Lim WK1,2, Davila S1,3, Teo JX1, Yang C4, Pua CJ4, Blöcker C2, Lim JQ5, Ching J3, Yap JJL6, Tan SY6, Sahlén A6, Chin CW6, Teh BT1,2,7,8,9, Rozen SG1,2,10, Cook SA1,3,4,11,12, Yeo KK6, Tan P1,2,9,13.
1 SingHealth Duke-NUS Institute of Precision Medicine, Singapore.
2 Cancer and Stem Biology Program, Duke-NUS Medical School, Singapore.
3 Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, Singapore.
4 National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore.
5 Lymphoma Genomic Translational Laboratory, Division of Medical Oncology, National Cancer Centre Singapore, Singapore.
6 Department of Cardiology, National Heart Centre Singapore, Singapore.
7 Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore
8 Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore.
9 Cancer Science Institute of Singapore, National University of Singapore, Singapore.
10 Centre for Computational Biology, Duke-NUS Medical School, Singapore.
11 National Heart and Lung Institute, Imperial College London, United Kingdom.
12 MRC Clinical Sciences Centre, Imperial College London, United Kingdom.
13 Biomedical Research Council, Agency for Science, Technology and Research, Singapore.
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.