Government of Navarre.
University of the Basque Country (UPV/EHU).
Res Q Exerc Sport. 2019 Dec;90(4):678-689. doi: 10.1080/02701367.2019.1643446. Epub 2019 Sep 3.
: The association between an overlooked classical Lactate Threshold (LT), named "Minimum Lactate Equivalent" (LE), with Maximal Lactate Steady State (MLSS) has been recently described with good MLSS prediction results in endurance-trained runners. This study aimed to determine the applicability of LE to predict MLSS in lower aerobic-conditioned individuals compared to well-established blood lactate-related thresholds (BLTs). : Fifteen soccer players [velocity at MLSS (MLSS) 13.2 ± 1.0 km·h; coefficient of variation (CV) 7.6%] conducted a submaximal discontinuous incremental running test to determine BLTs and 3-6 constant velocity running tests to determine MLSS. : LE did not differ from conventional LTs ( > .05) and was 24% lower than MLSS ( < .001; ES: 3.26). Among LTs, LE best predicted MLSS ( = 0.83; < .001; SEE = 0.59 km·h). There was no statistical difference between MLSS and estimated MLSS using LE prediction formula ( = .99; ES: 0.001). Mean bias and limits of agreement were 0.00 ± 0.58 km·h and ±1.13 km·h, respectively. LE best predicted MLSS ( = 0.92; < .001; SEE = 0.54 km·h) in the pooled data of soccer players and endurance-trained runners of the previous study ( = 28; MLSS range 11.2-16.5 km·h; CV 9.8%). : Results support LE to be one of the best single predictors of MLSS. This study is the sole study providing specific operational regression equations to estimate the impractical MLSS in soccer players by means of a BLT measured during a submaximal single-session test.
最近的研究表明,一种被忽视的经典乳酸阈(LT),即“最小乳酸等效值”(LE),与最大乳酸稳态(MLSS)之间存在关联,并且在耐力训练的跑步者中,能够很好地预测 MLSS。本研究旨在确定 LE 在预测低有氧条件下个体的 MLSS 方面的适用性,与已建立的与血乳酸相关的阈值(BLTs)相比。
15 名足球运动员(MLSS 速度为 13.2±1.0km·h;变异系数为 7.6%)进行了亚最大不连续递增跑步测试以确定 BLTs,并进行了 3-6 次恒定速度跑步测试以确定 MLSS。
LE 与传统的 LT 没有差异(>0.05),比 MLSS 低 24%(<0.001;ES:3.26)。在 LT 中,LE 能最好地预测 MLSS(=0.83;<0.001;SEE=0.59km·h)。使用 LE 预测公式预测的 MLSS 与实际 MLSS 之间没有统计学差异(=0.99;ES:0.001)。平均偏差和一致性界限分别为 0.00±0.58km·h 和±1.13km·h。LE 能够最好地预测足球运动员和之前研究中耐力训练跑步者的混合数据中的 MLSS(=0.92;<0.001;SEE=0.54km·h)(=28;MLSS 范围 11.2-16.5km·h;CV 9.8%)。
结果支持 LE 成为预测 MLSS 的最佳单一预测指标之一。本研究是唯一一项研究,它通过亚最大单次测试中测量的 BLT,提供了特定的操作回归方程,以估计足球运动员不切实际的 MLSS。