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Gender differences of health behaviors in the risk of metabolic syndrome for middle-aged adults: A national cross-sectional study in South Korea

Authors
Yoon, J.Kim, J.Son, H.
Issue Date
Apr-2021
Publisher
MDPI AG
Keywords
Gender differences; Health practice index; Metabolic syndrome; Middle-aged
Citation
International Journal of Environmental Research and Public Health, v.18, no.7
Journal Title
International Journal of Environmental Research and Public Health
Volume
18
Number
7
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6589
DOI
10.3390/ijerph18073699
ISSN
1661-7827
1660-4601
Abstract
This study examined gender differences in health behaviors for the risk of metabolic syndrome (MetS) among middle-aged adults using nationally representative data from the Seventh Korea National Health and Nutrition Examination Survey (2016–2018). The sample included data from 8677 middle-aged adults. The Health Practice Index measured health behaviors, including smoking, alcohol use, physical activity, sleeping, eating breakfast, working hours, nutritionally bal-anced diet, and mental stress. Complex sample multiple logistic regression analyses were conducted to determine the association between the Health Practice Index (HPI) and MetS. Men and women with poor or moderate HPI scores had significantly higher risks of having MetS than those with good HPI scores. Controlling for covariates, high-risk alcohol use (p < 0.001) and physical activity (p = 0.008) were associated with the risk of MetS in men and women, respectively. Men reporting alcohol use and women lacking a healthy diet were, respectively, 2.056 times (adjusted odds ratio (OR) = 2.056, 95% CI: 1.681–2.514) and 1.306 times (adjusted OR = 1.306, 95% CI: 1.075–1.587) more likely to have increased risks of MetS. Given these gender differences in health behaviors, developing tailored interventions could be beneficial in preventing MetS among middle-aged men and women. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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상경대학 (Department of Statistics and Data Science)
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