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

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          Abstract

          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.

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                Author and article information

                Contributors
                Journal
                Front Digit Health
                Front Digit Health
                Front. Digit. Health
                Frontiers in Digital Health
                Frontiers Media S.A.
                2673-253X
                02 July 2021
                2021
                : 3
                : 677043
                Affiliations
                [1] 1Graduate School of Engineering Science, Osaka University , Toyonaka, Osaka, Japan
                [2] 2Intasect Communications, Inc. , Tokyo, Japan
                [3] 3Graduate School of Medical Sciences, Nagoya City University , Nagoya, Japan
                [4] 4Graduate School of Education, The University of Tokyo , Tokyo, Japan
                Author notes

                Edited by: Liang Zhang, Xidian University, China

                Reviewed by: Jian Guo, RIKEN Center for Computational Science, Japan; Limin Hou, Shanghai University, China

                *Correspondence: Toru Nakamura t-nakamura@ 123456sangaku.es.osaka-u.ac.jp

                This article was submitted to Health Informatics, a section of the journal Frontiers in Digital Health

                †These authors have contributed equally to this work

                Article
                10.3389/fdgth.2021.677043
                8521927
                34713148
                dcd06352-70af-42b8-8d2e-64a96d1a2a6a
                Copyright © 2021 Li, Nakamura, Hayano and Yamamoto.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 March 2021
                : 02 June 2021
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 57, Pages: 11, Words: 7221
                Categories
                Digital Health
                Original Research

                sleep seasonality,meteorological factors,big data,acceleration data,japanese

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