Reliability and Data Integration of Duplicated Test Results Using Two Bioelectrical Impedence Analysis Machines in the Korean Genome and Epidemiology Study.
10.3961/jpmph.2010.43.6.479
- Author:
Boyoung PARK
1
;
Jae Jeong YANG
;
Ji Hyun YANG
;
Jimin KIM
;
Lisa Y CHO
;
Daehee KANG
;
Chol SHIN
;
Young Seoub HONG
;
Bo Youl CHOI
;
Sung Soo KIM
;
Man Suck PARK
;
Sue K PARK
Author Information
1. Department of Preventive Medicine, Seoul National University College of Medicine, Korea. suepark@snu.ac.kr
- Publication Type:Original Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
- Keywords:
Electric impedance;
Statistical model;
Regression
- MeSH:
Aged;
Biometry/*instrumentation/methods;
*Body Composition;
Cohort Studies;
Electric Impedance;
Female;
*Genome, Human;
Humans;
Male;
Middle Aged;
Observer Variation;
Reproducibility of Results;
Republic of Korea
- From:Journal of Preventive Medicine and Public Health
2010;43(6):479-485
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: The Korean Genome and Epidemiology Study (KoGES), a multicenter-based multi-cohort study, has collected information on body composition using two different bioelectrical impedence analysis (BIA) machines. The aim of the study was to evaluate the possibility of whether the test values measured from different BIA machines can be integrated through statistical adjustment algorithm under excellent inter-rater reliability. METHODS: We selected two centers to measure inter-rater reliability of the two BIA machines. We set up the two machines side by side and measured subjects' body compositions between October 2007 and December 2007. Duplicated test values of 848 subjects were collected. Pearson and intra-class correlation coefficients for inter-rater reliability were estimated using results from the two machines. To detect the feasibility for data integration, we constructed statistical compensation models using linear regression models with residual analysis and R-square values. RESULTS: All correlation coefficients indicated excellent reliability except mineral mass. However, models using only duplicated body composition values for data integration were not feasible due to relatively low R2 values of 0.8 for mineral mass and target weight. To integrate body composition data, models adjusted for four empirical variables that were age, sex, weight and height were most ideal (all R2>0.9). CONCLUSIONS: The test values measured with the two BIA machines in the KoGES have excellent reliability for the nine body composition values. Based on reliability, values can be integrated through algorithmic statistical adjustment using regression equations that includes age, sex, weight, and height.
