Dunst Anna Katharina, Hesse Clemens, Ueberschär Olaf
Institute for Applied Training Science, Department of Endurance Sports, Leipzig, Germany.
German Cycling Federation, Frankfurt am Main, Germany.
Eur J Appl Physiol. 2025 Apr;125(4):895-907. doi: 10.1007/s00421-024-05663-4. Epub 2025 Feb 4.
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.
This narrative review examined the effect of cycling cadence on its key input parameters, including oxygen demand per Watt ( ), resting oxygen uptake ( ), maximal oxygen uptake ( ), and maximal blood lactate accumulation rate (vLa). 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 (FAT).
A U-shaped relationship was found between cadence and both and , while remained largely cadence-independent within typical cadences. vLa 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 and while maintaining constant and vLa yielded polynomial power output-cadence relationships, with optimal cadences of 84 rpm at MAP, 80 rpm at MLSS, and 70 rpm at FAT. Incorporating cadence-dependent vLa produced implausible results, supporting cadence-independent maximal glycolytic flux. A hypothesized cadence-dependent improved alignment between model predictions and empirical data.
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.
马德的数学模型被广泛用于耐力表现预测，旨在准确呈现运动的代谢反应。然而，该模型传统上忽视了不同运动速度下代谢过程的动态变化。
本叙述性综述研究了骑行踏频对其关键输入参数的影响，这些参数包括每瓦特需氧量（ ）、静息摄氧量（ ）、最大摄氧量（ ）和最大血乳酸积累率（vLa）。这些研究结果被纳入模型，以评估踏频在最大有氧功率（MAP）、最大乳酸稳态（MLSS）和峰值脂肪氧化（FAT）时对预测功率输出的影响。
发现踏频与 和 之间呈U形关系，而在典型踏频范围内， 在很大程度上与踏频无关。vLa随踏频呈多项式增加，这归因于乳酸消除的变化，表明最大糖酵解通量与踏频无关。将这些发现纳入马德模型并考虑各种情况后发现，踏频会导致显著变化，功率输出差异高达100 W以上。在保持 和vLa不变的情况下，使用与踏频相关的 和 会产生多项式功率输出 - 踏频关系，MAP时的最佳踏频为84转/分钟，MLSS时为80转/分钟，FAT时为70转/分钟。纳入与踏频相关的vLa会产生不合理的结果，支持最大糖酵解通量与踏频无关的观点。一个假设的与踏频相关的 改善了模型预测与实验数据之间的一致性。
忽视不同运动速度下代谢过程的动态变化会导致建模结果不准确。将踏频与既定参数相结合可提高马德代谢模型预测骑行表现的精度。
