인지나이와 얼굴주름, 얼굴의 텍스처 및 인구학적데이터와의 상관성
Correlation Between Perceived Age and Facial Wrinkles, Facial Texture, and Demographic Data
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As the global population ages, the prevalence of age-related diseases is increasing. Preventing aging is necessary to reduce the burden of disease and protect population productivity. Biological age is an indicator of whether an individual is experiencing rapid, slow, or normal aging. Perceived age is highly correlated with biological age, which reflects health assessment, and is frequently used as an indicator of clinical aging. However, objective methods for quantifying perceived age are lacking. This study examines the correlations between perceived age and demographic data, facial wrinkles, and facial texture. This study used Korean medicine Data Center (KDC) data from 2017 to 2019. The study included 394 healthy men and 1,015 healthy women aged 40 years or older (range 40-58, 47.9±4.36). Facial wrinkles showed a correlation of 0.45, and facial texture variables GLCM_Information1_Mean and GLCM_Information2_Mean also showed correlations of 0.22 and -0.24, respectively. The results of this study will be helpful in developing healthcare services for middle-aged adults in Korea
Abstract
As the global population ages, the prevalence of age-related diseases is increasing. Preventing aging is necessary to reduce the burden of disease and protect population productivity. Biological age is an indicator of whether an individual is experiencing rapid, slow, or normal aging. Perceived age is highly correlated with biological age, which reflects health assessment, and is frequently used as an indicator of clinical aging. However, objective methods for quantifying perceived age are lacking. This study examines the correlations between perceived age and demographic data, facial wrinkles, and facial texture. This study used Korean medicine Data Center (KDC) data from 2017 to 2019. The study included 394 healthy men and 1,015 healthy women aged 40 years or older (range 40-58, 47.9±4.36). Facial wrinkles showed a correlation of 0.45, and facial texture variables GLCM_Information1_Mean and GLCM_Information2_Mean also showed correlations of 0.22 and -0.24, respectively. The results of this study will be helpful in developing healthcare services for middle-aged adults in Korea