Prevalence and related factors of sleep quality among Chinese undergraduates in Jiangsu Province: multiple models' analysis

Bin Hu; Wen Shen; Yun Wang; Qi Wu; Jiali Li; Xiaozhou Xu; Yaohui Han; Lishun Xiao; Dehui Yin

doi:10.3389/fpsyg.2024.1343186

Prevalence and related factors of sleep quality among Chinese undergraduates in Jiangsu Province: multiple models' analysis

Front Psychol. 2024 Apr 10:15:1343186. doi: 10.3389/fpsyg.2024.1343186. eCollection 2024.

Authors

Bin Hu^#¹, Wen Shen^#², Yun Wang^#³, Qi Wu¹, Jiali Li⁴, Xiaozhou Xu⁴, Yaohui Han⁴, Lishun Xiao^{4

5}, Dehui Yin¹

Affiliations

¹ Key Laboratory of Human Genetics and Environmental Medicine, School of Public Health, Xuzhou Medical University, Xuzhou, China.
² Department of Respiratory Diseases, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, Jiangsu, China.
³ Department of Dermatology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, Jiangsu, China.
⁴ Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
⁵ Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, China.

^# Contributed equally.

Abstract

Background and aims: In China, a significant number of undergraduates are experiencing poor sleep quality. This study was designed to investigate the prevalence of poor sleep quality and identify associated factors among undergraduates in Jiangsu Province, China.

Methods: A total of 8,457 participants were collected in 2022 using whole-group convenience sampling. The factors studied included basic demographics, family and social support, personal lifestyles, physical and mental health, mobile phone addiction index (MPAI), and the Connor-Davidson resilience scale (CD-RISC). The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Four models, including weighted multiple linear regression, binary logistic regression, weighted linear mixed model, and logistic regression with random effects, were applied to identify associated factors for sleep quality.

Results: Of the 8,457 participants analyzed, 26.64% (2,253) were classified into the poor sleep quality group with a PSQI score >7. No significant relationship was found between sleep quality and gender, native place, economic level of family, physical exercise, dormitory light, dormitory hygiene, and amativeness matter. Risk factors for sleep quality identified by the four models included lower CD-RISC, higher MPAI, fourth grade or above, smoking, drinking, greater academic pressure, greater employment pressure, roommate sleeping late, noisy dormitory, poorer physical health status, poorer mental health status, and psychological counseling.

Conclusions: These findings provide valuable insights for university administrators, enabling them to better understand the risk factors associated with poor sleep quality in undergraduates. By identifying these factors, administrators can provide targeted intervention measures and counseling programs to improve students' sleep quality.

Keywords: Connor-Davidson resilience scale; Pittsburgh Sleep Quality Index; mobile phone addiction index; risk factors; sleep quality; undergraduates.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (grant number 12001470), the China Postdoctoral Science Foundation (grant number 2020M671607), and the Science and Technology Development Fund project of the Affiliated Hospital of Xuzhou Medical University (grant number XYFY202245). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.