Marquette University
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Exercise Science Faculty Research and Publications
Health Sciences, College of
7-21-2016
Fast Men Slow More Than Fast Women in a 10
Kilometer Road Race
Robert O. Deaner
Grand Valley State University
Vittorio Addana
Macalester College
Rickey E. Carter
Mayo Clinic
Michael J. Joyner
Mayo Clinic
Sandra K. Hunter
Marquette University, [email protected]
Published version. PeerJ, Vol. 4, No. e2235 ( July 21, 2016). DOI. Copyright 2016 Deaner et al.
Fast men slow more than fast women in
a 10 kilometer road race
Robert O. Deaner1 , Vittorio Addona2 , Rickey E. Carter3 , Michael J. Joyner4 and
Sandra K. Hunter5
1
Psychology Department, Grand Valley State University, Allendale, MI, United States
Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, MN,
United States
3
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
4
Department of Anesthesiology, Mayo Clinic, Rochester, MN, United States
5
Exercise Science Program, Department of Physical Therapy, Marquette University, Milwaukee,
WI, United States
2
ABSTRACT
Submitted 13 May 2016
Accepted 20 June 2016
Published 21 July 2016
Corresponding author
Robert O. Deaner,
[email protected]
Background. Previous studies have demonstrated that men are more likely than women
to slow in the marathon (footrace). This study investigated whether the sex difference
in pacing occurs for a shorter race distance.
Materials & Methods. Data were acquired from the Bolder Boulder 10 km road race for
the years 2008–2013, which encompassed 191,693 performances. There were two pacing
measures, percentage change in pace of the first 3 miles relative to the final 3.2 miles
and percentage change in pace of the first mile relative to the final 5.2 miles. Pacing was
analyzed as a continuous variable and as two categorical variables, as follows: ‘‘maintain
the pace,’’ defined as slowing <5% and ‘‘marked slowing,’’ defined as slowing ≥10%.
Results. Among the fastest (men < 48:40; women < 55:27) and second fastest (men <
53:54; women < 60:28) sex-specific finishing time sextiles, men slowed significantly
more than women with both pacing measures, but there were no consistently significant
sex differences in pacing among the slower four sextiles. For the fastest sextile, the odds
for women were 1.96 (first pacing measure) and 1.36 (second measure) times greater
than men to maintain the pace. For the fastest sextile, the odds for women were 0.46
(first measure) and 0.65 (second measure) times that of men to exhibit marked slowing.
Multiple regression indicated that being older was associated with lesser slowing, but
the sex difference among faster runners persisted when age was controlled.
Conclusions. There was a sex difference in pacing during a 10 km race where glycogen
depletion is not typically relevant. These results support the hypothesis that the sex
difference in pacing partly reflects a sex difference in decision making.
Academic editor
Justin Keogh
Additional Information and
Declarations can be found on
page 14
Subjects Kinesiology, Psychiatry and Psychology
Keywords Gender, Risk taking, Endurance exercise, Distance running, Athletic performance,
Marathon, Pacing, Sex differences, Road races, Decision making
DOI 10.7717/peerj.2235
Copyright
2016 Deaner et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
INTRODUCTION
Scientists have long been interested in pacing in endurance events. They have characterized
successful pacing trajectories in races of varying lengths and also identified several
physiological, energetic, and environmental factors that influence pacing (Abbiss & Laursen,
2008; Tucker & Noakes, 2009; Roelands et al., 2013). Recently, researchers discovered a
How to cite this article Deaner et al. (2016), Fast men slow more than fast women in a 10 kilometer road race. PeerJ 4:e2235; DOI
10.7717/peerj.2235
robust pattern that suggests additional influences on pacing. The pattern is that men are
more likely than women to slow their pace in the marathon (42.195 km) (March et al.,
2011; Trubee et al., 2014; Deaner et al., 2015a; Krawczyk & Wilamowski, 2015; Hubble &
Jinger, 2015; Hanley, 2016). For example, a study of 14 US marathons, including more than
90,000 performances, found that, on average, men ran the second half of the marathon
15.6% slower than the first half, whereas women slowed by 11.7% (Deaner et al., 2015a).
Furthermore, the same study showed that men were approximately three times as likely as
women to slow by least 30%.
The sex difference in marathon pacing might be attributed to a sex difference in
physiology. In particular, it was hypothesized that the sex difference is due to men’s greater
susceptibility to muscle glycogen depletion (March et al., 2011; Trubee et al., 2014), a major
contributor to marathon slowing (Coyle, 2007; Rapoport, 2010). Supporting this hypothesis
are studies suggesting that, for any given intensity of endurance exercise, women are more
likely than men to spare glycogen (Roepstorff et al., 2002; Tarnopolsky, 2008).
It was also hypothesized that the sex difference in marathon pacing reflects a sex
difference in some aspect of decision making (Deaner et al., 2015a; Krawczyk & Wilamowski,
2015; Hubble & Jinger, 2015). Supporting this hypothesis are recent studies of the Warsaw
marathon (Krawczyk & Wilamowski, 2015) and the Houston marathon (Hubble & Jinger,
2015) that examined the relationship between runners’ pacing and the discrepancy between
their pre-race self-forecasts and their actual performances; in both marathons, pacing and
forecasting discrepancy were substantially correlated, and men’s greater slowing could
be largely attributed to their more discrepant forecasting, which may, in turn, be caused
by greater over-confidence or risk taking. Also consistent with the decision making
hypothesis is the fact that, in non-sport domains, men generally perceive lesser risks
than women do (Harris, Jenkins & Glaser, 2006), and laboratory studies indicate that
individuals with lesser perceptions of risk are more likely to select ambitious initial paces
(Micklewright et al., 2015).
The susceptibility to glycogen depletion hypothesis and the decision making hypothesis
are mutually compatible because the decision to adopt an ambitious or risky pace relative
to one’s ability will impose physiological challenges. Nevertheless, it could be fruitful to
attempt to differentiate these hypotheses in order to clarify the factors that contribute to
group and individual variation in pacing. One approach to differentiating these hypotheses
is to test for a sex difference in pacing in a distance running event where glycogen depletion
should be irrelevant, generally an event shorter than 25 km (Coyle, 2007; Rapoport, 2010). If
men’s greater susceptibility to glycogen depletion is the main cause of the sex difference in
pacing, then no sex difference should occur in a shorter race. However, if decision making
contributes to the sex difference in pacing, then a sex difference, where men slow more
than women, should occur in a shorter race.
Here we attempt to differentiate these hypotheses by examining pacing in the Bolder
Boulder 10 km road race, which is one of the largest road races in the US (Running USA,
2015) and one of the few large road races shorter than a marathon to record split times.
For races occurring between 2008 and 2013, we obtained splits at each mile for 191,693
performances. In addition to testing for an overall sex difference in pacing, we will explore
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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if finishing time and age modulate any sex difference; previous studies of marathon pacing
have indicated the importance of these factors (March et al., 2011; Trubee et al., 2014;
Santos-Lozano et al., 2014; Deaner et al., 2015a; Krawczyk & Wilamowski, 2015; Hubble &
Jinger, 2015; Hanley, 2016).
In summary, the aim of this study is to provide the first test of whether the sex difference
in pacing that has been documented for the marathon also occurs in a large 10 km road
race. The decision making hypothesis predicts there will be a sex difference in pacing
whereas the glycogen depletion hypothesis predicts there will not be a sex difference.
MATERIALS & METHODS
This study did not require formal approval by Grand Valley State’s institutional review
board (IRB) or the other IRBs (Macalester College, Marquette University, Mayo Clinic).
The Grand Valley State University IRB determined that the protocol (reference number
756083-1) was exempt under federal category 45 CFR 46.101(b) (4) because all data were
preexisting and public.
Results from the Bolder Boulder can be publicly accessed (http://onlineraceresults.com/
search/ with keyword ‘‘Bolder’’). Information is available on finishers’ names, hometowns,
sex, age, and finishing times for races beginning in 1980; beginning with the 2008 race, mile
splits are also available. To speed data acquisition, we requested that all data be sent to us
as a single file. The race organizers and timing company generously agreed to this request,
although they first removed finishers’ names. The data set spanned 6 years (2008–2013)
and initially included all finishers who had sex, age, finishing time, and all splits recorded
(n = 274,966). We excluded all finishers which we believe had one or more data entry
errors, according to the following rules: (i) one or more mile times less than 2 min 30 s or
(ii) mile times not consistent with their total finishing time (n = 9).
We also excluded all those who finished in 83 min 20 s or greater (n = 83,264; 67.76%
women). We did this because the transition speed between walking and running typically
occurs around 2.0 m/s (or 4.5 mph), so we assumed that runners whose overall speed was
slower than this (i.e., a finishing time ≥ 83 min and 20 s) were likely walking a substantial
portion of the race. Although there is individual variability in the transition speed between
walking and running, this variation appears unrelated to oxygen consumption (Rotstein et
al., 2005; Monteiro et al., 2011). Thus, we used this same exclusion criterion for men and
women of all ages. After making these exclusions, the sample was comprised of 191,693
usable performances, including 48.85% women (n = 93,643).
Statistical analysis
We considered two measures of pacing. For our first pacing measure, we calculated the
percentage change in the pace maintained over the first 3 miles relative to the final 3.2
miles. That is, % change = (minutes per mile in final 3.2 miles − minutes per mile in first
3 miles)/minutes per mile in first 3 miles. We used this measure because it is similar to
comparing the pace of the first half of the race to the second half of the race, which has
been done in marathon studies (Deaner et al., 2015a; Krawczyk & Wilamowski, 2015). (No
5 km split was available for this race, preventing a more direct comparison with previous
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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studies.) Our second pacing measure was the percentage change in the pace of the first
mile relative to the final 5.2 miles. That is, % change = (minutes per mile in final 5.2 miles
− minutes per mile in first mile)/minutes per mile in first mile. We selected this measure
because it should capture runners beginning the race at a speed which is too fast to be
sustainable.
We considered each of these two pacing measures as a continuous variable and as two
categorical variables. For the first categorization, percentage changes less than 5% were
considered ‘‘maintaining the pace.’’ For example, a runner who completed the first 3 miles
of the race in 6 min and 40 s per mile and the final 3.2 miles in 7 min per mile or better
maintained the pace; those who ran the final 3.2 miles in more than 7 min per mile failed
to maintain the pace. For the second categorization, percentage changes equal to or greater
than 10% were considered as ‘‘marked slowing.’’ For example, a runner who completed
the first 3 miles of the race in 6 min and 40 s per mile and the final 3.2 miles in 7 min and
20 s per mile or worse showed marked slowing. Total finishing time and all mile split times
were determined from electronic chip times (i.e., based on when each individual crossed
the starting line).
To explore the impact of finishing time on our categorical pacing measures, we also
categorized male and female performances into sex-specific sextiles (i.e., dividing all
performances of one sex into six equivalent sized groups). The fastest male group consisted
of men finishing in 48:40 or faster, and the other sextile boundaries for men were 53:54,
58:30, 63:40, and 71:05. The corresponding boundaries for women were 55:27, 60:28, 64:58,
69:53, and 75:41.
When considering pacing as a continuous response (or dependent) variable, we estimated
two multiple regression models for each of our two pacing measures. Model 1 used sex, age,
and finishing time as explanatory (or independent) variables. Model 2 included sex, age,
and finishing time, along with the pairwise interactions between these variables. For these
models and some figures, we followed a previous study (Deaner et al., 2015a) in dividing
women’ times by 1.12 to account for men’s greater maximal oxygen uptake (Joyner, 1993;
Sparling, O’Donnell & Snow, 1998; Cheuvront et al., 2005).
Descriptive summaries are presented as mean ± standard deviation (SD). All analyses
were conducted using the R programming language, version 3.1.1 (R Core Team, 0000).
Data are reported as mean ± SD in the text. Two-sided P-values less than 0.05 were taken
as statistically significant.
RESULTS
A total of 191,693 runners (48.85% women) were included in this analysis. The ages for
men and women, respectively, were 35.54 ± 15.27 and 32.90 ± 12.41 years. Finishing times
for men and women were 59:23 ± 10:51 and 65:10 ± 9:36 min, respectively, a difference
in means of 8.9%.
The percentage changes in pace of the first 3 miles relative to the final 3.2 miles were 2.02
± 6.69 and 1.71 ± 5.90% for men and women, respectively (P < 0.0001). The percentage
changes in pace of the first mile relative to the final 5.2 miles were 8.17 ± 9.38 and 8.04
± 8.62% for men and women, respectively (P = 0.0024).
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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Table 1 Pacing by sex and finishing time sextile. Pacing Measure 1 indicates percentage change in pace of the first 3 miles relative to the final 3.2
miles. Pacing Measure 2 indicates percentage change in pace of the first mile relative to the final 5.2 miles.
Sextile (# of Women, # of Men)
Pacing measure 1
Pacing measure 2
Women
Mean (SD)
Men
Mean (SD)
P-value
Women
Mean (SD)
Men
Mean (SD)
P-value
Fastest sextile (15,649, 16,344)
0.62 (3.61)
1.62 (3.88)
<0.0001
4.82 (5.39)
6.08 (5.77)
<0.0001
Second sextile (15,607, 16,352)
0.46 (4.50)
1.17 (4.97)
<0.0001
4.91 (6.41)
5.84 (7.12)
<0.0001
Third sextile (15,585, 16,334)
1.09 (5.09)
1.25 (5.76)
0.0071
6.04 (7.10)
6.14 (8.04)
0.23
Fourth sextile (15,606, 16,342)
1.96 (5.89)
1.48 (6.56)
<0.0001
8.05 (8.02)
7.01 (9.00)
<0.0001
Fifth sextile (15,608, 16,341)
2.79 (6.85)
2.75 (7.56)
0.62
10.69 (9.35)
9.72 (9.99)
<0.0001
Slowest sextile (15,588, 16,337)
3.35 (7.83)
3.83 (9.48)
<0.0001
13.78 (10.47)
14.24 (12.00)
0.00027
Table 2 Percentage of runners who maintained the pace (<5% slowing) by sex and finishing time sextile. Pacing Measure 1 indicates percentage
change in pace of the first 3 miles relative to the final 3.2 miles. Pacing Measure 2 indicates percentage change in pace of the first mile relative to the
final 5.2 miles. The odds ratio (OR) compares the odds of maintaining the pace for women compared with men.
Sextile (# of Women, # of Men)
Pacing Measure 1
Pacing Measure 2
Women
% maintained
pace
Men
% maintained
pace
OR (95% CI)
Women
% maintained
pace
Men
% maintained
pace
OR (95% CI)
Fastest sextile (15,649, 16,344)
91.29
84.28
1.96 (1.82,2.10)
52.43
44.78
1.36 (1.30,1.42)
Second sextile (15,607, 16,352)
87.22
82.29
1.47 (1.38,1.56)
51.49
46.46
1.22 (1.17,1.28)
Third sextile (15,585, 16,334)
81.07
78.64
1.16 (1.10,1.23)
45.17
45.40
0.99 (0.95,1.04)
Fourth sextile (15,606, 16,342)
73.27
74.45
0.94 (0.90,0.99)
35.47
42.71
0.74 (0.71,0.77)
Fifth sextile (15,608, 16,341)
65.91
65.85
1.00 (0.96,1.05)
24.72
32.08
0.70 (0.66,0.73)
Slowest sextile (15,588, 16,337)
61.96
59.17
1.12 (1.08,1.18)
16.37
18.74
0.85 (0.80,0.90)
Overall (93,643, 98,050)
76.80
74.12
1.16 (1.13,1.18)
37.62
38.36
0.97 (0.95,0.99)
Pacing by sex and finishing time
Table 1 reports the results for the two pacing measures (Measure 1: first 3 miles relative to
the final 3.2 miles; Measure 2: first mile relative to the final 5.2 miles) for each finishing
time sextile, separately for men and women. For both measures and both sexes, change
in pace varied significantly across sextiles (P < 0.0001 for both sexes and both measures),
with slower runners generally experiencing greater slowing. Among runners in the fastest
two sextiles, men slowed significantly more than women for both pacing measures. Among
the other sextiles, women sometimes slowed significantly more than men (fourth sextile,
measures 1 and 2; fifth sextile, measure 2), men sometimes slowed significantly more than
women (third sextile, measure 1; sixth sextile, measures 1 and 2), and in some cases, there
was no significant sex difference (fifth sextile, measure 1; third sextile, measure 2).
We next determined, for each pacing measure, the percentage of men and women in
each sextile that maintained the pace (<5% slowing). Table 2 shows that, for both pacing
measures, maintaining the pace varied across sextiles, as faster runners were more likely
than slower runners to maintain the pace (P < 0.0001 for both sexes and both measures).
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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Table 3 Percentage of runners who exhibited marked slowing (≥10% slowing) by sex and finishing time sextile. Pacing Measure 1 indicates
percentage change in pace of the first 3 miles relative to the final 3.2 miles. Pacing Measure 2 indicates percentage change in pace of the first mile relative to the final 5.2 miles. The odds ratio (OR) compares the odds of experiencing marked slowing for women compared with men.
Sextile (# of Women, # of Men)
Pacing measure 1
Pacing measure 2
Women
% marked
slowing
Men
% marked
slowing
OR (95% CI)
Women
% marked
slowing
Men
% marked
slowing
OR(95% CI)
Fastest sextile (15,649, 16,344)
0.97
2.10
0.46 (0.38,0.55)
15.37
21.97
0.65 (0.61,0.68)
Second sextile (15,607, 16,352)
1.97
3.27
0.59 (0.52,0.68)
19.39
24.74
0.73 (0.69,0.77)
Third sextile (15,585, 16,334)
3.70
5.19
0.70 (0.63,0.78)
26.08
27.89
0.91 (0.87,0.96)
Fourth sextile (15,606, 16,342)
7.11
7.39
0.96 (0.88,1.05)
36.82
32.52
1.21 (1.16,1.27)
Fifth sextile (15,608, 16,341)
11.15
13.29
0.82 (0.77,0.88)
50.95
45.15
1.26 (1.21,1.32)
Slowest sextile (15,588, 16,337)
15.04
18.69
0.77 (0.73,0.82)
65.23
63.67
1.07 (1.02,1.12)
Overall (93,643, 98,050)
6.65
8.32
0.79 (0.76,0.81)
35.63
35.99
0.98 (0.97,1.00)
We calculated the common (pooled) OR for maintaining the pace for women compared
with men across the sextiles (Table 2). Overall, the odds that women maintained the pace
were 1.16 times (95% CI [1.13–1.18]; P < 0.0001) greater compared to men for the first
pacing measure. For the second pacing measure, the odds that women maintained the pace
were 0.97 times as great as the odds that men maintained the pace (95% CI [0.95–0.99];
P < 0.0001). For both measures, there were significant differences among the sextiles
(P < 0.0001 for both measures, indicated by a Breslow–Day test for homogeneity). The
largest sex differences occurred in the fastest sextile (first measure: OR, 1.96; 95% CI
[1.82–2.10]; second measure: OR, 1.36; 95% CI [1.30–1.42]).
We conducted similar analyses of marked slowing (≥10% slowing). Table 3 shows that,
for both measures, marked slowing varied across sextiles, as faster runners were less likely
to exhibit marked slowing (P < 0.0001 for both sexes). Overall, the odds of exhibiting
marked slowing were 0.79 (95% CI [0.76–0.81]; P < 0.0001) times as great for women
compared with men for the first pacing measure, and 0.98 times (95% CI [0.97–1.00];
P = 0.10) as great for women compared with men for the second pacing measure (Table 3).
There were significant differences among the sextiles (P < 0.0001 for both measures). The
greatest sex differences occurred in the fastest sextile (first measure: OR, 0.46; 95% CI
[0.38–0.55]; second measure: OR, 0.65; 95% CI [0.61–0.68]).
To further explore whether the relationship between finishing time and pacing differed
for men and women, we plotted these relationships separately for each sex. In these plots, we
adjusted (i.e., decreased) female finishing times by 12% (see ‘Methods’). Figure 1 illustrates
the results, (A) Pacing Measure 1, and (B) Pacing Measure 2. This figure indicates that, for
both measures, slower finishers exhibited greater percentage slowing, although this trend
was most prominent for those who finished in approximately 60 min or more. In addition,
this figure shows that, for both measures, men tended to slow more than women among
the fastest runners (i.e., roughly those who finished in under 53 min, corresponding to a
time of 59:36 for women). Among slower runners, there was no consistent sex difference
in pacing for the first pacing measure (Fig. 1A). For the second pacing measure, among
slower runners, women showed a consistent tendency to slow more than men (Fig. 1B).
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Finishing time (min)
20
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0
−10
Percent change in pace
30
B
40
50
60
Finishing time (min)
Figure 1 Pacing as a function of finishing time for men and women. A random subset of 5,000 data
points are plotted, along with a loess smoother (obtained using all of the data) separately for men (red)
and women (black). Women’s finishing times presented on the x-axis have been adjusted (i.e., decreased)
by 12%. (A) Pacing Measure 1, percentage change in pace of the first 3 miles relative to the final 3.2 miles.
(B) Pacing Measure 2, percentage change in pace of the first mile relative to the final 5.2 miles.
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
7/17
Table 4 Summary of regression models of pacing (coefficients, with standard errors in parentheses). Pacing measure 1 indicates percentage
change in pace of the first 3 miles relative to the final 3.2 miles. Pacing measure 2 indicates percentage change in pace of the first mile relative to the
final 5.2 miles. See text for descriptions of Model 1 and Model 2.
Explanatory variable
Pacing measure 1
Model 1
Model 2
***
(Intercept)
−3.37 (0.092)
Sex male
0.21 (0.029)***
Finishing time (adjusted)
Age
Pacing measure 2
Model 1
***
−3.99 (0.25)
2.76 (0.19)***
***
0.092 (0.0015)
***
−0.0073 (0.001)
***
0.10 (0.0041)
−13.48 (0.34)***
−0.029 (0.039)
5.42 (0.26)***
0.42 (0.0055)***
0.30 (0.002)
***
−0.078 (0.0014)
0.0044 (0.0086)
***
–
−0.11 (0.0041)***
–
0.021 (0.0029)***
–
−0.0015 (0.0001)***
−0.038 (0.0063)
–
−0.045 (0.0030)
Sex male: age
–
0.0030 (0.0021)
***
–
−6.89 (0.13)
***
Sex male: finishing time
Finishing time: age
Model 2
***
0.00048 (0.0001)
***
Notes.
***
p-value < 0.0001.
We also plotted these relationships without making a 12% adjustment to women’ finishing
times (Fig. 2). With these absolute finishing times, men slowed more than women among
faster and slower runners, and this was true for both pacing measures.
Regression modeling to control for runner’s age
The preceding results investigated the relationships among pacing, sex and finishing time
through tabular and visual displays. In order to simultaneously address the potential impact
of runner’s age, we used multiple regression modeling. We fit two models for each of our
two continuous pacing measures. Model 1 used sex, age, and finishing time as explanatory
variables. Model 2 additionally included the pairwise interactions between sex, age, and
finishing time. In these models, finishing time was measured in minutes (adjusted by 12%
for women) and age was measured in years.
For Pacing Measure 1, older, faster, and female runners exhibited more even pacing
(Model 1). With regards to sex differences, Model 2 indicated that the effect of sex was
attenuated for slower finishers. That is, men generally showed greater slowing than women,
but this difference weakened as finishing time increased.
For Pacing Measure 2, older and faster runners showed more even pacing, while males
and females were not significantly different (Model 1). The lack of significance of the sex
variable in Model 1 reflects a significant interaction between sex and finishing time (Model
2). That is, among fast runners, men showed greater slowing but, among slower finishers,
women showed greater slowing.
These regression results corroborated the patterns illustrated in Fig. 1. Complete
information on the value of the coefficients and standard errors from these models are
shown in Table 4.
DISCUSSION
This study demonstrated that in the Bolder Boulder race, a large 10 km road race, there was
a sex difference in pacing, specifically among faster runners. In particular, men and women
in the fastest two sex-specific finishing time sextiles differed significantly in their pacing,
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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Figure 2 Pacing as a function of finishing time for men and women without 12% adjustment to
women’s times. A random subset of 5,000 data points are plotted, along with a loess smoother (obtained
using all of the data) separately for men (red) and women (black). Women’s finishing times presented on
the x-axis have not been adjusted by 12%. (A) Pacing Measure 1, percentage change in pace of the first
3 miles relative to the final 3.2 miles. (B) Pacing Measure 2, percentage change in pace of the first mile
relative to the final 5.2 miles
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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with men generally slowing more than women. This was true whether pacing was defined
as the change in pace of the first 3 miles relative to the final 3.2 miles (first pacing measure)
or as the change in pace of the first mile relative to the final 5.2 miles (second measure).
Moreover, categorical comparisons indicated that the magnitude of the sex difference was
not trivial (Tables 2 and 3). Perhaps most notably, among the fastest sextile, the odds of
women slowing by at least 10% was 0.46 (first pacing measure) and 0.65 (second pacing
measure) times that of the corresponding value for men (Table 3). The sex difference in
pacing among faster runners also occurred when comparisons were based on continuous
finishing times, rather than sex-specific finishing time sextiles, including when female
finishing times were adjusted by 12% to account for men’ greater maximal oxygen uptake
(Joyner, 1993; Sparling, O’Donnell & Snow, 1998; Cheuvront et al., 2005). Finally, the sex
difference in pacing persisted in multiple regression models that controlled for age (Table 4).
Although these results indicate that the sex difference in pacing that has been documented
in the marathon (March et al., 2011; Trubee et al., 2014; Deaner et al., 2015a; Krawczyk
& Wilamowski, 2015; Hubble & Jinger, 2015; Hanley, 2016) also occurs in shorter race
distances, there were some notable differences between pacing at these distances. First,
the magnitude of slowing in the present 10 km race was substantially less than has been
found in marathons. In this 10 km race, the mean slowing for the first 3 miles relative
to the final 3.2 miles was 2.0% for women and 1.7% for men, whereas, in a sample of
14 marathons, the mean slowing for the first half of the marathon relative to the second
half was 11.7% for women and 15.6% for men (Deaner et al., 2015a). A second important
difference is that, in this 10 km race, the pattern of men slowing more than women was
limited to relatively fast runners (Tables 1–3 and Fig. 1). Among slower runners there was
no consistent sex difference and, in some analyses, women slowed significantly more than
men did. In the 14 marathons, by contrast, men slowed substantially more than women
among faster runners, and this sex difference became larger among slower runners (Deaner
et al., 2015a). Third, among faster runners, men were consistently more likely than women
to slow in this 10 km race, but the magnitude of this sex difference was less than that
reported in the 14 marathons. Although ability groupings and categorical definitions of
slowing differed in these studies, it is reasonable to conclude there is a difference because
odds ratios indicating sex differences were consistently smaller in this 10 km race, even for
faster runners, (Tables 2 and 3) than reported in the 14 marathons (Deaner et al., 2015a).
Susceptibility to glycogen depletion hypothesis
The documentation of a sex difference in pacing in a 10 km road race challenges the
hypothesis that the sex difference in pacing is due to men’s greater susceptibility to
glycogen depletion (March et al., 2011; Trubee et al., 2014). According to that hypothesis,
no sex difference in pacing should have occurred for a 10 km race because glycogen
depletion is highly unlikely to be relevant for events of this duration (Coyle, 2007; Rapoport,
2010). Nevertheless, it is premature to completely reject the susceptibility to glycogen
depletion hypothesis because the present study of a 10 km race cannot discount that men’s
apparently greater susceptibility to glycogen depletion contributes to the sex difference
in pacing in marathons. The fact that the sex difference in pacing in this 10 km race was
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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limited to faster runners and was smaller in magnitude than the sex difference in marathon
pacing is consistent with this possibility.
Decision making hypothesis
The present study’s results support, albeit indirectly, the hypothesis that the sex difference
in pacing in distance running reflects, at least in part, some kind of sex difference in
decision making (Allen & Dechow, 2013; Deaner et al., 2015a; Krawczyk & Wilamowski,
2015; Hubble & Jinger, 2015). For example, men may be more likely than women to begin
a race with an ambitious or risky pace relative to their ability, and this would increase their
likelihood of slowing later.
The decision making hypothesis is supported by several lines of evidence besides the
present study’s results. First, men’s greater slowing in the Warsaw (Krawczyk & Wilamowski,
2015) and Houston marathons (Hubble & Jinger, 2015) was substantially associated with
their more discrepant forecasting, indicating men were typically more ambitious or
over-confident than were women. Second, a study of the Chicago marathon showed that
performances among non-elite runners clustered at round numbers (e.g., just under 4 h)
because some runners accelerated in the final 2.2 km of the race; although women were
more likely than men to have accelerated in the final 2.2 km, men were more likely to have
done so in order to achieve round numbers, indicating that the men were more likely to
modify their pace to pursue external goals (Allen & Dechow, 2013). Third, in non-sport
domains, men generally take greater risks, partly due to their lesser perceptions of risk
(Harris, Jenkins & Glaser, 2006), and laboratory studies of running and cycling indicate
that individuals with lesser perceptions of risk are more likely to adopt ambitious initial
paces (Micklewright et al., 2015). Finally, risk taking and competitiveness are believed to
be closely related (Croson & Gneezy, 2009), and there is mounting evidence that male
distance runners are typically more competitive than their female counterparts (Deaner,
2013; Deaner, Addona & Mead, 2014; Deaner et al., 2015b).
Other physiological factor(s) besides glycogen depletion may contribute to the sex
difference in pacing. The only suggestion of which we are aware is that the sex difference
in pacing might be due to men’s (supposedly) greater susceptibility to hyperthermia
(Trubee et al., 2014), a frequent contributor to slowing in distance running (Maughan,
Watson & Shirreffs, 2007; Gonzalez-Alonso, Crandall & Johnson, 2008; El Helou et al.,
2012). This hypothesis was suggested by the fact that the sex difference in marathon
pacing increases with warmer ambient temperatures (Trubee et al., 2014; Krawczyk &
Wilamowski, 2015). The present study’s results from the Bolder Boulder 10 km race do not
address the susceptibility to hyperthermia hypothesis. (We did not address the effect of
temperature because there was little variation in the race years we studied; the maximum
daily temperature in Boulder on race day was 23.06 ± 3.31 ◦ C.) We note, however, that
this hypothesis is weakened by the fact that, compared to women, men are generally
believed to have advantages in thermoregulation (Gagnon & Kenny, 2012). Moreover, the
sex difference in pacing has been documented in marathons (March et al., 2011; Trubee et
al., 2014; Krawczyk & Wilamowski, 2015) that occurred at temperatures sufficiently cool
(e.g., <10 ◦ C) that hyperthermia would have been irrelevant (El Helou et al., 2012).
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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Another crucial point is that although there is, at present, no compelling evidence that a
physiological difference between men and women can explain the sex difference in pacing,
the decision making hypothesis is not at odds with physiology contributing to individual
and group variation in pacing. This is because the decision to adopt an ambitious or risky
pace will undoubtedly impose physiological challenges (Coyle, 2007; Abbiss & Laursen,
2008; Tucker & Noakes, 2009).
Why men’s greater slowing was limited to faster runners
An interesting question is why the greater slowing by men than women in this 10 km
road race was limited to relatively fast runners, roughly men who finished in less than
53 min and women who finished in less than 60 min. By contrast, the sex difference in
marathon pacing occurs for runners of widely ranging abilities (March et al., 2011; Trubee
et al., 2014; Deaner et al., 2015a; Krawczyk & Wilamowski, 2015). Furthermore, research
on sex differences in risk taking in non-sports domains (e.g., driving) generally indicate
that sex differences occur across broad populations (Harris, Jenkins & Glaser, 2006; Croson
& Gneezy, 2009) or, if they vary across populations, the sex differences tend to be smaller
or non-existent among more selective sub-populations (Croson & Gneezy, 2009 but see
Deaner et al., 2015b). The sex difference in financial risk taking, for instance, apparently
weakens or disappears among professional investors (Croson & Gneezy, 2009).
Although we cannot definitively explain why the sex difference in pacing was limited
to relatively fast runners, we can offer a suggestion, based on two points. First, although
pacing in distance running can involve risk taking, it often may not. For instance, an evenly
paced performance may not indicate a runner whose gamble with an ambitious risky
pace paid off; it may simply indicate a runner who adopted a pace that was easy for them.
Conversely, a performance with dramatic slowing may not indicate a runner who began the
race with an ambitious pace but, despite great effort, failed to hold it; it may merely indicate
a runner who did not prioritize achieving an outstanding performance and was content
to slow when their initial pace became uncomfortable. Second, although any particular
running performance may or may not involve substantial risk taking, it can be expected
that faster runners generally will be more likely than slower runners to select an ambitious
pace and to exert themselves in maintaining it. This is supported by surveys showing that
faster runners are more likely than are slower runners to endorse competitiveness and
goal achievement items (e.g., ‘‘to push myself beyond my current limits’’) (Masters, Ogles
& Jolton, 1993; Deaner et al., 2015b). If these points are correct, then variation in pacing
among fast runners may provide important insights about risk taking, whereas variation
in pacing among slower runners may be less informative.
Our suggestion for why the sex difference in pacing was limited to faster runners in
this 10 km race does raise the question of why the sex difference in pacing in marathons
held even among slower runners (March et al., 2011; Trubee et al., 2014; Deaner et al.,
2015a; Krawczyk & Wilamowski, 2015). Although we cannot provide a definitive answer
to this question, we can suggest that 10 km races and marathons may differ in ways that
make marathon races more likely to reveal individual and group variation in decision
making. One difference is that somewhat different kinds of individuals may participate;
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marathoners tend to be more motivated by competition (Ogles, Masters & Richardson,
1995). A second difference involves the demands of maintaining an even pace, a reliable
correlate of performance in distance running (Abbiss & Laursen, 2008; Tucker & Noakes,
2009; March et al., 2011; Trubee et al., 2014; Santos-Lozano et al., 2014; Deaner et al., 2015a;
Krawczyk & Wilamowski, 2015; Hubble & Jinger, 2015; Hanley, 2016). Because fatigue in a
marathon generally reflects slowly cumulating processes, such as glycogen depletion (Coyle,
2007; Rapoport, 2010), hyperthermia (Maughan, Watson & Shirreffs, 2007; Gonzalez-Alonso,
Crandall & Johnson, 2008; El Helou et al., 2012), and muscle damage (Del Coso et al., 2013),
coaching handbooks advise that achieving an evenly paced marathon requires running
the first half of the race at a pace that seems sufficiently comfortable that the runner is
frequently tempted to accelerate in order to ‘‘bank some minutes’’ and get ahead of their
time goal; resisting this temptation may be crucial for avoiding dramatic slowing late
in the race (Humphrey, Hanson & Hanson, 2012; Magness, 2014). By contrast, in shorter
races, such conservatism seems less important, apparently because immediate feelings of
discomfort are generally sufficient to inform the runner that they should reduce their speed
to avoid severe distress. Underscoring the unusual pacing demands in the marathon is
that the mean percentage slowing in marathons (Deaner et al., 2015a) is far greater than
documented in this 10 km race.
Future work on decision making and pacing
Although the present study’s results, in conjunction with previous research, indicate that
decision making contributes to the sex difference in pacing, additional research must
be conducted to make strong tests of this hypothesis. Ideally, this work would investigate
when and how runners establish their pre-race goals, including their target pace(s); runners’
estimates of the benefits, costs, and likelihoods of various race outcomes (e.g., dramatic
slowing, setting a personal best, winning age group); runners’ psychological and
physiological experiences during the race as they adjust their pace and modify their goals;
and how these factors vary according to sex, age, experience, risk taking, training, ability,
coaching, course difficulty, and race distance. Besides allowing a strong test of whether
decision making contributes to the sex difference in pacing, such research may reveal
unexpected and interesting interactions among physiological and psychological factors.
If it turns out that the sex difference in pacing partly reflects a sex difference in some
aspect of decision making (e.g., over-confidence, risk perception, willingness to tolerate
discomfort), then coaches and athletes may benefit by considering individual and group
variation in these characteristics when planning training and racing. For example, training
sessions aiming to improve pacing for males (or more confident females) may need to
emphasize being conservative whereas corresponding sessions for females (or less confident
males) may require encouraging more ambitious pacing.
Limitations
The current study has several limitations. First, we evaluated pacing by comparing the
pace of the first 3 miles to the final 3.2 miles and by comparing the pace of the first mile
relative to the final 5.2 miles. Although these two pacing measures produced roughly similar
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outcomes with respect to sex differences among faster runners, other pacing measures could
be explored and might yield different results. Second, although we succeeded in identifying
sex, finishing time, and age as contributors to pacing variation, other factors must also
be important. For example, in the study of 14 US marathons, greater race experience was
associated with lesser slowing, although the effect was modest and did not eliminate the
sex difference in pacing (Deaner et al., 2015a). As noted above, other relevant factors might
include risk taking, training, and coaching. Third, although our sample size was large in
terms of runners, the sample was drawn entirely from six years of the same race, the Bolder
Boulder 10 km road race. This may limit the generalizability of our conclusions. Finally,
many runners participated in more than one year of the race, and we did not cluster
performances by runner. This could have led to p-values being smaller than they would
have been if clustering was modeled explicitly.
CONCLUSIONS
This study demonstrates that the sex difference in pacing that has been documented in
marathons also occurs in a large 10 km road race, although the magnitude of the sex
difference was smaller and limited to relatively fast runners, roughly men who finished
in less than 53 min and women who finished in less than 60 min. This sex difference
was robust in several respects, including that it occurred with two measures of pacing,
with continuous and categorical specifications of the pacing measures, and when age
was statistically controlled. These results suggest that the sex difference in pacing partly
reflects a sex difference in some aspect of decision making. To make a strong test of this
hypothesis, future research must directly address runners’ decision making and relate it to
their pacing.
ACKNOWLEDGEMENTS
We thank Josh Drew (Online Race Results) for assistance with data acquisition.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The authors received no funding for this work.
Competing Interests
Robert Deaner is an Academic Editor for PeerJ.
Author Contributions
• Robert O. Deaner conceived and designed the experiments, wrote the paper, reviewed
drafts of the paper.
• Vittorio Addona conceived and designed the experiments, analyzed the data, wrote the
paper, prepared figures and/or tables, reviewed drafts of the paper.
• Rickey E. Carter, Michael J. Joyner and Sandra K. Hunter conceived and designed the
experiments, reviewed drafts of the paper.
Deaner et al. (2016), PeerJ, DOI 10.7717/peerj.2235
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Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
This study did not require formal approval by Grand Valley State’s institutional review
board (IRB) or the other IRBs (Macalester College, Marquette University, Mayo Clinic).
The Grand Valley State University IRB determined that the protocol (reference number
756083-1) was exempt under federal category 45 CFR 46.101(b) (4) because all data were
preexisting and public.
Data Availability
The following information was supplied regarding data availability:
The raw data has been supplied as Data S1.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.2235#supplemental-information.
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