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The effects of an enforced fast-start on long distance performance are controversial and seem to depend on the athlete's capacity to delay and tolerate metabolic disruption. The aim of this study was to investigate the effects of an enforced fast-start on 10-km running performance and the influence of the some physiological and performance variables on the ability to tolerate an enforced fast-start during the running. Fifteen moderately-trained runners performed two 10-km time-trials: free-pacing (FP-TT) and fast-start (FS-TT). During FS-TT, speed during the first kilometer was 6% higher than in FP-TT. Maximal oxygen uptake (VO2max), peak velocity (PV), velocity associated with VO2max (vVO2max), ventilatory threshold, and running economy (RE) at 10 km·h-1, 12 km·h-1 and FP-TT average velocity (AV-10 km) were individually determined. There were no differences between FP-TT and FS-TT performance (45:01 ± 4:08 vs 45:11 ± 4:46 min:s, respectively, p=0.4). We observed that eight participants improved (+2.2%) their performance and were classified as positive responders (PR) and seven decreased (-3.3%) performance and were classified as negative responders (NR). Running speed was significantly higher for PR between 6 km and 9.2 km (p<0.05) during FS-TT. In addition, PR presented higher PV (p=0.02) and vVO2max (p= 0.01) than NR, suggesting the PV and vVO2max might influence the ability to tolerate a fast-start strategy. In conclusion, there was an individual response to the enforced fast-start strategy during 10-km running, and those who improved performance also presented higher vVO2max and PV, suggesting a possible association between these variables and response to the strategy adopted.
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... This is congruent with the idea that runners are continuously evaluating their momentary capabilities and are making active decisions about pacing on a moment-to-moment basis, based both on pre-race expectations and on homeostatic disturbances. 3,9,10,14,[17][18][19][20][21][22][23][24][25] The process of falling behind is probably an unconscious part of decision making, in that the runner cannot keep up with the pace, either through a subtly unacceptable rate of RPE growth or affective decline. In a race as important as the Olympics, most runners are likely to start at the pace of the eventual leaders/winner, but gradually fall behind as their body realizes, even before their mind, that the pace is unrealistic for them. ...
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Introduction: In distance running, pacing is characterized by changes in speed, leading to runners dropping off the leader’s pace until a few remain to contest victory with a final sprint. Pacing behavior has been well studied over the last 30 years, but much remains unknown. It might be related to finishing position, finishing time, and dependent on critical speed (CS), a surrogate of physiologic capacity. We hypothesized a relationship between CS and the distance at which runners “fell behind” and “let go” from the leader or were “outsprinted” as contributors to performance. Methods: 100-m split times were obtained for athletes in the men’s 10,000-m at the 2008 Olympics (N = 35). Split times were individually compared with the winner at the point of “falling behind” (successive split times progressively slower than the winner), “letting go” (large increase in time for distance compared with winner), or “outsprinted” (falling behind despite active acceleration) despite being with the leader with 400 m remaining. Results: Race times ranged between 26:55 and 29:23 (world record = 26:17). There were 3 groups who fell behind at ∼1000 (n = 11), ∼6000 (n = 16), and ∼9000 m (n = 2); let go at ∼4000 (n = 10), ∼7000 (n = 14), and ∼9500 m (n = 5); or were outkicked (n = 6). There was a moderate correlation between CS and finishing position ( r = .82), individual mean pace ( r = .79), “fell behind” distance ( r = .77), and “let go” distance ( r = .79). D′ balance was correlated with performance in the last 400 m ( r = .87). Conclusions: Athletes displayed distinct patterns of falling behind and letting go. CS serves as a moderate predictor of performance and final placing. Final placing during the sprint is related to preservation of D′ balance.
... Although early studies focused on self-paced activity, [6][7][8] more recent studies have focused on head-to-head competition, [9][10][11] and particularly on the decision-making process relative to changes in pace. [12][13][14][15] Another approach has been the comparison of historical performances relative to the evolution of pacing strategy. These studies show that world record (WR) performance typically evolves via more even pacing across time, although the pattern of pacing within a performer is remarkably consistent. ...
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Purpose : This study determined the evolution of performance and pacing for each winner of the men’s Olympic 1500-m running track final from 1924 to 2020. Methods : Data were obtained from publicly available sources. When official splits were unavailable, times from sources such as YouTube were included and interpolated from video records. Final times, lap splits, and position in the peloton were included. The data are presented relative to 0 to 400 m, 400 to 800 m, 800 to 1200 m, and 1200 to 1500 m. Critical speed and D′ were calculated using athletes’ season’s best times. Results : Performance improved ∼25 seconds from 1924 to 2020, with most improvement (∼19 s) occurring in the first 10 finals. However, only 2 performances were world records, and only one runner won the event twice. Pacing evolved from a fast start–slow middle–fast finish pattern (reverse J-shaped) to a slower start with steady acceleration in the second half (J-shaped). The coefficient of variation for lap speeds ranged from 1.4% to 15.3%, consistent with a highly tactical pacing pattern. With few exceptions, the eventual winners were near the front throughout, although rarely in the leading position. There is evidence of a general increase in both critical speed and D′ that parallels performance. Conclusions : An evolution in the pacing pattern occurred across several “eras” in the history of Olympic 1500-m racing, consistent with better trained athletes and improved technology. There has been a consistent tactical approach of following opponents until the latter stages, and athletes should develop tactical flexibility, related to their critical speed and D′, in planning prerace strategy.
... Regarding the physiological influence on pacing behaviour, Bertuzzi, Lima-Silva (Bertuzzi et al., 2014) reported that peak treadmill speed (PTS) during an incremental test, maximal oxygen consumption (VO 2 max), and maximum dynamic strength explained 80% of the speed variance during the middle phase of a 10-km race and the PTS alone explained 66% of the final 400m speed. In addition, Do Carmo, Barroso (Do Carmo, Barroso, Renfree, Gil, & Tricoli, 2016) observed that after an induced fast start, the ability to maintain speed during a 10-km race was associated with higher PTS. ...
Article
The effects of plyometric training on middle- and long-distance running performances are well established. However, its influence on pacing behavior is still unclear. The aim of this study was to evaluate the effects of plyometric training on pacing behavior. Also, verify whether the adaptations induced by plyometric training 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 plyometric training (PT). PT held two weekly plyometric training sessions for eight weeks. Drop jump (DJ) performance, 10-km running performance, pacing behavior, RPE and affective feelings, VO2peak, ventilatory thresholds (VT1 and VT2), peak treadmill speed (PTS), and RE were measured. For group comparisons, a mixed model analysis for repeated measures, effect size (ES) and 90% confidence interval (CI90%) 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 to 0.85)) and RE (4.5%; p ≤ 0.05; ES = -0.52 ((-0.73 to -0.31)) without changes in pacing behavior. While PT was effective for improving DJ and RE, there is no evidence that pacing behavior, RPE or affective feelings are directly affected by these adaptations during a 10-km time-trial run.
... Because such a behaviour would allow that all cardiovascular, respiratory and locomotor subsystems involved in HRV regulation could just focus on their specific functional mechanisation at an earlier point of the race. In addition, the accelerated decline in DFA-alpha1 may also relate to a faster VO 2 response especially at the beginning of the race (de Aguiar et al., 2015;do Carmo et al., 2016;Murgatroyd et al., 2011). Therefore, DFA-alpha1 could function as a means of observing the VO 2 kinetic as well as a performance differentiating metric. ...
Article
The present study examines the influence of a 10 km race of runners with different performance levels on time-domain measures and non-linear dynamics of HRV. Twenty-two male recreational to elite runners performed a self-paced 10 km race on asphalt with flat profile. The participants were divided into two performance groups based on their 10 km total time with a split at 40 min (fTT: fast total times, sTT: slow total times). During the race (Begin, Mid-Point, End), heart rate and RR-intervals were recorded continuously. Besides HRV time-domain measurements, fractal correlation properties using short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA) were calculated. Mean total time from fTT was significant faster compared to sTT (35:14 ± 03:15 min:sec vs. 46:34 ± 05:46 min:sec). While RMSSD and SDNN diminished strongly during the race with no differences between groups, we observed significant lower values in DFA-alpha1 at Begin for fTT. In comparison of Begin vs. Mid-Point as well as Begin vs. End a significant decrease could be determined in DFA-alpha1 for sTT. The earlier loss of correlation properties during Begin in fTT implies a fastened alteration of cardiac autonomic regulation in order to match an all-out performance attractor for maximal endurance performance.
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This study intended to analyze the pacing profiles in 5,000 m and 10,000 m race, men and women, in Brazil Athletics Trophy. Thirty-six high-level runners, being 19 women, participated in this study. The races were filmed (iPhone 8 Plus). The device was positioned on the outside of the track and a cone on the inside edge of the finish line. This procedure allowed the acquisition of lap times. The plotting of the percentage of average speed for each lap of the 400 m within a race was used to analyze the pacing profile adopted. Differences between the laps were analyzed through one-way ANOVA for repeated measures. The significance level was established as p<0.05. In the women’s 5,000 m (1.200 m - 4.000 m; p=0.003-0.045) and 10,000 m (2.400 m - 8.400; p= 0.01-0.04), was employed the positive pacing profile. In men’s race, the varied profile was adopted in 5,000 m race (800m - 1.600m, p=0.001-0.019; 3.200m, p=0.00-0.02; last 200m, p= 0.027) and the constant during 10,000 m race (p= 0.98). In these competitions in which the most important is the finishing positions, opponents' actions can influence the tactical decision. Therefore, to succeed, the athletes should remain in the group and sprint in the race's final meters when the result is defined.
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Objectives: the aim of the present study was to verify the pacing strategy during a season of a Cross-Country Mountain Bike (XCO-MTB) and the effects of performance level, age and sex. Method: Overall, 802 paces in 4 age-sex categories were analyzed: male elite (EliteM; n = 272), female elite (EliteF; n = 170), male under-23 (U23M; n = 247) and female under-23 (U23F; n = 113). Races were divided into Initial Lap; middle one (Middle1); middle two (Middle2) and Final Lap. The athletes were divided into high performance (HP); intermediated performance (IP) and low performance (LP). The magnitude-based inference and the effect size were assessed to check the changes clinically important. Results: it was observed a similar fast-start strategy for all categories. Both HP EliteM and EliteF athletes showed higher speed in Final Lap than LP (EliteM - ES = 0.5; 90%CI -0.8 to -0.2; very likely and EliteF - ES = 1.0; 90%CI -1.4 to -0.6; almost certain). The U23F athletes showed higher speeds in Initial Lap than EliteF (ES = 0.21; 90%CI - 0.1 to 0.5; likely), however in Final Lap the speeds was lower in U23F (ES = 1; 90%CI -1.3 to -0.6; very likely). Conclusion: the fast-start strategy is typically used during a XCO-MTB race independently of performance level, age or sex. HP Elite athletes are able to maintain higher speeds in the Final Lap. U23F athletes used to do a more variable pacing strategy with more aggressive fast-start strategy and lower speed in Final Lap than EliteF.
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The aim of this study is to analyse the influence of performance level, age and gender on pacing during a 100-km ultramarathon. Results of a 100-km race incorporating the World Masters Championships were used to identify differences in relative speeds in each 10-km segment between participants finishing in the first, second, third and fourth quartiles of overall positions (Groups 1, 2, 3 and 4, respectively). Similar analyses were performed between the top and bottom 50% of finishers in each age category, as well as within male and female categories. Pacing varied between athletes achieving different absolute performance levels. Group 1 ran at significantly lower relative speeds than all other groups in the first three 10-km segments (all P < 0.01), and significantly higher relative speeds than Group 4 in the 6th and 10th (both P < 0.01), and Group 2 in the 8th (P = 0.04). Group 4 displayed significantly higher relative speeds than Group 2 and 3 in the first three segments (all P < 0.01). Overall strategies remained consistent across age categories, although a similar phenomenon was observed within each category whereby 'top' competitors displayed lower relative speeds than 'bottom' competitors in the early stages, but higher relative speeds in the later stages. Females showed lower relative starting speeds and higher finishing speeds than males. 'Top' and 'bottom' finishing males displayed differing strategies, but this was not the case within females. Although pacing remained consistent across age categories, it differed with level of performance within each, possibly suggesting strategies are anchored on direct competitors. Strategy differs between genders and differs depending on performance level achieved in males but not females.
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Purpose: The objective of this study is to examine risk taking and risk perception associations with perceived exertion, pacing, and performance in athletes. Methods: Two experiments were conducted in which risk perception was assessed using the domain-specific risk taking (DOSPERT) scale in 20 novice cyclists (experiment 1) and 32 experienced ultramarathon runners (experiment 2). In experiment 1, participants predicted their pace and then performed a 5-km maximum effort cycling time trial on a calibrated Kingcycle mounted bicycle. Split times and perceived exertion were recorded every kilometer. In experiment 2, each participant predicted their split times before running a 100-km ultramarathon. Split times and perceived exertion were recorded at seven checkpoints. In both experiments, higher and lower risk perception groups were created using median split of DOSPERT scores. Results: In experiment 1, pace during the first kilometer was faster among lower risk perceivers compared with higher risk perceivers (t(18) = 2.0, P = 0.03) and faster among higher risk takers compared with lower risk takers (t(18) = 2.2, P = 0.02). Actual pace was slower than predicted pace during the first kilometer in both the higher risk perceivers (t(9) = -4.2, P = 0.001) and lower risk perceivers (t(9) = -1.8, P = 0.049). In experiment 2, pace during the first 36 km was faster among lower risk perceivers compared with higher risk perceivers (t(16) = 2.0, P = 0.03). Irrespective of risk perception group, actual pace was slower than predicted pace during the first 18 km (t(16) = 8.9, P < 0.001) and from 18 to 36 km (t(16) = 4.0, P < 0.001). In both experiments, there was no difference in performance between higher and lower risk perception groups. Conclusions: Initial pace is associated with an individual's perception of risk, with low perceptions of risk being associated with a faster starting pace. Large differences between predicted and actual pace suggest that the performance template lacks accuracy, perhaps indicating greater reliance on momentary pacing decisions rather than preplanned strategy.
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