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Profiling the socioeconomic characteristics, dietary intake, and health status of Korean older adults for nutrition plan customization: a comparison of principal component, factor, and cluster analyses
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Kyungsook Woo, Kirang Kim
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Epidemiol Health. 2024;46:e2024043. Published online April 12, 2024
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DOI: https://doi.org/10.4178/epih.e2024043
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 Abstract
Summary
 PDF Supplementary Material
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Abstract
OBJECTIVES
This study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques.
METHODS Data were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches.
RESULTS PCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01).
CONCLUSIONS The study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.
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Summary
Korean summary
동일한 데이터셋을 사용하여 PCA, FA, CA를 이용한 프로파일링 분석이 한국의 노인들을 특성화하고 분류하기 위해 수행되었습니다. 분석 결과, 세 가지 기법은 한국 노인들의 특성에서 유사한 패턴을 보였습니다.
Key Message
Profiling analysis using PCA, FA, and CA was performed to characterize and classify older adults in Korea on the same dataset. As a result of the analysis, the three techniques showed similar patterns in the characteristics of Korean older adults.
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