ArticlePLoS genetics2020
Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Pooled it
- Circadian Syndrome and Stress Urinary Incontinence in Adult American Women: A NHANES Cross-Sectional Study.Urology research & practice · 2026Article
- Dissecting the genetic architecture of circadian rhythms in human tissues using a quantitative circadian deviation score.Genome biology · 2025Article
- Preventive circadian medicine: improving health with sleep checkups.Npj biological timing and sleep · 2025Review
- Evaluating the causal effect of circulating proteome on the risk of Juvenile idiopathic arthritis: an omics pipeline study.Pediatric rheumatology online journal · 2025Article
- Investigating the non-linear association between sleep duration and type 2 diabetes: conventional and Mendelian randomization analyses from the UK Biobank.Journal of diabetes investigation · 2025Article
- Seasonal timing and interindividual differences in shiftwork adaptation.NPJ digital medicine · 2025Article
- The Interplay of Genetic Predisposition, Circadian Misalignment, and Metabolic Regulation in Obesity.Current obesity reports · 2025Review
- Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis.JMIR diabetes · 2024Article
- Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare.Biomedicines · 2024Review
- Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks.Nature genetics · 2024Article
- The Association between Circadian Syndrome and Frailty in US adults: a cross-sectional study of NHANES Data from 2007 to 2018.Aging clinical and experimental research · 2024Article
- Whole genome sequencing of mouse lines divergently selected for fatness (FLI) and leanness (FHI) revealed several genetic variants as candidates for novel obesity genes.Genes & genomics · 2024Article
- Cardiorespiratory fitness and circadian rhythms in adolescents: a pilot study.Journal of applied physiology (Bethesda, Md. : 1985) · 2024Article
- Circadian clocks: It's time for chronobiology.PLoS biology · 2023Article
- CARE as a wearable derived feature linking circadian amplitude to human cognitive functions.NPJ digital medicine · 2023Article
- Association between circadian syndrome and the prevalence of kidney stones in overweight adults: a cross-sectional analysis of NHANES 2007-2018.BMC public health · 2023Article
- Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study.Journal of medical Internet research · 2023Article
- A positive association between the prevalence of circadian syndrome and overactive bladder in United States adults.Frontiers in public health · 2023Article
- Insights about circadian clock in glioma: From molecular pathways to therapeutic drugs.CNS neuroscience & therapeutics · 2022Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Wearable devices have been increasingly used in research to provide continuous physical activity monitoring, but how to effectively extract features remains challenging for researchers. To analyze the generated actigraphy data in large-scale population studies, we developed computationally efficient methods to derive sleep and activity features through a Hidden Markov Model-based sleep/wake identification algorithm, and circadian rhythm features through a Penalized Multi-band Learning approach adapted from machine learning. Unsupervised feature extraction is useful when labeled data are unavailable, especially in large-scale population studies. We applied these two methods to the UK Biobank wearable device data and used the derived sleep and circadian features as phenotypes in genome-wide association studies. We identified 53 genetic loci with p<5×10-8 including genes known to be associated with sleep disorders and circadian rhythms as well as novel loci associated with Body Mass Index, mental diseases and neurological disorders, which suggest shared genetic factors of sleep and circadian rhythms with physical and mental health. Further cross-tissue enrichment analysis highlights the important role of the central nervous system and the shared genetic architecture with metabolism-related traits and the metabolic system. Our study demonstrates the effectiveness of our unsupervised methods for wearable device data when additional training data cannot be easily acquired, and our study further expands the application of wearable devices in population studies and genetic studies to provide novel biological insights.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.