Pacing in Long-Distance Running: Sex and Age Differences in 10-km Race and Marathon

Pacing in ultra-marathon running: the Western States 100-mile endurance run 2006-2023

Pacing has been investigated in different running races, including ultra-marathons. We have, however, little knowledge about pacing in ultra-trail running. To date, no study has investigated pacing in one of the most iconic ultra-trail running races, the 'Western States 100-Mile Endurance Run' (WSER), which covers 160 km (100 miles) and includes significant elevation changes (6000 vertical meters uphill and 7500 vertical meters downhill). Therefore, the aim of the study was to investigate pacing for successful finishers in WSER regarding gender, age, and performance level. Official results and split times for the WSER were obtained from the race website, including elevation data from 3837 runners, with 3068 men (80%) and 769 women (20%) competing between 2006 and 2023. The mean race speed was calculated for each participant, as well as the average mean checkpoint speed for each of the 18 race checkpoints (17 aid stations and finish point). The percentage of change in checkpoint speed (CCS) in relation to the average race speed was calculated. CCS was calculated for each of the 18 checkpoints to evaluate each runner's pacing strategy. The average change in checkpoint speed (ACCS) of each participant was calculated as a mean of the 18 CCSs. Eight age groups were formed. Since there were very few runners younger than 25 and older than 65 years, these age groups were merged into < 30 and 60 > groups, respectively. Four performance groups were formed by four quartiles, each consisting of 25% of the total sample separately for men and women. Pacing shows great variability between checkpoints in both men and women, mainly influenced by elevation. Although the race profile is mostly downhill, it appears that the pacing trend is towards positive pacing. The differences between men and women were mainly at the beginning of the race (men start faster) and towards the end (men slow down more). Men have more pacing variability than women, with significant differences in the youngest age group, as well as the 40-44 and 50-54 age groups. In addition, younger men have more variability in pace compared to older men. There are no significant differences in age groups in women. Finally, the slowest and fastest ultra runners had less pacing variability than medium level runners. Pacing in WSER-runners shows great variability between checkpoints in both men and women. Pacing is positive and highly influenced by elevation. Men start faster than women, and men slow down more than women. Pacing differs in male but not in female age group runners. The slowest and fastest ultra runners had less pacing variability than medium level runners.

Fractional utilization of the 10-minute treadmill test velocity in running performance

This study aimed to evaluate the applicability of the 10-minute submaximal treadmill test (T10 test), a self-paced test, in determining critical speed (CS) and predicting running performance. Specifically, we sought to identify the percentage of T10 velocity (vT10) that runners performed in official distance races, and to compare physiological and performance indicators between sexes. 60 recreational runners (n=34 males and n=26 females) underwent a maximum incremental test, the novel T10 test, and ran 1200-m and 2400-m on the track. Runners self-reported their best performance times. Generalized Linear Model was used to compare running performances between sexes. For both males and females, the %vT10 in 5 km, 10 km, and half-marathon races occurred at 107.5% and 106.5%, 99.9% and 100.8%, and 92.6% and 97.1%, respectively. There was no interaction effect (p=0.520) and no main effect of sex (p=0.443). There was a main effect of distance (p<0.001), indicating that %vT10 in the 5km race differed from that found in the 10 km race (p=0.012), as well as in the half-marathon (p<0.001). Our findings suggest that %vT10 values can be used to determine pace in recreational endurance runners for race distances regardless of sex.

The fastest 24-hour ultramarathoners are from Eastern Europe

Ultramarathon running is of increasing popularity, where the time-limited 24-hour run is one of the most popular events. Although we have a high scientific knowledge about different topics for this specific race format, we do not know where the best 24-hour runners originate from and where the fastest races are held. The purpose of the present study was to investigate the origin of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed based on the athlete´s age, gender, country of origin and the country where the race takes place. Model explainability tools were used to investigate how each independent variable would influence the predicted running speed. A sample of 171,358 race records from 63,514 unique runners from 73 countries participating in 24-hour races held in 57 countries between 1807 and 2022 was analyzed. Most of the athletes originated from the USA, France, Germany, Great Britain, Italy, Japan, Russia, Australia, Austria, and Canada. Tunisian athletes achieved the fastest average running speed, followed by runners from Russia, Latvia, Lithuania, Island, Croatia, Slovenia, and Israel. Regarding the country of the event, the ranking looks quite similar to the participation by the athlete, suggesting a high correlation between the country of origin and the country of the event. The fastest 24-hour races are recorded in Israel, Romania, Korea, the Netherlands, Russia, and Taiwan. On average, men were 0.4 km/h faster than women, and the fastest runners belonged to age groups 35-39, 40-44, and 45-49 years. In summary, the 24-hour race format is spread over the world, and the fastest athletes mainly originate from Eastern Europe, while the fastest races were organized in European and Asian countries.

A preliminary exploration of the regression equation for performance in amateur half-marathon runners: a perspective based on respiratory muscle function

This document presents a study on the relationship between physical characteristics, respiratory muscle capacity, and performance in amateur half-marathon runners. The aim of this study was to establish a preliminary predictive model to provide insights into training and health management for runners. Participants were recruited from the 2023 Beijing Olympic Forest Park Half-Marathon, comprising 233 individuals. Personal information including age, gender, height, weight, and other relevant factors were collected, and standardized testing methods were used to measure various parameters. Correlation analysis revealed significant associations between gender, height, weight, maximum expiratory pressure, maximal inspiratory pressure, and half-marathon performance. Several regression equations were developed to estimate the performance of amateur marathon runners, with a focus on gender, weight, maximum expiratory pressure, and height as predictive factors. The study found that respiratory muscle training can delay muscle fatigue and improve athletic performance. Evaluating the level of respiratory muscle capacity in marathon athletes is crucial for defining the potential speed limitations and achieving optimal performance. The information from this study can assist amateur runners in optimizing their training methods and maintaining their physical wellbeing.

Running Variability in Marathon-Evaluation of the Pacing Variables

Background and Objectives: Pacing analyses for increasingly popular long-distance running disciplines have been in researchers' spotlight for several years. In particular, assessing pacing variability in long-distance running was hardly achievable since runners must repeat long-running trials for several days. Potential solutions for these problems could be multi-stage long-distance running disciplines. Therefore, this study aimed to assess the long-distance running variability as well as the reliability, validity, and sensitivity of the variables often used for pacing analyses. Materials and Methods: This study collected the split times and finish times for 20 participants (17 men and three women; mean age 55.5 years ± 9.5 years) who completed the multiday marathon running race (five marathons in 5 days), held as part of the Bretzel Ultra Tri in Colmar, France, in 2021. Seven commonly used pacing variables were subsequently calculated: Coefficient of variation (CV), Change in mean speed (CS), Change in first lap speed (CSF), Absolute change in mean speed (ACS), Pace range (PR), Mid-race split (MRS), and First 32 km-10 km split (32-10). Results: Multi-stage marathon running showed low variability between days (Intraclass correlation coefficient (ICC) > 0.920), while only the CV, ACS, and PR variables proved to have moderate to good reliability (0.732 < ICC < 0.785). The same variables were also valid (r > 0.908), and sensitive enough to discern between runners of different performance levels (p < 0.05). Conclusions: Researchers and practitioners who aim to explore pacing in long-distance running should routinely utilize ACS, CV, and PR variables in their analyses. Other examined variables, CS, CSF, MRS, and 32-10, should be used cautiously. Future studies might try to confirm these results using different multi-stage event's data as well as by expanding sensitivity analysis to age and gender differences.

Prediction of Marathon Performance using Artificial Intelligence

Although studies used machine learning algorithms to predict performances in sports activities, none, to the best of our knowledge, have used and validated two artificial intelligence techniques: artificial neural network (ANN) and k-nearest neighbor (KNN) in the running discipline of marathon and compared the accuracy or precision of the predicted performances. Official French rankings for the 10-km road and marathon events in 2019 were scrutinized over a dataset of 820 athletes (aged 21, having run 10 km and a marathon in the same year that was run slower, etc.). For the KNN and ANN the same inputs (10-km race time, body mass index, age and sex) were used to solve a linear regression problem to estimate the marathon race time. No difference was found between the actual and predicted marathon performances for either method (p>0,05). All predicted performances were significantly correlated with the actual ones, with very high correlation coefficients (r>0,90; p<0,001). KNN outperformed ANN with a mean absolute error of 2,4 vs 5,6%. The study confirms the validity of both algorithms, with better accuracy for KNN in predicting marathon performance. Consequently, the predictions from these artificial intelligence methods may be used in training programs and competitions.

Pacing and packing behavior in elite and world record performances at Berlin marathon

The aim of this study was to compare pacing and packing behaviors between sex and performance level at elite Berlin marathon races. Official electronic split and finishing times from 279 (149 male and 130 female) marathon performances, including 5 male world records, were obtained from eleven Berlin marathon races held from 2008 to 2018, and from two previous world records and the second world all-time fastest performance also achieved at that same Berlin course. Male performances displaying an even pacing behavior were significantly faster than those adopting a positive behavior (p < 0.001; d = 0.75). Male world records were characterized by even profiles with fast endspurts, being especially remarkable at world all-time two fastest performances which were assisted by the use of a new shoe technology. Female marathon runners decreased their speed less than men during the second half marathon and especially from the 35^th km onwards (p < 0.001; 0.51 ≤ d≤0.55). The latest race stages were usually run individually in both sexes. Significant pace differences between performance groups at every race segment were found in women (p < 0.01; 1.0 ≤ d≤2.0), who also covered an important part of the race alone. Prior to participation in meet marathon races such as Berlin marathon, elite runners should select the group that they will join during the race according to their current performance level as a preassigned pace set by a pacemaker will be adopted. Therefore, they could follow an even rather than positive pacing behavior which will allow them to achieve a more optimal performance.

Gender and Age Differences in Performance of Over 70,000 Chinese Finishers in the Half- and Full-Marathon Events

(1) Background: The aim of the present study was to examine the characteristics of over 70,000 long-distance finishers over the last four years in Chinese half- and full-marathon events; (2) Methods: The available data of all finishers (n = 73,485; women, n = 17,134; men, n = 56,351) who performed half- and full-marathon events in Hangzhou from 2016 to 2019 were further analyzed for the characteristics of gender, age and average running speed; (3) Results: The total men-to-women ratio was the lowest in the half-marathon event (1.86) and the highest in the full-marathon event (17.42). Faster running performance in males than in females and faster average running speed in short-distance runners were shown. Gender and race distance were observed to have the most significant effects on average running speed (p < 0.01). For both male and female finishers, the slowest running speed was shown in older age groups (p < 0.01) during the full marathon. Our results indicated that the gender difference in performance was attenuated in the longer race distances and older age groups; (4) Conclusions: Understanding the participation and performances across different running distances would provide insights into physiological and biomechanical characteristics for training protocols and sports gear development in different groups.

Consistency of pacing profile according to performance level in three different editions of the Chicago, London, and Tokyo marathons

Running pacing has become a focus of interest over recent years due to its relationship with performance, however, it is still unknown the consistency of each race in different editions. The aim of this study is to analyze the consistency of pacing profile in three consecutive editions of three marathon races. A database of 282,808 runners, compiled from three different races (Chicago, London, and Tokyo Marathon) and three editions (2017, 2018, and 2019) was analyzed. Participants were categorized according to their time performance in the marathon, every 30 min from 2:30 h to sub-6 h. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. The intraclass correlation coefficients (ICC) of relative speed at the different pacing section, taking into account the runner time categories, was excellent over the three marathon editions (ICC > 0.93). The artificial intelligence model showed an accuracy of 86.8% to classify the runners' data in three marathons, suggesting a consistency between editions with identifiable differences between races. In conclusion, although some differences have been observed between editions in certain sections and marathon runner categories, excellent consistency of the pacing profile was observed. The study of pacing profile in a specific marathon can, therefore, be helpful for runners, coaches and marathon organizers for planning the race and improving its organization.

Oxygen Saturation Behavior by Pulse Oximetry in Female Athletes: Breaking Myths

The myths surrounding women’s participation in sport have been reflected in respiratory physiology. This study aims to demonstrate that continuous monitoring of blood oxygen saturation during a maximal exercise test in female athletes is highly correlated with the determination of the second ventilatory threshold (VT2) or anaerobic threshold (AnT). The measurements were performed using a pulse oximeter during a maximum effort test on a treadmill on a population of 27 healthy female athletes. A common behavior of the oxygen saturation evolution during the incremental exercise test characterized by a decrease in saturation before the aerobic threshold (AeT) followed by a second significant drop was observed. Decreases in peripheral oxygen saturation during physical exertion have been related to the athlete’s physical fitness condition. However, this drop should not be a limiting factor in women’s physical performance. We found statistically significant correlations between the maximum oxygen uptake and the appearance of the ventilatory thresholds (VT1 and VT2), the desaturation time, the total test time, and between the desaturation time and the VT2. We observed a relationship between the desaturation time and the VT2 appearance. Indeed, a linear regression model between the desaturation time and the VT2 appearance can predict 80% of the values in our sample. Besides, we suggest that pulse oximetry is a simple, fairly accurate, and non-invasive technique for studying the physical condition of athletes who perform physical exertion.