McMorris T, Sproule J, Draper S, Child R, Sexsmith J R, Forster C D, Pattison J
Centre for Sports Science and Medicine, University College Chichester, West Sussex, UK.
Eur J Appl Physiol. 2000 Jul;82(4):262-7. doi: 10.1007/s004210000225.
Plasma lactate and catecholamine thresholds were calculated using three different variations of linear regression, an algorithmic linear regression method, a log-log transformation method and a semi-log method. A group of 18 male sports science students undertook an incremental test to exhaustion on a cycle ergometer. A 5-ml blood sample was drawn at rest, after 4 min of exercise and every 2 min thereafter until the cessation of the test. Lactate, adrenaline and noradrenaline concentrations were measured. Lactate threshold (Th1a), adrenaline threshold (ThA) and noradrenaline threshold (ThNA) were calculated using each of the three methods. The best fits of the methods were examined by comparing their standard error of estimates (SEEs). The algorithmic method demonstrated a higher SEE than the other two methods, but only for Th1a and ThNA. The power output for which each method calculated the thresholds demonstrated a main effect for method. Tukey post hoc tests showed that the algorithmic method produced significantly higher outputs than the other two methods, which did not differ significantly from one another. Comparison of these power outputs showed that Th1a and ThA differed significantly, regardless of method, there were no other significant differences. Plasma concentrations of lactate, adrenaline and noradrenaline showed that the values of Th1a and ThNA calculated by the algorithmic method were significantly higher than those calculated using the other two methods, which did not differ significantly from one another. The only significant difference for ThA was between the algorithmic and semi-log methods. Correlations between the power outputs at which each method calculated the thresholds varied greatly between methods, and were at best only moderate (r = 0.63). It was concluded that the algorithmic method was less powerful than the other two methods, and that Th1a, ThA and ThNA are not highly correlated.
使用三种不同的线性回归变体、一种算法线性回归方法、一种对数-对数变换方法和一种半对数方法计算血浆乳酸和儿茶酚胺阈值。一组18名男性运动科学专业学生在自行车测力计上进行递增负荷测试直至力竭。在静息状态、运动4分钟后以及此后每2分钟抽取一次5毫升血样,直至测试结束。测量乳酸、肾上腺素和去甲肾上腺素浓度。使用这三种方法分别计算乳酸阈值(Th1a)、肾上腺素阈值(ThA)和去甲肾上腺素阈值(ThNA)。通过比较它们的估计标准误差(SEEs)来检验这些方法的最佳拟合情况。算法方法显示出比其他两种方法更高的SEEs,但仅针对Th1a和ThNA。每种方法计算阈值时的功率输出显示出方法的主效应。Tukey事后检验表明,算法方法产生的输出显著高于其他两种方法,而这两种方法之间没有显著差异。这些功率输出的比较表明,无论采用何种方法,Th1a和ThA均存在显著差异,其他方面无显著差异。血浆乳酸、肾上腺素和去甲肾上腺素浓度表明,算法方法计算出的Th1a和ThNA值显著高于使用其他两种方法计算出的值,而这两种方法之间没有显著差异。ThA的唯一显著差异存在于算法方法和半对数方法之间。每种方法计算阈值时的功率输出之间的相关性在不同方法之间差异很大,且充其量仅为中等程度(r = 0.63)。得出的结论是,算法方法的效能低于其他两种方法,并且Th1a、ThA和ThNA之间的相关性不高。