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

Abad, César Cavinato Cal; Barros, Ronaldo Vilela; Bertuzzi, Rômulo Cássio de Moraes; Gagliardi, João Fernando Laurito; Lima‐Silva, Adriano Eduardo; Lambert, Mike

doi:10.1515/hukin-2015-0182

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…The positive relationship between cardiovascular parameters such as maximal oxygen uptake (VO2 max), ventilatory threshold (VT), running economy (RE) and endurance performance is well established (Costill et al, 1973;Daniels, 1985;Morgan et al, 1989). The majority of the studies which have investigated the prediction factors of performance in races from 5 km to ultramarathon support the "classical model" (Joyner, 1991) especially when they are expressed in velocity terms [velocity of maximal oxygen uptake (vVO2 max), and velocity ventilatory threshold (vVT); Abad et al, 2016;Abe et al, 1998;Mclaughlin et al, 2010;Scott & Houmard, 1994;Sjodin & Jacobs, 1981;Sjodin & Svedenhag, 1985;Stratton et al, 2009). In addition, there is evidence that body composition and especially fat-free mass is also a significant performance parameter (Bale et al, 1985;Gomez-Molina et al, 2017;Hagan et al, 1981;Oguela-Alday et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Physiological Determinants of Short Trail Running

Kolyfa¹,

Geladas²,

Paradisis³

et al. 2021

EJPE

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The recent worldwide popularity of trail running has raised the necessity of studying the physiological profile of this sport. Although trail running races are long distance endurance events, the variety of their terrain, incline and duration prevents the application of the classical predictive model of level running. Thus, the aim of the present study was to investigate the physiological and anthropometric parameters that determine short trail race performance. Twenty-five moderately trained trail runners participated in a 15 km trail running race, consisting of 9 km positive and 6 km negative incline. Four days after the race they followed a laboratory protocol for the measurement and estimation of anthropometric and physiological parameters (maximal oxygen uptake, velocity at maximal oxygen uptake, ventilatory threshold, velocity at ventilatory threshold, running economy, flexibility, muscle power, aerobic capacity). The results revealed high correlations between the 15 km race performance and velocity at maximal oxygen uptake (r = 0.81), ventilatory threshold (r = 0.88), muscle power of knee extensor (r = 0.50 – 0.53), anaerobic capacity (r = 0.65) and body fat percentage (r = 0.7). Another two parameters that were highly correlated with the 15 km mountain trail race performance were both the positive and negative incline time (r = 0.95 and r = 0.96, respectively). Our conclusions confirmed previous findings that performance in trail running cannot be predicted with the same variable model as level running. Article visualizations:<img src="/-counters-/edu_01/0985/a.php" alt="Hit counter" />

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Section: Introductionmentioning

confidence: 99%

Physiological Determinants of Short Trail Running

Kolyfa¹,

Geladas²,

Paradisis³

et al. 2021

EJPE

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“…For 3 km, Slattery et al [24] proposed a vPeak-based model which induced SEE of 24 s, and when velocity at the lactate threshold and peak blood lactate values were added to the model, SEE decreased to 15 s. Altini and Amft, [8] used multiple variables (performance, training, anthropometrics, resting physiology) for their estimation of 10-km time and reported MAPE of 4.0 % and root mean square error of 2.7 min for the most accurate model. Abad et al [25] in turn, created a model for 10 km that used either vPeak (SEE = 1.9 min) or a combination of vPeak and submaximal running economy (SEE = 1.5 min). Current SEE values of 23 s for 3 km and 1.8 min for 10 km are well in line with previous models and both could be regarded as accurate estimations for the current group of recreational runners.…”

Section: Estimation Of Distance Running Performancementioning

confidence: 99%

Predicting Running Performance and Adaptations from Intervals at Maximal Sustainable Effort

et al. 2023

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This study examined the predictive quality of intervals performed at maximal sustainable effort to predict 3-km and 10-km running times. In addition, changes in interval performance and associated changes in running performance were investigated. Either 6-week (10-km group, n = 29) or 2-week (3-km group, n = 16) interval training periods were performed by recreational runners. A linear model was created for both groups based on the running speed of the first 6x3-min interval session and the test run of the preceding week (T1). The accuracy of the model was tested with the running speed of the last interval session and the test run after the training period (T2). Pearson correlation was used to analyze relationships between changes in running speeds during the tests and interval sessions. At T2, the mean absolute percentage error of estimate for 3-km and 10-km test times were 2.3 % and 3.4 %, respectively. The change in running speed of intervals and test runs from T1 to T2 correlated (r = 0.75, p < 0.001) in both data sets. Thus, the maximal sustainable effort intervals were able to predict 3-km and 10-km running performance and training adaptations with good accuracy, and current results demonstrate the potential usefulness of intervals as part of the monitoring process.

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“…Running economy (RE), together with maximal oxygen uptake (VO 2max ) and the fraction of oxygen uptake that can be utilized over a given period, delineates aerobic performance capacity ( Bassett and Howley, 2000 ). RE is defined as the energy cost of running per unit of distance or time at a given steady state velocity ( Saunders et al, 2004 ) and is an important variable of performance in endurance runners with equally high levels of VO 2max ( Abad et al, 2016 ; Morgan et al, 1989 ). Typically, lower RE indicates a lower energy cost of running which is associated with a lower vertical displacement of the body’s centre of mass ( Moore, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of Plyometric Training on Soft and Hard Surfaces for Improving Running Economy

Lännerström¹,

Nilsson²,

Cardinale³

et al. 2021

Journal of Human Kinetics

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The present study investigated the effects of plyometric jump training on hard and soft surfaces on running economy (RE), maximal oxygen uptake (VO2max), running performance and the rate of force development in orienteers. Nineteen orienteers (11 women and 8 men, body mass 61.1 ± 7.3 kg, age 21 ± 5.8 yrs) were randomly stratified based on sex, age, VO2max and RE to plyometric jumping training (8 sessions over 4 weeks) on either a hard or a soft surface. RE, VO2max and running performance were assessed on a treadmill and outdoor on- and off-trail loops. Moreover, ground reaction forces and force development were assessed during a one leg drop-jump test. The training intervention led to an overall 2-7% improvement in treadmill and off-trail RE, independent of the jumping surface and running velocity assessed. These improvements were not explained by force development during drop jump tests, which remained unchanged following the intervention. The changes in time-trial performance were associated with changes in RE. Plyometric training improved RE with no difference between the hard or the soft training surface and improved RE was also independent of the running speed assessed. Furthermore, improved running performance was associated with changes in RE after the intervention.

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

Cited by 9 publications

References 25 publications

Physiological Determinants of Short Trail Running

Physiological Determinants of Short Trail Running

Predicting Running Performance and Adaptations from Intervals at Maximal Sustainable Effort

Effects of Plyometric Training on Soft and Hard Surfaces for Improving Running Economy

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