Evaluation of custom-made VR exergame for at-home Stroke rehabilitation. A longitudinal single-arm study.
Bibliographic record
Abstract
Abstract Exercise games (Exergames) based on Virtual Reality (VR) have emerged as a promising option for supporting physical rehabilitation in stroke users. As a com- plementary therapy, they offer valuable benefits such as therapy engagement and enjoyment. In this study, we assessed the effectiveness of an immersive, custom- made VR exergame designed for upper limb rehabilitation in stroke participants aged 50 and above. We conducted 14 sessions of 15 minutes involving ten par- ticipants (6 females, ages 58.1 ± 7.5 years old) who volunteered to participate in an assisted at-home rehabilitation process. The study employed a range of evaluation tests to measure physical rehabilitation and game user experience out- comes. The tests included pre- and post-assessments of range of motion (ROM), the Ashworth spasticity test, and the Borg rating of perceived fatigue question- naire. To evaluate the game participant experience, we used the VR Neuroscience Questionnaire (VRNQ), and the Immersive Tendencies Questionnaire (ITQ). Our results revealed significant improvements in the range of motion for elbow and shoulder flexion, extension, adduction, and abduction. Furthermore, we observed a reduction in Ashworth spasticity, and the fatigue scale showed reduced per- ception comparing the last with the first session, although the difference was insignificant. The VRNQ questionnaire indicated significant enhancements in the domains related to ”Game Experience” and ”Game Mechanics” and an overall reduction of the perceived “Motion Sickness”. In the ITQ questionnaire, partic- ipants reported high levels of ”Attention,” and while there were no significant differences in ”Immersion” and ”Enjoyment,” a considerable improvement was observed in ”Excitement”. In summary, our results indicate that the immersive VR exergame improved the range of motion, spasticity, and overall game user experience among participants with stroke in a longitudinal, single-arm inter- vention. We conclude that using custom-made VR exergames is an effective and motivating tool for upper limb rehabilitation, with positive changes in both clin- ical and perception outcomes, and the positive and measurable effects persist after the first sessions. These findings support using VR exergames as a comple- mentary tool for at-home rehabilitation therapy with good ease of use, improved physical rehabilitation outcomes, and high treatment adherence.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".