A 1-day maximal lactate steady-state assessment protocol for trained cyclists - Lillo-Bevia, J. R.; Moran-Navarro, R.; Martinez-Cava, A.; Cerezuela, V.; Pallares, J. G. (Journal of Science and Cycling, 2018)
The main aim of this study is to assess the validity of a new cycling protocol to estimate the Maximal Lactate Steady-State workload (MLSS) through a one-day incremental protocol (1day_MLSS). Eleven well-trained male cyclists performed 3 to 4 trials of 30-min constant load test (48-72h in between) to determine their respective MLSS workload. Then, on separate days, each cyclist carried out two identical graded exercise tests, comprised of four 10-minute long stages, with the initial load at 63% of their respective maximal aerobic power, 0.2 W·Kg-1 increments, and blood lactate concentration (BLC) determinations each 5 min. The results of the 1day_MLSS tests were analysed through three different constructs: i) BLC difference between 5th and 10th min of each stage (DIF_5to10), ii) BLC difference between the 10th min of two consecutive stages (DIF_10to10), and iii) difference in the mean BLC between the 5th and 10th min of two consecutive stages (DIF_mean). For all constructs, the physiological steady state was determined as the highest workload that could be maintained with a BLC rise lower than 1mmol/L. No significant differences were detected between the MLSS workload (247 ± 22W) and any of the 1day_MLSS data analysis (250 ± 24W, 245 ± 23W and 243 ± 21W, respectively; p>0.05). When compared to the MLSS workload, strong ICCs and low bias values were found for these three constructs, especially for the DIF_10to10 workload (r=0.960; Bias=2.2 W). High within-subject reliability data were found for the DIF10_10 construct (ICC=0.846; CV=0.4%; Bias=2.2 ± 6.4W). The 1day_MLSS test and DIF_10to10 data analysis is a valid assessment to predict the MLSS workload in cycling, that considerably reduces the dedicated time, effort and human resources that requires the original test. The validity and reliability values reported in this project are higher than those achieved by other previous MLSS estimation tests.