Peak Running Velocity or Critical Speed Under Field Conditions: Which Best Predicts 5-km Running Performance in Recreational Runners?

Peak Running Velocity vs. Critical Speed: Which One Is Better to Prescribe Endurance Training to Recreational Runners?

ABSTRACT

Figueiredo, DH, Figueiredo, DH, Manoel, FA, and Machado, FA. Peak running velocity vs. critical speed: which one is better to prescribe endurance training to recreational runners? J Strength Cond Res 37(9): 1783-1788, 2023-This study aimed to evaluate the effects of 5 weeks of training prescribed by peak running velocity obtained on the track (Vpeak_TR) and their respective time limit (Tlim), as well as by critical speed (CS), on physiological and endurance performance parameters in recreational runners. Twenty-two male runners were distributed into a Vpeak_TR group (GVP) and CS group (GCS) with a predefined program, alternating moderate-intensity continuous training and high-intensity interval training. Maximum oxygen uptake (V̇O2max), and its respective velocity (vV̇O2max), Vpeak_TR, Tlim at 100% Vpeak_TR, 5-km running performance, CS, and D' (maximum distance covered above CS) were assessed at pretraining and posttraining period. There was a significant increase from pretraining to posttraining in Vpeak_TR (GVP = 4.5 ± 3.1% vs. GCS = 7.5 ± 4.2%), vV̇O2max (GVP = 3.9 ± 3.8% vs. GCS = 8.6 ± 6.7%), and mean velocity 5-km (GVP = 5.6 ± 3.3% vs. GCS = 7.3 ± 3.5%) and decrease in 5-km time (GVP = -5.1 ± 3.0% vs. GCS = -6.8 ± 3.0%). CS and V̇O2max significantly improved in GCS (9.3 ± 8.4% and 6.0 ± 6.8%, respectively), with no difference for GVP (2.8 ± 5.6% and 1.3 ± 8.4%, respectively). No differences were observed between groups for all variables. These findings give further supports to the notion that both variables obtained on the track are valid tools to prescribed training in recreational runners.

Modeling Physiological Predictors of Running Velocity for Endurance Athletes

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.