The Validity of an Updated Metabolic Power Algorithm Based upon di Prampero’s Theoretical Model in Elite Soccer Players

Abstract

The aim of this study was to update the metabolic power (MP) algorithm ( ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$ , W·kg ⁻¹) related to the kinematics data (P _GPS, W·kg ⁻¹) in a soccer-specific performance model. For this aim, seventeen professional (Serie A) male soccer players ( $\dot{\mathrm{V}}{\mathrm{O}}_{2\max }$ 55.7 ± 3.4 mL·min ⁻¹·kg ⁻¹) performed a 6 min run at 10.29 km·h ⁻¹ to determine linear-running energy cost (C _r). On a separate day, thirteen also performed an 8 min soccer-specific intermittent exercise protocol. For both procedures, a portable Cosmed K4b ² gas-analyzer and GPS (10 Hz) was used to assess the energy cost above resting (C). From this aim, the MP was estimated through a newly derived C equation (P _GPSn) and compared with both the commonly used (P _GPSo) equation and direct measurement ( ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$ ). Both P _GPSn and P _GPSo correlated with ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$ (r = 0.66, p < 0.05). Estimates of fixed bias were negligible (P _GPSn = −0.80 W·kg ⁻¹ and P _GPSo = −1.59 W·kg ⁻¹), and the bounds of the 95% CIs show that they were not statistically significant from 0. Proportional bias estimates were negligible (absolute differences from one being 0.03 W·kg ⁻¹ for P _GPSn and 0.01 W·kg ⁻¹ for P _GPSo) and not statistically significant as both 95% CIs span 1. All variables were distributed around the line of unity and resulted in an under- or overestimation of P _GPSn, while P _GPSo routinely underestimated MP across ranges. Repeated-measures ANOVA showed differences over MP conditions ( F _1,38 = 16.929 and p < 0.001). Following Bonferroni post hoc test significant differences regarding the MP between P _GPSo and ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$ /P _GPSn ( p < 0.001) were established, while no differences were found between ${\mathrm{P}}_{\dot{\mathrm{V}}{\mathrm{O}}_2}$ and P _GPSn ( p = 0.853). The new approach showed it can help the coaches and the soccer trainers to better monitor external training load during the training seasons.