Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors

Lirong Zhang; Shaocong Zhao; Wei Yang; Zhongbing Yang; Zhi'an Wu; Hua Zheng; Mingxing Lei

doi:10.3389/fpsyt.2024.1447281

Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors

Front Psychiatry. 2024 Nov 7:15:1447281. doi: 10.3389/fpsyt.2024.1447281. eCollection 2024.

Authors

Lirong Zhang¹, Shaocong Zhao¹, Wei Yang¹, Zhongbing Yang², Zhi'an Wu³, Hua Zheng⁴, Mingxing Lei^{5

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Affiliations

¹ Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, China.
² School of Physical Education, Guizhou Normal University, Guiyang, Guizhou, China.
³ Department of Physical Education, Guangzhou Institute of Physical Education, Guangzhou, China.
⁴ College of Physical Education and Health Sciences, Chongqing Normal University, Chongqing, China.
⁵ Department of Orthopaedics, Hainan Hospital of Chinse PLA General Hospital, Sanya, China.
⁶ Nursing Department, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
⁷ Chinese PLA Medical School, Beijing, China.

Abstract

Background: Adolescents often experience difficulties with sleep quality. The existing literature on predicting severe sleep disturbance is limited, primarily due to the absence of reliable tools.

Methods: This study analyzed 1966 university students. All participants were classified into a training set and a validation set at the ratio of 8:2 at random. Participants in the training set were utilized to establish models, and the logistic regression (LR) and five machine learning algorithms, including the eXtreme Gradient Boosting Machine (XGBM), Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), CatBoosting Machine (CatBM), were utilized to develop models. Whereas, those in the validation set were used to validate the developed models.

Results: The incidence of severe sleep disturbance was 5.28% (104/1969). Among all developed models, the XGBM model performed best in AUC (0.872 [95%CI: 0.848-0.896]), followed by the CatBM model (0.853 [95% CI: 0.821-0.878]) and DT model (0.843 [95% CI: 0.801-0.870]), whereas the AUC of the logistic regression model was only 0.822 (95% CI: 0.777-0.856). Additionally, the XGBM model had the best accuracy (0.792), precision (0.780), F1 score (0.796), Brier score (0.143), and log loss (0.444).

Conclusions: The XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents.

Keywords: Pittsburgh sleep quality index; adolescents; epidemiology; machine learning; prediction model; sleep disturbance.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the teaching reform research project of Xiamen University of Technology (JYCG202459) and National Social Science General Project (24BTY029).