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

Overview

Journal Front Digit Health

Specialty Medical Informatics

Date 2021 Oct 29

PMID 34713148

Authors

Li Li

Toru Nakamura

Junichiro Hayano

Yoshiharu Yamamoto

Affiliations

Soon will be listed here.

Abstract

Seasonal changes in meteorological factors [e.g., ambient temperature (), 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, had a practically meaningful association with sleep quality, indicating that sleep quality worsened with the increase of . This association would partly explain seasonal variations in sleep quality among seasons. In conclusion, had a principal role for seasonality in sleep quality, and the sunrise time chiefly determined the get-up time.

References

Suzuki M, Taniguchi T, Furihata R, Yoshita K, Arai Y, Yoshiike N . Seasonal changes in sleep duration and sleep problems: A prospective study in Japanese community residents. PLoS One. 2019; 14(4):e0215345. PMC: 6472875. DOI: 10.1371/journal.pone.0215345. View

Hashizaki M, Nakajima H, Shiga T, Tsutsumi M, Kume K . A longitudinal large-scale objective sleep data analysis revealed a seasonal sleep variation in the Japanese population. Chronobiol Int. 2018; 35(7):933-945. DOI: 10.1080/07420528.2018.1443118. View

Yuda E, Ueda N, Kisohara M, Hayano J . Redundancy among risk predictors derived from heart rate variability and dynamics: ALLSTAR big data analysis. Ann Noninvasive Electrocardiol. 2020; 26(1):e12790. PMC: 7816809. DOI: 10.1111/anec.12790. View

Montmayeur , Buguet . Sleep patterns of European expatriates in a dry tropical climate. J Sleep Res. 1992; 1(3):191-196. DOI: 10.1111/j.1365-2869.1992.tb00037.x. View

Malafeev A, Laptev D, Bauer S, Omlin X, Wierzbicka A, Wichniak A . Automatic Human Sleep Stage Scoring Using Deep Neural Networks. Front Neurosci. 2018; 12:781. PMC: 6232272. DOI: 10.3389/fnins.2018.00781. View

Hochreiter S, Schmidhuber J . Long short-term memory. Neural Comput. 1997; 9(8):1735-80. DOI: 10.1162/neco.1997.9.8.1735. View

Supratak A, Dong H, Wu C, Guo Y . DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG. IEEE Trans Neural Syst Rehabil Eng. 2017; 25(11):1998-2008. DOI: 10.1109/TNSRE.2017.2721116. View

Hayano J, Ohashi K, Yoshida Y, Yuda E, Nakamura T, Kiyono K . Increase in random component of heart rate variability coinciding with developmental and degenerative stages of life. Physiol Meas. 2018; 39(5):054004. DOI: 10.1088/1361-6579/aac007. View

Sadeh A, Sharkey K, Carskadon M . Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep. 1994; 17(3):201-7. DOI: 10.1093/sleep/17.3.201. View

10.

Zhao X, Sun G . A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals. Entropy (Basel). 2021; 23(1). PMC: 7830686. DOI: 10.3390/e23010116. View

11.

Friborg O, Bjorvatn B, Amponsah B, Pallesen S . Associations between seasonal variations in day length (photoperiod), sleep timing, sleep quality and mood: a comparison between Ghana (5°) and Norway (69°). J Sleep Res. 2011; 21(2):176-84. DOI: 10.1111/j.1365-2869.2011.00982.x. View

12.

Bliwise D . Sleep in normal aging and dementia. Sleep. 1993; 16(1):40-81. DOI: 10.1093/sleep/16.1.40. View

13.

Ohayon M, Partinen M . Insomnia and global sleep dissatisfaction in Finland. J Sleep Res. 2002; 11(4):339-46. DOI: 10.1046/j.1365-2869.2002.00317.x. View

14.

Teicher M . Actigraphy and motion analysis: new tools for psychiatry. Harv Rev Psychiatry. 1995; 3(1):18-35. DOI: 10.3109/10673229509017161. View

15.

Okamoto-Mizuno K, Mizuno K, Michie S, Maeda A, Iizuka S . Effects of humid heat exposure on human sleep stages and body temperature. Sleep. 1999; 22(6):767-73. View

16.

Ancoli-Israel S, Martin J, Blackwell T, Buenaver L, Liu L, Meltzer L . The SBSM Guide to Actigraphy Monitoring: Clinical and Research Applications. Behav Sleep Med. 2015; 13 Suppl 1:S4-S38. DOI: 10.1080/15402002.2015.1046356. View

17.

Li L, Nakamura T, Hayano J, Yamamoto Y . Age and gender differences in objective sleep properties using large-scale body acceleration data in a Japanese population. Sci Rep. 2021; 11(1):9970. PMC: 8113448. DOI: 10.1038/s41598-021-89341-x. View

18.

de Souza L, Benedito-Silva A, Pires M, Poyares D, Tufik S, Calil H . Further validation of actigraphy for sleep studies. Sleep. 2003; 26(1):81-5. DOI: 10.1093/sleep/26.1.81. View

19.

Kohsaka M, Fukuda N, Honma K, Honma S, Morita N . Seasonality in human sleep. Experientia. 1992; 48(3):231-3. DOI: 10.1007/BF01930461. View

20.

Husby R, Lingjaerde O . Prevalence of reported sleeplessness in northern Norway in relation to sex, age and season. Acta Psychiatr Scand. 1990; 81(6):542-7. DOI: 10.1111/j.1600-0447.1990.tb05009.x. View