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1000
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Abstract/Summary
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<jats:sec><jats:title>Background</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Graphical Abstract</jats:title><jats:p> </jats:p></jats:sec>
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1000
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Hinweis
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DeepGreen-ID: d2aae0a240064f8aa0d311727f4404c0 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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