Machine learning in physical activity, sedentary, and sleep behavior research

The nature of human movement and non-movement behaviors is complex and multifaceted, making their study complicated and challenging. Thanks to the availability of wearable activity monitors, we can now monitor the full spectrum of physical activity, sedentary, and sleep behaviors better than ever before—whether the subjects are elite athletes, children, adults, or individuals with pre-existing medical conditions. The increasing volume of generated data, combined with the inherent complexities of human movement and non-movement behaviors, necessitates the development of new data analysis methods for the research of physical activity, sedentary, and sleep behaviors. The characteristics of machine learning (ML) methods, including their ability to deal with complicated data, make them suitable for such analysis and thus can be an alternative tool to deal with data of this nature. ML can potentially be an excellent tool for solving many traditional problems related to the research of physical activity, sedentary, and sleep behaviors such as activity recognition, posture detection, profile analysis, and correlates research. However, despite this potential, ML has not yet been widely utilized for analyzing and studying these behaviors. In this review, we aim to introduce experts in physical activity, sedentary behavior, and sleep research—individuals who may possess limited familiarity with ML—to the potential applications of these techniques for analyzing their data. We begin by explaining the underlying principles of the ML modeling pipeline, highlighting the challenges and issues that need to be considered when applying ML. We then present the types of ML: supervised and unsupervised learning, and introduce a few ML algorithms frequently used in supervised and unsupervised learning. Finally, we highlight three research areas where ML methodologies have already been used in physical activity, sedentary behavior, and sleep behavior research, emphasizing their successes and challenges. This paper serves as a resource for ML in physical activity, sedentary, and sleep behavior research, offering guidance and resources to facilitate its utilization.