Dataset Information

Physiological Predictors of Maximal Incremental Running Performance.

ABSTRACT: Purpose:The aim of this study was to verify whether physiological components [vertical jumps (Squat Jump - SJ and Countermovement Jump - CMJ), eccentric utilization ratio (EUR) of vertical jumps, running economy (RE), metabolic cost (C MET ), first and second ventilatory threshold (VT1 and VT2) maximal oxygen uptake (VO2MAX)] can predict maximal endurance running performance. Methods:Twenty male runners performed maximal vertical jumps, submaximal running at constant speeds, and maximal incremental running test. Before, we measured anthropometric parameters (body mass and height) and registered the training history and volume. SJ and CMJ tests were evaluated prior to running tests. Initially, the oxygen uptake (VO2) was collected at rest in the orthostatic position for 6 min. Soon after, a 10-min warm-up was performed on the treadmill at 10 km?h-1, followed by two 5-min treadmill rectangular tests at 12 and 16 km?h-1 monitored by a gas analyzer. After that, the runners performed a maximal incremental test, where the VT1, VT2, and VO2MAX were evaluated, as well as the maximum running speed (vVO2MAX). Thus, RE and C MET were calculated with data obtained during rectangular running tests. Multivariate stepwise regression analyses were conducted to measure the relationship between independent variables (height and power of SJ and CMJ, EUR; RE and C MET 12 and 16 km?h-1 ; VT1, VT2, and VO2MAX), as predictors of maximal running performance (vVO2MAX), with significance level at ? = 0.05. Results:We found that VO2MAX and RE at 16 km?h-1 predict 81% of performance (vVO2MAX) of endurance runners (p < 0.001). Conclusion:The main predictors of the maximal incremental running test performance were VO2MAX and RE.

SUBMITTER: Lanferdini FJ

PROVIDER: S-EPMC7419685 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Physiological Predictors of Maximal Incremental Running Performance.

Lanferdini Fábio J FJ Silva Edson S ES Machado Esthevan E Fischer Gabriela G Peyré-Tartaruga Leonardo A LA

Frontiers in physiology 20200805

<h4>Purpose</h4>The aim of this study was to verify whether physiological components [vertical jumps (Squat Jump - SJ and Countermovement Jump - CMJ), eccentric utilization ratio (EUR) of vertical jumps, running economy (RE), metabolic cost (C <sub><i>MET</i></sub> ), first and second ventilatory threshold (VT<sub>1</sub> and VT<sub>2</sub>) maximal oxygen uptake (VO<sub>2MAX</sub>)] can predict maximal endurance running performance.<h4>Methods</h4>Twenty male runners performed maximal vertical ...[more]

PMID: 32848890

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Project description: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 (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, 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 (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m−2; VO2max = 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 VAT 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 VRCP 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 Vmax 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.

Project description:This study aimed to investigate and compare the effects of preferred music on anaerobic threshold determination in an incremental running test, as well the physiological responses and perceived exertion at this intensity, in physically active men and women. Additionally, by using area under the curve (AUC) analysis of the parameters of interest during the graded test, we studied the effects of music at two physiological moments-before and after anaerobic threshold intensity (iAT)-in men and women. Twenty (men = 10; women = 10) healthy and active participants completed four visits to the laboratory. The first and second sessions were used for sample characterization. In the third and fourth sessions, participants performed an incremental running test (started at 7 km.h-1 with increments of 1 km.h-1 at each 3-minute stage) under preferred music and non-music conditions. Blood lactate ([Lac]), heart rate (HR), and perceived exertion were measured by two scales (RPEBorg and the estimation of time limit ‒ ETL) during all tests, and the total time of effort (TT) was considered as performance. Individual curves of the "intensity vs blood lactate" analyzed by the bissegmentation method provide the iAT and the AUC of [Lac], HR, RPEBorg, and ETL before and after the iAT attainment were calculated. The iAT for men (non-music: 11.5±0.9km.h-1 vs music: 11.6±1.1km.h-1) and women (non-music: 9.8±0.7km.h-1 vs music: 9.7±0.7km.h-1) was not affected by music, and for both sexes, there was no difference between non-music and music conditions in all variables obtained at iAT. The AUC of all variables were not affected by music before the iAT attainment. However, [Lac], HR, and RPEBorg presented higher values of AUC after iAT for the female group with preferred music. This may be due to the fact that 70% of women have increased TT under music conditions. Overall, preferred music did not affect the iAT determination in an incremental running test. However, some physiological responses and perceived exertion after iAT of female subjects seems to be influenced by preferred music.

Project description:BackgroundThe maximal running speed (VMAX) determined on a graded treadmill test is well-recognized as a running performance predictor. However, few studies have assessed the variables that predict VMAX in recreationally active runners.MethodsWe used a mathematical procedure combining Fick's law and metabolic cost analysis to verify the relation between (1) VMAX versus anthropometric and physiological determinants of running performance and, (2) theoretical metabolic cost versus running biomechanical parameters. Linear multiple regression and bivariate correlation were applied. We aimed to verify the biomechanical, physiological, and anthropometrical determinants of VMAX in recreationally active runners. Fifteen recreationally active runners participated in this observational study. A Conconi and a stead-steady running test were applied using a heart rate monitor and a simple video camera to register the physiological and mechanical variables, respectively.ResultsStatistical analysis revealed that the speed at the second ventilatory threshold, theoretical metabolic cost, and fat-mass percentage confidently estimated the individual running performance as follows: VMAX = 58.632 + (-0.183 * fat percentage) + (-0.507 * heart rate percentage at second ventilatory threshold) + (7.959 * theoretical metabolic cost) (R2 = 0.62, p = 0.011, RMSE = 1.50 km.h-1). Likewise, the theoretical metabolic cost was significantly explained (R2 = 0.91, p = 0.004, RMSE = 0.013 a.u.) by the running spatiotemporal and elastic-related parameters (contact and aerial times, stride length and frequency, and vertical oscillation) as follows: theoretical metabolic cost = 10.421 + (4.282 * contact time) + (-3.795 * aerial time) + (-2.422 * stride length) + (-1.711 * stride frequency) + (0.107 * vertical oscillation).ConclusionCritical determinants of elastic mechanism, such as maximal vertical force and vertical and leg stiffness were unrelated to the metabolic economy. VMAX, a valuable marker of running performance, and its physiological and biomechanical determinants can be effectively evaluated using a heart rate monitor, treadmill, and a digital camera, which can be used in the design of training programs to recreationally active runners.

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Physiological Predictors of Maximal Incremental Running Performance.

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Physiological Predictors of Maximal Incremental Running Performance.

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