Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration Data

Seasonal changes in meteorological factors [e.g., ambient temperature (Ta), humidity, and sunlight] could significantly influence a person's sleep, possibly resulting in the seasonality of sleep properties (timing and quality). However, population-based studies on sleep seasonality or its assoc...

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Main Authors: Li Li, Toru Nakamura, Junichiro Hayano, Yoshiharu Yamamoto
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Digital Health
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2021.677043/full
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spelling doaj-a211a21a867d498f957ae6186330d0732021-07-02T04:25:40ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2021-07-01310.3389/fdgth.2021.677043677043Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration DataLi Li0Li Li1Toru Nakamura2Junichiro Hayano3Yoshiharu Yamamoto4Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, JapanIntasect Communications, Inc., Tokyo, JapanGraduate School of Engineering Science, Osaka University, Toyonaka, Osaka, JapanGraduate School of Medical Sciences, Nagoya City University, Nagoya, JapanGraduate School of Education, The University of Tokyo, Tokyo, JapanSeasonal changes in meteorological factors [e.g., ambient temperature (Ta), humidity, and sunlight] could significantly influence a person's sleep, possibly resulting in the seasonality of sleep properties (timing and quality). However, population-based studies on sleep seasonality or its association with meteorological factors remain limited, especially those using objective sleep data. Japan has clear seasonality with distinctive changes in meteorological variables among seasons, thereby suitable for examining sleep seasonality and the effects of meteorological factors. This study aimed to investigate seasonal variations in sleep properties in a Japanese population (68,604 individuals) and further identify meteorological factors contributing to sleep seasonality. Here we used large-scale objective sleep data estimated from body accelerations by machine learning. Sleep parameters such as total sleep time, sleep latency, sleep efficiency, and wake time after sleep onset demonstrated significant seasonal variations, showing that sleep quality in summer was worse than that in other seasons. While bedtime did not show clear seasonality, get-up time varied seasonally, with a nadir during summer, and positively correlated with the sunrise time. Estimated by the abovementioned sleep parameters, Ta had a practically meaningful association with sleep quality, indicating that sleep quality worsened with the increase of Ta. This association would partly explain seasonal variations in sleep quality among seasons. In conclusion, Ta had a principal role for seasonality in sleep quality, and the sunrise time chiefly determined the get-up time.https://www.frontiersin.org/articles/10.3389/fdgth.2021.677043/fullsleep seasonalitymeteorological factorsbig dataacceleration dataJapanese
collection DOAJ
language English
format Article
sources DOAJ
author Li Li
Li Li
Toru Nakamura
Junichiro Hayano
Yoshiharu Yamamoto
spellingShingle Li Li
Li Li
Toru Nakamura
Junichiro Hayano
Yoshiharu Yamamoto
Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration Data
Frontiers in Digital Health
sleep seasonality
meteorological factors
big data
acceleration data
Japanese
author_facet Li Li
Li Li
Toru Nakamura
Junichiro Hayano
Yoshiharu Yamamoto
author_sort Li Li
title Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration Data
title_short Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration Data
title_full Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration Data
title_fullStr Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration Data
title_full_unstemmed Seasonal Sleep Variations and Their Association With Meteorological Factors: A Japanese Population Study Using Large-Scale Body Acceleration Data
title_sort seasonal sleep variations and their association with meteorological factors: a japanese population study using large-scale body acceleration data
publisher Frontiers Media S.A.
series Frontiers in Digital Health
issn 2673-253X
publishDate 2021-07-01
description Seasonal changes in meteorological factors [e.g., ambient temperature (Ta), humidity, and sunlight] could significantly influence a person's sleep, possibly resulting in the seasonality of sleep properties (timing and quality). However, population-based studies on sleep seasonality or its association with meteorological factors remain limited, especially those using objective sleep data. Japan has clear seasonality with distinctive changes in meteorological variables among seasons, thereby suitable for examining sleep seasonality and the effects of meteorological factors. This study aimed to investigate seasonal variations in sleep properties in a Japanese population (68,604 individuals) and further identify meteorological factors contributing to sleep seasonality. Here we used large-scale objective sleep data estimated from body accelerations by machine learning. Sleep parameters such as total sleep time, sleep latency, sleep efficiency, and wake time after sleep onset demonstrated significant seasonal variations, showing that sleep quality in summer was worse than that in other seasons. While bedtime did not show clear seasonality, get-up time varied seasonally, with a nadir during summer, and positively correlated with the sunrise time. Estimated by the abovementioned sleep parameters, Ta had a practically meaningful association with sleep quality, indicating that sleep quality worsened with the increase of Ta. This association would partly explain seasonal variations in sleep quality among seasons. In conclusion, Ta had a principal role for seasonality in sleep quality, and the sunrise time chiefly determined the get-up time.
topic sleep seasonality
meteorological factors
big data
acceleration data
Japanese
url https://www.frontiersin.org/articles/10.3389/fdgth.2021.677043/full
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