Korean Journal of Community Nutrition (대한지역사회영양학회지)

The Korean Society of Community Nutrition (대한지역사회영양학회)
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A Comparison of Cluster and Factor Analysis to Derive Dietary Patterns in Korean Adults Using Data from the 2005 Korea National Health and Nutrition Examination Survey

군집분석과 요인분석 이용한 우리나라 성인의 식사패턴 비교 분석 - 2005년도 국민건강영양조사 자료 이용하여

  • Song, Yoon-Ju (School of Human Ecology, The Catholic University of Korea) ;
  • Paik, Hee-Young (Department of Food & Nutrition, Seoul National University) ;
  • Joung, Hyo-Jee (Graduate School of Public Health, Seoul National University)
  • 송윤주 (가톨릭대학교 생활과학부 식품영양학) ;
  • 백희영 (서울대학교 식품영양학과) ;
  • 정효지 (서울대학교 보건대학원)
  • Published : 2009.12.31
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Abstract

The purpose of this study was to explore dietary patterns and compare dietary patterns using cluster and factor analysis in Korean adults. This study analyzed data of 4,182 adult populations who aged 30 and more and had all of socio-demographic, anthropometric, and dietary data from 2005 Korean Health and Nutrition Examination Survey. Socio-demographic data was assessed by questionnaire and dietary data from 24-hour recall method was used. For cluster analysis, the percent of energy intake from each food group was used and 4 patterns were identified: "traditional", "bread, fruit & vegetable, milk", "noodle & egg", and "meat, fish, alcohol". The "traditional" pattern group was more likely to be old, less educated, living in a rural area and had higher percentage of energy intake from carbohydrates than other pattern groups. "Meat, fish, alcohol" group was more likely to be male and higher percentage of energy intake from fat. For factor analysis, mean amount of each food group was used and also 4 patterns were identified; "traditional", "modified", "bread, fruit, milk", and "noodle, egg, mushroom". People who showed higher factor score of "traditional" pattern were more likely to be elderly, less educated, and living in a rural area and higher proportion of energy intake from carbohydrates. In conclusion, three dietary patterns defined by cluster and factor analysis separately were similar and all dietary patterns were affected by socio-demographic factors and nutrient profile.

Keywords

References

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