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# Expanding the Gap: An Updated Look Into Sex Differences in Running Performance

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Males consistently outperform females in athletic endeavors, including running events of standard Olympic distances (100 m to Marathon). The magnitude of this percentage sex difference, i.e., the sex gap, has evolved over time. Two clear trends in sex gap evolution are evident; a narrowing of the gap during the 20th century, followed by a period of stability thereafter. However, an updated perspective on the average sex gap from top 20 athlete performances over the past two decades reveals nuanced trends over time, indicating the sex gap is not fixed. Additionally, the sex gap varies with performance level; the difference in absolute running performance between males and females is lowest for world record/world lead performances and increases in lower-ranked elite athletes. This observation of an increased sex gap with world rank is evident in events 400 m and longer and indicates a lower depth in female competitive standards. Explanations for the sex difference in absolute performance and competition depth include physical (physiological, anatomical, neuromuscular, biomechanical), sociocultural, psychological, and sport-specific factors. It is apparent that females are the disadvantaged sex in sport; therefore, measures should be taken to reduce this discrepancy and enable both sexes to reach their biological performance potential. There is scope to narrow the sex performance gap by addressing inequalities between the sexes in opportunities, provisions, incentives, attitudes/perceptions, research, and media representation.
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REVIEW
published: 04 January 2022
doi: 10.3389/fphys.2021.804149
Edited by:
Ana Cristina Rodrigues Lacerda,
Federal University of Jequitinhonha
and Mucuri Valleys (UFVJM), Brazil
Reviewed by:
José Ramón Alvero Cruz,
University of Malaga, Spain
Antonio La Torre,
University of Milan, Italy
*Correspondence:
Lydia C. Hallam
lydiahallam@outlook.com
Specialty section:
Exercise Physiology,
a section of the journal
Frontiers in Physiology
Accepted: 29 November 2021
Published: 04 January 2022
Citation:
Hallam LC and Amorim FT (2022)
Expanding the Gap: An Updated Look
Into Sex Differences in Running
Performance.
Front. Physiol. 12:804149.
doi: 10.3389/fphys.2021.804149
Expanding the Gap: An Updated
Look Into Sex Differences in Running
Performance
Lydia C. Hallam*and Fabiano T. Amorim
Exercise Physiology Laboratory, Department of Health, Exercise, and Sport Science, University of New Mexico,
Albuquerque, NM, United States
Males consistently outperform females in athletic endeavors, including running events
of standard Olympic distances (100 m to Marathon). The magnitude of this percentage
sex difference, i.e., the sex gap, has evolved over time. Two clear trends in sex gap
evolution are evident; a narrowing of the gap during the 20th century, followed by
a period of stability thereafter. However, an updated perspective on the average sex
gap from top 20 athlete performances over the past two decades reveals nuanced
trends over time, indicating the sex gap is not ﬁxed. Additionally, the sex gap varies with
performance level; the difference in absolute running performance between males and
females is lowest for world record/world lead performances and increases in lower-
ranked elite athletes. This observation of an increased sex gap with world rank is
evident in events 400 m and longer and indicates a lower depth in female competitive
standards. Explanations for the sex difference in absolute performance and competition
depth include physical (physiological, anatomical, neuromuscular, biomechanical),
sociocultural, psychological, and sport-speciﬁc factors. It is apparent that females are
the disadvantaged sex in sport; therefore, measures should be taken to reduce this
discrepancy and enable both sexes to reach their biological performance potential.
There is scope to narrow the sex performance gap by addressing inequalities between
the sexes in opportunities, provisions, incentives, attitudes/perceptions, research, and
media representation.
Keywords: track and ﬁeld, running, women, performance, sex difference
INTRODUCTION
Competitive sport is one of the clearest examples of high-stakes human endeavor, whereby athletes,
coaches and researchers seek to push the limits of human physical performance. Time, money
and energy is invested with the aim of athletes obtaining the upper limit of physical capacity and
surpassing one another in sporting competitions. Within this context, performance diﬀerences
between males and females, herein called the “sex gap,” have been studied across sporting events,
including athletics, swimming, cycling and rowing (Coast et al., 2004;Cheuvront et al., 2005;Seiler
et al., 2007;Thibault et al., 2010;Hunter et al., 2011;Knechtle et al., 2016;Joyner, 2017;Keenan
et al., 2018;Millard-Staﬀord et al., 2018). The best male athletes consistently outperform their
female peers, with the magnitude of this sex gap typically ranging between 5 and 17%, depending
on the sporting discipline, event duration and competitive standard. For example, the average sex
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gap for elite level Olympic distance running events is 10.7%,
compared to 17.5% for jumps, 8.9% for swimming, and 8.7% for
sprint cycling (Thibault et al., 2010). In athletics the sex gap is
usually lower for sprints than middle- and long-distances (Coast
et al., 2004;Cheuvront et al., 2005;Millard-Staﬀord et al., 2018).
The sex gap within ultra-endurance events is as low as 4.4% in
ultra-marathon (Waldvogel et al., 2019). Finally, the sex gap is
smaller between elite males and females (Cheuvront et al., 2005;
Thibault et al., 2010), compared to sub-elite and recreational
runners (Vickers and Vertosick, 2016;Nuell et al., 2019).
The sex gap in sports performance is primarily rooted in
biological diﬀerences between the sexes, namely in relation
to male’s superior skeletal muscle mass, oxidative capacities
and lower fat mass (Joyner, 2017). However, there is a range
of sociocultural, psychological and sport-speciﬁc factors that
could explain some of the variance between male and female
athletic performance (Deaner, 2006, 2012;Capranica et al.,
2013;Donnelly and Donnelly, 2013;Senne, 2016). The relative
contribution of these diﬀerent biological and environmental
factors to the sex gap is unclear. Therefore, the aims of this
review manuscript are three-fold: (a) provide a summary of
the literature on the evolution of the sex gap in running
performance, (b) provide an updated analysis on the running
sex gap to explore if and how the sex gap has changed in recent
years across running events and between performance levels,
(c) summarize potential explanations. We chose to focus on
the sport of running, speciﬁcally ﬂat Olympic distances, because
of the availability of result databases and literature on the sex
diﬀerences in running physiology and performance. Addressing
these questions provides insight into the debate surrounding the
limits of human athletic performance (Berthelot et al., 2015) and
hold practical signiﬁcance for athletes, coaches and practitioners.
The Evolution of the Sex Gap: World
Analyses of world record (WR) performances between 1891 and
2008 reveal two major trends in the sex gap evolution for athletic
events: a fast reduction in gap magnitude until the mid-1980s,
followed by a period of stability thereafter (Thibault et al., 2010).
Within running disciplines 100 m to Marathon distance, the
sex gap in WRs decreased from an average of 30% in 1922 to
10.7% at the point of stabilization (1985) (Thibault et al., 2010).
Table 1 shows the WR sex gaps for speciﬁc running events at the
established stabilization year [based on Thibault et al.’s analysis
(Thibault et al., 2010)], as of 2004 (Cheuvront et al., 2005) and
the present day (February 2021). From the mid-1980s to present,
there has been an increase in the WR sex gap for all ﬂat Olympic
running events, except the 5,000 m and Marathon, where the gap
has narrowed slightly (Table 1).
The trends in the sex gap for WR running performances –
rapid decline followed by a period of stability – reﬂect the changes
in the rate at which females were setting WRs compared to
males. An analysis of WR progressions reveal that there were
signiﬁcant initial jumps in the rate of female WR advancement
which eventually plateaued; whereas, the advancement of male
WRs was more gradual throughout the 20th century and into
the 21st century (Cheuvront et al., 2005). This reﬂected the
advancement in training, globalization of sport, increase in
competitive opportunities and professionalism throughout the
20th century (Joyner, 1993, 2017). Females began competing
in sport later than males and were exposed to these changes
within a shorter time frame, prompting a drastic fall in female
running times unrivaled by that of males (Joyner, 2017). This
trend led some to predict that females would eventually outrun
males (Whipp, 1992); however, it is apparent that the rate of
improvement in female WR performances have leveled, and the
sexes now “evolve in parallel” (Thibault et al., 2010). However, the
present-day WR sex gaps are slightly wider than they have been
in the past (Table 1), and males appear to be breaking WRs more
frequently than females (Holden, 2004). As such, males may be
improving at a greater rate than females at the world class level.
Considering many female WRs have not been surpassed since
the 1980s (Table 1), it is also insightful to consider progression
in the annual world lead (WL) performances for both sexes.
Sparling et al. found the sex gaps for WL 1,500 m and Marathon
performances were relatively stable between 1980 and 1996,
averaging at 11.1% and 11.2%, respectively (Sparling et al., 1998).
TABLE 1 | The World Record (WR) times and year for Males and Females, and the WR Sex Gap for Running Events 100 m to Marathon (Cheuvront et al., 2005;
Thibault et al., 2010).
Running
distance
Male WR time
(Year set)a
Female WR time
(Year set)a
WR sex gap (%) during 20th century,
taken from Thibault et al. (2010)b
WR sex gap (%) as of 2004, taken
from Cheuvront et al. (2005)b
WR sex gap (%) as of
February 2021b,c
100 m 9.58 (2009) 10.49 (1988) 6.5 7.3 9.5
200 m 19.19 (2009) 21.34 (1988) 9.2 10.5 11.2
400 m 43.03 (2016) 47.60 (1985) 10.0 10.2 10.6
800 m 1:40.91 (2012) 1:53.28 (1983) 10.4 12.0c12.3
1,500 m 3:26.00 (1998) 3:50.07 (2015) 10.6 11.9 11.7
5,000 m 12:35.36 (2020) 14:06.62 (2020) 12.9 14.1 12.1
10,000 m 26:11.00 (2020) 29:17.45 (2016) 10.8 12.1 11.9
Marathon 2:01:39 (2018) 2:14:04 (2019) 10.6 8.4 10.2
Range 1998 – 2020 1983 – 2020 6.5 – 12.9 7.3 – 14.1 9.5 – 12.1
aTime format ss.ms, mm:ss.ms, h:mm:ss.
bThe sex gap (%) is calculated as follows: {[Female WR (s) – Male WR(s)]/ Male WR(s)} X 100.
cCalculated using data from World Athletics public database (https://www.worldathletics.org).
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This indicates that the magnitude and trends in the WL sex gap
are similar to that of WR performances (Cheuvront et al., 2005).
The literature on the sex gap evolution in running often
focuses on changes in WR or WL performances (Sparling et al.,
1998;Cheuvront et al., 2005;Thibault et al., 2010;Sandbakk et al.,
2017). Whilst these analyses give insight to the upper limits of
human performance (Coast et al., 2004), we should be cautious
when drawing conclusions about general trends in the sex gap
based on these rare and extraordinary individual performances
(Capranica et al., 2013). These studies use a single data point
per year (i.e., percent diﬀerence between the #1 ranked male
and female); therefore, “anomalous” performances could skew
the trends and subsequent conclusions drawn. This is of concern
within the context of doping scandals and whether some WRs
were achieved by athletes taking performance-enhancing drugs.
World Athletics have considered a proposal to erase all WRs
set before 2005, the year when blood and urine samples were
ﬁrst stored for future testing (Edwards et al., 2018). If this reset
took place, the sex gap for some events would be impacted
quite drastically, for example the women’s 100 m WR gap would
increase from 9.5 to 11.1%. This brings into the question the
relevance of studying WRs to understand the sex gap evolution,
since such records may not always be reliable or trustworthy
markers of the natural limits of human performance.
The Evolution of the Sex Gap: Elite
Performances
Whilst coaches, athletes, scientists and sports teams are looking
to push the limits of human athletic performance by achieving
WR times, the world of high-performance sport is also concerned
with advancing competitive standards within the elite category as
a whole. Looking beyond WRs/WLs and taking the average sex
gap between a small group of the top ranked male and female
runners will expand the pool of data analyzed each year. This
can provide a more representative picture of trends in the sex gap
evolution for world class runners and allow detection of changes
not reﬂected in WRs/WLs alone (Seiler et al., 2007;Capranica
et al., 2013;Sandbakk et al., 2017). Such an approach would also
help account for the confounding eﬀect of performances achieved
by doping and/or anomalies in sport.
A few studies have adopted this approach (Seiler et al., 2007;
Thibault et al., 2010;Millard-Staﬀord et al., 2018). Millard-
Staﬀord et al. compared the top 8 male and female performers
at the U.S. Olympic track and ﬁeld Trials and found the average
sex gap for running events was 12.1% in 2016, which had been
stable since 1980 (Millard-Staﬀord et al., 2018). The gap had
decreased from 17.3% in 1972 following the instigation of Title
IX, a federal law which legislated equal opportunities for females
in the United States education system, including within sport.
Additionally, Thibault et al. examined the sex gap evolution using
the top 10 male and female running performances in athletics
worldwide (Thibault et al., 2010). The average gap decreased
from 25.3% in the early 20th century to 11.2% in 1984, after
which followed a stabilization period (Thibault et al., 2010). Seiler
et al. studied the sex gap between the top 6 ﬁnishers in male
and female World Championship and Olympic Games ﬁnals for
sprint events. The average sex gap for the 100 m, 200 m and
400 m events decreased between the 1950s and 1980s to reach
an average low of 9.8%, before increasing to 11.2% by 2005
(Seiler et al., 2007).
Moreover, the patterns in the sex gap evolution are similar
when studying the elite pool and WR/WL performances – rapid
decline until the 1980s then stabilizing (Thibault et al., 2010).
However, as demonstrated by Seiler et al., looking at the top 6
annual performances at major championships allows sensitivity
to detect the widening of the sex gap over a shorter time frame
in more recent years (Seiler et al., 2007). It is unclear whether
a similar change has occurred in middle- and long-distance
running events, or if there are trends that have occurred in the sex
gap in the last decade. Further, the above studies are insightful but
limited because they only consider performances from a single
nation/at a single event [United States Olympic Trials (Millard-
Staﬀord et al., 2018)], or do not include the most recent years’
data on sex gaps [data until 2008 (Thibault et al., 2010), data until
2005 (Seiler et al., 2007)].
An Update on the Sex Gap in Running
Performance
Sex Gap Trends for Top 20 Performances Over the
Last 20 Years
Understanding trends in the sex gap in the modern athletic
era will give insight into the current competitive standard
of male and female running and how the sexes perform
relative to one another. This should guide discussion around
explanations for the sex gap and how to advance athletic
performance in both sexes.
We conducted an up-to-date analysis on the sex gap between
elite male and female runners using the annual top 20 world
best performances over the past two decades. Data was extracted
from the World Athletics public database (1Season Top Lists,
date extracted: 02/01/2021), with the top 20 best by athlete marks
selected for male and female Olympic running distances (ﬂat
events: 100 m, 200 m, 400 m, 800 m, 1,500 m, 5,000 m, 10,000 m,
Marathon) each year between 2001 and 2020. For each event
and each year, a pairwise comparison was made between the nth
ranked male and female performance time (seconds), using the
following equation:
sex gap (%)=nh Femalen(sec)Malen(sec)
Malen(sec)i×100o
20
For each event each year, the average sex gap across all 20 ranks
was calculated as follows:
Top 20 sex gap (%)=(XFemalen(sec)Malen(sec)
Malen(sec))×100
As per Seiler et al. (2007), we did not analyze the data with
inferential statistics, considering data points were compiled from
multiple individuals across multiple years. The sex gap (%) in
performance time for each pairwise rank was plotted against
1https://www.worldathletics.org
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historical time (year) using GraphPad Prism 8 (San Diego, CA)
for each running event. Two regression lines were ﬁtted to
the data: a linear regression (y = ax +b) and a second order
polynomial regression (y = ax2 +bx +c). If the R2 value
was improved by at least 0.02 when using the second order
polynomial regression, this was used as the line of best ﬁt,
otherwise the linear regression was used (Seiler et al., 2007).
As inferred from the regression lines plotted to the data
(Figures 1A–H), the sex gap for top 20 performers over the past
two decades has been relatively stable for the 100 m, 200 m and
800 m events, has increased slightly for the 400 m, has decreased
slightly for the 1,500 m and 5,000 m, and has ﬂuctuated for the
10,000 m and Marathon. The current trends in the top 20 sex
gap for sprints (100 m, 200 m, 400 m), middle-distances (800 m,
1,500 m) and long-distances (5,000 m, 10,000 m, Marathon)
will be discussed in relation to the WR sex gaps and the
literature below.
Sprints
In accordance with other studies (Coast et al., 2004;Millard-
Staﬀord et al., 2018), the present analysis shows that the top 20
sex gap for sprinters (speciﬁcally 100 m and 200 m) is consistently
lower than middle- and long-distance runners over the years
(Figure 1). The average top 20 sex gap for the 100 m ranges from
9.75 to 11% between 2001 and 2020 (Figure 1A), consistently
higher than the current 100 m WR sex gap of 9.5% (Table 1).
For the 200 m, the average top 20 gap is, on occasion, similar to
the WR sex gap of 11.2%, but most years it exceeds 11.5%. The
400 m top 20 sex gap has exceeded 12% every year in the past two
decades, whereas the WR sex gap is 10.6%.
Seiler et al. observed a narrower sex gap in the sprints in
the 1980s compared to the 2000 – 2005 period (Seiler et al.,
2007). This may have been an artifact of doping prior to the
initiation of randomized drug testing in 1989; the gains that
male athletes receive from performance-enhancing drugs may
be smaller than that of females due to the male’s pre-existing
high levels of circulating testosterone (Seiler et al., 2007). It
appears that since the early 2000 s, the average sex gap has
not changed drastically for the 100 m or 200 m and may be
increasing very slightly in the 400 m (Figure 2). This implies
that the confounding eﬀect of performance-enhancing drugs are
minimized, allowing a more accurate assessment of the limits of
male and female performance.
Middle-Distance
The average top 20 sex gaps for the 800 m and 1,500 m
exceeded 13% in all but one year, and the present WR sex gaps
for these events are 12.3% and 11.7%, respectively (Table 1).
Similarly, Thibault et al. established the stable sex gap for the
top 10 800 m and 1,500 m performances at 11.3% and 12.3%,
compared to 10.4% and 10.6% for WR performances, respectively
(Thibault et al., 2010).
The sex gap in elite 1,500 m appears to have decreased
in recent years (Figure 1E), an insight which is not apparent
based on earlier studies (Sparling et al., 1998;Thibault et al.,
2010). Whilst speculative, the narrowing of the gap could reﬂect
rising standards of elite female 1,500 m, particularly amongst
British and American runners. Between 2005 and 2008, either
no athletes or a single female athlete was represented by these
countries on the top 20 world rankings. In comparison, four
American and four British female runners were ranked in the
top 20 in 2019. Whilst the WR sex gap has not narrowed
signiﬁcantly since 2004 (Table 1), the top 20 sex gap for
1,500 m is 1% lower, and it will be of interest to see if this
trend continues.
The overall trend in the 800 m sex gap is stable based on the
ﬁtted regression line (Figure 1D), which is consistent with the
ﬁndings of Thibault et al. (2010). However, visual inspection of
the graph reveals nuances in 800 m sex gap evolution; there is a
cyclical trend in the sex gap – decreasing between 2001 and 2008,
then increasing to 2013, decreasing to 2017 before increasing in
the subsequent 3 years.
Long-Distance
On average, the top 20 sex gap was highest of all running events
for the 10,000 m. There was no systematic increase in the sex
gap with increasing event distance as suggested elsewhere (Coast
et al., 2004), since the 5,000 m and Marathon gaps were typically
lower than that of the middle-distances (Figure 1).
The sex gap has narrowed in recent years for the 5,000 m
(Figure 1F); at the start of the era (early 2000 s), the sex gap
appeared relatively stable around 13.8%, yet has been 1 – 2%
lower in comparison in the last 5 years (with the exception
of 2018). Alongside this, there appears to be a concomitant
narrowing of the sex gap in the 5,000 m WR; in 2004 this was
14% (Table 1), compared to the current gap of 12%.
As seen in Figure 1G, there is large annual variability and
ﬂuctuations in the sex gap for the 10,000 m. This is not
particularly evident in the sprints and middle-distances, where
year-on-year variations in the sex gap are smaller and more
cyclical. The average top 20 sex gap for the 10,000 m was lowest
at 12.8% in 2016 and highest at 16.2% in 2007, and consistently
higher than the current WR sex gap of 11.9% (Table 1).
The Marathon event also displays similar variability in the sex
gap, with an overall inverted-U shape trend, increasing for the
ﬁrst decade then decreasing (Figure 1H). The average sex gap
always exceeded 12%, compared to the current Marathon WR sex
gap of 10.2%. Moreover, Hunter et al. found that, between the late
1990’s and 2009, the average sex gap for the top 5 runners at major
world class marathon races diﬀered signiﬁcantly over the years;
although, there was no systematic trend to indicate the sex gap
narrowing or widening (Hunter et al., 2011).
These observations indicate that, although there may be
overall stability in the sex gap for the longer distances, annual
changes occur in how females perform relative to their male
peers, and vice versa. Whilst it is currently unclear why such
variations exist it could be inferred that multiple underlying
variables are involved.
Changes With Performance Level Over Top 20 Ranks
To further understand the relationship between the sex gap and
performance level, we averaged the sex gap across the 20-year
period (2001 – 2020) for each rank position (1st to 20th) using
the pairwise sex gaps for the top 20 performers across Olympic
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FIGURE 1 | The Sex Gap Over Time in Running Events 100 m to Marathon between 2001 and 2020. The sex gap % is calculated as the percent difference between
male and female running times. The pairwise sex gaps for each top 20 ranked individuals (annually, worldwide) are plotted as black circles and the average top 20
sex gap for each year is plotted as a red diamond. Regression lines are ﬁtted as the line of best ﬁt. (A) 100 m, (B) 200 m, (C) 400 m, (D) 800 m, (E) 1,500 m, (F)
5,000 m, and (G) Marathon.
running events. For each event (100 m to Marathon), we plotted
the averaged sex gap against rank position.
As illustrated in Figure 2, the overall trend for most running
events is an increase in the sex gap as the rank increases from the
1st to 20th athlete. For the sprint events (Figure 2A), the increase
in sex gap with rank is most clearly seen for the 400 m. For the
100 m, the sex gap decreases from 1st to 4th ranked individuals,
increases to the 15th rank, then plateaus. For the 200 m, the gap
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FIGURE 2 | The Sex Gap and Performance Level in Running Events 100 m to Marathon. The sex gap % is calculated as the percent difference between male and
female running times, averaged over a 20-year period (2001 – 2020) for each worldwide rank position (1 through 20) for (A) sprint events, (B) middle-distances, (C)
long-distances.
is highest for the ﬁrst three ranked individuals and is relatively
similar between 4th and 20th ranks. For the middle-distances
(Figure 2B), changes in sex gap with rank position are similar;
there is a clear trend toward a gradual expanding of the sex gap
from 1st to 13th rank, after which the gap is relatively stable at
14% for higher ranks. There is a linear trend of increase in the
5,000 m sex gap from 1st to 20th ranked athlete. For the 10,000 m
and Marathon, there is a relatively large jump in the sex gap from
1st to 4th rank, and a more gradual expansion of the gap with
each rank position thereafter. The diﬀerence in average sex gap
between 1st and 20th ranks is smallest for distances 100 m to
1,500 m (<1%), and larger for 5,000 m to Marathon (1.3 – 2.1%).
The widening of the sex gap with rank position or ﬁnishing
place in elite level runners is evident in other studies (Sparling
et al., 1998;Hunter et al., 2011, 2015;Hunter and Stevens,
2013;Senefeld et al., 2015). The sex gap increases from the
WL performers to the 100th ranked athletes in the 1,500 m
(Sparling et al., 1998) and Marathon (Sparling et al., 1998;
Hunter et al., 2015), from the 1st place to 5th place ﬁnisher
(Hunter et al., 2011) and 1st to 10th place ﬁnisher (Hunter
and Stevens, 2013;Senefeld et al., 2015) in world class
marathon races. This is due to a greater relative drop oﬀ
in performance time with increasing rank/position for female
athletes (Hunter and Stevens, 2013).
Collectively, these ﬁndings indicate that male athletes are
relatively faster runners and closer to the sex speciﬁc WR/WL
marks than females, and that the competitive pool of male
runners is more homogenous. This is more apparent in middle-
and long-distance running than in short sprints, where the sex
gap is not noticeably diﬀerent between 1st and 20th world class
ranks (Figure 2). Nonetheless, there is evidence of a greater
drop oﬀ in female performance at lower performance levels for
sprinters; the sex gap for sub-elite sprinters is 15% across 80 m
(Nuell et al., 2019), which is higher than the 100 m sex gap for WR
and elite performances. Moreover, there is a sex gap in relative
performance and depth of competition within running.
EXPLANATIONS
Underpinning diﬀerences in running performance between
males and females are a range of potential physical (physiological,
anatomical, neuromuscular, biomechanical), sociocultural,
psychological and sport-speciﬁc factors. The role each variable
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contributes to the sex gap may vary between event disciplines,
across historical time, across cultures, and with performance
level. Many assert that the gap in running today is explained
purely by biological sex diﬀerences (Cheuvront et al., 2005;
Thibault et al., 2010;Millard-Staﬀord et al., 2018). However,
there are ﬂuctuations and small changes in the sex gap for WRs
(Table 1) and elite level running (Figure 1), which warrant
further investigation into other explanatory factors. This has
signiﬁcance to the discussion of whether runners, both male
and female, have reached the limits of human performance, or
whether advancements in technology, training, opportunities
and talent identiﬁcation could see notable jumps in one or both
of the sexes (Berthelot et al., 2015). The following section will
discuss the biological and environmental explanations for the sex
gap in running performance.
Physiological, Anatomical,
Neuromuscular and Biomechanical
Explanations
Sprints
The sex diﬀerences in muscle anatomy and physiology are
signiﬁcant in explaining the performance gap in sprint running.
Male runners have superior muscle volumes than females (Nuell
et al., 2019), due to longer muscle segment sizes and 25 –
40% more skeletal muscle mass (Sandbakk et al., 2017). The
latter is due to male’s larger muscle ﬁber cross sectional areas, as
opposed to greater ﬁber numbers (Miller et al., 1993;Jaworowski
et al., 2002). Since muscle cross sectional area is closely related to
the force producing capacity of the muscle (Miller et al., 1993),
males are naturally stronger and more powerful than females,
contributing to their advantage in sprinting (Sandbakk et al.,
2017). Additionally, males have superior anaerobic metabolic
power than females (Sandbakk et al., 2017), potentially due to
a larger relative area of fast twitch muscle ﬁbers which have
large glycolytic capacity (Jaworowski et al., 2002) and higher
peak tension (Schiaﬃno and Reggiani, 2012). These diﬀerences
in muscle morphology and function arise during puberty and are
underpinned by the increase in circulating testosterone in males
(Handelsman, 2017).
Biomechanical and neuromuscular factors are also pertinent
to this discussion for sprinters. It is apparent that sprint speed
is limited by the ability to apply large ground reaction forces
over short contact times, as opposed to the anaerobic energy
supply (Bundle and Weyand, 2012). Male sprinters possess
anthropometric, structural and mechanical properties that favor
their ability to produce horizontal ground forces at high speeds
(Nuell et al., 2019). These include their larger skeletons, longer
legs and therefore longer stride lengths, higher center of gravity
and greater muscle and tendon stiﬀness (Blackburn et al.,
2006;Slawinski et al., 2015;Nuell et al., 2019). These variables
contribute to sex diﬀerences in mechanical sprint properties in
both elite (Slawinski et al., 2015) and sub-elite sprinters (Nuell
et al., 2019). Males obtain greater maximal force, velocity and
power, have longer acceleration phases and shorter deceleration
phases than females.
Moreover, if sex diﬀerences in body composition are critical
in explaining the male advantage in running performance, one
may expect the sex gap to be greater in disciplines that rely
predominantly on muscular strength and power. However, this is
not the case; the sex gap for short sprint events (100 m and 200 m)
are typically smaller than distance events (Coast et al., 2004;
Sandbakk et al., 2017;Millard-Staﬀord et al., 2018) (Figures 1,2
and Table 1). A potential reason for this is that female sprinters
have somewhat of an advantage due to their relatively smaller
upper-body mass, meaning there is less inertia to overcome when
accelerating (Sandbakk et al., 2017).
Middle-Distance
Middle-distance running, particularly the 800 m, is an interesting
and challenging ﬁeld for sports physiologists to study because it
represents the “middle ground” between aerobic and anaerobic
energy domains (Duﬃeld and Dawson, 2003). The estimated
anaerobic energy contribution to 800 m running is higher in
males than females (Duﬃeld and Dawson, 2003;Duﬃeld et al.,
2005) which could be related to sex diﬀerences in muscle ﬁber
type distribution, with male middle-distance runners displaying a
greater proportion of fast twitch ﬁbers (Costill et al., 1976, 1979).
Furthermore, it is recognized that elite male 800 m runners
possess biomechanical and neuromuscular abilities that underpin
fast maximal sprinting speed (MSS), which is a requirement for
competitive success (Sandford et al., 2018, 2019). Sandford et al.
found that elite male 800 m runners have a large anaerobic
speed reserve (ASR) – the diﬀerence between MSS and the
velocity at VO2max (Sandford et al., 2018). Possessing a large
ASR may be advantageous because it signiﬁes the athlete has
a large “race pace” speed bandwidth in which they can adjust
velocity to produce mid-race surges and end-kicks, as well as the
force application abilities to execute very fast starting velocities
(Sandford et al., 2019). The role of the ASR in female middle-
distance running is an unstudied area, and it is unclear whether
female 800 m runners possess these characteristics that are critical
for success in male running.
Hence, the physiological, biomechanical and neuromuscular
proﬁles of a typical female middle-distance runner could mean
they have greater potential to excel at longer aerobic-based events
than the 800 m (Duﬃeld and Dawson, 2003;Duﬃeld et al.,
2005). If this is true, then you may expect the sex gap to be
greater in the 800 m than other middle- and long-distances. This
was not clear from the top 20 sex gap analysis; however, we
did see a narrowing of the gap over time for the 1,500 m and
5,000 m and this trend was absent for the 800 m (Figure 1).
Perhaps females are “training smarter” in these longer events,
leading to greater relative improvement compared to males.
There may be scope for female 800 m runners to narrow the
sex gap by tapping into the ASR domain, which is evidently a
successful approach for male middle-distance runners (Sandford
et al., 2018). There is a male sex bias in middle-distance running
research (Mpholwane, 2007) which means that coaches tend to
train female athletes using strategies that have been validated in
males, but do not take into consideration aforementioned sex
diﬀerences. Hence, more research is needed using female runners
to ensure that training strategies will enable them to reach their
biological potential.
It is also important to consider the large diversity in
physiological and mechanical proﬁles of middle-distance runners
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(Sandford and Stellingwerﬀ, 2019). Sandford identiﬁed speed-
based and endurance-based subtypes in male 800 m runners,
and asserted that both are capable of executing successful
performances through adopting tactics/pacing that favor their
underlying physiologies and mechanics (Sandford et al., 2018).
We have observed similar subtype categorizations in female
800 m runners (unpublished data). Hence, coaches and athletes
should be aware of their unique proﬁle and use this information
to advise training and competition practices.
Long-Distance
Over long-distances, the variance between sexes is primarily
related to diﬀerences in maximal oxygen consumption
(VO2max). Elite male marathon runners have relative VO2max
values that are 10% higher than their female peers (Pate
and O’Neill, 2007). This is because males have less fat mass
and greater skeletal muscle mass, the most metabolically
active tissue in the body, than do females (Wang et al., 2010).
Additional physiological variables likely contribute to the
superior cardiorespiratory capacity in males. Males have greater
red blood cell and hemoglobin concentrations than females,
therefore a higher blood oxygen carrying capacity (Cureton
et al., 1986;Murphy, 2014). Additionally, males have larger
hearts and lungs relative to body size (Purkiss and Huckell,
1997;Harms et al., 1998;LoMauro and Aliverti, 2018), as well
as larger cardiovascular preload and stroke volumes (Huxley,
2007;Wheatley et al., 2014). Since VO2max represents the
upper limits in aerobic capacity and maximal rate of oxidative
ATP production (Bassett and Howley, 2000), it is clearly a key
determinant of endurance running performance diﬀerence
between the sexes (Cheuvront et al., 2005;Joyner, 2017).
Physiological diﬀerences related to thermoregulation
(Cheuvront et al., 2005) and metabolism (Tarnopolsky et al.,
1990;Carter et al., 2001) could explain some of the sex variance
in long-distance running performance; although, their role is less
clear. Females have greater reliance on lipid oxidation during
prolonged submaximal exercise than males (Tarnopolsky et al.,
1990;Carter et al., 2001), and this may give them an advantage
over marathon distances due to glycogen sparing and delaying
fatigue (Sandbakk et al., 2017). In support, the average top 20
sex gap for the Marathon is typically lower than other long-
and middle-distance events (Figure 1). However, this advantage
in fuel regulation may be negated in Marathon racing, where
both sexes use regular carbohydrate feeding which will reduce
reliance on lipid metabolism (Coast et al., 2004). There is no
conclusive evidence for sex diﬀerences in mitochondrial capacity,
lactate threshold or running economy (Joyner, 2017), which
are also key determinants of endurance running performance
(Bassett and Howley, 2000).
Gender Equality in Participation and
Opportunity
The sociocultural conditions hypothesis states that sex diﬀerences
in opportunities and participation explain variance between
male and female sports performance (above that which is
rooted in biological diﬀerences) and the lower depth of female
competition (Hunter et al., 2015;Keenan et al., 2018). Female
participation in athletics has increased drastically since the ﬁrst
modern Olympic games in 1896, where not a single female
competed. Legislative changes throughout the 20th century
meant that females were permitted to compete in events
from which they were previously banned, for example in the
Olympic 1,500 m in 1972 and the Marathon in 1982 (Cheuvront
et al., 2005). Three milestones were achieved at the London
2012 Olympic Games: 44.3% of participants were females, the
highest percentage of any Summer Olympic Games; females
competed in every event; all nations were represented by female
athletes (Donnelly and Donnelly, 2013). Title IX legislation
has promoted equal opportunities for females to train and
compete at the U.S. collegiate level and has equalized the
ﬁnancial aid awarded to males and females within an institution
(Senne, 2016).
Despite the progress made in legislation, policies and
participation, sex inequality still pervades sport and sociocultural
factors are likely to inﬂuence the sex gap today (Capranica
et al., 2013). There is a discrepancy in funding and ﬁnancial
incentives, social support provided by governing bodies and
sporting federations, and media representation between male
and female athletes (Capranica et al., 2013;Donnelly and
Donnelly, 2013;Senne, 2016). Alongside this, stereotypes
and perceptions remain a barrier to female athletes; sport
is viewed by many as a masculinized domain and despite
opportunities, females are still less likely to participate than males
(Senne, 2016).
Some have argued against the sociocultural conditions
hypothesis as an explanation for the sex gap in absolute
performance and competition depth. The frequency at which
females set WRs in athletics decreased between 1980s and
2008, despite growing numbers of female participants at the
Olympics, indicating that participation is not a limiting factor
for the progression of female performance (Thibault et al., 2010).
However, this is an incomplete argument; despite roughly equal
numbers of male and female Olympic competitors, females
are still underrepresented at the sub-elite and grassroots level
(Deaner, 2006;Capranica et al., 2013;Senne, 2016). This could
be the source of the sex gap in competitive depth at higher levels
of competition. Additionally, whilst female athletes in countries
such as the United States beneﬁt from legislation promoting equal
opportunities and participation in sport, this is not universal
(Capranica et al., 2013).
Furthermore, Keenan et al. (2018) found that female collegiate
rowers improved more than their male peers between 1997
and 2016, narrowing the sex gap in absolute performance and
competition depth. These changes coincided with an increase in
female, but not male, participation. Additionally, lower female
participation is associated with an increase in the sex gap in
Marathon running (Hunter and Stevens, 2013). These examples
demonstrate that participation is an important factor related
to changes in female performance and the sex gap evolution,
corroborating the sociocultural conditions hypothesis.
The expanding of the sex gap with rank position, an indicator
of lower depth in the female ﬁeld, is more apparent in the longer
distances (Figure 2). In the 5,000 m, 10,000 m and Marathon,
there is a larger increase in the sex gap with rank, compared
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to the 100 m and 200 m (and to a lesser extent 800 m and
1,500 m) where the changes between 1st and 20th ranks are
small. This could be because females have been competing over
long-distances for a shorter time (the ﬁrst Olympic Marathon
for females took place in 1984) resulting in a time lag eﬀect.
We may see an improvement in depth of long-distance running
over time as more females have the opportunity to train and
compete in the sport.
Competitiveness and Psychology
The evolved predispositions hypothesis argues that there are
innate and evolved sex diﬀerences in competitiveness and
motivation that explain the sex gap in competition depth
(Deaner, 2006, 2012;Keenan et al., 2018). Deaner (2012) argues
that males have a predisposition for enduring competitiveness,
meaning they are attracted to performance-based environments
where they display and compete for status. As such, males
are more likely to be dedicated to the training regimens that
are required for success in sport; this drives up the standard
and depth of male competition. In support, Deaner found that
in the United States more male high school, collegiate and
professional track runners ran relatively fast compared to the sex-
speciﬁc world class standards than did their females counterparts
(Deaner, 2006).
When investigating potential sex diﬀerences in the
psychological disposition toward competitive environments,
it is useful to analyze how male and female runners execute
performance, i.e., their pacing strategy. Pacing can be described
as the distribution of metabolic resources over the course of a
race (Abbiss and Laursen, 2008). Female runners typically display
more even pacing proﬁles across middle- and long-distances at
the elite (Hanley, 2016;Filipas et al., 2018) and recreational level
(March et al., 2011;Trubee et al., 2014;Deaner et al., 2015, 2016).
Male runners are more likely to adopt risky and ambitious pacing
strategies – starting too fast and slowing signiﬁcantly in the
second half of the race – which could reﬂect an overconﬁdence in
their abilities. This is consistent with the evolved predispositions
hypothesis. Conversely, Hanley and Hettinga (2018) found
that both sexes displayed “ego-oriented behavior” in major
championship 800 m and 1,500 m races. The eventual gold
medalists ran to win during qualifying rounds, as opposed to
doing just enough to qualify and preserve energy reserves for
the ﬁnal. Other studies demonstrate similarities in race strategies
between the sexes, particularly amongst medalists (Hanley et al.,
2019;Hettinga et al., 2019). This indicates elite female runners
display equally competitive or risky racing behaviors as males;
however, perhaps relatively fewer females have such a disposition
at lower competitive levels.
One could integrate aspects of both the enduring
competitiveness and sociocultural conditions hypotheses to
explain why fewer females engage in competitive running.
Perhaps males possess a greater competitive drive to pursue
sporting success from a young age because they are primed by
their sociocultural environment to desire/expect this. Likewise,
females may anticipate fewer opportunities, less acceptance and
less support, so have less motivation to engage in sport.
PRACTICAL APPLICATIONS
Why should we be concerned with how the sex gap in elite
runners is evolving or the underlying explanations? The purpose
of this discussion is not to create a “battle of the sexes” (Tanaka,
2002), since males and females are not in direct competition
with each other but, rather, with members of their own sex. This
discussion should instead be approached with the view of seeing
both male and female athletes maximize their biological potential
and push the limits of human performance. If this is achieved by
both sexes, the magnitude of the gap is insigniﬁcant. However,
if it is apparent that one sex has more opportunities, support,
incentives and provisions to reach their performance potential,
we are faced with an inequality that should be challenged. As
argued in this review, females historically and presently are the
It appears that there is scope to narrow the sex gap for elite
runners, particularly in relation to competition depth. Sex gaps
as low as 10 – 11% are biologically possible for the best male and
female runners (Table 1), but we consistently observe increasing
sex gaps at lower performance levels (Figure 2). To address the
sex gap in competitive depth, governing bodies and sporting
federations should implement strategies to expand the talent pool
from which the world’s best athletes are drawn. This should focus
on increasing female participation in grassroots sport, improving
talent identiﬁcation schemes to recognize young female athletic
talent, and retaining these athletes in the transition from junior
to senior level competition. Media campaigns should be used to
challenge the stereotypical view that sport is a masculine domain
and grow commercial interest in female sport. More research
in female runners is needed so that coaches and practitioners
better understand the unique training responses, race demands,
physiologies and mechanics of their female athletes. Finally,
ﬁnancial provisions and incentives should be equalized between
the sexes, particularly in countries where there are fewer policies
and legislations in place that support the female athlete.
A limitation of the present study is the lack of use of
inferential statistics. Although the data set used is representative
of the population studied (top 20 best male and female runners
between 2001 and 2020), some runners appeared in diﬀerent
years and events multiple times. These multiple appearances
for the same runners violate the independence assumption
where each data element must be selected independently of
data previously selected. Another limitation is the analyses of
a relatively short time frame (20 years) and the observed small
ﬂuctuations in the sex gap over the last 20 years. Therefore, we
recommended here, a word of caution, in regard to meaningful
and practical signiﬁcance.
CONCLUSION
It is undeniable that the drastic narrowing of the sex gap
during the 20th century has leveled oﬀ, and the current data
indicates that performance will not be completely equalized
between the sexes, at least for Olympic running distances
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Hallam and Amorim Sex Gap and Running Performance
(Cheuvront et al., 2005;Thibault et al., 2010;Millard-Staﬀord
et al., 2018). However, the assertions that the sex gap is now
ﬁxed and explained entirely by biological sex diﬀerences is a
limited explanation. The sex gap in athletic performance is not
a stable entity; elite male and female runners are constantly
evolving in relation to their same-sex competitors and to one
another. In the last two decades, the top 20 sex gap has
widened slightly for the 400 m, narrowed slightly for the
1,500 m and 5,000 m, and had ﬂuctuated for the 10,000 m
and Marathon (Figure 1). This could be due to a range of
integrated biological and environmental factors; shifts in training
strategies, technologies, research, event demands, opportunities,
participation, provisions, legislation and perceptions can all play
a role in performance progression in runners. When one sex
experiences diﬀerential advantages from these factors, this is
likely to be reﬂected in a change – narrowing or widening – in
the sex gap. The present review demonstrates that there is not
just a sex gap in absolute performance, but also in competition
depth. The sex gap increases with rank position, i.e., at a lower
performance level, in events 400 m and longer (Figure 2). This
suggests there are more male runners that are closer to the sex-
speciﬁc world class standard than females, and that the male
competitive ﬁeld is more homogenous. Seeking to rectify this gap
will require a multidisciplinary approach to the address the sex
biases that pervade sport and research.
AUTHOR CONTRIBUTIONS
LH conducted the literature review and data analysis. LH and
FA contributed to the conceptualization of ideas, drafting, and
critically revising the work. Both authors contributed to the
article and approved the submitted version.
ACKNOWLEDGMENTS
LH and FA acknowledge the contributions of Ann Gibson,
Christine Mermier, and Leonard Kravitz for reviewing the
manuscript prior to submission.
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Conﬂict of Interest: The authors declare that the research was conducted in the
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and do not necessarily represent those of their aﬃliated organizations, or those of
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Frontiers in Physiology | www.frontiersin.org 11 January 2022 | Volume 12 | Article 804149
... 6 Importantly, any predictive model will be biased if the data being modeled contains participation bias, whether for social, economic, or other reasons. [9][10][11] The odds of an athletic woman choosing to become a miler seem far worse today than the odds of being born with the phenotype required to be a successful miler. Numerous factors 9-11 create differential participation in running between men and women, resulting in a decrease in the depth of track running talent among the latter 12,13 that may help explain why women are not closer to the 4-minute mile today. ...
... Participation disparity is a known confounder for comparing performances between the sexes or when forecasting future trends. 5,6,9,11 A closer look at what is required physiologically to go sub-four, juxtaposed against the documented best performances of women, offers a different and potentially more optimistic outlook. ...
... As with men in the early 1950s, this might stir greater interest, excitement, participation, and depth in the women's mile, the present absence of which likely contributes to more pessimistic mathematical modeling forecasts. 5,6,9,11 The extrapolation of metabolic cost savings to forecast performance improvements is sound, 34 but not without limitations, 39 particularly for running events like the mile which require significant unsustainable energy sources. 24,40 The concepts and conclusions presented herein are theoretical-but they are also reasonable. ...
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When will women run a sub-4-minute mile? The answer seems to be a distant future given how women’s progress has plateaued in the mile, or its better studied metric placeholder, the 1500 m. When commonly accepted energetics principles of running, along with useful field validation equations of the same, are applied to probe the physiology underpinning the 10 all-time best women’s mile performances, insights gained may help explain the present 12.34-second shortfall. Insights also afford estimates of how realistic improvements in the metabolic cost of running could shrink the difference and bring the women’s world record closer to the fabled 4-minute mark. As with men in the early 1950s, this might stir greater interest, excitement, participation, and depth in the women’s mile, the present absence of which likely contributes to more pessimistic mathematical modeling forecasts. The purpose of this invited commentary is to provide a succinct, theoretical, but intuitive explanation for how women might get closer to their own watershed moment in the mile.
... Further research is needed to improve safety, preparation measures and performance for such extreme conditions. Sex differences in sports performance of various sports disciplines have been of significant scientific interest over the last years 14,15,16 . It has been established that males outperform women 16 , although the magnitude of the sex gap depends on different aspects such as sports discipline 16,17 , distance 18 , age 17,19,20 and performance level [15][16][17] . ...
... Sex differences in sports performance of various sports disciplines have been of significant scientific interest over the last years 14,15,16 . It has been established that males outperform women 16 , although the magnitude of the sex gap depends on different aspects such as sports discipline 16,17 , distance 18 , age 17,19,20 and performance level [15][16][17] . The sex gap of performance was reported to be smaller in swimming than in running and cycling for triathlon events 21 . ...
... Sex differences in sports performance of various sports disciplines have been of significant scientific interest over the last years 14,15,16 . It has been established that males outperform women 16 , although the magnitude of the sex gap depends on different aspects such as sports discipline 16,17 , distance 18 , age 17,19,20 and performance level [15][16][17] . The sex gap of performance was reported to be smaller in swimming than in running and cycling for triathlon events 21 . ...
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... Interestingly, the prevalence of several types of running injuries (e.g., hamstring injuries, patellofemoral pain, and iliotibial band syndrome) differs between the sexes [2,4]. When considering the sex differences, different performance between males and females (i.e., sex gap) is relatively high in recreational runners [5,6], of which biological differences between the sexes (e.g., hormonal factors, skeletal muscle mass, and oxidative capacities) are accepted as the primary cause [7,8]. Moreover, the sex-specific manner in which individuals perform running tasks is reported [9][10][11] and listed as one potential risk factor for musculoskeletal injuries [2,3]. ...
... Table 2. The eigenvalue (%) and descriptive movements of the first five principal movements (PM [1][2][3][4][5] analyzed from all participants when performing self-preferred speed treadmill walking. The first PM resembles the swing-phase movement. ...
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A sex-specific manner in running tasks is considered a potential internal injury risk factor in runners. The current study aimed to investigate the sex differences in running stability in recreational runners during self-preferred speed treadmill running by focusing on a whole-body movement. To this end, principal component analysis (PCA) was applied to kinematic marker data of 22 runners (25.7 ± 3.3 yrs.; 12 females) for decomposing the whole-body movements of all participants into a set of principal movements (PMs), representing different movement synergies forming together to achieve the task goal. Then, the sex effects were tested on three types of PCA-based variables computed for individual PMs: the largest Lyapunov exponent (LyE) as a measure of running variability; the relative standard deviation (rSTD) as a measure of movement structures; and the root mean square (RMS) as a measure of the magnitude of neuromuscular control. The results show that the sex effects are observed in the specific PMs. Specifically, female runners have lower stability (greater LyE) in the mid-stance-phase movements (PM4−5) and greater contribution and control (greater rSTD and RMS) in the swing-phase movement (PM1) than male runners. Knowledge of an inherent sex difference in running stability may benefit sports-related injury prevention and rehabilitation.
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The purpose of this study was to determine and compare thigh muscle volumes (MVs), and sprint mechanical properties and performance between male and female national-level sprinters. We also studied possible relationships between thigh MVs and sprint performance. Nine male and eight female national-level sprinters participated in the study. T1-weighted magnetic resonance images of the thighs were obtained to determine MVs of quadriceps, hamstrings and adductors. Sprint performance was measured as the time to cover 40 and 80 m. Instantaneous sprint velocity was measured by radar to obtain theoretical maximum force (F0), theoretical maximum velocity (V0) and maximum power (Pmax). When MVs were normalized by height–mass, males showed larger hamstrings (13.5%, ES = 1.26, P < 0.05) compared with females, while quadriceps and adductors showed no statistically significant differences. Males were extremely faster than females in 40 m (14%, ES = 6.68, P < 0.001) and in 80 m (15%, ES = 5.01, P < 0.001. Males also showed increased sprint mechanical properties, with larger F0 (19%, ES = 1.98, P < 0.01), much larger Pmax (46%, ES = 3.76, P < 0.001), and extremely larger V0 (23%, ES = 6.97, P < 0.001). With the pooled data, hamstring and adductor MVs correlated strongly (r = -0.685, P < 0.01) and moderately (r = -0.530, P < 0.05), respectively, with sprint performance; while quadriceps showed no association. The sex-stratified analysis showed weaker associations compared with pooled data, most likely due to small sample size. In conclusion, males were faster than females and showed larger MVs, especially in hamstrings. Moreover, regarding the thigh muscles, hamstrings MV seems the most related with sprint performance as previously proposed.
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Middle-distance running provides unique complexity where very different physiological and structural/mechanical profiles may achieve similar elite performances. Training and improving the key determinants of performance and applying interventions to athletes within the middle-distance event group are probably much more divergent than many practitioners and researchers appreciate. The addition of maximal sprint speed and other anaerobic and biomechanical based parameters, alongside more commonly captured aerobic characteristics, shows promise to enhance our understanding and analysis within the complexities of middle-distance sport science. For coaches, athlete diversity presents daily training programming challenges in order to best individualize a given stimulus according to the athletes profile and avoid “non-responder” outcomes. It is from this decision making part of the coaching process, that we target this mini-review. First we ask researchers to “question their categories” concerning middle-distance event groupings. Historically broad classifications have been used [from 800 m (~1.5 min) all the way to 5,000 m (~13–15 min)]. Here within we show compelling rationale from physiological and event demand perspectives for narrowing middle-distance to 800 and 1,500 m alone (1.5–5 min duration), considering the diversity of bioenergetics and mechanical constraints within these events. Additionally, we provide elite athlete data showing the large diversity of 800 and 1,500 m athlete profiles, a critical element that is often overlooked in middle-distance research design. Finally, we offer practical recommendations on how researchers, practitioners, and coaches can advance training study designs, scientific interventions, and analysis on middle-distance athletes/participants to provide information for individualized decision making trackside and more favorable and informative study outcomes.
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The purpose of this study was to examine whether World Championship and Olympic medallist endurance athletes pace similarly to their race opponents, where and when critical differences in intra-race pacing occur, and the tactical strategies employed to optimally manage energy resources. We analyzed pacing and tactics across the 800, 1,500, 5,000, 10,000 m, marathon and racewalk events, providing a broad overview for optimal preparation for racing and pacing. Official electronic splits from men's (n = 275 performances) and women's (n = 232 performances) distance races between 2013 and 2017 were analyzed. Athletes were grouped for the purposes of analysis and comparison. For the 800 m, these groups were the medalists and those finishing 4th to 8th (“Top 8”). For the 1,500 m, the medalists and Top 8 were joined by those finishing 9th to 12th (“Top 12”), whereas for all other races, the Top 15 were analyzed (those finishing 9th to 15th). One-way repeated measures analysis of variance was conducted on the segment speeds (p < 0.05), with effect sizes for differences calculated using Cohen's d. Positive pacing profiles were common to most 800 m athletes, whereas negative pacing was more common over longer distances. In the 1,500 m, male medalists separated from their rivals in the last 100 m, whereas for women it was after 1,200 m. Similarly, over 5,000 m, male medalists separated from the slowest pack members later (4,200 m; 84% of duration) than women (2,500 m; 50% of duration). In the 10,000 m race, the effect was very pronounced with men packing until 8,000 m, with the Top 8 athletes only dropped at 9,600 m (96% of duration). For women, the slowest pack begin to run slower at only 1,700 m, with the Top 8 finishers dropped at 5,300 m (53% of duration). Such profiles and patterns were seen across all events. It is possible the earlier separation in pacing for women between the medalists and the other runners was because of tactical racing factors such as an early realization of being unable to sustain the required speed, or perhaps because of greater variation in performance abilities.
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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-miles races (i.e. 231,980 records from 91,665 finishers) and from 1953 to 2017 for 100-miles races (i.e. 107,445 records from 39,870 finishers) were analyzed. In 50-miles, races times were 11.74±1.95 h for men and 12.31±1.69 h for women. In 100-miles, 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-miles (4.41%) than in 50-miles races (9.13%). The overall age of peak performance was 33 years in both distances. In summary, women reduced the performance difference to men with advancing age, the relative difference being smaller in 100-miles compared to 50-miles. These findings might aid coaches and ultra-marathon runners setting long-term training goals considering their sex and age. Keywords: age of peak performance; athlete; sex difference; ultra-endurance
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Purpose: This was the first study to analyze high-resolution pacing data from multiple global championships, allowing for deeper and rigorous analysis of pacing and tactical profiles in elite-standard middle-distance racing. The aim of this study was to analyze successful and unsuccessful middle-distance pacing profiles and variability across qualifying rounds and finals. Methods: Finishing and 100-m split speeds and season's best times (SB) were collected for 265 men and 218 women competing in 800 m and 1500 m races, with pace variability expressed using coefficient of variation (CV). Results: In both events, successful athletes generally separated themselves from slower athletes in the final 200 m, not by speeding up, but by avoiding slowing compared with competitors. This was despite different pacing profiles between events in the earlier part of the race preceding the endspurt. Approximately 10% of athletes ran SBs, showing a tactical approach to elite-standard middle-distance racing, and possible fatigue across rounds. Men's and women's pacing profiles were remarkably similar within each event, but the previously undescribed seahorse-shaped profile in the 800 m (predominantly positive pacing) differed from the J-shaped negative pacing of the 1500 m. Pacing variability was high compared with world records, especially in the finals (CV: 5.2 - 9.1%), showing that athletes need to be able to vary pace and cope with surges. Conclusions: Previous studies have focussed more on athletes in finals, but the present study showed that the best athletes had the physiological capacity to vary pace and respond to surges through successive competition rounds.
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Recent evidence indicates that the modern-day men’s 800 m runner requires a speed capability beyond that of previous eras. In addition, the appreciation of different athlete subgroups (400–800, 800, 800–1500 m) implies a complex interplay between the mechanical (aerial or terrestrial) and physiological characteristics that enable success in any individual runner. Historically, coach education for middle-distance running often emphasises aerobic metabolic conditioning, while it relatively lacks consideration for an important neuromuscular and mechanical component. Consequently, many 800 m runners today may lack the mechanical competence needed to achieve the relaxed race pace speed required for success, resulting in limited ability to cope with surges, run faster first laps or close fast. Mechanical competence may refer to the skilled coordination of neuromuscular/mechanical (stride length/frequency/impulse) and metabolic components needed to sustain middle-distance race pace and adjust to surges efficiently. The anaerobic speed reserve (ASR) construct (difference between an athlete’s velocity at maximal oxygen uptake [v$$\dot{V}$$O2max]—the first speed at which maximal oxygen uptake [$$\dot{V}$$O2max] is attained) and their maximal sprint speed (MSS) offers a framework to assess a runner’s speed range relative to modern-day race demands. While the smooth and relaxed technique observed in middle-distance runners is often considered causal to running economy measured during submaximal running, little empirical evidence supports such an assumption. Thus, a multidisciplinary approach is needed to examine the underpinning factors enabling elite 800 m running race pace efficiency. Here, we argue for the importance of utilising the ASR and MSS measurement to ensure middle-distance runners have the skills to compete in the race-defining surges of modern-day 800 m running.
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Purpose:: In recent years (2011-2016), men's 800m championship running performances have required greater speed than previous eras (2000-2009). The "Anaerobic speed reserve" (ASR) may be a key differentiator of this performance, but profiles of elite 800m runners and its relationship to performance time have yet to be determined. Methods:: The ASR - determined as the difference between maximal sprint speed (MSS) and predicted maximal aerobic speed (MAS) - of 19 elite 800m and 1500m runners was assessed using 50m sprint and 1500m race performance times. Profiles of three athlete sub-groups were examined using cluster analysis and the speed reserve ratio (SRR), defined as MSS/MAS. Results:: For the same MAS, MSS and ASR showed very large negative (both r=-0.74±0.30, ±90% confidence limits; very likely) relationships with 800m performance time. In contrast, for the same MSS, ASR and MAS had small negative relationships (both r=-0.16±0.54), possibly) with 800m performance. ASR, 800m personal best, and SRR best defined the three sub-groups along a continuum of 800m runners, with SRR values as follows: 400-800m ≥1.58, 800m ≤1.57 to ≥1.47, and 800-1500m as ≤1.47 to ≥ 1.36. Conclusions:: MSS had the strongest relationship with 800m performance, whereby for the same MSS, MAS and ASR showed only small relationships to differences in 800m time. Further, our findings support coaching observation of three 800m sub-groups, with the SRR potentially representing a useful and practical tool for identifying an athlete's 800m profile. Future investigations should consider the SRR framework and its application for individualised training approaches in this event.
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The aim of this study was to analyse qualification patterns in middle distance running and identify whether athletes adopt theoretically optimal tactics, or whether the will to win overrides these. The performances of 295 men and 258 women finalists in the Olympic and IAAF World Championship 800 m and 1500 m events from 1999 to 2017 were analysed across all three rounds of competition. Finishing position, time and ranking amongst all competitors were found for each athlete. Position in the final was correlated with finishing position in the heats and semi-finals (all P < 0.001), but not with finishing times in those rounds. Of the 57 champions, 40 won both their heat and semi-final, even though a lower automatic qualification position would have been sufficient, and only 18 achieved a season’s best time in the final. The will to win amongst the eventual champions (and other medallists) suggests predominantly ego oriented behaviour that is encouraged by a performance climate, and which did not appear to differ between men and women. Coaches and athletes are recommended to note that championship-specific physiological and psychological factors are important to develop in training and prior competition to improve both short- and long-term championship strategies.
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Men Are from Mars, Women Are from Venus. John Gray used this provocative title for his book to describe the fundamental psychological differences between the sexes. Many other controlled studies and brain scans demonstrate that men and women are physically and mentally different. The purpose of this physiology masterclass is to illustrate how sex-related differences are present in respiratory function and their possible clinical implications.
Article
Purpose: To analyse the pacing profiles of the world top 800-m annual performances between 2010 and 2016, comparing male and female strategies. Methods: One hundred and forty two performances were characterised for overall race times and 0-200 m, 200-400 m, 400-600 m, 600-800 m split times using available footage from YouTube. Only the best annual performance for each athlete was considered. Overall race and splits speed were calculated so that each lap speed could be expressed as a percentage of the mean race speed. Results: The mean speed of the men's 800-m was 7.73 ± 0.06 m.s-1, with the 0-200 m split faster than the others. After the first split, the speed decreased significantly during the three subsequent splits (p <0.001). The mean speed of the women's 800-m was 6.77 ± 0.05 m.s-1, with a significative variation in speed during the race (p <0.001). The first split was faster than the others (p <0.001). During the rest of the race, speed is almost constant and no difference observed between the other splits. Comparison between men and women revealed that there was an interaction between split and gender (p <0.001), showing a different pacing behaviour in 800-m competitions. Conclusion: The world best 800-m performances revealed an important difference in pacing profile between men and women. Tactics could play a greater role in this difference, but physiological and behavioural characteristics are likely also important.