Evaluation of sleep quality and influencing factors among medical and non-medical students using machine learning techniques in Fujian during the public health emergencies

• Published in: Frontiers in psychiatry Vol. 16; p. 1533875
• Main Authors: Lin, Yifei, Chen, Qingquan, Chen, Zeshun, Qiu, Shengxun, Wang, Liangming
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 2025
• Subjects: COVID-19; machine learning; medical students; non-medical students; sleep quality; COVID-19; machine learning; medical students; sleep quality; non-medical students
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2025.1533875

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.