うつ病と不安に関するジャーナル

うつ病と不安に関するジャーナル
オープンアクセス

ISSN: 2167-1044

概要

Analysis of the Potential Profile and Predictors of Sleep Quality among University Students during the COVID-19 Pandemic in Hubei Province, China

Chaoyi Chen, Da Feng, Suyi Song, Ziqi Yan, Gang Yin, Zhanchun Feng*, Zhuo Chen, Zhiming Jiao

Introduction: Insufficient and poor quality sleep has plagued contemporary university students in China. As young adults fail to cope with their stress properly, they become vulnerable to psychological distress and sleep disorders during the COVID-19 pandemic.

Methods: A cross-sectional survey was conducted amongst 1326 university students from Hubei Province, China. Latent profile analysis was conducted on results of class-difference tests of sleep patterns. Multiple logistic regressions were used to explore the relationship between the influencing factors and the three classes of sleep quality.

Results: The overall score of sleep quality (9.17 ± 3.22) amongst university students was assessed by using the PSQI scale, and 427 (32.20%) students reported poor sleep quality. Three distinct classes of sleep patterns were identified, namely, good sleepers (Class 1, 70.44%), poor sleep quality with less medication use (Class 2, 26.55%) and poor sleepers (Class 3, 3.01%). Compared with ‘good sleepers’, students having ‘poor sleep quality with less hypnotic drug use’ were affected by their education stage, smoking habits, physical activity, depression and anxiety. Meanwhile, ‘poor sleepers’ were affected by their age, origins, smoking habits, mental stress, depression and anxiety.

Conclusion: Results confirmed a significant heterogeneity in the sleep patterns of university students, and their behavioral lifestyles and mental-health-related factors demonstrated different relationship patterns with sleep quality. Multiple sleep promotion interventions, including moderate aerobic exercises, psychological counseling and mindfulness training, should be performed in groups regularly to improve their sleep quality.

免責事項: この要約は人工知能ツールを使用して翻訳されたものであり、まだレビューまたは検証されていません。
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