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 Data원문보기
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.
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 resul...
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.
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.
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문제 정의
The conceptual model is shown in Figure 1. This study will find meaningful effect of soybean food intervention for criterion variables that effect before and after the metabolic syndrome studies, on the basis of the results of a meta-analysis. The papers included in this study meta-analysis were identified using the keywords “Metabolic Syndrome Soybean”, “Metabolic Syndrome Isoflavone”, specifying on RISS, DBpia, eArticle, and Kyobo Scholar in database articles of social science.
제안 방법
By using collected raw data, the calculated number of standard deviations and samples were coded that verified studies influencing pre and post in metabolic syndrome risk factors. Therefore, the mean, standard deviation, and the number of samples calculated the effect size using Equation 1.
To address variation between professional guidelines, the NHLBI, AHA, International Diabetes Foundation (IDF), and others have proposed a harmonized definition of metabolic syndrome [1]. Complaints of chest pain, dyspnea, or claudication (symptoms of possible complications) may warrant additional studies, including the following: Electrocardiography (rest/stress ECG), Ultrasonography (vascular, or rest/stress echocardiography), Stress single-photon emission computed tomography (SPECT) or cardiac positron emission tomography (PET). Investigation into other causes of or exacerbating factors in metabolic syndrome should be considered.
This study reanalyzed the research papers with the purpose of classifying the results of the previous studies which analyzed causal relationships between pre and post of waist circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose, triglyceride and high density lipoprotein cholesterol in the designed metabolic syndrome risk factors included 8 studies for journals and theses published in Korea between 2000 and 2016. Based on information from these literature reviews, paths presented in the conceptual model in this study are converted to values of average effect size by using calibrated inverse variance weighting values and a random-effects model, as the effect size (r) shown Table 3.
대상 데이터
The papers included in this study meta-analysis were identified using the keywords “Metabolic Syndrome Soybean”, “Metabolic Syndrome Isoflavone”, specifying on RISS, DBpia, eArticle, and Kyobo Scholar in database articles of social science.
참고문헌 (16)
K. G. Alberti, R. H. Eckel, S. M. Grundy, P. Z. Zimmet, J. I. Cleeman and K. A. Donato, "Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity," Journal of the American Heart Association, vol. 120, no. 16, pp. 1640-1645, Oct. 2009.
E. Tasali and S. M. Mary, "Obstructive sleep apnea and metabolic syndrome: alterations in glucose metabolism and inflammation," Proceedings of the American Thoracic Society, vol. 5, no. 2, pp. 207-224, Feb. 2008.
K. G. Alberti, and P. Z. Zimmet, "Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation," Diabetic Medicine, vol. 15, no. 7, pp. 539-553, July 1998.
E. Kim, and S. W. Oh, "Gender differences in the association of occupation with metabolic syndrome in Korean adults," The Korean Journal of Obesity, vol. 21, no. 2, pp. 108-114, june 2012.
A. S. Gami, B. J. Witt, D. E. Howard, P. J. Erwin, L. A. Gami, and V. K. Somers, "Metabolic syndrome and risk of incident cardiovascular events and death: A systematic review and meta-analysis of longitudinal studies," Journal of the American College of Cardiology, vol. 49, no. 4, pp. 403-414, Jan. 2007.
S. H. Kim, O. K. Yu, M. S. Byun, Y. S. Cha, and T. S. Park, "Effects of Weight Management Program for Middle Aged Women with Metabolic Syndrome Risk Factors," The Korean Journal of Obesity, vol. 23, no. 2, pp. 106-115, Jan. 2014.
O. K. Yu, Y. S. Cha, C. Y. Jin, D. G. Kim, and S. T. Nam, "A Meta-analysis of Influencing Mediator Athletics on the Metabolic Syndrome Risk Factors Utilized Big Data Analysis," J. Korea Inst. Inf. Commun. Eng., vol. 19, no. 11, pp. 2590-2596, Jan. 2015.
H. Y. Lee, "Effectiveness of Obesity Management Programs: Systematic Rewiew and Meta-analysis," Journal of Korean Society for Health Education and Promotion, vol. 24, no. 4, pp. 131-146, Dec. 2007.
J. E. Kim and K. H. Choi, "A meta analysis for anti-hyperlipidemia effect of soybeans," Journal of the Korean Data & Information Science Society, vol. 21, no. 4, pp. 651-667, July 2010.
Y. A. Jeon and N. Woo, "A Meta-Analysis of Obesity Management Effects of Aromatherapy Use," Kor. J. Aesthet. Cosmetol., vol. 12, no. 2, pp. 275-281, Apr. 2014.
L. V. Hedges and W. Stock, "The Effects of Class Size: An Examination of Rival Hypotheses," American Education Res. Journal, vol. 20. no. 1, pp. 63-85, Mar. 1983.
R. G. Orwin, "A fail-safe N for Effect Size," Journal of Educational Statistics, vol. 8, no. 2, pp. 157-159, Sum. 1983.
J. Cohen, Statistical Power Analysis for the Behavioral Sciences (Revised Edition), New York: Academic Press, 1977.
S. T. Nam, C. Y. Jin, and J. S. Sim, "A Meta-analysis of the Relationship between Mediator Factors and Purchasing Intention in E-commerce Studies," Journal of Information and Communication Convergence Engineering," vol. 12, no. 4, pp. 257-262, Dec. 2014.
L. V. Hedges, and I. Olkin, "Clustering Estimates of Effect Magnitude From Independent Studies," Psychological Bulletin, vol. 93, no. 1, pp. 563-573, May 1983.
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