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

• Published in: PLoS genetics Vol. 16; no. 10; p. e1009089
• Main Authors: Li, Xinyue, Zhao, Hongyu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.10.2020; Public Library of Science (PLoS)
• Subjects: Accelerometers; Actigraphy - methods; Biobanks; Biology and Life Sciences; Body mass index; Central nervous system; Chromosomes; Circadian rhythm; Circadian Rhythm - genetics; Circadian Rhythm - physiology; Circadian rhythms; Diaries; Electronic data processing; Exercise; Female; Gene loci; Genetic aspects; Genetic factors; Genetic Predisposition to Disease; Genome-wide association studies; Genome-Wide Association Study; Genomes; Humans; Identification and classification; Insomnia; Learning algorithms; Machine learning; Male; Markov Chains; Medicine and Health Sciences; Mental disorders; Metabolism; Middle Aged; Neurological diseases; Phenotypes; Physical activity; Physical sciences; Population; Population genetics; Population studies; Quantitative trait loci; Sleep; Sleep - genetics; Sleep - physiology; Sleep and wakefulness; Sleep disorders; Sleep Wake Disorders - genetics; Sleep Wake Disorders - pathology; Wearable computers; Wearable Electronic Devices; United States; United Kingdom > UK; United States > US

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...