Background
Mader’s mathematical model, widely employed for endurance performance prediction, aims to accurately represent metabolic response to exercise. However, it traditionally overlooks dynamic changes in metabolic processes at varying movement velocities.
Methods
This narrative review examined the effect of cycling cadence on its key input parameters, including oxygen demand per Watt ( \(\text{CE}_{{\dot{\text{V}}}\text{O2}}\) ), resting oxygen uptake ( \({{\dot{\text{V}}}\text{O}_{\text{2Base}}}\) ), maximal oxygen uptake ( \({{\dot{\text{V}}}\text{O}_{\text{2max}}}\) ), and maximal blood lactate accumulation rate (vLamax). These findings were integrated into the model to assess cadence-induced variations in predicted power output at maximal aerobic power (MAP), maximal lactate steady state (MLSS), and peak fat oxidation (FATmax).
Results
A U-shaped relationship was found between cadence and both \(\text{CE}_{{\dot{\text{V}}}\text{O2}}\) and \({{\dot{\text{V}}}\text{O}_{\text{2Base}}}\) , while \({{\dot{\text{V}}}\text{O}_{\text{2max}}}\) remained largely cadence-independent within typical cadences. vLamax exhibited a polynomial increase with cadence, attributed to changes in lactate elimination, suggesting cadence-independent maximal glycolytic flux. Incorporating these findings into Mader’s model considering various scenarios revealed significant cadence-induced variations, with power output differences of up to > 100 W. Using cadence-dependent \(\text{CE}_{{\dot{\text{V}}}\text{O2}}\) and \({{\dot{\text{V}}}\text{O}_{\text{2Base}}}\) while maintaining constant \({{\dot{\text{V}}}\text{O}_{\text{2max}}}\) and vLamax yielded polynomial power output-cadence relationships, with optimal cadences of 84 rpm at MAP, 80 rpm at MLSS, and 70 rpm at FATmax. Incorporating cadence-dependent vLamax produced implausible results, supporting cadence-independent maximal glycolytic flux. A hypothesized cadence-dependent \({{\dot{\text{V}}}\text{O}_{\text{2max}}}\) improved alignment between model predictions and empirical data.
Conclusion
Neglecting dynamic changes in metabolic processes across different movement velocities can lead to inaccurate modelling results. Incorporating cadence alongside established parameters enhances the precision of Mader’s metabolic model for cycling performance prediction.
Graphical abstract
