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Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms

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  • Xinyue Li
  • Hongyu Zhao

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

Suggested Citation

  • Xinyue Li & Hongyu Zhao, 2020. "Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms," PLOS Genetics, Public Library of Science, vol. 16(10), pages 1-22, October.
    • Handle: RePEc:plo:pgen00:1009089
    DOI: 10.1371/journal.pgen.1009089
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    References listed on IDEAS

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    1. Aiden Doherty & Karl Smith-Byrne & Teresa Ferreira & Michael V. Holmes & Chris Holmes & Sara L. Pulit & Cecilia M. Lindgren, 2018. "GWAS identifies 14 loci for device-measured physical activity and sleep duration," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    2. Tom White & Kate Westgate & Nicholas J Wareham & Soren Brage, 2016. "Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    3. Aiden Doherty & Dan Jackson & Nils Hammerla & Thomas Plötz & Patrick Olivier & Malcolm H Granat & Tom White & Vincent T van Hees & Michael I Trenell & Christoper G Owen & Stephen J Preece & Rob Gillio, 2017. "Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-14, February.
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