Dunst Anna Katharina, Hesse Clemens, Ueberschär Olaf, Holmberg Hans-Christer
Department of Endurance Sports, Institute for Applied Training Science, Marschnerstraße 29, 04109 Leipzig, Germany.
German Cycling Federation, 60528 Frankfurt am Main, Germany.
Sports (Basel). 2023 Jan 28;11(2):29. doi: 10.3390/sports11020029.
During maximal cycling sprints, efficiency (η) is determined by the fiber composition of the muscles activated and cadence-dependent power output. To date, due to methodological limitations, it has only been possible to calculate gross efficiency (i.e., the ratio of total mechanical to total metabolic work) in vivo without assessing the impact of cadence and changes during exercise. Eliminating the impact of cadence provides optimal efficiency (η), which can be modeled as a function of time. Here, we explain this concept, demonstrate its calculation, and compare the values obtained to actual data. Furthermore, we hypothesize that the time course of maximal power output (P) reflects time-dependent changes in η.
Twelve elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) and a maximal-pedaling test on a cycle ergometer. Crank force and cadence were monitored continuously to determine fatigue-free force-velocity profiles (F/v) and fatigue-induced changes in P. Respiratory gases were measured during and for 30 min post-exercise. Prior to and following each sprint, lactate in capillary blood was determined to calculate net blood lactate accumulation (ΔBLC). Lactic and alactic energy production were estimated from ΔBLC and the fast component of excess post-exercise oxygen consumption. Aerobic energy production was determined from oxygen uptake during exercise. Metabolic power (MP) was derived from total metabolic energy (W). η was calculated as P divided by MP. Temporal changes in P, W and η were analyzed by non-linear regression.
All models showed excellent quality (R > 0.982) and allowed accurate recalculation of time-specific power output and gross efficiency (R > 0.986). The time-constant for P(t) (τ) was closely correlated with that of η (τ; r = 0.998, < 0.001). Estimating efficiency using τ for τ led to a 0.88 ± 0.35% error.
Although efficiency depends on pedal force and cadence, the latter influence can be eliminated by η(t) using a mono-exponential equation whose time constant can be estimated from P(t).
在最大强度自行车冲刺过程中,效率(η)由被激活肌肉的纤维组成和与踏频相关的功率输出决定。迄今为止,由于方法学上的限制,在不评估踏频影响和运动过程中变化的情况下,仅能在体内计算总效率(即总机械功与总代谢功的比值)。消除踏频影响可得到最佳效率(η),其可被建模为时间的函数。在此,我们解释这一概念,展示其计算方法,并将所得值与实际数据进行比较。此外,我们假设最大功率输出(P)的时间进程反映了η随时间的变化。
12名精英场地自行车运动员在自行车测力计上进行了4次最大冲刺(3、8、12、60秒)和一次最大踏频测试。持续监测曲柄力和踏频,以确定无疲劳力 - 速度曲线(F/v)以及疲劳引起的P变化。在运动期间及运动后30分钟测量呼吸气体。在每次冲刺前后,测定毛细血管血中的乳酸,以计算净血乳酸积累(ΔBLC)。根据ΔBLC和运动后过量耗氧的快速成分估算乳酸和非乳酸能量产生。根据运动期间的摄氧量确定有氧能量产生。代谢功率(MP)由总代谢能量(W)得出。η计算为P除以MP。通过非线性回归分析P、W和η的时间变化。
所有模型均显示出优良品质(R > 0.982),并能准确重新计算特定时间的功率输出和总效率(R > 0.986)。P(t)的时间常数(τ)与η的时间常数(τ)密切相关(r = 0.998,< 0.001)。用τ估计τ的效率导致0.88 ± 0.35%的误差。
尽管效率取决于踏板力和踏频,但通过使用单指数方程η(t)可消除后者的影响,该方程的时间常数可从P(t)估算得出。