Evaluation of Body Composition
In the selection of body composition field methods and prediction equations, exercise and health practitioners must consider their clients’ demographics. Factors, such as age, gender, level of adiposity, physical activity and ethnicity influence the choice of method and equation. Also, it is important to evaluate the relative worth of prediction equations in terms of the criterion method used to derive reference measures of body composition for equation development. Given that hydrodensitometry, hydrometry and dual-energy x-ray absorptiometry are subject to measurement error and violation of basic assumptions underlying their use, none of these should be considered as a ‘gold standard’ method for in vivo body composition assessment.
Reference methods, based on whole-body, 2-component body composition models, are limited, particularly for individuals whose fat-free body (FFB) density and hydration differ from values assumed for 2-component models. Use of field method prediction equations developed from 2-component model (Siri equation) reference measures of body composition will systematically underestimate relative body fatness of American Indian women, Black men and women, and Hispanic women because the average FFB density of these ethnic groups exceeds the assumed value (1.1 g/ml). Thus, some researchers have developed prediction equations based on multicomponent model estimates of body composition that take into account interindividual variability in the water, mineral, and protein content of the FFB. One multicomponent model approach adjusts body density (measured via hydrodensitometry) for total body water (measured by hydrometry) and/or total body mineral estimated from bone mineral (measured via dual-energy x-ray absorptiometry).
Skinfold (SKF), bioelectrical impedance analysis (BIA), and near-infrared interactance (NIR) are 3 body composition methods used in clinical settings. Unfortunately, the overwhelming majority of field method prediction equations have been developed and cross-validated for White populations and are based on 2-component model reference measures. Because ethnicity may affect the composition of the FFB and regional fat distribution, race-specific prediction equations may need to be developed for some ethnic groups. To date, race-specific SKF (American Indian women, Black men, and Asian adults), BIA (American Indian women and Asian adults), and NIR (American Indian women and White women) equations have been developed. However, these equations need to be cross-validated on additional samples from these ethnic groups.
In summary, research strongly suggests that multicomponent models need to be used in order to quantify differences in FFB composition due to ethnicity so that accurate SKF, BIA, and NIR prediction equations can be developed. Assessment of body composition in vivo may be enhanced by using advanced technologies such as dual-energy x-ray absorptiometry and hydrometry to refine hydrodensitometry. Practitioners should carefully select and use only those prediction equations that have been developed and cross-validated for specific ethnic groups. Additional research is needed to test the accuracy and applicability of previously published prediction equations for the American Indian, Asian, Black, and Hispanic populations.
KeywordsBody Composition Total Body Water Bioelectrical Impedance Analysis Multicomponent Model American Indian Woman
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- 1.Wilmore JH. The use of actual, predicted, and constant residual volumes in the assessment of body composition by underwater weighing. Med Sci Sports Exerc 1969; 1: (87–90)Google Scholar
- 3.Pollock ML, Wilmore JH. Exercise in health and disease: evaluation and prescription for prevention and rehabilitation. 2nd rev. ed. Philadelphia: WB Saunders, 1990Google Scholar
- 4.Siri WE. Body composition from fluid spaces and density: analysis of methods. In: Brozek J, Henschel A, editors. Techniques for measuring body composition. Washington DC: National Academy of Sciences, 1961: 223–44Google Scholar
- 6.Baumgartner RN, Heymsfield SB, Lichtman S, et al. Body composition in elderly people: effect of criterion estimates on predictive equations. Am J Clin Nutr 1991; 53: (1–9)Google Scholar
- 7.Lohman TG. Advances in body composition assessment. In: Current issues in exercise science series Vol. 3. Champaign (IL): Human Kinetics, 1992Google Scholar
- 9.Williams DP, Going SB, Massett MP, et al. Aqueous and mineral fractions of the fat-free body and their relation to body fat estimates in men and women aged 49–82 years. In: Ellis KJ, Eastman JD, editors. Human body composition: in vivo methods, models and assessment. New York: Plenum, 1993: 109–13Google Scholar
- 13.Schoeller DA, Kushner RF, Taylor P, et al. Measurement of total body water: isotope dilution techniques. Report of the Sixth Ross Conference on Medical Research. Columbus (OH): Ross Laboratories, 1985: 24–9Google Scholar
- 15.Pace N, Rathbun EN. Studies in body composition III. The body water and chemically combined nitrogen content in relation to fat content. J Biol Chem 1945; 158: (685–91)Google Scholar
- 18.Hicks VL, Heyward VH, Baumgartner RN, et al. Body composition of native american women estimated by dual-energy x-ray absorptiometry. In: Ellis KJ, Eastman JD, editors. Human body composition: in vivo methods, models and assessment. New York: Plenum, 1993: 89–92Google Scholar
- 21.Lohman TG. Dual-energy x-ray absorptiometry. In: Roche AF, Heymsfield SB, Lohman TG, editors. Human body composition. Champaign (IL): Human Kinetics, 1996: 63–78Google Scholar
- 34.Hicks VL. Validation of near-infrared interactance and skinfold methods for estimating body composition of Amerian Indian women [dissertation]. Albuquerque: University of New Mexico, 1992Google Scholar
- 35.Boileau RA, Lohman TG, Slaughter MH. Exercise and body composition of children and youth. Scand J Sports Sci 1985; 7: (17–27)Google Scholar
- 37.Lohman TG, Boileau RA, Slaughter MH. Body composition in children and youth. In: Boileau RA, editor. Advances in pe-diatric sport sciences. Champaign (IL): Human Kinetics, 1984: 29–57Google Scholar
- 40.Lohman TG. Applicability of body composition techniques and constants for children and youth. In: Pandolf KB, editor. Exercise and sport sciences reviews. New York: Macmillan, 1986: 325–57Google Scholar
- 41.Heyward VH, Stolarczyk LM. Applied body composition assessment. Champaign (IL): Human Kinetics, 1996Google Scholar
- 52.Heyward VH, Cook KL, Hicks VL, et al. Predictive accuracy of three field methods for estimating body fatness of non-obese and obese women. Int J Sports Nutr 1992; 2: (75–86)Google Scholar
- 54.Hortobagyi T, Israel RG, Houmard JA, et al. Comparison of four methods to assess body composition in black and white athletes. Int J Sports Nutr 1992; 2: (60–74)Google Scholar
- 55.Heyward VH, Stolarczyk LM, Goodman JA, et al. Predictive accuracy of skinfold (SKF) and near-infrared interactance (NIR) equations in estimating body density of hispanic women [abstract]. Sports Med Train Rehab 1995; 6: (238)Google Scholar
- 57.Hicks V, Heyward V, Flores A, et al. Validation of near-infrared interactance (NIR) and skinfold (SKF) methods for estimating body composition of American indian women [abstract]. Med Sci Sports Exerc 1993; 25: S152Google Scholar
- 60.VanLoan MD, Mayclin PL. Bioelectrical impedance analysis: is it a reliable estimator of lean body mass and total body water? Hum Biol 1987; 59: 299–309Google Scholar
- 61.Stolarczyk LM, Heyward VH. An alternative to categorizing normal and obese subjects using the Segal fatness-specific bioimpedance equations [abstract]. Southwest Chapter 1995 Annual meeting, American College of Sports Medicine; 1995 Nov 11–12, San DiegoGoogle Scholar
- 62.Heyward VH, Wilson WL, Stolarczyk LM. Predictive accuracy of BIA equations for estimating fat-free mass of American Indian, Black, and Hispanic men [abstract]. Med Sci Sports Exerc 1994; 26: S202Google Scholar
- 66.Heyward, VH, Stolarczyk LM, Goodman JA, et al. Comparison of two component and multi-component models in estimating body composition of Hispanic women [abstract]. Med Sci Sports Exerc 1995; 27: S118Google Scholar
- 68.Malina RM. Comparative studies of Blacks and Whites in the United States. In: Miller KS, Dreger RM, editors. Biological substrata. New York: Seminar Press, 1973: 53–123Google Scholar
- 69.Futrex, Inc. Futrex-5000 research manual. Gaithersburg (MD): Futrex, Inc., 1988Google Scholar
- 72.Houmard JA, Israel RG, McCammon MR, et al. Validity of near-infrared device for estimating body composition in a college football team. J Appl Sport Sci Res 1991; 5: (53–9)Google Scholar
- 73.Nielsen DH, Cassady SL, Wacker LM, et al. Validation of the Futrex-5000 near-infrared spectrophotometer analyzer for assessment of body composition. J Orthop Sports Phys Ther 1992; 16: (281–7)Google Scholar
- 74.Wilson WL, Heyward VH. Validation of near-infrared interactance method for Black, Hispanic, Native American and White men, 20 to 50 years. In: Ellis KJ, Eastman JD, editors. Human body composition: in vivo methods, models and assessment. New York: Plenum, 1993: 389–92Google Scholar
- 75.Yee EM, Going SB, Milliken SD, et al. Cross-validation of prediction equations for measuring percent body fat and changes in percent body fat in obese women [abstract]. Southwest Chapter 1995 Annual meeting, American College of Sports Medicine; 1995 Nov 11–12, San DiegoGoogle Scholar
- 76.Wilson WL, Heyward VH. Effects of skintone, skinfold, and mid-arm muscle area on optical density measurements at the biceps site [abstract]. Med Sci Sports Exerc 1993; 25: S60Google Scholar