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  • Title: Predicting fat percent by skinfolds in racial groups: Durnin and Womersley revisited.
    Author: Davidson LE, Wang J, Thornton JC, Kaleem Z, Silva-Palacios F, Pierson RN, Heymsfield SB, Gallagher D.
    Journal: Med Sci Sports Exerc; 2011 Mar; 43(3):542-9. PubMed ID: 20689462.
    Abstract:
    PURPOSE: Despite their widespread use in research and fitness settings, Durnin and Womersley's (DW) 1974 prediction equations using skinfold thickness to estimate body fat percent by hydrodensitometry have not been systematically evaluated in racial or ethnic groups using body fat percent measured by dual-energy x-ray absorptiometry (%BF(DXA)) as the standard. METHODS: This cross-sectional, population-based study examined whether the DW skinfold equations predict %BF(DXA) in a large, multiracial sample. Four skinfold measures (biceps, triceps, subscapular, and suprailiac), other clinical anthropometrics, and %BF(DXA) were obtained from 1675 healthy adults, age 18-110 yr, who were classified into four racial or ethnic categories: Caucasian, African American, Hispanic, or Asian. Predicted body fat percent using DW equations was compared with %BF(DXA) and evaluated within race/ethnicity- and sex-specific groups. RESULTS: Mean body fat percent predicted by DW equations was significantly different from %BF(DXA) in four of eight race/ethnicity- and sex-specific groups, particularly in Asian women and African American men (3.3 and 2.4 percentage point overestimates, respectively, P < 0.0001). New linear regression equations were developed estimating %BF(DXA) specific to each race/ethnicity and sex group, using the original DW skinfold sites. Body weight, height, and waist circumference independently predicted fat percent and were also included in the new equations. CONCLUSIONS: The 1974 DW equations did not predict %BF(DXA) uniformly in all races or ethnicities. Using %BF(DXA) as the criterion measure, the original DW skinfold equations have been updated specific to sex and race/ethnicity while maintaining the DW options for a minimalistic model using fewer predictors.
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