J Prev Med Public Health.
2010 Nov;43(6):479-485. 10.3961/jpmph.2010.43.6.479.
Reliability and Data Integration of Duplicated Test Results Using Two Bioelectrical Impedence Analysis Machines in the Korean Genome and Epidemiology Study
- Affiliations
-
- 1Department of Preventive Medicine, Seoul National University College of Medicine, Korea. suepark@snu.ac.kr
- 2Center for Genome Science, National Health Institute, Korea Centers for Disease Control and Prevention, Korea.
- 3Department of Internal Medicine, Korea University College of Medicine, Korea.
- 4Department of Preventive Medicine, Dong-a University College of Medicine, Korea.
- 5Department of Preventive Medicine, Hanyang University College of Medicine, Korea.
- 6Institute of Health Policy and Management, Seoul National University College of Medicine, Korea.
- KMID: 1098205
- DOI: http://doi.org/10.3961/jpmph.2010.43.6.479
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.
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