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Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Health and Fitness

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

H. Yuan, T. Plekhanova, et al.

This study conducted by Hang Yuan, Tatiana Plekhanova, and others delves into the effectiveness of wrist-worn accelerometers for classifying sleep stages and examines how sleep duration and efficiency relate to mortality risk. The findings reveal that short sleep duration may increase mortality risk, regardless of sleep quality.

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Playback language: English
Abstract
This study investigated the accuracy of wrist-worn accelerometers for sleep stage classification and the association between sleep duration and efficiency with mortality. A self-supervised deep neural network (SleepNet) was developed for sleep stage classification using polysomnography and accelerometry data from 1113 participant nights. SleepNet showed fair to moderate agreement with polysomnography. In the UK Biobank (66,262 participants), short sleepers (<6h) had a higher mortality risk compared to those with normal sleep duration (6-7.9h), regardless of sleep efficiency. The study suggests short sleep duration increases mortality risk, irrespective of sleep continuity.
Publisher
npj Digital Medicine
Published On
May 20, 2024
Authors
Hang Yuan, Tatiana Plekhanova, Rosemary Walsmley, Amy C. Reynolds, Kathleen J. Maddison, Maja Bucan, Philip Gehrman, Alex Rowlands, David W. Ray, Derrick Bennett, Joanne McVeigh, Leon Straker, Peter Eastwood, Simon D. Kyle, Aiden Doherty
Tags
sleep stage classification
accelerometers
mortality risk
sleep duration
sleep efficiency
deep neural network
polysomnography

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