Virtual Reality for Vestibular Rehabilitation: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Vertigo is a debilitating symptom, leading to increased healthcare utilization and lost patient productivity. Vestibular rehabilitation is used to manage the symptomatic manifestations of vestibular disease. However, vestibular rehabilitation is limited by accessibility and time commitment. Recently, virtual reality has been described as a vestibular rehabilitation tool that may circumvent these barriers to treatment. Despite this, the efficacy of virtual reality for vestibular rehabilitation remains unclear. This study aims to review and summarize the current literature on the effectiveness of virtual reality-based vestibular rehabilitation. METHODS: A systematic review of the MEDLINE, EMBASE, and Alternative and Complementary Medicine databases was conducted for prospective studies describing virtual reality-based vestibular rehabilitation. RESULTS: Our search identified 382 unique articles. Six randomized controlled trials and four other studies were ultimately included. Study sample sizes ranged from 13 to 70 participants and varied in diagnoses from any unilateral peripheral vertigo to specific pathologies. Different virtual reality interventions were used. Comparator groups ranged from supervised vestibular rehabilitation to independent Cawthorne-Cooksey exercises. Outcomes consisted of validated questionnaires, objective clinical tests, and measurements of balance or reflexes. CONCLUSION: The studies reviewed in this study are preliminary evidence to suggest the benefit of virtual reality-based vestibular rehabilitation. However, these studies are limited by their inclusion criteria, heterogeneity, comparator design, and evidence-based clinical outcomes. Further research should address these limitations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".