Identification of Smartwatch-Collected Lifelog Variables Affecting Body Mass Index in Middle-Aged People Using Regression Machine Learning Algorithms and SHapley Additive Explanations

Abstract

Body mass index (BMI) plays a vital role in determining the health of middle-aged people, and a high BMI is associated with various chronic diseases. This study aims to identify important lifelog factors related to BMI. The sleep, gait, and body data of 47 middle-aged women and 71 middle-aged men were collected using smartwatches. Variables were derived to examine the relationships between these factors and BMI. The data were divided into groups according to height based on the definition of BMI as the most influential variable. The data were analyzed using regression and tree-based models: Ridge Regression, eXtreme Gradient Boosting (XGBoost), and Category Boosting (CatBoost). Moreover, the importance of the BMI variables was visualized and examined using the SHapley Additive Explanations Technique (SHAP). The results showed that total sleep time, average morning gait speed, and sleep efficiency significantly affected BMI. However, the variables with the most substantial effects differed among the height groups. This indicates that the factors most profoundly affecting BMI differ according to body characteristics, suggesting the possibility of developing efficient methods for personalized healthcare.