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Abstract
Motion sickness (MS) is a common physiological response that often occurs when individuals are exposed to environments with repeated acceleration stimuli. MS results from a mismatch between the vestibular system and visual and proprioceptive inputs. As a crucial organ for sensing acceleration stimuli, the vestibular system is closely related to the onset of MS. However, the complex pathogenesis of MS has led to its diagnosis primarily relying on subjective questionnaires, with a lack of objective indicators for evaluation. To identify objective indicators for evaluating MS, we conducted rotating chair stop experiments based on the principle of vestibulo-ocular reflex with 65 volunteers, obtaining their nystagmus slow-phase velocity and related time constants. Additionally, we conducted detailed MS questionnaires with these volunteers to assess their MS susceptibility. Through correlation analysis, we explored whether the nystagmus slow-phase velocity and related time constants significantly correlated with the MS questionnaire scores. The results showed significant positive correlations between the maximal nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and the nystagmus duration with the MS questionnaire scores in the 65 volunteers. These results indicated that these nystagmus parameters could serve as objective indicators for assessing MS susceptibility. Using K-Means clustering analysis, we classified MS susceptibility into categories I, II, III, and IV, and conducted K-Means clustering analysis on the corresponding nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and nystagmus duration. The magnitude and range of these indicators at different levels was quantified, offering objective and quantitative indicators for the clinical diagnosis of MS.
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Details
1 Xuzhou Medical University, School of Medical Imaging, Xuzhou, China (GRID:grid.413458.f) (ISNI:0000 0000 9330 9891)
2 Xuzhou Medical University, The First Clinical Medical College, Xuzhou, China (GRID:grid.413458.f) (ISNI:0000 0000 9330 9891)
3 Dalian University of Technology, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0000 9247 7930)