Analysis and prediction of addiction among virtual reality users.

Abstract

To understand the addiction situation and influencing factors of virtual reality users, and provide reference basis for timely and effective prevention and intervention of user addiction. Based on a questionnaire survey, univariate analysis, multivariate analysis, and model prediction were conducted on the data of 1164 participants in VR related Facebook groups and Reddit subedits. The single factor analysis results show that the user's own attributes, usage duration, perception level, and application types of virtual reality devices can significantly affect the degree of addiction; The results of multivariate analysis showed that the age of users, the number of days used per week, the number of hours used per day, and the perceived level of the device can significantly affect the probability of addiction. In addition, this study used decision tree algorithm to predict adolescent virtual reality device addiction, with a prediction accuracy of 0.957. The addiction of virtual reality users is related to multiple factors such as gender, age, usage time, application type, and perception level. When developing VR applications and content, consideration should be given to balancing user immersion and healthy use, and reasonable control of usage time is also an effective means to prevent VR addiction.