Literature DB >> 31036415

The relative accuracy of skinfolds compared to four-compartment estimates of body composition.

Brett S Nickerson1, Michael V Fedewa2, Zackary Cicone2, Michael R Esco2.   

Abstract

BACKGROUND: Body composition estimates using skinfold thickness are common in field settings and can provide a reasonably accurate measure when more advanced technology is unavailable. However, the observed error between skinfolds and criterion body composition measures may be influenced by the methodology used to derive the criterion measure. AIMS: The aim of this study was to examine the relative accuracy of body composition estimates derived from measures of skinfold thickness when compared to four-compartment (4C) models that utilize underwater weighing (UWW) and dual energy X-ray absorptiometry (DXA)-derived body volume (BV).
METHODS: The sample consisted of adult males (n = 96) and females (n = 91) who were apparently healthy (age = 21.85 ± 4.82 years; BMI = 24.45 ± 4.62 kg/m2). %Fat was assessed via skinfold using three common equations. BV assessed via UWW and DXA were used to estimate %Fat derived as part of a 4C model. Between group differences were assessed using a repeated measures analysis of variance.
RESULTS: %Fat4C ranged from 4.7 to 39.7 %Fat (21.9 ± 8.1 %Fat). Estimated %FatSF using the SF7 Jackson, SF7 Evans, and SF4 Peterson were significantly lower than %Fat4C as measured via UWW (-4.8 ± 3.5 %Fat, -3.3 ± 3.9 %Fat,-3.1 ± 6.9 %Fat, respectively, all p < .001). The estimated %FatSF error was lowest when compared to the %Fat4C that used the Smith-Ryan DXA-derived BV equation (-0.2 ± 7.3 to -1.9 ± 4.6 %Fat) and highest for the Wilson DXA-derived BV equation (-6.5 ± 7.1 to -8.3 ± 3.3 %Fat).
CONCLUSIONS: Skinfold prediction methods can provide reasonable accuracy when estimating %Fat in field settings when more advanced methods are unavailable or undesirable due to increased participant burden. In addition, clinicians and researchers should use caution when selecting a method of estimating body volume via DXA, as the methodology and equation used to derive body volume as part of the 4C model can introduce differences in error.
Copyright © 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Entities:  

Keywords:  Adiposity; Body fat; Dual energy X-ray absorptiometry; Hydrostatic weighing; Multi-compartment model

Year:  2019        PMID: 31036415     DOI: 10.1016/j.clnu.2019.04.018

Source DB:  PubMed          Journal:  Clin Nutr        ISSN: 0261-5614            Impact factor:   7.324


  4 in total

1.  Predictive equations for fat mass in older Hispanic adults with excess adiposity using the 4-compartment model as a reference method.

Authors:  Rogelio González-Arellanes; Rene Urquidez-Romero; Alejandra Rodríguez-Tadeo; Julián Esparza-Romero; Rosa Olivia Méndez-Estrada; Erik Ramírez-López; Alma-Elizabeth Robles-Sardin; Bertha-Isabel Pacheco-Moreno; Heliodoro Alemán-Mateo
Journal:  Eur J Clin Nutr       Date:  2022-06-15       Impact factor: 4.016

2.  Agreement Between A 2-Dimensional Digital Image-Based 3-Compartment Body Composition Model and Dual Energy X-Ray Absorptiometry for The Estimation of Relative Adiposity.

Authors:  Katherine Sullivan; Casey J Metoyer; Bjoern Hornikel; Clifton J Holmes; Brett S Nickerson; Michael R Esco; Michael V Fedewa
Journal:  J Clin Densitom       Date:  2021-09-24       Impact factor: 2.963

3.  Associations Between Adult Triceps Skinfold Thickness and All-Cause, Cardiovascular and Cerebrovascular Mortality in NHANES 1999-2010: A Retrospective National Study.

Authors:  Weiya Li; Han Yin; Yilin Chen; Quanjun Liu; Yu Wang; Di Qiu; Huan Ma; Qingshan Geng
Journal:  Front Cardiovasc Med       Date:  2022-05-10

4.  Generalized Equations for Predicting Percent Body Fat from Anthropometric Measures Using a Criterion Five-Compartment Model.

Authors:  Zackary S Cicone; Brett S Nickerson; Youn-Jeng Choi; Clifton J Holmes; Bjoern Hornikel; Michael V Fedewa; Michael R Esco
Journal:  Med Sci Sports Exerc       Date:  2021-12-01       Impact factor: 5.411

  4 in total

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