Current Status and Influencing Factors of Insomnia Severity in Adolescents: A Cross-Sectional Study

Hai-Shan Huang,1 Zhuo-Yan Zhang,2 Rong Xv1 1Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaCorrespondence: Zhuo-Yan Zhang, School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China, Email [email protected]: To investigate the status and influencing factors of insomnia severity among Chinese urban adolescents and to develop a predictive model for early identification.Design: A multi-center cross-sectional study.Methods: A survey was conducted with 4288 secondary school students from Wuhan, China. A nested case-control sampling method was used within the cross-sectional survey to enhance statistical power for predictive modeling. Data were collected using standardized scales including the Insomnia Severity Index (ISI), the Three-dimensional Psychological Pain Scale (TDPPS), and the Patient Health Questionnaire-9 (PHQ-9). Statistical analyses included univariate analysis, correlation analysis, and multivariable binary logistic regression to identify independent predictors and construct a prediction model.Results: Psychological pain was the strongest risk factor (OR=1.444), followed by depression (OR=1.292) and parental absence > 3 months (OR=1.290). Increasing age (OR=0.361) and higher paternal education (OR=0.644) were protective. The final optimized prediction model demonstrated excellent goodness-of-fit (Nagelkerke R2 = 0.761), with an overall correct classification rate of 88.6% and a sensitivity of 87.2%.Conclusion: This study identifies psychological pain as the strongest predictor of adolescent insomnia, exceeding the influence of depression and anxiety. We developed a streamlined 9-variable prediction model with high accuracy (88.6%), offering a practical tool for efficient, large-scale insomnia screening in school settings. This enables early identification and paves the way for targeted, preventive interventions.Implications for Practice: Clinical assessments for adolescent insomnia should incorporate the evaluation of psychological pain. The model enables school nurses and mental health professionals to efficiently identify at-risk adolescents, promoting precision-based preventive care.Impact: This study addresses the multifactorial nature of adolescent insomnia and the lack of practical screening tools. Findings will benefit school health systems and mental health practitioners.Reporting Method: This study was reported in accordance with the STROBE guidelines for cross-sectional studies.Patient or Public Contribution: Patients or the public were not involved in the design, conduct, analysis, interpretation, or preparation of the manuscript for this study.Keywords: adolescent, insomnia, binary logistic regression, risk factors, protective factors