Polynomial modeling for the identification of lactate minimum velocity by different methods

Area of Science:

• Exercise Physiology
• Sports Science
• Biochemistry

Background:

• The lactate minimum (LM) protocol is a standard method for assessing aerobic fitness.
• It is used to predict exercise intensity at the maximal blood lactate steady state.
• Accurate identification of lactate minimum velocity (LMV) is crucial for training prescription.

Purpose of the Study:

• To compare different methods for identifying the lactate minimum velocity (LMV) in cycling.
• To evaluate the reliability and practicality of various LMV identification techniques.
• To determine if fewer data points can yield accurate LMV results.

Main Methods:

• Fourteen male cyclists underwent a specific LM protocol involving a time trial and incremental cycling bouts.
• Blood lactate concentrations (bLAC) were measured after each bout.
• LMV was identified visually (vLMV), using polynomial functions on 6 (pLMV(6)) and 3 (pLMV(3)) bouts, and via lactate-to-work velocity quotients (LMVQ(6), LMVQ(3)).

Main Results:

• No significant differences were found between the visually identified LMV (vLMV) and the values obtained from polynomial modeling (pLMV(6), pLMV(3)) or quotient methods (LMVQ(6), LMVQ(3)).
• All tested methods showed high correlation, indicating consistent results across different approaches.
• The mean LMV across all methods was approximately 33 km/h.

Conclusions:

• The lactate minimum velocity (LMV) can be reliably identified using the proposed methods.
• Modeling with as few as three data points using a polynomial function provides accurate LMV results.
• This simplified approach makes the LM test more practical, economical, and less uncomfortable for athletes.