Performance and Age of the Fastest Female and Male 100-KM Ultramarathoners Worldwide From 1960 to 2012

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.

Energetics as a driver of human morphological thermal adaptation; evidence from female ultra-endurance athletes

Functional benefits of the morphologies described by Bergmann's and Allen's rules in human males have recently been reported. However, the functional implications of ecogeographical patterning in females remain poorly understood. Here, we report the findings of preliminary work analysing the association between body shape and performance in female ultramarathon runners (n = 36) competing in hot and cold environments. The body shapes differed between finishers of hot and cold races, and also between hot race finishers and non-finishers. Variability in race performance across different settings supports the notion that human phenotype is adapted to different thermal environments as ecogeographical patterns have reported previously. This report provides support for the recent hypothesis that the heightened thermal strain associated with prolonged physical activity in hot/cold environments may have driven the emergence of thermally adaptive phenotypes in our evolutionary past. These results also tentatively suggest that the relationship between morphology and performance may be stronger in female vs. male athletes. This potential sex difference is discussed with reference to the evolved unique energetic context of human female reproduction. Further work, with a larger sample size, is required to investigate the observed potential sex differences in the strength of the relationship between phenotype and performance.

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.

Tactical behavior of high-level male marathon runners

This study analyzes the strategy used by the best male runners who participated in one of the major city marathons (Frankfurt Marathon, 2008-2018), the all-time performances <2:04:00, the male world records achieved during the 21st century and the Nike Breaking2 Project and INEOS 1:59 Challenge (total = 235 races). The races of the best runners in the Frankfurt Marathon (top 10) were analyzed (n = 110 runners, range: 2:03:42-2:14:05 hours); the runners were divided into two groups according to the tactical used. The pace of Group A (stable pace) remained steady throughout the race, while in Group B (decrease in running speed toward the end of the race) a moderate, but significant drop in speed was detected (P ≤ .001), starting from halfway through the race and getting sharper from the 30th kilometer (30-35 km = 1.6%, P ≤ .001 - 35-40 km = 4.3%, P ≤ .001 - 40-42.195 km: 3.9%, P ≤ .001, total = ≈10%). In the races in which the world record is achieved, the running speed tends to be steady and relatively conservative during the first stretch of the race, increasing smoothly in the second half and achieving a significant increase in the last 2195 m of the race (P = .016, ES = 1.14). Among all the possible strategies, running at a steady pace throughout the race seems the most effective option, especially when priority is given to time rather than position (ie, world records and best all-time races).

Even Pacing Is Associated with Faster Finishing Times in Ultramarathon Distance Trail Running-The "Ultra-Trail du Mont Blanc" 2008-2019

In recent years, there has been an increasing number of investigations analyzing the effects of sex, performance level, and age on pacing in various running disciplines. However, little is known about the impact of those factors on pacing strategies in ultramarathon trail running. This study investigated the effects of age, sex, and performance level on pacing in the UTMB^® (Ultra-trail du Mont Blanc) and aimed to verify previous findings obtained in the research on other running disciplines and other ultramarathon races. Data from the UTMB^® from 2008 to 2019 for 13,829 race results (12,681 men and 1148 women) were analyzed. A general linear model (two-way analysis of variance (ANOVA)) was applied to identify a sex, age group, and interaction effect in pace average and pace variation. A univariate model (one-way ANOVA) was used to identify a sex effect for age, pace average, and pace variation for the fastest men and women. In our study, pace average and a steadier pace were positively correlated. Even pacing throughout the UTMB^® correlated with faster finishing times. The average pace depended significantly on sex and age group. When considering the top five athletes in each age group, sex and age group also had significant effects on pace variation. The fastest women were older than the fastest men, and the fastest men were faster than the fastest women. Women had a higher pace variation than men. In male competitors, younger age may be advantageous for a successful finish of the UTMB^®. Faster male runners seemed to be younger in ultramarathon trail running with large changes in altitude when compared to other distances and terrains.

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.

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.

Ultra-endurance athletic performance suggests that energetics drive human morphological thermal adaptation

Both extinct and extant hominin populations display morphological features consistent with Bergmann's and Allen's Rules. However, the functional implications of the morphologies described by these ecological laws are poorly understood. We examined this through the lens of endurance running. Previous research concerning endurance running has focused on locomotor energetic economy. We considered a less-studied dimension of functionality, thermoregulation. The performance of male ultra-marathon runners (n = 88) competing in hot and cold environments was analysed with reference to expected thermoregulatory energy costs and the optimal morphologies predicted by Bergmann's and Allen's Rules. Ecogeographical patterning supporting both principles was observed in thermally challenging environments. Finishers of hot-condition events had significantly longer legs than finishers of cold-condition events. Furthermore, hot-condition finishers had significantly longer legs than those failing to complete hot-condition events. A degree of niche-picking was evident; athletes may have tailored their event entry choices in accordance with their previous race experiences. We propose that the interaction between prolonged physical exertion and hot or cold climates may induce powerful selective pressures driving morphological adaptation. The resulting phenotypes reduce thermoregulatory energetic expenditure, allowing diversion of energy to other functional outcomes such as faster running.

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.

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.

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.

Age of Peak Competitive Performance of Elite Athletes: A Systematic Review

BACKGROUND

Knowledge of the age at which elite athletes achieve peak performance could provide important information for long-term athlete development programmes, event selection and strategic decisions regarding resource allocation.

OBJECTIVES

The objective of this study was to systematically review published estimates of age of peak performance of elite athletes in the twenty-first century.

METHODS

We searched SPORTDiscus, PubMed and Google Scholar for studies providing estimates of age of peak performance. Here we report estimates as means only for top (international senior) athletes. Estimates were assigned to three event-type categories on the basis of the predominant attributes required for success in the given event (explosive power/sprint, endurance, mixed/skill) and then plotted by event duration for analysis of trends.

RESULTS

For both sexes, linear trends reasonably approximated the relationships between event duration and estimates of age of peak performance for explosive power/sprint events and for endurance events. In explosive power/sprint events, estimates decreased with increasing event duration, ranging from ~27 years (athletics throws, ~1-5 s) to ~20 years (swimming, ~21-245 s). Conversely, estimates for endurance events increased with increasing event duration, ranging from ~20 years (swimming, ~2-15 min) to ~39 years (ultra-distance cycling, ~27-29 h). There was little difference in estimates of peak age for these event types between men and women. Estimations of the age of peak performance for athletes specialising in specific events and of event durations that may best suit talent identification of athletes can be obtained from the equations of the linear trends. There were insufficient data to investigate trends for mixed/skill events.

CONCLUSION

Differences in the attributes required for success in different sporting events likely contribute to the wide range of peak-performance ages of elite athletes. Understanding the relationships between age of peak competitive performance and event duration should be useful for tracking athlete progression and talent identification.

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.

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

Recent findings suggested that the age of peak ultra-marathon performance seemed to increase with increasing race distance. The present study investigated the age of peak ultra-marathon performance for runners competing in time-limited ultra-marathons held from 6 to 240 h (i.e. 10 days) during 1975-2013. Age and running performance in 20,238 (21%) female and 76,888 (79%) male finishes (6,863 women and 24,725 men, 22 and 78%, respectively) were analysed using mixed-effects regression analyses. The annual number of finishes increased for both women and men in all races. About one half of the finishers completed at least one race and the other half completed more than one race. Most of the finishes were achieved in the fourth decade of life. The age of the best ultra-marathon performance increased with increasing race duration, also when only one or at least five successful finishes were considered. The lowest age of peak ultra-marathon performance was in 6 h (33.7 years, 95% CI 32.5-34.9 years) and the highest in 48 h (46.8 years, 95% CI 46.1-47.5). With increasing number of finishes, the athletes improved performance. Across years, performance decreased, the age of peak performance increased, and the age of peak ultra-marathon performance increased with increasing number of finishes. In summary, the age of peak ultra-marathon performance increased and performance decreased in time-limited ultra-marathons. The age of peak ultra-marathon performance increased with increasing race duration and with increasing number of finishes. These athletes improved race performance with increasing number of finishes.

Will the age of peak ultra-marathon performance increase with increasing race duration?

Background

Recent studies found that the athlete’s age of the best ultra-marathon performance was higher than the athlete’s age of the best marathon performance and it seemed that the athlete’s age of peak ultra-marathon performance increased in distance-limited races with rising distance.

Methods

We investigated the athlete’s age of peak ultra-marathon performance in the fastest finishers in time-limited ultra-marathons from 6 hrs to 10 d. Running performance and athlete’s age of the fastest women and men competing in 6 hrs, 12 hrs, 24 hrs, 48 hrs, 72 hrs, 144 hrs (6 d) and 240 hrs (10 d) were analysed for races held between 1975 and 2012 using analysis of variance and multi-level regression analysis.

Results

The athlete’s ages of the ten fastest women ever in 6 hrs, 12 hrs, 24 hrs, 48 hrs, 72 hrs, 6 d and 10 d were 41 ± 9, 41 ± 6, 42 ± 5, 46 ± 5, 44 ± 6, 42 ± 4, and 37 ± 4 yrs, respectively. The athlete’s age of the ten fastest women was different between 48 hrs and 10 d. For men, the athlete’s ages were 35 ± 6, 37 ± 9, 39 ± 8, 44 ± 7, 48 ± 3, 48 ± 8 and 48 ± 6 yrs, respectively. The athlete’s age of the ten fastest men in 6 hrs and 12 hrs was lower than the athlete’s age of the ten fastest men in 72 hrs, 6 d and 10 d, respectively.

Conclusion

The athlete’s age of peak ultra-marathon performance did not increase with rising race duration in the best ultra-marathoners. For the fastest women ever in time-limited races, the athlete’s age was lowest in 10 d (~37 yrs) and highest in 48 hrs (~46 yrs). For men, the athlete’s age of the fastest ever in 6 hrs (~35 yrs) and 12 hrs (~37 yrs) was lower than the athlete’s age of the ten fastest in 72 hrs (~48 yrs), 6 d (~48 yrs) and 10 d (~48 yrs). The differences in the athlete’s age of peak performance between female and male ultra-marathoners for the different race durations need further investigations.

Sex difference in age and performance in elite Swiss freestyle swimmers competing from 50 m to 1,500 m

Recent studies reported different ages for peak freestyle swimming performances for 50 m and 1,500 m. The aims of the present study were (i) to determine the age of peak freestyle swimming speed for distances including 50 m, 100 m, 200 m, 400 m, 800 m, and 1,500 m and to (ii) analyze the sex difference in peak freestyle swimming speed for all distances between 50 m and 1,500 m for elite female and male swimmers competing at national level. Data from the ‘Swiss Swimming Federation’ between 2006 and 2010 from 10,405 men and 9,552 women were analyzed using regression analyses and analyses of variance (ANOVA). Women achieved peak freestyle swimming speed at ~20–21 years from 50 m to 400 m, at ~24–25 years in 1,500 m and at ~25–27 years in 800 m. In men, the age of peak freestyle swimming speed varied between ~22–23 years and ~25–27 years for 50 m to 1,500 m. Between the age of 10 and 29 years, the sex difference in freestyle swimming speed increased from 2.2 ± 0.4% to 19.0 ± 6.7% in 50 m (r² = 0.87, P < 0.001), from 2.4 ± 0.7% to 10.8 ± 2.8% in 100 m (r² = 0.67, P = 0.004) and from 3.6 ± 1.9% to 10.2 ± 3.4% in 200 m (r² = 0.60, P = 0.008). In 400 m (r² = 0.24), 800 m (r² = 0.39) and 1,500 m (r² = 0.34), the sex difference showed no changes (P > 0.05) with 6.9 ± 3.0%, 5.8 ± 3.5%, and 9.7 ± 8.6%, respectively. The sex difference in freestyle swimming speed showed no change with increasing race distance (r² = 0.12, P > 0.05). To summarize, the age of peak freestyle swimming speed increased for women with the length of the race distance from 50 m to 200 m, but not from 400 m to 1,500 m. For men, the age of peak freestyle swimming speed varied between ~22–23 years and ~25–27 years from 50 m to 1,500 m. The sex difference in freestyle swimming speed of 9.1 ± 2.5% showed no change with increasing race distance. Future studies need to confirm these findings in elite swimmers competing at international level such as the World Championships and the Olympic Games.