Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging.
Jing Xian TeoSonia DavilaChengxi YangAn An HiiChee Jian PuaJonathan YapSwee Yaw TanAnders SahlénCalvin Woon-Loong ChinBin Tean TehSteven G RozenStuart Alexander CookKhung Keong YeoPatrick TanWeng Khong LimPublished in: Communications biology (2019)
Sleep is associated with various health outcomes. Despite their growing adoption, the potential for consumer wearables to contribute sleep metrics to sleep-related biomedical research remains largely uncharacterized. Here we analyzed sleep tracking data, along with questionnaire responses and multi-modal phenotypic data generated from 482 normal volunteers. First, we compared wearable-derived and self-reported sleep metrics, particularly total sleep time (TST) and sleep efficiency (SE). We then identified demographic, socioeconomic and lifestyle factors associated with wearable-derived TST; they included age, gender, occupation and alcohol consumption. Multi-modal phenotypic data analysis showed that wearable-derived TST and SE were associated with cardiovascular disease risk markers such as body mass index and waist circumference, whereas self-reported measures were not. Using wearable-derived TST, we showed that insufficient sleep was associated with premature telomere attrition. Our study highlights the potential for sleep metrics from consumer wearables to provide novel insights into data generated from population cohort studies.
Keyphrases
- physical activity
- sleep quality
- cardiovascular disease
- data analysis
- electronic health record
- healthcare
- blood pressure
- heart rate
- risk assessment
- depressive symptoms
- mental health
- health information
- gene expression
- dna methylation
- social media
- genome wide
- climate change
- big data
- human health
- cardiovascular risk factors
- body weight
- patient reported