Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Oct 4:2:361.
doi: 10.1038/s42003-019-0605-1. eCollection 2019.

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

Jing Xian Teo  1 , Sonia Davila  1   2 , Chengxi Yang  3 , An An Hii  3 , Chee Jian Pua  3 , Jonathan Yap  4 , Swee Yaw Tan  4 , Anders Sahlén  4   5 , Calvin Woon-Loong Chin  4 , Bin Tean Teh  1   6   7   8   9 , Steven G Rozen  1   6   10 , Stuart Alexander Cook  1   2   3   11   12 , Khung Keong Yeo  4 , Patrick Tan  1   6   9   13 , Weng Khong Lim  1   6
Affiliations

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

Jing Xian Teo et al. Commun Biol. .

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.

Keywords: Data integration; Predictive markers; Risk factors; Senescence.

PubMed Disclaimer

Conflict of interest statement

Competing interestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Comparison between wearable-derived and self-reported sleep metrics. a Wearable-derived TST and PSQI Component 3 score (sleep duration). b Wearable-derived SE and PSQI Component 4 score (sleep efficiency). c Wearable-derived nocturnal awakenings and PSQI Component 5b score (nocturnal awakenings). Asterisks denote significance of component score in linear model compared to reference score of 0. *p < 0.01, ***p < 0.001. TST total sleep time, SE sleep efficiency, PSQI Pittsburgh Sleep Quality Index
Fig. 2
Fig. 2
Wearable sleep duration and demographic factors. a Distribution of volunteer sleep and wake times. b–d Wearable-derived TST by b age-group, c gender and d occupation type. Asterisks denote significance of factor in linear model compared to reference level (leftmost factor). *p < 0.05, **p < 0.01, ***p < 0.001. TST total sleep time
Fig. 3
Fig. 3
Wearable-derived TST predicts leukocyte telomere length. a Adjusted WGS-LTL by age-group. b Adjusted wearable-derived TST and adjusted WGS-LTL. c Adjusted WGS-LTL and adjusted qPCR-LTL of volunteers with insufficient (<5 h) and adequate (>7 h) of TST. d Adjusted wearable-derived TST and adjusted qPCR-LTL. Asterisks denote significance of component score in linear model compared to reference score of 0. **p < 0.01, ***p < 0.001. LTL leukocyte telomere length, WGS-LTL LTL estimated using whole-genome sequencing, qPCR-LTL LTL estimated using quantitative PCR, TST total sleep time, bp base pairs, T/S T/S ratio. All LTL values are adjusted for age, gender, ethnicity, and BMI
See this image and copyright information in PMC

References

    1. Taheri S. The link between short sleep duration and obesity: we should recommend more sleep to prevent obesity. Arch. Dis. Child. 2006;91:881–884. doi: 10.1136/adc.2005.093013. - DOI - PMC - PubMed
    1. Wu Y, Zhai L, Zhang D. Sleep duration and obesity among adults: a meta-analysis of prospective studies. Sleep. Med. 2014;15:1456–1462. doi: 10.1016/j.sleep.2014.07.018. - DOI - PubMed
    1. Pepin J-L, et al. Hypertension and sleep: overview of a tight relationship. Sleep. Med. Rev. 2014;18:509–519. doi: 10.1016/j.smrv.2014.03.003. - DOI - PubMed
    1. Gangwisch JE. A review of evidence for the link between sleep duration and hypertension. Am. J. Hypertens. 2014;27:1235–1242. doi: 10.1093/ajh/hpu071. - DOI - PMC - PubMed
    1. Wang Y, et al. Relationship between duration of sleep and hypertension in adults: a meta-analysis. J. Clin. Sleep. Med. 2015;11:1047–1056. - PMC - PubMed

Publication types

LinkOut - more resources