Author(s): BRIAND, J., DI PRAMPERO, P.E., OSGNACH, C., PAVEI, G., FARACI, G., THIBAULT, G., TREMBLAY, J., Institution: UNIVERSITÉ DE MONTRÉAL, Country: CANADA, Abstract-ID: 1777

INTRODUCTION:
Field-based running in soccer displays repeated accelerations and decelerations. di Prampero et al. (2005) developed an equivalent slope method to quantify the added energy cost of accelerated and decelerated running (1). Since accelerations can cost up to 5-fold more than steady-state running, this approach better captures metabolic power variations characteristics of team sports. However, total metabolic power does not reveal how energy is supplied across alactic, glycolytic and aerobic pathways. This quantification is relevant to practitioners because athletes with different metabolic profiles deplete energy stores at different rates despite identical external loads, affecting fatigue onset and recovery needs (2). Mader proposed a dynamic model allowing the partition of energy supply during exercise with power variations, based on individual capacities (3). This study explores the integration of GPS- derived metabolic power within Mader’s framework and its practical insights.
METHODS:
All numerical values are expressed as mean ± standard deviation. Twenty-one male soccer players, with measured maximal oxygen consumption rates (VO2max) of 60.7 ± 6.8 mL/kg/min, completed self-paced 50-m shuttle runs to exhaustion (duration: 19.7 ± 2.1 min; peak speed: 21.1 ± 1.5 km/h), wearing GPS units (GPEXE, 20 Hz) and portable metabolic analyzers (Cosmed K5). Metabolic power was computed from GPS speed and acceleration (1). A five-state ordinary differential equation system modelled muscle VO2, muscle and blood lactate concentrations, and mouth-level VO2 (introducing a time delay relative to muscle VO2) throughout the test. For each athlete, maximal rate of glycolysis (VLamax: 0.54 ± 0.20 mmol/kg/s) and steady-state energy cost of running (4.57 ± 0.36 J/kg/m) were fitted to match experimental end-test blood lactate concentration ([La]) and measured VO2.
RESULTS:
VO2 agreement showed a root mean square error of 5.3 ± 1.5 mL/kg/min. The differences between simulated and measured total oxygen consumption were 0.4 ± 4.6%. Modelled [La] error was -0.4 ± 1.6 mmol/L. Athletes achieving similar [La] showed VLamax differing up to 4-fold depending on VO2max and running economy. Players with lower VO2max required higher VLamax to reach equivalent [La]. Simulations also indicate that athletes with a higher VLamax deplete their carbohydrate stores more quickly.
CONCLUSION:
Integrating GPS-derived metabolic power with Mader’s dynamic bioenergetic model reveals insights that initial testing fails to capture. The energy partition helps identify how athletes’ VO2max and VLamax interactions can affect energy use and availability. This information could orientate individualized nutrition and recovery strategies. Future efforts will focus on enhancing parameter-fitting methods and conducting tests across a variety of scenarios, including simulated games.

1) di Prampero et al. J Exp Biol, 2005; 2) Wackerhage et al. Sports Med, 2025; 3) Mader Eur J Appl Physiol, 2003