DOI QR코드

DOI QR Code

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

빅데이터 분석을 활용한 콩 식품 중재가 대사증후군 위험요인에 미치는 영향 메타분석

  • Received : 2016.05.19
  • Accepted : 2016.06.08
  • Published : 2016.06.30

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

References

  1. 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.
  2. 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. https://doi.org/10.1513/pats.200708-139MG
  3. 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. https://doi.org/10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S
  4. 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. https://doi.org/10.7570/kjo.2012.21.2.108
  5. 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. https://doi.org/10.1016/j.jacc.2006.09.032
  6. 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. https://doi.org/10.7570/kjo.2014.23.2.106
  7. 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. https://doi.org/10.6109/jkiice.2015.19.11.2590
  8. 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.
  9. 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.
  10. 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.
  11. 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. https://doi.org/10.3102/00028312020001063
  12. R. G. Orwin, "A fail-safe N for Effect Size," Journal of Educational Statistics, vol. 8, no. 2, pp. 157-159, Sum. 1983.
  13. J. Cohen, Statistical Power Analysis for the Behavioral Sciences (Revised Edition), New York: Academic Press, 1977.
  14. 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. https://doi.org/10.6109/jicce.2014.12.4.257
  15. 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. https://doi.org/10.1037/0033-2909.93.3.563
  16. R. Rosenthal, "The File Drawer Problem and Tolerance for Null Results," Psychological Bulletin, vol. 86, no. 2, pp. 638-641, Feb. 1978.