medicina
Article
Pacing in Long-Distance Running: Sex and Age Differences in
10-km Race and Marathon
Ivan Cuk 1
, Pantelis T. Nikolaidis 2,3
, Elias Villiger 4 and Beat Knechtle 4,5,*


Citation: Cuk, I.; Nikolaidis, P.T.;
Villiger, E.; Knechtle, B. Pacing in
Long-Distance Running: Sex and Age
Differences in 10-km Race and
Marathon. Medicina 2021, 57, 389.
https://doi.org/10.3390/medicina
57040389
Academic Editor:
Edgaras Stankeviˇcius
Received: 25 February 2021
Accepted: 15 April 2021
Published: 17 April 2021
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1
Faculty of Physical Education and Sports Management, Singidunum University, 11000 Belgrade, Serbia;
ivan_cuk84@yahoo.com
2
Exercise Physiology Laboratory, 18450 Nikaia, Greece; pademil@hotmail.com
3
School of Health and Caring Sciences, University of West Attica, 10679 Athens, Greece
4
Institute of Primary Care, University of Zurich, 8006 Zurich, Switzerland; eviliger@gmail.com
5
Medbase St. Gallen Am Vadianplatz, 9000 St. Gallen, Switzerland
*
Correspondence: beat.knechtle@hispeed.ch; Tel.: +41-(0)-71-226-93-00
Abstract: Background and objective: The recent availability of data from mass-participation run-
ning events has allowed researchers to examine pacing from the perspective of non-elite distance
runners. Based on an extensive analysis of the literature, we concluded that no study utilizing
mass-participation events data has ever directly compared pacing in the 10-km race, with other
long-distance races. Therefore, the main aim of this study was to assess and compare pacing between
10-km runners and marathoners, in regards to their sex and age. Materials and methods: For the
purpose of this study, ofﬁcial results from the Oslo marathon (n = 8828) and 10-km race (n = 16,315)
held from 2015 to 2018 were included. Results: Both 10-km runners and marathoners showed positive
pacing strategies. Moreover, two-way analysis of variance showed that women were less likely to
slow in the marathon than men (9.85% in comparison to 12.70%) however, not in the 10-km race
(3.99% in comparison to 3.38%). Finally, pace changing is more prominent in youngest and oldest
marathoners comparing to the other age groups (12.55% in comparison to 10.96%). Conclusions:
Based on these ﬁndings, practitioners should adopt different training programmes for marathoners
in comparison to shorter long-distance runners.
Keywords: running; endurance; health; marathoners; recreation
1. Introduction
Pacing can be deﬁned as the distribution of exercise intensity during a prolonged
time [1]. Optimal pacing is one of the most important contributors to achieving the best
results in long-distance running [1,2], while signiﬁcantly decreasing the risk of muscu-
loskeletal injuries [3]. Although a pacing strategy that can classify as optimal depends
on many factors (e.g., race length and proﬁle, altitude, or weather conditions [4,5]) an
even pacing strategy with an end-spurt has often been the best choice for long-distance
events [6]. This strategy was best seen in the recent successful sub-2-h marathon challenge
by Eliud Kipchoge, where the pacing was artiﬁcially controlled to be even throughout the
race, with the spontaneous end-spurt by Kipchoge in the last several hundred meters [7].
Different pacing strategies in long-distance running were extensively investigated by sev-
eral inﬂuential studies, both on track [4] and road racing [8,9]. However, those studies
only involved a small sample size of professional athletes. On the other hand, the recent
availability of mass-participation events data has allowed researchers to examine pacing
from the perspective of a wider range of athletes including recreational distance runners of
all ages [10,11].
The ﬁrst studies using mass-participation events data were focused on independent
long-distance events, primarily marathons and half-marathons. Contrary to elite runners,
when a wider range of athletes was investigated, previous research reported positive pacing
Medicina 2021, 57, 389. https://doi.org/10.3390/medicina57040389
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in endurance running races, where pace (time/distance) increased after approximately
3/4 of the race [12,13]. This decline in running speed was more prominent in men than in
women due to several physiological [12] and psychological factors [14,15], such as a riskier
and faster start by men or women’s better fat utilization to obtain energy.
Moreover, studies investigating pacing in age group endurance runners proved
to be somewhat inconsistent in their ﬁndings, with either no differences between age
groups [11,12] or with more even pacing in older age groups [13]. Regarding the 10-km
race, several studies examined pacing in this long-distance event, mainly in a small samples
of track runners [1,4], time trial runners on either track [16] or treadmill [17], as well as in
triathletes [18]. Pacing in the aforementioned elite 10-km runners proved to be rather even
with an end-spurt. However, in recent years, 10-km races became increasingly popular
in recreational runners, especially among women and beginners of both sexes [19,20].
Recreational athletes would participate in a 10-km race in the context of their preparation
for a subsequent longer race [19], as well as a training tool to ameliorate pace time [21].
Therefore, further investigation of pacing in recreational 10-km runners is crucial to better
understand the mechanisms controlling the pacing in long-distance events. This might
help runners to enjoy running more as well as to achieve better results.
In recent years, several studies using new methodological approaches attempted
to compare pacing in mass-participation events [10,20]. Nevertheless, the performance
of different distances and events was not adjusted in the abovementioned studies; e.g.,
some events might be under different environmental conditions, opposition ﬁelds, or race
proﬁles. On the other hand, a new methodological approach allowed researchers to directly
compare pacing between half-marathon and marathon on the same race and track, with
rather similar weather conditions [10,22], thus providing more detailed and comparable
results. For example, a novel ﬁnding was that women’s pacing was similar to men’s in
half-marathon, whereas in a marathon women had more even pacing compared to men.
Speciﬁcally, physiological rather than psychological factors can inﬂuence the additional
lack of speed in marathoners (and not half-marathoners), such as better utilization of fat by
women or men’s muscle glycogen depletion [10,22]. Accordingly, women did not differ by
age group in pace variability, whereas youngest and oldest men, showed larger variability
in pace [10]. However, further proof is needed that the observed sex and age differences
are not speciﬁc to only a few races (i.e., Vienna and Ljubljana) and only two long-distance
running events (i.e., half-marathon and marathon). This can be achieved by investigating
and comparing pacing strategies in other long-distance races, such as longer ultra-races,
or shorter and increasingly popular 10km races with already popular and investigated
half-marathon or marathon.
Based on an extensive analysis of the literature, we concluded that no study utilizing
mass-participation events data has ever directly compared pacing in a 10-km race, with
other long-distance races. Such a comparison could shed additional light on the importance
of the mechanisms underlying pacing behaviour of the long distance runners, as well as
to better understand potential training requirements for both recreational and proﬁcient
runners. Therefore, the main aim of this study was to assess and compare pacing between
the increasingly popular 10-km race and the most popular long-distance race—marathon,
in regards to their sex and age. We hypothesized that 10-km runners will show more even
pacing than marathoners, particularly women and middle age runners.
2. Materials and Methods
For this study, ofﬁcial results from the Oslo marathon and Oslo 10-km race held from
2015 to 2018 were included [23]. Split times from the middle of the race were also included
(i.e., 5 km and 21.0975 km for the 10-km race and marathon respectively).
The Oslo marathon was chosen as an ofﬁcially certiﬁed race because it was held on a
rather ﬂat track (elevation difference 60 m). For reference, the Berlin Marathon considered
“the fastest marathon”, has an elevation difference of 21 m [24]. Moreover, both the 10-km
race and marathon were held on the same day, whereas the 10-km race was entirely
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contained within the marathon race. Finally, note that hyperthermia can signiﬁcantly affect
pacing in both elite and recreational runners [5,12]. The Oslo Marathon is traditionally
held in Norway at the end of September, usually in colder weather conditions, which can
reduce the chances of hyperthermia in runners.
2.1. Participants
In total, 25,143 participants of all performance levels were considered for this study
(10-km race, n = 16,315; Marathon, n = 8828), however, most of them were recreational
runners. Participants who did not ﬁnish any of the races, or did not have recorded any of
the split times were excluded from the initial sample.
The present research was approved by the Institutional Review Board of Kanton
St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the par-
ticipants as the research concerned the study of publicly available data (Ethical Committee
St. Gallen 1 June 2010). This research was conducted in accordance with ethical standards
derived from the Declaration of Helsinki adopted in 1964 and revised in 2013.
2.2. Data Acquisition
All data was acquired from the ofﬁcial Oslo Marathon results page [23]. First, overall
times, athlete details and a link to each athlete’s split times were copied from the results
page and pasted into an Excel document. This was done separately per year, distance and
gender. The split times were then added in a second step using custom Python scripts that
followed the ofﬁcial link to each athlete’s split times and extracted all available split times.
2.3. Procedures
In the ﬁrst step of data analysis, the average running speed of all runners was cal-
culated for the ﬁrst and second half of both the 10-km race and marathon. A particular
novelty of this study was the use of time (i.e., minutes and seconds) per kilometre as a unit
of speed. This “runners friendly” measurement of speed was chosen as a very practical
tool for both professional and recreational runners as well as their coaches. For example,
GPS watches, often utilized by runners to monitor running speed, presents minutes per
kilometre by default. Moreover, in long-distance races, each kilometre is usually marked.
Therefore, participants can see the time they consumed running between each kilometre.
As a result, runners and running coaches often rely on the time needed to run one kilometre
when assessing and comparing someone’s running speed or pace maintenance.
Considering that even pacing is the best choice for long-distance running [6], pacing
assessment from the aspect of speed maintenance was selected for this study. Thereafter,
speed variation was calculated based on the percentage difference in speed observed
between the second and the ﬁrst half of the race (i.e., % change = (second half time − ﬁrst
half time)/ﬁrst half time). Percentage change was considered as a continuous variable [14].
Applying this method, it was possible to normalize pace and compare it between different
race distances [10]. Criteria for inclusion in the ﬁnal data set were having timing data for
the halfway mark and the full race in proper sequence (e.g., ﬁnishing time greater than
split time); a net time less than the gun time; and a slowing less than 400% [14].
2.4. Statistical Analysis
Prior to all statistical tests, descriptive statistics were calculated as mean and standard
deviation. Since the Kolmogorov-Smirnov or similar data normality tests are not sensi-
tive when using a large sample size, data distribution normality was veriﬁed by visual
inspection of histograms and QQ plots [10,22].
To assess pacing differences between the ﬁrst and second half of the 10-km race and
marathon, two 2-way between-within ANOVAs were performed (separately for women
and men). The main effect of pace (ﬁrst half and second half), race (10-km race and
marathon), and the interaction pace x race were observed.
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To assess pace change between women and men in 10-km race and marathon, two-way
ANOVA with between factors was performed. The main effect of sex (women and men),
race (10-km race and marathon), and the interaction sex × race were observed.
Finally, to assess pace change between age groups in 10-km race and marathon, two
2-way between-within ANOVAs were performed (separately for women and men). The
main effect of age group (18–23; 24–34; 34–39; 40–44; 45–49; 50–54; 55–59; 60–64; 65+), race
(10-km race and marathon) and the interaction age × race were observed.
In addition, for all ANOVAs, Bonferroni post-hoc test was performed. The effect size
was calculated as eta squared ( 2), where the values of 0.01, 0.06, and above 0.14 were
considered small, medium, and large, respectively [25]. Alpha level was set at p ≤ 0.05. All
statistical tests were performed using Microsoft Ofﬁce Excel 2007 (Microsoft Corporation,
Redmond, WA, USA) and SPSS 20 (IBM, Armonk, NY, USA).
3. Results
The ﬁrst and second half pacing of participants is presented in Table 1. Regardless of
their sex and age, both 10-km runners and marathoners showed a positive pacing strategy
(i.e., second half of the race was slower than the ﬁrst half). Further examination of pacing
between 10-km runners and marathoners, in regards to their sex and age, is presented in
Figures 1–3.
Table 1. Speed indicators (in min/km) of 10-km and marathon runners showed as mean ± standard deviation.
Women (n10-km = 9932; nmarathon = 2048)
Men (n10-km = 6383; nmarathon = 6780)
10-km Race (min/km)
Marathon (min/km)
10-km Race (min/km)
Marathon (min/km)
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Age: 18–23
n = 1589
First half
6:03.2
1:05.9
5:50.0
0:38.9
5:03.1
1:06.3
5:17.8
0:42.4
Second half
6:16.0
1:12.9
6:27.7
1:04.9
5:12.7
1:10.0
6:06.0
1:11.6
Total
6:09.6
1:08.5
6:08.9
0:49.4
5:07.9
1:07.1
5:41.9
0:54.3
Age: 24–34
n = 7777
First half
6:07.7
1:02.7
5:41.2
0:40.7
5:13.2
1:06.0
5:10.0
0:42.5
Second half
6:19.1
1:09.6
6:15.0
1:02.9
5:22.9
1:10.7
5:49.1
1:07.1
Total
6:13.4
1:05.2
5:58.1
0:49.8
5:18.1
1:07.4
5:29.6
0:52.4
Age: 34–39
n = 3695
First half
6:14.7
1:02.2
5:36.1
0:37.5
5:17.7
1:06.3
5:09.9
0:43.7
Second half
6:27.7
1:08.2
6:04.4
0:52.4
5:28.4
1:10.1
5:47.5
1:04.0
Total
6:21.2
1:04.4
5:50.2
0:43.4
5:23.1
1:07.4
5:28.7
0:51.9
Age: 40–44
n = 3784
First half
6:17.9
1:01.1
5:46.3
0:42.6
5:19.3
1:05.2
5:12.5
0:39.4
Second half
6:32.3
1:07.1
6:17.4
0:59.5
5:29.3
1:09.5
5:51.4
1:00.4
Total
6:25.1
1:03.3
6:01.8
0:49.6
5:24.3
1:06.6
5:32.0
0:47.6
Age: 45–49
n = 3514
First half
6:18.7
1:01.4
5:47.7
0:37.4
5:29.0
1:06.4
5:14.6
0:40.1
Second half
6:34.0
1:08.5
6:21.5
0:55.4
5:41.5
1:11.9
5:51.0
0:59.8
Total
6:26.4
1:04.2
6:04.6
0:44.6
5:35.3
1:08.4
5:32.8
0:47.9
Age: 50–54
n = 2342
First half
6:23.4
1:04.8
5:52.8
0:43.1
5:34.1
1:07.2
5:23.2
0:41.6
Second half
6:41.4
1:11.5
6:29.3
0:58.5
5:48.3
1:13.0
6:05.0
1:02.4
Total
6:32.4
1:07.4
6:11.0
0:49.2
5:41.2
1:09.3
5:44.1
0:49.9
Age: 55–59
n = 1256
First half
6:45.1
1:06.2
6:00.3
0:39.7
5:43.7
1:06.4
5:23.6
0:39.1
Second half
7:05.0
1:12.7
6:40.1
0:51.9
5:58.0
1:13.7
6:03.0
0:58.0
Total
6:55.1
1:08.5
6:20.2
0:44.5
5:50.8
1:09.4
5:43.3
0:46.7
Age: 60–64
n = 646
First half
6:52.6
1:09.2
6:29.6
0:50.1
5:53.3
1:17.4
5:37.0
0:47.1
Second half
7:10.9
1:11.8
7:26.8
1:07.5
6:09.4
1:22.8
6:17.3
1:05.3
Total
7:01.7
1:09.7
6:58.2
0:56.8
6:01.3
1:19.0
5:57.2
0:53.5
Age: 65+
n = 540
First half
7:03.6
1:03.8
6:44.0
0:41.5
6:18.7
1:09.1
6:02.3
0:44.0
Second half
7:28.2
1:11.2
7:27.9
1:02.7
6:35.4
1:16.1
6:54.7
1:06.9
Total
7:15.9
1:06.7
7:06.0
0:50.5
6:27.1
1:11.6
6:28.5
0:53.1
n = number of participants, SD = standard deviation of data, min/km = minutes per kilometer.
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Figure 1. Women’s (upper panel) and men’s (lower panel) running time in the first and second 
half of 10-km race and marathon. Data showed as mean ± standard deviation. **—Significant dif-
ferences at p< 0.01. 
3.1. Pacing in 10-km and Marathon 
For women (Figure 1, upper panel), the two-way ANOVA showed significant main 
effects of pace(F(3,11978) = 6513.1, ŋ2 = 0.02, p< 0.01), race(F(3,11978) = 187.7, ŋ2= 0.01, p< 0.01) as 
well as pace × raceinteraction (F(3,11978) = 1086.1, ŋ2< 0.01, p< 0.01), whereas for men (Figure 
Figure 1. Women’s (upper panel) and men’s (lower panel) running time in the ﬁrst and second half
of 10-km race and marathon. Data showed as mean ± standard deviation. **—Signiﬁcant differences
at p < 0.01.
3.1. Pacing in 10-km and Marathon
For women (Figure 1, upper panel), the two-way ANOVA showed signiﬁcant main
effects of pace(F(3,11978) = 6513.1,
2 = 0.02, p < 0.01), race(F(3,11978) = 187.7,
2 = 0.01, p < 0.01)
Medicina 2021, 57, 389
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as well as pace × raceinteraction (F(3,11978) = 1086.1,
2 < 0.01, p < 0.01), whereas for
men (Figure 1, lower panel), the two-way ANOVA showed signiﬁcant main effects of
pace(F(3,13161) = 8720.9,
2 = 0.04, p < 0.01), race(F(3,13161) = 14.6,
2 < 0.01, p < 0.01) as well as
sex × race interaction (F(3,13161) = 2619.0,
2 = 0.01, p < 0.01).
3.2. Pace Change in Men and Women in 10-km and Marathon
Regarding pace change (Figure 2), the two-way ANOVA showed signiﬁcant main
effects of sex(F(3,25139) = 129.6,
2 < 0.01, p < 0.01), race(F(3,25139) = 3717.2,
2 = 0.13, p < 0.01)
as well as sex × race interaction (F(3,25139) = 149.3,
2 = 0.01, p < 0.01).
3.3. Age Group Pace Change in 10-km and Marathon
For women (Figure 3, upper panel), the two-way ANOVA showed signiﬁcant main
effects of age(F(17,11962) = 359.3,
2 = 0.01, p < 0.01), race(F(17,11962) = 441.1,
2 = 0.04, p < 0.01)
as well as age × race interaction (F(17,11962) = 189.3,
2 < 0.01, p < 0.01), whereas for
men (Figure 3, lower panel), the two-way ANOVA showed signiﬁcant main effects of
age(F(17,13145) = 3.9,
2 < 0.01, p < 0.01), race(F(17,13145) = 1678.2,
2 = 0.11, p < 0.01) as well as
age × race interaction (F(17,13145) = 4.0,
2 < 0.01, p < 0.01).
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1, lower panel), the two-way ANOVA showed significant main effects of pace(F(3,13161) =
8720.9, ŋ2 = 0.04, p< 0.01), race(F(3,13161) = 14.6, ŋ2< 0.01, p< 0.01) as well as sex × race interaction
(F(3,13161) = 2619.0, ŋ2= 0.01, p< 0.01). 
3.2. Pace Change in Men and Women in 10-km and Marathon 
Regarding pace change (Figure 2), the two-way ANOVA showed significant main
effects of sex(F(3,25139) = 129.6, ŋ2 < 0.01, p< 0.01), race(F(3,25139) = 3717.2, ŋ2 = 0.13, p< 0.01) a
well as sex × race interaction (F(3,25139) = 149.3, ŋ2 = 0.01, p< 0.01). 
3.3. Age Group Pace Change in 10-km and Marathon 
For women (Figure 3, upper panel), the two-way ANOVA showed significant main
effects of age(F(17,11962) = 359.3, ŋ2 = 0.01, p< 0.01), race(F(17,11962) = 441.1, ŋ2 = 0.04, p< 0.01) a
well as age × race interaction (F(17,11962) = 189.3, ŋ2< 0.01, p< 0.01), whereas for men (Figure 3
lower panel), the two-way ANOVA showed significant main effects of age(F(17,13145) = 3.9
ŋ2 < 0.01, p< 0.01), race(F(17,13145) = 1678.2, ŋ2 = 0.11, p< 0.01) as well as age × race interaction
(F(17,13145) = 4.0, ŋ2 < 0.01, p< 0.01). 
 
Figure 2. Pace change in 10-km race and marathon for women and men. Data showed as mean ± 
standard deviation. **—Significant differences at p< 0.01. 
Figure 2. Pace change in 10-km race and marathon for women and men. Data showed as mean ±
standard deviation. **—Signiﬁcant differences at p < 0.01.
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Figure 3. Pace change in 10-km race and marathon for women’s (upper panel) and men’s (lower 
panel) age groups. Data showed as mean ± standard deviation. **—Significant differences at p< 
0.01. 
Figure 3. Pace change in 10-km race and marathon for women’s (upper panel) and men’s (lower
panel) age groups. Data showed as mean ± standard deviation. **—Signiﬁcant differences at p < 0.01.
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4. Discussion
The main aim of this study was to assess and compare pacing between 10-km runners
and marathoners, in regards to their sex and age. The hypothesis, that 10-km runners
would show more even pacing than marathoners, particularly women and middle age
runners, was partially conﬁrmed.
Contrary to the previous results obtained on elite 10-km runners, where an even or
negative pacing proﬁle was observed [1,4], recreational runners (both men and women)
showed a positive pacing proﬁle (Figure 1). However, when compared to the marathoners,
the pace slowing that occurred in the second half of the 10-km race was signiﬁcantly
less. It seems that running performance does not affect pacing in the 10-km, as it does in
the marathon [11,26]. In our study, marathoners achieved a faster speed than the 10-km
runners (Table 1), thus we can assume that the 10-km runners were mostly beginners in
comparison to the marathoners. Similar results were previously obtained for recreational
half-marathoners [10,22], where plummet in pace was less prominent in recreational half-
marathoners than the marathoners. Therefore, we can assume that the pacing difference
between 10-km race and marathon was not related to the runners’ performance level,
but could be attributed to the increased fatigue when participating in the longer distance
race [22].
4.1. Pacing in 10-km and Marathon
Finally, note that time (expressed as minutes and seconds) per kilometre was utilized
as a unit of speed. Similar studies have often used meters per second or kilometres per
hour [10,11] as more common units of speed, particularly in the ﬁeld of sports science.
However, practitioners (e.g., coaches, runners of all levels, as well as some sports scientists)
regularly use time per kilometre as a very comprehensive practical tool when assessing
and comparing someone’s running speed or pace maintenance (see Materials and Methods
for additional information).
4.2. Pace Change in Men and Women in 10-km and Marathon
When women and men were compared, no differences in pace change were obtained
in the 10-km race (Figure 2). In contrast to the 10-km race, the marathon women had a signif-
icantly lower pace change in comparison to the marathon men. When we relate this ﬁnding
to the previous studies comparing pacing in recreational half-marathoners and marathon-
ers [10,22], it appears that in long-distance races shorter than a marathon, the men’s pacing
strategy is equally good as the women’s one, potentially event slightly better (Figure 2).
That would conﬁrm the previously observed hypothesis that women have an advantage in
pacing over men only in distances equal to or longer than marathons [10]. The obtained
ﬁndings can possibly diminish the previously reported psychological effect on men’s pac-
ing (i.e., a riskier and faster start by men, due to greater competitiveness [14,15,27]). It can
be assumed that less variation in pacing, as observed in recreational female marathoners,
was due to a better fat utilization to obtain energy [28], rather than burning glycogen stored
in muscles [12].
4.3. Age Group Pace Change in 10-km and Marathon
In all age groups (in both women and men), the 10-km runners showed a lower pace
change in comparison to the marathon runners (Figure 3). It appears that both the youngest
and the oldest marathoners change pace more than other age groups (Figure 3, lower panel),
which is also conﬁrmed in the 2017 Vienna marathon and half-marathon [29]. Younger, less
experienced marathoners might encounter an inadequate control mechanism of pacing
by altering pace often, which in turn might induce an excess of fatigue. The control
mechanism of pacing depends on the information of the endpoint and race duration, an
inherent clock setting scalar timing, and the knowledge of pacing from previously ﬁnished
races [30]. Younger inexperienced runners could lack this pacing template in the brain,
hence they cannot control pace during prolonged activities, such as a marathon. On the
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other hand, elderly men spend more time running (i.e., run slowly). As a consequence,
fatigue-induced change in pace is more likely to occur, regardless of the well-developed
pacing template [29]. Similar results are obtained in women, whereas the youngest and
oldest marathoners change pace the most (Figure 3, upper panel). This phenomenon is,
however, less pronounced compared to men due to previously explained sex differences
(see preceding paragraph).
4.4. Limitations
Several limitations of this study should be noted. First, it was impossible to include
the half-marathon mid-race split in this study, since the organizers usually don’t provide
a split at 10.550 km. Also, this split is not quite popular among runners (both elite and
recreational). Second, additional splits in the 10-km race would provide better insight
into the runners’ pacing strategies. Unfortunately, almost no mass participation events
provide these splits in races shorter than half-marathon. Third, this study has assessed
only one event in four consecutive years, thus limiting the potential generalization of the
obtained ﬁndings. On the other hand, this allows a greater sample of participants. Finally,
combining all runners older than 65 into the one age group (since there is a limited number
of runners older than 65), limits our knowledge about pacing in older 10-km and marathon
runners.
4.5. Practical Applications
Based on these ﬁndings, strength and conditioning coaches (e.g., running coaches)
should adopt different training programmes for marathoners, in comparison to the par-
ticipants in the shorter long-distance events. Particular emphasis should be placed on
individualized training plans for beginners, with the purpose of achieving an even or
negative pacing proﬁle (or at least to reduce plummet in the speed in the second half of the
race). For example, recreational runners could run several 10-km races or half-marathons
with the goal of achieving an even or negative pacing proﬁle, before they attempt to run a
marathon. This pacing strategy might aid athletes to have a faster race time, decrease the
risk of musculoskeletal injuries, and enhance the enjoyment of endurance running.
Finally, a particular novelty of this study was the use of minutes per kilometre as a unit
of speed, as a very “practitioner friendly” and quite comprehensive tool when assessing
and comparing someone’s running speed or pace maintenance (see Methods for additional
information). Similar studies could utilize this measurement of speed more often, thus
providing more comparable ﬁndings.
5. Conclusions
In conclusion, both 10-km runners and marathoners showed positive pacing strate-
gies. Moreover, women are less likely to slow in the marathon, however not in shorter
long-distance events. Finally, pace changing is more prominent in youngest and oldest
marathoners comparing to the other age groups.
Author Contributions: Conceptualization, I.C. and P.T.N.; methodology, I.C. and P.T.N.; software,
I.C., P.T.N. and E.V.; validation, I.C. and P.T.N.; formal analysis, I.C., P.T.N. and E.V.; investigation,
I.C., P.T.N. and E.V.; resources, I.C., P.T.N. and E.V.; data curation, I.C., P.T.N. and E.V.; writing—
original draft preparation, I.C., P.T.N. and B.K.; writing—review and editing, I.C., P.T.N. and B.K.;
visualization, I.C. and P.T.N.; supervision, P.T.N. and B.K.; project administration, E.V. and B.K.;
funding acquisition, E.V. and B.K. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The present research was approved by the Institutional
Review Board of Kanton St. Gallen, Switzerland, with a waiver of the requirement for informed
consent of the participants as the research concerned the study of publicly available data (Ethical
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Committee St. Gallen 1 June 2010). This research was conducted in accordance with ethical standards
derived from the Declaration of Helsinki adopted in 1964 and revised in 2013.
Informed Consent Statement: Patient consent was waived due to as the research concerned the
study of publicly available data.
Data Availability Statement: BMW Oslo Maraton. Available online: https://oslomaraton.no/
(accessed on 25April 2019).
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Pacing in Long-Distance Running: Sex and Age Differences in 10-km Race and Marathon.
Cuk, Ivan,Nikolaidis, Pantelis T,Villiger, Elias,Knechtle, Beat