Predicting cybersickness using individual and task characteristics.

The experience of cybersickness in virtual reality (VR) drastically differs between users, likely due to variability in individual and task characteristics, leaving cybersickness as a substantial barrier to the widespread adoption of VR technology. While these characteristics have been connected to cybersickness, analyses do not commonly consider the simultaneous effects of multiple factors on cybersickness. As such, the current research aims to evaluate how multiple individual and task characteristics impact cybersickness, as well as how much variance in cybersickness these characteristics account for. In this study, 150 participants were exposed to the 3D Cybersickness Corn Maze that was designed with cybersickness-inducing stimuli and could choose to exit at any time. Participants completed one of three tasks that varied in mental workload during their exposure. Hierarchical multiple regression models were used to examine how individual characteristics (i.e., motion sickness history, previous VR use, gender, age, and personality) and task characteristics (i.e., workload, presence) impacted cybersickness. Analyses revealed that both individual characteristics (particularly motion sickness history) and task characteristics (particularly workload) were important for predicting cybersickness, accounting for between 43.6% and 47.7% of the variance in cybersickness experiences. This study's results suggest that models of cybersickness that do not include task and individual characteristics can be shown to be lacking by not considering these important factors. Designers of virtual environments may also benefit from evaluating the impact of their tasks and their users' variable characteristics during design.