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Sleep Status and Menstrual Problems among Chinese Young Females.


ABSTRACT:

Background

The association between sleep disturbance and the menstruation in the young women population has been scarcely studied. The aim of this study is to assess the association between sleep status and phase of the menstrual cycle in healthy, young, ovulating women.

Methods

This cross-sectional study used the data collected from healthy young, ovulating Chinese females from September to December 2018. The association was analyzed by using linear regression and binary logistic analyses.

Results

2260 women aged 17 to 30 were included in the analysis. The average sleep duration of the respondents was 7.24 hours (SD = 0.92). 61.7% of them admitted that they were accompanied by at least one of sleep symptoms including difficulty initiating sleep, difficulty maintaining sleep, dreaminess, early morning awakening, and somnolence. Sleep quality was significantly associated with dysmenorrhea (OR [95%CI] = 1.74 [1.40-2.17], P < 0.001) and self-awareness menstrual regularity (OR [95%CI] = 1.29 [1.06-1.56], P = 0.011).

Conclusion

This study found that poor sleep quality is significantly associated with dysmenorrhea and self-awareness menstrual irregularity among healthy, young, ovulating, Chinese females.

SUBMITTER: He H 

PROVIDER: S-EPMC8560254 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Sleep Status and Menstrual Problems among Chinese Young Females.

He Hong H   Yu Xiaoxuan X   Chen Tingjia T   Yang Fei F   Zhang Min M   Ge Hui H  

BioMed research international 20211025


<h4>Background</h4>The association between sleep disturbance and the menstruation in the young women population has been scarcely studied. The aim of this study is to assess the association between sleep status and phase of the menstrual cycle in healthy, young, ovulating women.<h4>Methods</h4>This cross-sectional study used the data collected from healthy young, ovulating Chinese females from September to December 2018. The association was analyzed by using linear regression and binary logistic  ...[more]

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