The underlying characteristics of sleep behavior and its relationship to sleep-related cognitions: a latent class analysis of college students in Wuhu city, China.
The aim of this study was to gain insight into the sleep quality of college students and related factors from a new perspective by using Latent Class Analysis (LCA). A total of 1,288 college students from four universities in Wuhu city participated in the study. LCA was used to identify the classes...
| Publicado en: | Psychology, Health & Medicine Vol. 25; no. 7; pp. 887 - 898 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Aug2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144826569&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144826569 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13548506 0UX jtl: Psychology, Health & Medicine issn: 13548506 maglogo: N pubinfo: dt: Aug2020 vid: 25 iid: 7 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 144826569 144826569 144826569 10.1080/13548506.2019.1687915 144826569 ppf: 887 ppct: 11 formats: tig: atl: The underlying characteristics of sleep behavior and its relationship to sleep-related cognitions: a latent class analysis of college students in Wuhu city, China. aug: au: Zhou, Jun Jin, Lai-Run Tao, Meng-Jun Peng, Hui Ding, Shu-Shu Yuan, Hui affil: School of Public Health, Wannan Medical College, Wuhu, Anhui, People's Republic of China sug: subj: Students, College Psychosocial Factors Sleep Evaluation Cognition Evaluation Human China Multiple Logistic Regression Learning Evaluation Mental Status Female Sleep Disorders Risk Factors Risk Assessment Female ab: The aim of this study was to gain insight into the sleep quality of college students and related factors from a new perspective by using Latent Class Analysis (LCA). A total of 1,288 college students from four universities in Wuhu city participated in the study. LCA was used to identify the classes of sleep behaviors. Differences in class membership related to selected research factors were examined using multinomial logistic regression analysis.Four distinct classes of behaviors were identified: (1) good sleep (Class 1, 31.8%), (2) prolonged sleep latency (Class 2, 49.1%), (3) sleep disturbances and daytime dysfunction (Class 3, 6.8%), (4) multiple poor sleep behavior (Class 4, 12.3%). The latent classes of sleep behavior were correlated with the DBAS-16 total score (rs = −0.109, P < 0.001). Learning pressure and mental state during the day could affect overall sleep (Class 2, Class 3 and Class 4), and female students were at higher risk of severe sleep problems (Class 3 and Class 4), while bedtime exercised could improve mild sleep problems (Class 2). The sleep behavior of college students in Wuhu city has obvious class heterogeneity, and different influencingfactors may affect sleep to varying degrees. In addition, our research provides a basis for targeted intervnetion in college student's sleep.. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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