designs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".