Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging

• DOI: 10.1038/s42003-019-0605-1
• Publication Date: 2019-10-04T10:02:49Z
• Authors: Jing Xian Teo; Sonia Davila; Chengxi Yang; An An Hii; Chee Jian Pua; Jonathan Yap; Swee Yaw Tan; Anders Sahlén; Calvin Woon-Loong...; Bin Tean Teh; Steven G. Rozen; Stuart Alexander ...; Khung Keong Yeo; Patrick Tan; Weng Khong Lim
• Keywords: Sleep; Wearable Technology

Abstract Sleep is associated with various health outcomes. Despite their growing adoption, the potential for consumer wearables to contribute sleep metrics sleep-related biomedical research remains largely uncharacterized. Here we analyzed tracking data, along questionnaire responses and multi-modal phenotypic data generated from 482 normal volunteers. First, compared wearable-derived self-reported metrics, particularly total time (TST) efficiency (SE). We then identified demographic, socioeconomic lifestyle factors TST; they included age, gender, occupation alcohol consumption. Multi-modal analysis showed that TST SE were cardiovascular disease risk markers such as body mass index waist circumference, whereas measures not. Using TST, insufficient was premature telomere attrition. Our study highlights provide novel insights into population cohort studies.