What is the age for the fastest ultra-marathon performance in time-limited races from 6 h to 10 days?

Race course characteristics are the most important predictors in 48 h ultramarathon running

Ultra-marathon running - where races are held in distance-limited (50 km, 50 miles, 100 km, 100 miles, etc.), time-limited (6 h, 12 h, 24 h, 48 h, 72 h, etc.), and multi-stage races - is gaining in popularity. However, we have no knowledge of where the fastest 48-hour runners originate and where the fastest 48-hour races are held. This study tried to determine the origin of the fastest 48-hour runners and the predictor factors associated with 48-hour ultra-marathon performance, such as age, gender, event country, country of origin and race course specific characteristics. A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). Model explainability tools were then used to investigate how each independent variable would influence the predicted result. A sample of 16,233 race records from 7,075 unique runners originating from 60 different countries and participating in races held in 36 different countries between 1980 and 2022 was analyzed. Participation was spread across many countries, with USA, France, Germany, and Australia at the top of the participants' rankings. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. The XG Boost model showed that elevation of the course (flat course) and the running surface (track) were the variables that had a larger influence on the running speed. The country of origin of the athlete and the country where the event was hold were the most important features by the SHAP analysis, yielding the broader range of model outputs. Men were ~ 0.5 km/h faster than women. Most finishers were 45-49 years old, and runners in this age group achieved the fastest running speeds. In summary, elevation of the course (flat course) and the running surface (track) were the most important variables for fast 48-hour races, whilst the country of origin of the athlete and the country where the event was hold would lead to the broadest difference in the predicted running speed range. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. Any athlete intending to achieve a personal best performance in this race format can benefit from these findings by selecting the most appropriate race course.

Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model

Although the 50-mile ultramarathon is one of the most common race distances, it has received little scientific attention. The objective of this study was to assess how an athlete's age group, sex, nationality, and the race location, affect race speed. Utilizing a dataset with ultramarathon races from 1863 to 2022, a machine learning model based on the XGBoost algorithm was developed to predict the race speed based on the aforementioned variables. Model explainability tools, including model features relative importances and prediction distribution plots were then used to investigate how each feature affects the predicted race speed. The most important features, with respect to the predictive power of the XGBoost model, were the location of the race and the athlete's gender. The top 3 countries with the fastest predicted median race speeds were Slovenia, New Zealand, and Bulgaria for nationality and New Zealand, Croatia, and Serbia for the race location. The fastest median race speed was predicted for the age group 20-24 years, but a marked age-related performance decline only became apparent from the age group 40-44 years onward. Model predictions for male athletes were faster than for female athletes. This study offers insights into factors influencing race speed in 50-mile ultramarathons, which may be beneficial for athletes, coaches, and race organizers. The identification of nationalities and event countries with fast race speeds provides a foundation for further exploration in the field of ultramarathon events.

Running trends in Switzerland from 1999 to 2019: An exploratory observational study

BACKGROUND

Several single race events (5 km, 10 km, half-marathon, marathon, ultra-marathon) in different countries and different years have been analyzed in multiple studies, representing the rising interest in endurance-based activity and thus physical health. With focus on participation numbers, performance or sex difference, many single study results were obtained. The running trends in a whole country covering a longer period of time and several race distances are missing so far.

OBJECTIVES

The aim of this study is therefore to examine 5 km, 10 km, half-marathon, marathon and ultra-marathon races by age, sex, participation numbers and performance during two decades (1999-2019) for one country (Switzerland).

METHODS

In this exploratory observational study, we analyzed 1,172,836 finishers (370,517 women and 802,319 men) competing between 1999 and 2019 in 5 different race distances in Switzerland. We used publicly available data about the athletes and examined total finishing numbers, sex, age and performances (measured in m/s) via descriptive analyses and linear mixed models. Do-not-finishers were excluded.

RESULTS

The most frequented race was the half-marathon (33.1% of finishers), the less frequented was the ultra-marathon distance (8.5% of finishers). In most recent years, only the number of finishers in ultra-marathon, especially in trail runs increased. In total, there were more male finishers (68.4%) than female finishers (31.6%) and only in 5 km races, more women finished than men (55.3%). Men were faster than women and both sexes were running slower in all race distances across years. Athletes in 10 km races had the best performance within the five analyzed race distances. Median age increased with longer race distance and decreased in ultra-marathon in recent years.

CONCLUSION

In summary, finishing numbers especially in ultra-marathons increased with a focus on trail runs, female and male athletes had a declining performance across years in all race distances and men ran faster than women. Median age increased with longer race distance leading to more aged endurance-trained athletes. A downtrend in median age is found only in ultra-marathon in recent years. The results are important for athletes, race directors and coaches with regard to training schedules and the trend towards long distance races as well as for the medical attendance especially of older athletes, being more and more interested in endurance running.

Analysis of the 10-day ultra-marathon using a predictive XG boost model

OBJECTIVE

Ultra-marathon running races are held as distance-limited or time-limited events, ranging from 6 h to 10 days. Only a few runners compete in 10-day events, and so far, we have little knowledge about the athletes' origins, performance, and event characteristics. The aim of the present study was to investigate the origin and performance of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, country where the race takes place, the type of race and the kind of running surface. The model explainability tools were then used to investigate how each independent variable would influence the predicted running speed.

RESULTS

The model rated the origin of the athlete as the most important predictor, followed by age group, running on dirt path, gender, running on asphalt, and event location. Running on dirt path led to a significant reduction of running speed, while running on asphalt showed faster running speeds compared to other surfaces. Most athletes came from USA, followed by Russia, Germany, Ukraine, the Czech Republic, and Slovakia. Most of the runners competed in USA. The fastest 10-day runners were from Finland and Israel. The fastest 10-day races were held in Greece.

CONCLUSIONS

Most 10-day runners originated from USA, but the fastest runners originate from Finland and Israel. The fastest race courses were in Greece. Running on dirt paths leads to a significant reduction in running speed while running on asphalt leads to faster running speeds.

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.

Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathons

The 50-km ultra-marathon is a popular race distance, slightly longer than the classic marathon distance. However, little is known about the country of affiliation and age of the fastest 50-km ultra-marathon runners and where the fastest races are typically held. Therefore, this study aimed to investigate a large dataset of race records for the 50-km distance race to identify the country of affiliation and the age of the fastest runners as well as the locations of the fastest races. A total of 1,398,845 50-km race records (men, n = 1,026,546; women, n = 372,299) were analyzed using both descriptive statistics and advanced regression techniques. This study revealed significant trends in the performance of 50-km ultra-marathoners. The fastest 50-km runners came from African countries, while the fastest races were found to occur in Europe and the Middle East. Runners from Ethiopia, Lesotho, Malawi, and Kenya were the fastest in this race distance. The fastest 50-km racecourses, providing ideal conditions for faster race times, are in Europe (Luxembourg, Belarus, and Lithuania) and the Middle East (Qatar and Jordan). Surprisingly, the fastest ultra-marathoners in the 50-km distance were found to fall into the age group of 20-24 years, challenging the conventional belief that peak ultra-marathon performance comes in older age groups. These findings contribute to a better understanding of the performance models in 50-km ultra-marathons and can serve as valuable insights for runners, coaches, and race organizers in optimizing training strategies and racecourse selection.

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.

Elderly female ultra-marathoners reduced the gap to male ultra-marathoners in Swiss running races

Recent studies showed that female runners reduced the performance gap to male runners in endurance running with increasing age and race distance. However, the investigated samples were generally small. To investigate this further, the present study examined sex differences by age across various race distances (5, 10 km, half-marathon, marathon, and ultra-marathon) using a large dataset of over 1,100,000 race records from Switzerland over two decades (1999-2019). The study explored performance and participation disparities between male and female runners by employing diverse methods, such as descriptive statistics, histograms, scatter and line plots, correlations, and a predictive machine learning model. The results showed that female runners were more prevalent in shorter races (5, 10 km, half-marathon) and outnumbered male runners in 5 km races. However, as the race distance increased, the male-to-female ratio declined. Notably, the performance gap between sexes reduced with age until 70 years, after which it varied depending on the race distance. Among participants over 75 years old, ultra-marathon running exhibited the smallest sex difference in performance. Elderly female ultra-marathoners (75 years and older) displayed a performance difference of less than 4% compared to male ultra-marathoners, which may be attributed to the presence of highly selected outstanding female performers.

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.

Trends in Participation, Sex Differences and Age of Peak Performance in Time-Limited Ultramarathon Events: A Secular Analysis

Background and Objectives: Increases in the number of participants in time-limited ultra-marathons have been reported. However, no information is available regarding the trends in participation, performance and age in 12 h and 24 h time-limited events. The aim of the study was to describe the trends in runners’ participation, performance and age in 12 h and 24 h ultra-marathons for both sexes and to identify the age of peak performance, taking into account the ranking position and age categories. Materials and Methods: The sample comprised 210,455 runners in time-limited ultra-marathons (female 12 h = 23,706; female 24 h = 28,585; male 12 h = 61,594; male 24 h = 96,570) competing between 1876 and 2020 and aged 18 to 86 years. The age of peak performance was tested according to their ranking position (first−third; fourth−tenth and >tenth position) and taking into account their running speed in different age categories (<30 years; 31−40 years; 41−50 years; 51−60 years; >60 years), using the Kruskal−Wallis test, followed by the Bonferroni adjustment. Results: An increase in the number of participants and a decrease in running speed were observed across the years. For both events, the sex differences in performance decreased over time. The sex differences showed that male runners performed better than female runners, but the lowest differences in recent years were observed in the 24 h ultra-marathons. A positive trend in age across the years was found with an increase in mean age (“before 1989” = 40.33 ± 10.07 years; “1990−1999” = 44.16 ± 10.37 years; “2000−2009” = 45.99 ± 10.33 years; “2010−2020” = 45.62 ± 10.80 years). Male runners in 24 h races were the oldest (46.13 ± 10.83 years), while female runners in 12 h races were the youngest (43.46 ± 10.16 years). Athletes ranked first−third position were the youngest (female 12 h = 41.19 ± 8.87 years; female 24 h = 42.19 ± 8.50 years; male 12 h = 42.03 ± 9.40 years; male 24 h = 43.55 ± 9.03 years). When age categories were considered, the best performance was found for athletes aged between 41 and 50 years (female 12 h 6.48 ± 1.74 km/h; female 24 h 5.64 ± 1.68 km/h; male 12 h 7.19 ± 1.90 km/h; male 24 h 6.03 ± 1.78 km/h). Conclusion: A positive trend in participation in 12 h and 24 h ultra-marathons was shown across the years; however, athletes were becoming slower and older. The fastest athletes were the youngest ones, but when age intervals were considered, the age of peak performance was between 41 and 50 years.

The Sex Difference in 6-h Ultra-Marathon Running-The Worldwide Trends from 1982 to 2020

Background and Objectives: The 6-h ultra-marathon is the shortest time-limited ultra-marathon race, but little has been investigated regarding this race format. Previously, only the age of peak performance in the context of longer time-limited ultra-marathons was determined. The purpose of this study was to investigate the trends in 6-h ultra-marathon races from 1982 to 2020 for female and male ultra-runners, the participation and performance by countries, the age of peak performance, and the differences in performance regarding countries. Materials and Methods: The sample included 23,203 female ultra-runners, aged 18-83 years, and 87,264 male ultra-runners, aged 18-85 years, who were finishers in a 6-h ultra-marathon held between 1982 and 2020. The age of peak performance was tested using the Kruskal-Wallis test, followed by the Bonferroni Correction. The difference in performance by countries was verified using a linear regression model with the fastest runners from Russia in women, and Tunisia in men, used as reference. Results: Over the years, the men-to-women ratio decreased. The mean age was 43.20 ± 9.30 years for female and 46.09 ± 10.17 years for male runners. Athletes in younger age groups were faster than athletes in older age groups. Most female and male participants originated from Germany. Women from Russia (10.01 ± 1.28 km/h) and men from Tunisia (12.16 ± 1.46 km/h) were the fastest. Conclusions: In summary, in 6-h ultra-marathons held between 1982 and 2020, the participation for both women and men increased, while the men-to-women ratio decreased. The mean age was higher in men compared to women. Most female and male runners originated from Germany, but the fastest women were from Russia, and the fastest men from Tunisia. Future studies need to investigate whether Russian women and Tunisian men are also the best in other distance-limited ultra-marathon races, such as 12-h and 24-h.

From Athens to Sparta-37 Years of Spartathlon

(1) Background: Recent studies analyzed the participation and performance trends of historic races such as the oldest ultra-marathon (Comrades) or the oldest 100-km ultra-marathon (Biel). One of the toughest and historic ultra-marathons in the world is the ‘Spartathlon’ (246-km ultra-marathon from Athens to Sparta). The present study aimed to analyze the trends in participation and performance of this race. (2) Methods: Different general linear models were applied as follows: the first model was a two-way ANOVA (Decade × Sex), with separate models for all participants and for only the top five finishers in each race; the second model was a two-way ANOVA (Age Group × Sex); the third model was a two-way ANOVA (Nationality × Sex). (3) Results: Between 1982 and 2019, 3504 ultra-marathoners (3097 men and 407 women) officially finished the Spartathlon at least once. Athletes from Japan were the majority with 737 participants, followed by far by runners from Germany (n = 393), Greece (n = 326), and France (n = 274). The nations with the highest numbers of athletes amongst the top five performers were Japan (n = 71), followed by Germany (n = 59), and Great Britain (n = 31). Runners from the USA were the fastest in men, and runners from Great Britain were the fastest in women. Female and male runners improved performance across the decades. The annual five fastest women and men improved their performance over time. Runners achieved their best performance earlier in life (20–29 and 30–39 years) than female runners (30–39 and 40–49 years). Runners in age group 30–39 years were the fastest for all nationalities, except for Greece. (4) Conclusions: Successful finishers in the Spartathlon improved performance in the last four decades and male runners achieved their best performance ~10 years earlier in life than female runners.

Physiological Changes, Activity, and Stress During a 100-km-24-h Walking-March

Background

Long-endurance exercises like ultramarathons are known to elicit various metabolic and physiological changes in the human body. However, little is known about very long-duration exercise at low intensities regarding healthy human subjects.

Aim

The purpose of this study was to evaluate changes in body composition and metabolism in long-endurance but low-intensity events.

Methods

Twenty-five male and 18 female healthy recreational athletes (age 34.6 ± 8.8 years; BMI: 22.4 ± 2.0 kg/m²) of the "100 km Mammutmarsch" were recruited for participation during the events in 2014-2016. Other than classical ultramarathons, the "Mammutmarsch" is a hiking event, in which participants were required to walk but not run or jog. It was expected to complete the 100-km distance within 24 h, resulting in a calculated mean speed of 4.17 km/h, which fits to the mean speed observed (4.12 ± 0.76 km/h). As not all participants reached the finish line, comparison of finishers (FIN, n = 11) and non-finishers (NON, n = 21) allowed differential assessment of performance. Body composition measured through bioelectrical impedance analysis (BIA) was determined pre- and post-event, and serum samples were taken pre-event, at 30, 70, and 100 km to determine NT-pro-BNP, troponin T, C-reactive protein (CRP), cortisol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, total cholesterol, total creatine kinase (CK), CK-MB, aminotransferase (AST), ALT, and sodium levels. Nineteen participants wore actimeter armbands (SenseWear^®) to gain information about body activity and exercise intensity [metabolic equivalent of task (MET)]. Sixteen participants wore mobile heart rate monitors to assess mean heart rate during the race. Serum parameter alterations over the course of the race were analyzed with mixed-effects ANOVA and additional t-tests. All serum parameters were analyzed for correlation concerning different MET levels, speed, age, BMI, baseline NT-pro-BNP, mean heart rate during the race, and sex with linear regression analysis.

Results

We found significant elevations for muscle and cardiac stress markers (CRP, CK, CK-MB, AST, ALT, cortisol, and NT-pro-BNP) as well as decreasing markers of lipid metabolism (cholesterol, triglycerides, LDL). Although the intensity level demanded from our participants was low compared with other studies on (ultra-) marathons, the alteration of tested parameters was similar to those of high-intensity exercise, e.g., NT-pro-BNP showed a fourfold increase (p < 0.01) and LDL decreased by 20% (p = 0.05). Besides the duration of exercise, age, BMI, training status, and sex are relevant parameters that influence the elevation of stress factors. Notably, our data indicate that NT-pro-BNP might be a marker for cardiovascular fitness also in healthy adults.

Conclusion

This low-intensity long-endurance walk evoked a strong systemic reaction and large cell stress and shifted to a favorable lipid profile, comparable to higher intensity events. Despite increasing cardiac stress parameters, there were no indications of cardiac cell damage. Remarkably, the duration seems to have a greater influence on stress markers and metabolism than intensity.

Influence of Psychological Factors on the Success of the Ultra-Trail Runner

The aim of this study was to analyze the psychological variables of runners of ultra-trail mountain races and their association with athletic performance and success. The sample was made up of 356 mountain runners, 86.7% men and 13.2% women, with a mean age of 42.7 years and 5.7 years of experience. Using pre- and post-race questionnaires, data were collected regarding mental toughness, resilience, and passion. The performance of each runner in the race was also recorded. The results showed very high values in the psychological variables analyzed compared with other sports disciplines. Completion of the race (not withdrawing) and the elite quality of the runners were presented as the most relevant indicators in the processes of resilience, mental toughness, and obsessive passion. Differences were noted between the pre- and post-race results, suggesting that the competition itself is a means of training those psychological factors that are essential to this sports discipline. It can be concluded that psychological factors are decisive to athletic performance and race completion in mountain ultra-marathon races.

An Analysis of Participation and Performance of 2067 100-km Ultra-Marathons Worldwide

This study aimed to analyze the number of successful finishers and the performance of the athletes in 100-km ultra-marathons worldwide. A total of 2067 100-km ultra-marathon races with 369,969 men and 69,668 women competing between 1960 and 2019 were analyzed, including the number of successful finishers, age, sex, and running speed. The results showed a strong increase in the number of running events as well as a strong increase in the number of participants in the 100-km ultra-marathons worldwide. The performance gap disappeared in athletes older than 60 years. Nevertheless, the running speed of athletes over 70 years has improved every decade. In contrast, the performance gap among the top three athletes remains persistent over all decades (F = 83.4, p < 0.001; _pη² = 0.039). The performance gap between the sexes is not significant in the youngest age groups (20–29 years) and the oldest age groups (>90 years) among recreational athletes and among top-three athletes over 70 years. In summary, especially for older athletes, a 100-km ultra-marathon competition shows an increasing number of opponents and a stronger performance challenge. This will certainly be of interest for coaches and athletes in the future, both from a scientific and sporting point of view.

Participation and Performance in the Oldest Ultramarathon-Comrades Marathon 1921-2019

Participation and performance trends have been analyzed for different ultramarathons for limited time periods. This study examined trends in participation and performance in the oldest ultramarathon in the world, the 'Comrades Marathon' (South Africa), during a century (1921-2019). Data from www.ultra-marathon.org on 100 000 unique finishers were analysed using different general linear models. Women represented 4.2% of the total sample (n=4152), and the first women ran this race in 1978. Before the year 1965, the number of participants in the race ranged between 5 and 35 athletes, then started to grow exponentially until mid 90's. An increase in finishers in the 70 s mainly due to an increase in male athletes in age groups 30-39, 40-49 and 50-59 years was observed (p<0.001). A stable running speed for overall women and men but an improvement in performance for the annual top five women and men were shown (p<0.001). Male runners were faster than female runners for all age groups (p<0.001). While overall performance was not improved across years, the annual top five women and men were able to improve their performance over years.

Performance in 100-km Ultramarathoners-At Which Age, It Reaches Its Peak?

Nikolaidis, PT and Knechtle, B. Performance in 100-km ultramarathoners-At which age, it reaches its peak? J Strength Cond Res 34(5): 1409-1415, 2020-The number of those participating in 100-km ultramarathon has increased over the past years; however, we have limited knowledge about performance trends in this sport, and particularly, the effect of age. The aim of this study was to analyze the age when women and men runners achieve their peak performance considering 1- and 5-year age group intervals, and examining all or the fastest (i.e., top 10) participants in each age group. We analyzed 370,051 athletes (i.e., 44,601 women and 325,450 men) who finished a 100-km ultramarathon between 1959 and 2016, and studied the age of peak performance using a second-order nonlinear regression analysis. The age of peak performance was 40-44 years in women and 45-49 years in men when all finishers were analyzed, whereas it was 30-34 years in women and 35-39 years in men when the top 10 finishers were considered in 5-year age groups. When we analyzed finishers in 1-year age groups, we found the age of peak performance at 41 years in women and 45 years in men considering all finishers, and at 39 years in women and 41 years in men considering the top 10 finishers. In conclusion, the age of peak performance was younger in women than in men, which might reflect the overall younger age of women participants than men. Compared with previous studies, we observed the peak performance at an age older by ∼10 years, which could be attributed to an increase of finishers' age across calendar years. Because the knowledge of the age of peak performance is unique for each sport, coaches and fitness trainers might benefit from the findings of this study in the long-term training of their athletes.

Tower Running-Participation, Performance Trends, and Sex Difference

Though there are exhaustive data about participation, performance trends, and sex differences in performance in different running disciplines and races, no study has analyzed these trends in stair climbing and tower running. The aim of the present study was therefore to investigate these trends in tower running. The data, consisting of 28,203 observations from 24,007 climbers between 2014 and 2019, were analyzed. The effects of sex and age, together with the tower characteristics (i.e., stairs and floors), were examined through a multivariable statistical model with random effects on intercept, at climber’s level, accounting for repeated measurements. Men were faster than women in each age group (p < 0.001 for ages ≤69 years, p = 0.003 for ages > 69 years), and the difference in performance stayed around 0.20 km/h, with a minimum of 0.17 at the oldest age. However, women were able to outperform men in specific situations: (i) in smaller buildings (<600 stairs), for ages between 30 and 59 years and >69 years; (ii) in higher buildings (>2200 stairs), for age groups <20 years and 60–69 years; and (iii) in buildings with 1600–2200 stairs, for ages >69 years. In summary, men were faster than women in this specific running discipline; however, women were able to outperform men in very specific situations (i.e., specific age groups and specific numbers of stairs).

Participation and Performance Trends in the Oldest 100-km Ultramarathon in the World

Participation and performance trends in ultramarathon running have been investigated for large datasets and long period of times with an increase in participants and an improvement in performance. However, the analysis of ultramarathons across many decades is missing. We analyzed these trends for 96,036 athletes (88,286 men and 7750 women) from 67 countries competing between 1956 and 2019 in ‘100 km Lauf Biel’ in Switzerland, the oldest 100-km ultramarathon in the world. More men than women participated in all years. The number of male participants reached a peak at around 1985 and a decline in participation occurred thereafter. Women started competing in 1962. Men were always faster than women and both women and men reduced their race times over years. After about 1985, both overall women and men and both female and male winners were not able to improve race times. For men, athletes from all age groups below the age of 49 years old reached a peak of participation in the 1980s, and showed a decrease since then. Regarding age groups, the decrease first started in age group 20–29 years, followed by 30–39, 40–49, 50–59, and 60–69 years. For athletes in age groups 70–79 and 80–89 years, no decrease occurred. For women, age group athletes in age groups 40–49, 50–59, and 60–69 years increased their participation, whereas age groups 20–29 and 30–39 peaked in the late 1980s and started to decrease or stabilize, respectively. Switzerland, Germany, and France were the countries with the highest numbers of participants throughout the history of the race. In men, race times increased after about 1990 for most nationalities; only runners from Germany seemed to stabilize their performance. In women, runners from Italy, France, and Austria improved their performance over the years. In summary, the analysis of the oldest 100-km ultramarathon in the world showed a decrease in participation and an impairment in performance in the last 60 years. These changes were due to a decrease in the number of male ultramarathoners in around the 1980s, where mainly the number of age group runners younger than 70 years decreased.

Participation and Performance Analysis in Children and Adolescents Competing in Time-Limited Ultra-Endurance Running Events

Ultra-endurance running is of increasing popularity in the adult population, mainly due to master runners older than 35 years of age. However, youth runners younger than 19 years of age are also competing in ultra-endurance events, and an increase has been observed in distance-limited events, but no data is available on time-limited ultra-endurance events in this age group. This study investigated participation and performance trends in time-limited ultra-endurance races, including multi-day events, in runners younger than 19 years of age. Between the period 1990 and 2018, the most popular events recorded a total of 214 finishes (from 166 unique finishers (UF)) for 6-h events, 247 (212 UF) for 12-h events, and 805 (582 UF) for 24-h events, respectively. The majority of athletes originated from Europe and North America. Only a minority participated in multi-day events. Overall, speed increased with age, but the overall performance speed decreased across calendar years for 6- and 24-h events as participation numbers grew. In summary, in youth ultra-endurance runners, differences were observed regarding participation and performance across the different time-limited events, the age of the athletes and their country of origin

Women Reduce the Performance Difference to Men with Increasing Age in Ultra-Marathon Running

Age and sex are well-known factors influencing ultra-marathon race performance. The fact that women in older age groups are able to achieve a similar performance as men has been documented in swimming. In ultra-marathon running, knowledge is still limited. The aim of this study was to analyze sex-specific performance in ultra-marathon running according to age and distance. All ultra-marathon races documented in the online database of the German Society for Ultra-Marathon from 1964 to 2017 for 50-mile races (i.e., 231,980 records from 91,665 finishers) and from 1953 to 2017 for 100-mile races (i.e., 107,445 records from 39,870 finishers) were analyzed. In 50-mile races, race times were 11.74 ± 1.95 h for men and 12.31 ± 1.69 h for women. In 100-mile races, race times were 26.6 ± 3.49 h for men and 27.47 ± 3.6 h for women. The sex differences decreased with older age and were smaller in 100-mile (4.41%) than in 50-mile races (9.13%). The overall age of peak performance was 33 years for both distances. In summary, women reduced the performance difference to men with advancing age, the relative difference being smaller in 100-mile compared to 50-mile races. These findings might aid coaches and ultra-marathon runners set long-term training goals considering their sex and age.

Different Predictor Variables for Women and Men in Ultra-Marathon Running-The Wellington Urban Ultramarathon 2018

Ultra-marathon races are increasing in popularity. Women are now 20% of all finishers, and this number is growing. Predictors of performance have been examined rarely for women in ultra-marathon running. This study aimed to examine the predictors of performance for women and men in the 62 km Wellington Urban Ultramarathon 2018 (WUU2K) and create an equation to predict ultra-marathon race time. For women, volume of running during training per week (km) and personal best time (PBT) in 5 km, 10 km, and half-marathon (min) were all associated with race time. For men, age, body mass index (BMI), years running, running speed during training (min/km), marathon PBT, and 5 km PBT (min) were all associated with race time. For men, ultra-marathon race time might be predicted by the following equation: (r² = 0.44, adjusted r² = 0.35, SE = 78.15, degrees of freedom (df) = 18) ultra-marathon race time (min) = −30.85 ± 0.2352 × marathon PBT + 25.37 × 5 km PBT + 17.20 × running speed of training (min/km). For women, ultra-marathon race time might be predicted by the following equation: (r² = 0.83, adjusted r² = 0.75, SE = 42.53, df = 6) ultra-marathon race time (min) = −148.83 + 3.824 × (half-marathon PBT) + 9.76 × (10 km PBT) − 6.899 × (5 km PBT). This study should help women in their preparation for performance in ultra-marathon and adds to the bulk of knowledge for ultra-marathon preparation available to men.

An integrative modeling approach to the age-performance relationship in mammals at the cellular scale

Physical and cognitive performances change across lifespan. Studying cohorts of individuals in specific age ranges and athletic abilities remains essential in assessing the underlying physiological mechanisms that result in such a drop in performance. This decline is now viewed as a unique phenotypic biomarker and a hallmark of the aging process. The rates of decline are well documented for sets of traits such as running or swimming but only a limited number of studies have examined the developmental and senescent phases together. Moreover, the few attempts to do so are merely descriptive and do not include any meaningful biological features. Here we propose an averaged and deterministic model, based on cell population dynamics, replicative senescence and functionality loss. It describes the age-related change of performance in 17 time-series phenotypic traits, including human physical and cognitive skills, mouse lemur strength, greyhound and thoroughbred speed, and mouse activity. We demonstrate that the estimated age of peak performance occurs in the early part of life (20.5% ± 6.6% of the estimated lifespan) thus emphasizing the asymmetrical nature of the relationship. This model is an initial attempt to relate performance dynamics to cellular dynamics and will lead to more sophisticated models in the future.

Pacing During and Physiological Response After a 12-Hour Ultra-Marathon in a 95-Year-Old Male Runner

In recent years, outstanding performances of elderly people up to 100 years have been reported. In this case study, pacing during and recovery after a 12-h ultra-marathon were described for a 95-year old runner. The athlete achieved a total distance of 52.987 km. Pacing followed a parabolic pattern (U-shaped), where the speed decreased till the middle of the race and then increased. However, no end spurt was observed. A large main effect of lap quartile on speed was observed, where the second quartile was slower than the first quartile and forth. The smallest variability was shown in the first quartile and the largest in the second quartile. During recovery, erythrocytes, hemoglobin and hematocrit increased whereas thrombocytes and leucocytes decreased. CRP, GOT, GPT, y-GT, CK, and LDH were increased post-race and decreased to reference range during recovery. Also, creatinine and urea decreased during recovery. Creatinine clearance increased during recovery. Sodium increased during recovery and remained constantly within the reference range. During recovery body fat and visceral fat mass decreased, whereas body water and lean body mass increased. In summary, a 95-year-old man was able to run during 12 h using a U-shaped pacing and achieving a total distance of nearly 53 km. Increased selected hematological and biochemical parameters returned to pre-race values within a recovery phase of 5 days.

Neuropsychology of aging

All people want to age "successfully," maintaining functional capacity and quality of life as they reach advanced age. Achieving this goal depends on preserving optimal cognitive and brain functioning. Yet, significant individual differences exist in this regard. Some older adults continue to retain most cognitive abilities throughout their lifetime. Others experience declines in cognitive and functional capacity that range from mild decrements in certain cognitive functions over time to severe dementia among those with neurodegenerative diseases. Even among relatively healthy "successful agers," certain cognitive functions are reduced from earlier levels. This is particularly true for cognitive functions that are dependent on cognitive processing speed and efficiency. Working memory and executive and attentional functions tend to be most vulnerable. Learning and memory functions are also usually reduced, although in the absence of neurodegenerative disease learning and retrieval efficiency rather than memory storage are affected. Other functions, such as visual perception, language, semantics, and knowledge, are often well preserved. Structural, functional, and physiologic/metabolic brain changes correspond with age-associated cognitive decline. Physiologic and metabolic mechanisms, such as oxidative stress and neuroinflammation, may contribute to these changes, along with the contribution of comorbidities that secondarily affect the brain of older adults. Cognitive frailty often corresponds with physical frailty, both affected by multiple exogenous and endogenous factors. Neuropsychologic assessment provides a way of measuring the cognitive and functional status of older adults, which is useful for monitoring changes that may be occurring. Neuroimaging is also useful for characterizing age-associated structural, functional, physiologic, and metabolic brain changes, including alterations in cerebral blood flow and metabolite concentrations. Some interventions that may enhance cognitive function, such as cognitive training, neuromodulation, and pharmacologic approaches, exist or are being developed. Yet, preventing, slowing, and reversing the adverse effects of cognitive aging remains a challenge.

World Single Age Records in Running From 5 km to Marathon

This study investigated the relationship between race times and age, in 1-year intervals, by using the world single age records, from 5 km to marathon running (i. e., 5 km, 4 miles, 8, 10, 12, 15 km, 10 miles, 20 km, half-marathon, 25 km, 30 km, and marathon). For each race, a regression model was fitted. Effects of sex, alone and in interaction with age, and the effect of country of origin on performance were examined in a multi-variable model. The relationship between age and race time was modeled through a 4th order-polynomial function. Women achieved their best half-marathon and marathon race time, respectively, 1 year and 3 years earlier in life than men. On the contrary, in the other races, the best women performances were achieved later in life than men (i.e., 4 miles and 30 km: 2 years later, 8 km: 3 years later, 15–20–25 km: 1 year later, 10 miles: 4 years) or at the same age (i.e., 5, 10, 12 km). Moreover, age of peak performance did not change monotonically with the distance of race. For all races, except 12 km, sex differences had an absolute maximum at old ages and a relative maximum near the age of peak performance. From 8 km onward, estimated sex differences were increasing with increasing race distance. Regarding country, runners from Canada were slower than runners from the United States of America in 5 km by 00:10:05 h:min:s (p < 0.001) and in half-marathon by 00:18:43 h:min:s (p < 0.01). On the contrary, in marathon, they were 00:18:43 h:min faster (p < 0.05). Moreover, in 10 miles, runners from Great Britain were 00:02:53 h:min:s faster (p < 0.05) than runners from the United States of America. In summary, differences seem to exist in the age of peak performance between women and men and for nearly all distances sex differences showed an absolute maximum at old ages and relative maximum near the age of peak performance. Thus, these findings highlight the need for sex-specific training programs, especially near the age of peak performance and for elder runners.

Peak Age and Performance Progression in World-Class Track-and-Field Athletes

The aim of this study was to quantify peak age and improvements over the preceding years to peak age in elite athletic contestants according to athlete performance level, sex, and discipline. Individual season bests for world-ranked top 100 athletes from 2002 to 2016 (14,937 athletes and 57,049 individual results) were downloaded from the International Association of Athletics Federations’ website. Individual performance trends were generated by fitting a quadratic curve separately to each athlete’s performance and age data using a linear modeling procedure. Mean peak age was typically 25–27 y, but somewhat higher for marathon and male throwers (∼28–29 y). Women reached greater peak age than men in the hurdles and middle- and long-distance running events (mean difference, ±90% CL: 0.6, ±0.3 to 1.9, ±0.3 y: small to moderate). Male throwers had greater peak age than corresponding women (1.3, ±0.3 y: small). Throwers displayed the greatest performance improvements over the 5 y prior to peak age (mean [SD]: 7.0% [2.9%]), clearly ahead of jumpers, long-distance runners, hurdlers, middle-distance runners, and sprinters (3.4, ±0.2% to 5.2, ±0.2%; moderate to large). Similarly, top 10 athletes showed greater improvements than top 11–100 athletes in all events (1.0, ±0.9% to 1.8, ±1.1%; small) except throws. Women improved more than men in all events (0.4, ±0.2% to 2.9, ±0.4%) except sprints. This study provides novel insight on performance development in athletic contestants that are useful for practitioners when setting goals and evaluating strategies for achieving success.

How accurate are runners' prospective predictions of their race times?

Metacognition is a domain which has illuminated our understanding of the regulation of cognition, but has yet to be applied in detail to more physical activities. We used half marathon finish time predictions from 7211 runners to investigate the factors that influence running performance metacognitive accuracy. In particular, we were concerned with the effects of experience, gender, and age on calibration. We expected more experienced runners to be better calibrated than less experienced ones. Given analogous findings in the domain of metacognition, we expected women to be less overconfident in their predictions, and better calibrated than male runners. Based on the metacognition literature, we expected that if older runners have effectively learned from previous experience, they would be as well-calibrated as younger runners. In contrast, uninformed inferences not based on performance feedback would lead to overestimating performance for older compared to younger runners. As expected, experience in terms of both club membership and previous race completion improved calibration. Unexpectedly though, females were more overconfident than males, overestimating their performance and demonstrating poorer calibration. A positive relationship was observed between age and prediction accuracy, with older runners showing better calibration. The present study demonstrates that data, collected before a test of physical activity, can inform our understanding of how participants anticipate their performance, and how this ability is affected by a number of demographic and situational variables. Athletes and coaches alike should be aware of these variables to better understand, organise, plan, and predict running performance, potentially leading to more appropriate training sessions and faster race finish times.

[Ultramarathon with Type 1 Diabetes]

Ultramarathon with Type 1 Diabetes Abstract. We report the case of a 63-year-old runner with type 1 diabetes mellitus requiring insulin since the age of 21. At the age of 32, he ran his first marathon, and at the age of 34 the first ultramarathon. So far, he has finished more than 90 marathons and ultramarathons. Thanks to an insulin pump and continuous glucose monitoring, he has so far completed 48 24-h-runs with an average distance of 133 km. The analysis of running volume and HbA1c values showed a significant increase in monthly exercise volume, a significant decrease in HbA1c values over the years, and a significant correlation between monthly running kilometers and HbA1c values.

The Age-Related Performance Decline in Ironman Triathlon Starts Earlier in Swimming Than in Cycling and Running

Käch, I, Rüst, CA, Nikolaidis, PT, Rosemann, T, and Knechtle, B. The age-related performance decline in Ironman triathlon starts earlier in swimming than in cycling and running. J Strength Cond Res 32(2): 379-395, 2018-In Ironman triathlon, the number of overall male and female finishers increased in the past 30 years, while an improvement in performance has been reported. Studies concluding these numbers only analyzed the top 10 athletes per age group instead of all finishers; therefore, a selection bias might have occurred. The aim of this study was to investigate participation, performance, and the age-related performance decline of all pro- and age-group triathletes ranked in all Ironman triathlons held worldwide between 2002 and 2015. Split and overall race times of 329,066 (80%) male and 81,815 (20%) female athletes competing in 253 different Ironman triathlon races were analyzed. The number of finishers increased in all age groups with the exception of women in age group 75-79 years. In pro athletes, performance improved in all disciplines. In age-group athletes, performance improved in younger age groups for running (from 18-24 to 40-44 years) and older age groups for swimming (from 50-54 to 65-69 years) and cycling (from 35-39 to 55-59 years), whereas it impaired in younger age groups for swimming (from 18-24 to 45-49 years) and cycling (from 18-24 to 30-34 years), and older age groups in running (from 45-49 to 70-74 years). The age-related performance decline started in women in age group 25-29 years in swimming and in age group 30-34 years in cycling, running, and overall race time, whereas it started in men in age group 25-29 years in swimming and in age group 35-39 years in cycling, running, and overall race time. For athletes and coaches, performance improved in younger age groups for running and older age groups for swimming and cycling, and the age-related decline in performance started earlier in swimming than in cycling and running. In summary, women should start competing in Ironman triathlon before the age of 30 years and men before the age of 35 years to achieve their personal best Ironman race time.

Physiology and Pathophysiology in Ultra-Marathon Running

In this overview, we summarize the findings of the literature with regards to physiology and pathophysiology of ultra-marathon running. The number of ultra-marathon races and the number of official finishers considerably increased in the last decades especially due to the increased number of female and age-group runners. A typical ultra-marathoner is male, married, well-educated, and ~45 years old. Female ultra-marathoners account for ~20% of the total number of finishers. Ultra-marathoners are older and have a larger weekly training volume, but run more slowly during training compared to marathoners. Previous experience (e.g., number of finishes in ultra-marathon races and personal best marathon time) is the most important predictor variable for a successful ultra-marathon performance followed by specific anthropometric (e.g., low body mass index, BMI, and low body fat) and training (e.g., high volume and running speed during training) characteristics. Women are slower than men, but the sex difference in performance decreased in recent years to ~10–20% depending upon the length of the ultra-marathon. The fastest ultra-marathon race times are generally achieved at the age of 35–45 years or older for both women and men, and the age of peak performance increases with increasing race distance or duration. An ultra-marathon leads to an energy deficit resulting in a reduction of both body fat and skeletal muscle mass. An ultra-marathon in combination with other risk factors, such as extreme weather conditions (either heat or cold) or the country where the race is held, can lead to exercise-associated hyponatremia. An ultra-marathon can also lead to changes in biomarkers indicating a pathological process in specific organs or organ systems such as skeletal muscles, heart, liver, kidney, immune and endocrine system. These changes are usually temporary, depending on intensity and duration of the performance, and usually normalize after the race. In longer ultra-marathons, ~50–60% of the participants experience musculoskeletal problems. The most common injuries in ultra-marathoners involve the lower limb, such as the ankle and the knee. An ultra-marathon can lead to an increase in creatine-kinase to values of 100,000–200,000 U/l depending upon the fitness level of the athlete and the length of the race. Furthermore, an ultra-marathon can lead to changes in the heart as shown by changes in cardiac biomarkers, electro- and echocardiography. Ultra-marathoners often suffer from digestive problems and gastrointestinal bleeding after an ultra-marathon is not uncommon. Liver enzymes can also considerably increase during an ultra-marathon. An ultra-marathon often leads to a temporary reduction in renal function. Ultra-marathoners often suffer from upper respiratory infections after an ultra-marathon. Considering the increased number of participants in ultra-marathons, the findings of the present review would have practical applications for a large number of sports scientists and sports medicine practitioners working in this field.

Pacing of an Untrained 17-Year-Old Teenager in a Marathon Attempt

Although there has been increased scientific interest for physiological responses to endurance running and pacing, limited information exists for adolescents participating in endurance events. We are reporting the case of an untrained 17-year-old female teenager (body mass 50.6 kg, height 167 cm and body mass index 18.1 kg/m2) who intended to run a marathon within 6 hours without preparation. The young woman missed her goal by just 2 km. When the average running speed per hour was analysed, there was a major effect of race hour on running speed (p = 0.013, η2 = 0.320), where the running speed in the fifth hour (6.3 ± 0.2 km/h) was lower than in the second hour (6.9 ± 0.1 km/h). Despite a progressive decrease in running speed, she was still able to put on a final spurt, indicated by a 4th degree non-linear regression (R2=0.55). Creatine-kinase reached the initial value again after 5 days and the fall of hemoglobin and hematocrit indicated expansion of plasma volume. Running a marathon as a teenager did not impair physical health, especially when a self-selected pace was adopted. Laboratory parameters during running showed similar changes as have been reported for teenagers and adults after running a marathon. Increased values returned to base line within a few days. In summary, a female teenager at the age of 17 years without specific running preparation is able to achieve nearly a marathon distance during 6 hours of continuous running without harmful effects on health.

Age of peak performance in 50-km ultramarathoners - is it older than in marathoners?

PURPOSE

Despite the increasing popularity of 50-km ultramarathons during the last few years, only limited information is available regarding the trends in its performance and participation. The aim of the present study was to examine the age of peak running performance in female and male 50-km ultramarathoners using second-order nonlinear regression analyses.

METHODS

Data from 494,414 runners (124,045 women and 370,369 men) who finished a 50-km ultramarathon between 1975 to 2016 were analyzed.

RESULTS

When the top ten finishers in 1-year age-groups were analyzed, the age of peak running speed was 41 years in both women and men. When the fastest finishers in 1-year age-group intervals were analyzed, the age of peak running speed was 40 years in women and 39 years in men.

CONCLUSION

In summary, the age of peak running speed in 50-km ultramarathoners is older than what has been reported by previous studies for marathons. Women seem to achieve the best race time in a 50-km ultramarathon later in life compared with men. These findings are of great practical value for coaches and fitness trainers when setting performance goals for 50-km ultramarathon runners.

Running Performance, Nationality, Sex, and Age in the 10-km, Half-Marathon, Marathon, and the 100-km Ultramarathon IAAF 1999-2015

Nikolaidis PT, Onywera VO, and Knechtle B. Running performance, nationality, sex, and age in the 10-km, half-marathon, marathon, and the 100-km ultramarathon IAAF 1999-2015. J Strength Cond Res 31(8): 2189-2207, 2017-The aim of this study was to examine the performance of the world's best runners in the 10-km, half-marathon, marathon, and 100-km races by age, sex, and nationality during 1999-2015, using data from the International Association of Athletics Federations (IAAF). A total of 38,895 runners (17,136 women and 21,759 men) were evaluated, with 2,594 (1,360 women and 1,234 men) in the 10-km; 11,595 (5,225 women and 6,370 men) in the half-marathon; 23,973 (10,208 women and 13,765 men) in the marathon; and 733 (343 women and 390 men) in 100-km events. Most runners in the 10-km event (women 40%, men 67%) and the half-marathon (women 30%, men 57%) were Kenyans. In the marathon, most female and male runners were Ethiopians (women 17%, men 14%) and Kenyans (women 15%, men 43%), respectively. In the 100-km event, most runners were Japanese (20% women, and 80% men). Women were older than the men in the 10-km (32.0 ± 6.0 vs. 25.3 ± 4.3 years, p < 0.001), half-marathon (27.5 ± 4.7 vs. 25.9 ± 4.1 years, p < 0.001), and marathon events (29.5 ± 5.5 vs. 29.1 ± 4.3 years, p < 0.001), but not in 100-km event (36.6 ± 6.1 vs. 35.9 ± 5.5 years, p = 0.097). Men were faster than the women in the 10-km (28:04 ± 0:17 vs. 32:08 ± 0.31 (minutes:seconds), p < 0.001), half-marathon (1:01:58 ± 0:00:52 vs. 1:11:21 ± 0:01:18 (hours:minutes:seconds), p < 0.001), marathon (2:13:42 ± 0:03:01 vs. 2:35:04 ± 0:05:21 (hours:minutes:seconds), p < 0.001), and 100-km events (6:48:01 ± 0:11:29 vs. 7:53:51 ± 0:16:37 (hours:minutes:seconds), p < 0.001). East Africans were not the fastest compared with athletes originating from other countries where only the Ethiopian men were faster than all other men in the marathon. In summary, (a) in the 10-km, half-marathon and marathon events, most runners were from Kenya and Ethiopia, and from Japan and Russia in the 100-km event; (b) women were older than the men in all distance events except the 100-km event;

The age of the best ultramarathon performance - the case of the "Comrades Marathon"

The aim of the present study was to determine the age of the fastest running speed in 202,370 runners (34,090 women and 168,280 men) competing in the "Comrades Marathon" between 1994 and 2015 using non-linear regression analysis (second order polynomial function). When all runners were considered in 1-year age intervals, the fastest running speed (9.61 ± 1.65 km/h) was achieved at the age of 29.89 years in men, whereas women achieved it at the age of 35.96 years 8.60 ± 1.10 km/h. When the fastest runners were considered in 1-year intervals, the fastest running speed (16.65 km/h) was achieved in men at the age of 36.38 years. For the fastest women, the age of the fastest running speed (13.89 km/h) was 32.75 years. To summarize, for all runners, men achieved the best ultramarathon performance ~6 years earlier than women. When the fastest runners were considered, however, men achieved the best performance ~4 years later than women.

The Age in Swimming of Champions in World Championships (1994⁻2013) and Olympic Games (1992⁻2012): A Cross-Sectional Data Analysis

(1) Background: We investigated the age of swimming champions in all strokes and race distances in World Championships (1994–2013) and Olympic Games (1992–2012); (2) Methods: Changes in age and swimming performance across calendar years for 412 Olympic and world champions were analysed using linear, non-linear, multi-level regression analyses and MultiLayer Perceptron (MLP); (3) Results: The age of peak swimming performance remained stable in most of all race distances for world champions and in all race distances for Olympic champions. Longer (i.e., 200 m and more) race distances were completed by younger (~20 years old for women and ~22 years old for men) champions than shorter (i.e., 50 m and 100 m) race distances (~22 years old for women and ~24 years old for men). There was a sex difference in the age of champions of ~2 years with a mean age of ~21 and ~23 years for women and men, respectively. Swimming performance improved in most race distances for world and Olympic champions with a larger trend of increase in Olympic champions; (4) Conclusion: Swimmers at younger ages (<20 years) may benefit from training and competing in longer race distances (i.e., 200 m and longer) before they change to shorter distances (i.e., 50 m and 100 m) when they become older (>22 years).

The aspect of experience in ultra-triathlon races

Previous experience seems to be an important predictor for endurance and ultra-endurance performance. The present study investigated whether the number of previously completed races and/or the personal best times in shorter races is more predictive for performance in longer non-stop ultra-triathlons such as a Deca Iron ultra-triathlon. All female and male ultra-triathletes who had finished between 1985 and 2014 at least one Double Iron ultra-triathlon (i.e. 7.6 km swimming, 360 km cycling and 84.4 km running), one Triple Iron ultra-triathlon (i.e. 11.4 km swimming, 540 km cycling and 126.6 km running), one Quintuple Iron ultra-triathlon (i.e. 19 km swimming, 900 km cycling and 221 km running) and one Deca Iron ultra-triathlon (i.e. 38 km swimming, 1,800 km cycling and 422 km running) were identified and their best race times for each distance were recorded. Multiple regression analysis (stepwise, forward selection, p of F for inclusion <0.05, p of F for exclusion >0.1, listwise deletion) was used to determine all variables correlating to overall race time and performance in split disciplines for both Quintuple and Deca Iron ultra-triathlon. The number of finished shorter races (i.e. Double and Triple Iron ultra-triathlon) was not associated with the number of finished longer races (i.e. Quintuple and Deca Iron ultra-triathlon) whereas both split and overall race times correlated to split and overall race times of the longer races with the exception of the swimming split times in Double Iron ultra-triathlon showing no correlation with swimming split times in both Quintuple and Deca Iron ultra-triathlon. In summary, previous experience seemed of importance in performance for longer ultra-triathlon races (i.e. Quintuple and Deca Iron ultra-triathlon) where the personal best times of shorter races (i.e. Double and Triple Iron ultra-triathlon) were important, but not the number of previously finished races. For athletes and coaches, fast race times in shorter ultra-triathlon races (i.e. Double and Triple Iron ultra-triathlon) are more important than a large of number finished races in order to achieve a fast race time in a longer ultra-triathlon (i.e. Quintuple and Deca Iron ultra-triathlon).

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.

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.