Recursive Partitioning Methods to Examine the Effects of Physical Activity on Physical and Mental Health: A Case Study at One Public University in the United States

Abstract

The purpose of this study was to examine the effects of physical activity (PA) on physical and mental health within an adult sample, while controlling for demographic characteristics. Data were analyzed from the 2018–2019 Exercise is Medicine® On Campus (EIM-OC) assessment of students, faculty, and staff at one public university in the United States. Participants completed a survey including a demographic questionnaire, the 12-Item Short Form Health Survey (SF-12), and the International Physical Activity Questionnaire (IPAQ). Classification and Regression Trees, a supervised learning technique based upon recursive partitioning, were utilized to explore the relationships between PA and health. Variable-importance was assessed via the permutation method and confirmed with ensemble learning methods. Results indicated that PA, especially in leisure, was predictive of both physical and mental health. Further, leisure PA was the single most important variable contributing to mental health. Greater levels of physical health were observed in adults achieving at least 672 MET minutes of leisure PA per week, while greater levels of mental health were observed in adults achieving at least 950 MET minutes of leisure PA per week. Despite current recommendations for PA (irrespective of domain), more PA (especially in leisure) improves physical and mental health.