Beyond fitness tracking: The use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research

The use of consumer-grade wearables for purposes beyond fitness tracking has not been comprehensively explored. We generated and analyzed multidimensional data from 233 normal volunteers, integrating wearable data, lifestyle questionnaires, cardiac imaging, sphingolipid profiling, and multiple clinical-grade cardiovascular and metabolic disease markers. We show that subjects can be stratified into distinct clusters based on daily activity patterns and that these clusters are marked by distinct demographic and behavioral patterns. While resting heart rates (RHRs) performed better than step counts in being associated with cardiovascular and metabolic disease markers, step counts identified relationships between physical activity and cardiac remodeling, suggesting that wearable data may play a role in reducing overdiagnosis of cardiac hypertrophy or dilatation in active individuals. Wearable-derived activity levels can be used to identify known and novel activity-modulated sphingolipids that are in turn associated with insulin sensitivity. Our findings demonstrate the potential for wearables in biomedical research and personalized health.Author summary: Little is known about how data from wearable sensors can be used apart from fitness tracking. We comprehensively studied 233 normal volunteers, integrating data from wearable sensors with lifestyle questionnaires, cardiac imaging, sphingolipid profiling, and clinical measurements of various heart and metabolic disease markers. Apart from showing that wearable sensors can be used to identify groups of volunteers with distinct behavioral and demographic characteristics, we showed that resting heart rate (RHR) from wearables performed better than step counts in predicting heart and metabolic disease risk markers. Notably, we further demonstrated that wearable data could be used in 2 areas of biomedical research. In the field of cardiac imaging, we showed that activity data from wearables can be used to determine how the size of heart is influenced by physical activity. Wearable data could also identify active individuals that are more likely than others to have enlarged hearts and potentially be misdiagnosed with heart disease. In the field of lipidomics, we showed that wearable data can be used to identify species of sphingolipids that are affected by how active a person is. Some of these compounds are known to be associated with obesity, diabetes, and heart disease.