Huang Shangbin, Chen Qingquan, Qiu Shengxun, Dai Rongrong, Yao Ling, Zhuang Jiajing, Wu Zhijie, Zeng Yifu, Fan Jimin, Zhang Yixiang
The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
The School of Public Health, Fujian Medical University, Fuzhou, China.
Front Public Health. 2025 Mar 26;13:1406062. doi: 10.3389/fpubh.2025.1406062. eCollection 2025.
The factors associated with sleep quality among medical personnel providing support on the frontline during the height of the COVID-19 pandemic remain unclear, and appropriate predictive and screening tools are lacking. This study was designed and conducted to investigate whether factors such as weight change, job title, and tea consumption influence the sleep quality of these workers. Additionally, the study aims to develop predictive models to analyze the sleep problems experienced by healthcare workers during periods of epidemic instability, and to provide relevant data and tools to support effective intervention and prevention strategies.
A cross-sectional study was conducted from June 25 to July 14, 2022, using a self-administered general information questionnaire and the Pittsburgh Sleep Quality Index (PSQI) to investigate the sleep quality of medical personnel providing aid in Shanghai. The relevant influencing factors were obtained via univariate analysis and multivariate stepwise logistic regression analysis, and 80% of the data were used in the training-test set ( = 1,060) and 20% were used in the independent validation set ( = 266). We used snowball sampling to establish the six models of logistics (LG), deep learning (DL), naïve Bayes (NB), artificial neural networks (ANN), random forest (RF), and gradient-boosted trees (GBT) and perform model testing.
Among the participants, 75.8% were female. Those under 35 years of age comprised 53.7% of the medical staff, while those over 35 years accounted for 46.3%. The educational background of the participants included 402 individuals with an associate degree (30.3%), 713 with a bachelor's degree (53.8%), and 211 with a master's degree or higher (15.9%).Weight, job title, and tea consumption during the aid period were the main factors influencing the sleep quality of medical personnel during the aid period. The areas under the curve (AUC) of LG, DL, NB, ANN, RF, and GBT were 0.645, 0.656, 0.626, 0.640, 0.551, and 0.582, respectively. The DL model has the best prediction performance (specificity = 86.1%, sensitivity = 45.5%) of all the models.
During the height of the COVID-19 pandemic, the sleep quality of frontline medical personnel providing aid in Shanghai was influenced by multiple factors, and the DL model was found to have the strongest overall predictive efficacy for sleep quality.
在新冠疫情高峰期，一线支援医务人员睡眠质量的相关影响因素尚不明确，且缺乏合适的预测和筛查工具。本研究旨在调查体重变化、职称和茶饮等因素是否会影响这些工作人员的睡眠质量。此外，该研究旨在建立预测模型，以分析医护人员在疫情不稳定期间所经历的睡眠问题，并提供相关数据和工具，以支持有效的干预和预防策略。
于2022年6月25日至7月14日进行了一项横断面研究，使用自行填写的一般信息问卷和匹兹堡睡眠质量指数（PSQI）来调查在上海提供援助的医务人员的睡眠质量。通过单因素分析和多因素逐步逻辑回归分析获得相关影响因素，80%的数据用于训练测试集（n = 1060），20%的数据用于独立验证集（n = 266）。我们采用雪球抽样法建立了逻辑回归（LG）、深度学习（DL）、朴素贝叶斯（NB）、人工神经网络（ANN）、随机森林（RF）和梯度提升树（GBT）六种模型并进行模型测试。
参与者中，75.8%为女性。35岁以下的医务人员占53.7%，35岁以上的占46.3%。参与者的教育背景包括402人具有大专学历（30.3%），713人具有本科学历（53.8%），211人具有硕士及以上学历（15.9%）。援助期间的体重、职称和茶饮是影响医务人员援助期间睡眠质量的主要因素。LG、DL、NB、ANN、RF和GBT的曲线下面积（AUC）分别为0.645、0.656、0.626、0.640、0.551和0.582。DL模型在所有模型中具有最佳的预测性能（特异性 = 86.1%，敏感性 = 45.5%）。
在新冠疫情高峰期，在上海提供援助的一线医务人员的睡眠质量受多种因素影响，且发现DL模型对睡眠质量的总体预测效能最强。
