Comparative analysis of BIA, IBC and DXA for determining body fat in American Football players

BACKGROUND: Body composition is frequently measured by sports, fitness, and healthcare professionals. Dual-Energy X-ray Absorptiometry (DXA) analysis is a validated measurement of body composition and is considered a criterion or gold-standard measurement. However, due to long scan times, accessibility and cost, conducting DXA scans on larger athletes (i.e., football players) is difficult. Hence fitness professionals, notably strength and conditioning coaches, typically use other methods to measure body composition. The aim of this study was to assess the accuracy of the Bioelectrical Impedance Analysis (BIA) and Integrative Body Composition (IBC) techniques to DXA body fat percent (BF%) in collegiate American Football players. METHODS: Participants performed all three modes of body composition measurement: DXA, BIA (BIA-A [athlete]and BIA-NA [non-athlete modes]), and IBC, on the same day during early morning hours in a fasted state. RESULTS: The BF% measured via all methods significantly correlated with BF% measured via DXA (i.e., BIA-A [P<0.001, r=0.903], BIA-NA [P<0.001, r=0.891], and IBC [P<0.001, r=0.867]). However, values obtained via BIA-A (athlete) (P<0.001) and IBC (P<0.001) methods under predicted BF%. CONCLUSIONS: BIA and IBC can be used as an alternative to DXA for measuring BF% in American Football players. The BIA-A and IBC under predicted BF% compare to DXA, therefore, a correction formula can be utilized by coaches and athletes to predict BF% more accurately compared to IBC and BIA-A methods in American Football

Automated summary

This study evaluated the accuracy of Bioelectrical Impedance Analysis (BIA) and Integrative Body Composition (IBC) techniques in measuring body fat percentage in collegiate American Football players compared to Dual-Energy X-ray Absorptiometry (DXA). The results showed that both BIA and IBC can be used as alternatives to DXA, but they tended to under predict body fat percentage. Therefore, a correction formula may be necessary to improve the accuracy of predictions.