Application of machine learning models in predicting physical literacy in 4–6-year-old children: A comprehensive analysis of individual and family factors

Abstract

Physical literacy in children has become a significant research topic in both education and psychology. Recently, machine learning, as a cutting-edge AI technology, has started to play a crucial role in these fields. This study aimed to apply machine learning models to predict physical literacy in 4–6-year-old children and to comprehensively analyze the influence of individual and family factors. We evaluated the physical literacy of 1,734 children aged 4–6 and systematically examined the impact of both individual factors (such as gender, age, body type, sedentary behavior, screen time, moderate-to-vigorous physical activity (MVPA), sleep duration, and sleep quality) and family factors (such as parents’ education level, occupation, exercise frequency, support for children’s physical activity, household annual income, and family exercise environment) using various machine learning models. Results showed that the ensemble learning model achieved the best performance in predicting physical literacy, with an AUC of 86.2%. Among all predictive factors, mother’s exercise frequency, family exercise environment, and time spent on MVPA were identified as the most important. These findings provide new insights into enhancing children’s physical literacy and underscore the critical role of family environment and lifestyle in its development.