Test of the Classic Model for Predicting Endurance Running Performance

McLaughlin, James E.; Howley, Edward T.; Bassett, David R.; Thompson, Dixie L.; Fitzhugh, Eugene C.

doi:10.1249/mss.0b013e3181c0669d

Cited by 157 publications

(184 citation statements)

References 33 publications

Supporting

Mentioning

148

Contrasting

Unclassified

Order By: Relevance

“…This result contradicts the suggestion that the higher the PTV the better the endurance performance. In accordance with maximal aerobic power and RE, these results also suggest that differences in body mass may affect the PTV, as steeplechase runners were heavier than the marathoners (McLaughlin et al, 2010). …”

Section: Introductionsupporting

confidence: 73%

“…Given the non-linear relationship between body size and a metabolic rate, some studies have used allometric scaling of body mass to improve the predictive power of maximal and submaximal VO 2 values (Agutter and Wheatley, 2004; Bergh et al, 1991; McLaughlin et al, 2010). Therefore, the lack of allometric scaling may have biased results of those studies, decreasing the power of VO 2max , RE and PTV in predicting endurance performance.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the controversy, the prediction of running endurance performance by VO 2max test variable(s) still remains as current debate, and recent studies have investigated the correlations between VO 2max test outputs and middle and long-distance running performance (Legaz-Arrese et al, 2011; McLaughlin et al, 2010; Stratton et al, 2009). For example, McLaughlin et al (2010) submitted 17 runners to a VO 2max test, and using a stepwise regression model they found that the velocity at VO 2max was the best predictor of a 16 km running time trial.…”

Section: Introductionmentioning

confidence: 99%

“…For example, McLaughlin et al (2010) submitted 17 runners to a VO 2max test, and using a stepwise regression model they found that the velocity at VO 2max was the best predictor of a 16 km running time trial. However, this study only analyzed unadjusted variables so it was not determined whether allometrically adjusted VO 2max and RE, together with the PTV, would have improved the endurance performance prediction.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables

Abad

Barros

Bertuzzi

et al. 2016

Journal of Human Kinetics

View full text Add to dashboard Cite

The aim of this study was to verify the power of VO2max, peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO2max and PTV; 2) a constant submaximal run at 12 km·h−1 on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO2max, PTV and RE) and adjusted variables (VO2max0.72, PTV0.72 and RE0.60) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO2max. Significant correlations (p < 0.01) were found between 10 km running time and adjusted and unadjusted RE and PTV, providing models with effect size > 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV0.72 and RE0.60 and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation.

show abstract

Section: Introductionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables

Abad

Barros

Bertuzzi

et al. 2016

Journal of Human Kinetics

View full text Add to dashboard Cite

show abstract

“…Therefore, some attempts have been made to predict the performance in endurance sports based on VO 2 max. For instance, McLaughlin, Howley, Bassett Jr., Thompson and Fitzhugh (2010) used VO 2 max (as well as other variables that are linked to endurance performance) in order to predict running performance in a 16 km time trial. Also in shorter distances such as 5 and 10 km, VO 2 max seems to be a strong predictor of running speed (Bird, Theakston, Owen & Nevill, 2003).…”

Section: Introductionmentioning

confidence: 99%

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

Jäger

Kurz

Müller

2017

International Journal of Computer Science in Sport

View full text Add to dashboard Cite

Maximal oxygen uptake (VO 2 max) 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 VO 2 max or performance based on VO 2 max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO 2 max 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 4x1000 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 for MLP. Results showed that the accuracy of the applied MLP was comparable to the MLR, but did not outperform the linear approach.

show abstract

The effects of plyometric versus resistance training on running economy and 5‐km running time in middle‐aged recreational runners

Eihara,

Takao,

Sugiyama

et al. 2024

European Journal of Sport Science

View full text Add to dashboard Cite

The effects of plyometric training (PT) versus resistance training (RT) on running economy and performance are unclear, especially in middle‐aged recreational runners. We examined (1) the efficacy of PT versus RT on running economy and performance in middle‐aged recreational runners and (2) the relationships between the main training outcomes. Twenty middle‐aged recreational runners were randomly allocated to a PT or RT group (n = 10/group). Training was conducted twice/week for 10 weeks combined with daily running. PT included the countermovement jump (CMJ), rebound jump, hurdle hop, and drop jump. RT consisted of leg press, leg curl, and calf raise with 50%–90% of one‐repetition maximum (1RM). Before and after the intervention, 1RM of the three lifting tasks, CMJ and drop jump performances, oxygen cost at 8–12 km/h, and 5 km running time were assessed. PT enhanced 1RM of leg curl only (8.5% and p = 0.007), whereas RT increased 1RM of the three lifting tasks (19.0%–21.1% and p < 0.001). Both groups improved CMJ height (6.4%–8.3% and p = 0.016) and drop jump performance (height: 9.7%–19.4%, p = 0.005, height/contact time: 11.4%–26.3% and p = 0.009) and oxygen cost regardless of running velocity (2.0% and p = 0.001) without significant group differences. However, neither group changed the 5‐km running time (p ≥ 0.259). A significant correlation was found between the changes in calf raise 1RM and oxygen cost (r = −0.477 and p = 0.046) but not between the other measured variables. These results suggest that for middle‐aged recreational runners, PT and RT can similarly improve running economy albeit not necessarily the 5‐km running time, and enhancing plantarflexion strength may particularly contribute to improving running economy.

show abstract

Test of the Classic Model for Predicting Endurance Running Performance

Abstract: Among well-trained subjects heterogeneous in VO2max and running performance, vVO2max is the best predictor of running performance because it integrates both maximal aerobic power and the economy of running. The PTV is linked to the same physiological variables that determine vVO2max.

Cited by 157 publications

References 33 publications

10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables

10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables

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

The effects of plyometric versus resistance training on running economy and 5‐km running time in middle‐aged recreational runners

Contact Info

Product

Resources

About