Development and validation of skinfold-thickness prediction equations with a 4-compartment model.

Published

Journal Article

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.

Full Text

Cited Authors

  • Peterson, MJ; Czerwinski, SA; Siervogel, RM

Published Date

  • May 2003

Published In

Volume / Issue

  • 77 / 5

Start / End Page

  • 1186 - 1191

PubMed ID

  • 12716670

Pubmed Central ID

  • 12716670

International Standard Serial Number (ISSN)

  • 0002-9165

Digital Object Identifier (DOI)

  • 10.1093/ajcn/77.5.1186

Language

  • eng

Conference Location

  • United States