scholarly journals Predictive Performance Models in Long-Distance Runners: A Narrative Review

2020 ◽  
Vol 17 (21) ◽  
pp. 8289
Author(s):  
José Alvero-Cruz ◽  
Elvis Carnero ◽  
Manuel García ◽  
Fernando Alacid ◽  
Lorena Correas-Gómez ◽  
...  
Keyword(s):  
Oxygen Uptake ◽  
Maximal Oxygen Uptake ◽  
Prediction Models ◽  
Exercise Physiology ◽  
Field Tests ◽  
Training Load ◽  
Running Economy ◽  
Anthropometric Parameters ◽  
Long Distance ◽  
Physiological Variables

Physiological variables such as maximal oxygen uptake (VO2max), velocity at maximal oxygen uptake (vVO2max), running economy (RE) and changes in lactate levels are considered the main factors determining performance in long-distance races. The aim of this review was to present the mathematical models available in the literature to estimate performance in the 5000 m, 10,000 m, half-marathon and marathon events. Eighty-eight articles were identified, selections were made based on the inclusion criteria and the full text of the articles were obtained. The articles were reviewed and categorized according to demographic, anthropometric, exercise physiology and field test variables were also included by athletic specialty. A total of 58 studies were included, from 1983 to the present, distributed in the following categories: 12 in the 5000 m, 13 in the 10,000 m, 12 in the half-marathon and 21 in the marathon. A total of 136 independent variables associated with performance in long-distance races were considered, 43.4% of which pertained to variables derived from the evaluation of aerobic metabolism, 26.5% to variables associated with training load and 20.6% to anthropometric variables, body composition and somatotype components. The most closely associated variables in the prediction models for the half and full marathon specialties were the variables obtained from the laboratory tests (VO2max, vVO2max), training variables (training pace, training load) and anthropometric variables (fat mass, skinfolds). A large gap exists in predicting time in long-distance races, based on field tests. Physiological effort assessments are almost exclusive to shorter specialties (5000 m and 10,000 m). The predictor variables of the half-marathon are mainly anthropometric, but with moderate coefficients of determination. The variables of note in the marathon category are fundamentally those associated with training and those derived from physiological evaluation and anthropometric parameters.

Sprinting Ability as an Important Indicator of Performance in Elite Long-Distance Runners

2020 ◽  
Vol 15 (1) ◽  
pp. 141-145
Author(s):  
Ryo Yamanaka ◽  
Hayato Ohnuma ◽  
Ryosuke Ando ◽  
Fumiya Tanji ◽  
Toshiyuki Ohya ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Oxygen Uptake ◽  
Maximal Oxygen Uptake ◽  
Running Economy ◽  
Distance Runners ◽  
Final Phase ◽  
Improve Performance ◽  
Long Distance ◽  
Important Indicator ◽  
Sprint Time

Purpose: Increases in maximal oxygen uptake () and running economy improve performance in long-distance runners. Nevertheless, long-distance runners require sprinting ability to win, especially in the final phase of competitions. The authors determined the relationships between performance and sprinting ability, as well as other abilities in elite long-distance runners. Methods: The subjects were 12 elite long-distance runners. Mean official seasonal best times in 5000-m (5000 m-SB) and 10,000-m (10,000 m-SB) races within 1 year before or after the examination were 13:58.5 (0:18.7) and 28:37.9 (0:25.2) (mean [SD]), respectively. The authors measured 100-m and 400-m sprint times as the index of sprinting ability. They also measured and running economy ( at 300 m·min−1 of running velocity). They used a single correlation analysis to assess relationships between 5000 m-SB or 10,000 m-SB and other elements. Results: There were significant correlations between 5000 m-SB was significantly correlated with 100-m sprint time (13.3 [0.7] s; r = .68, P = .014), 400-m sprint time (56.6 [2.7] s; r = .69, P = .013), and running economy (55.5 [3.9] mL·kg−1·min−1; r = .59, P = .045). There were significant correlations between 10,000 m-SB and 100-m sprint time (r = .72, P = .009) and 400-m sprint time (r = .85, P < .001). However, there was no significant correlation between 5000 m-SB or 10,000 m-SB and (72.0 [3.8] mL·kg−1·min−1). Conclusions: The authors' data suggest that sprinting ability is an important indicator of performance in elite long-distance runners.

Reliability of time-to-exhaustion and selected psycho-physiological variables during constant-load cycling at the maximal lactate steady-state

2017 ◽  
Vol 42 (2) ◽  
pp. 142-147 ◽  
Author(s):  
Oliver Faude ◽  
Anne Hecksteden ◽  
Daniel Hammes ◽  
Franck Schumacher ◽  
Eric Besenius ◽  
...  
Keyword(s):  
Steady State ◽  
Oxygen Uptake ◽  
Blood Lactate ◽  
Maximal Oxygen Uptake ◽  
Perceived Exertion ◽  
Constant Load ◽  
Time To Exhaustion ◽  
Maximal Lactate Steady State ◽  
Physiological Variables ◽  
Load Tests

The maximal lactate steady-state (MLSS) is frequently assessed for prescribing endurance exercise intensity. Knowledge of the intra-individual variability of the MLSS is important for practical application. To date, little is known about the reliability of time-to-exhaustion and physiological responses to exercise at MLSS. Twenty-one healthy men (age, 25.2 (SD 3.3) years; height, 1.83 (0.06) m; body mass, 78.9 (8.9) kg; maximal oxygen uptake, 57.1 (10.7) mL·min−1·kg−1) performed 1 incremental exercise test, and 2 constant-load tests to determine MLSS intensity. Subsequently, 2 open-end constant-load tests (MLSS 1 and 2) at MLSS intensity (3.0 (0.7) W·kg−1, 76% (10%) maximal oxygen uptake) were carried out. During the tests, blood lactate concentrations, heart rate, ratings of perceived exertion (RPE), variables of gas exchange, and core body temperature were determined. Time-to-exhaustion was 50.8 (14.0) and 48.2 (16.7) min in MLSS 1 and 2 (mean change: −2.6 (95% confidence interval: −7.8, 2.6)), respectively. The coefficient of variation (CV) was high for time-to-exhaustion (24.6%) and for mean (4.8 (1.2) mmol·L−1) and end (5.4 (1.7) mmol·L−1) blood lactate concentrations (15.7% and 19.3%). The CV of mean exercise values for all other parameters ranged from 1.4% (core temperature) to 8.3% (ventilation). At termination, the CVs ranged from 0.8% (RPE) to 11.8% (breathing frequency). The low reliability of time-to-exhaustion and blood lactate concentration at MLSS indicates that the precise individual intensity prescription may be challenging. Moreover, the obtained data may serve as reference to allow for the separation of intervention effects from random variation in our sample.

scholarly journals Linear and Nonlinear Prediction Models Show Comparable Precision for Maximal Mean Speed in a 4x1000 m Field Test

2017 ◽  
Vol 16 (2) ◽  
pp. 78-87
Author(s):  
J. M. Jäger ◽  
J. Kurz ◽  
H. Müller
Keyword(s):  
Oxygen Uptake ◽  
Field Test ◽  
Maximal Oxygen Uptake ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Endurance Performance ◽  
Step Test ◽  
Nonlinear Prediction ◽  
Linear And Nonlinear ◽  
Mlr Model

AbstractMaximal oxygen uptake (VO2max) is one of the most distinguished parameters in endurance sports and plays an important role, for instance, in predicting endurance performance. Different models have been used to estimate VO2max or performance based on VO2max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO2max in healthy adults based on the Queens College Step Test (QCST) as well as the Shuttle Run Test (SRT) and to use these values for linear and nonlinear models in order to predict the performance in a maximal 1000 m run (i.e. the speed in an incremental 4×1000 m Field Test (FT)). 53 female subjects participated in these three tests (QCST, SRT, FT). Maximal oxygen uptake values from QCST and SRT were used as (a) predictor variables in a multiple linear regression (MLR) model and as (b) input variables in a multilayer perceptron (MLP) after scaling in preprocessing. Model output was speed [km·h−1] in a maximal 1000 m run. Maximal oxygen uptake values estimated from QCST (40.8 ± 3.5 ml·kg−1·min−1) and SRT (46.7 ± 4.5 ml·kg−1·min−1) were significantly correlated (r = 0.38, p < 0.01) and maximal mean speed in the FT was 12.8 ± 1.6 km·h−1. Root mean squared error (RMSE) of the cross validated MLR model was 0.89 km·h−1while it was 0.95 km·h−1for MLP. Results showed that the accuracy of the applied MLP was comparable to the MLR, but did not outperform the linear approach.

scholarly journals Can categorised values of maximal oxygen uptake discriminate patterns of exercise dysfunction in pectus excavatum: a prospective cohort study?

2021 ◽  
Vol 8 (1) ◽  
pp. e000940
Author(s):  
Christopher M R Satur ◽  
Ian Cliff ◽  
Nicholas Watson
Keyword(s):  
Oxygen Uptake ◽  
Pulmonary Function ◽  
Exercise Test ◽  
Maximal Oxygen Uptake ◽  
Pectus Excavatum ◽  
Exercise Physiology ◽  
Cardiopulmonary Exercise Test ◽  
Cardiopulmonary Exercise ◽  
Group A ◽  
Group B

Cohort studies of patients with pectus excavatum have inadequately characterised exercise dysfunction experienced. Cardiopulmonary exercise test data were delineated by maximal oxygen uptake values >80%, which was tested to examine whether patterns of exercise physiology were distinguished.MethodsSeventy-two patients considered for surgical treatment underwent assessment of pulmonary function and exercise physiology with pulmonary function tests and cardiopulmonary exercise test between 2006 and 2019. Seventy who achieved a threshold respiratory gas exchange ratio of >1.1 were delineated by maximal oxygen uptake >80%, (group A, n=33) and <80% (group B, n=37) and comparison of constituent physiological parameters performed.ResultsThe cohort was 20.8 (±SD 6.6) years of age, 60 men, with a Haller’s Index of 4.1 (±SD 1.4). Groups A and B exhibited similar demography, pulmonary function test results and Haller’s index values. Exercise test parameters of group B were lower than group A; work 79.2% (±SD 11.3) versus 97.7 (±SD 10.1), anaerobic threshold 38.1% (±SD 7.8) versus 49.7% (±SD 9.1) and O2 pulse 77.4% (±SD 9.8) versus 101.8% (±SD 11.7), but breathing reserve was higher, 54.9% (±SD 13.1) versus 44.2% (±SD 10.8), p<0.001 for each. Both groups exhibited similar incidences of carbon dioxide retention at peak exercise. A total of 65 (93%) exhibited abnormal values of at least one of four exercise test measures.ConclusionThis study showed that patients with pectus excavatum exhibited multiple physiological characteristics of compromised exercise function. It is the first study that defines differing patterns of exercise dysfunction and provides evidence that patients with symptomatic pectus excavatum should be considered for surgical treatment.

scholarly journals Physiological Determinants of Ultramarathon Trail-Running Performance

2020 ◽  
pp. 1-8
Author(s):  
Alexandra M. Coates ◽  
Jordan A. Berard ◽  
Trevor J. King ◽  
Jamie F. Burr
Keyword(s):  
Oxygen Uptake ◽  
Body Mass ◽  
Aerobic Fitness ◽  
Maximal Oxygen Uptake ◽  
Cardiovascular Health ◽  
Peak Velocity ◽  
Running Economy ◽  
Running Performance ◽  
Uptake Test ◽  
Trail Running

Context: The physiological determinants of ultramarathon success have rarely been assessed and likely differ in their contributions to performance as race distance increases. Purpose: To examine predictors of performance in athletes who completed either a 50-, 80-, or 160-km trail race over a 20-km loop course on the same day. Methods: Measures of running history, aerobic fitness, running economy, body mass loss, hematocrit alterations, age, and cardiovascular health were examined in relation to race-day performance. Performance was defined as the percentage difference from the winning time at a given race distance, with 0% representing the fastest possible time. Results: In the 50-km race, training volumes, cardiovascular health, aerobic fitness, and a greater loss of body mass during the race were all related to better performance (all P < .05). Using multiple linear regression, peak velocity achieved in the maximal oxygen uptake test (β = −11.7, P = .002) and baseline blood pressure (β = 3.1, P = .007) were the best performance predictors for the men’s 50-km race (r = .98, r2 = .96, P < .001), while peak velocity achieved in the maximal oxygen uptake test (β = −13.6, P = .001) and loss of body mass (β = 12.8, P = .03) were the best predictors for women (r = .94, r2 = .87, P = .001). In the 80-km race, only peak velocity achieved in the maximal oxygen uptake test predicted performance (β = −20.3, r = .88, r2 = .78, P < .001). In the 160-km race, there were no significant performance determinants. Conclusions: While classic determinants of running performance, including cardiovascular health and running fitness, predict 50-km trail-running success, performance in longer-distance races appears to be less influenced by such physiological parameters.

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