Vestibular rehabilitation potential of commercially available virtual reality video games
Bibliographic record
Abstract
BACKGROUND: Peripheral vestibular disorders affect 2.8-6.5% of people. Standard treatment is vestibular rehabilitation therapy, and virtual reality (VR) could improve outcomes. The objective of this study was to identify the commercially available VR video game that is most congruent to vestibular rehabilitation therapy. METHODS: A term search "virtual reality racing" was performed on the App Store in March 2022. Results were screened for free point-of-view racing games compatible with Android and iOS devices. An investigator was filmed playing each game and videos were distributed to 237 physiotherapists. Physiotherapists completed a survey of 5-point Likert scale questions that assessed the video games vestibular rehabilitation potential. Survey responses were analyzed using Friedman Two-Way ANOVA (alpha = 0.05) and paired samples sign test with Bonferroni correction. RESULTS: The search yielded 58 games, 4 were included. Forty physiotherapists participated. VR Tunnel Race (VRTR) and VR Real World Bike Racing (VRWBR) had the greatest vestibular rehabilitation potential (median global scores = 18.00). VRTR replicated habituation exercises significantly (p < 0.001) better than Derby VR, and VRWBR replicated physiotherapist-prescribed exercises significantly (p < 0.001) better than VR X-Racer. There were no discernable significant differences between VRWBR and VRTR. CONCLUSIONS: VRTR and VRWBR are the most congruent VR games to standard vestibular rehabilitation. VRWBR is preferable to VRTR with respect to ease of use and the ability to alter the amount of optokinetic stimulation. Prospective studies are needed to confirm the efficacy of these videos games and to determine if they could be used as solitary treatments. TRIAL REGISTRATION: Not applicable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".