Influence Analysis of the Screen Time on Daily Exercise Based on the Personal Activity Factor Model

Abstract

The lockdown caused by the COVID-19 epidemic has led to using smartphones and decreasing physical activity in the world. It is known that increased screen time and decreased physical activity have bad effects on physical and mental health. However, few studies investigate the influence of screen time on the amount of exercise. By analyzing the influence, exercise promotion services can be improved. Therefore, in this study, we challenge to clarify the relationship between exercise and the screen time. Using a machine learning method, we verify the influence importance of screen time on the amount of exercise. We collect the data on smartphone screen time, tablet screen time, steps, and sleep score from a male college student. In addition, we gather weather of the location, and weekdays/weekends data during our experiment period. To analyze the influence, Gradient Boosting Decision Tree (GBDT) is used. GBDT is a kind of decision tree-based method, and it can show the importance of explainable variables. As a result, the variables that were more important for exercise were, in order of importance, total screen time (total screen times of his smartphone and tablet), screen time of smartphone, and sleep score. The result showed that using electronic devices such as smartphones and tablets influence on the exercise. Moreover, the sleep score also had an influence.