Gimenez Philippe, Busso Thierry
Laboratoire de Physiologie de l'Exercice, Université de Saint-Etienne, France.
Respir Physiol Neurobiol. 2008 Aug 31;162(3):238-41. doi: 10.1016/j.resp.2008.07.004. Epub 2008 Jul 17.
We tested that the breath-by-breath V(O2) determination by an algorithm termed BR did not bias the kinetics parameters. It was compared to two other algorithms using a correction for changes in lung gas stores between two successive breaths (AU, W) and one without correction (NC). Ten healthy male subjects cycled 10 min at 15 and 30 W below and above ventilatory threshold (VT) after 3 min at 0 W. The breath-by-breath V(O2) variability was lower with BR than the other methods during the last 3 min at each power. V(O2) kinetics was described by a mono-exponential model at power below VT and a bi-exponential model above VT. Differences in parameter were only observed for the primary component between estimates using AU and NC. The between-subject variability in the parameters of the slow component at 15 W above VT was lower with AU and BR than W and NC. It was concluded that the BR algorithm could be used to analyse the V(O2) kinetics during exercise.
我们测试了一种名为BR的算法逐次呼吸测定V（O2）不会使动力学参数产生偏差。将其与另外两种算法进行比较，一种算法对连续两次呼吸之间肺气体储备的变化进行校正（AU，W），另一种算法不进行校正（NC）。10名健康男性受试者在0 W下运动3分钟后，分别在低于和高于通气阈值（VT）15 W和30 W的功率下进行10分钟的骑行运动。在每个功率下的最后3分钟内，BR算法的逐次呼吸V（O2）变异性低于其他方法。在低于VT的功率下，V（O2）动力学用单指数模型描述，在高于VT的功率下用双指数模型描述。仅在使用AU和NC的估计值之间观察到主要成分的参数差异。在高于VT 15 W时，AU和BR算法的慢成分参数的受试者间变异性低于W和NC算法。得出的结论是，BR算法可用于分析运动期间的V（O2）动力学。
