Pacing during an ultramarathon running event in hilly terrain

Pacing strategies in marathons: A systematic review

Background

The pacing strategy embodies the tactical behavior of athletes in distributing their energy across different segments of a race; therefore, a quantitative analysis of pacing strategies in marathon races could deepen the understanding of both pacing behavior and physical capacity in marathon athletics.

Objective

The objective of this systematic review was to synthesize and characterize pacing strategies in marathon road races by exploring the categories of pacing strategies and the factors that influence these strategies during marathon events.

Methods

Preferred Reporting Items for Systematic Reviews guidelines were followed for systematic searches, appraisals, and syntheses of literature on this topic. Electronic databases such as Science Direct, SPORTDiscuss, PubMed, and Web of Science were searched up to July 2024. Records were eligible if they included pace performance measurements during competition, without experimental intervention that may influence their pace, in healthy, adult athletes at any level.

Results

A total of 39 studies were included in the review. Twenty-nine were observational studies, and 10 were experimental (randomized controlled trials). The assessment of article quality revealed an overall median NOS score of 8 (range 5-9). The included studies examined the pacing profiles of master athletes and finishers in half-marathon (n = 7, plus numbers compared to full marathon), full-marathon (n = 21), and ultramarathon (n = 11) road races. Considering that some studies refer to multiple pacing strategies, in general, 5 studies (∼13 %) reported even pacing, 3 (∼8 %) reported parabolic pacing, 7 (∼18 %) reported negative pacing, and 30 (∼77 %) reported positive pacing during marathon competitions. Gender, age, performance, pack, and physiological and psychological factors influence pacing strategies.

Conclusion

This study synthesized pacing performance in marathons and highlighted the significance of examining pacing strategies in these events, offering valuable insights for coaches and athletes. Several key findings were highlighted: (1) pacing profiles and pacing ranges were identified as the primary indicators of pacing strategies; (2) the pacing strategy was found to be dynamic, with the most substantial effects attributed to gender and distance; and (3) three distinct types of pacing strategies for marathons were classified: positive, negative, and even pacing. These findings advance the understanding of marathon pacing strategies by shedding light on the factors that influence athletes' pacing decisions and behaviors. Additionally, these findings offer practical benefits, aiding athletes in making well-informed tactical choices and developing effective pace plans to enhance marathon performance. However, due to the complex nature of marathon racing, further research is required to explore additional factors that might impact pacing strategies. A better grasp of optimal pacing strategies will foster progress in this area and serve as a basis for future research and advancements.

Pacing Variation in Multistage Ultramarathons: Internet-Based Cross-Sectional Study

BACKGROUND

Ultramarathon running is the most popular ultraendurance competition in terms of the number of races and runners competing annually worldwide; however, no study has compared pacing and performance over a long period.

OBJECTIVE

This study analyzes the pacing of successful finishers and nonfinishers in multistage ultramarathons worldwide.

METHODS

A total of 4079 athletes (men=3288; women=791) competing in 99 multistage ultramarathon events from 1983 to 2021 were analyzed, including the number of participants, age, gender, rank, and running speed of successful finishers.

RESULTS

The results showed a significant increase in the number of events (n=338) and a significant increase in the number of finishers and nonfinishers (n=5575) in the ultramarathons worldwide during this period. The general linear models (GLMs) of pacing variation showed nonsignificant effects for gender (F1,36.2=2.5; P=.127; ηp2=0.063) and age group (F10,10=0.6; P=.798; ηp2=0.367), but it showed a significant interaction (gender × age) effect (F10,2689=2.3; P=.008; ηp2=0.009). Post hoc analyses showed that men have a higher pacing variation than women in the under 30 years (U30), U35, U45, and U50 groups. Additionally, the fastest women's age group (U35) had the lowest pacing variation. The GLM of pacing variation by gender and event distance showed significant effects for both gender (F1,3=18.5; P<.001; ηp2=0.007) and distance (F2,3=20.1; P<.001; ηp2=0.015). Post hoc analyses showed a growing pacing variation with increasing race distance for both men and women. In addition, men had a higher variation in long events. Furthermore, there was a significant main effect for both genders (F1,3=33.7; P<.001; ηp2=0.012) and rank (F1,3=136.6; P<.001; ηp2=0.048) on performance, with men being faster than women. Pacing varied greatly due to gender (F1,3=4.0; P=.047; ηp2=0.001), with a lower (ie, more even) pacing variation for male athletes in the top 3 finishers. Male nonfinishers showed a higher performance than female nonfinishers (F1,1340=25.6; P<.001), and no difference was identified for pacing variation (F1,789=1.5; P=.228) based on gender. In addition, a weak but significant correlation (r=-0.130; P<.001) was identified between the average running speed and pacing variation for both female and male nonfinishers.

CONCLUSIONS

In summary, multistage ultramarathon competitions showed an increasing number of competitors and a higher performance challenge. Men have a higher pacing (ie, less even) variation than women, especially observed in longer events. A higher pacing variation was associated with lower performance for men, women, and nonfinishers.

Variable Pacing Is Associated with Performance during the OCC® Ultra-Trail du Mont-Blanc® (2017-2021)

The current evidence suggests that pacing may not be affected by performance level or sex in trail-running races as may occur in road running races. However, the previous studies included races of >100 km. Therefore, we aimed to verify the influence of performance level and sex on pacing in the last four (2017, 2018, 2019, and 2021) editions of a shorter (56.3 km) ultra-trail running race (i.e., Orsières-Champex-Chamonix; OCC®) that maintained the same race profile. The mean finishing time for the 5656 participants was 10 h 20 min 33 s ± 2 h 01 min 19 s. Pacing variability (CV%) was higher in high-level participants, thus showing a greater ability to adapt their pace to the race profile than low-level runners. Males also had a higher pacing variability than females although the effect sizes were small. Based on the current findings, we may recommend for non-elite OCC® participants to adapt their pace to the race profile with a slower pace during uphills and a faster pace during downhills. Further studies including participants' experience are necessary to confirm the effectiveness of this suggestion in trail-running races of variable distances.

The Effects of Sex, Age and Performance Level on Pacing in Ultra-Marathon Runners in the ‘Spartathlon’

Background

Pacing has been investigated in different kinds of ultra-marathon races, but not in one of the toughest ultra-marathons in the world, the ‘Spartathlon’.

Objective

The aim of the present study was to analyse the pacing of female and male finishers competing in the ‘Spartathlon’ in regards to their age and performance groups.

Methods

A total of 2598 runners (2255 men and 343 women) finishing ‘Spartathlon’ between 2011 and 2019 were analysed. We selected 10 checkpoints with split times corresponding to important race sections. Average running speed was calculated for each participant and the average checkpoint running speed for each of the 10 race checkpoints. Furthermore, to assess the pacing strategy of each runner, the percentage of change in checkpoint speed (CCS) in relation to the average race speed was calculated (for each of 10 checkpoints). Finally, the average change in checkpoint speed (ACCS) was calculated for each participant as a mean of the 10 CCSs.

Results

Both women and men slowed down through the first 7 checkpoints but increased running speed towards the end of the race (reverse J-shaped pacing). Men showed a significantly greater CCS in the first and second checkpoint (p < 0.01 and p < 0.05, respectively), whereas women showed a more significant change in CCS in the last checkpoint (p < 0.05). Furthermore, age and sex showed no effect on ACCS, whereas ACCS differed between the performance groups. In particular, the slowest and the fastest runners showed a more minor change in ACCS than the two medium groups of both men and women (p < 0.01).

Conclusions

In summary, successful finishers in ‘Spartathlon’ showed a reverse J-shaped pacing curve with a decrease in running speed from the start to the 7th checkpoint and an increase in running speed thereafter. This strategy was most probably due to the profile of the race course. Men showed a more significant change in checkpoint speed in the first two checkpoints, whereas women showed a more substantial change in the last checkpoint. Age and sex did not affect average checkpoint speed, whereas this speed was different between the different performance groups. The slowest and the fastest runners showed fewer changes in average checkpoint speed than the two medium groups in men and women.

Downhill Sections Are Crucial for Performance in Trail Running Ultramarathons-A Pacing Strategy Analysis

Trail running is an increasingly popular discipline, especially over long-distance races (>42.195 km). Pacing strategy, i.e., how athletes modulate running speed for managing their energies during a race, appears to have a significant impact on overall performance. The aims of this study were to investigate whether performance level, terrain (i.e., uphill or downhill) and race stage affect pacing strategy and whether any interactions between these factors are evident. Race data from four race courses, with multiple editions (total races = 16), were retrieved from their respective events websites. A linear mixed effect model was applied to the full dataset, as well as to two subgroups of the top 10 male and female finishers, to assess potential differences in pacing strategy (i.e., investigated in terms of relative speed). Better finishers (i.e., athletes ranking in the best positions) tend to run downhill sections at higher relative speeds and uphill sections at lower relative speeds than slower counterparts (p < 0.001). In the later race stages, the relative speed decrease is larger in downhill sections than in uphill ones (p < 0.001) and in downhill sections, slower finishers perform systematically worse than faster ones, but the performance difference (i.e., between slower and faster finishers) becomes significantly larger in the later race stages (p < 0.001). Among elite athletes, no difference in pacing strategy between faster and slower finishers was found (p > 0.05). Both men (p < 0.001) and women (p < 0.001), in the later race stages, slow down more in downhill sections than in uphill ones. Moreover, elite women tend to slow down more than men (p < 0.001) in the later race stages, regardless of the terrain, in contrast to previous studies focusing on road ultramarathons. In conclusion, running downhill sections at higher relative speeds, most likely due to less accentuated fatigue effects, as well as minimizing performance decrease in the later race stages in downhill sections, appears to be a hallmark of the better finishers.

Pacing and Body Weight Changes During a Mountain Ultramarathon: Sex Differences and Performance

The study was aimed at comparing pacing adopted by males and females in a 107-km mountain ultramarathon and assessing whether pacing-related variables were associated with intracompetition body weight changes and performance. Forty-seven athletes (29 males; 18 females) were submitted to a cardiopulmonary exercise test before the race. Athletes were also weighted before the start of the race, at three midpoints (33 km, 66 km and 84 km) and after the race. Pacing was analyzed using absolute and relative speeds and accelerometry-derived sedentary time spent during the race. Results showed that females spent less sedentary time (4.72 ± 2.91 vs. 2.62 ± 2.14%; p = 0.035; d = 0.83) and displayed a smaller body weight loss (3.01 ± 1.96 vs. 4.37 ± 1.77%; p = 0.048; d = 0.77) than males. No significant sex differences were revealed for speed variability, absolute and relative speed. In addition, finishing time was correlated with: speed variability (r = 0.45; p = 0.010), index of pacing (r = -0.63; p < 0.001) and sedentary time (r = 0.64; p < 0.001). Meanwhile, intracompetition body weight changes were related with both the absolute and relative speed in the first and the last race section. These results suggest that females, as compared with males, take advantage of shorter time breaks at aid stations. Moreover, performing a more even pacing pattern may be positively associated with performance in mountain ultramarathons. Finally, intracompetition body weight changes in those races should be considered in conjunction with running speed fluctuations.

The Impact of Sex and Performance Level on Pacing Behavior in a 24-h Ultramarathon

Purpose: We analyzed the impact of sex, performance level and substantial speed reductions (SSR) on pacing in the VI Rio 24-h Marines Ultramarathon. This will provide insights into the importance of minimizing speed variations in relation to optimal pacing in endurance events.

Methods: Runners (30 males and 21 females), classified as high- (HP) and low-performance (LP) ran the race while having their time recorded every 400 m. The pacing was analyzed as the first 10% (initial epoch), the following 80% (intermediate epoch) and the last 10% of the race (final epoch). The time percentage spent at speeds <3.5 km·h⁻¹ (SSR), 3.5 to 5.9 km·h⁻¹ (walking speed), 6.0 to 8.0 km·h⁻¹ (walk-to-running transition speed) and > 8.0 km·h⁻¹ (running speed) was calculated.

Results: Runners showed a reverse J-shaped pacing (P < 0.001) regardless of sex and performance level, although male (P < 0.004) and HP runners (P < 0.001) have preserved a higher mean speed throughout the race. Male and HP runners spent more time at running speed (P < 0.001) and less time at SSR (P < 0.001) than female and LP runners. Total distance was inversely correlated with the number of SSR and speed CV in male (r = −0.47 and r = −0.64, respectively) and female (r = −0.61 and r = −0.47, respectively).

Conclusion: Male, HP runners showed less SSR, conserving a higher mean speed with less variation throughout the race. Results suggest that conservative pacing strategies, with lower speeds in the beginning and higher speeds toward the end, may be the most adequate for different endurance running disciplines. Results also show different competition dynamics between men and women, which warrants further exploration in ultramarathons as well as other IAAF events.

Effect of marathon characteristics and runners' time category on pacing profile

This study aimed to analyse differences in pacing profiles in four marathon competitions and to explore that pacing per time category. A database of 91,493 runners gathered from 4 different races was analysed (Valencia, Chicago, London and Tokyo Marathon). Participants were categorized in accordance with their completion time. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. In the four marathons studied, the first 5 km differed widely, presenting London the highest relative speeds (5 km: CI95% London vs. Valencia [12.1, 13.6%], p < 0.001 and ES = 2.1; London vs. Chicago [5.5, 7.1%], p < 0.001 and ES = 1.1; London vs. Tokyo [15.2, 16.8%], p < 0.001 and ES = 2.3). Races did not differ at each section for high-performance runners (sub-2:30), but differences between races increased as the time category increases (e.g. 35 km and sub-3:00: CI95% London vs. Tokyo [-3.1, -1.8%], p < 0.001 and ES = 0.7; 35 km and sub-5:00: London vs. Tokyo [-9.8, -9.2%], p < 0.001 and ES = 1.3). The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons (e.g. CI95% London vs. Valencia [10.3, 10.8%], p < 0.001 and ES = 1.3). In conclusion, although race characteristics affect pacing, this effect was higher as the category time increases. Race pacing characteristics should be taken into consideration for runners and coaches choosing the race and working on pacing strategies, for researches to extrapolate or interpret results, or for race organizations to improve its pacing characteristics.Highlights The first 5 km differed widely on pacing profiles between the four marathons assessed.London had the highest relative speeds in the first 5 km.Race characteristics affect pacing, but this effect was higher as the category time increases.The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons.

Physiological factors determining downhill vs uphill running endurance performance

OBJECTIVES

Recent studies investigated the determinants of trail running performance (i.e., combining uphill (UR) and downhill running sections (DR)), while the possible specific physiological factors specifically determining UR vs DR performances (i.e., isolating UR and DR) remain presently unknown. This study aims to determine the cardiorespiratory responses to outdoor DR vs UR time-trial and explore the determinants of DR and UR performance in highly trained runners.

DESIGN

Randomized controlled trial.

METHODS

Ten male highly-trained endurance athletes completed 5-km DR and UR time-trials (average grade: ±8%) and were tested for maximal oxygen uptake, lower limb extensor maximal strength, local muscle endurance, leg musculotendinous stiffness, vertical jump ability, explosivity/agility and sprint velocity. Predictors of DR and UR performance were investigated using correlation and commonality regression analyses.

RESULTS

Running velocity was higher in DR vs UR time-trial (20.4±1.0 vs 12.0±0.5km·h^-1, p<0.05) with similar average heart rate (95±2% vs 94±2% maximal heart rate; p>0.05) despite lower average V̇O₂ (85±8% vs 89±7% V̇O_2max; p<0.05). Velocity at V̇O_2max (vV̇O_2max) body mass index (BMI) and maximal extensor strength were significant predictors of UR performance (r²=0.94) whereas vV̇O_2max, leg musculotendinous stiffness and maximal extensor strength were significant predictors of DR performance (r²=0.84).

CONCLUSIONS

Five-km UR and DR running performances are both well explained by three independent predictors. If two predictors are shared between UR and DR performances (vV̇O_2max and maximal strength), their relative contribution is different and, importantly, the third predictor appears very specific to the exercise modality (BMI for UR vs leg musculotendinous stiffness for DR).

The Natural Environmental Factors Influencing the Spatial Distribution of Marathon Event: A Case Study from China

PURPOSE

The purpose of this study was to investigate the influence of natural environmental factors on the spatial distribution of marathon events in China, and to identify the suitable natural environmental factors for the marathon events.

METHODS

Geographic information system (GIS) spatial analysis tools were used to perform coupling analysis, e.g. overlap, neighborhood, intersection and buffer for terrain, climate, air quality, mountains and water resources with 342 marathon events held in China in 2018.

RESULTS

The results indicate that the spatial distribution of marathon events in China is negatively correlated with the elevation of the terrain (plain > hill > plateau > mountain > basin); climate (subtropical monsoon climate > temperate monsoon climate > temperate continental climate > tropical monsoon climate > plateau alpine climate), air quality (level 3 > level 2 > level 4 > level 1). Results indicate that buffer zones can protect water resources: there are 24 items in the buffer zone of river 0.5 km and lake 1 km, 131 items in the buffer zone of river 3 km and lake 5 km, 191 items in the buffer zone of river 5 km and lake 10 km, 298 items in the buffer zone of river 10 km and lake 20 km. Results indicate for mountain range buffer: 13 items in the 20 km buffer and 39 items in the 50 km buffer.

CONCLUSIONS

Marathon events are more likely to be held on the third rung of China's topography where a city has a typical landform (plains, basins, hills, or mountain) with good climate and air quality. Meanwhile a city with water and mountain resources for recreational events such as cross-country or obstacle course are essential. The contribution of this study is to systematically and intuitively reflect the influence of natural environment factors on the distribution of marathon events in China, and to provide evidence for the medium and long-term planning of marathon events in China, the selection of venues for different types of marathon events and how to attract participants.

The Differences in Pacing Among Age Groups of Amateur Cross-Country Skiers Depend on Performance

Pacing strategies have mainly been investigated for runners, but little is known for cross-country skiers. The aim of the present study was to examine the effects of performance and age on pacing strategies in cross-country skiing. All finishers (women, n = 19,375; men, n = 86,190) in the 'Engadin Ski Marathon' (42 km) between 1998 and 2016 were analysed for the percentage change of speed at 10 km (Change A), 20 km (Change B) and 35 km (Change C). They were classified in performance groups according to quartiles of average race speed (Q1, Q2, Q3 and Q4) and in 5-year age groups (<20, 20-24, 25-29… 85-89 years). Men were faster than women by +14.3% (15.2 ± 4.0 vs. 13.3 ± 3.3 km/h; p < 0.001, η² = 0.215). A small impact of age group × performance group interaction on Change A was shown in women (p < 0.001, η² = 0.026) and men (p < 0.001, η² = 0.025), where Q1 augmented and Q4 attenuated the decrease in speed with aging. However, the impact of age group × performance group interaction on Change B and C was trivial (p = 0.002, η² ≤ 0.010). Based on these findings, it was concluded that the differences in pacing among age groups depended on the performance level. Thus, the coaches and fitness trainers working with cross-country skiers should advise their athletes to consider both age and performance.

Pacing Decision Making in Sport and the Effects of Interpersonal Competition: A Critical Review

An athlete's pacing strategy is widely recognised as an essential determinant for performance during individual events. Previous research focussed on the importance of internal bodily state feedback, revealed optimal pacing strategies in time-trial exercise, and explored concepts such as teleoanticipation and template formation. Recently, human-environment interactions have additionally been emphasized as a crucial determinant for pacing, yet how they affect pacing is not well understood. Therefore, this literature review focussed on exploring one of the most important human-environment interactions in sport competitions: the interaction among competitors. The existing literature regarding the regulation of exercise intensity and the effect of competition on pacing and performance is critically reviewed in this paper. The PubMed, CINAHL and Web of Science electronic databases were searched for studies about pacing in sports and (interpersonal) competition between January 2000 to October 2017, using the following combination of terms: (1) Sports AND (2) Pacing, resulting in 75 included papers. The behaviour of opponents was shown to be an essential determinant in the regulation of exercise intensity, based on both observational (N = 59) and experimental (N = 16) studies. However, adjustment in the pacing response related to other competitors appears to depend on the competitive situation and the current internal state of the athlete. The findings of this review emphasize the importance of what is happening around the athlete for the outcome of the decision-making process involved in pacing, and highlight the necessity to incorporate human-environment interactions into models that attempt to explain the regulation of exercise intensity in sports and exercise.

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.