Prediction of race pace in long distance running from blood lactate concentration around race pace

Pérez, Iker Muñoz; Perez, Diego Moreno; González, Claudia Cardona; Esteve-Lanao, Jonathan

doi:10.4100/jhse.2012.74.04

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…The main strength of this study is the comparison of prediction models in which the v VO 2 max is found as the most frequently variable related to performance in runners (Morgan et al, 1989; Noakes et al, 1990; Muñoz et al, 2012) and an easy-to-perform field test in a training schedule.…”

Section: Discussionmentioning

confidence: 99%

Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests

et al. 2019

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This study compared the ability to predict performance in half-marathon races through physiological variables obtained in a laboratory test and performance variables obtained in the Cooper field test. Twenty-three participants (age: 41.6 ± 7.6 years, weight: 70.4 ± 8.1 kg, and height: 172.5 ± 6.3 cm) underwent body composition assessment and performed a maximum incremental graded exercise laboratory test to evaluate maximum aerobic power and associated cardiorespiratory and metabolic variables. Cooper’s original protocol was performed on an athletic track and the variables recorded were covered distance, rating of perceived exertion, and maximum heart rate. The week following the Cooper test, all participants completed a half-marathon race at the maximum possible speed. The associations between the laboratory and field tests and the final time of the test were used to select the predictive variables included in a stepwise multiple regression analysis, which used the race time in the half marathon as the dependent variable and the laboratory variables or field tests as independent variables. Subsequently, a concordance analysis was carried out between the estimated and actual times through the Bland-Altman procedure. Significant correlations were found between the time in the half marathon and the distance in the Cooper test (r = −0.93; p < 0.001), body weight (r = 0.40; p < 0.04), velocity at ventilatory threshold 1, (r = −0.72; p < 0.0001), speed reached at maximum oxygen consumption (vVO2max), (r = −0.84; p < 0.0001), oxygen consumption at ventilatory threshold 2 (VO2VT2) (r = −0.79; p < 0.0001), and VO2max (r = −0.64; p < 0.05). The distance covered in the Cooper test was the best predictor of time in the half-marathon, and might predicted by the equation: Race time (min) = 201.26 – 0.03433 (Cooper test in m) (R2 = 0.873, SEE: 3.78 min). In the laboratory model, vVO2max, and body weight presented an R2 = 0.77, SEE 5.28 min. predicted by equation: Race time (min) = 156.7177 – 4.7194 (vVO2max) – 0.3435 (Weight). Concordance analysis showed no differences between the times predicted in the models the and actual times. The data indicated a high predictive power of half marathon race time both from the distance in the Cooper test and vVO2max in the laboratory. However, the variable associated with the Cooper test had better predictive ability than the treadmill test variables. Finally, it is important to note that these data may only be extrapolated to recreational male runners.

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Section: Discussionmentioning

confidence: 99%

Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests

et al. 2019

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“…In addition to the abovementioned correlation studies, an approach to study determinants of performance in HM is to develop prediction equations of race time based on correlates (Pérez et al 2012;Gómez-Molina et al 2017), which include usually two steps, first, the development of an equation in a sample of runners, and second, the validation of this equation in another sample. For instance, a study on runners considered training-related and anthropometric variables, and laboratory data from a graded exercise test (GXT) on a treadmill (VO 2 max, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) (Gómez-Molina et al 2017).…”

Section: Physiological Correlates Of Performancementioning

confidence: 99%

“…This study found that HM race time could be predicted to 90.3% by variables related to training and anthropometry, 94.9% by physiological variables, 93.7% by biomechanical parameters and 96.2% by a general equation, and using these equations, the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively) (Gómez-Molina et al 2017). Moreover, HM race speed could be predicted as V21k (km/h) = (V2*1.085) + (− −0.131, r2 = 0.97, ETE = 0.414 km/h, where V2 was the speed during a test in track at constant pace over 2400 m slightly higher than the competition expected pace and bLA2 blood lactate (Pérez et al 2012). A research examined the ratio between running speed and heart rate (HR) as predictor for aerobic capacity (based on the assumption that lower HR at a given speed is expected for more fit individuals), and subsequently race time in 10 km, HM and FM (Altini et al 2017).…”

Section: Physiological Correlates Of Performancementioning

confidence: 99%

Participation and performance characteristics in half-marathon run: a brief narrative review

Nikolaïdis

Knechtle

2022

J Muscle Res Cell Motil

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Half-marathon (HM) is a running sport of increasing popularity in both sexes and in all age groups worldwide during the last years. Many studies have examined several aspects of HM, such as performance and participation trends, sex and age differences, physiological correlates, and training; however, no comprehensive review has ever been contacted to summarize the recently accumulated knowledge. Therefore, the aim of the present study was to review all previous research in this sport, focusing on participation and performance aspects. It was shown that HM runners had similar anthropometric and physiological characteristics as full-marathon runners which should be attributed to the affinity of these two races in terms of metabolic demands. Performance in HM was related with superior scores in aerobic capacity (maximal oxygen uptake, anaerobic threshold and running economy) and training characteristics (sport experience, weekly distance, training speed, frequency of sessions and long single endurance run distance), and lower scores in adiposity-related scores (e.g. body mass, body mass index, body fat percentage and skinfold thickness). Considering the popularity of HM race and the lack of many original studies (compared to FM race), this is an exciting field for scientific research with a large potential for practical applications, since the majority of HM runners are amateur runners in need of sex-, age- and performance-tailored exercise prescription.

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“…In performance diagnostics, blood lactate (LA) concentrations are frequently used to determine endurance capacity (Broich et al, 2012;Goodwin et al, 2007), to give specific training recommendations (Stegmann et al, 1981) and to predict athletic performance in competitions (Muñoz Perez et al, 2012). Skeletal muscle produces and releases large amounts of LA during high intensity exercise.…”

Section: Introductionmentioning

confidence: 99%

Lactate distribution in red blood cells and plasma after a high intensity running exercise in aerobically trained and untrained subjects

Tomschi

Bizjak

Predel

et al. 2018

jhse

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Introduction: To determine endurance capacity and to give specific training recommendations, blood lactate (LA) concentrations are frequently used in performance diagnostics. In blood, LA is stored in red blood cells (RBC) and in plasma. Higher LA uptake by RBC might lead to delayed muscle fatigue since RBC serve as a dilution space for LA and more LA can be taken up by plasma which is released from the working muscle. Therefore, the aim of this study was to investigate the distribution of lactate in plasma and RBC in aerobically well-trained athletes (AA) in comparison to an untrained control group (CG). Materials and Methods: 13 AA and 13 CG participated in this study and conducted a high intensive treadmill test consisting of 2x4 minutes of running at 95% of the maximal running velocity with an active break of 4 minutes. Venous blood was drawn before and after the test. LA was measured in whole blood, plasma and RBC. Further, the ratio (LARatio) was calculated using the following formula: LA of RBC / LA of plasma. Results: AA exhibit significantly higher values in VO2peak and maximum running velocity. After the running test, LA in whole blood, RBC and plasma is increased significantly in both groups. No interaction effect (group X time point) was observed in any parameter. Values of LARatio did not show any significant differences. Conclusion: This study showed that the LA distribution in RBC and plasma after a high intensity running test is very similar in well trained endurance athletes and in untrained control subjects. Hence, LA uptake by RBC cannot or only in part be seen as a

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Prediction of race pace in long distance running from blood lactate concentration around race pace

Cited by 7 publications

References 18 publications

Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests

Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests

Participation and performance characteristics in half-marathon run: a brief narrative review

Lactate distribution in red blood cells and plasma after a high intensity running exercise in aerobically trained and untrained subjects

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