International  Journal  of
Environmental Research
and Public Health
Article
Women Reduce the Performance Diﬀerence to Men
with Increasing Age in Ultra-Marathon Running
Karin J. Waldvogel 1, Pantelis T. Nikolaidis 2,3
, Stefania Di Gangi 1, Thomas Rosemann 1
and
Beat Knechtle 1,4,*
1
Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland
2
Exercise Physiology Laboratory, 18450 Nikaia, Greece
3
School of Health and Caring Sciences, University of West Attica, 12243 Athens, Greece
4
Medbase St. Gallen Am Vadianplatz, 9001 St. Gallen, Switzerland
*
Correspondence: beat.knechtle@hispeed.ch; Tel.: +41-(0)-71-226-93-00
Received: 8 June 2019; Accepted: 2 July 2019; Published: 4 July 2019


Abstract: 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.
Keywords: age of peak performance; athlete; sex diﬀerence; ultra-endurance
1. Introduction
The oldest entry in the collection of ultra-marathon running statistics provided by the “German
Society for Ultra-Marathon” [1] was a 89 km run from London to Brighton taking place in 1837. Since
then, the popularity of ultra-marathon running has substantially increased [2–5]. Ultra-marathon
running competitions are mainly speciﬁed by duration in hours or days (e.g., six hours to ten days) or
by distance in km or miles (e.g., 50 km, 100 km, 50 miles, and 100 miles). For a race to be considered
as an ultra-marathon, the duration has to be at least 6 hours, or the distance has to be longer than
42.195 km (26.2 miles) [5,6].
Over the last decades, the number of ultra-marathon competitions [7] as well as the number of
participants in these races has increased exponentially [8]. This increase appears to be mostly due
to increasing numbers of athletes aged over 40 years (i.e., master athletes) [7], as well as women
increasingly participating [3,8]. While very few women participated in the ﬁrst ultra-marathon running
competitions, their share has increased ever since [7,9,10]. Since 2004, approximately 20% of the
runners have been women, but there are no records documenting women participating in the USA
161 km ultra-marathon distance in the 1970s [7].
Int. J. Environ. Res. Public Health 2019, 16, 2377; doi:10.3390/ijerph16132377
www.mdpi.com/journal/ijerph
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Multiple determinants of the ultra-marathon’s success have been identiﬁed. One very important
factor is age [10–12]. Knowing the age of peak performance has been assessed as being indispensable
for optimization of the training schedule and to plan a successful career as an ultra-runner [13].
Comparing marathon and ultra-marathon running, diﬀerences in the age of peak performance have
been very recently reported. In marathon running, the best performances of women and men are
achieved between 25 and 35 years of age [4,11,14–16]. In above-marathon distances, the age of peak
performance is higher [4]. Several studies report that the best results are observed in men aged 30 to
49 years and in women aged 30 to 54 years in 100 km ultra-marathon races [13,17]. One explanation
might be that most runners start their careers with marathons, only later in life enhancing the challenge
with ultra-marathons [17]. Moreover, compared to marathon races, ultra-marathons require the
necessary level of performance, and this depends even more on the critical factors of adequate training
preparation with an appropriate nutrition plan and mental strength [6].
Another essential factor inﬂuencing race performance is the athlete’s sex. Even though the
performance of women compared to men in endurance running was inferior in the past [9],
the sex-related gap has decreased in the last couple of decades [18]. This observation led to speculation
about whether and how women could reduce the diﬀerence in running times to a level where they
might outperform men in long-distance races. Other authors have hypothesized that this might be
more likely to happen with very long distances, such as in ultra-marathons [18–21]. In contrast to
such expectations, some results seem to indicate a larger sex gap in ultra-marathons compared to
marathons [20], although there might be a potential bias underlying these results. Most studies either
did not consider all participants and only focused on the top athletes [15,22,23], or had a limited
sample size of athletes, only investigating a small number of races and/or a limited period [11,16].
Comparing only the top ten world record performances carries the risk of the results being aﬀected by
athletes with the highest performance level. For example, Lepers et al. [22] restricted their analysis of
triathletes to the top ten men of each age group in the Olympic triathlon and Ironman triathlon world
championships of 2006 and 2007 (440 athletes in total) and found an age-related performance decline
at the age of 50 years in swimming and at the age of 45 years in cycling and running. In contrast,
Käch et al. [24], investigating 329,066 men and 81,815 women participating in Ironman triathlon
competitions held between 2002 and 2015, found a performance decline at profoundly earlier ages
(in swimming, at 25–29 years of age in women and men; in cycling and running, at 30–34 years of age
in women and at 35–39 years of age in men). According to Käch et al. [24], the participants in Ironman
triathlons are not only the top-performing athletes of each age group, but also recreational athletes,
the latter typically not being included in an analysis of top-ten athletes. Top-performing athletes tend
to have more experience, mental strength, training volume, and training intensity than recreational
athletes and are thus more likely to be included in analyses of top-ten performers [24]. This could
also explain that the performance of unselected athletes (i.e., investigation of performance of every
participating athlete, as found by Käch et al. [24], tends to decline at an earlier age than performance in
top-ten athletes (as found by Lepers et al. [22]).
Could a similar mechanism also explain discrepant ﬁndings on the performance of men compared
to women? Recent studies investigating master swimmers in pool and open-water swimming showed
that women in older age groups (80 years and older) achieved a similar performance to men in
an investigation of 65,584 freestyle pool swimmers (29,467 women and 36,117 men) competing in
50 to 800 m [25] races and when 7592 freestyle open-water swimmers (2829 women and 4768 men)
competing in 3000 m [26] races in the FINA World championships from 1986–2014 and 1992–2014,
respectively. In contrast, Senefeld et al. [27], who conducted a similar study except that they focused on
the top ten swimmers in the years between 1986 and 2011 (6760 athletes in total, men and women), found
that the performance of women in every age group was inferior and, contrary to Knechtle et al. [25,26],
that the sex gap increased with age.
The diﬀerences in the selection of performance levels could possibly explain these discrepant
ﬁndings in comparisons of men versus women. In support of this interpretation, other studies found
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a reduction in the sex gap in swimming performance with increasing age for diﬀerent disciplines
such as breaststroke [28], backstroke [29], butterﬂy [30], and in the individual medley event [31].
The commonality across these studies is that they included all participants in their investigation,
rather than only the top ten performance participants. The results indicate that selection based on
performance levels has an inﬂuence on the results regarding sex-speciﬁc performance diﬀerences,
usually in favor of men. In contrast, studies performed with all athletes tend to display a lower or no
sex gap.
The fact that women in older age groups (i.e., older than 80 years) achieve a similar performance to
men has only been reported for diﬀerent swimming disciplines, but not for running. Knechtle et al. [32]
reported results on ultra-marathon performance in men and women and found that with increasing
age and race distance, the sex gap increased rather than decreased. However, our knowledge of
whether women in older age groups would be able to achieve a similar performance for longer running
distances is still limited. For instance, a recent study on road running records from 5 km to 6 days
showed that men were faster than women, the sex gap decreased with increasing age, and it did not
vary by race distance or duration [33].
We therefore investigated whether women in 50-mile and 100-mile ultra-marathon races would
be able to reduce the gap to men in older age groups. In contrast to previous studies, we analyzed a
much larger data set, containing all 50-mile ultra-marathon races held between 1964 and 2017 and all
100-mile races between 1953 and 2017, thus avoiding selection bias by not only focusing on the top
participants. Based on previous ﬁndings for master swimmers, we hypothesized that the sex gap in
performance in ultra-marathons would decrease with increasing age, and that this decrease would be
independent from the race distance.
2. Materials and Methods
2.1. Ethical Approval
This study was approved by the Institutional Review Board of the Kanton St. Gallen, Switzerland,
with a waiver of the requirement for informed consent of the participants, as the study involved the
analysis of publicly available data (1 June 2010).
2.2. Data Sampling
The investigation comprised all ultra-marathon competitions with running distances documented
in “miles” in the online database of the German Society for Ultra-Marathon (Deutsche Ultramarathon
Vereinigung e.V.). A total of 7769 competitions with 456,167 men and women participating in the years
from 1928 to 2017 [34] were extracted.
The data set was retrieved in multiple steps. First, we used the Google Chrome browser (Version
66.0.3359.139) with the add-on “Web Scraper” (Version 0.3.7) to retrieve the Uniform Resource Locator
(URL) of each ultra-marathon competition registered in the online database. Each URL was saved
in Microsoft Excel 2013 (Version 15.0.4569.1504). Subsequently, the Microsoft Excel-integrated Visual
Basic Application (VBA) was used to ﬁlter the database contents, excluding every URL of competitions
that did not have a distance speciﬁed in miles. In a ﬁnal step, also using Excel-VBA, the raw data of
each competition was extracted and uniformly formatted. The resulting ﬁle was visually controlled
for inconsistences, and these were corrected in accordance with the original data. For the purpose of
the present study, we analyzed 339,425 records of athletes either ﬁnishing a 50-mile race from 1964 to
2017 or a 100-mile race from 1953 to 2017. Other race distances were excluded due to insuﬃcient data.
The following variables were extracted: year of race, race distance, name of race, country of race, race
time (h), running speed (km/h), name of athlete, year of birth, nationality of athlete, and sex of athlete.
Age was derived by subtracting the year of birth from 2017.
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2.3. Statistical Analysis
Descriptive statistics are presented as means ± standard deviations. Performance, or race time,
was recorded in the format “hours:minutes:seconds” (h:min:s) and converted into hours, as a numerical
variable. For 50- and 100-mile ultra-marathon races, t-tests were performed to compare the average
performance between men and women by age group and by country. It was acknowledged that
analyses of variance (ANOVAs) might have been easier to interpret; however, the mixed regression
analysis was preferred since it was necessary to correct for clustered observations within runners who
participate more than once. ANOVA would have not accounted for clustered observations. The age
groups were 10–19, 20–29, 30–39, 40–49, 50–59, 60–74, and 75–95 years, and only observations with
non-missing ages were considered in analyses involving age. Country groups were identiﬁed through
participation prevalence by country: United States of America (USA), Canada (CAN), Great Britain
(GBR), and Republic of South Africa (RSA). The other countries were grouped together. Age was
considered as a continuous variable, in 1-year intervals, when deﬁned as a predictor variable for
ultra-marathon time. A non-linear regression mixed model with basis splines was performed to ﬁnd
the age of peak performance, which is the age at which the time record-ﬁtted value has a minimum.
The mixed model was used to correct for repeated measurements within runners (clusters) through the
random eﬀects of intercepts. Diﬀerent regression model speciﬁcations were initially considered, with
age–sex, age–country, and country–sex interaction terms and with diﬀerent hypotheses about the age
and time trend. Model selection was performed using both the Akaike information criterion (AIC) and
the Bayes information criterion (BIC). In the ﬁnal selected model, age, calendar year, sex, country, and
a country–sex interaction term were considered as ﬁxed eﬀect predictors. The statistical model was
speciﬁed as follows:
Ultra − marathon time (Y) ∼ [ fixed ef fects (X) = BS(year, df = 3) + BS(age, df = 3)
+ sex + country + country ∗ sex] + [random ef fects of intercept = runners]
where BS (year, df=3) and BS (age, df = 3) are 3 degrees of freedom (df) basis splines changing with
calendar year and age, respectively; country*sex denotes the country–sex interaction term.
Two diﬀerent analyses were performed, one for 50-mile and one for 100-mile races. In the 50 miles
analysis, South Africa was combined with other countries because of the low number of runners.
Results of the regression models are presented as estimates and standard errors. In addition, sex
diﬀerences (%) in performance were examined, deﬁned as 100 × (women’s race time-men’s race
time)/men’s race time. For all tests and regressions, statistical signiﬁcance was deﬁned as p < 0.05.
All statistical analyses were carried out with R [35]. The packages ggplot2, lme4, and lmerTest were
used, respectively, for data visualization and for the mixed model.
3. Results
Between 1964 and 2017, a total of n = 231,980 records on 91,665 diﬀerent ﬁnishers with information
on age were retrieved from the database on 50-mile ultra-marathon races. For 100-mile races, a total
of n = 107,445 records on 39,870 diﬀerent ﬁnishers was available for the period between 1953 and
2017. Overall, the average number of observations per runner was 2.53 in 50-mile and 2.69 in 100-mile
races. In 50-/100-mile races, the number of women was 23,548 (26%)/7789 (20%) with 55,540 (24% of
the total observations)/20,154 (19% of the total observations) records, and the number of men was
68,107 (74%)/32,081 (80%) with 176,440 (76%)/87,291 (81%) records.
The proportions of observations of ﬁnishers aged 50 years and above were 24.4% (men) and
17% (women) in 50-mile races and 24.9% (men) and 18.3% (women) in 100-mile races, indicating that
ﬁnishing men tended to be slightly older than women. The vast majority of ﬁnishers participated in
races in the USA (85.2%); 6.1%, 3.8%, and 0.1% of the sample participated in Great Britain, Canada,
and South Africa, respectively, and 4.1% in races taking place in 43 other countries.
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In Table 1, the number of observations and the average performance by sex, age group, and country
are reported for 50- and 100-mile races. In both 50-/100-mile races, the shortest average race times were
observed in the 20–29 years age group, both in men (10.30 h/26.07 h) and in women (11.18 h/27.14 h);
the lowest average performances were observed in the 75–95 years age group, again both in men
(14.20 h/29.73 h) and in women (13.40 h/29.00 h). In 50-mile races, the shortest average race times were
observed in Canada (10.47 h in men and 11.35 h in women) and the longest in Great Britain (11.67 h in
men and 12.87 h in women). In 100-mile races, the shortest average running times occurred in South
Africa (21.82 h in men, 23.19 h in women) and the longest in the group of the 43 “other” countries (27.73
h in men and 28.11 h in women). Performance diﬀerences between sexes were signiﬁcant (p < 0.001)
for all age groups <75 years in the 50-mile races and for age groups between 20 and 60 years in the
100-mile races. In the ≥75 years of age group, better performances occurred in women compared to
men, even though the diﬀerence failed to attain statistical signiﬁcance due to the small sample size.
The magnitude of the diﬀerence was, however, similar to that seen in younger age groups, where men
are faster than women, and particularly in 5- mile races. With the largest performance sex gap in favor
of men seen in the youngest age group (10–19 years), a clear performance trend over age is visible for
both distances.
Table 1.
Mean ultra-marathon performance (50 and 100 miles) by sex, age group, and country
(South Africa, due to a small sample size for 50-mile races, is combined with other countries). p-values
of a t-test of mean performance between sexes are shown.
50 miles, n = 231,980
100 miles, n = 107,445
Age group
Sex
n
Mean
(hours)
Sd
(hours)
p
n
Mean
(hours)
Sd
(hours)
p
10–19
Men
1312
10.9778
2.3152
<0.001
131
26.6158
5.1444
0.057
Women
177
12.0706
2.0483
12
30.9698
7.0097
20–29
Men
18,124
10.3022
2.1982
<0.001
5966
26.0697
5.4017
<0.001
Women
6409
11.1797
2.2465
1410
27.1409
4.9447
30–39
Men
53,553
10.3663
2.1998
<0.001
26,069
26.1852
5.6369
<0.001
Women
19,256
11.2043
2.2210
6778
27.3619
5.1514
40–49
Men
60,351
10.6421
2.1462
<0.001
33,387
26.9095
5.5481
<0.001
Women
20,234
11.5077
2.2047
8257
27.7625
5.0640
50–59
Men
33,857
11.1670
2.0841
<0.001
17,867
27.8913
5.2604
<0.001
Women
8210
12.1018
2.1911
3350
28.5333
5.0671
60–74
Men
9054
12.0289
2.1031
<0.001
3844
28.9254
4.9203
0.358
Women
1230
12.9829
2.4215
342
28.6806
4.6994
75–95
Men
189
14.1952
3.6604
0.199
27
29.7292
6.2894
0.571
Women
24
13.4018
2.6675
5
29.0034
0.8413
Country
Sex
n
Mean
(hours)
Sd
(hours)
p
n
Mean
(hours)
Sd
(hours)
p
Canada
Men
6208
10.4748
2.2036
<0.001
2924
26.2718
4.4521
<0.001
Women
2563
11.3481
2.2938
1000
27.3393
4.6320
Great Britain
Men
11,249
11.6678
3.0373
<0.001
4724
26.7545
6.1957
0.009
Women
2805
12.8717
3.3837
785
27.3641
6.0431
United States
Men
149,514
10.6281
2.0732
<0.001
62,949
27.1163
4.9311
<0.001
Women
48,307
11.4174
2.1129
15,679
27.8919
4.5937
South Africa
Men
3830
21.8187
3.3647
<0.001
Women
585
23.1866
3.9734
Other
Men
Women
9469
1865
10.8642
11.4223
2.6745
2.7839
<0.001
12,864
2105
27.7271
28.1111
7.6246
7.5810
0.031
(Note: Due to the small sample size for 50-mile races, South Africa was combined with other countries; p-values are
from comparisons of mean performances between sexes).
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Regarding country, in both distances and for all country groups, performance diﬀerences were
signiﬁcant (p < 0.001) between sexes due to a better performances in men, the largest diﬀerences being
observed in South Africa. Table 2 describes, for both distances, the results of the statistical models, as
described in the methods section (model selection statistics omitted). For 50 miles, race times were 11.74
(Sd = 1.95) h for men and 12.31 (Sd = 1.69) for women, with a sex diﬀerence of 9.13%. For 100 miles,
race times were 26.6 (Sd = 3.49) h for men and 27.47 (Sd = 3.6) h for women, with a sex diﬀerence of
4.41%. Women were signiﬁcantly slower than men (p < 0.001), the estimated sex diﬀerences being 0.74
(SE = 0.017) and 0.81 (SE = 0.075) hours in 50- and 100-mile races, respectively. For 50 miles, compared
to the USA, ﬁnishers in Canada and in other countries were signiﬁcantly (p < 0.001) faster by 0.19
(SE = 0.040) and 0.092 (SE = 0.028) hours, respectively. In contrast, ﬁnishers in GBR were signiﬁcantly
(p < 0.001) slower by 0.656 hours. For 100 miles, compared to the USA, ﬁnishers in GBR, CAN, and the
RSA were signiﬁcantly (p < 0.001) faster, with runners in the RSA being faster than in the USA by an
estimated 3.938 hours. Other countries were slower by 0.893 hours, p < 0.001, compared to the USA.
Table 2. Regression analysis (mixed model) of ultra-marathons (50 and 100 miles). Estimates and
standard errors (SEs) of ﬁxed eﬀects are reported. P-value ranges are marked with asterisks (see note).
Smoothing terms, basis splines, are denoted with BS(x) t, where x = year, age; t = 1,2,3.
50 miles Estimate (SE)
100 miles Estimate (SE)
Intercept
12.462 ***
23.658 ***
(0.152)
(1.766)
Year
BS (year) 1
−4.009 ***
1.399
(0.236)
(2.416)
BS (year) 2
−1.218 ***
5.882 ***
(0.126)
(1.609)
BS (year) 3
−0.313 *
6.651***
(0.148)
(1.750)
Age
BS (age) 1
−2.427 ***
−8.204***
(0.176)
(1.055)
BS (age) 2
−0.511***
3.674 ***
(0.090)
(0.652)
BS (age) 3
4.039 ***
−0.637
(0.189)
(1.523)
Country (ref. United States)
Canada
−0.190 ***
−1.001***
(0.040)
(0.156)
Great Britain
0.656 ***
−0.322 **
(0.028)
(0.109)
Other
−0.092 ***
0.893 ***
(0.028)
(0.070)
South Africa
−3.938 ***
(0.128)
Sex (ref. Men)
Women (W)
0.740 ***
0.810 ***
(0.017)
(0.075)
Int. J. Environ. Res. Public Health 2019, 16, 2377
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Table 2. Cont.
50 miles Estimate (SE)
100 miles Estimate (SE)
Country*Sex
Canada*W
0.057
0.751*
(0.074)
(0.311)
Great Britain*W
0.562 ***
−0.133
(0.061)
(0.273)
Other countries*W
−0.191 **
−0.339
(0.067)
(0.180)
South Africa*W
0.129
(0.331)
Observations
231,980
107,445
Note: * p < 0.05; ** p < 0.01; *** p < 0.001.
The Country*Sex interaction terms, for example the term Great Britain*W, estimates how much
greater the eﬀect of being a woman in a particular country (e.g., Great Britain) was on race time,
compared to the USA. The interaction eﬀects (Table 2) are visualized in Figure 1 (50 miles) and Figure 2
(100 miles). They were particularly pronounced for GBR in 50-mile races (0.562 hours) and for CAN in
100-mile races (0.751 hours), where the performances of women and men diﬀered clearly more than in
the USA. The distance between the ﬁtted curves in men and women is largest for GBR in 50-mile races
and for CAN in 100-mile races.
Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 
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(0.061) 
(0.273) 
Other countries*W 
−0.191 ** 
−0.339 
 
(0.067) 
(0.180) 
South Africa*W 
 
0.129 
 
 
(0.331) 
Observations 
231,980 107,445 
Note: * p < 0.05; ** p < 0.01; *** p < 0.001. 
234 
The Country*Sex interaction terms, for example the term Great Britain*W, estimates how much 
235 
greater the effect of being a woman in a particular country (e.g., Great Britain) was on race time, 
236 
compared to the USA. The interaction effects (Table 2) are visualized in Figures 1 (50 miles) and 2 
237 
(100 miles). They were particularly pronounced for GBR in 50-mile races (0.562 hours) and for CAN 
238 
in 100-mile races (0.751 hours), where the performances of women and men differed clearly more 
239 
than in the USA. The distance between the fitted curves in men and women is largest for GBR in 
240 
50-mile races and for CAN in 100-mile races. 
241 
 
242 
Figure 1. Ultra-marathon speed, 50 miles, by sex, age (in years), and country. Points are race-time averages. 
243 
Lines are fitted curves (mixed model). Vertical lines with numeric labels are the ages at peak performance. 
244 
USA = United States of America, CAN = Canada, GBR = Great Britain, W = women, M = men. 
245 
 
246 
Figure 1. Ultra-marathon speed, 50 miles, by sex, age (in years), and country. Points are race-time averages.
Lines are fitted curves (mixed model). Vertical lines with numeric labels are the ages at peak performance.
USA = United States of America, CAN = Canada, GBR = Great Britain, W = women, M = men.
Int. J. Environ. Res. Public Health 2019, 16, 2377
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247 
Figure 2. Ultra-marathon speed, 100 miles, by sex, age (in years), and country. Points are race-time averages. 
248 
Lines are fitted curves (mixed model). Vertical lines with numeric labels are the ages at peak performance. The 
249 
sample sizes decrease towards the minimum and maximum of the age axes, with some of the points reflecting 
250 
only individuals; for example, the five points corresponding with GBR men 75+ years of age reflect one 
251 
individual each, one of the three remarkable individuals (Geoffrey Oliver) accounting for three of the five 
252 
points [36]. USA = United States of America, CAN = Canada, GBR = Great Britain, RSA = Republic of South 
253 
Africa, W = women, M = men. 
254 
  
255 
All the effects in Table 2, together with the age and year of peak performance, are shown 
256 
graphically in Figures 1–4. Both in 50-mile (Figure 1) and 100-mile (Figure 2) races, running times 
257 
decreased and, after reaching a minimum at 33 years (peak performance), increased with increasing 
258 
age. Regarding the calendar period, in 50-mile races, 1985 was the year of the best performance 
259 
(Figure 3), whereas in 100-mile races, performance worsened consistently over time (Figure 4). In 
260 
Figure 5, the estimated sex differences in performance by country are shown over age. For both 
261 
distances, the differences in favor of men increased up to about 33 years, and the increase was 
262 
subsequently followed by a decrease. In 100- but not in 50-mile races, the differences re-increased 
263 
slightly after about 80 years of age. For both distances, the estimated sex differences were smaller for 
264 
100- than for 50-mile races. Over calendar time, from 1953 to 2017, the sex difference in performance 
265 
decreased continuously in all countries in 100-mile races. For roughly the same period, the sex 
266 
difference in performance peaked at around 1985 in all countries for 50-mile races (Figure 6). 
267 
Figure 2. Ultra-marathon speed, 100 miles, by sex, age (in years), and country. Points are race-time
averages. Lines are ﬁtted curves (mixed model). Vertical lines with numeric labels are the ages at peak
performance. The sample sizes decrease towards the minimum and maximum of the age axes, with
some of the points reﬂecting only individuals; for example, the ﬁve points corresponding with GBR
men 75+ years of age reﬂect one individual each, one of the three remarkable individuals (Geoﬀrey
Oliver) accounting for three of the ﬁve points [36]. USA = United States of America, CAN = Canada,
GBR = Great Britain, RSA = Republic of South Africa, W = women, M = men.
All the eﬀects in Table 2, together with the age and year of peak performance, are shown graphically
in Figures 1–4. Both in 50-mile (Figure 1) and 100-mile (Figure 2) races, running times decreased and,
after reaching a minimum at 33 years (peak performance), increased with increasing age. Regarding
the calendar period, in 50-mile races, 1985 was the year of the best performance (Figure 3), whereas in
100-mile races, performance worsened consistently over time (Figure 4). In Figure 5, the estimated sex
diﬀerences in performance by country are shown over age. For both distances, the diﬀerences in favor
of men increased up to about 33 years, and the increase was subsequently followed by a decrease.
In 100- but not in 50-mile races, the diﬀerences re-increased slightly after about 80 years of age. For
both distances, the estimated sex diﬀerences were smaller for 100- than for 50-mile races. Over calendar
time, from 1953 to 2017, the sex diﬀerence in performance decreased continuously in all countries in
100-mile races. For roughly the same period, the sex diﬀerence in performance peaked at around 1985
in all countries for 50-mile races (Figure 6).
Int. J. Environ. Res. Public Health 2019, 16, 2377
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268 
 
269 
Figure 3. Ultra-marathon speed, 50 miles, by sex, calendar year, and country. Points are race-time averages. 
270 
Lines are fitted curves (mixed model). Vertical lines with numeric labels are the ages at peak performance. 
271 
USA = United States of America, CAN = Canada, GBR = Great Britain, W = women, M = men. 
272 
 
273 
4. Discussion 
274 
The aim of this study was to examine the sex gap in performance in ultra-marathons. We 
275 
hypothesized a decrease of the sex gap with increasing age and that this decrease would be 
276 
independent from race distance. The main findings were that the (i) sex difference in performance 
277 
was smaller in older than in younger athletes; (ii) the relative sex difference in performance was 
278 
smaller in 100- than in 50-mile races; (iii) the sex difference in performance approaches a historical 
279 
minimum; (iv) the peak performance age was 33 years; (v) the average performance worsened over 
280 
the last three decades. Minor findings were that (vi) men were slightly older than women; (vii) more 
281 
than two thirds (70%) of the finishers had participated in 50-mile races; (viii) three quarters (76%) of 
282 
all finishers were men; (ix) the proportion of men was higher in 100-mile races (80%) than in 50-mile 
283 
races (74%); (x) in South African races, men and women demonstrated the best 100-mile 
284 
performances.  
285 
 
286 
Figure 3. Ultra-marathon speed, 50 miles, by sex, calendar year, and country. Points are race-time averages.
Lines are fitted curves (mixed model). Vertical lines with numeric labels are the ages at peak performance.
USA = United States of America, CAN = Canada, GBR = Great Britain, W = women, M = men.
Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 
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287 
Figure 4. Ultra-marathon speed, 100 miles, by sex, calendar year, and country. Points are race-time averages. 
288 
Lines are fitted curves (mixed model). Vertical lines with numeric labels are the ages at peak performance. 
289 
USA = United States of America, CAN = Canada, GBR = Great Britain, RSA = Republic of South Africa, W = 
290 
women, M = men. 
291 
 
292 
4.1. The Sex difference in performance was smaller in older than in younger athletes 
293 
In 50-mile races, the decline in the sex difference always decreasing up to the highest age. There 
294 
are multiple possible physiological mechanisms in men for the reduction in the performance sex gap 
295 
with increasing age, including lower levels of anabolic hormones [37], a decrease in neuromuscular 
296 
efficiency [38], and a reduced ability to synthesize protein [39] as well as body fat [40]. In addition, 
297 
the loss in skeletal muscle mass is more pronounced in men at the age of 60 years and above 
298 
Figure 4. Ultra-marathon speed, 100 miles, by sex, calendar year, and country. Points are race-time
averages. Lines are ﬁtted curves (mixed model). Vertical lines with numeric labels are the ages at peak
performance. USA = United States of America, CAN = Canada, GBR = Great Britain, RSA = Republic
of South Africa, W = women, M = men.
Int. J. Environ. Res. Public Health 2019, 16, 2377
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4. Discussion
The aim of this study was to examine the sex gap in performance in ultra-marathons. We
hypothesized a decrease of the sex gap with increasing age and that this decrease would be independent
from race distance. The main ﬁndings were that the (i) sex diﬀerence in performance was smaller
in older than in younger athletes; (ii) the relative sex diﬀerence in performance was smaller in 100-
than in 50-mile races; (iii) the sex diﬀerence in performance approaches a historical minimum; (iv) the
peak performance age was 33 years; (v) the average performance worsened over the last three decades.
Minor ﬁndings were that (vi) men were slightly older than women; (vii) more than two thirds (70%) of
the ﬁnishers had participated in 50-mile races; (viii) three quarters (76%) of all ﬁnishers were men;
(ix) the proportion of men was higher in 100-mile races (80%) than in 50-mile races (74%); (x) in South
African races, men and women demonstrated the best 100-mile performances.
Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 
12 of 17 
 
308 
Figure 5. Sex differences by age (in years) and country in 50- and 100-mile ultra-marathons. Curves represent 
309 
fitted values. For 50-mile races, South Africa was combined with other countries. 
310 
USA = United States of America, CAN = Canada, GBR = Great Britain, RSA = Republic of South Africa, W = 
311 
women, M = men. Sex differences (%) in performance were defined as 100× (women’s race time–men’s race 
312 
time)/(men’s race time). 
313 
 
314 
In contrast to 50-mile races, in 100-mile races, the age-related downward trend in the sex 
315 
difference reversed, the sex difference again increasing after about 80 years of age. It has to be noted, 
316 
however, that the number of athletes in the oldest age group was rather small, in particular in 
317 
100-mile races. Thus, the increase in the sex gap in 100-mile races could simply be due to chance. 
318 
Alternatively, however, the possibility of an increasing out-selection of relatively slow men at higher 
319 
ages, in particular in 100-mile races, cannot be excluded. This does not appear completely 
320 
implausible as physical performance is predictive of longevity at older ages [42,43], possibly 
321 
underlying a deficit in high-performing men. However, as the increase of the sex gap at very high 
322 
ages did not occur in 50-mile races, plain chance appears to be the more plausible explanation.  
323 
 
324 
 
325 
Figure 5. Sex diﬀerences by age (in years) and country in 50- and 100-mile ultra-marathons. Curves
represent ﬁtted values. For 50-mile races, South Africa was combined with other countries. USA =
United States of America, CAN = Canada, GBR = Great Britain, RSA = Republic of South Africa, W =
women, M = men. Sex diﬀerences (%) in performance were deﬁned as 100× (women’s race time–men’s
race time)/(men’s race time).
4.1. The Sex Diﬀerence in Performance Was Smaller in Older Than in Younger Athletes
In 50-mile races, the decline in the sex diﬀerence always decreasing up to the highest age. There
are multiple possible physiological mechanisms in men for the reduction in the performance sex gap
with increasing age, including lower levels of anabolic hormones [37], a decrease in neuromuscular
eﬃciency [38], and a reduced ability to synthesize protein [39] as well as body fat [40]. In addition,
the loss in skeletal muscle mass is more pronounced in men at the age of 60 years and above
compared to women of the same age, with sarcopenia present in ~53% of men compared to ~47%
Int. J. Environ. Res. Public Health 2019, 16, 2377
11 of 16
of women [41]. Our ﬁnding of a sex gap reduction with increasing age in ultra-marathon running
is consistent with recent ﬁndings of studies analyzing master swimmers competing in pool and
open-water races [6,25,26,29–31]. The factor of sarcopenia was also suggested by Knechtle et al. [25],
who investigated 65,584 freestyle master swimmers between 1986 and 2014. Sarcopenia might thus
be an important factor in ultra-marathon running as well. Finally, compared to men, women tend to
live longer and to be in better physical condition later in life [30]. A larger higher-age population of
high-performing women as compared to men in 50-mile ultra-marathon races can thus be expected
based on these considerations.
Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 
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326 
Figure 6. Sex differences by calendar year and country in 50-mile and 100-mile ultra-marathons. 
327 
Curves represent fitted values. For 50-mile races, South Africa was combined with other countries. 
328 
USA = United States of America, CAN = Canada, GBR = Great Britain, RSA = Republic of South 
329 
Africa, W = women, M = men. Sex differences (%) in performance were defined as 100 × (women’s 
330 
race time–men’s race time) / men’s race time. 
331 
4.2. Relative sex difference in performance was smaller in 100- than in 50-Mile Races  
332 
Our second hypothesis of the decrease in the sex gap in performance with increasing age being 
333 
independent from race distance was not confirmed. For both race distances, a decline of the relative 
334 
sex difference is clearly visible, and the decline is more pronounced in 50-mile races than 100-mile 
335 
races. One explanation for this finding could be that in extremely long distances, like 100-mile races, 
336 
there might exist a sex-independent pace limit [44]. 
337 
This limit might constitute a performance maximum, outweighing sex differences at increasing 
338 
ages (“ceiling effect”). Nikolaidis et al. [11] also found a decrease in the sex gap with increasing race 
339 
distance from half-marathon to marathon and to 100-km ultra-marathon races. In contrast, Coast et 
340 
al. [20] found, more than 10 years earlier, an increasing sex gap with increasing running distances. 
341 
However, these authors had restricted their analysis to world-best running performances at 
342 
distances from 100 m to 200 km, and results might thereby have been biased by the selection of 
343 
mostly top athletes. Furthermore, the authors indicate that their results might have been confounded 
344 
by the reduced number of women in longer-distance events. The question of whether the sex gap 
345 
depends on race distance thus requires further research. 
346 
Figure 6. Sex diﬀerences by calendar year and country in 50-mile and 100-mile ultra-marathons. Curves
represent ﬁtted values. For 50-mile races, South Africa was combined with other countries. USA =
United States of America, CAN = Canada, GBR = Great Britain, RSA = Republic of South Africa, W =
women, M = men. Sex diﬀerences (%) in performance were deﬁned as 100 × (women’s race time–men’s
race time)/men’s race time.
In contrast to 50-mile races, in 100-mile races, the age-related downward trend in the sex diﬀerence
reversed, the sex diﬀerence again increasing after about 80 years of age. It has to be noted, however, that
the number of athletes in the oldest age group was rather small, in particular in 100-mile races. Thus,
the increase in the sex gap in 100-mile races could simply be due to chance. Alternatively, however, the
possibility of an increasing out-selection of relatively slow men at higher ages, in particular in 100-mile
races, cannot be excluded. This does not appear completely implausible as physical performance is
predictive of longevity at older ages [42,43], possibly underlying a deﬁcit in high-performing men.
However, as the increase of the sex gap at very high ages did not occur in 50-mile races, plain chance
appears to be the more plausible explanation.
Int. J. Environ. Res. Public Health 2019, 16, 2377
12 of 16
4.2. Relative Sex Diﬀerence in Performance Was Smaller in 100- Than in 50-Mile Races
Our second hypothesis of the decrease in the sex gap in performance with increasing age being
independent from race distance was not conﬁrmed. For both race distances, a decline of the relative
sex diﬀerence is clearly visible, and the decline is more pronounced in 50-mile races than 100-mile
races. One explanation for this ﬁnding could be that in extremely long distances, like 100-mile races,
there might exist a sex-independent pace limit [44].
This limit might constitute a performance maximum, outweighing sex diﬀerences at increasing
ages (“ceiling eﬀect”). Nikolaidis et al. [11] also found a decrease in the sex gap with increasing race
distance from half-marathon to marathon and to 100-km ultra-marathon races. In contrast, Coast et
al. [20] found, more than 10 years earlier, an increasing sex gap with increasing running distances.
However, these authors had restricted their analysis to world-best running performances at distances
from 100 m to 200 km, and results might thereby have been biased by the selection of mostly top
athletes. Furthermore, the authors indicate that their results might have been confounded by the
reduced number of women in longer-distance events. The question of whether the sex gap depends on
race distance thus requires further research.
4.3. Sex Diﬀerence in Performance Approaches a Historical Minimum
Over the period of 1953 to 2017 (end of study period), the sex diﬀerence decreased across all
countries in 100-mile races. In contrast, in 50-mile races, the decline was restricted, across all countries,
to the period of about 1985 to 2017, whereas in the preceding period from 1964 on, the sex diﬀerence
had increased across all countries. For either distance, the range of sex diﬀerences between countries
exceeds that within countries and is larger for 50- than for 100-mile races, the ranking of the countries
being diﬀerent in 50- and 100-mile races.
4.4. Peak Performance Age Was 33 Years
In spite of some outliers at certain ages and in certain countries, based on very few or even single
individuals per year of age, as demonstrated in Figures 1 and 2, the model-based peak performance
age was 33 years, for both race distances. It has to be noted that due to the age*sex and age*country
interaction terms being dismissed during the course of the pre-speciﬁed stepwise model-building
process, the performance peaks do not show variation across sex and country. The model-based
estimate of the age of peak performance of 33 years is in line with the available literature. Most previous
studies suggested that the age of peak performance in ultra-marathons lies between 30 to 49 years for
men and between 30 to 54 years for women [3,4,13,17,45]. However, our result is more in the suggested
range of marathon peak performance age of 25 to 35 years for men and women [11,14–16].
4.5. Average Performance Worsened Over the Last Three Decades
In 50-mile races, the average running speed improved in both sexes from 1964 (start of the study
period) up to 1985, subsequently worsening until the end of the study period (2017). In 100-mile
races, a decline of the average running speed occurred over the whole study period, i.e., from 1953
to 2017. The performance improvement in 50-mile races up to 1985 is essentially attributable to US
races, with relatively low average running speeds (see Figure 3). As about 85% of the races in the
data set were from the USA, these particular races have inevitably impacted the overall shape of the
performance-over-calendar-year-curve, the model speciﬁcation not allowing for a country-speciﬁc
shape due to not considering a country*calendar year interaction term. Had these low-performance
US races been omitted from ﬁtting the speciﬁed model, this would have resulted in a continuous
performance decrease over the whole study period for 50-mile races, as it did for 100-mile races.
Historically, the USA was the ﬁrst country where ultra-marathon running became a popular activity
among 436 recreational athletes [46]. It is reasonable to assume that initially, these recreational athletes
had engaged preferably in 50-mile rather than 100-mile races, lowering the average performance.
Int. J. Environ. Res. Public Health 2019, 16, 2377
13 of 16
For example, 494,414 runners participated in 50-km ultra-marathon races between 1975 and 2016 [4], as
compared to only 370,051 runners who participated in 100-km ultra-marathon races between 1959 and
2016 [13]. This is conﬁrmed with the present data, where more than two thirds (70%) of the ﬁnishers
had participated in 50-mile races.
Despite this impact of the US-speciﬁc phenomenon in shaping the performance model curve,
the general trend visible in the data is a performance decline over the study period. One factor for this
general decrease in running speed across calendar years could be the popularity of ultra-marathon
races gradually increasing worldwide and the races increasingly attracting recreational (i.e., master)
athletes. As a consequence, the average performance would have gradually shifted to lower levels.
It can be assumed that 100-mile races have always been less attractive to recreational athletes, as they
require a more rigorous preparation than 50-mile races. An example for the more rigorous preparation
with increasing race distances is provided by Rüst et al., who compared training characteristics between
marathoners and 100-km ultra-marathoners and found that ultra-marathoners completed signiﬁcantly
more hours and kilometers during their training. It is therefore likely that the inﬂux of recreational
athletes into 100-mile races has been more gradual than in, for example, 50-mile races, and this trend
leads to a non-linearity of the performance trend curve.
4.6. Limitations and Strength
A limitation of the present study was that it considered speciﬁc ultra-running race distances
(50 and 100 miles) and thus, caution would be needed to generalize the ﬁndings to ultra-running
races of other distances or durations [33]. On the other hand, a strength was the large data set that
was available for analysis, which was not restricted to top athletes and covered the whole range of
performance levels. The depth of the dataset both temporally as well as geographically, and the number
of race distances included resulted in the opportunity to provide a comprehensive historic coverage of
both 50- and100-mile ultra-marathon results. The data was publicly available, and the collection of data
was independent from the data analysis, and therefore the replication of the present analyses is possible.
The study ﬁndings may aid coaches and ultra-marathon runners in setting long-term training goals
based on an athlete’s age and sex. For example, knowledge of the peak age of performance (33 years
in both race distances) may inﬂuence individuals seeking to race in these distances and recruitment
from a coaching perspective. Furthermore, the variation in performance by sex might also inﬂuence
the training stimulus to be more homogeneous because the sex diﬀerence is small (e.g., 100 miles or
elder age groups). In addition to performance, the abovementioned practical applications were also
relevant from a health perspective. The role of exercise in the prevention and treatment of diseases
(e.g., coronary artery disease, stroke, hypertension, diabetes, arthritis, osteoporosis, dyslipidemia,
obesity, depression, cancer, and chronic obstructive pulmonary disease) has been well recognized [47].
The ﬁndings of the present study can aid physicians prescribing endurance exercise considering sex
and age [48].
5. Conclusions
In summary, as age increases, the performance diﬀerence between women and men decreases and
also becomes lower with longer distances. Based on the model, the overall age of peak performance
was 33 years. Future investigations should not just include races measured in miles but also those
measured in kilometers, and also analyze time-limited races. Potentially relevant covariates should be
considered and whenever possible, acquired prospectively through interviews when they cannot be
accessed through administrative databases. If this data had been available, a more comprehensive
analysis could have been conducted, including covariate-adjusted time-series analysis.
Int. J. Environ. Res. Public Health 2019, 16, 2377
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Author Contributions: Conceptualization, B.K., K.J.W., and P.T.N.; methodology, S.D.G.; software, S.D.G.;
validation, S.D.G.; formal analysis, S.D.G.; investigation, B.K.; resources, B.K.; data curation, S.D.G.;
writing—original draft preparation, K.J.W., B.K., and P.T.N.; writing—review and editing, S.D.G. and T.R.;
visualization, S.D.G.; supervision, B.K.; project administration, B.K.; funding acquisition, T.R.
Funding: This research received no external funding.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Women Reduce the Performance Difference to Men with Increasing Age in Ultra-Marathon Running.
Waldvogel, Karin J,Nikolaidis, Pantelis T,Di Gangi, Stefania,Rosemann, Thomas,Knechtle, Beat