비만의 자가보고와 실측치에 따른 비만율의 차이와 비만에 영향을 주는 요인의 차이 연구
Differences in Obesity Prevalence and Associated Factors between Self-Reported and Measured Data
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Objectives: This study aimed to examine differences in obesity prevalence between self-reported and directly measured data and to explore how associated factors vary accordingly. Methods: Data from the 2020 Korea Community Health Survey (n=224,184) and Korea National Health and Nutrition Examination Survey (n=5,826) were analyzed. Differences in obesity prevalence were assessed across sociodemographic characteristics (sex, age, housing, etc.), health behaviors (walking, smoking, drinking, etc.), and health status variables (stress, hypertension, diabetes, etc.). Statistical analyses included descriptive statistics, chi-square tests, and logistic regression. Results: The prevalence of underweight was similar between the self-reported and measured data; however, the former indicated higher rates of normal weight. In contrast, the measured data showed a higher prevalence of overweight or obesity. Self-reported data consistently underestimated obesity, with larger discrepancies observed among women and individuals in their 20s and 30s. Conclusions: When relying on self-reported obesity data, even individuals classified as having “normal weight” may benefit from tailored education that addresses unhealthy behaviors and promotes better health outcomes. Among younger adults, particularly those in their 20s and 30s, interventions should focus on improving awareness of actual body weight and encouraging healthy lifestyles. Factors that showed inconsistent associations with obesity in this study warrant further investigation in comparative research.
Abstract
Objectives: This study aimed to examine differences in obesity prevalence between self-reported and directly measured data and to explore how associated factors vary accordingly. Methods: Data from the 2020 Korea Community Health Survey (n=224,184) and Korea National Health and Nutrition Examination Survey (n=5,826) were analyzed. Differences in obesity prevalence were assessed across sociodemographic characteristics (sex, age, housing, etc.), health behaviors (walking, smoking, drinking, etc.), and health status variables (stress, hypertension, diabetes, etc.). Statistical analyses included descriptive statistics, chi-square tests, and logistic regression. Results: The prevalence of underweight was similar between the self-reported and measured data; however, the former indicated higher rates of normal weight. In contrast, the measured data showed a higher prevalence of overweight or obesity. Self-reported data consistently underestimated obesity, with larger discrepancies observed among women and individuals in their 20s and 30s. Conclusions: When relying on self-reported obesity data, even individuals classified as having “normal weight” may benefit from tailored education that addresses unhealthy behaviors and promotes better health outcomes. Among younger adults, particularly those in their 20s and 30s, interventions should focus on improving awareness of actual body weight and encouraging healthy lifestyles. Factors that showed inconsistent associations with obesity in this study warrant further investigation in comparative research.
