Severity-based Phenotyping of Motion Sickness Symptoms Reveals Multi-Symptom Burden and Sex-Specific Susceptibility

Abstract

Nausea is one of the most commonly reported symptoms in the area of motion sickness and a continuum of physiological illnesses. Although studied in the past as a single symptom, empirical clinical evidence has continuously shown that it occurs concomitantly with a cluster of systemic signs, such as dizziness, cephalgia, diaphoresis, and fatigue. The idea of nausea being considered as part and parcel of a broad symptom network will offer a more detailed understanding of the accompanying physiological burden that the impacted persons have to bear. The current study values nausea as part of a paradigm of multiple symptoms by implementing severity-based symptom phenotyping. A cross-sectional population survey of 432 respondents was done; data cleaning provided after it yielded 377 valid respondents to be used in rigorous analysis. The severity of five different symptoms including nausea, dizziness, headache, sweating and fatigue, was rated using a Likert scale ranging from 1 and 10. To question the distribution of the severities of the symptoms, descriptive statistical procedures were used. The application of visual analytic techniques, using severity rankings, sex-based comparisons were used to define patterns of symptoms. Results revealed the severity measures had a drastic severity order: fatigue proved to have the greatest mean severity, followed by the headache and sweating, and finally, the dizziness and nausea were characterized by a relatively reduced severity measure. Sex-specificity of the analytic results revealed that female respondents displayed higher levels of the severity of symptoms and more frequent episodes of motion sickness in comparison with male ones. The arguments of these observations point to the need to incorporate nausea into a more extensive multi-symptom physiological reaction paradigm, not as a standalone pathology. This work, as a whole, shows that the use of severity-based symptom phenotyping can be used to detect the patterns of burden of population-level data, which in turn makes it easier to understand the susceptibility of motion sickness.