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http://dx.doi.org/10.6109/jkiice.2016.20.6.1074

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)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.20, no.6, 2016 , pp. 1074-1080 More about this Journal
Abstract
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.
Keywords
Meta-analysis; Metabolic syndrome; Soybean; Obesity; Big data;
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