Age, sex, and finish time as determinants of pacing in the marathon

The effect of sex and performance level on pacing in cross-country skiers: Vasaloppet 2004-2017

BACKGROUND

Pacing, defined as percentage changes of speed between successive splits, has been extensively studied in running and cycling endurance sports; however, less information about the trends in change of speed during cross-country (XC) ski racing is available. Therefore, the aim of the present study was to examine the effect of performance (quartiles of race time (Q), with Q1 the fastest and Q4 the slowest) level on pacing in the Vasaloppet ski race, the largest XC skiing race in the world.

METHODS

For this purpose, we analyzed female (n = 19,465) and male (n = 164,454) finishers in the Vasaloppet ski race from 2004 to 2017 using a one-way (2 sexes) analysis of variance with repeated measures to examine percentage changes of speed between 2 successive splits. Overall, the race consisted of 8 splits.

RESULTS

The race speeds of Q1, Q2, Q3, and Q4 were 13.6 ± 1.8, 10.6 ± 0.5, 9.2 ± 0.3, and 8.1 ± 0.4 km/h, respectively, among females and 16.7 ± 1.7, 13.1 ± 0.7, 10.9 ± 0.6, and 8.9 ± 0.7 km/h, respectively, among males. The overall pacing strategy of finishers was variable. A small sex × split interaction on speed was observed (η ² = 0.016, p < 0.001), with speed difference between sexes ranging from 14.9% (Split 7) to 27.0% (Split 1) and larger changes in speed between 2 successive splits being shown for females (p < 0.001, η ² = 0.004). A large performance × split interaction on speed, with Q1 presenting the smallest changes of speed between splits, was shown for females (η ² = 0.149, p < 0.001) and males (η ² = 0.169, p < 0.001).

CONCLUSION

Male and fast XC skiers are more even pacers. Coaches and athletes should develop tailored sex- and performance-level pacing strategies; for instance, they should advise fast XC skiers to start fast and maintain their speed, rather than starting slowly and trying to make up time by going faster at times during the race.

Monitoring heart rates to evaluate pacing on a 75-km MUM

BACKGROUND

To examine pacing among twelve males on a 75-km mountain ultra marathon (MUM) and to determine whether pacing relates to final performance.

METHODS

Speed and heart rates (HR) were measured continuously using a HR monitor and a global position system device. An Index of Pacing (IP) was calculated by dividing the average race speed by the speed on the first race segment. In addition, percentage (%) of heart rate reserve (HRres), coefficient of variation (CV) in speed and in percentage of HRres were analyzed throughout the race.

RESULTS

Performance time was correlated with IP (r=-0.88, P<0.01), % of HRres (r=- 0.72, P<0.05), and CV in % of HRres (r=0.80, P<0.05), but not with CV in speed (r=-0.12, P=0.9). On the entire race, evolution of HR was not dependent on the elevation gain.

CONCLUSIONS

Tracking HR is a safer way to rate pacing than speed tracking on a hilly course.

Pacing strategies by age in marathon cross-country skiing

OBJECTIVES

Pacing strategies have mainly been investigated for runners, but little is known for cross-country skiers. The aim of the present study was to investigate whether differences in pacing strategies do exist between younger and older cross-country skiers competing in the 42 km 'Engadin Ski Marathon'.

METHODS

Pacing was studied in 105,565 cross-country skiers (classified in 5-year age groups) competing between 1998 and 2016 in this race by examining changes of mean section velocity in 10 km (Change A, i.e. 100×(velocity in the 10-20 km section - velocity in the 0-10 km section)/velocity in the 0-10 km section), 20 km (Change B) and 35 km (Change C).

RESULTS

A small sex×distance (i.e. Change A versus Change B versus Change C) interaction on change of velocity was shown (P < .001, η² = 0.016), with women showing a less even pacing than men. In women, there was a trivial main effect of age group on Change A (P < .001, η² = 0.008) with a smaller decrease in velocity in age group <20 (-7.4%) and larger decrease in velocity in age group 75-79 (-12.8%), and Change B (P = .006, η² = 0.004) with smaller increase in velocity in age group 75-79 (+30.6%) and larger increase in velocity in age group 40-44 (+37.7%), but not on Change C (P = .784, η² = 0.003). In men, a small main effect of age group on Change A was shown (P < .001, η² = 0.019), with a smaller decrease of velocity in age group <20 (-3.5%) and larger in age group 70-74 (-10.5%). Trivial main effects of age group on Change B (P < .001, η² = .002), with a smaller increase of velocity in age group 85-89 (+25.8%) and larger increase in age group 70-74 (+33.0%), and Change C (P < .001, η² = 0.003), with smaller decrease of velocity in age group 85-89 (-38.2%) and larger decrease in age group 80-84 (-41.0%), were found.

CONCLUSIONS

Based on these findings, it was concluded that men and young cross-country skiers had a more even pacing than women and older cross-country skiers, which was in contrast with previous findings in other endurance sports, suggesting that the sex- and age-related differences in pacing might be sport-dependent.

Effect of the Pacing Strategies on the Open-Water 10-km World Swimming Championships Performances

PURPOSE

To (1) compare the pacing strategies of different-level open-water swimmers during the 10-km race of the FINA 2015 Swimming World Championships and (2) relate these pacing strategies to the race performance.

METHODS

Final and intermediate split times, as well as intermediate race positions, from the 10-km race participants (69 men and 51 women) were collected from the public domain and were divided into 5 groups (G1-G5) depending on their finishing positions.

RESULTS

Medalists and finalists (G1 and G2, respectively) presented an even pacing profile with swimming velocities similar to those of the less successful swimmers (G3-G5) on the initial and middle stages of the race but a 1.5-3% increase in swimming velocity in the last quarter of the race. This acceleration toward the end of the race, or "end spurt," was largely related to the race performance and was not observed in the G3 and G4 (even-paced profile) or G5 (positive pacing profile) groups. Intermediate race positions and lap rankings were negatively related to finishing position, indicating a delayed positioning of the most successful swimmers at 25%, 50%, and 75% of race distance.

CONCLUSIONS

The adoption of a conservative starting strategy by open-water swimmers with a negative pacing profile and delayed partial positioning seems to increase the chances of overall race success, as it allows a fast end spurt that is closely related to successful finishing race positions.

Comparison of the Effects of Performance Level and Sex on Sprint Performance in the Biathlon World Cup

Biathlon is an Olympic sport combining cross-country skiing with the skating technique and rifle shooting. The sprint (7.5 km for women and 10 km for men) includes 2 shootings between 3 laps of skiing. The aims of the current study were to compare biathletes of different performance levels and sex on total race time and performance-determining factors of sprint races in the biathlon World Cup. The top-10 performers (G1-10) and results in ranks 21-30 (G21-30) in 47 sprint races during the 2011-12 to 2015-16 World Cup seasons were compared regarding total race time, course time, shooting time, range time, shooting performance (rate of hits), and penalty time. G21-30 men and women were on average 3-5% behind G1-10 in total race time, in which course time accounted for 59-65% of the overall performance difference, followed by 31-35% explained by penalty time. The remainder (ie, 4-6%) was explained by differences in shooting time and range time. The G1-10 women exhibited on average 12% slower speeds than the G1-10 men, and course time accounted for 93% of the total time difference of 13% between sexes. The average total hit rates were 92-93% among the G1-10 and 85% among the G21-30 in both sexes. In total, men shot on average 6 s faster than women. Course time is the most differentiating factor for overall biathlon performance between performance levels and sex in World Cup races. No sex difference in shooting performance was found.

Sex Differences in World-Record Performance: The Influence of Sport Discipline and Competition Duration

The current review summarizes scientific knowledge concerning sex differences in world-record performance and the influence of sport discipline and competition duration. In addition, the way that physiological factors relate to sex dimorphism is discussed. While cultural factors played a major role in the rapid improvement of performance of women relative to men up until the 1990s, sex differences between the world’s best athletes in most events have remained relatively stable at approximately 8–12%. The exceptions are events in which upper-body power is a major contributor, where this difference is more than 12%, and ultraendurance swimming, where the gap is now less than 5%. The physiological advantages in men include a larger body size with more skeletal-muscle mass, a lower percentage of body fat, and greater maximal delivery of anaerobic and aerobic energy. The greater strength and anaerobic capacity in men normally disappear when normalized for fat-free body mass, whereas the higher hemoglobin concentrations lead to 5–10% greater maximal oxygen uptake in men with such normalization. The higher percentage of muscle mass in the upper body of men results in a particularly large sex difference in power production during upper-body exercise. While the exercise efficiency of men and women is usually similar, women have a better capacity to metabolize fat and demonstrate better hydrodynamics and more even pacing, which may be advantageous, in particular during long-lasting swimming competitions.

Pacing in age group marathoners in the "New York City Marathon"

The aim of the study was to investigate how women and men age group runners pace during a large city marathon. We analysed changes in running speed by splits of 5 km in 20,283 women and 28,282 men age group runners competing in the 2015 edition of the "New York City Marathon". A moderate split×sex interaction on running speed (p < 0.001, η² = 0.108) was observed with men showing a larger decrease in speed from the fastest split (5-10 km) to the slowest one (35-40 km) than women (21.1 vs. 16.7%), and a different pattern was observed in the 25-30 km split (increase in women, decrease in men). A trivial split×age group interaction on speed was observed in women (p < 0.001, η² = 0.003) and men (p < 0.001, η² = 0.004). In summary, men and women of all age groups reduced running speed during the marathon with a final spurt in the last segment (i.e. 40-42.2 km).

Effect of age and performance on pacing of marathon runners

Pacing strategies in marathon runners have previously been examined, especially with regard to age and performance level separately. However, less information about the age × performance interaction on pacing in age-group runners exists. The aim of the present study was to examine whether runners with similar race time and at different age differ for pacing. Data (women, n=117,595; men, n=180,487) from the “New York City Marathon” between 2006 and 2016 were analyzed. A between–within subjects analysis of variance showed a large main effect of split on race speed (p<0.001, η²=0.538) with the fastest speed in the 5–10 km split and the slowest in the 35–40 km. A small sex × split interaction on race speed was found (p<0.001, η²=0.035) with men showing larger increase in speed at 5 km and women at 25 km and 40 km (end spurt). An age-group × performance group interaction on Δspeed was shown for both sexes at 5 km, 10 km, 15 km, 20 km, 25 km, 30 km, 35 km, and 40 km (p<0.001, 0.001≤η²≤0.004), where athletes in older age-groups presented a relatively more even pace compared with athletes in younger age-groups, a trend that was more remarkable in the relatively slow performance groups. So far, the present study is the first one to observe an age × performance interaction on pacing; ie, older runners pace differently (smaller changes) than younger runners with similar race time. These findings are of great practical interest for coaches working with marathon runners of different age, but similar race time.

Aging and Half-Ironman Performance

Background/Study Context: Previous research on triathlon performance analyzed age trends for the Top Ten or Top Five finishers in world championship or national races at Olympic, Half-Ironman, and Ironman distances. The findings indicated higher age declines and/or earlier onset of decline in swimming and running than cycling. However, the designs of those studies took no account of possible differences between cross-sectional and longitudinal trends (i.e., cohort differences versus age changes).

METHODS

This study analyzed performance times over the inaugural 5 years of the Half-Ironman world championship held in Clearwater, Florida, from 2006 to 2010. Only one previous study is known that examined age trends in performance for this triathlon distance. The data from the official race results showed 5549 age class competitors that provided 6541 sets of observations. Analyses by mixed linear modeling (MLM) partitioned the data to compare discrete and interactive cross-sectional and longitudinal trends for swimming, cycling, and running, respectively.

RESULTS

The findings showed an historical decrease in cycling and running but not swimming times. Performance times were lower by men than women, with the gender discrepancy higher in some older age classes. Comparable to earlier findings for the Half-Ironman triathlon, cross-sectional performance decline was apparent for all triathlon activities from an early cohort age (i.e., 35-39 years). Although longitudinal trend showed significant gains for swimming, running, and overall times, interactions between cohort age and age change showed longitudinal decline that began at a younger cohort age for running (35-39 years) than swimming (50-55 years), but the interaction was nonsignificant for cycling. These interactions add to the knowledge about cohort differences and age changes in triathlon performance.

CONCLUSIONS

Practical applications of the findings suggest that conservation of effort might explain the absence of longitudinal change in cycling performance at older cohort ages. The authors reason that increased effort in cycling might benefit overall times of older triathletes.

Declines in marathon performance: Sex differences in elite and recreational athletes

The first aim of this study was to determine the age group at which marathon performance declines in top male and female runners and to compare that to the runners of average ability. Another aim of this of this study was to examine the age-related yearly decline in marathon performance between age group winners and the average marathon finisher. Data from the New York (NYC), Boston, and Chicago marathons from 2001–2016 were analyzed. Age, sex, and location were used in multiple linear regression models to determine the rate of decline in marathon times. Winners of each age group were assessed in 5-year increments from 16 through 74 years old (n = 47 per age group). The fastest times were between 25–34 years old, with overall champion males at 28.3 years old, and overall champion females at 30.8 years old (p = 0.004). At 35 years of age up to 74 years of age, female age group winners had a faster yearly decline in marathon finishing times compared to male age group winners, irrespective of marathon location [women = (min:sec) 2:33 per year, n = 336; men = 2:06 per year, n = 373, p < 0.01]. The median times between each age group only slowed beginning at 50 years old, thereafter the decline was similar between both men and women (women = 2:36, n = 140; men = 2:57, n = 150, p = 0.11). The median times were fastest at Boston and similar between Chicago and NYC. In conclusion, the rate of decline at 35 years old up to 74 years old is roughly linear (adjusted r² = 0.88, p < 0.001) with female age group winners demonstrating 27 s per year greater decline per year compared to male age group winners.

Sex differences in pacing during 'Ultraman Hawaii'

Background

To date, little is known for pacing in ultra-endurance athletes competing in a non-stop event and in a multi-stage event, and especially, about pacing in a multi-stage event with different disciplines during the stages. Therefore, the aim of the present study was to examine the effect of age, sex and calendar year on triathlon performance and variation of performance by events (i.e., swimming, cycling 1, cycling 2 and running) in ‘Ultraman Hawaii’ held between 1983 and 2015.

Methods

Within each sex, participants were grouped in quartiles (i.e., Q1, Q2, Q3 and Q4) with Q1 being the fastest (i.e., lowest overall time) and Q4 the slowest (i.e., highest overall time). To compare performance among events (i.e., swimming, cycling 1, cycling 2 and running), race time in each event was converted in z score and this value was used for further analysis.

Results

A between-within subjects ANOVA showed a large sex × event (p = 0.015, η² = 0.014) and a medium performance group × event interaction (p = 0.001, η² = 0.012). No main effect of event on performance was observed (p = 0.174, η² = 0.007). With regard to the sex × event interaction, three female performance groups (i.e., Q2, Q3 and Q4) increased race time from swimming to cycling 1, whereas only one male performance group (Q4) revealed a similar trend. From cycling 1 to cycling 2, the two slower female groups (Q3 and Q4) and the slowest male group (Q4) increased raced time. In women, the fastest group decreased (i.e., improved) race time from swimming to cycling 1 and thereafter, maintained performance, whereas in men, the fastest group decreased race time till cycling 2 and increased it in the running.

Conclusion

In summary, women pace differently than men during ‘Ultraman Hawaii’ where the fastest women decreased performance on day 1 and could then maintain on day 2 and 3, whereas the fastest men worsened performance on day 1 and 2 but improved on day 3.

An empirical study of race times in recreational endurance runners

Background

Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment.

Methods

We examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. An Internet survey was used to collect data from recreational endurance runners (N = 2303). The cohort was split 2:1 into a training set and validation set to create models to predict race time.

Results

Sex, age, BMI and race training were associated with mean race velocity for all race distances. The difference in velocity between males and females decreased with increasing distance. Tempo runs were more strongly associated with velocity for shorter distances, while typical weekly training mileage and interval training had similar associations with velocity for all race distances. The commonly used Riegel formula for race time prediction was well-calibrated for races up to a half-marathon, but dramatically underestimated marathon time, giving times at least 10 min too fast for half of runners. We built two models to predict marathon time. The mean squared error for Riegel was 381 compared to 228 (model based on one prior race) and 208 (model based on two prior races).

Conclusions

Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing.

Electronic supplementary material

The online version of this article (doi:10.1186/s13102-016-0052-y) contains supplementary material, which is available to authorized users.

Pacing in age-group freestyle swimmers at The XV FINA World Masters Championships in Montreal 2014

Pacing strategies have been investigated for elite-standard freestyle swimmers, but little is known about pacing in age-group freestyle swimmers. We investigated changes in swimming time across distances in 4,481 women and men swimmers who competed in 100, 200, 400, and 800 m freestyle age groups from 25-29 years to 90-94 years in the FINA World Masters Championships 2014. In 100 to 800 m, there was a small lap×sex interaction (P < 0.001, 0.033 ≤ η² ≤ 0.045) whereby women had larger lap-to-lap changes in swimming time than men. From 100 to 800 m, there were moderate to large lap×age group interactions (P < 0.001, 0.054 ≤ η² ≤ 0.235), i.e., pacing patterns differed by age groups. There were small main effects of lap on time in 100, 200, 400 and 800 m freestyle events (P < 0.001, 0.033 ≤ η² ≤ 0.045). In summary, (i) the largest increase in swimming time occurred during the second lap and a decrease in time occurred during the last lap, except in the 100 m, and (ii) the effect of participants' sex on lap time indicated larger percentage changes of pacing in women than in men. These findings should help coaches to develop age- and event-tailored pacing strategies.

Fast men slow more than fast women in a 10 kilometer road race

Background. Previous studies have demonstrated that men are more likely than women to slow in the marathon (footrace). This study investigated whether the sex difference in pacing occurs for a shorter race distance. Materials & Methods. Data were acquired from the Bolder Boulder 10 km road race for the years 2008-2013, which encompassed 191,693 performances. There were two pacing measures, percentage change in pace of the first 3 miles relative to the final 3.2 miles and percentage change in pace of the first mile relative to the final 5.2 miles. Pacing was analyzed as a continuous variable and as two categorical variables, as follows: "maintain the pace," defined as slowing <5% and "marked slowing," defined as slowing ≥10%. Results. Among the fastest (men < 48:40; women < 55:27) and second fastest (men < 53:54; women < 60:28) sex-specific finishing time sextiles, men slowed significantly more than women with both pacing measures, but there were no consistently significant sex differences in pacing among the slower four sextiles. For the fastest sextile, the odds for women were 1.96 (first pacing measure) and 1.36 (second measure) times greater than men to maintain the pace. For the fastest sextile, the odds for women were 0.46 (first measure) and 0.65 (second measure) times that of men to exhibit marked slowing. Multiple regression indicated that being older was associated with lesser slowing, but the sex difference among faster runners persisted when age was controlled. Conclusions. There was a sex difference in pacing during a 10 km race where glycogen depletion is not typically relevant. These results support the hypothesis that the sex difference in pacing partly reflects a sex difference in decision making.

The influence of sex, age, and race experience on pacing profiles during the 90 km Vasaloppet ski race

The purpose of this study was to investigate pacing-profile differences during the 90 km Vasaloppet ski race related to the categories of sex, age, and race experience. Skiing times from eight sections (S1 to S8) were analyzed. For each of the three categories, 400 pairs of skiers were matched to have a finish time within 60 seconds, the same start group, and an assignment to the same group for the other two categories. Paired-samples Student’s t-tests were used to investigate sectional pacing-profile differences between the subgroups. Results showed that males skied faster in S2 (P=0.0042), S3 (P=0.0049), S4 (P=0.010), and S1–S4 (P<0.001), whereas females skied faster in S6 (P<0.001), S7 (P<0.001), S8 (P=0.0088), and S5–S8 (P<0.001). For the age category, old subjects (40 to 59 years) skied faster than young subjects (19 to 39 years) in S3 (P=0.0029), and for the other sections, there were no differences. Experienced subjects (≥4 Vasaloppet ski race completions) skied faster in S1 (P<0.001) and S1–S4 (P=0.0054); inexperienced skiers (<4 Vasaloppet ski race completions) had a shorter mean skiing time in S5–S8 (P=0.0063). In conclusion, females had a more even pacing profile than that of males with the same finish time, start group, age, and race experience. No clear age-related pacing-profile difference was identified for the matched subgroups. Moreover, experienced skiers skied faster in the first half whereas inexperienced skiers had higher skiing speeds during the second half of the race.

Optimal [Formula: see text] ratio for predicting 15 km performance among elite male cross-country skiers

The aim of this study was 1) to validate the 0.5 body-mass exponent for maximal. oxygen uptake [Formula: see text] as the optimal predictor of performance in a 15 km classical-technique skiing competition among elite male cross-country skiers and 2) to evaluate the influence of distance covered on the body-mass exponent for [Formula: see text] among elite male skiers. Twenty-four elite male skiers (age: 21.4±3.3 years [mean ± standard deviation]) completed an incremental treadmill roller-skiing test to determine their [Formula: see text]. Performance data were collected from a 15 km classical-technique cross-country skiing competition performed on a 5 km course. Power-function modeling (ie, an allometric scaling approach) was used to establish the optimal body-mass exponent for [Formula: see text] to predict the skiing performance. The optimal power-function models were found to be [Formula: see text] and [Formula: see text], which explained 69% and 81% of the variance in skiing speed, respectively. All the variables contributed to the models. Based on the validation results, it may be recommended that [Formula: see text] divided by the square root of body mass (mL · min(-1) · kg(-0.5)) should be used when elite male skiers' performance capability in 15 km classical-technique races is evaluated. Moreover, the body-mass exponent for [Formula: see text] was demonstrated to be influenced by the distance covered, indicating that heavier skiers have a more pronounced positive pacing profile (ie, race speed gradually decreasing throughout the race) compared to that of lighter skiers.

Men are more likely than women to slow in the marathon

Studies on nonelite distance runners suggest that men are more likely than women to slow their pace in a marathon.

PURPOSE

This study determined the reliability of the sex difference in pacing across many marathons and after adjusting women's performances by 12% to address men's greater maximal oxygen uptake and also incorporating information on racing experience.

METHODS

Data were acquired from 14 US marathons in 2011 and encompassed 91,929 performances. For 2929 runners, we obtained experience data from a race-aggregating Web site. We operationalized pace maintenance as the percentage change in pace observed in the second half of the marathon relative to the first half. Pace maintenance was analyzed as a continuous variable and as two categorical variables, as follows: "maintain the pace," defined as slowing <10%, and "marked slowing," defined as slowing ≥30%.

RESULTS

The mean change in pace was 15.6% and 11.7% for men and women, respectively (P < 0.0001). This sex difference was significant for all 14 marathons. The odds for women were 1.46 (95% confidence interval, 1.41-1.50; P < 0.0001) times higher than men to maintain the pace and 0.36 (95% confidence interval, 0.34-0.38; P < 0.0001) times that of men to exhibit marked slowing. Slower finishing times were associated with greater slowing, especially in men (interaction, P < 0.0001). However, the sex difference in pacing occurred across age and finishing time groups. Making the 12% adjustment to women's performances lessened the magnitude of the sex difference in pacing but not its occurrence. Although greater experience was associated with less slowing, controlling for the experience variables did not eliminate the sex difference in pacing.

CONCLUSIONS

The sex difference in pacing is robust. It may reflect sex differences in physiology, decision making, or both.

The influence of performance level, age and gender on pacing strategy during a 100-km ultramarathon

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.

Does the sex difference in competitiveness decrease in selective sub-populations? A test with intercollegiate distance runners

Sex differences in some preferences and motivations are well established, but it is unclear whether they persist in selective sub-populations, such as expert financial decision makers, top scientists, or elite athletes. We addressed this issue by studying competitiveness in 1,147 varsity intercollegiate distance runners. As expected, across all runners, men reported greater competitiveness with two previously validated instruments, greater competitiveness on a new elite competitiveness scale, and greater training volume, a known correlate of competitiveness. Among faster runners, the sex difference decreased for one measure of competitiveness but did not decrease for the two other competitiveness measures or either measure of training volume. Across NCAA athletic divisions (DI, DII, DIII), the sex difference did not decrease for any competitiveness or training measure. Further analyses showed that these sex differences could not be attributed to women suffering more injuries or facing greater childcare responsibilities. However, women did report greater commitment than men to their academic studies, suggesting a sex difference in priorities. Therefore, policies aiming to provide men and women with equal opportunities to flourish should acknowledge that sex differences in some kinds of preferences and motivation may persist even in selective sub-populations.

Pacing strategy in male elite and age group 100 km ultra-marathoners

Pacing strategy has been investigated in elite 100 km and elite 161 km (100 mile) ultra-marathoners, but not in age group ultra-marathoners. This study investigated changes in running speed over segments in male elite and age group 100 km ultra-marathoners with the assumption that running speed would decrease over segments with increasing age of the athlete. Running speed during segments in male elite and age group finishers for 5-year age groups (ie, 18-24 to 65-69 years) in the 100 km Lauf Biel in Switzerland was investigated during the 2000-2009 period. Average running speed over segment time station (TS) TS1-TS2 (56.1 km) was compared with running speed Start-TS1 (38 km) and Start-TS3 (76.7 km) and running speed TS2-TS3 was compared with running speed Start-Finish. For the top ten athletes in each edition, running speed decreased from 2000 to 2009 for TS1-TS2 and TS2-TS3 (P<0.0001) but not in TS3-Finish (P>0.05). During TS1-TS2, athletes were running at 98.0%±2.1% of the running speed of Start-TS1. In TS2-TS3, they were running at 94.6%±3.4% of the running speed of TS1-TS2. In TS3-Finish, they were running at 95.5%±3.8% of running speed in TS2-TS3. For age group athletes, running speed decreased in TS1-TS2 and TS2-TS3. In TS3-Finish, running speed remained unchanged with the exception of the age group 40-44 years for which running speed increased. Running speed showed the largest decrease in the age group 18-24 years. To summarize, the top ten athletes in each edition maintained their running speed in the last segment (TS3-Finish) although running speed decreased over the first two segments (TS1-TS2 and TS2-TS3). The best pacers were athletes in the age group 40-44 years, who were able to achieve negative pacing in the last segment (TS3-Finish) of the race. The negative pacing in the last segment (TS3-Finish) was likely due to environmental conditions, such as early dawn and the flat circuit in segment TS3-Finish of the race.

Effects of heat stress and sex on pacing in marathon runners

Recent research suggests that women tend to exhibit less of a precipitous decline in run velocity during the latter stages of a marathon than men when the covariates of age and run time are controlled for. The purpose of this study was to examine this sex effect with the added covariate of heat stress on pacing, defined as the mean velocity of the last 12.2 km divided by the mean velocity of the first 30 km. A secondary purpose of this investigation was to compare the pacing profiles of the elite men and women runners and the pacing profiles of the elite and nonelite runners. Subjects included 22,990 men and 13,233 women runners from the 2007 and 2009 Chicago marathons for which the mean ambient temperatures were 26.67° C and 2.77° C, respectively. Each 5-km split time was measured via an electronic chip worn on the participants' shoe. Multiple regression analysis indicated that age, sex, heat stress, and overall finish time (p < 0.01 for each) were simultaneous independent elements of pacing. Nonelite women were consistently better pacers than nonelite men in both marathons, and this sex difference was magnified from cold to warm race temperatures. No difference (p < 0.05) in pacing was found between elite men and women runners. Elite men and women had enhanced pacing over their nonelite counterparts. In hotter temperatures, coaches of novice runners should advise their athletes to implement a slower initial velocity to maintain or increase running velocity later in the race.

Do non-elite older runners slow down more than younger runners in a 100 km ultra-marathon?

Background

This study investigated changes in normalised running speed as a proxy for effort distribution over segments in male elite and age group 100 km ultra-marathoners with the assumption that older runners would slow down more than younger runners.

Methods

The annual ten fastest finishers (i.e. elite and age group runners) competing between 2000 and 2009 in the ‘100 km Lauf Biel’ were identified. Normalised average running speed (i.e. relative to segment 1 of the race corrected for gradient) was analysed as a proxy for pacing in elite and age group finishers. For each year, the ratio of the running speed from the final to the first segment for each age cohort was determined. These ratios were combined across years with the assumption that there were no ‘extreme’ wind events etc. which may have impacted the final relative to the first segment across years. The ratios between the age cohorts were compared using one-way ANOVA and Tukey’s post-hoc test. The ratios between elite and age group runners were investigated using one-way ANOVA with Dunnett’s multiple comparison post-hoc tests. The trend across age groups was investigated using simple regression analysis with age as the dependent variable.

Results

Normalised average running speed was different between age group 18–24 years and age groups 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59 and 65–69 years. Regression analysis showed no trend across age groups (r² = 0.003, p > 0.05).

Conclusion

To summarize, (i) athletes in age group 18–24 years were slower than athletes in most other age groups and (ii) there was no trend of slowing down for older athletes.

Pacing strategies during the swim, cycle and run disciplines of sprint, Olympic and half-Ironman triathlons

PURPOSE

This study investigated the influence of distance on self-selected pacing during the swim, cycle and run disciplines of sprint, Olympic and half-Ironman (HIM) distance triathlon races.

METHOD

Eight trained male triathletes performed the three individual races in <2 months. Participants' bikes were fitted with Schoberer Rad Meßtechnik to monitor speed, power output and heart rate during the cycle discipline. Global positioning system was worn to determine speed and heart rate during the swim and run disciplines.

RESULT

An even swim pacing strategy was adopted across all distances. A more stochastic pacing was observed during the HIM cycle [standard deviation of exposure variation analysis (EVASD) = 3.21 ± 0.61] when compared with the sprint cycle discipline (EVASD = 3.84 ± 0.44, p = 0.018). Only 20.9 ± 4.1 % of the cycling time was spent more than 10 % above the mean power output in the HIM, compared with 43.8 ± 2.9 % (p = 0.002) and 37.7 ± 11.1 % (p = 0.039) during the sprint and Olympic distance triathlons, respectively. Conversely, 13.6 ± 5.1 % of the cycling time was spent 5-10 % below the mean power output during the HIM, compared with 5.9 ± 1.2 % (p = 0.034) and 8.0 ± 5.1 % (p = 0.045) during the sprint and Olympic distance triathlons, respectively. A negative pacing strategy was adopted during the sprint distance run, compared with positive pacing strategy during the Olympic and HIM.

CONCLUSION

Results of this study suggest that pacing strategies during triathlon are highly influenced by distance and discipline, and highlight the importance of developing pacing strategies based on distance, strengths and individual fitness.

Factors influencing pacing in triathlon

Triathlon is a multisport event consisting of sequential swim, cycle, and run disciplines performed over a variety of distances. This complex and unique sport requires athletes to appropriately distribute their speed or energy expenditure (ie, pacing) within each discipline as well as over the entire event. As with most physical activity, the regulation of pacing in triathlon may be influenced by a multitude of intrinsic and extrinsic factors. The majority of current research focuses mainly on the Olympic distance, whilst much less literature is available on other triathlon distances such as the sprint, half-Ironman, and Ironman distances. Furthermore, little is understood regarding the specific physiological, environmental, and interdisciplinary effects on pacing. Therefore, this article discusses the pacing strategies observed in triathlon across different distances, and elucidates the possible factors influencing pacing within the three specific disciplines of a triathlon.

Relationship between age and elite marathon race time in world single age records from 5 to 93 years

BACKGROUND

The aims of the study were (i) to investigate the relationship between elite marathon race times and age in 1-year intervals by using the world single age records in marathon running from 5 to 93 years and (ii) to evaluate the sex difference in elite marathon running performance with advancing age.

METHODS

World single age records in marathon running in 1-year intervals for women and men were analysed regarding changes across age for both men and women using linear and non-linear regression analyses for each age for women and men.

RESULTS

The relationship between elite marathon race time and age was non-linear (i.e. polynomial regression 4(th) degree) for women and men. The curve was U-shaped where performance improved from 5 to ~20 years. From 5 years to ~15 years, boys and girls performed very similar. Between ~20 and ~35 years, performance was quite linear, but started to decrease at the age of ~35 years in a curvilinear manner with increasing age in both women and men. The sex difference increased non-linearly (i.e. polynomial regression 7(th) degree) from 5 to ~20 years, remained unchanged at ~20 min from ~20 to ~50 years and increased thereafter. The sex difference was lowest (7.5%, 10.5 min) at the age of 49 years.

CONCLUSION

Elite marathon race times improved from 5 to ~20 years, remained linear between ~20 and ~35 years, and started to increase at the age of ~35 years in a curvilinear manner with increasing age in both women and men. The sex difference in elite marathon race time increased non-linearly and was lowest at the age of ~49 years.

Prediction of half-marathon race time in recreational female and male runners

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r² = 0.42, adjusted r² = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) – 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r² = 0.68, adjusted r² = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) – 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the models.

The relationship between age and running time in elite marathoners is U-shaped

Several investigations have demonstrated that running performance gradually decreases with age by using runners >25 years grouped in 5-year age brackets. The aim of this study was to determine the relationship between race time in marathon and age in elite marathoners by including all ages and 1-year intervals. Running times of the top ten men and women at 1-year intervals (from 18 to 75 years) in the New York City marathon were analyzed for the 2010 and 2011 races. Gender differences in performance times were analyzed between 18 and 70 years of age. The relationship between running time and runner's age was U-shaped: the lowest race time was obtained at 27 years (149 ± 14 min) in men and at 29 years (169 ± 17 min) in women. Before this age (e.g., 27 years for men and 29 years for women), running time increased by 4.4 ± 4.0 % per year in men and 4.4 ± 4.3 % per year in women. From this age on, running time increased by 2.4 ± 8.1 % per year in men and 2.5 ± 9.9 % per year in women. The sex difference in running time remained stable at ~18.7 ± 3.1 % from 18 to 57 years of age. After this, sex difference progressively increased with advancing age. In summary, endurance runners obtained their best performance in the marathon at 27 years in men and 29 in women. Thus, elite marathon runners should program their long-term training to obtain maximal performance during their late 20s.

Running world cross-country championships: a unique model for pacing

The aim of this study was to describe the pacing distribution during 6 editions of the world cross-country championships.

METHODS

Data from the 768 male runners participating from 2007 to 2013 were considered for this study. Blocks of 10 participants according to final position (eg, 1st to 10th, 11 to 20th, etc) were considered.

RESULTS

Taking data from all editions together, the effect of years was found to be significant (F(5,266) = 3078.69, P < .001, ω² = 0.31), as well as the effect of blocks of runners by final position (F(4,266) = 957.62, P < .001, ω² = 0.08). A significant general decrease in speed by lap was also found (F(5,1330) = 2344.02, P < .001, ω² = 0.29). Post hoc analyses were conducted for every edition where several pacing patterns were found. All correlations between the lap times and the total time were significant. However, each lap might show different predicting capacity over the individual outcome.

DISCUSSION

Top athletes seem to display different strategies, which allow them to sustain an optimal speed and/or kick as needed during the critical moments and succeed. After the first group (block) of runners, subsequent blocks always displayed a positive pacing pattern (fast to slow speed). Consequently, a much more stable pacing pattern should be considered to maximize final position.

CONCLUSIONS

Top-10 finishers in the world cross-country championships tend to display a more even pace than the rest of the finishers, whose general behavior shows a positive (fast-to-slow) pattern.

Did recent world record marathon runners employ optimal pacing strategies?

We apply statistical analysis of high frequency (1 km) split data for the most recent two world-record marathon runs: Run 1 (2:03:59, 28 September 2008) and Run 2 (2:03:38, 25 September 2011). Based on studies in the endurance cycling literature, we develop two principles to approximate 'optimal' pacing in the field marathon. By utilising GPS and weather data, we test, and then de-trend, for each athlete's field response to gradient and headwind on course, recovering standardised proxies for power-based pacing traces. The resultant traces were analysed to ascertain if either runner followed optimal pacing principles; and characterise any deviations from optimality. Whereas gradient was insignificant, headwind was a significant factor in running speed variability for both runners, with Runner 2 targeting the (optimal) parallel variation principle, whilst Runner 1 did not. After adjusting for these responses, neither runner followed the (optimal) 'even' power pacing principle, with Runner 2's macro-pacing strategy fitting a sinusoidal oscillator with exponentially expanding envelope whilst Runner 1 followed a U-shaped, quadratic form. The study suggests that: (a) better pacing strategy could provide elite marathon runners with an economical pathway to significant performance improvements at world-record level; and (b) the data and analysis herein is consistent with a complex-adaptive model of power regulation.

An Analysis of Pacing Profiles of World-Class Racewalkers

Purpose:

The aim of this study was to describe the pacing profiles used by racewalkers competing in IAAF World Championships.

Methods:

The times for each 5-km segment were obtained for 225 men competing over 20 km, 214 women competing over 20 km, and 232 men competing over 50 km, of whom 49 did not finish. Athletes were grouped based on finishing position (for medalists) or finishing time.

Results:

Different pacing profiles were used by athletes grouped by finishing time, with 20-km medalists using negative pacing and those finishing within 5% of the winning time matching the medalists’ early pace but failing to maintain it. Lower-placed 20-km athletes tended to start more quickly relative to personal-best pace and experienced significant decreases in pace later. Across all competitions, the fastest finishers started the slowest relative to previous best performance. All 50-km athletes slowed toward the finish, but lower-placed finishers tended to decrease pace earlier (with up to 60% of the race remaining). After halfway in the 50-km, 8 of the 15 athletes who had a 5-km split more than 15% slower than the previous split dropped out.

Conclusions:

The negative pacing profile used by 20-km medalists required the ability to start fast and maintain this pace, and similarly paced training may be beneficial in race preparation. Over 50 km, the tactic of starting slower than personal-best pace was generally less risky; nonetheless, any chosen pacing strategy should be based on individual strengths.

Master runners dominate 24-h ultramarathons worldwide-a retrospective data analysis from 1998 to 2011

BACKGROUND

The aims of the present study were to examine (a) participation and performance trends and (b) the age of peak running performance in master athletes competing in 24-h ultra-marathons held worldwide between 1998 and 2011.

METHODS

Changes in both running speed and the age of peak running speed in 24-h master ultra-marathoners (39,664 finishers, including 8,013 women and 31,651 men) were analyzed.

RESULTS

The number of 24-h ultra-marathoners increased for both women and men across years (P < 0.01). The age of the annual fastest woman decreased from 48 years in 1998 to 35 years in 2011. The age of peaking running speed remained unchanged across time at 42.5 ± 5.2 years for the annual fastest men (P > 0.05). The age of the annual top ten women decreased from 42.6 ± 5.9 years (1998) to 40.1 ± 7.0 years (2011) (P < 0.01). For the annual top ten men, the age of peak running speed remained unchanged at 42 ± 2 years (P > 0.05). Running speed remained unchanged over time at 11.4 ± 0.4 km h-1 for the annual fastest men and 10.0 ± 0.2 km/h for the annual fastest women, respectively (P > 0.05). For the annual ten fastest women, running speed increased over time by 3.2% from 9.3 ± 0.3 to 9.6 ± 0.3 km/h (P < 0.01). Running speed of the annual top ten men remained unchanged at 10.8 ± 0.3 km/h (P > 0.05). Women in age groups 25-29 (r2 = 0.61, P < 0.01), 30-34 (r2 = 0.48, P < 0.01), 35-39 (r2 = 0.42, P = 0.01), 40-44 (r2 = 0.46, P < 0.01), 55-59 (r2 = 0.41, P = 0.03), and 60-64 (r2 = 0.57, P < 0.01) improved running speed; while women in age groups 45-49 and 50-54 maintained running speed (P > 0.05). Men improved running speed in age groups 25-29 (r2 = 0.48, P = 0.02), 45-49 (r2 = 0.34, P = 0.03), 50-54 (r2 = 0.50, P < 0.01), 55-59 (r2 = 0.70, P < 0.01), and 60-64 (r2 = 0.44, P = 0.03); while runners in age groups 30-34, 35-39, and 40-44 maintained running speed (P > 0.05).

CONCLUSIONS

Female and male age group runners improved running speed. Runners aged >40 years achieved the fastest running speeds. By definition, runners aged >35 are master runners. The definition of master runners aged >35 years needs to be questioned for ultra-marathoners competing in 24-h ultra-marathons.

Participation and performance trends in ultra-endurance running races under extreme conditions - 'Spartathlon' versus 'Badwater'

BACKGROUND

The aim of the present study was to compare the trends in participation, performance and age of finishers in 'Badwater' and 'Spartathlon' as two of the toughest ultramarathons in the world of more than 200 km of distance.

METHODS

Running speed and age of male and female finishers in Badwater and Spartathlon were analyzed from 2000 to 2012. Age of peak performance and sex difference in running speed were investigated during the studied period.

RESULTS

The number of female and male finishes increased in Badwater and Spartathlon. Women accounted on average for 21.5% ± 6.9% in Badwater and 10.8% ± 2.3% in Spartathlon. There was a significant increase in female participation in Badwater from 18.4% to 19.1% (p < 0.01) and in Spartathlon from 11.9% to 12.5% (p = 0.02). In men, the age of finishers was higher in Badwater (46.5 ± 9.3 years) compared to Spartathlon (44.8 ± 8.2 years) (p < 0.01). The age of female finishers of both races was similar with 43.0 ± 7.5 years in Badwater and 44.5 ± 7.8 years in Spartathlon (p > 0.05). Over the years, the age of the annual five fastest men decreased in Badwater from 42.4 ± 4.2 to 39.8 ± 5.7 years (p < 0.05). For women, the age remained unchanged at 42.3 ± 3.8 years in Badwater (p > 0.05). In Spartathlon, the age was unchanged at 39.7 ± 2.4 years for men and 44.6 ± 3.2 years for women (p > 0.05). In Badwater, women and men became faster over the years. The running speed increased from 7.9 ± 0.7 to 8.7 ± 0.6 km/h (p < 0.01) in men and from 5.4 ± 1.1 to 6.6 ± 0.5 km/h (p < 0.01) in women. The sex difference in running speed remained unchanged at 19.8% ± 4.8% (p > 0.05). In Spartathlon, the running speed was stable over time at 10.8 ± 0.7 km/h for men and 8.7 ± 0.5 km/h for women (p > 0.05). The sex difference remained unchanged at 19.6% ± 2.5% (p > 0.05).

CONCLUSIONS

These results suggest that for both Badwater and Spartathlon, (a) female participation increased, (b) the fastest finishers were approximately 40 to 45 years, and (c) the sex difference was at approximately 20%. Women will not outrun men in both Badwater and Spartathlon races. Master ultramarathoners can achieve a high level of performance in ultramarathons greater than 200 km under extreme conditions.

Analysis of participation and performance in athletes by age group in ultramarathons of more than 200 km in length

Background

Participation and performance trends for athletes by age group have been investigated for marathoners and ultramarathoners competing in races up to 161 km, but not for longer distances of more than 200 km.

Methods

Participation and performance trends in athletes by age group in the Badwater (217 km) and Spartathlon (246 km) races were compared from 2000 to 2012.

Results

The number of female and male finishers increased in both races across years (P < 0.05). The age of the annual five fastest men decreased in Badwater from 42.4 ± 4.2 years to 39.8 ± 5.7 years (r² = 0.33, P = 0.04). For women, the age remained unchanged at 42.3 ± 3.8 years in Badwater (P > 0.05). In Spartathlon, the age of the annual five fastest finishers was unchanged at 39.7 ± 2.4 years for men and 44.6 ± 3.2 years for women (P > 0.05). In Badwater, running speed increased in men from 7.9 ± 0.7 km/hour to 8.7 ± 0.6 km/hour (r² = 0.51, P < 0.01) and in women from 5.4 ± 1.1 km/hour to 6.6 ± 0.5 km/hour (r² = 0.61, P < 0.01). In Spartathlon, running speed remained unchanged at 10.8 ± 0.7 km/hour in men and 8.7 ± 0.5 km/hour in women (P > 0.05). In Badwater, the number of men in age groups 30–34 years (r² = 0.37, P = 0.03) and 40–44 years (r² = 0.75, P < 0.01) increased. In Spartathlon, the number of men increased in the age group 40–44 years (r² = 0.33, P = 0.04). Men in age groups 30–34 (r² = 0.64, P < 0.01), 35–39 (r² = 0.33, P = 0.04), 40–44 (r² = 0.34, P = 0.04), and 55–59 years (r² = 0.40, P = 0.02) improved running speed in Badwater. In Spartathlon, no change in running speed was observed.

Conclusion

The fastest finishers in ultramarathons more than 200 km in distance were 40–45 years old and have to be classified as “master runners” by definition. In contrast to reports of marathoners and ultramarathoners competing in races of 161 km in distance, the increase in participation and the improvement in performance by age group were less pronounced in ultramarathoners competing in races of more than 200 km.

Influence of different performance levels on pacing strategy during the Women's World Championship marathon race

PURPOSE

To analyze pacing strategies displayed by athletes achieving differing levels of performance during an elite-level marathon race.

METHODS

Competitors in the 2009 IAAF Women's Marathon Championship were split into groups 1, 2, 3, and 4 comprising the first, second, third, and fourth 25% of finishers, respectively. Final, intermediate, and personal-best (PB) times of finishers were converted to mean speeds, and relative speed (% of PB speed) was calculated for intermediate segments.

RESULTS

Mean PB speed decreased from groups 1 to 4, and speeds maintained in the race were 98.5% ± 1.8%, 97.4% ± 3.2%, 95.0% ± 3.1%, and 92.4% ± 4.4% of PB speed for groups 1-4 respectively. Group 1 was fastest in all segments, and differences in speed between groups increased throughout the race. Group 1 ran at lower relative speeds than other groups for the first two 5-km segments but higher relative speeds after 35 km. Significant differences (P < .01) in the percentage of PB speed maintained were observed between groups 1 and 4 and groups 2 and 4 in all segments after 20 km and groups 3 and 4 from 20 to 25 km and 30 to 35 km.

CONCLUSIONS

Group 1 athletes achieved better finishing times relative to their PB than athletes in other groups, who selected unsustainable initial speeds resulting in subsequent significant losses of speed. It is suggested that psychological factors specific to a major competitive event influenced decision making by athletes, and poor decisions resulted in final performances inferior to those expected based on PB times.

Can neuromuscular fatigue explain running strategies and performance in ultra-marathons?: the flush model

While the industrialized world adopts a largely sedentary lifestyle, ultra-marathon running races have become increasingly popular in the last few years in many countries. The ability to run long distances is also considered to have played a role in human evolution. This makes the issue of ultra-long distance physiology important. In the ability to run multiples of 10 km (up to 1000 km in one stage), fatigue resistance is critical. Fatigue is generally defined as strength loss (i.e. a decrease in maximal voluntary contraction [MVC]), which is known to be dependent on the type of exercise. Critical task variables include the intensity and duration of the activity, both of which are very specific to ultra-endurance sports. They also include the muscle groups involved and the type of muscle contraction, two variables that depend on the sport under consideration. The first part of this article focuses on the central and peripheral causes of the alterations to neuromuscular function that occur in ultra-marathon running. Neuromuscular function evaluation requires measurements of MVCs and maximal electrical/magnetic stimulations; these provide an insight into the factors in the CNS and the muscles implicated in fatigue. However, such measurements do not necessarily predict how muscle function may influence ultra-endurance running and whether this has an effect on speed regulation during a real competition (i.e. when pacing strategies are involved). In other words, the nature of the relationship between fatigue as measured using maximal contractions/stimulation and submaximal performance limitation/regulation is questionable. To investigate this issue, we are suggesting a holistic model in the second part of this article. This model can be applied to all endurance activities, but is specifically adapted to ultra-endurance running: the flush model. This model has the following four components: (i) the ball-cock (or buoy), which can be compared with the rate of perceived exertion, and can increase or decrease based on (ii) the filling rate and (iii) the water evacuated through the waste pipe, and (iv) a security reserve that allows the subject to prevent physiological damage. We are suggesting that central regulation is not only based on afferent signals arising from the muscles and peripheral organs, but is also dependent on peripheral fatigue and spinal/supraspinal inhibition (or disfacilitation) since these alterations imply a higher central drive for a given power output. This holistic model also explains how environmental conditions, sleep deprivation/mental fatigue, pain-killers or psychostimulants, cognitive or nutritional strategies may affect ultra-running performance.