Development and Validation of a Risk Prediction Model for Sleep Disorders among Senior High School Students in China

Abstract

Abstract Background Sleep disorders are extremely harmful to the health and learning abilities of senior high school students. This issue has garnered significant societal attention. The study aims to develop and validate a risk prediction model for identifying sleep disorders among senior high school students in China, thereby enabling schools and parents to identify high-risk individuals and implement timely interventions. Methods This study is a cross-sectional study. Cluster sampling was employed to recruit participants from senior high school students in China for the purpose of conducting a questionnaire survey from July to August 2021. The questionnaire includes demographic information, psychological status, lifestyle habits, and sleep status. We divided the data into training and validation sets using a 7:3 ratio. The logistic regression method was used to construct a prediction model, and the model was visualized using a nomogram. To evaluate the model’s discrimination, we utilized the area under the receiver operating characteristic curve. Calibration plots and the Hosmer-Lemeshow test were also used to evaluate calibration. Furthermore, decision-curve analysis was used to assess clinical practicality. Results This study included 4793 senior high school students, 24.2% of whom had sleep disorders. Multivariate logistic regression analysis showed interpersonal sensitivity, anxiety, depression, high academic pressure, coffee consumption, alcohol consumption, smoking, eating before bedtime, staying up late, a poor sleep environment, and prolonged use of hand-held electronic devices were the risk factors for sleep disorders in senior high school students. We used these factors to construct a nomogram model. The AUC values for the training and validation sets were 0.862 (95% CI = 0.847-0.876) and 0.853 (95% CI = 0.830-0.876), respectively. Additionally, the Hosmer-Lemeshow test values for the training and validation sets were P = 0.682 and P = 0.1859, respectively. Conclusion The prediction model constructed in this research has good predictive performance. It is helpful for schools to identify high-risk groups for sleep disordersand provide references for subsequent prevention and treatment.