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

Abstract

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.

Low frequency variants associated with leukocyte telomere length in the Singapore Chinese population

“…The SingHEART/Biobank study was established at the National Heart Centre Singapore and is a cohort of normal volunteers enrolled to characterize normal reference values for various cardiovascular and metabolic disease-related markers in Singaporeans 47 , 48 . Participants were aged between 21 and 69 years without a medical history of myocardial infarction (MI), coronary artery disease (CAD), peripheral arterial disease, stroke, cancer, autoimmune/genetic disease, endocrine disease, diabetes mellitus, psychiatric illness, asthma, or chronic lung disease and chronic infective disease and without a family history of cardiomyopathies 48 . Among study participants, whole genome sequencing (WGS) data for 154 Chinese participants were available for analysis.…”

“…In SingHEART/Biobank, WGS data was utilized to estimate LTL 48 . The Telomerecat 51 program was utilized to calculate the ratio between read-pairs mapping to the telomere with reads spanning the telomere boundary and provide base-pair length predictions.…”

Digital Phenotyping in Livestock Farming

“…Sleep and resting play a crucial role in the development and health of most animals. Some studies have even found that inefficient sleep patterns can shorten the telomers on an animal's chromosomes, leading to shorter lifespans [36]. This important factor for production is not currently observable for all livestock, and it cannot be easily monitored by human caretakers without the risk of observer influence.…”

Digital health data-driven approaches to understand human behavior

“…And, digital data derived from mobile sensing (e.g., calling, texting, conversation and app use) have also been used to characterize behavioral sociability patterns and to map these behaviors onto personality traits [ 60 ]. Further, phenotypic data gathered via wearable sensors have shown that several metrics of sleep (total sleep time and sleep efficiency) are associated with cardiovascular disease risk markers, such as waist circumference and [ 61 ] body mass index and that insufficient sleep is linked to premature telomere attrition. Thus, these digitally derived health risk data can provide real time insights into biological aging.…”