The research group of Data Analytics in Sport Scince is overall interested in applied machine learning and data science on fields of applications realted to movement, sports, and health. Currently, our research is primarily dedicated to exploring innovative methodologies for investigating the interrelationships among wearable-measured 24-hour physical activity, sedentary behavior, sleep, and health. Our goal is to understand how the use of time and patterns of movement and non-movement behaviors are related to several health outcomes. The research group utilizes data from large population-based cohort studies, integrating data from wearables, health records, and behavioral factors. Machine learning, deep learning, and other statistical approaches are utilized to make sense of these data, facilitating a comprehensive understanding of health and diseases at population level.