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

• Published in: PLoS biology Vol. 16; no. 2; p. e2004285
• Main Authors: Lim, Weng Khong, Davila, Sonia, Teo, Jing Xian, Yang, Chengxi, Pua, Chee Jian, Blöcker, Christopher, Lim, Jing Quan, Ching, Jianhong, Yap, Jonathan Jiunn Liang, Tan, Swee Yaw, Sahlén, Anders, Chin, Calvin Woon-Loong, Teh, Bin Tean, Rozen, Steven G, Cook, Stuart Alexander, Yeo, Khung Keong, Tan, Patrick
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.02.2018; Public Library of Science (PLoS)
• Subjects: Activity patterns; Adult; Biology; Biology and Life Sciences; Biomedical research; Cancer; Cardiology; Cardiomegaly - diagnosis; Cardiovascular disease; Cardiovascular Physiological Phenomena; Clusters; Data processing; Demographics; Diabetes; Exercise; Female; Fitness; Fitness Trackers; Funding; Healthy Volunteers; Heart diseases; Heart Rate; Humans; Hypertrophy; Insulin; Insulin Resistance; Laboratories; Life Style; Male; Markers; Medical Overuse - prevention & control; Medical schools; Medicine; Medicine and Health Sciences; Metabolic disorders; Middle Aged; Multidimensional data; Physical activity; Physical fitness; Research and Analysis Methods; Sensors; Software; Sphingolipids; Sphingolipids - blood; Stretching; Supervision; Surveys and Questionnaires; Tracking; Ventricular Remodeling; Wearable technology; United Kingdom > UK; Singapore
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.2004285

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.