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Sarah Lanham – Undergraduate Student, Georgia Southern University

Emily L. Langford – Graduate Assistant, Georgia Southern University

Gina R. Hogan – Undergraduate Student, Georgia Southern University

Michelle L. Eisenman

Greg A. Ryan, PhD, CSCS*D, TSAC-F – Assistant Professor of Exercise Science, Georgia Southern University

Ronald L. Snarr, PhD, CSCS*D, NSCA-CPT – Assistant Professor, Georgia Southern University


Estimations of anthropometric measures (i.e., body fat percentage [BF%]) are important to consider in athletic populations as variations in body composition may impact health and performance, particularly in power and weight-controlled sports. However, most laboratory and field-based devices estimate BF% using algorithms based upon, and intended for, general populations, which tend to be less physically active. Therefore, these algorithms may not be applicable to special populations (i.e. collegiate athletes), leading to misrepresentation of BF%. PURPOSE: The purpose of this study was to compare six body composition methods: foot-to-foot bioelectrical impedance analysis (FF_BIA); hand-to-foot bioelectrical impedance analysis (HF_BIA); bioelectrical impedance spectroscopy (BIS); air displacement plethysmography (ADP); dual-energy x-ray absorptiometry (DXA); and a 3-site skinfold test (3SF) against the criterion of both a 3-compartment (3C) and 4-compartment (4C) model in Division-I collegiate athletes. METHODS: Sixty-eight athletes (males: n = 38; female: n = 30) from various sports, volunteered for participation in the study. Each participant performed all measures on the same visit to the laboratory, following all standard operating procedures for each test. A repeated measures ANOVA, with Bonferroni pairwise comparisons, was used to determine differences (p ≤ 0.05) within gender between BF% estimation measures compared to the 3C and 4C models. RESULTS: For the male athletes, when compared to 3C (13.9  6.74%) results indicated a significant mean difference with DXA (17.2 ± 9.5%; p < 0.01), BIS (18.8 6.4%; p < 0.01), and HF_BIA (18.9 5.4%; p < 0.01). There were no statistical differences between 3C and ADP (12.4 8.1%; p = 0.24), FF_BIA (11.9  5.9%; p = 0.21), or 3SF (13.5  8.4%; p = 1.0). Similar differences were noted between 4C (12.2 7.1%) and DXA, BIS, and HF_BIA (all p < 0.01). Additionally, no differences existed between ADP (p = 1.0), FF_BIA (p = 1.0), or 3SF (p = 1.0) compared to 4C. For the female athletes, when compared to 3C (22.9  6.6%) results indicated a significant mean difference with DXA (27.7 ± 5.6%) and HF_BIA (26.0  4.3%) (each p < 0.01). There were no significant differences observed between the 3C model and ADP (21.3 ± 5.5%; p =1.0), BIS (25.2 5.1%; p = 0.60), FF_BIA (22.1  4.6%; p = 1.0), or 3SF (24.1  4.9%; p = 1.0). Similar differences were noted between 4C (22.0  6.8%) and DXA (p < 0.01) and HF_BIA (p < 0.01), with no differences compared to ADP (p = 1.0), BIS (p = 0.11), FF_BIA (p = 1.0), or 3SF (p = 0.92). Interestingly, the 3C and 4C models were significantly different from each other (p < 0.01) in both genders. CONCLUSION: Results indicated that some laboratory measures (DXA, BIS, HF_BIA) may over predict BF% in athletic populations when compared to more thorough 3C and 4C models. Therefore, consideration on reliance of these tests, as well as the calculating of new algorithms to estimate BF% in athletes may be warranted. PRACTICAL APPLICATIONS: These results suggest that reliance on single laboratory measures of estimating BF% in athletes are likely to result in a misrepresentation of BF%. Additionally, while these results indicated no differences in group means between ADP, FF_BIA, or 3SF compared to 3C and 4C in either gender, practitioners may want to consider incorporating a 3C or 4C model, as these offer a more complete representation of body composition in athletic populations.


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