일개 제조업 근로자의 3년간 체중변화와 대사증후군 진단 구성요소의 비교 분석: 국가건강검진(2015~2017년) 자료를 중심으로
A Study on the Three-Year Weight Changes of Workers at a Manufacturing Company and the Comparison of Metabolic Syndrome Diagnosis Components: Focused on the Data of Korean National Health Screening (2015~2017)
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Purpose: This study aims to provide preliminary data for weight management, and prevention and management of cardiovascular and cerebrovascular diseases. We examined the effect of changes in the weight of workers at a manufacturing company over three years on their metabolic syndrome and metabolic syndrome diagnosis components. Methods: Necessary data were collected from the questionnaire and the results of the Korean National Health Screening of 2015 and 2017, which included 228 workers at a manufacturing company in G region. The collected data were analyzed using the SPSS/WIN 23.0 program. ANCOVA was used to examine the differences in the metabolic syndrome diagnosis components according to weight change. In addition, multiple logistic regression analysis was used to obtain the odds ratios of metabolic syndrome and metabolic syndrome analysis component, based on the weight changes in the normal weight group and the obesity group. Results: Waist measure, systolic blood pressure, and blood pressure were found to have significant effects based on participants’ weight change over three years. These factors increased with a larger increase in weight at a statistically significant level. This study analyzed the weight changes of the normal weight group and the obesity group considering the data from the National Health Screening of 2015, and found that the risk of metabolic syndrome increased at a statistically significant level as body weight increased; thus, the obesity group showed a higher risk in this regard. It was also found that waist measure, fasting blood sugar, and high-density low cholesterol increased at a statistically significant level as body weight increased. Conclusion: Health administrators need to recognize the importance of workers’ weight management, select an intensive management group based on a time series analysis of weight changes, and develop and implement programs to manage the metabolic syndrome diagnosis components.
Abstract
Purpose: This study aims to provide preliminary data for weight management, and prevention and management of cardiovascular and cerebrovascular diseases. We examined the effect of changes in the weight of workers at a manufacturing company over three years on their metabolic syndrome and metabolic syndrome diagnosis components. Methods: Necessary data were collected from the questionnaire and the results of the Korean National Health Screening of 2015 and 2017, which included 228 workers at a manufacturing company in G region. The collected data were analyzed using the SPSS/WIN 23.0 program. ANCOVA was used to examine the differences in the metabolic syndrome diagnosis components according to weight change. In addition, multiple logistic regression analysis was used to obtain the odds ratios of metabolic syndrome and metabolic syndrome analysis component, based on the weight changes in the normal weight group and the obesity group. Results: Waist measure, systolic blood pressure, and blood pressure were found to have significant effects based on participants’ weight change over three years. These factors increased with a larger increase in weight at a statistically significant level. This study analyzed the weight changes of the normal weight group and the obesity group considering the data from the National Health Screening of 2015, and found that the risk of metabolic syndrome increased at a statistically significant level as body weight increased; thus, the obesity group showed a higher risk in this regard. It was also found that waist measure, fasting blood sugar, and high-density low cholesterol increased at a statistically significant level as body weight increased. Conclusion: Health administrators need to recognize the importance of workers’ weight management, select an intensive management group based on a time series analysis of weight changes, and develop and implement programs to manage the metabolic syndrome diagnosis components.
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