Citation: Yang, S.-J.; Yang, F.; Gao, Y.;
Su, Y.-F.; Sun, W.; Jia, S.-W.; Wang, Y.;
Lam, W.-K. 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 2022, 19,
7802. https://doi.org/10.3390/
ijerph19137802
Academic Editors: Paul B.
Tchounwou and Ukadike
Chris Ugbolue
Received: 11 May 2022
Accepted: 23 June 2022
Published: 25 June 2022
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International  Journal  of
Environmental Research
and Public Health
Article
Gender and Age Differences in Performance of Over
70,000 Chinese Finishers in the Half- and Full-Marathon Events
San-Jun Yang 1, Fan Yang 1,2,*
, Yuan Gao 3, Yan-Feng Su 4, Wei Sun 4, Sheng-Wei Jia 2, Yu Wang 5,*
and Wing-Kai Lam 6,*
1
Department of Physical Education and Research, China University of Mining and Technology—Beijing,
Beijing 100083, China; 108947@cumtb.edu.cn
2
Li Ning Sports Science Research Center, Li Ning (China) Sports Goods Company Limited,
Beijing 101111, China; jiashengwei@li-ning.com.cn
3
School of Physical Education, Yanshan University, Qinhuangdao 066004, China; gaoyuan1107@163.com
4
School of Physical Education and Coaching, Shanghai University of Sport, Shanghai 200438, China;
suhui0909@163.com (Y.-F.S.); sunwei1@sus.edu.cn (W.S.)
5
School of Kinesiology and Health, Capital University of Physical Education and Sports, Beijing 100091, China
6
Sports Information and External Affairs Centre, Hong Kong Sports Institute, Sha Tin, Hong Kong
*
Correspondence: yangfan6@li-ning.com.cn (F.Y.); wangyu@cupes.edu.cn (Y.W.);
gilbert.lam@connect.polyu.hk (W.-K.L.); Tel.: +86-189-11326682 (F.Y.); +86-135-01126242 (Y.W.);
+86-186-11783188 (W.-K.L.)
Abstract: (1) Background: The aim of the present study was to examine the characteristics of over
70,000 long-distance ﬁnishers over the last four years in Chinese half- and full-marathon events;
(2) Methods: The available data of all ﬁnishers (n = 73,485; women, n = 17,134; men, n = 56,351) who
performed half- and full-marathon events in Hangzhou from 2016 to 2019 were further analyzed for
the characteristics of gender, age and average running speed; (3) Results: The total men-to-women
ratio was the lowest in the half-marathon event (1.86) and the highest in the full-marathon event
(17.42). Faster running performance in males than in females and faster average running speed
in short-distance runners were shown. Gender and race distance were observed to have the most
signiﬁcant effects on average running speed (p < 0.01). For both male and female ﬁnishers, the slowest
running speed was shown in older age groups (p < 0.01) during the full marathon. Our results
indicated that the gender difference in performance was attenuated in the longer race distances and
older age groups; (4) Conclusions: Understanding the participation and performances across different
running distances would provide insights into physiological and biomechanical characteristics for
training protocols and sports gear development in different groups.
Keywords: gender; men-to-women ratio; marathon; age; running speed
1. Introduction
The health beneﬁts of endurance exercise might partially explain the increase in
participation in marathon races during the last decades [1]. In recent years, marathon
running has been considered a globally popular physical activity that can cater to the
various healthy lifestyle needs of urban residents [2,3]. This running boom has gradually
spread around the world. Well-known New York, London, Paris, and Berlin marathon
events all had between 30,000 and 40,000 ﬁnishers [4]. While distance running used to
be a male-dominated sport, today females account for 43% of marathon runners in the
USA [5]. Marathon events have developed later in China than in Western countries. The
number of marathon events held in China increased from 12 to 53 between 2010 and
2014 [6] to approximately 1100 in 2017 [7], which involved nearly 5 million participants
and increased to over 2.2 million participants from 2016 to 2017 [7]. The number further
increased to a total of 1900 events in 2019 [1]. The growing popularity of running has
inspired a large amount of research on running biomechanics, performance and sport
Int. J. Environ. Res. Public Health 2022, 19, 7802. https://doi.org/10.3390/ijerph19137802
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022, 19, 7802
2 of 9
gears in the past decades [8]. Studies have found that proper pace can effectively reduce
the risk of musculoskeletal injury [9], and different running strategies should be used in
long-distance running according to gender, age and the event the runner is training for [10].
Moreover, differences in running biomechanics between Chinese men and women were
observed, with female runners showing greater range of motion in the hip and knee joints,
and a smaller shoe-to-ground angle during the heel touch-down phase. This is believed to
be a form of self-regulation that women use to reduce the impact of landing, and men rely
more on the performance of their shoes to achieve the purpose of buffering [11].
Following the increase in female participation in distance running [12], investigations
into gender differences in running mechanics were intensiﬁed in the Western world [12–14].
The gender difference studies indicated clear differences in females’ body fat and running
speed [3], resulting in distinct movement characteristics and injury etiology. Nikolaidis
et al. studied the performance and age composition of different genders during marathons,
where they found that women achieved their best marathon race time ~5 years earlier
in life compared to men. Women’s participation increased disproportionately to men’s
participation, leading to an increase in the ratio of men to women [15]. In addition, more
and more seniors are joining in marathon races [16]. The sex gap between elite female
marathon racers and elite male runners may have reached its limit [17]. The age structure
of most male marathon runners is larger and older than that of females (male: 40–44 years;
female: 30–34 years) [18].
Although studies have been conducted on anthropometry, physiology and training
characteristics have improved our understanding of the predictors of race time [19], as well
as age- and gender-related differences in pacing during endurance running [20–23]. While
Western counterparts have been extensively analyzed regarding their running characteris-
tics, little attention has been paid to the Chinese population. Several articles have shown
differences in running between Chinese and Western populations; one study found that,
compared with Western women, Chinese women use the medial forefoot more during the
push-off phase of running [24]. Western female runners have greater ankle valgus angles
than males, while there is no signiﬁcant difference between Chinese females and males [25].
Thus, research ﬁndings obtained from Western runners may not be directly applicable to
the Chinese population because of racial differences. The present study examined trends of
the men-to-women ratio, number of ﬁnishers and performances by gender and age groups
across four years in half- and full-marathon events, respectively. It was expected that
there would be a different men-to-women ratio and different performances across different
running events. The comparison could help to better understand potential training require-
ments for both elite and recreational runners of different age groups, as well as to effectively
estimate the demand of running shoes for different gender and age groups. To optimize
female performance and health in sport, we need to include women in our analyses in
order to better understand peculiarities that may exist in physiology. Therefore, we are
happy to enrich the existing pool of knowledge with more data on female participation
and performance in marathon racing. Understanding the participation and performance
across different running distances (half- and full-marathon events) would provide insights
into the physiological and biomechanical characteristics for training protocols for different
gender and age groups.
2. Materials and Methods
2.1. Participants and Data Acquisition
The complete marathon event data for this study were ofﬁcially obtained from
Hangzhou Marathon Organizing Committee (https://www.hzim.org) [26]. The records
were collected from all half marathons and full marathons between 2016 and 2019, and
were ofﬁcially certiﬁed by the World Athletics Organization. The Hangzhou marathon
event included both full and half marathons and runs. Regretfully, the Hangzhou marathon
event has been suspended due to the spread of COVID-19 in 2019. The study data included
participants who completed the race in the appropriate amount of time. Age and gender
Int. J. Environ. Res. Public Health 2022, 19, 7802
3 of 9
information was provided for the period of 4 years. Ultimately, the study included a total
of 73,485 participants (male, n = 56,351; female, n = 17,134).
2.2. Procedures
Age intervals of ﬁve years were selected to represent age groups among younger
and older ﬁnishers in their categories. All runners over 71 years old were placed in one
category, as there were only a few male runners in the oldest age group, while the oldest
male runner was 74 years old. In total, the ﬁnishers were classiﬁed into 11 age groups;
21–25, 26–30, 31–35, 36–40, 41–45, 45–50, 51–55, 56–60, 61–65, 66–70 and 71+ years. Changes
in gender participation are described by the men-to-women ratio (MWR, the quotient of
males divided by female completers) [27].
2.3. Statistical Analysis
The ofﬁcial race time (i.e., accurate in seconds) was obtained for all ﬁnishers in both
races. The average running speed in km/h was calculated using the ﬁnal race time (h)
divided by race distance (km) to allow comparison of performances between two long-
distance races. All descriptive statistics were reported as mean and standard deviation.
Prior to statistical analyses, data distribution normality was veriﬁed by visual inspection
of histograms and QQ plots [18]. To assess age and gender distribution among ﬁnishers
in the half- and full-marathon events, a chi-square test (χ2) was performed. Statistical
differences in marathon performance between 11 age groups and two events were observed.
Meanwhile, their interactions were calculated using a two-way ANOVA, post hoc with
Bonferroni-corrected tests, and the signiﬁcance level was controlled at 0.05. All data were
organized and summarized using Microsoft Ofﬁce Excel 2019 (Microsoft Corporation,
Redmond, WA, USA) and statistical testing was performed using SPSS 20.0 (IBM, Armonk,
NY, USA).
3. Results
3.1. Participation by Gender, Race Distance, and Age Group
The MWR as well as the total number of male and female ﬁnishers in each age group
and race distance are presented in Table 1.
Table 1. Distribution of male and female ﬁnishers in each age group and race distance.
Half-Marathon
Full-Marathon
Age Groups
Males
Females
Total
MWR
Males
Females
Total
MWR
21–25
940
505
1445
1.86
639
158
797
4.04
26–30
3667
2166
5833
1.69
3046
714
3760
4.27
31–35
4381
2063
6444
2.12
5110
1073
6183
4.76
36–40
4360
1712
6072
2.55
6274
1249
7523
5.02
41–45
3514
1538
5052
2.28
6733
1325
8058
5.08
46–50
2820
1404
4224
2.01
6573
1386
7959
4.74
51–55
1428
633
2061
2.26
3569
678
4247
5.26
56–60
618
179
797
3.45
1540
193
1733
7.98
61–65
208
47
255
4.43
502
64
566
7.84
66–70
111
20
131
5.55
209
12
221
17.42
71+
28
0
28
-
18
0
18
-
Total
22,075
10,267
32,342
2.15
34,213
6852
41,065
4.99
MWR = men-to-women ratio.
The total MWR was 2.15 and 4.99 in the half and full marathon, respectively. A
gender × race distance association in participation was shown (χ2 = 2294.505, p < 0.01,
ϕ = 0.177). A gender × age group association in participation was observed in the half
marathon (χ2 = 89.091, p < 0.01, ϕ = 0.081) and in the full marathon (χ2 = 53.431, p < 0.01,
ϕ = 0.050). Furthermore, a race distance × age group association in participation was
Int. J. Environ. Res. Public Health 2022, 19, 7802
4 of 9
shown for male ﬁnishers (χ2 = 1687.054, p < 0.01, ϕ = 0.187) and for female ﬁnishers
(χ2 = 600.388, p < 0.01, ϕ = 0.197) of different age groups. In the half marathon, the lowest
MWR was observed in the age group of 26–30 years (1.69), whereas the highest MWR was
observed in the age group of 36–40 years (6.12). In the full marathon, the lowest MWR was
observed in the youngest age group (4.04), whereas the highest MWR was observed in the
age group of 66–70 years (17.41).
3.2. Performance (Average Running Speed)
3.2.1. Overall Effects
The two-way ANOVA showed significant effects of gender [F (1, 73,365) = 612.757,
p < 0.001] and race distance [F (1, 73,365) = 7.914, p < 0.005], as well as age group
[F (10, 73,365) = 119.550, p < 0.001]. Moreover, we found significant interactions for age
group × race distance [F (10, 73,365) = 3.763, p < 0.001], while no interaction was observed
for gender × race distance [F (1, 73,365) = 3.270, p = 0.071] and for gender × age group
[F (9, 73,365) = 1.329, p = 0.216].
3.2.2. Performance by Gender and Race Distance
A signiﬁcant effect of gender on average running speed is shown (p < 0.001) in Figure 1,
where male ﬁnishers (with the performance of 10.03 ± 1.67 km/h) were faster than female
ﬁnishers (with the performance of 9.08 ± 1.27 km/h). In addition, a signiﬁcant effect of
race distance on average running speed was observed (p < 0.001). Figure 1 also shows
that performance in the full-marathon event (9.95 ± 1.71 km/h) was faster than that in the
half-marathon event (9.63 ± 1.53 km/h).
 
0.177). A gender × age group association in participation was observed in the half mara
thon (χ2 = 89.091, p < 0.01, φ = 0.081) and in the full marathon (χ2 = 53.431, p < 0.01, φ = 
0.050). Furthermore, a race distance × age group association in participation was shown 
for male finishers (χ2 = 1687.054, p < 0.01, φ = 0.187) and for female finishers (χ2 = 600.388, 
p < 0.01, φ = 0.197) of different age groups. In the half marathon, the lowest MWR was 
observed in the age group of 26–30 years (1.69), whereas the highest MWR was observed 
in the age group of 36–40 years (6.12). In the full marathon, the lowest MWR was observed 
in the youngest age group (4.04), whereas the highest MWR was observed in the age group 
of 66–70 years (17.41). 
3.2. Performance (Average Running Speed) 
3.2.1. Overall Effects 
The two-way ANOVA showed significant effects of gender [F (1, 73,365) = 612.757, p < 
0.001] and race distance [F (1, 73,365) = 7.914, p < 0.005], as well as age group [F (10, 73,365) = 
119.550, p < 0.001]. Moreover, we found significant interactions for age group × race dis-
tance [F (10, 73,365) = 3.763, p < 0.001], while no interaction was observed for gender × race 
distance [F (1, 73,365) = 3.270, p = 0.071] and for gender × age group [F (9, 73,365) = 1.329, p = 0.216]. 
3.2.2. Performance by Gender and Race Distance 
A significant effect of gender on average running speed is shown (p < 0.001) in Figure 
1, where male finishers (with the performance of 10.03 ± 1.67 km/h) were faster than fe-
male finishers (with the performance of 9.08 ± 1.27 km/h). In addition, a significant effect 
of race distance on average running speed was observed (p < 0.001). Figure 1 also shows 
that performance in the full-marathon event (9.95 ± 1.71 km/h) was faster than that in the 
half-marathon event (9.63 ± 1.53 km/h). 
No gender × race distance interaction on average running speed was found (p > 0.05), 
while the gender difference was lower in the half-marathon event (+9.98%) than in the 
full-marathon event (+10.25%). Half-marathon finishers were slower than full-marathon 
finishers among females (9.02 ± 1.22 versus 9.17 ± 1.35 km/h, respectively, p < 0.001), as 
well as among males (9.92 ± 1.57 versus 10.11 ± 1.73 km/h, respectively, p < 0.001). 
 
Figure 1.
Race speed by race distance and gender.
Error bars represent standard deviations.
ˆˆ p < 0.001; ** p < 0.001.
No gender × race distance interaction on average running speed was found (p > 0.05),
while the gender difference was lower in the half-marathon event (+9.98%) than in the
full-marathon event (+10.25%). Half-marathon ﬁnishers were slower than full-marathon
ﬁnishers among females (9.02 ± 1.22 versus 9.17 ± 1.35 km/h, respectively, p < 0.001), as
well as among males (9.92 ± 1.57 versus 10.11 ± 1.73 km/h, respectively, p < 0.001).
Int. J. Environ. Res. Public Health 2022, 19, 7802
5 of 9
3.2.3. Performance by Age Group and Race Distance
The age group × race distance interaction had a signiﬁcant effect on average running
speed (p < 0.001). Under closer examination of the performance, male ﬁnishers had the
fastest average running speed of 10.33 ± 1.60 km/h, while female ﬁnishers had the slowest
average running speed of 8.54 ± 1.03 km/h, regardless of the type of event.
In the half-marathon event, the fastest male age group was 61–65 years (average
running speed of 10.32 ± 1.45 km/h), while the slowest male age group was 26–30 years
(average running speed of 9.61 ± 1.57 km/h). In the full-marathon event, the male ﬁnishers
had the fastest speed of 10.39 ± 1.62 km/h in the 46–50 age interval and the slowest speed
of 9.16 ± 1.18 km/h in the 71+ age interval (Figure 2a).
 
Figure 1. Race speed by race distance and gender. Error bars represent standard deviations. ^^ p < 
0.001; ** p < 0.001. 
3.2.3. Performance by Age Group and Race Distance 
The age group × race distance interaction had a significant effect on average running 
speed (p < 0.001). Under closer examination of the performance, male finishers had the 
fastest average running speed of 10.33 ± 1.60 km/h, while female finishers had the slowest 
average running speed of 8.54 ± 1.03 km/h, regardless of the type of event. 
In the half-marathon event, the fastest male age group was 61–65 years (average run-
ning speed of 10.32 ± 1.45 km/h), while the slowest male age group was 26–30 years (av-
erage running speed of 9.61 ± 1.57 km/h). In the full-marathon event, the male finishers 
had the fastest speed of 10.39 ± 1.62 km/h in the 46–50 age interval and the slowest speed 
of 9.16 ± 1.18 km/h in the 71+ age interval (Figure 2a). 
In the half-marathon event, female finishers had the fastest average running speed of 
9.61 ± 1.21 km/h in the 66–70 age interval, but the slowest average running speed of 8.65 ± 
1.13 km/h in the 26–30 age group. In the full-marathon event, the fastest female finisher 
was observed in the 46–50 age group (9.43 ± 1.28 km/h), while the slowest female was in 
the 66–70 age group (8.48 ± 0.91 km/h) (Figure 2b). 
 
Figure 2. Race speed by age group and gender in the half-marathon event (a) and in the full-mara-
thon event (b). Error bars represent standard deviations. ## p < 0.01; # p < 0.05. ^^ p < 0.01; ^ p < 0.05. 
3.2.4. Performance by Gender and Age Group 
Although the main effect of gender × age group interaction on the average running 
speed (p = 0.216) was obtained, Bonferroni post hoc comparisons revealed a significant 
difference (Table 2). 
In both male and female finishers, there was a significant difference between the age 
group of 21–25 years and the age group of 36–60 years (p < 0.01), as well as the age group 
of 61–55 years (p < 0.05). The same difference was found in the age groups of 26–30 years 
and 31–65 years (p < 0.01).  
There was also a difference in the average running speed between age groups of 31–
35 years, 36–60 years (p < 0.01) and 61–55 years (p < 0.05). Another significant difference 
was observed between the 36–40 age interval and the 41–55 age interval (p < 0.01). The 
performance of the 46–50 age interval showed a significant difference with the 41–45 age 
interval (p < 0.01) and the 51–55 age interval (p < 0.01). 
Figure 2. Race speed by age group and gender in the half-marathon event (a) and in the full-marathon
event (b). Error bars represent standard deviations. ˆˆ p < 0.01; ˆ p < 0.05.
In the half-marathon event, female ﬁnishers had the fastest average running speed
of 9.61 ± 1.21 km/h in the 66–70 age interval, but the slowest average running speed of
8.65 ± 1.13 km/h in the 26–30 age group. In the full-marathon event, the fastest female
ﬁnisher was observed in the 46–50 age group (9.43 ± 1.28 km/h), while the slowest female
was in the 66–70 age group (8.48 ± 0.91 km/h) (Figure 2b).
3.2.4. Performance by Gender and Age Group
Although the main effect of gender × age group interaction on the average running
speed (p = 0.216) was obtained, Bonferroni post hoc comparisons revealed a signiﬁcant
difference (Table 2).
In both male and female ﬁnishers, there was a signiﬁcant difference between the age
group of 21–25 years and the age group of 36–60 years (p < 0.01), as well as the age group
of 61–55 years (p < 0.05). The same difference was found in the age groups of 26–30 years
and 31–65 years (p < 0.01).
There was also a difference in the average running speed between age groups of
31–35 years, 36–60 years (p < 0.01) and 61–55 years (p < 0.05). Another signiﬁcant difference
was observed between the 36–40 age interval and the 41–55 age interval (p < 0.01). The
performance of the 46–50 age interval showed a signiﬁcant difference with the 41–45 age
interval (p < 0.01) and the 51–55 age interval (p < 0.01).
Int. J. Environ. Res. Public Health 2022, 19, 7802
6 of 9
Table 2. Bonferroni post hoc tests of age groups.
Age Groups
21–25
26–30
31–35
36–40
41–45
46–50
51–55
56–60
61–65
66–70
71+
21–25
-
-
-
**
**
**
**
**
*
-
-
26–30
-
-
**
**
**
**
**
**
**
-
-
31–35
-
##
-
**
**
**
**
**
*
-
-
36–40
##
##
##
-
**
**
**
-
-
-
-
41–45
##
##
##
##
-
**
-
-
-
-
-
46–50
##
##
##
##
##
-
**
-
-
-
-
51–55
##
##
##
##
##
-
-
-
-
-
56–60
##
##
##
-
-
-
-
-
-
-
-
61–65
#
##
#
-
-
-
-
-
-
-
-
66–70
-
-
-
-
-
-
-
-
-
-
-
71+
-
-
-
-
-
-
-
-
-
-
-
* represent male, # represent female, ** or ## p < 0.01; * or # p < 0.05.
4. Discussion
The aim of this study was to assess the age, gender and average speed characteristics
of over 70,000 long-distance ﬁnishers over the last four years.
We found that the 31–35 and 36–40 age groups had the largest number of male
ﬁnishers in the half-marathon event, while the 26–30 and 36–40 age groups had the largest
number of female ﬁnishers. These ﬁndings are partly in agreement with previous studies,
which reported that, regardless of gender, the largest number of ﬁnishers was found
in the 24–34 age group [28]. In addition, a study conducted in Switzerland found that
the two largest numbers of full-marathon ﬁnishers were in the age ranges 40–44 and
45–49 in men, but 35–39 and 40–44 in women; this study also reported that most half-
marathon participants were 30–34 years old and 45–49 years old in men, but 25–29 years
old and 30–34 years old in women [29]. Although there are differences among age groups
in Asian and European populations, over 30 years of age appeared to be the dominant
age in participants in long-distance running, which may be related to the relationship
with physical needs, social inﬂuences and disposable time [30]. The study showed that
the largest age group participating in full marathon events was usually older than the
half-marathon runners. Such results may be due to older runners being more emotionally
stable and responsible than younger people [31].
In terms of the number of ﬁnishers by race distance, there were ~1.3 times more
marathon runners than half-marathon runners, which is not in agreement with the previous
ﬁndings. The reason for this phenomenon may be that the marathon organizing committee
limited the number of half-marathon runners during the registration period. In Switzerland,
the total number of half-marathon runners was about 2.6 times higher than the marathon
runners between 2000 and 2010 [32]. In Greece, the total number of half-marathon runners
was about 3.5 times higher than that in the Oslo Marathon between 2008 and 2018 [28].
This could be explained by the fact that the registered number of half-marathon events is
substantially lower than the full-marathon events held in China [33].
It is not difﬁcult to understand the phenomenon that the number of people who ﬁnish
the half marathon is higher than the number of people who ﬁnish the full marathon. The
demand for physical ﬁtness in the full marathon is undoubtedly higher. Running long
distances can lead to dehydration, damage to muscle tissue and an increase in body tem-
perature, which must be more difﬁcult to overcome during a full marathon [34]. Moreover,
the depletion of glycogen stores in the body causes athletes to “hit the wall (HTW)”; the
frequency of HTW among elite and non-elite runners was 51%, with the greatest proportion
being found in non-elite runners. Thus, HTW also increases the risk of withdrawal as the
distance of the competition increases [35].
Our data conﬁrmed that a higher number of male ﬁnishers than females was also
observed in a longer-distance event, which is similar to the “Marathon des Sables” (7-day
competition) with a men-to-women ratio (MWR) of 6.76 [3], the “Western States 100-Mile
Endurance Run” (161 km) with an MWR of 5.28 [36], or Double Iron Ultra-Triathlon
Int. J. Environ. Res. Public Health 2022, 19, 7802
7 of 9
(MWR: 8.96) to Deca Iron Ultra-Triathlon (MWR: 6.94) [37]. However, over the past few
years, an increase in women ﬁnishers was observed in half- and full-marathon events, as
indicated by the decreased trend of MWR. A plausible explanation for the MWR variation
by race distances/intensities might be the consideration of females as relatively “novice”
runners compared to males. One study reported that the MWR was decreased from 10.2
in the 1970s to 1.5 in the 2010s in the “New York City Marathon” [38]. Women complete
shorter-distance races ﬁrst and then longer-distance races according to their ability and
physiological features. The majority opted for the half marathon, resulting in a higher
number of female ﬁnishers in the half marathon than in the full marathon. Regarding
changes in ﬁnishers by gender and age group, similar trends were shown across all race
distances, with older groups having a higher MWR than younger age groups.
The current ﬁndings showed faster running performance in males than in females
and faster average running speed in shorter race distances. This may be attributable to the
innate physiological advantage of male runners and gender differences in training habits,
with males having greater body weights and lower body fat percentages in terms of physiol-
ogy [3]. The research pointed out that men and women are born with different muscle ﬁber
properties, and the advantage of men is that the circumference of muscle ﬁbers is larger
than that of women, so they are more powerful [39]. This is the difference between men
and women in gene expression in human skeletal muscle [40]. Furthermore, male athletes
exhibit higher maximal oxygen uptake (VO2max) and anaerobic thresholds than females in
long-distance running [41]; there is a positive correlation between VO2max and thermoreg-
ulatory ability [42]. Some articles have pointed out that in the non-competition training
phase, men have longer training distances than women and have more weekly training
sessions, so the running experience of male runners is higher than that of women [43];
the same goes for training differences for a half marathon [3]. Some researchers have also
found that the larger male-to-female ratio in the older group is due to the lower number of
female ﬁnishers [44]. Our data also found that the average pace of women in half-marathon
events increased with age. In previous research, the slowest pace occurred in the older age
groups [22]. One of the reasons for this could be that most young female participants in the
half marathon were attempting long-distance running for the ﬁrst time, which makes the
pace slower in younger age groups. At the same time, the running speeds of older Chinese
women groups were better than Western women. Eastern women are morphologically
thinner than Western women [38], thus saving more running economy in long-distance
running and are, therefore, faster on average.
There were several limitations in the present study. First of all, the role of environ-
mental conditions, such as detailed data on temperature, humidity, and wind, was not
considered. It is reasonable to involve the effect of these environmental parameters on
the endurance performance [45]. Secondly, the number of participants was higher than
other studies, as four-year data were accessible. Long-term research should be conducted
in future. Thirdly, other endurance events such as 10 km races should be included.
5. Conclusions
This study found that the number of female ﬁnishers in the half- and full-marathon
races has increased, but, overall, there are still more males than females. A higher number
of younger female ﬁnishers participated in both race distances. Moreover, the overall
performance of male’s running is better than female’s running, but as the race distance and
the age of participants increased, the difference in performance caused by gender gradually
weakened. It should be emphasized that the analysis of performance trends is related to
changes in MWR based on age group and race distance.
Int. J. Environ. Res. Public Health 2022, 19, 7802
8 of 9
Author Contributions: Conceptualization, Y.-F.S., F.Y. and S.-J.Y.; methodology, F.Y. and S.-J.Y. and
W.-K.L.; software, F.Y., Y.G. and S.-W.J.; validation, Y.G., S.-J.Y. and W.-K.L.; formal analysis, F.Y.;
investigation, S.-J.Y., W.S. and Y.-F.S.; resources, S.-J.Y.; data curation, S.-W.J., S.-J.Y., Y.W. and W.S.;
writing—original draft preparation, S.-J.Y.; writing—review and editing, W.-K.L. and F.Y.; visualiza-
tion, S.-J.Y. and W.-K.L.; supervision, S.-J.Y. and Y.W.; project administration, F.Y.; funding acquisition,
F.Y. and W.-K.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Beijing Technology and Innovation Service Development
Research Fund, grant number 154218001.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
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Gender and Age Differences in Performance of Over 70,000 Chinese Finishers in the Half- and Full-Marathon Events.
Yang, San-Jun,Yang, Fan,Gao, Yuan,Su, Yan-Feng,Sun, Wei,Jia, Sheng-Wei,Wang, Yu,Lam, Wing-Kai