Traditionally, the focus of many investigations has been centered only on overall running performance (13,17,21,24). However, it has been proposed in the last 20 years that success in long-distance events might be influenced by pacing strategy (1,10,14). During a 10-km running race, athletes usually adopt a pacing strategy with a speed distribution consisting of 3 distinct phases. The 10-km running race is characterized by a fast start (phase 1), followed by a period of slower speed during the middle of the race (phase 2), and a significant increase in running speed towards the end (phase 3) (28). Based on the rating of perceived exertion (RPE) responses during the race, some authors have suggested that this triphasic speed distribution profile (so-called “U-shaped” pacing strategy) could reflect a centrally regulated control system (10,25). It has been theorized that athletes monitor their perception of effort based on the biomechanical pattern of motion and muscular metabolism to minimize physiological strain and to prevent a premature exercise termination (29).
In addition to a theoretical perception of effort monitoring, it has been demonstrated that pacing strategy may be determined by some physiological variables related to endurance performance (4,12,15). Lima-Silva et al. (15) observed that runners with a higher running economy (RE) and a faster speed corresponding to onset of blood lactate accumulation were able to adopt a more aggressive U-shaped speed curve, employing faster speeds at the beginning (first 400 m), middle (400–9,600 m), and end (last 400 m) phases of a 10-km running race. Additionally, Hettinga et al. (12) reported that the endspurt during a 4,000-m cycling time trial was related to an elevated anaerobic energy production when compared with previous parts of the race. Taken together, these findings suggest that traditional physiological variables related to energetic cost and metabolite accumulation (i.e., H+) may also be important to choose an appropriate pacing strategy during long-distance events.
Over the past 3 decades, a wide number of studies have reported a close association between muscular parameters and endurance performance (2,19,27). Støren et al. (26) founded a significant increase in maximum dynamic strength (1 repetition maximum [1RM]) (∼33%) and time to exhaustion during high-intensity aerobic exercise (∼21%) after an 8-week strength training program in long-distance runners. In a more recent study, Mikkola et al. (19) also observed a significant increase in 1RM (∼4%) and peak treadmill speed (PTS) (∼3%) in long-distance runners after 8 weeks of strength training. It has been hypothesized that an increased stiffness of the body structures and greater elastic energy utilization after strength training may increase athlete's ability to sustain high speeds during a race (26). However, it is still unknown in which phase of a long-distance race, the maximum dynamic strength could be more relevant.
The knowledge regarding the predictors of speed distribution during a 10-km running time trial may contribute to determining the relative importance of specific variables to each phase of the race. This could enable greater efficacy in athletic performance prediction and training prescription. Although previous studies have proposed that perceptive (9) and physiological (4,15) parameters are relevant to the pacing strategy, it is unclear whether these parameters have similar contributions to the different phases of the race. Furthermore, previous models describing the factors related to pacing strategy are incomplete because the contribution of muscular parameters has not been taken into account. Therefore, the aim of this study was to determine the contribution of RPE, physiological (V[Combining Dot Above]O2max, respiratory compensation point [RCP], RE, and PTS), and muscular (1RM) parameters to the adopted pacing strategy by long-distance runners during a 10-km running time trial.
Experimental Approach to the Problem
Runners were required to visit the laboratory on 4 separate occasions, at least 72 hours apart, over a 3-week period. In the first session, anthropometric measurements (body mass and height) and a maximal incremental treadmill test were performed. In the second session, a submaximal constant speed test was performed to assess RE. Familiarization with the maximum strength test was conducted at the end of the first and second experimental sessions after a 30-minute rest. Maximum dynamic strength was assessed in the third session. In the fourth session, the runners performed a 10-km running time trial on an outdoor track. Participants were asked to refrain from any exhaustive or unaccustomed exercise and from taking nutritional supplements during the experimental period.
Thirty-three recreational long-distance runners from local sport clubs were initially invited to participate in this study. During data acquisition, 3 participants dropped out because of scheduling conflicts, whereas 2 participants dropped out because of the development of muscular injuries not associated with running training. Their data were not included in the statistical analyses. Thus, 28 male long-distance runners (age, 36 ± 8 years; body mass, 69.1 ± 4.7 kg; height, 174.4 ± 8.1 cm; weekly training volume, 65.6 ± 30 km) participated in this study. All participants competed regularly in 10-km running races at regional levels and had been training for the last 2 years without interruption. The subjects participated in local competitions, and their best performances in 10-km competitions ranged from 35 to 45 minutes. They performed only low-intensity continuous aerobic training (50–70% V[Combining Dot Above]O2max) and reported no previous strength or plyometric training experience. The participants were medication-free, nonsmokers, and had neither neuromuscular disorders nor cardiovascular dysfunctions. The participants received a verbal explanation about the possible benefits, risks, and discomfort associated with the study and signed a written informed consent before participation in the study. All methods and procedures were approved by the local Ethics Committee for Human Studies.
Maximal Incremental Treadmill Test
Participants performed a maximal incremental test on a motor-driven treadmill (model TK35; CEFISE, Nova Odessa, Brazil) to determine V[Combining Dot Above]O2max, the RCP, and PTS. After a 3-minute warm-up at 8 km·h−1, the speed was increased by 1 km·h−1 every minute until exhaustion. The slope was kept at 0% throughout the test. The participants received strong verbal encouragement to ensure attainment of maximal values. Oxygen uptake (V[Combining Dot Above]O2) was measured breath-by-breath using a gas analyzer (Cortex Metamax 3B; Cortex Biophysik, Leipzig, Germany) and subsequently averaged over 30-second intervals throughout the test. Before each test, the gas analyzer was calibrated using ambient air and gas of a known composition (12% O2 and 5% CO2). The turbine flowmeter was calibrated using a 3-liter syringe (Quinton Instruments, Seattle, WA, USA). Heart rate was monitored during the test with a heart rate transmitter (model S810; Polar Electro Oy, Kempele, Finland) coupled with the gas analyzer. Maximal heart rate (HRmax) was defined as the heart rate at the time in which exhaustion occurred. Blood samples (25 μL) were collected from the ear lobe immediately, 3, and 5 minutes after the completion of the test to determine peak blood lactate concentration ([La]peak). Lactate concentration was measured with an automatic blood lactate analyzer (model 1500; Yellow Springs Instruments, Ohio, USA).
V[Combining Dot Above]O2max was determined when at least 2 of the following criteria were met: an increase in V[Combining Dot Above]O2 of <2.1 ml·kg−1·min−1 between 2 consecutive stages, a respiratory exchange ratio >1.1, a blood lactate concentration >8.0 mmol·l−1, and a maximum heart rate within ±10 b·min−1 of the predicted maximal (i.e., 220-age) (13). The RCP was determined by 3 independent investigators as the point of a nonlinear increase in the VE/V[Combining Dot Above]CO2, a constant increase in the Ve/V[Combining Dot Above]O2, and the first decrease in the expiratory fraction of CO2 (18). The PTS was established as the highest speed obtained in the last stage maintained for at least 1 minute (21).
Participants performed constant speed running at 12 km·h−1 on a motor-driven treadmill (model TK35; CEFISE, Nova Odessa, Brazil) to determine RE (2). This intensity corresponded to ∼78% of the RCP. Before the constant speed running test, the participants performed a standardized 5-minute warm-up at 8 km·h−1 followed by 5 minutes of light stretching. Running economy was determined by measuring V[Combining Dot Above]O2 (Cortex Metamax 3B; Cortex Biophysik, Leipzig, Germany) while running for 10 minutes and calculated by averaging V[Combining Dot Above]O2 values during the last 30 seconds of the test (2).
Maximum Dynamic Strength
The 1RM tests were performed in a Smith machine (Hammer; Life Fitness, Chicago, IL, USA) to measure the maximum dynamic strength of the lower limbs. Body position and foot placement were individually determined with measuring tape fixed on both the bar and on the ground. In addition, an adjustable height wooden seat was placed behind the participants to keep the bar displacement and knee flexion angle constant during each squat repetition. Participants' settings on the Smith machine were recorded to guarantee the same positioning across familiarization and testing sessions. The 1RM squat test was performed according to the standard procedures (6). Briefly, participants performed a 5-minute warm-up run on a treadmill at 9 km·h−1 followed by light stretching exercises with the lower limbs and 2 light squat sets. In the first set, individuals performed 5 repetitions with 50% of the estimated 1RM. In the second set, individuals performed 3 repetitions with 70% of the estimated 1RM. A 3-minute interval was allowed between sets. After the second warm-up set, participants rested for 3 minutes and had up to 5 trials to achieve the 1RM load (e.g., maximum weight that could be lifted once with proper technique) with a 3-minute interval between attempts.
A 10-km Running Performance Test
Runners performed individually a 10-km running time trial on an outdoor 400-m track. They were instructed to maintain regular water consumption within 6 hours of testing, and water was provided ad libitum during the entire event. The runners performed a 10-minute warm-up consisting of a free-paced run followed by 5 minutes of light stretching. The participants were instructed to finish the race as quickly as possible, as in a competitive event. Verbal encouragement was provided during the entire event. However, runners were not advised of their lap splits. Speed was registered every 50 m through a global positioning system (GPS Forerunner 305; Garmin, Kansas City, OR, USA), and the average speed of the start (first 400 m), middle (400–9,600 m), and end (last 400 m) phases were calculated as previously reported (15). The fatigue index (FI) was expressed as the percentage decline from the highest speed (HSP) to the lowest speed (LSP) during the entire 10-km time trial (Equation 1). The running speed variability (RUSV) was determined as the coefficient of variation of the running speed measured every 50 m.
Before the test, the RPE scale was explained to each participant by the same investigator. The RPE was reported by participants every 400 m using the Borg's 15-point scale (5). Copies of this scale were laminated and reduced to 10 × 5 cm and affixed to the wrist of the dominant arm of the individuals. The RPE was expressed as the mean of the start (RPESTART, first 400 m), middle (RPEMIDDLE, 400–9,600 m), and end (RPEEND, last 400 m) phases. All the tests were performed between 7 and 8 AM, and the mean values of the ambient temperature and air relative humidity were 21 ± 1° C and 59 ± 4%, respectively.
Data normality was assessed through the Shapiro-Wilk test, and all variables showed a normal distribution. The results of the descriptive statistics were reported as mean values and SDs. Three separate stepwise multiple linear regression models were used to identify perceptual, physiological, and muscular parameters, which best explained the speed variance for each running phase of the 10-km running time trial (start, middle, or end phase). The parameters V[Combining Dot Above]O2max, PTS, RCP, RE, RPE, and 1RM were considered as independent variables, whereas average speed of the different phases (start, middle, and end phases) were considered as dependent variables. Repeated measures analysis of variance was used to compare the speed for each running phase (start, middle, and end phases) of the 10-km running time trial. Similarly, repeated measures analysis of variance was also used to compare RPE measured during the start, middle, and end phases of the 10-km running time trial. When a significant effect was found, the main effect was analyzed using the Bonferroni's correction for multiple comparisons. Pearson's product-moment coefficients were used to determine the relationship between FI and RUSV with the other measured variables. Statistical significance was set at p ≤ 0.05.
Table 1 shows the physiological variables measured during the maximal incremental treadmill and constant speed running tests. The mean time to complete the 10-km running time trial was 44 minutes 3 seconds ± 4 minutes 3 seconds. The speed-distance curve showed a classical U shape, while the RPE increased linearly throughout the test (Figure 1). Running speed was significantly greater in the first (p = 0.009) and the end (p = 0.042) phases than in the middle phase, but there was no difference between the first and end phases (p = 0.536). Rating of perceived exertion was statistically lower in the first phase compared with the middle (p = 0.044) and end (p = 0.040) phases. Rating of perceived exertion was also significantly lower in the middle phase compared with the end phase (p = 0.001).
The stepwise multiple regression model selected 5 independent variables (RPESTART, PTS, V[Combining Dot Above]O2max, 1RM, and RCP) that explained the speed variance during the 3 phases. Figure 2 shows a schematic representation of the contribution of each variable. The RPESTART was the only variable selected in the start phase (first 400 m) and explained 72% of the speed variance (p = 0.001) (Equation 2). Peak treadmill speed explained 52% of the speed variance during the middle phase (p = 0.001), whereas V[Combining Dot Above]O2max and 1RM accounted for additional 23% (p = 0.002) and 5% (p = 0.003) (Equation 3), respectively. In the end phase, 66% of the speed variance (p = 0.003) was explained only by PTS (Equation 4). No other variable was selected by the stepwise regression model.
where RPESTART = RPE measured during start phase, V[Combining Dot Above]O2max = maximal oxygen uptake, 1RM = maximum dynamic strength.
The mean values of the HSP, LSP, FI, and RUSV were 16.2 ± 2.0 km·h−1, 12.0 ± 1.2 km·h−1, 24.7 ± 8.1%, and 6.2 ± 1.2%, respectively. Figure 3 shows the running phase in which individual values of the HSP and LSP were discovered. Most of the participants (68%) reached the HSP at or near the start phase, while LSP was predominantly obtained during the middle phase (96%). In turn, FI and RUSV were both negatively correlated with RCP while positively correlated with RPESTART, respectively (Table 2). Both FI and RUSV were not significantly correlated with any other variables measured.
The purpose of this study was to determine the contributions of the perceptive, physiological, and muscular parameters on the pacing strategy adopted by long-distance runners during a 10-km running time trial. For this purpose, we used a stepwise multiple regression model to evaluate the contribution of each one of these parameters to the pacing variance during the different phases of the trial. The main results were that RPESTART alone accounted for the greatest variance of speed during the start phase, whereas physiological and muscular parameters predominantly explained the speed variance during the last 2 running phases.
Previous investigations had centered only on the overall athletic performance without determination of the predictors of each running phase (13,17,21,24). To the best of our knowledge, this is the first study providing experimental observations that perceptive, physiological, and muscular factors were associated with the speed distribution during a simulated competitive running event. Stepwise analysis revealed RPESTART as the best single predictor of performance during the start phase, explaining 72% of the total variance in the speed. This suggests the existence of an initial perceptive zone of speed control (Figure 2). It is well established that RPE responses during exercise are related to some physiological variables, such as ventilation and oxygen uptake (5). However, it is also known that during the transition from rest to onset of exercise, the time needed for the stabilization of these physiological variables is greater (3–6 minutes) than the time required to complete the first 400 m of the running race in this study (∼90 seconds) (30). As the internal clues are low during the transition from rest to the onset of exercise, RPESTART phase may not have been influenced by the processing of any physiological systems. Instead, psychological factors may be important determinants of running speed during the early stages of a running race.
Data from this study showed that PTS, V[Combining Dot Above]O2max, and 1RM were the variables selected by the stepwise regression model to best explain the speed variance during the middle phase. The importance of these variables to endurance performance has been described by others (2,13–15,17,19,22). Traditionally, V[Combining Dot Above]O2max has been considered as an upper limit for ATP resynthesis through oxidative phosphorylation (13,24), whereas PTS combines into a single factor neuromuscular characteristics and the anaerobic contribution of the exercised muscles (23). In turn, maximum dynamic strength has been related to the capacity to store and use elastic energy during the stride cycle (26). Thus, these findings suggest that the ability of PTS, V[Combining Dot Above]O2max, and 1RM to predict running performance during the middle phase may be related to ATP availability and efficiency of the stretch-shortening cycle of exercised muscles. This is in accordance with a previous suggestion that the peripheral locus of fatigue has an important role in pacing strategy determination (12).
In this study, PTS was selected as the best single predictor of performance during the end phase. The identification of the PTS as a good predictor of performance is in agreement with other investigations (15,17,21). Studies investigating the factors associated with running performance have centered on predictive variables that are makers of the oxidative system (13,14), although middle- and long-distance events also require substantial anaerobic energy production (7,12). It has been demonstrated that the intensity of the endspurt might be dependent of the anaerobic energy expenditure (7,12). Hettinga et al. (12) observed that acceleration during the last part of a 4,000-m cycling time trial was accompanied by an increased anaerobic energy production. Similarly, Corbett (7) reported a close relationship between anaerobic metabolism contribution and increased power output during the end phase of a 2,000-m cycling time trial. As mentioned, PTS is influenced not only by maximal aerobic power but also by anaerobic characteristics of the lower limbs (23). Thus, the association between PTS and the endspurt may be due its relationship with anaerobic metabolism.
The results from the current investigation complement a growing body of evidence demonstrating that pacing strategy during a 10-km running time trial is composed of 3 distinct phases (Figure 1) (4,15,28). Tucker et al. (28) reported that the first and final kilometers of the 5-km and 10-km running world records were faster than the intermediate phases, indicating a predominant U-shaped pacing strategy. Billat et al. (4) confirmed that U-shaped was the most common pacing strategy adopted by highly trained athletes during a 10-km running race. The comprehension of the mechanisms related to fatigue process remains an interesting topic of debate among sport scientists (9,12,16,17,25). It is interesting to note that many studies have used time trials to investigate the mechanisms related to the fatigue process (4,12,28). It is believed that there are more motivational exercises and simulate a more realistic scenario of competition when compared with constant workload tests (12,22). However, considering that fatigue is defined as the inability to maintain the speed or power output (11), our findings demonstrated that the athletes did not reach running speeds that were intense enough to induce the fatigue process. In fact, the results from the current investigation are in agreement with other studies demonstrating that the lowest running speed was observed during the middle phase (Figure 3) (15,28). Thus, caution should be exercised when interpreting the findings from studies that have used long-distance time trial tests to investigate the fatigue process.
The ability to sustain high speeds over long distances has been considered a relevant aspect for success in the endurance events (17). Thus, successful athletes have to run at a high percentage of their RCP to obtain a better performance in a race. Interestingly, our results revealed that the FI was correlated with the RPESTART, RUSV, and speed corresponding to RCP. In addition, it was found that HSP and LSP used for FI determination were mainly reached during the start and middle phase, respectively (Figure 3). These results suggest that relationship among FI, RPESTART, and RUSV may to be dependent on the neural and metabolic control during the start phase. It has been proposed that RPE may be the result of the efferent signals from increased motor unit recruitment (8). It is believed that efferent copies are sent directly from the motor to the sensory areas in the brain, contributing to the RPE response during exercise (16). Thus, it is plausible to suggest that the brain might interpret efferent signals from increased motor unit recruitment as the first cues for running speed adjustments. However, because peripheral disturbances were probably reduced during the start phase, this increased motor unit recruitment resulted in speeds above RCP during the first 400 m. As pH decreases during intensities above the RCP (20,24), the negative impact of acidosis on the contractile machinery during the start phase may be responsible for the reduced speed during the middle phase. Therefore, these findings suggest that the increased motor unit recruitment during the start phase and the peripheral tolerance to acidosis during the middle phase may be the main factors responsible by speed variation during long-distance events.
In conclusion, our results indicated that distinct psychophysiological factors were able to predict the performance during the 3 phases of a 10-km running time trial. The perceived exertion accounted for the greatest variance of speed during the start phase (phase 1), whereas physiological and muscular parameters explained the speed variance during the last 2 running phases (phases 2 and 3). This suggests that predictors of the performance have a transitional behavior from perceptive (start phase) to muscular and physiological factors (middle and end phases).
It has been proposed that success in long-distance events is influenced by pacing strategy (1,10,14). Thus, we were interested to analyze the contribution of perceptive, physiological, and muscular variables on pacing strategy during a 10-km time trial. Our results suggest that distinct factors are related to different running phases of the 10-km running race. These findings are highly relevant to athletic performance and could contribute to a better training prescription and pacing strategy decision. For example, it was observed that PTS, V[Combining Dot Above]O2max, and 1RM are the main contributors to running performance during the middle phase. Thus, coaches could use Equation 3 to predict the performance during the middle phase of a 10-km running race. The prediction of the maximal sustainable intensity of a specific running phase may help the athletes to avoid premature fatigue and consequently to determine an appropriated pacing strategy during a 10-km race. In addition, because strength training increases both 1RM and PTS in long-distance runners (3,19,26), an improvement on the running performance during the middle phase might be expected after strength training. However, further studies are necessary to confirm if strength training regimes could improve running performance during the middle phase of a 10-km running time trial.
This study was supported by São Paulo Research Foundation (FAPESP: 2011/10742-9). Mayara Damasceno and Leonardo Pasqua are supported by scholarships from São Paulo Research Foundation (FAPESP: 2011/02769-4 and FAPESP: 2010/13913-6, respectively).
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Keywords:Copyright © 2014 by the National Strength & Conditioning Association.
endurance performance; maximal oxygen uptake; respiratory compensation point; peak treadmill speed; maximum dynamic strength; rating of perceived exertion