Sport-specific variability in the energy cost of constant speed running: Implications for metabolic power estimations

Metabolic power is essential for assessing the physical demands of team sports. Accurately determining the energy cost of constant speed running (EC0), is crucial for refining these. EC0 depends on factors like running velocity and V̇O₂max and varies between athlete groups due to training adaptations and sport-specific body characteristics. To ensure accurate energy expenditure, EC0 should be individually determined based on the specific team sport, improving player monitoring, recovery, and load management.

An experimental cohort study collected data from 339 incremental treadmill tests in elite team sports athletes: 11 male handball players, 120 male soccer players, 23 male and 185 female field hockey players. Athletes performed a treadmill protocol to exhaustion while O2-uptake, CO2-output, respiratory exchange ratio and ventilation were measured breath-by-breath. Data processing verified steady-state conditions. Net EC0 was calculated as energy expenditure above rest divided by velocity. Sport, speed, sex and V̇O2max were defined as fixed effect variables.

Our random intercept and slope model with all predictors performed best. Handball players had the highest EC0 (estimated total mean) with 4.04 J/kg/m (CI95% 3.88, 4.20), field hockey players with 3.95 J/kg/m (CI95% 3.90, 4.00) and soccer players with 3.79 J/kg/m (CI95% 3.73, 3.85). For the whole group, EC0 showed a curvilinear dependence on speed: increasing with speed up to ~3.5 m/s and then remaining relatively constant at higher velocities. However, grouping athletes by similar treadmill performance, EC0 remained constant across speeds.

Our data show multiple predictors must be considered to determine an appropriate EC₀ for each athlete. Although EC₀ remains stable across velocities for individuals, it varies significantly between sports and V̇O₂max levels, highlighting the need for individualized assessment. Calculating EC₀ per athlete may improve energy cost estimations, enhance the metabolic power approach and allow for more accurate analysis of metabolic data based on positional tracking.