Running economy in long-distance runners is positively affected by running experience and negatively by aging.

INTRODUCTION: The maximum oxygen uptake (V˙O2max), the maximum rate of oxygen that can be sustained before the onset of blood lactate accumulation, and the metabolic cost of locomotion are the main physiological factors associated with long-distance running performance. The latter is known as the running economy. Generally, runners reach peak performance in long races between 25 and 30 years of age, with a progressive decline occurring thereafter. However, it is not known whether the running economy is affected or how it is affected by aging. AIM: To investigate the effect of age and years of running experience on the running economy of amateur long-distance runners aged 20-80 years. METHODS: Sixty-nine recreational long-distance runners, divided into five age groups according to decade of life, participated in this study: Group 1 (n= 9) 27.2 ± 1.3 years, Group 2 (n= 18) 35.9 ± 2.2 years, Group 3 (n= 17) 43.4 ± 2.8 years, Group 4 (n= 17) 53.0 ± 2.3 years, and Group 5 (n= 8) 65.5 ± 2.9 years. For running economy assessment, oxygen cost (OC) and energy cost (EC) were measured. Furthermore, the participants were interviewed on their running experience. RESULTS: For EC, the two independent variables composing the regression model were age (ß = 0.703, t= 5.443, p < 0.001) and running experience (ß = -0.230, t = -1.785, p= 0.07), and 34% of the energy cost variation can be explained by these two factors. EC and OC were compared among the groups. There were no significant differences between Groups 1 and 2 (p= 0.999), Groups 1 and 3 (p= 1.000), and Groups 1 and 4 (p= 0.528). However, Group 5 had a significantly higher energy cost than Group 1 (p < 0.001), Group 2 (p < 0.001), Group 3 (p < 0.001) and Group 4 (p < 0.001). CONCLUSION: The number of years of running experience has a positive effect on running economy, but it is insufficient to overcome the negative effect of the aging process. Furthermore, running economy was significantly worse in participants aged ≥60 years compared with that in younger athletes.

Can plyometric training change the pacing behaviour during 10-km running?

The effects of plyometric training (PT) on middle- and long-distance running performances are well established. However, its influence on pacing behaviour is still unclear. The aim of this study was to evaluate the effects of PT on pacing behaviour. In addition, verify whether the adaptations induced by PT would change ratings of perceived exertion (RPE) and/or affective feelings during the race. Twenty-eight male runners were assigned to two groups: control (C) and PT. PT held two weekly PT sessions for eight weeks. Drop jump (DJ) performance, 10-km running performance, pacing behaviour, RPE and affective feelings, VO2peak, ventilatory thresholds (VT1 and VT2), peak treadmill speed (PTS), and running economy (RE) were measured. For group comparisons, a mixed model analysis for repeated measures, effect size (ES) and 90% confidence interval (90% CI) were calculated for all dependent variables. Significant differences pre-to-post was observed for PT group in DP (7.2%; p ≤ 0.01; ES = 0.56 (0.28-0.85)) and RE (4.5%; p ≤ 0.05; ES = -0.52 ((-0.73 to -0.31)) without changes in pacing behaviour. While PT was effective for improving DJ and RE, there is no evidence that pacing behaviour, RPE or affective feelings are directly affected by these adaptations during a 10-km time trial run.

Could middle- and long-distance running performance of well-trained athletes be best predicted by the same aerobic parameters?

The prediction of running performance at different competitive distances is a challenge, since it can be influenced by several physiological, morphological and biomechanical factors. In experienced male runners heterogeneous for maximal oxygen uptake (VO2max), endurance running performance can be well predicted by several key parameters of aerobic fitness such as VO2max and its respective velocity (vVO2max), running economy, blood lactate response to exercise, oxygen uptake kinetics and critical velocity. However, for a homogeneous group of well-trained endurance runners, the relationship between aerobic fitness parameters and endurance running performance seems to be influenced by the duration of the race (i.e., middle vs. long). Although middle-distance and ultramarathon runners present high aerobic fitness levels, there is no accumulating evidence showing that the aerobic key parameters influence both 800-m and ultramarathon performance in homogeneous group of well-trained runners. The vVO2max seems to be the best predictor of performance for 1500 m. For 3000 m, both vVO2max and blood lactate response to exercise are the main predictors of performance. Finally, for long distance events (5000 m, 10,000 m, marathon and ultramarathon), blood lactate response seems to be main predictor of performance. The different limiting/determinants factors and/or training-induced changes in aerobic parameters can help to explain this time- or distance-dependent pattern.

The Impact of ACTN3 Gene Polymorphisms on Susceptibility to Exercise-Induced Muscle Damage and Changes in Running Economy Following Downhill Running.

This study aimed to investigate if ACTN3 gene polymorphism impacts the susceptibility to exercise-induced muscle damage (EIMD) and changes in running economy (RE) following downhill running. Thirty-five healthy men were allocated to the two groups based on their ACTN3 gene variants: RR and X allele carriers. Neuromuscular function [knee extensor isometric peak torque (IPT), rate of torque development (RTD), and countermovement, and squat jump height], indirect markers of EIMD [muscle soreness, mid-thigh circumference, knee joint range of motion, and serum creatine kinase (CK) activity], and RE (oxygen uptake, minute ventilation, blood lactate concentration, and perceived exertion) for 5-min of running at a speed equivalent to 80% of individual maximal oxygen uptake speed were assessed before, immediately after, and 1-4 days after a 30-min downhill run (-15%). Neuromuscular function was compromised (P < 0.05) following downhill running with no differences between the groups, except for IPT, which was more affected in the RR individuals compared with the X allele carriers immediately (-24.9 ± 6.9% vs. -16.3 ± 6.5%, respectively) and 4 days (-16.6 ± 14.9% vs. -4.2 ± 9.5%, respectively) post-downhill running. EIMD manifested similarly for both the groups except for serum CK activity, which was greater for RR (398 ± 120 and 452 ± 126 U L-1 at 2 and 4 days following downhill running, respectively) compared with the X allele carriers (273 ± 121 and 352 ± 114 U L-1 at the same time points). RE was compromised following downhill running (16.7 ± 8.3% and 11 ± 7.5% increases in oxygen uptake immediately following downhill running for the RR and X allele carriers, respectively) with no difference between the groups. We conclude that although RR individuals appear to be more susceptible to EIMD following downhill running, this does not extend to the changes in RE.

Influence of sports background on the bouncing mechanism of running.

During running, the mechanical energy of the centre of mass of the body (COM) oscillates throughout the step like a spring-mass system, where part of its mechanical energy is stored during negative phases to be released during the following positive phases. This storage-release of energy improves muscle-tendon efficiency, which is related to lower-limb stiffness. This study explores the effect of sports background on the bouncing mechanism, by examining differences in stiffness and step spatiotemporal parameters between swimmers and football athletes. All athletes performed three consecutive running bouts on an instrumented treadmill at three different speeds (3.9, 4.4 and 5.0 m·s-1). The ground reaction forces were recorded. Vertical stiffness and step spatiotemporal parameters were analysed and compared using a two-way ANOVA. Vertical stiffness of football players was on average 21.0 ± 1.1% higher than swimmers. The modification of step spatiotemporal parameters also suggests a more elastic rebound by increasing the stretch of tendons relative to muscle within muscle-tendon units in football players. Compared to swimmers, they (1) decrease the effective contact time by 9.7 ± 2.4% and (2) decrease the duration of the push by 15.0 ± 6.4%, suggesting that background training adaptations influence spring-mass behaviour during running.

Physiological Predictors of Maximal Incremental Running Performance.

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 α = 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.

Consumption of An Anthocyanin-Rich Antioxidant Juice Accelerates Recovery of Running Economy and Indirect Markers of Exercise-Induced Muscle Damage Following Downhill Running.

This study examined the effects of anthocyanin-rich antioxidant juice (AJ) on the recovery of exercise-induced muscle damage (EIMD) and the running economy (RE) following downhill running (DHR). Thirty healthy young men were randomly divided into two blinded groups and consumed either AJ or placebo (PLA) for nine days (240 mL twice-a-day). On day 5, the participants from both groups ran downhill (-15%) for 30 min at 70% of their maximal oxygen uptake (VO2max) speeds. The changes in RE (oxygen uptake (VO2) and perceived effort (PE) during 5-min runs at 80%VO2max) and EIMD (isometric peak torque (IPT), muscle soreness (SOR) and serum creatine kinase activity (CK)) were compared over time and between the groups on the 4 days following DHR. VO2 and PE increased (p < 0.05) immediately following DHR for both groups and remained elevated for PLA until 48h post-DHR while fully recovering 24 h post-DHR for AJ. SOR was greater (p < 0.05) for PLA throughout the study. CK increased for both groups and was greater (p < 0.05) for PLA at 96 h post-DHR. IPT decreased for both groups but recovered faster for AJ (72 h) compared to PLA (no full recovery). AJ accelerated recovery of RE and EIMD and should be used in specific contexts, but not chronically.

Effects of a 4-week high-intensity interval training on pacing during 5-km running trial

This study analyzed the influence of a 4-week high-intensity interval training on the pacing strategy adopted by runners during a 5-km running trial. Sixteen male recreational long-distance runners were randomly assigned to a control group (CON, n=8) or a high-intensity interval training group (HIIT, n=8). The HIIT group performed high-intensity interval-training twice per week, while the CON group maintained their regular training program. Before and after the training period, the runners performed an incremental exercise test to exhaustion to measure the onset of blood lactate accumulation, maximal oxygen uptake (VO2max), and peak treadmill speed (PTS). A submaximal constant-speed test to measure the running economy (RE) and a 5-km running trial on an outdoor track to establish pacing strategy and performance were also done. During the 5-km running trial, the rating of perceived exertion (RPE) and time to cover the 5-km trial (T5) were registered. After the training period, there were significant improvements in the HIIT group of ∼7 and 5% for RE (P=0.012) and PTS (P=0.019), respectively. There was no significant difference between the groups for VO2max (P=0.495) or onset of blood lactate accumulation (P=0.101). No difference was found in the parameters measured during the 5-km trial before the training period between HIIT and CON (P>0.05). These findings suggest that 4 weeks of HIIT can improve some traditional physiological variables related to endurance performance (RE and PTS), but it does not alter the perception of effort, pacing strategy, or overall performance during a 5-km running trial.

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

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.

Estratégia de corrida em média e longa distância: como ocorrem os ajustes de velocidade ao longo da prova? / La estratégia de la carrera del media y larga distancia: como se producen los ajustes de velocidad a lo largo de la carrera? / Pacing strategy in middle and long distance running: how are velocities adjusted during the race?

A estratégia de corrida tem sido apontada como um fator decisivo para o sucesso do atleta. Durante corridas de média e longa duração, a melhor estratégia será aquela capaz de poupar as "reservas fisiológicas", permitindo ao atleta terminar a prova no menor tempo possível. O controle da estratégia de corrida parece ser influenciado por fatores fisiológicos e psicológicos, que são ajustados constantemente durante a corrida, permitindo ao atleta determinar a intensidade do esforço. Diversas variáveis fisiológicas têm sido relacionadas aos ajustes da estratégia de corrida, no entanto, os resultados observados são controversos e foco de grandes discussões. Assim, a presente revisão tem como objetivo propiciar aos profissionais do esporte um melhor entendimento de questões como: 1) quais as melhores estratégias adotadas para determinado tipo de prova?; 2) como os ajustes da estratégia de corrida são realizados?; e 3) quais variáveis fisiológicas influenciam no controle da estratégia de corrida?.

The pacing strategy has been shown as an important factor for athletes' performance. During middle and long distance races, the best pacing strategy will be able to maintain a "physiological reserve" and to allow the athlete to run in his/her best time. It should be mentioned that the pacing strategy can be altered by physiological and psychological factors. These factors are frequently adjusted throughout the race and they will be used to control runner's pace. Several physiological variables have been suggested to have an important role in pacing strategy control. However, there is still controversial findings among the studies. Thus, the aim of this review was provide a better understanding about some questions such as: 1) what is the best pacing strategy for a specific race?; 2) how pacing strategy is controlled by the runner?; and 3) how physiological variables can induce changes on pacing strategy?.

La estrategia de la carrera ha sido identificada como un factor decisivo para el éxito del atleta. Durante las carreras de médio y largo plazo, la mejor estrategia es aquella que puede salvar a la "reserva fisiológica" que permite al atleta terminar la carrera en el menor tiempo posible. El control de la estrategia de carrera parece estar influenciada por algunos factores fisiológicos y psicológicos, que se ajustan com frecuencia durante la carrera, lo que permite al atleta determinar la intensidad del esfuerzo. Muchas de las variables fisiológicas se han relacionadas con los ajustes de la estrategia de carrera, sin embargo, los resultados observados son controvertidos y foco de muchos debates. Por lo tanto, esta revisión tiene como objetivo proporcionar a los profesionales del deporte una mejor comprensión de temas tales como: 1) cuáles son las mejores estrategias adoptadas para un tipo particular de prubea?; 2) cómo los ajustes de estrategia de prubea se hacen?; y 3) las variables fisiológicas que influyen en el control de la estrategia de carrera?.