Chen Qingquan, Chen Zeshun, Zhu Xi, Zhuang Jiajing, Yao Ling, Zheng Huaxian, Li Jiaxin, Xia Tian, Lin Jiayi, Huang Jiewei, Zeng Yifu, Fan Chunmei, Fan Jimin, Song Duanhong, Zhang Yixiang
The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.
The School of Public Health, Fujian Medical University, Fuzhou, Fujian Province, China.
Digit Health. 2024 Oct 1;10:20552076241287363. doi: 10.1177/20552076241287363. eCollection 2024 Jan-Dec.
The sleep quality of medical staff was severely affected during COVID-19, but the factors influencing the sleep quality of frontline staff involved in medical assistance remained unclear, and screening tools for their sleep quality were lacking. From June 25 to July 14, 2022, we conducted an Internet-based cross-sectional survey. The Pittsburgh Sleep Quality Index (PSQI), a self-designed general information questionnaire, and a questionnaire regarding the factors influencing sleep quality were combined to understand the sleep quality of frontline medical staff in Fujian Province supporting Shanghai in the past month. A chi-square test was used to compare participant characteristics, and multivariate unconditional logistic regression analysis was used to determine the predictors of sleep quality. Stratified sampling was used to divide the data into a training test set ( = 1061, 80%) and an independent validation set ( = 265, 20%). Six models were developed and validated using logistic regression, artificial neural network, gradient augmented tree, random forest, naive Bayes, and model decision tree. A total of 1326 frontline medical staff were included in this survey, with a mean PSQI score of 11.354 ± 4.051. The prevalence of poor sleep quality was 80.8% ( = 1072, PSQI >7). Six variables related to sleep quality were used as parameters in the prediction model, including type of work, professional job title, work shift, weight change, tea consumption during assistance, and basic diseases. The artificial neural network (ANN) model produced the best overall performance with area under the curve, accuracy, sensitivity, specificity, precision, F1 score, and kappa of 71.6%, 68.7%, 66.7%, 69.2%, 34.0%, 45.0%, and 26.2% respectively. In this study, the ANN model, which demonstrated excellent predictive efficiency, showed potential for application in monitoring the sleep quality of medical staff and provide some scientific guidance suggestions for early intervention.
在新冠疫情期间，医务人员的睡眠质量受到严重影响，但影响参与医疗救助的一线工作人员睡眠质量的因素仍不明确，且缺乏针对他们睡眠质量的筛查工具。2022年6月25日至7月14日，我们开展了一项基于互联网的横断面调查。将匹兹堡睡眠质量指数（PSQI）、一份自行设计的一般信息问卷以及一份关于影响睡眠质量因素的问卷相结合，以了解福建省支援上海的一线医务人员过去一个月的睡眠质量。采用卡方检验比较参与者特征，并使用多因素无条件逻辑回归分析确定睡眠质量的预测因素。采用分层抽样将数据分为训练测试集（n = 1061，80%）和独立验证集（n = 265，20%）。使用逻辑回归、人工神经网络、梯度增强树、随机森林、朴素贝叶斯和模型决策树开发并验证了六个模型。本调查共纳入1326名一线医务人员，PSQI平均得分为11.354 ± 4.051。睡眠质量差的患病率为80.8%（n = 1072，PSQI>7）。预测模型中使用了六个与睡眠质量相关的变量作为参数，包括工作类型、专业职称、工作班次、体重变化、援助期间饮茶情况和基础疾病。人工神经网络（ANN）模型的总体表现最佳，曲线下面积、准确率、灵敏度、特异度、精准度、F1分数和kappa分别为71.6%、68.7%、66.7%、69.2%、34.0%、45.0%和26.2%。在本研究中，表现出优异预测效率的人工神经网络模型显示出在监测医务人员睡眠质量方面的应用潜力，并为早期干预提供了一些科学指导建议。
