Tracking body composition over a competitive season in elite soccer players using laboratory- and field-based assessment methods - Bongiovanni, T.; Lacome, M.; Rodriguez, C.; Tinsley, G. M. (The Journal of Strength and Conditioning Research, 2024)
The purpose of this study was to describe body composition changes in professional soccer players over the course of a competitive playing season and compare the ability of different assessment methods to detect changes. Twenty-one elite male soccer players (age: 23.7 ± 4.8 years; height: 185.0 ± 5.2 cm; body mass: 80.7 ± 5.5 kg; body fat: 12.8 ± 2.2%) playing for an Italian national second league (Serie B) championship team were assessed at 4 time points throughout a competitive season: T0 (mid-October), T1 (mid-December), T2 (mid-February), and T3 (end of April). Dual-energy x-ray absorptiometry (DXA), skinfolds (SKF), and bioelectrical impedance analysis were performed at each time point, and multiple SKF-based equations were applied. A modified 4-compartment (4C) model was also produced. Data were analyzed using repeated measures analysis of variance, relevant post hoc tests, and Pearson's correlations. Dual-energy x-ray absorptiometry, 4C, and the SKF-based equations of Reilly and Civar detected differences in fat-free mass (FFM) between time points, with the most differences observed for DXA. Fat-free mass increased from T0 values to a peak at T2, followed by a decrease by T3, although FFM values remained higher than T0. Fat-free mass gain was primarily driven by increases in the lower limbs. Fat-free mass changes between all methods were significantly correlated, with correlation coefficients of 0.70-0.97. No significant differences between time points were observed for absolute fat mass or body fat percentage, although significant correlations between several methods for change values were observed. Select laboratory and field methods can detect changes in FFM over the course of a season in elite, professional soccer athletes, with a more limited ability to detect changes in adiposity-related variables. For SKF in this population, the equation of Reilly is recommended.