School of Life Sciences, Pharmacy and Chemistry, Faculty of Science Engineering and Computing, Kingston University London, Penryhn Road, Kingston upon Thames, KT1 2EE, UK.
Eur J Appl Physiol. 2021 Jun;121(6):1759-1771. doi: 10.1007/s00421-021-04643-2. Epub 2021 Mar 11.
The objective of this study was to investigate the changes in metabolic variables, running energetics and spatiotemporal gait parameters during an 80.5 km treadmill ultramarathon and establish which key predictive variables best determine ultramarathon performance.
Twelve participants (9 male and 3 female, age 34 ± 7 years, and maximal oxygen uptake ([Formula: see text]O) 60.4 ± 5.8 ml·kg·min) completed an 80.5 km time trial on a motorised treadmill in the fastest possible time. Metabolic variables: oxygen consumption ([Formula: see text]O), carbon dioxide production ([Formula: see text]CO) and pulmonary ventilation ([Formula: see text]) were measured via indirect calorimetry every 16.1 km at a controlled speed of 8 km·h and used to calculate respiratory exchange ratio (RER), the energy cost of running (Cr) and fractional utilisation of [Formula: see text]O (F). Spatiotemporal gait parameters: stride length (SL) and cadence (SPM) were calculated via tri-axial accelerometery.
Trial completion time was 09:00:18 ± 01:14:07 (hh:mm:ss). There were significant increases in [Formula: see text]O, Cr, F, [Formula: see text] and heart rate (HR) (p < 0.01); a significant decrease in RER (p < 0.01) and no change in SL and SPM (p > 0.05) across the measured timepoints. F and Cr accounted for 61% of the variance in elapsed finish time ([Formula: see text] = 0.607, p < 0.01).
A treadmill ultramarathon elicits significant changes in metabolic variables, running energetics and spatiotemporal gait parameters. With F and Cr explaining 61% of variance in finish time. Therefore, those able to maintain a higher F, while adopting strategies to minimise an increase in Cr may be best placed to maximise ultramarathon performance.
本研究旨在探讨 80.5 公里跑步机超长马拉松比赛中代谢变量、跑步能量学和时空步态参数的变化，并确定哪些关键预测变量能最好地决定超长马拉松比赛的成绩。
12 名参与者（9 名男性和 3 名女性，年龄 34±7 岁，最大摄氧量（[Formula: see text]O）60.4±5.8 ml·kg·min）以最快速度在电动跑步机上完成 80.5 公里的计时赛。代谢变量：通过间接测热法每 16.1 公里测量一次[Formula: see text]O、[Formula: see text]CO 和[Formula: see text]（[Formula: see text]），在控制速度为 8 km·h 下使用这些数据计算呼吸交换率（RER）、跑步能量成本（Cr）和[Formula: see text]O 的分数利用（F）。时空步态参数：通过三轴加速度计计算步长（SL）和步频（SPM）。
试验完成时间为 09:00:18±01:14:07（小时：分钟：秒）。在测量的时间点上，[Formula: see text]O、Cr、F、[Formula: see text]和心率（HR）显著增加（p<0.01）；RER 显著降低（p<0.01），SL 和 SPM 无变化（p>0.05）。F 和 Cr 占完赛时间（[Formula: see text]）的 61%（方差解释率，p<0.01）。
跑步机超长马拉松比赛会引起代谢变量、跑步能量学和时空步态参数的显著变化。F 和 Cr 能解释完赛时间（[Formula: see text]）方差的 61%。因此，那些能够保持更高的 F 水平，同时采取策略将 Cr 的增加最小化的人，可能更有机会在超长马拉松比赛中取得最佳成绩。
