Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Area of Science:

• Exercise Physiology
• Sports Science
• Cardiorespiratory Fitness Assessment

Background:

• Maximal heart rate (HRmax) is a key indicator of cardiorespiratory fitness.
• Predicting HRmax offers an alternative to maximal cardiopulmonary exercise testing (CPET).
• The accuracy of HRmax prediction models in endurance athletes (EA) requires independent validation.

Purpose of the Study:

• To externally validate existing HRmax prediction equations specifically for elite runners and cyclists.
• To assess the accuracy and potential biases of these prediction models in EA populations.
• To compare the reliability of predicted HRmax versus measured HRmax from CPET.

Main Methods:

• Utilized data from 4043 runners and 1026 cyclists who completed maximal CPET.
• Applied statistical analyses including Student's t-test, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE).
• Externally validated eight running and five cycling HRmax prediction equations.

Main Results:

• Significant differences (p = 0.001) were observed between measured and predicted HRmax for 9 out of 13 (69.2%) models.
• HRmax was overestimated by 61.5% of models and underestimated by 38.5%.
• Prediction models demonstrated limited precision, with MAPE up to 4.7% and RMSE ranging from 9.1 to 10.5.

Conclusions:

• Current HRmax prediction models exhibit notable inaccuracies and limited precision for endurance athletes.
• HRmax was more frequently underestimated than overestimated by the tested formulae.
• While predicted HRmax can serve as a supplementary tool, maximal CPET remains the gold standard for accurate assessment in EA.