Modeling Physiological Predictors of Running Velocity for Endurance Athletes

Szczepan Wiecha; Przemysław Seweryn Kasiak; Igor Cieśliński; Marcin Maciejczyk; Artur Mamcarz; Daniel Śliż

doi:10.3390/jcm11226688

Modeling Physiological Predictors of Running Velocity for Endurance Athletes

J Clin Med. 2022 Nov 11;11(22):6688. doi: 10.3390/jcm11226688.

Authors

Szczepan Wiecha¹, Przemysław Seweryn Kasiak², Igor Cieśliński¹, Marcin Maciejczyk³, Artur Mamcarz⁴, Daniel Śliż⁴

Affiliations

¹ Department of Physical Education and Health in Biala Podlaska, Faculty in Biala Podlaska, Józef Piłsudski University of Physical Education in Warsaw, 21-500 Biala Podlaska, Poland.
² Student's Scientific Circle of Lifestyle Medicine, 3rd Department of Internal Medicine and Cardiology, Medical University of Warsaw, 04-749 Warsaw, Poland.
³ Department of Physiology and Biochemistry, Faculty of Physical Education and Sport, University of Physical Education in Krakow, 31-571 Kraków, Poland.
⁴ 3rd Department of Internal Medicine and Cardiology, Medical University of Warsaw, 04-749 Warsaw, Poland.

Abstract

Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (V_AT), respiratory compensation point (V_RCP), and maximal velocity (V_max) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of V_AT, V_RCP, V_max, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (n_males = 3300, n_females = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m^-2; VO_2max = 53.20 ± 7.17 mL·min^-1·kg^-1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For V_AT R² = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For V_RCP R² = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For V_max R² = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA.

Keywords: anaerobic threshold; endurance training; machine learning; prediction models; respiratory compensation point; running; velocity.

Grants and funding

The authors received no funding from an external source.