Safety and Health at Work

  • 제14권1호
  • /
  • Pages.59-65
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  • 2023
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  • 2093-7911(pISSN)
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  • 2093-7997(eISSN)
산업안전보건연구원 (Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency)
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Impact of Reduced Working Hours and Night Work Hours on Metabolic Syndrome: A Quasi-Experimental Study

  • Hye-Eun Lee (Department of Social and Preventive Medicine, Hallym University College of Medicine) ;
  • Ichiro Kawachi (Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health)
  • 투고 : 2022.06.30
  • 심사 : 2022.11.09
  • 발행 : 2023.03.30
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초록

Objectives: Epidemiological evidence linking long working hours and shift work to metabolic syndrome remains inadequate. We sought to evaluate the impact of reducing working hours on metabolic syndrome. Methods: We compared the prevalence of metabolic syndrome among male manual workers in a manufacturing company (N = 371) before and after the introduction of policy to reduce daily work hours from 10 to 8 hours. Components of metabolic syndrome were measured in periodic health examinations before the intervention, 6-9 months after, and 1.5-2 years after the intervention. Generalized estimating equation models were used to estimate changes in the prevalence of metabolic syndrome. Analyses were stratified by day work versus shift work. Results: The results showed a significantly decreased prevalence of metabolic syndrome 6-9 months following the intervention in day workers (risk ratio = 0.68, 95% confidence interval 0.52-0.88), but the benefit disappeared after 1.5-2 years. Shift workers showed a decreased prevalence of metabolic syndrome for the whole follow-up duration after the intervention, although the change was not statistically significant. Conclusion: Reducing working hours was associated with short-term improvement in metabolic syndrome in male manual workers.

키워드

과제정보

The authors are grateful to the labor union of Doowon for their support for collecting data. The authors would also like to thank Dr. Min Choi, Dr. Jinwoo Lee and Dr. Se-Eun Kim for data cleaning and preliminary analysis.

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