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 (Wkg-1), peak oxygen uptake (V˙O2peak) (mlkg-1min-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±44W; SEE 4.8W; 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.

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

Fontana, Federico Y;Pogliaghi, Silvia;
2018-01-01

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 (Wkg-1), peak oxygen uptake (V˙O2peak) (mlkg-1min-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±44W; SEE 4.8W; 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.
2018
MLSS; Multi-level multiple linear regression analysis; RCP; VO(2peak)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/981968
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