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빅데이터 분석을 활용한 콩 식품 중재가 대사증후군 위험요인에 미치는 영향 메타분석

A Meta-Analysis of Influencing Soybean Food Interventions on the Metabolic Syndrome Risk Factors Utilizing Big Data

  • Jin, Chan-Yong (Div. of Information EC(Institute of Convergence and Creativity), Wonkwang University) ;
  • Yu, Ok-Kyeong (Obesity Research Center(Agrobio Food R & D Institute), Chonbuk National University) ;
  • Nam, Soo-Tai (Div. of Information EC(Institute of Convergence and Creativity), Wonkwang University)
  • 투고 : 2016.05.19
  • 심사 : 2016.06.08
  • 발행 : 2016.06.30

초록

빅데이터 분석은 기존 데이터베이스 관리 도구로부터 데이터를 수집, 저장, 관리, 분석할 수 있는 역량을 말한다. 따라서 메타분석은 여러 실증연구의 정량적인 결과를 통합과 분석을 통해 전체 결과를 조망할 수 있는 기회를 제공하는 통계적 통합 방법이다. 일반적으로 대사증후군 위험요인을 허리둘레, 수축기혈압, 이완기혈압, 공복혈당, 중성지방 그리고 고밀도지단백콜레스테롤 요인으로 정의한다. 메타분석 결과 공복혈당 사전 사후 경로에서 가장 큰 효과크기(r = -.324)인 것으로 나타났다. 따라서 콩 식품의 중재효과는 10%의 설명력을 확인할 수 있었다. 두 번째 큰 효과크기는 허리둘레 사전 사후 경로(r = .256)인 것으로 나타났다. 그런데 콩 식품의 습취는 허리둘레 (복부비만) 개선효과가 없는 것을 확인할 수 있었다. 이러한 결과를 바탕으로 학문적 실무적 의의를 논의하였다.

Big data analysis refers the ability to store, manage and analyze collected data from an existing database management tool. Thus, meta-analysis is a statistical integration method that delivers an opportunity to overview the entire result of integrating and analyzing many quantitative research results. Commonly, factors of metabolic syndrome can be defined as abdominal obesity, systolic blood pressure, diastolic blood pressure, triglycerides, and high density lipoprotein cholesterol. In this meta-analysis, we concluded that the path between pre and post of the fasting blood glucose had the largest effect size of (r = -.324). Therefore, the effect of soybean food intervention showed an explanatory power of 10%. The second biggest effect size (r = .256) was found the path between pre and post in the waist circumference. Unfortunately, soybean food intake showed no improvement on abdominal obesity. Thus, we present the theoretical and practical implications of these results.

키워드

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