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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
Seasonal Variations in Sleep Quality and Stability Among Patients with Chronic Insomnia: Real-World Evidence Across
Lin-Lin Hu1, Jia-Xin Wang1, Ming-Fen Song2
1Sleep Medicine Center, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, People's Republic of China.
Background:
The 24 Solar Terms of the traditional Chinese lunisolar calendar reflect seasonal and climatic changes that may influence sleep. Few large-scale studies have examined sleep quality and stability across these seasonal markers in chronic insomnia.
Methods:
This retrospective observational study analyzed anonymized data from 25,428 chronic insomnia patients using the "Good Sleep 365" platform at Zhejiang University's Affiliated Mental Health Center (2018-2023). Sleep quality and stability were assessed via the Pittsburgh Sleep Quality Index (PSQI), incorporating the total score to reflect overall sleep quality, along with score reduction and reduction rate to capture changes and stability over time. Seasonal autoregressive integrated moving average (SARIMA) models were applied to time-series data; 2023 data validated the models, and 2024 predictions were generated.
Results:
Sleep quality was poorer during Grain Rain (Guyu) and Cold Dew (Hanlu), with mean PSQI scores of 9.53 and 9.48, respectively, whereas it was better during Major Snow (Daxue) and Minor Snow (Xiaoxue), with mean PSQI scores of 8.91 and 8.96, respectively. Women were more sensitive to seasonal variations than men (P<0.05), while patients aged 45-59 showed greater fluctuations (P<0.05). No significant associations were found between sleep and key solar terms such as Beginning of Spring (Lichun), Beginning of Summer (Lixia), Beginning of Autumn (Liqiu), and Beginning of Winter (Lidong), etc. SARIMA(1,0,1)(0,1,1)[24] best modeled sleep quality and fluctuations, and SARIMA(1,0,1)(1,0,1)[24] for improvements; both models demonstrated good fit and predictions are mostly contained within the confidence intervals.
Conclusion:
Sleep quality and stability vary across the 24 Solar Terms, with notable gender and age differences. SARIMA models are able to reflect these patterns to a certain extent, with the majority of predictions lying within the confidence intervals, which may contribute to personalized insomnia management. Cultural context of the 24 Solar Terms adds interpretive value. Limitations of this study include reliance on self-reported PSQI scores, lack of direct meteorological data, and other factors.
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