Wearables for population health
The research group is pioneering the development and design of innovative algorithms for wearable activity monitors, specifically tailored to assess 24-hour movement behaviors across diverse populations. The group utilizes advanced and innovative analytical methods to reveal the complex relationship between behaviors, health, and physical activity.
Research group information
Unit and faculty
Contact information
Research group leader
- Assistant professor and research fellow
- Associate professor (Docent) of Population Health Technology
Researchers
Research group description
The research group is 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.