A simplified approach to estimating the maximal lactate steady state

Area of Science:

• Exercise Physiology
• Sports Science
• Training Optimization

Background:

• Maximal steady state (MSS) is considered an optimal exercise intensity for endurance training.
• Identifying MSS typically requires direct measurement of blood lactate or respiratory metabolism.
• A simpler method for MSS identification is needed for practical exercise prescription.

Purpose of the Study:

• To evaluate the efficacy of heart rate (HR) in identifying the maximal steady state (MSS) during constant load exercise.
• To develop and validate HR-based models for predicting MSS in trained athletes.
• To assess the accuracy of HR models compared to direct blood lactate measurements.

Main Methods:

• Trained runners and cyclists performed incremental and constant load exercise tests.
• Maximal steady state (MSS) was defined by blood lactate concentration changes (< 1.0 mM increase from 10 to 30 minutes).
• Heart rate (HR) models were developed using confidence intervals around MSS and above MSS thresholds, then cross-validated.

Main Results:

• Heart rate models correctly predicted MSS in 84% of cycling and 76% of running bouts.
• The models predicted non-steady state conditions when steady state occurred in 62% of prediction errors.
• Accuracy was sufficient for practical application in exercise prescription.

Conclusions:

• Simple heart rate (HR) models can predict maximal steady state (MSS) exercise intensity with considerable accuracy.
• These HR models provide a viable alternative to direct blood lactate measurements for exercise prescription.
• Further refinement may improve the accuracy of HR-based MSS identification.