Frontiers in Psychology (Apr 2024)

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,
  • Lishun Xiao,
  • Dehui Yin

DOI
https://doi.org/10.3389/fpsyg.2024.1343186
Journal volume & issue
Vol. 15

Abstract

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Background and aimsIn 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.MethodsA 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.ResultsOf 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.ConclusionsThese 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.

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