Development of the simplified method for estimating lactate threshold

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The true response according to the concept of anaerobic during a progressive incrementalload exercise, corresponds to the onset of blood lactate accumulation from a resting level, and it is called lactate threshold (LT). The measurement and analysis of blood lactate, however, is complicated and not popular, so LT is often estimated by 2 criteria of gas exchange parameters (GEPs) described below visually, 1) non-1inear increase in VE and 2) increase in VE/VO_2 Without corresponding increase in VE/VCO2, and it is called ventilatory threshold (VT). The visual inspection to detect VT contains the subjective variation of intra and inter-detectors, so its reliability and reproducibility are poor. The purpose of this study are 1) to examine if the LT is consistent with VT determinated by the modeling of each criterion to avoid the subjective variations, or not, and 2) if either of VT by modeling is consistent with LT well, to develop the new method (algorithm) that executes the estimation of its consisted VT by on-line and real-time processing of GEPs. 21 young male subjects performed an incremental-load ergometer exercise in which the initial work rate was the 4 min unloaded cycling and thereafter its rate was increased 150 kgm every 2 min until exhaustion. GEPS were measured by Douglas bag method continuously, and blood samples for lactate analysis were obtained last 30 sec of each work rate frofn a warmed ear lobe. To detect LT and VT from criterion l, we employed the segmented regression analysis to the data below 85% of VO_2max of LA and VE vs. VO_2, and these were called LT and VT(85%). To detect VT from crit-erion 2, we employed the multiple regression analysis to all data of VE/VO_2 vs. VO_2, and selected the model's order according to AIC. The lowest value of the fitting model was determinated as VT detected by criterion 2, and it is called VE/ VO_2>(lowest). ' As a result, VT(85%) was not always consistent with LT, but VE/VO_2(lowest) was consistent with LT well, so VE/VO_2(10west) was a good predictor to estimate LT by GEPS objectively. The consistence of LT and VE/VO_2(lowest) shows the cause-effect relationship between LT (cause) and VT (effect). It is not safe to perform the exercise test until exhaustion, in particular, for older subjects. So, it is necessary to develop the algorithm that executes the on-line and real-time processing of VE/VO2 data successively during a test. This algorithm can be stopped a test at the time of LT estimation. To consider the on-Iine and real-time processing, we use the point at whlch the slope between 2 adjacent VE/VO2 Changes from minus to plus, that equals VE/VO_2(lowest) substantially, and it is called VE/VO_2(minimum). To be concrete, this algorithm consists of three parts : moving average processing of VE/VO_2(raw data), calculation of the slope between 2 adjacent moving averages, and decision of VE/VO_2(minimum) using the change in slope.