Lin Yifei, Chen Qingquan, Chen Zeshun, Qiu Shengxun, Wang Liangming
The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian, China.
Front Psychiatry. 2025 Jun 6;16:1533875. doi: 10.3389/fpsyt.2025.1533875. eCollection 2025.
The COVID-19 pandemic has significantly affected the sleep quality of medical and non-medical students, yet the influencing factors remain unclear. Objective: This study aimed to assess sleep quality of 20,645 full-time undergraduate and graduate students aged between 17-35 years old in Fujian Province who were enrolled in universities and colleges in the province and to explore key influencing factors while establishing predictive models.
A cross-sectional survey was conducted using an online questionnaire from April 5 to 16, 2022, employing demographic survey components, coffee use, internet use, psychological factors and the Pittsburgh Sleep Quality Index (PSQI). Data were analyzed with a training set (70%) and testing set (30%), utilizing four machine learning techniques: naive Bayes, artificial neural networks, decision trees, and gradient boosting trees.
Non-medical students exhibited poorer sleep quality than medical students (P<0.001). Risk factors for non-medical students included age ≥20 years and fear of infection, while graduation class was a determinant for medical students. The developed models demonstrated high clinical efficiency, with strong agreement between predictions and observations, as shown by calibration curves. Decision curve analysis indicated net benefits for all models.
Non-medical students faced more factors affecting their sleep quality. The validated prediction models provide accurate estimations of sleep disorders in college students, offering valuable insights for campus management.
新冠疫情对医学生和非医学生的睡眠质量产生了重大影响，但其影响因素仍不明确。目的：本研究旨在评估福建省20645名年龄在17至35岁之间的全日制本科和研究生的睡眠质量，这些学生就读于该省的高校，并在建立预测模型的同时探索关键影响因素。
于2022年4月5日至16日使用在线问卷进行横断面调查，问卷包括人口统计学调查内容、咖啡饮用情况、互联网使用情况、心理因素以及匹兹堡睡眠质量指数（PSQI）。数据分为训练集（70%）和测试集（30%）进行分析，采用四种机器学习技术：朴素贝叶斯、人工神经网络、决策树和梯度提升树。
非医学生的睡眠质量比医学生差（P<0.001）。非医学生的风险因素包括年龄≥20岁和对感染的恐惧，而毕业年级是医学生的一个决定因素。所建立的模型显示出较高的临床效率，预测结果与观察结果之间有很强的一致性，校准曲线表明了这一点。决策曲线分析表明所有模型都有净效益。
非医学生面临更多影响其睡眠质量的因素。经过验证的预测模型能够准确估计大学生的睡眠障碍情况，为校园管理提供了有价值的见解。
