期刊
AMERICAN JOURNAL OF CLINICAL NUTRITION
卷 77, 期 5, 页码 1186-1191出版社
OXFORD UNIV PRESS
DOI: 10.1093/ajcn/77.5.1186
关键词
body composition; skinfold thickness; body compartments; anthropometry; adipose tissue; nutritional assessment; body fat; obesity
资金
- NICHD NIH HHS [R01HD12252, R01 HD012252] Funding Source: Medline
Background: Skinfold-thickness measurements are commonly obtained for the indirect assessment of body composition. Objective: We developed new skinfold-thickness equations by using a 4-compartment model as the reference. Additionally, we compared our new equations with the Durnin and Womersley and Jackson and Pollock skinfold-thickness equations to evaluate each equation's validity and precision. Design: Data from 681 healthy, white adults were used. Percentage body fat (%BF) values were calculated by using the 4-compartment model. The cohort was then divided into validation and cross-validation groups. Equations were developed by using regression analyses and the 4-compartment model. All equations were then tested by using the cross-validation group. Tests for accuracy included mean differences, R-2, and Bland-Altman plots. Precision was evaluated by comparing root mean squared errors. Results: Our new equations' estimated means for %BF in men and women (22.7% and 32.6%, respectively) were closest to the corresponding 4-compartment values (22.8% and 32.8%). The Durnin and Womersley equation means in men and women (20.0% and 31.0%, respectively) and the Jackson and Pollock mean in women (26.2%) underestimated %BF. All equations showed a tendency toward underestimation in subjects with higher %BF. Bland-Altman plots showed limited agreement between Durnin and Wormersley, Jackson and Pollock, and the 4-compartment model. Precision was similar among all the equations. Conclusions: We developed accurate and precise skinfold-thickness equations by using a 4-compartment model as the method of reference. Additionally, we found that the skinfold-thickness equations frequently used by clinicians and practitioners underestimate %BF.
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