An equation to predict the maximal lactate steady state from ramp-incremental exercise test data in cycling

Abstract

Objectives: The maximal lactate steady state (MLSS) represents the highest exercise intensity at which an elevated blood lactate concentration ([Lac]b) is stabilized above resting values. MLSS quantifies the boundary between the heavy-to-very-heavy intensity domains but its determination is not widely performed due to the number of trials required. Design: This study aimed to: (i) develop a mathematical equation capable of predicting MLSS using variables measured during a single ramp-incremental cycling test and (ii) test the accuracy of the optimized mathematical equation. Methods: The predictive MLSS equation was determined by stepwise backward regression analysis of twelve independent variables measured in sixty individuals who had previously performed ramp-incremental exercise and in whom MLSS was known (MLSSobs). Next, twenty-nine different individuals were prospectively recruited to test the accuracy of the equation. These participants performed ramp-incremental exercise to exhaustion and two-to-three 30-min constant-power output cycling bouts with [Lac]b sampled at regular intervals for determination of MLSSobs. Predicted MLSS (MLSSpred) and MLSSobs in both phases of the study were compared by paired t-test, major-axis regression and Bland–Altman analysis. Results: The predictor variables of MLSS were: respiratory compensation point (W kg−1), peak oxygen uptake (O2peak) (ml kg−1 min−1) and body mass (kg). MLSSpred was highly correlated with MLSSobs (r = 0.93; p < 0.01). When this equation was tested on the independent group, MLSSpred was not different from MLSSobs (234 ± 43 vs. 234 ± 44 W; SEE 4.8 W; r = 0.99; p < 0.01). Conclusions: These data support the validity of the predictive MLSS equation. We advocate its use as a time-efficient alternative to traditional MLSS testing in cycling.

Publication
In: Journal of Science and Medicine in Sport, http://dx.doi.org/10.1016/j.jsams.2018.05.004
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