中国浙江新冠疫情后大流行时代普通人群失眠的特征及相关因素：一项横断面研究

Characteristics and Associated Factors of Insomnia Among the General Population in the Post-Pandemic Era of COVID-19 in Zhejiang, China: A Cross-Sectional Study.

作者信息

Da Miao, Mou Shaoqi, Hou Guangwei, Shen Zhongxia

机构信息

Department of Sleep Medicine Center, Huzhou Third Municipal Hospital, the Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, People's Republic of China.

Department of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.

出版信息

Int J Gen Med. 2025 Jan 15;18:191-206. doi: 10.2147/IJGM.S473269. eCollection 2025.

DOI:10.2147/IJGM.S473269

PMID:39830141

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11742635/

Abstract

OBJECTIVE

This study aimed to analyze the changes in insomnia characteristics among the general population and explore associated factors during the COVID-19 pandemic and post-pandemic periods.

METHODS

A cross-sectional study was conducted using an anonymous online survey. Questionnaires were administered at two-time points (T1: March 1-31, 2022; T2: March 1-31, 2023), which included an Insomnia Severity Index (ISI) and questions related to sleep risk factors, including the COVID-19 pandemic, familial influences, work and study conditions, social activities, physical health, use of electronic devices before sleep, sleep environment, food intake and exercise before sleep, etc. Insomnia characteristics were compared at two points, with logistic regression testing associations with sociodemographic covariates and risk factors. Six machine learning models were employed to develop a predictive model for insomnia, namely logistic regression, random forest, neural network, support vector machine, CatBoost, and gradient boosting decision tree.

RESULTS

The study obtained 2769 and 1161 valid responses in T1 and T2, respectively. The prevalence of insomnia increased from 23.4% in T1 to 34.83% in T2. Univariate analyses indicated the factors of the COVID-19 pandemic, familial influences, social activity, physical health, food intake, and exercise before sleep significantly differed in T1 (p<0.05) between insomnia and non-insomnia groups. In T2, significant differences (p<0.05) were observed between the two groups, including the factors of the COVID-19 pandemic, family structure, work and study conditions, social activity, and physical health status. The random forest model had the highest prediction accuracy (90.92% correct and 86.59% correct in T1 and T2, respectively), while the pandemic was the most critical variable at both time points.

CONCLUSION

The prevalence and severity of insomnia have worsened in the post-pandemic period, highlighting an urgent need for effective interventions. Notably, the COVID-19 pandemic and physical health status were identified as significant risk factors for insomnia.

摘要

目的

本研究旨在分析新冠疫情期间及疫情后普通人群失眠特征的变化，并探索相关因素。

方法

采用匿名在线调查进行横断面研究。在两个时间点（T1：2022年3月1日至31日；T2：2023年3月1日至31日）进行问卷调查，内容包括失眠严重程度指数（ISI）以及与睡眠风险因素相关的问题，包括新冠疫情、家庭影响、工作和学习条件、社交活动、身体健康、睡前使用电子设备、睡眠环境、睡前饮食和运动等。比较两个时间点的失眠特征，并通过逻辑回归分析与社会人口统计学协变量和风险因素的关联。使用六种机器学习模型建立失眠预测模型，即逻辑回归、随机森林、神经网络、支持向量机、CatBoost和梯度提升决策树。

结果

该研究在T1和T2分别获得2769份和1161份有效回复。失眠患病率从T1的23.4%上升至T2的34.83%。单因素分析表明，新冠疫情、家庭影响、社交活动、身体健康、饮食摄入和睡前运动等因素在T1失眠组和非失眠组之间存在显著差异（p<0.05）。在T2，两组之间观察到显著差异（p<0.05），包括新冠疫情、家庭结构、工作和学习条件、社交活动以及身体健康状况等因素。随机森林模型具有最高的预测准确率（T1和T2分别为90.92%和86.59%正确），而疫情在两个时间点都是最关键的变量。