1
Vol.:(0123456789)
Scientific Reports |        (2023) 13:12521  
| https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports
Elderly female ultra‑marathoners 
reduced the gap to male 
ultra‑marathoners in Swiss running 
races
Beat Knechtle 1,2*, Anja Witthöft 3, David Valero 4, Mabliny Thuany 5, Pantelis T. Nikolaidis 6, 
Volker Scheer 4, Pedro Forte 7,8,9 & Katja Weiss 2
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.
Running is high  popular1 and is organized in different event formats such as track  running2,3, road  running4 and 
trail running (off-road running)5. Road-based running races are held over different distances, such as 5  km6, 
10  km4, half-marathon7,  marathon8 and ultra-marathon9. Recently, the interest in sports science to study female 
athletes profiles has grown (i.e., physiology, professionalism, and contextual factors that affect performance)10,11, 
with the aspect of sex difference in running garnering high-interest12–17. Twenty years ago, it was assumed that 
longer running distances were associated with higher sex differences. This might have been confounded by 
the reduced number of female runners in longer running  distances15. It was also assumed that a sex difference 
of ~ 11–12% would be unchanged independent of the  distance15 and would not change over  years17,18. Today, the 
sex difference is still higher in longer running distances compared to shorter  distances16, and the sex difference 
of different sports disciplines remained stable at ~ 10%12. It highlighted the need for researching the sex differ-
ences in different running distances.
Recent studies showed that elderly female ultra-marathoners reduced the gap to male ultra-marathoners of 
the same  age13,14. Age seems to be of higher importance than the length of a race. It has been shown that female 
runners reduced the gap to male runners with increasing age, not with the increasing length of a  race14. It is 
important to understand the contextual and environmental factors that may explain sex differences in running 
competitions. Modality popularity is dependent on the number of participants and competitive level. Therefore, 
females reducing the performance gap to male runners may improve participation in the sports modality. Upon 
that, it is important to understand the performance differences between sex (i.e., running speed), the ratio of 
OPEN
1Medbase St. Gallen am Vadianplatz, Vadianstrasse 26, 9001 St. Gallen, Switzerland. 2Institute of Primary Care, 
University of Zurich, Zurich, Switzerland. 3Kinderspital St. Gallen, St. Gallen, Switzerland. 4Ultra Sports Science 
Foundation, Pierre-Benite, France. 5Faculty of Sports, University of Porto, Porto, Portugal. 6School of Health and 
Caring Sciences, University of West Attica, Athens, Greece. 7CI-ISCE, Higher Institute of Educational Sciences of 
the Douro, Penafiel, Portugal. 8Instituto Politécnico de Bragança, Bragança, Portugal. 9Research Center in Sports, 
Health and Human Development, Covilhã, Portugal. *email: beat.knechtle@hispeed.ch
2
Vol:.(1234567890)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
participants between sexes and the evolution of the participant number over time to explain the sex differences 
in running. This first step may allow forecasting strategies to reduce sex differences in running.
Studies investigating the sex difference in endurance running performance analyzed rather small samples 
and/or single  distances19. The present study investigated the sex difference in performance in running races of 
5, 10 km, half-marathon, marathon, and ultra-marathon with race records of two decades of a single country 
with a sample size of more than one million race records. Based upon recent findings regarding the reduction of 
the sex difference in longer running distances and older age groups, we hypothesized to confirm recent findings.
Methods
Ethical approval. 
This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzer-
land, with a waiver of the requirement for informed consent of the participants as the study involved the analysis 
of publicly available data (EKSG 01/06/2010). The study was conducted in accordance with recognized ethical 
standards according to the Declaration of Helsinki adopted in 1964 and revised in 2013.
Data set. 
Athletes’ data from all 5, 10 km, half-marathon, marathon and ultra-marathon races held in Swit-
zerland between 1999 and 2019 were collected from different sources such as “swiss-running” (www. swiss- runni 
ng. ch), “runme” (www. runme. ch/ de/ laufk alend er/ schwe iz), “datasport” (www. datas port. com/ de) and “DUV” 
(https:// stati stik.d- u-v. org/ calen dar. php). For all race distances, road-based races and trail runs were included. 
For ultra-marathons, any running races longer than the official marathon distance of 42.195 km and longer than 
6 h were  combined20.
Data preparation. 
For each race, data from successful participants, including name and surname, sex, age, 
year of birth, distance and race time, event name and terrain type, were obtained from the websites and recorded 
in an EXCEL file. The average running speed (km/h) was calculated from the race distance and time. While 
the continuous age variable was available, race records were also classified (and later aggregated) by age group 
according to the official 5-year age group intervals. Records from runners younger than 18 were discarded. The 
junior category (18 years) includes only runners aged 18 and 19, while runners older than 75 were considered 
75+ years. Race records were also aggregated by year for male and female runners separately. The male-to-female 
ratio was calculated by dividing the number of male records by the number of female records each year.
Statistical analysis. 
Data normality was assessed by plotting histograms of the race speed in each race 
distance and for each sex. Descriptive statistics is then presented through the mean and standard deviation. 
For each race distance and age group, the male-to-female ratio was calculated by dividing the number of male 
records by the number of female records in each age group. The percentage difference in average speed was 
calculated as 100 * (male speed − female speed)/male speed. Pearson correlations were calculated between the 
male-to-female ratio, the percentage difference of speed and the age, where the male-to-female ratio and per-
centage of speed difference were calculated for each year of age (18 through to 100). Statistical significance 
was assessed through the calculation of p-values, where the threshold set at p < 0.05. A machine learning (ML) 
predictive model was built and evaluated through the  R2 and MAE metrics, with further analysis through SHAP 
values and feature importance/interaction analysis.
All data processing, analysis and visualization were done in a Google Colab notebook using Python and 
statistical/machine learning free packages such as numpy, pandas, matplotlib, seaborn, statsmodels, scipy.stats, 
sklearn, catboost, shap.
Results
A total of 1,149,182 race records from 419,042 runners competing in 243 race events were considered. Table 1 
presents the number of runners by distance and sex. Female runners were more numerous than male runners 
in the 5-km run. The male-to-female ratio increased with increasing race distance.
Figure 1 summarizes the trend in the number of runners over the years by distance and sex, along with the 
male-to-female ratio. The male-to-female ratio generally decreased in all race distances over the years, caused by 
a more progressive increase in the number of female runners (Fig. 1, pink lines) compared to the number of male 
runners (Fig. 1, blue lines). The number of male marathoners in Swiss races has steadily decreased since 2005.
Figure 2 shows the male-to-female ratio by age group and race distance. There is a general growing trend in 
the ratio from age 25 years until age group 70 years. Thereafter, the ratio increased further in 5 km and marathon, 
remained flat in 10 km, but decreased in half-marathon and ultra-marathon.
Table 1.  Number of male and female runners by race distance and male-to-female ratio.
Race distance
Male
Female
Total
Male-to-female ratio
5 km
44,866
56,954
101,820
0.78
10 km
204,648
125,469
330,117
1.63
Half-marathon
268,486
117,275
385,761
2.28
Marathon
187,778
43,777
231,555
4.28
Ultra-marathon
83,040
16,889
99,929
4.91
3
Vol.:(0123456789)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
Figure 3 presents the histograms of the average running speed by distance and sex. In general, male runners 
were running faster than female runners in all race distances. The fastest average running speed was achieved at 
10 km for both female and male runners. According to descriptive statistics, female runners participated more 
in the shorter distances and less in marathons and ultra-marathons, but the performance by sex did not differ 
so much in the longer distances.
Figure 4 presents the sex difference in percent difference by age for the five-race distances. According to 
descriptive statistics, there is a decreasing trend from the age 20 years to age 70 years in the 5, 10 km, and half 
Figure 1.  The trend in the number of runners and male-to-female ratio over the years by distance and sex.
4
Vol:.(1234567890)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
marathon distances. This decreasing trend starts at 45 years in marathon distances and at age 50 years in ultra-
marathons. In 5 km and marathon distances, the sex difference increases after age 65 years, while in 10 km, 
half-marathon and ultra-marathon, the sex difference continues decreasing beyond this age.
Table 2 presents the associations between the male-to-female ratio versus the % difference in running speed, 
between the male-to-female ratio and age group, and between the % difference in running speed and age group.
According to descriptive statistics, the male-to-female ratio increased with age, with some exceptions in the 
youngest and the oldest age. Furthermore, there were always more male than female runners (except for 5 km), 
and males were always faster than females, where the differences in running speed declined with increasing age, 
except in the marathon distance.
In 5 km, we found a significant and positive association (r = 0.57, p < 0.05) between the male-to-female ratio 
and the age and a significant and negative association (r = − 0.43, p < 0.05) between the difference in running 
speed and age. In 10 km, similar significant correlations can be observed, with slightly higher values. In the 
half-marathon, we found a significant and negative association (r = − 0.45, p < 0.05) between the male-to-female 
ratio and the percent difference in running speed and a significant and positive correlation (r = 0.67, p < 0.05) 
between the M/W ratio and the age. In the marathon, there was a significant and positive association (r = 0.28, 
p = 0.03) between the male-to-female ratio and the percent difference in running speed and a significant and 
positive association (r = 0.73, p < 0.05) between the male-to-female ratio and the age. Last, a significant and 
negative association (r = − 0.42, p = 0.0004) between the percent difference in running speed and age was found 
in ultra-marathon running.
Predictive model. 
To explore potential non-linear relationships between the variables of interest, an ML 
tree-ensemble/gradient boosting model was built and evaluated. The model uses the Cat Boost Regressor algo-
rithm with 200 learners to predict the average race speed (km/h) from the runner´s age and sex, the distance 
(km) and the terrain type (flat/trail).
The model was trained with over 860 K race records or 75% of the full sample of 1,149,182, and later evaluated 
over the remaining 287 K records (25%), achieving predictive accuracy scores of  R2 = 0.53, MAE = 1.32 km/h. The 
model features relative importance were also computed (Terrain 44%, Distance 32%, Sex 18%, Age 6%) along 
with the SHAP values and feature interactions.
Predictive model key indicators
Sample size
1,149,182
CatBoost model
200 trees
MAE (km/h) 1.32
R2 0.53
Figure 2.  The male-to-female ratio across age groups for all race distances.
5
Vol.:(0123456789)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
Features relative importances
Terrain_type
44.13
Distance_km
32.06
Runner_sex
17.66
Runner_age
6.14
Figure 3.  Histograms of average running speed by distance and sex.
6
Vol:.(1234567890)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
SHAP summary plots. 
Figure 5 presents the SHAP summary plots of individual prediction dots relative 
to reference value zero. They show how the model distributes its predictions for different values of each feature.
Terrain and Distance make the broadest contributions to the model output and are rated as the model’s most 
important features. Flat races (Terrain_type = 0, blue dots) score on the positive side of the x-axis (higher speeds), 
while trail races (Terrain_type = 1, red strip of dots) get reduced speeds. A similar pattern can be observed in the 
distance chart. Longer distances (red dots, but also violet ones—distance is continuous) can be seen increasingly 
towards the left (negative side of the x-axis), whilst low values of the distance predictor accumulate in the positive 
side. The sex distinction is clear, with males (’1’, red dots) accumulating almost completely on the right side of 
the chart. Finally, the age predictor, also a continuous variable, shows a similar pattern to the first two: lower ages 
(blue points) obtain the model’s best predictions of speed, and from there the x-axis turns darker blue, purple, 
and red towards the left as the age increases.
SHAP dependence plots. 
Figure 6 shows the SHAP dependence plots for the age of the runners, race 
distance, sex, and terrain. Each row represents the SHAP values for one predictor while its interactions with the 
other tree predictors are shown in each column. Regarding age, the first chart shows the blue dots corresponding 
to female, and the red dots to male, and shows an interesting trend changing gradually between 40 and 50 years. 
The model gives females better predictions than males from the age of 50 and up to 60 or 65 years. The second 
chart is largely dominated by blue (Terrain 0, flat races) and just shows the performance decline with age. The 
third chart has some interest, given that some red dots (long-distance races) obtain better speeds in age ranges 
(between 30 and 50 years and between 60 and 80 years). A possible explanation is the specialization of runners.
For the race distance predictor, the first chart shows a broad dispersion (blue and red dots overlapping) across 
the distance axis, indicating a small interaction of sex with the distance. An interesting chunk of blue points 
Figure 4.  % speed difference by sex for all race distances.
Table 2.  Correlations between the male-to-female ratio, % difference in running speed, and age 
(r = correlation coefficient, p = p value).
5 km
10 km
Half-Marathon
Marathon
Ultra-marathon
r
p
r
p
r
p
r
p
r
p
M/W ratio vs % running speed difference
0.04
0.73
− 0.01
0.92
− 0.45
0.0001
0.28
0.03
0.025
0.84
M/W ratio vs age
0.57
2.97e−07
0.66
6.3e−10
0.67
6.9e−10
0.73
2.8e−11
0.18
0.16
% running speed difference vs age
− 0.43
0.0002
− 0.55
7.1e−07
− 0.11
0.35
0.39
0.001
−0.42
0.0004
7
Vol.:(0123456789)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
(Runner_sex 0, female) can be seen from the 150 km distance, indicating the model gives a significant number 
of higher speed predictions to female than to male runners in these long distances. The second chart shows that 
blue (flat run) dominates high-speed predictions for very short distances, but then red is above blue from approx. 
50 km and up to 150 km. The third chart (distance/age interaction) shows equally a significant dispersion, where 
runners of all ages get the full range of predictions across all distances.
For sex, the first chart shows red dots (trail race predictions) closer between male and female runners, with 
the larger differences being between the flat races (blue points). The second chart shows that female performance 
(Sex 0, left bar) is more distant to male performance in the early ages (blue points) or, in other words, the model 
predicts the performance gap decreases with age. A similar pattern can be seen in the third chart, where blue 
points (performance in short-distance races) present the largest difference between male and female runners, 
progressively coming closer through violets and red colors (mid and old ages).
For terrain, the first chart shows that in trail races male runners obtain the lowest model predictions (red strip 
at the bottom of the right bar). The standard deviation of the data in the second chart is relatively high, indicating 
significant dispersion. The third and last chart shows that mid distances get higher speed predictions than high 
and short distances in both flat and trail races. This is more noticeable in trail terrain (ID 1).
Discussion
This study investigated the sex difference in running performance in a sample size of more than one million 
race records with the hypothesis to confirm the recent finding of reducing the sex difference in longer running 
distances and older age groups. The main important findings were (i) female runners participated more in the 
shorter race distances and less often in marathons and ultra-marathons, (ii) female runners were more numerous 
than male runners in the 5-km races, (iii) the male-to-female ratio increased with increasing race distance, (iv) 
the male-to-female ratio decreased in all race distances over the years, (v) the number of male marathoners con-
tinuously decreased since 2005, (vi) male runners were running faster than female runners in all distances, (vii) 
the fastest average running speed was in the 10 km events, and (viii) female runners reduced the performance gap 
with male runners gradually from age group 20–24 years to age group 65–69 years in 5, 10 km and half-marathon, 
but not so much in marathon and ultra-marathon, with differences thereafter for the different race distances.
Smallest sex difference in ultra‑marathon running. 
The smallest sex difference was found in the 
longest race distance (ultra-marathon) and oldest age group (75+), supporting the hypothesis. Female runners 
reduced the gap to male runners in ultra-marathon by increasing the age group from 50 to 54 years. The male-
to-female ratio decreased after age group 65–69 years in ultra-marathon, while it increased in marathons. The 
male-to-female ratio increased with age in most race distances but not in ultra-marathon. The decrease in sex 
Figure 5.  SHAP summary plots.
8
Vol:.(1234567890)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
difference and the increase in male-to-female ratio with increasing age was due to a higher number of faster and 
more competitive female runners in older age groups. This pattern was consistent across most race distances, 
except for an outlier in the oldest age group in marathons. The correlations between running speed difference, 
male-to-female ratio, and age group were similar across all race distances, with the few female runners in older 
age groups and ultra-marathon likely being exceptional performers.
A study of ultra-marathoners in timed and multi-day events found that peak performance age increased with 
longer race durations and more race finishes, indicating that successful ultra-marathoners improved with age 
and  experience21. A study investigating the effect of age and years of running experience in runners aged from 
20 to 80 years showed that the number of years of running had a positive effect on running  economy22. Master 
Figure 6.  SHAP dependence plots for age of the runners, race distance, sex, and terrain (from top to down).
9
Vol.:(0123456789)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
ultra-marathoners improved their performance in long ultra-marathons such as ‘Badwater’ and ‘Spartathlon’23. 
It was also shown by Van den Berghe et al. that a low-loading and high-load-bearing tolerance running style 
could be advantageous for completing endurance running events for long-term  runners24. A study comparing 
50- and 100-mile races showed that female runners reduced the gap to male runners with increasing age, where 
the sex difference was smaller in 100 miles compared to 50  miles13. Another study investigating data from the 
American Master Road Running Records from 5 km to 144 h showed that female runners reduced the gap to 
male runners with increasing age, not with increasing length or duration of the  performance14.
The sex difference in performance can be due to different reasons such as biological differences (e.g., muscle 
mass, body fat, body size, muscle strength, limb length)25–28, physiological differences (e.g., aerobic capacity, 
running economy, fatigue resistance, substrate efficiency, energetic demands)29–31,  participation9,32, experience 
and decision  making33,34,  motivation35,  sociocultural36, and psychological  differences3,37. Elderly female runners 
may have advantages over elderly male runners due to their ability to store and use elastic energy more efficiently, 
as studies have  shown27. Female runners have a higher proportional area of type I muscle fibers and are better 
at using fatty acids during prolonged exercise and preserving  carbohydrates25,38. This may lead to a more even 
pacing and less fatigue than male  runners25,38. Considering physiological differences, it has been assumed that 
female ultra-marathoners have better fatigue resistance compared to equally trained male ultra-marathoners who 
are faster in the marathon than the female ultra-marathoners30. Since males have a higher aerobic capacity and a 
higher skeletal muscle mass than females, the distance running gap will not narrow between females and  males28. 
Finally, decreasing testosterone levels due to aging also compromises male physical  performance39. However, 
female runners seem more prone to support fatigue, so the aging effects in performance may be  lower30. This 
may also support the reduced gap in sex differences with aging.
Participation of females in ultra-marathon is an important aspect since the percentage of female runners is 
generally low in these  races9,40. Lower participation of female runners compared to male runners overestimates 
the decline in age-related performance, especially in very old  females41. A small sex difference in ultra-marathon 
running is more likely due to a low number of participants than an outstanding  physiology42. An analysis of 20 
ultra-marathons from 45 to 160 km showed that the sex difference in running was lower in the longer distances 
and the largest when fewer female and male runners were in a  race43.
Another aspect is  competitiveness3,35,44–46. It is well-known that males are more engaged in direct competi-
tion than  females3 due to their higher  competitiveness47. However, another study refuted that females were 
less competitive than  males48. A study investigating 10-km races showed a significant annual decrease in the 
male-to-female ratio of finishers, with increasingly more female runners finishing in the sub-hour  range4. Fur-
thermore, it has been reported that females prefer smaller  competitions49, which is the case in ultra-marathon 
running with lower numbers of participants. The sex difference in performance can also be due to the motivat-
ing factors to participate in competitions. Between 1975 and 2013, master athletes improved their performance, 
where the magnitude of improvement was higher in the older age groups leading to gradual closing to younger 
 athletes50. It has been reported that the motivation to enter an athletic competition is based on social conditions 
and  predisposition3. A study of marathoners aged 20–79 found that sex differences in running speed increased 
with age, primarily attributed to the lower number of females than  males51. In marathon running, however, a 
successful finisher can achieve a similar race performance from 20 to 55  years52, which would not be possible 
in ultra-marathon running. The sex difference in marathon versus ultra-marathon in the 75+ age group may be 
attributed to the declining number of male marathoners after 2005. Conversely, the number of female maratho-
ners in the Venice Marathon increased from 2003 to 2019, providing a  counterpoint53.
Differences in the trend of sex difference by race distance. 
There was a general trend of decreas-
ing sex difference from the 20–24 age group to the 65–69 age group, except for 5 km and the marathon, which 
increased after this age. In contrast, the sex difference continuously decreased with increasing age in 10 km, 
half-marathon, and ultra-marathon races. The male-to-female ratio by age group and distance may explain these 
trends, as observed in an analysis of races held in Oslo from 2008 to 2018. Female runners comprised a higher 
percentage of finishers in the 10 km race, but fewer in the half-marathon and marathon, and the male-to-female 
ratio was lowest in the 10 km and highest in the  marathon54.
Another explanation could be the age itself. The age of ~ 65–70 years is also important regarding the age-
related performance  decline55,56. In age group athletes, performance declines curvilinear from the age of 35 years 
until the age of ~ 65–70  years57. McClelland and  Weyand16 recently noted a sex difference of ~ 12% for running 
distances from 800 m to 10 km, attributed to differences in energy supply and demands. Accordingly, Jobe 
et al.58 observed that males were 9–13% faster than females in all running events of the United States Olympic 
trials (from 100 m to marathon). Considering the physiological mechanisms underpinning the decrease of sex 
difference with the increasing race distance, an explanation might be the different taxing of the energy trans-
fer systems in races varying for  distance58. As the race distance increases, there is a larger contribution of the 
aerobic processes and a smaller anaerobic  mechanism59. Thus, the existence of smaller sex differences in aerobic 
than in anaerobic capacity might relate to the decreased sex difference in increasing  distances60. This is also in 
accordance with the latest study by Le Mat et al.61, although the race distances analyzed by the authors exceeded 
the ultra-marathon distances analyzed in this paper (45–260 km) a clear trend of decreasing sex difference with 
increasing race distance was shown.
Limitations, strengths, and practical applications. 
A limitation of the present study was that it ana-
lyzed race data from a single country; thus, considering the differences in performance and participation trends 
among countries, the findings should be generalized with caution to other countries. On the other hand, the 
strength was the large dataset that allowed drawing safe conclusions about the variation of sex difference by race 
10
Vol:.(1234567890)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
distance and age group. Another potential limitation is the reliance on descriptive statistics in the result interpre-
tation. The finding has practical applications for scientists and professionals working with long-distance runners 
to set optimal training goals for female athletes. Specifically, the main goals in training optimization considered 
should be body composition, sex differences in performance and performance differences throughout the aging 
process.
Conclusion
In summary, elderly female ultra-marathoners (age group 75+) show the smallest performance difference from 
male ultra-marathoners compared to other running distances from 5 km to a marathon. This is probably due to 
‘highly selected’ female ultra-marathoners who perform exceptionally well. Future studies might investigate the 
experience and motivation of elderly female ultra-marathoners.
Data availability
For this study, we have included official results and split times from “swiss-running” (www. swiss- runni ng. ch), 
“runme” (www. runme. ch/ de/ laufk alend er/ schwe iz), “datasport” (www. datas port. com/ de) and “DUV” (https:// 
stati stik.d- u-v. org/ calen dar. php). The datasets used and/or analyzed during the current study are available from 
the corresponding author on reasonable request.
Received: 4 March 2023; Accepted: 29 July 2023
References
 1. Scheerder, J., Breedveld, K. & Borgers, J. Who is doing a run with the running boom? In Running across Europe: The Rise and Size 
of One of the Largest Sport Markets (eds Scheerder, J. et al.) 1–27 (Palgrave Macmillan UK, 2015).
 2. Ganse, B., Kleerekoper, A., Knobe, M., Hildebrand, F. & Degens, H. Longitudinal trends in master track and field performance 
throughout the aging process: 83,209 results from Sweden in 16 athletics disciplines. Geroscience 42, 1609–1620. https:// doi. org/ 
10. 1007/ s11357- 020- 00275-0 (2020).
 3. Deaner, R. O., Addona, V. & Mead, M. P. U. S. masters track participation reveals a stable sex difference in competitiveness. Evol. 
Psychol. 12, 848–877 (2014).
 4. Cushman, D. M., Markert, M. & Rho, M. Performance trends in large 10-km road running races in the United States. J. Strength 
Cond. Res. 28, 892–901. https:// doi. org/ 10. 1519/ jsc. 00000 00000 000249 (2014).
 5. Scheer, V. et al. Defining off-road running: A position statement from the ultra sports science foundation. Int. J. Sports Med. 41, 
275–284. https:// doi. org/ 10. 1055/a- 1096- 0980 (2020).
 6. Deaner, R. O. & Lowen, A. Males and females pace differently in high school cross-country races. J. Strength Cond. Res. 30, 
2991–2997. https:// doi. org/ 10. 1519/ jsc. 00000 00000 001407 (2016).
 7. Yang, S. et al. Gender and age differences in performance of over 70,000 Chinese finishers in the half- and full-marathon events. 
Int. J. Environ. Res. Public Health https:// doi. org/ 10. 3390/ ijerp h1913 7802 (2022).
 8. Reusser, M. et al. Increased participation and decreased performance in recreational master athletes in “Berlin Marathon” 1974–
2019. Front. Physiol. https:// doi. org/ 10. 3389/ fphys. 2021. 631237 (2021).
 9. Thuany, M. et al. Trends in participation, sex differences and age of peak performance in time-limited ultramarathon events: A 
secular analysis. Medicina https:// doi. org/ 10. 3390/ medic ina58 030366 (2022).
 10. Emmonds, S., Heyward, O. & Jones, B. The challenge of applying and undertaking research in female sport. Sports Med. Open 5, 
51. https:// doi. org/ 10. 1186/ s40798- 019- 0224-x (2019).
 11. Elliott-Sale, K. J. et al. Methodological considerations for studies in sport and exercise science with women as participants: A work-
ing guide for standards of practice for research on women. Sports Med. 51, 843–861. https:// doi. org/ 10. 1007/ s40279- 021- 01435-8 
(2021).
 12. Thibault, V. et al. Women and men in sport performance: The gender gap has not evolved since 1983. J. Sports Sci. Med. 9, 214–223 
(2010).
 13. Waldvogel, K. J., Nikolaidis, P. T., Di Gangi, S., Rosemann, T. & Knechtle, B. Women reduce the performance difference to men 
with increasing age in ultra-marathon running. Int. J. Environ. Res. Public Health https:// doi. org/ 10. 3390/ ijerp h1613 2377 (2019).
 14. Sousa, C. V., da Silva Aguiar, S., Rosemann, T., Nikolaidis, P. T. & Knechtle, B. American masters road running records-the per-
formance gap between female and male age group runners from 5 km to 6 days running. Int. J. Environ. Res. Public Health https:// 
doi. org/ 10. 3390/ ijerp h1613 2310 (2019).
 15. Coast, J. R., Blevins, J. S. & Wilson, B. A. Do gender differences in running performance disappear with distance?. Can. J. Appl. 
Physiol. 29, 139–145. https:// doi. org/ 10. 1139/ h04- 010 (2004).
 16. McClelland, E. L. & Weyand, P. G. Sex differences in human running performance: Smaller gaps at shorter distances?. J. Appl. 
Physiol. 133, 876–885. https:// doi. org/ 10. 1152/ jappl physi ol. 00359. 2022 (1985).
 17. Millard-Stafford, M., Swanson, A. E. & Wittbrodt, M. T. Nature versus nurture: Have performance gaps between men and women 
reached an asymptote?. Int. J. Sports Physiol. Perform. 13, 530–535. https:// doi. org/ 10. 1123/ ijspp. 2017- 0866 (2018).
 18. Sparling, P. B., O’Donnell, E. M. & Snow, T. K. The gender difference in distance running performance has plateaued: An analysis 
of world rankings from 1980 to 1996. Med. Sci. Sports Exerc. 30, 1725–1729. https:// doi. org/ 10. 1097/ 00005 768- 19981 2000- 00011 
(1998).
 19. Costello, J. T., Bieuzen, F. & Bleakley, C. M. Where are all the female participants in sports and exercise medicine research?. Eur. 
J. Sport Sci. 14, 847–851. https:// doi. org/ 10. 1080/ 17461 391. 2014. 911354 (2014).
 20. Zaryski, C. & Smith, D. J. Training principles and issues for ultra-endurance athletes. Curr. Sports Med. Rep. 4, 165–170 (2005).
 21. Knechtle, B., Valeri, F., Zingg, M. A., Rosemann, T. & Rüst, C. A. What is the age for the fastest ultra-marathon performance in 
time-limited races from 6 h to 10 days?. Age https:// doi. org/ 10. 1007/ s11357- 014- 9715-3 (2014).
 22. Dos Anjos Souza, V. R. et al. Running economy in long-distance runners is positively affected by running experience and negatively 
by aging. Physiol. Behav. 258, 114032. https:// doi. org/ 10. 1016/j. physb eh. 2022. 114032 (2023).
 23. Zingg, M. A., Knechtle, B., Rüst, C. A., Rosemann, T. & Lepers, R. Analysis of participation and performance in athletes by age 
group in ultramarathons of more than 200 km in length. Int. J. Gen. Med. 6, 209–220. https:// doi. org/ 10. 2147/ ijgm. S43454 (2013).
 24. Van den Berghe, P., Breine, B., Haeck, E. & De Clercq, D. One hundred marathons in 100 days: Unique biomechanical signature 
and the evolution of force characteristics and bone density. J. Sport Health Sci. 11, 347–357. https:// doi. org/ 10. 1016/j. jshs. 2021. 
03. 009 (2022).
 25. Sandbakk, Ø., Solli, G. S. & Holmberg, H. C. Sex differences in world-record performance: The influence of sport discipline and 
competition duration. Int. J. Sports Physiol. Perform. 13, 2–8. https:// doi. org/ 10. 1123/ ijspp. 2017- 0196 (2018).
11
Vol.:(0123456789)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
 26. Wells, C. L. & Plowman, S. A. Sexual differences in athletic performance: Biological or behavioral?. Phys. Sportsmed 11, 52–63. 
https:// doi. org/ 10. 1080/ 00913 847. 1983. 11708 602 (1983).
 27. Lindle, R. S. et al. Age and gender comparisons of muscle strength in 654 women and men aged 20–93 yr. J. Appl. Physiol. 1997(83), 
1581–1587. https:// doi. org/ 10. 1152/ jappl. 1997. 83.5. 1581 (1985).
 28. Cheuvront, S. N., Carter, R., Deruisseau, K. C. & Moffatt, R. J. Running performance differences between men and women: An 
update. Sports Med. 35, 1017–1024. https:// doi. org/ 10. 2165/ 00007 256- 20053 5120- 00002 (2005).
 29. Bassett, A. J. et al. The biology of sex and sport. JBJS Rev. 8, e0140. https:// doi. org/ 10. 2106/ jbjs. Rvw. 19. 00140 (2020).
 30. Bam, J., Noakes, T. D., Juritz, J. & Dennis, S. C. Could women outrun men in ultramarathon races?. Med. Sci. Sports Exerc. 29, 
244–247. https:// doi. org/ 10. 1097/ 00005 768- 19970 2000- 00013 (1997).
 31. Tiller, N. B. et al. Do sex differences in physiology confer a female advantage in ultra-endurance sport?. Sports Med. 51, 895–915. 
https:// doi. org/ 10. 1007/ s40279- 020- 01417-2 (2021).
 32. Baker, A. B. & Tang, Y. Q. Aging performance for masters records in athletics, swimming, rowing, cycling, triathlon, and weightlift-
ing. Exp. Aging Res. 36, 453–477. https:// doi. org/ 10. 1080/ 03610 73x. 2010. 507433 (2010).
 33. Deaner, R. O., Carter, R. E., Joyner, M. J. & Hunter, S. K. Men are more likely than women to slow in the marathon. Med. Sci. Sports 
Exerc. 47, 607–616. https:// doi. org/ 10. 1249/ mss. 00000 00000 000432 (2015).
 34. Deaner, R. O., Addona, V., Carter, R. E., Joyner, M. J. & Hunter, S. K. Fast men slow more than fast women in a 10 kilometer road 
race. PeerJ 4, e2235. https:// doi. org/ 10. 7717/ peerj. 2235 (2016).
 35. Deaner, R. O. Distance running as an ideal domain for showing a sex difference in competitiveness. Arch. Sex Behav. 42, 413–428. 
https:// doi. org/ 10. 1007/ s10508- 012- 9965-z (2013).
 36. Hallam, L. & Amorim, F. Expanding the gap: An updated look into sex differences in running performance. Front. Physiol. 12, 
804149–804149. https:// doi. org/ 10. 3389/ fphys. 2021. 804149 (2022).
 37. Deaner, R. O., Addona, V. & Hanley, B. Risk taking runners slow more in the marathon. Front. Psychol. 10, 1–12. https:// doi. org/ 
10. 3389/ fpsyg. 2019. 00333 (2019).
 38. Besson, T. et al. Sex differences in endurance running. Sports Med. https:// doi. org/ 10. 1007/ s40279- 022- 01651-w (2022).
 39. Schaap, L. A. et al. Low testosterone levels and decline in physical performance and muscle strength in older men: Findings from 
two prospective cohort studies. Clin. Endocrinol. 68, 42–50. https:// doi. org/ 10. 1111/j. 1365- 2265. 2007. 02997.x (2008).
 40. Thuany, M. et al. A macro to micro analysis to understand performance in 100-mile ultra-marathons worldwide. Sci. Rep. 13, 1415. 
https:// doi. org/ 10. 1038/ s41598- 023- 28398-2 (2023).
 41. Senefeld, J. W. & Hunter, S. K. Are masters athletic performances predictive of human aging in men and women?. Mov. Sport Sci. 
Sci Mot. 200, 5–12. https:// doi. org/ 10. 1051/ sm/ 20190 18 (2019).
 42. Deaner, R. O. Physiology does not explain all sex differences in running performance. Med. Sci. Sports Exerc. 45, 146–147. https:// 
doi. org/ 10. 1249/ MSS. 0b013 e3182 690110 (2013).
 43. Senefeld, J., Smith, C. & Hunter, S. K. Sex differences in participation, performance, and age of ultramarathon runners. Int. J. Sports 
Physiol. Perform. 11, 635–642. https:// doi. org/ 10. 1123/ ijspp. 2015- 0418 (2016).
 44. Deaner, R. O. & Mitchell, D. More men run relatively fast in U.S. road races, 1981–2006: A stable sex difference in non-elite run-
ners. Evol. Psychol. 9, 600–621 (2011).
 45. Flory, J., Gneezy, U., Leonard, K. & List, J. Gender, age, and competition: A disappearing gap?. J. Econ. Behav. Organ. 150, 256–276 
(2018).
 46. Carpenter, J., Frank, R. & Huet-Vaughn, E. Gender differences in interpersonal and intrapersonal competitive behavior. J. Behav. 
Exp. Econ. 77, 170–176. https:// doi. org/ 10. 1016/j. socec. 2018. 10. 003 (2018).
 47. Deaner, R. O., Lowen, A., Rogers, W. & Saksa, E. Does the sex difference in competitiveness decrease in selective sub-populations? 
A test with intercollegiate distance runners. PeerJ 3, e884. https:// doi. org/ 10. 7717/ peerj. 884 (2015).
 48. Wieland, A. & Sarin, R. Domain specificity of sex differences in competition. J. Econ. Behav. Organ. 83, 151–157. https:// doi. org/ 
10. 1016/j. jebo. 2011. 06. 019 (2012).
 49. Hanek, K. J., Garcia, S. M. & Tor, A. Gender and competitive preferences: The role of competition size. J. Appl. Psychol. 101, 
1122–1133. https:// doi. org/ 10. 1037/ apl00 00112 (2016).
 50. Akkari, A., Machin, D. & Tanaka, H. Greater progression of athletic performance in older Masters athletes. Age Ageing 44, 683–686. 
https:// doi. org/ 10. 1093/ ageing/ afv023 (2015).
 51. Hunter, S. K. & Stevens, A. A. Sex differences in marathon running with advanced age: Physiology or participation?. Med. Sci. 
Sports Exerc. 45, 148–156. https:// doi. org/ 10. 1249/ MSS. 0b013 e3182 6900f6 (2013).
 52. Leyk, D. et al. Performance, training and lifestyle parameters of marathon runners aged 20–80 years: Results of the PACE-study. 
Int. J. Sports Med. 30, 360–365. https:// doi. org/ 10. 1055/s- 0028- 11059 35 (2009).
 53. Albertin, G. et al. “Venice marathon”: Participation of female Master Athletes shows a constant increase from 2003 to 2019. Eur. 
J. Transl. Myol. https:// doi. org/ 10. 4081/ ejtm. 2021. 10266 (2021).
 54. Nikolaidis, P., Cuk, I., Clemente-Suárez, V., Villiger, E. & Knechtle, B. Number of finishers and performance of age group women 
and men in long-distance running: Comparison among 10km, half-marathon and marathon races in Oslo. Res. Sports Med. 29, 
56–66. https:// doi. org/ 10. 1080/ 15438 627. 2020. 17267 45 (2021).
 55. Ganse, B., Drey, M., Hildebrand, F., Knobe, M. & Degens, H. Performance declines are accelerated in the oldest-old track and field 
athletes 80 to 94 years of age. Rejuvenation Res. 24, 20–27. https:// doi. org/ 10. 1089/ rej. 2020. 2337 (2021).
 56. Ransdell, L. B., Vener, J. & Huberty, J. Masters athletes: An analysis of running, swimming and cycling performance by age and 
gender. J. Exerc. Sci. Fit. 7, S61–S73. https:// doi. org/ 10. 1016/ S1728- 869X(09) 60024-1 (2009).
 57. Reaburn, P. & Dascombe, B. Endurance performance in masters athletes. Eur. Rev. Aging Phys. Activity 5, 31–42. https:// doi. org/ 
10. 1007/ s11556- 008- 0029-2 (2008).
 58. Jobe, T. K., Shaffer, H. N., Doci, C. L. & Gries, K. J. Sex differences in performance and depth of field in the United States olympic 
trials. J. Strength Cond. Res. 36, 3122–3129. https:// doi. org/ 10. 1519/ jsc. 00000 00000 004295 (2022).
 59. Trappe, S. Marathon runners: How do they age?. Sports Med. 37, 302–305. https:// doi. org/ 10. 2165/ 00007 256- 20073 7040- 00008 
(2007).
 60. Galloway, M. T., Kadoko, R. & Jokl, P. Effect of aging on male and female master athletes’ performance in strength versus endur-
ance activities. Am. J. Orthop. 31, 93–98 (2002).
 61. Le Mat, F. et al. Running endurance in women compared to men: Retrospective analysis of matched real-world big data. Sports 
Med. 53, 917–926. https:// doi. org/ 10. 1007/ s40279- 023- 01813-4 (2023).
Author contributions
B.K. and K.W. drafted the manuscript, D.V. performed the statistical analysis and prepared methods and results, 
A.W. obtained the data, M.T., P.N., V.S., and P.F. helped in drafting the final version. All authors read and 
approved the final manuscript.
Competing interests 
The authors declare no competing interests.
12
Vol:.(1234567890)
Scientific Reports |        (2023) 13:12521  | 
https://doi.org/10.1038/s41598-023-39690-6
www.nature.com/scientificreports/
Additional information
Correspondence and requests for materials should be addressed to B.K.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and 
institutional affiliations.
Open Access  This article is licensed under a Creative Commons Attribution 4.0 International 
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or 
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the 
Creative Commons licence, and indicate if changes were made. The images or other third party material in this 
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the 
material. If material is not included in the article’s Creative Commons licence and your intended use is not 
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from 
the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
© The Author(s) 2023

Elderly female ultra-marathoners reduced the gap to male ultra-marathoners in Swiss running races.
Knechtle, Beat,Witthöft, Anja,Valero, David,Thuany, Mabliny,Nikolaidis, Pantelis T,Scheer, Volker,Forte, Pedro,Weiss, Katja