Longitudinal correlation between cell phone use and sleep quality in college students.
10.3760/cma.j.cn112150-20220105-00019
- VernacularTitle:大学生手机依赖与睡眠质量轨迹的纵向关联
- Author:
Dan ZHANG
1
;
Ya Ye ZHAO
1
;
Ru NIU
1
;
Shu Man TAO
2
;
Ya Juan YANG
3
;
Li Wei ZOU
4
;
Yang XIE
1
;
Ting Ting LI
1
;
Yang QU
1
;
Shuang ZHAI
1
;
Fang Biao TAO
5
;
Xiao Yan WU
5
Author Information
1. Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei 230032, China.
2. MOE Key Laboratory of Population Health Across Life Cycle/Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei 230032, China The Second Hospital of Anhui Medical University, Hefei 230032, China.
3. MOE Key Laboratory of Population Health Across Life Cycle/Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei 230032, China School of Nursing, Anhui Medical University, Hefei 230032, China.
4. The Second Hospital of Anhui Medical University, Hefei 230032, China.
5. Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei 230032, China MOE Key Laboratory of Population Health Across Life Cycle/Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei 230032, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Sleep Quality;
Cohort Studies;
Cell Phone Use;
Surveys and Questionnaires;
Students;
Sleep Initiation and Maintenance Disorders;
Cell Phone;
Sleep
- From:
Chinese Journal of Preventive Medicine
2022;56(12):1828-1833
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the current situation of cell phone use and sleep quality among college students, establish a sleep quality trajectory model and explore the influence of cell phone use on the sleep quality trajectory. Methods: Based on data from the College Student Behavior and Health Cohort Study 2019-2020, a latent class growth modeling was used to establish a sleep quality trajectory model among college students. The baseline influencing factors of sleep quality trajectories among college students were analyzed by χ2 test, and the effects of cell phone use on sleep quality trajectories were analyzed by binary logistic regression. Results: A total of 1 092 college students were included in the analysis. The detection rates of cell phone use and poor sleep quality were 24.5% and 13.3%. Latent class growth model identified two groups of sleep quality trend trajactories: an improved sleep quality group (86.0%) and a decreased sleep quality group (14.0%). The result of binary logistic regression showed that the cell phone use was a risk factor of sleep quality trajectories. Conclusion: The cell phone use during college period could increase the risk of poor sleep quality. Targeted intervention measures about cell phone use should be adopted to improve the sleep quality among college students.
