International  Journal  of
Environmental Research
and Public Health
Article
Using Accelerometry for Evaluating Energy
Consumption and Running Intensity Distribution
Throughout a Marathon According to Sex
Carlos Hernando 1,2,*
, Carla Hernando 3, Ignacio Martinez-Navarro 4,5,
Eladio Collado-Boira 6
, Nayara Panizo 6 and Barbara Hernando 7
1
Sport Service, Jaume I University, 12071 Castellon, Spain
2
Department of Education and Speciﬁc Didactics, Jaume I University, 12071 Castellon, Spain
3
Department of Mathematics, Carlos III University of Madrid, 28911 Leganés, Madrid, Spain;
cahernan@math.uc3m.es
4
Department of Physical Education and Sport, University of Valencia, 46010 Valencia, Spain;
ignacio.martinez-navarro@uv.es
5
Sports Health Unit, Vithas-Nisa 9 de Octubre Hospital, 46015 Valencia, Spain
6
Faculty of Health Sciences, Jaume I University, 12071 Castellon, Spain; colladoe@uji.es (E.C.-B.);
nayapanizo@gmail.com (N.P.)
7
Department of Medicine, Jaume I University, 12071 Castellon, Spain; hernandb@uji.es
*
Correspondence: hernando@uji.es; Tel.: +34-964-728808
Received: 28 July 2020; Accepted: 25 August 2020; Published: 26 August 2020


Abstract: The proportion of females participating in long-distance races has been increasing in the last
years. Although it is well-known that there are diﬀerences in how females and males face a marathon,
higher research may be done to fully understand the intrinsic and extrinsic factors aﬀecting sex
diﬀerences in endurance performance. In this work, we used triaxial accelerometer devices to monitor
74 males and 14 females, aged 30 to 45 years, who ﬁnished the Valencia Marathon in 2016. Moreover,
marathon split times were provided by organizers. Several physiological traits and training habits
were collected from each participant. Then, we evaluated several accelerometry- and pace-estimated
parameters (pacing, average change of speed, energy consumption, oxygen uptake, running intensity
distribution and running economy) in female and male amateur runners. In general, our results
showed that females maintained a more stable pacing and ran at less demanding intensity throughout
the marathon, limiting the decay of running pace in the last part of the race. In fact, females ran at
4.5% faster pace than males in the last kilometers. Besides, their running economy was higher than
males (consumed nearly 19% less relative energy per distance) in the last section of the marathon.
Our results may reﬂect well-known sex diﬀerences in physiology (i.e., muscle strength, fat metabolism,
VO2max), and in running strategy approach (i.e., females run at a more conservative intensity level in
the ﬁrst part of the marathon compared to males). The use of accelerometer devices allows coaches
and scientiﬁc community to constantly monitor a runner throughout the marathon, as well as during
training sessions.
Keywords: accelerometry; sex; physical activity; running intensity; energy consumption; pacing;
marathoners; running economy
1. Introduction
Marathons have growth in popularity and therefore in participants worldwide at a record
pace [1–3]. However, the increase in the number of female marathoners has been delayed, as compared
to male, due to diﬀerent social and behavioral causes previously pointed out by Joyner and colleagues
Int. J. Environ. Res. Public Health 2020, 17, 6196; doi:10.3390/ijerph17176196
www.mdpi.com/journal/ijerph
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in 2017 [4]. Although sex ratios are still far of being equivalent (i.e., 18.7% of females from the total
number of participants in the Valencia Marathon 2019), female’s participation in marathon races has
increased exponentially since Kathy Swizer ﬁnished the Boston Marathon in 1967.
This situation has encouraged scientiﬁc community to study female’s behavior in long-distance
races and compare them with males. Studies have been focused on analyzing diﬀerent factors
aﬀecting running performance such as running speed [5–8], pacing [9], physiological traits [4,10],
running economy [7,11,12] and predominant type of metabolism used [13–15], as well as physical,
biomechanical, psychological and social factors [16–19].
The assessment of physiological parameters aﬀecting running performance has been carried out
in lab-based conditions—normally by measuring the volume of expired gases (the gold standard
test) [11–13,19]. However, lab-based conditions are far from normal race conditions. Up to now,
ﬁeld-based studies have been focused on estimating the energy consumption throughout a long-distance
race by analyzing changes in running speed [7,10,20–23]. The use of portable measurement systems to
obtain parameters for estimating energy consumption in real conditions is nowadays a reality [24–29].
In particular, the use of triaxial accelerometry has strongly emerged as a tool that allows the
evaluation of a physical activity, in terms of duration, frequency and intensity, performed by an individual
in free-living conditions [30–33]. Thus, using the cut-oﬀ points previously established for a speciﬁc
population and/or an activity, the accelerometer output data allowed to indirectly estimate the energy
cost of an activity [34–38].
With the aim of monitoring middle-aged recreational marathoners during a marathon using
accelerometry-based devices, our research group has established the GENEActiv® cut-oﬀ points,
under lab-based conditions, for discriminating the six relative-intensity activity levels in female and
male marathoners [39]. Once cut-oﬀ points were established, we used accelerometer output data for
analyzing the running intensity distribution and energy consumption of runners during a marathon
race (a free-living condition) [40]. Interestingly, accelerometer output data can also be used for inferring
other useful parameters (i.e., running economy of the runner) in real conditions.
Since sex was not taken into account in our previous work, this study focused on evaluating
several accelerometry-estimated parameters (energy consumption, running intensity distribution
and running economy) according to the individual’s sex. The use of accelerometers allowed us to
directly and constantly monitor a total of 88 recreational marathon runners (74 males and 14 females)
throughout the marathon race. Here, accelerometry- and pace-based data collected from females and
males were analyzed separately. In this study, we also pointed out the valuable additional information
that accelerometry oﬀers to athletes, coaches and scientiﬁc community, as compared to the evaluation
of running speed.
2. Materials and Methods
2.1. Sample Set and Data Collection
From all participants of the Valencia Fundación Trinidad Alfonso EDP 2016 Marathon
(20 November 2016), a total of 103 recreational marathon runners, aged 30 to 45 years, were selected
to participate in this study. Eight runners did not start the race and were discarded from our study
population. Finally, a total of 88 recreational marathon runners (74 males and 14 females) crossed the
ﬁnish line of the Valencia Fundacion Trinidad Alfonso EDP 2016 Marathon and thus were analyzed in
this study. The entire process of sampling, as well as the weather and track conditions of the race, has
been previously described [41].
Details of data collection, processing and analysis have been previously described [40]. Four weeks
before the marathon, participants completed a cardiopulmonary test. In this appointment, we also
collected anthropometric data, demographics, medical information, training program and competition
history. One hour before the marathon, all participants were weighed. During the race, participants
wore a GENEActiv accelerometer (Activinsights Ltd., Kimbolton, Cambridgeshire, United Kingdom)
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on the non-dominant wrist as a watch. Accelerometers were adjusted to record acceleration data at a
rate of 85.7 Hz, and data was summarized into Signal Vector Magnitude gravity subtracted (SVMgs)
per minute. Recording and processing of acceleration data has been previously explained in detail [39].
All individuals underwent the same testing under the same experimental conditions. Raw data of this
study is available in Supplementary File S1.
All individuals included in the current study were fully informed and gave their written consent
to participate. All experiments were performed in accordance with international guidelines and
regulations that govern human research. The research was approved by the Research Ethics Committee
of the University Jaume I of Castellon and is enrolled in the ClinicalTrails.gov database (NCT03155633).
2.2. Data Analysis
The marathon race was divided into nine sections as previously described [40]. All analyses were
performed for each of the nine race sections, as well as for the entire marathon distance.
Firstly, the physical eﬀort distribution of each runner throughout the marathon, in terms of relative
intensity levels of physical activity, was estimated using accelerometry-based devices and following
the methodology previously described by our research group [39]. Diﬀerent cut-oﬀ points were used
to discriminate the six relative-intensity activity levels in female and male recreational marathoners
(Table 1). Then, we estimated the time of each participant running at each one of the six-relative intensity
levels (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous). This estimation
was performed for each of the nine race sections and for the whole race (Tables S1 and S2).
Table 1. Values established for delineating the six-relative intensity levels of physical activity according
to runner’s sex.
Reference Values Established for Each Intensity
Level in Males by Hernando et al. (2018)
Values used for Energy
Consumption Estimation
Sex
Relative-Intensity
Levels of Physical
Activity #
VO2
(mL·kg−1·min−1)
METs *
%VO2max
(mL·kg−1·min−1)
VO2
(mL·kg−1·min−1)
METs *
Males
Sedentary
X < 10%
VO2 < 5.57
METs < 1.59
8.1
4.5
1.29
Light
10% ≤ X <25%
5.57 ≤ VO2 <13.94
1.59 ≤ METs < 3.97
17.5
9.75
2.79
Moderate
25% ≤ X < 45%
13.94 ≤ VO2 < 25.08
3.97 ≤ METs < 7.15
35.0
19.51
5.57
Vigorous
45% ≤ X < 65%
25.08 ≤ VO2 < 36.23
7.15 ≤ METs < 10.33
55.0
30.66
8.76
Very Vigorous
65% ≤ X < 85%
36.23 ≤ VO2 < 47.38
10.33 ≤ METs < 13.54
75.0
41.81
11.94
Extremely Vigorous
X ≥ 85%
VO2 ≥ 47.38
METs ≥ 13.54
92.5
51.56
14.73
Females
Sedentary
X < 10%
VO2 < 4.82
METs < 1.38
8.1
3.91
1.12
Light
10% ≤ X <25%
4.82 ≤ VO2 <12.07
1.38 ≤ METs < 3.45
17.5
8.44
2.41
Moderate
25% ≤ X < 45%
12.07 ≤ VO2 < 21.72
3.45 ≤ METs < 6.21
35.0
16.89
4.83
Vigorous
45% ≤ X < 65%
21.72 ≤ VO2 < 31.38
6.21 ≤ METs < 8.97
55.0
26.55
7.59
Very Vigorous
65% ≤ X < 85%
31.38 ≤ VO2 < 41.03
8.97 ≤ METs < 11.72
75.0
36.20
10.34
Extremely Vigorous
X ≥ 85%
VO2 ≥ 41.03
METs ≥ 11.72
92.5
44.65
12.76
Abbreviations: N, number of individuals; VO2max, maximum oxygen consumption; VO2, oxygen consumption;
MET, metabolic equivalent task. Each minute of the cardiopulmonary test was classiﬁed into one of the six intensity
categories of physical activity relative to an individual’s level of cardiorespiratory (VO2max). # X denotes the
percentage of an individual’s aerobic capacity (VO2max) used to classify each one of the six relative-intensity
categories. * 1 MET = 3.5 mLO2·kg−1·min−1. 1 MET = 1 Kcal·h−1.
Next, energy consumption was calculated by using the median %VO2max value of the range
delimiting each intensity category in males and females (Table 1). That was applied for all intensity
levels except for the sedentary category, in which the standing oxygen cost (4.5 mLO2·kg−1·min−1) was
Int. J. Environ. Res. Public Health 2020, 17, 6196
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applied as reference value in males [42]. The reference value corresponds to the 8.1% of the maximum
oxygen uptake in our males. For females, we used the 8.1% of the maximum oxygen uptake seen in our
females as the VO2 standing (3.91 mLO2·kg−1·min−1). Following previous recommendations [21], we
considered that one MET is equal to 3.5 mLO2·kg−1·min−1, and one MET is equal to one kcal·kg−1·h−1.
As the energy consumption depends on the individual’s body mass, we calculated (i) the calories
consumed per kilogram of body weight per minute (kcal·kg−1·min−1), in order to obtain the physical
eﬀort intensity [20,21,43]; and (ii) the calories consumed per kilogram of body weight per kilometer
(kcal·kg−1·km−1), to infer the running economy of runners [16,44]. Indeed, accelerometry-derived
data was also used for estimating the %VO2max maintained during the marathon by each runner,
an indicator of the physical eﬀort degree respect to the maximum value [19,42,43,45]. Following
the methodology described previously, we also inferred the VO2net and the energy of cost running
above standing (Crnet) for each participant included in the study [12,46,47]. These estimations were
performed by applying the corresponding reference values for females and males.
The split-times in minutes of the marathon sections were recorded for calculating the average
running speed of all sections and the whole marathon distance. Then, the average change in speed
(ACS) for each segment, related to the average race speed, was calculated. The average change in
speed through the whole race was estimated by averaging the ACS values of all sections. The ACS is a
valuable measure for assessing maintenance of running pace [9,48].
The squat jump test was performed to measure the runner’s strength before and after the race.
Jumping height was estimated using the ﬂight time of the jump, which was measured by a contact
platform (Chronojump, Barcelona, Spain) [49]. Individuals were familiarized with the test’s procedure
before to carry out it. Before the marathon race, all individuals performed a total of three jumps,
and the best jump was recorded. After crossing the ﬁnish line, the number of attempts was conditioned
by the capacity of each runner to jump (due to muscular fatigue). No more than three attempts were
performed per runner, and again only the best jump was recorded.
Sex comparisons were performed by calculating the percentage of diﬀerences (gap) in all measures
between males and females, as previously proposed [7,10]. Brieﬂy, we applied the following formula:
Gap = ((Xfemales − Xmales)/Xfemales) × 100.
2.3. Statistical Analysis
Statistical analyses were done using the IBM SPSS Statistics v.26 software, and null hypothesis
was rejected when the two-sided p-value was lower than 0.05.
The Kolgomorov–Smirnov test was used for testing data normality. Since variables were not
normally distributed, all statistical analyses were performed by applying non-parametric statistical
tests. The Chi-squared test was used for comparing categorical variable between males and females,
while the Mann–Whitney U test was applied for comparing quantitative variables between sex groups.
The meaningfulness of the outcomes was additionally estimated by inferring its eﬀect size via the
calculation of Cohen’s d, as following described [50,51]. Outcomes with values of d lower than 0.5 were
considered to have an small relevance; those with d values between 0.5 and 0.8 presented moderate
relevance; and those with values greater than 0.8 had large signiﬁcance [52].
3. Results
A detailed description of individuals included in this study is summarized in Table 2. Sex differences
were observed for several physiological traits, such as the body mass index (BMI; p-value = 0.001),
the percentage of body fat (p-value = 2.03 × 10−5), the maximum oxygen uptake (VO2max; p-value =
6.59 × 10−6), and the maximum metabolic equivalent of task (MET; p-value = 6.77 × 10−6). Moreover,
sex diﬀerences were also observed in the sessions of training performed per week (p-value = 0.04) and
in the number of marathons completed (p-value = 0.03).
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Table 2. Population description.
Variables
Males (N = 74)
Females (N = 14)
p-Value
Physiological
characteristics *
age
38.58 ± 3.70
39.21 ± 3.14
0.61
BMI
23.15 ± 1.46
21.65 ± 1.93
0.001
% body fat
13.76 ± 3.68
19.94 ± 4.26
2.03 × 10−5
VO2max (mL·kg−1·min−1)
55.55 ± 5.25
48.39 ± 3.60
6.59 × 10−6
maximum METs
15.87 ± 1.50
13.83 ± 1.03
6.77 × 10−6
Training
indicators *
years of running
6.42 ± 2.89
6.43 ± 2.17
0.99
sessions per week
4.97 ± 0.83
4.50 ± 0.76
0.04
kilometers per week
64.32 ± 13.16
58.93 ± 11.96
0.14
hours per week
7.54 ± 2.57
6.46 ± 1.82
0.16
History as
marathoner *
marathons ﬁnished
3.62 ± 3.11
2.00 ± 2.15
0.03
marathon per year
1.12 ± 0.64
1.00 ± 0.55
0.58
Work intensity #
high intensity
9.46%
0.00%
0.44
medium intensity
31.08%
28.57%
low intensity
59.46%
71.43%
Levels of study #
school graduate
4.11%
7.14%
0.72
high school graduate
19.18%
7.14%
professional certiﬁcate
6.85%
7.14%
undergraduate degree
69.86%
78.57%
Abbreviations: N, number of samples; BMI, body mass index; SD, standard deviation. * Values are presented as
mean ± SD. #. Values are presented as percentage. Mann–Whitney U test was used for comparing quantitative
variables among groups. Chi-square test was used for comparing categorical variables among groups. Bold denotes
signiﬁcant results.
Using the squat jump test, we measured the level of lower body strength of each runner before
and after running the marathon (Table 3). As expected, the basal squat jump height was signiﬁcantly
higher in males compared to females (27.34 ± 4.28 cm versus 23.84 ± 3.82 cm; p-value = 0.007; Cohen’s
d = 0.60). No signiﬁcant sex diﬀerences in the squat jump height were observed after crossing the ﬁnish
line (21.88 ± 6.19 cm versus 20.53 ± 6.72 cm; p-value = 0.300; Cohen’s d = 0.22). Therefore, the lower
body strength of females seemed to be less altered by running a marathon, one of the most challenging
endurance competitions.
Table 3. Comparison of the diﬀerent variables collected over the whole marathon distance between
males and females.
Variable
Males (N = 74)
Females (N = 14)
p-Value
Cohen’s d
Gap
Speed (m·min−1)
201.29 ± 17.84
180.96 ± 14.07
1.74 × 10−4
0.87
−11.24%
Energy consumed (kcal)
3274.07 ± 599.82
2423.01 ± 239.76
9.32 × 10−7
1.23
−35.12%
Relative energy consumed per minute
(kcal·kg−1·min−1)
0.21 ± 0.03
0.19 ± 0.02
9.91 × 10−4
0.75
−14.42%
Relative energy consumed per kilometer
(kcal·kg−1·km−1)
1.07 ± 0.16
1.04 ± 0.11
0.34
0.21
−2.91%
Cost running net (Crnet)
4.22 ± 0.69
4.11 ± 0.49
0.38
0.19
−2.82%
Percentage of VO2max (%)
80.76 ± 11.51
81.57 ± 7.59
0.68
0.09
1.00%
Basal Metabolic Rate (BMR)
12.87 ± 1.84
11.24 ± 1.06
8.29 × 10−4
0.76
−14.47%
Marathon time (minutes)
211.28 ± 19.16
234.50 ± 18.46
1.74 × 10−4
0.87
9.90%
Squat jump at the start line (cm)
27.24 ± 4.29
23.84 ± 3.82
0.007
0.60
−14.26%
Squat jump at the ﬁnish line (cm)
21.89 ± 6.19
20.53 ± 6.72
0.30
0.22
−6.62%
Average change in speed (%)
5.39 ± 2.62
6.29 ± 2.58
0.15
0.31
14%
% of time at sedentary level
0.01 ± 0.12
0.00 ± 0.00
0.66
0.02
NA
% of time at light level
0.09 ± 0.5
0.07 ± 0.27
0.81
0.02
−29%
% of time at moderate level
3.61 ± 7.40
1.07 ± 1.59
0.11
0.33
−237%
% of time at vigorous level
11.58 ± 19.58
4.79 ± 8,11
0.10
0.35
−142%
% of time at very vigorous level
30.82 ± 29.33
50.50 ± 30.29
0.02
0.52
39%
% of time at extremely vigorous level
53.88 ± 39.29
43.64 ± 34.86
0.27
0.24
−23%
Abbreviations: N, number of samples; SD, standard deviation; NA, not available; Gap, percentage of sex diﬀerences.
Values are presented as mean ± SD. Mann–Whitney U test was used for comparing quantitative variables among
groups. Cohen’s d was calculated for inferring the eﬀect size of a variable. Bold denotes signiﬁcant results.
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Firstly, accelerometer-derived data was used to determine the running intensity distribution of
female and male runners throughout the marathon. In this regard, the time consumed at each intensity
level was expressed as the percentage respect to the total time needed for covering each one of the nine
race sections and to the marathon time (Figure 1 and Table 3).
Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 
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distribution of time running at the very vigorous intensity level, females seemed to spend a higher 
percentage of time running at this intensity level than males, with significant differences in the 10–15 
km section, the 15-HM section, the HM-25 km section, the 25–30 km section, the 30–35 km section 
and the entire marathon distance (Figure 1 and Table 3). 
 
Figure 1. Bar plots showing the percentage of time performing at each of the six-relative intensity 
levels of physical activity. Running intensity distribution was analyzed (A–I) in each one of the nine 
race sections, and (J) for the whole marathon distance. Bars represent the average values for males 
Figure 1. Bar plots showing the percentage of time performing at each of the six-relative intensity
levels of physical activity. Running intensity distribution was analyzed (A–I) in each one of the nine
race sections, and (J) for the whole marathon distance. Bars represent the average values for males
(orange) and females (blue), and error bars represent the standard deviation of the mean. Mean time
spent (±standard deviation) to cover each race sections and the whole race by males (M) and females
(F) is showed in the corresponding panel. A Mann–Whitney U test was used for testing sex diﬀerences.
* p-value < 0.05; ** p-value < 0.01.
Int. J. Environ. Res. Public Health 2020, 17, 6196
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Time racing at extremely vigorous intensity level was similar in females and males at all race
sections and in the entire marathon distance (Figure 1 and Table 3). However, males tended to spend
more time running at extremely vigorous intensity than females, except for the last race section (from
40km to the ﬁnish line) (61.05 ± 40.69 % versus 62.93 ± 38.08%, respectively). Regarding the distribution
of time running at the very vigorous intensity level, females seemed to spend a higher percentage of
time running at this intensity level than males, with signiﬁcant diﬀerences in the 10–15 km section,
the 15-HM section, the HM-25 km section, the 25–30 km section, the 30–35 km section and the entire
marathon distance (Figure 1 and Table 3).
Nevertheless, males were more time running at vigorous intensity than females, showing
signiﬁcant diﬀerences in the 10–15 km and the 25–30 km race sections (Figure 1). There were sex
diﬀerences in the percentage of time running at moderate intensity in the 10–15 km and the 15-HM
race sections, with males presenting higher values than females. As for the extremely vigorous level
of physical intensity, females showed a higher, but not signiﬁcantly, percentage of time running at
moderate intensity than males in the last race section (from 40 km to the ﬁnish line) (5.50 ± 20.58%
versus 4.41 ± 13.23%, respectively).
As expected by the conditions of the activity, the percentage of time running at both sedentary and
light intensities was minimum for both males and females. In fact, the time running at the two highest
intensity levels (very vigorous and extremely vigorous) represented the 84.95 ± 23.70% of the marathon
time for males and the 94.21 ± 9.64% for females (Figure 2). Therefore, running at these high intensities
is crucial for achieving the marathon goal time of runners. The diﬀerences in absolute percentages
denoted that females had a minimum decay rate in their running intensity, while males tended to drop
from running at a very high intensity level (mainly at extremely vigorous) to a vigorous intensity level.
Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 
8 of 15 
 
(orange) and females (blue), and error bars represent the standard deviation of the mean. Mean time 
spent (± standard deviation) to cover each race sections and the whole race by males (M) and females 
(F) is showed in the corresponding panel. A Mann–Whitney U test was used for testing sex 
differences. * p-value < 0.05; ** p-value < 0.01. 
Nevertheless, males were more time running at vigorous intensity than females, showing 
significant differences in the 10–15 km and the 25–30 km race sections (Figure 1). There were sex 
differences in the percentage of time running at moderate intensity in the 10–15 km and the 15-HM 
race sections, with males presenting higher values than females. As for the extremely vigorous level 
of physical intensity, females showed a higher, but not significantly, percentage of time running at 
moderate intensity than males in the last race section (from 40 km to the finish line) (5.50 ± 20.58% 
versus 4.41 ± 13.23%, respectively). 
As expected by the conditions of the activity, the percentage of time running at both sedentary 
and light intensities was minimum for both males and females. In fact, the time running at the two 
highest intensity levels (very vigorous and extremely vigorous) represented the 84.95 ± 23.70% of the 
marathon time for males and the 94.21 ± 9.64% for females (Figure 2). Therefore, running at these 
high intensities is crucial for achieving the marathon goal time of runners. The differences in absolute 
percentages denoted that females had a minimum decay rate in their running intensity, while males 
tended to drop from running at a very high intensity level (mainly at extremely vigorous) to a 
vigorous intensity level. 
 
Figure 2. Bar plot showing the percentage of time performing at the two highest intensity levels of 
physical activity. Bars represent the average values for males (orange) and females (blue). Dots 
represent each runner included in our study. Mean percentage (±standard deviation) of time 
performing at these intensity levels is showed in the corresponding panel. A Mann–Whitney U test 
was used for testing differences between females (F) and males (M). * p-value < 0.05; ** p-value < 0.01. 
We also compared the evolution of running speed throughout the marathon race (Figure 3A and 
Table 3). Overall, running speed was higher in males than in females, except for the last section of the 
race (from 40 km to the finish line) (238.16 ± 51.50 m·min−1 for females versus 227.44 ± 40.16 m·min−1 
for males). In fact, the Gap (percentage of the relative sex difference) in running speed was decreasing 
during the course of the marathon. For the entire marathon distance, the running speed of females 
was an 11.24% slower than males (Gap = −11.24%). However, a positive Gap value (4.50%) was 
Figure 2. Bar plot showing the percentage of time performing at the two highest intensity levels of
physical activity. Bars represent the average values for males (orange) and females (blue). Dots represent
each runner included in our study. Mean percentage (±standard deviation) of time performing at these
intensity levels is showed in the corresponding panel. A Mann–Whitney U test was used for testing
diﬀerences between females (F) and males (M). * p-value < 0.05; ** p-value < 0.01.
We also compared the evolution of running speed throughout the marathon race (Figure 3A and
Table 3). Overall, running speed was higher in males than in females, except for the last section of the
race (from 40 km to the ﬁnish line) (238.16 ± 51.50 m·min−1 for females versus 227.44 ± 40.16 m·min−1
for males). In fact, the Gap (percentage of the relative sex diﬀerence) in running speed was decreasing
Int. J. Environ. Res. Public Health 2020, 17, 6196
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during the course of the marathon. For the entire marathon distance, the running speed of females was
an 11.24% slower than males (Gap = −11.24%). However, a positive Gap value (4.50%) was obtained
in the last race section (from 40 km to the ﬁnish line), denoting the faster speed achieved by females
compared to males in the last kilometers.
Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 
9 of 15 
 
obtained in the last race section (from 40 km to the finish line), denoting the faster speed achieved by 
females compared to males in the last kilometers. 
 
Figure 3. Evolution of (A) speed, (B) percentage of average change speed, (C) relative energy 
consumed per minute, (D) relative energy consumed per kilometer, (E) percentage of maximum 
oxygen uptake, and (F) cost running above the standing level. Dots represent the average values for 
males (orange) and females (blue), and error bars represent the standard deviation of the mean. Bars 
represent the percentage of sex difference (Gap). Mean values (±standard deviation) of each variable 
analyzed for the whole marathon distance are showed in the corresponding panel. A Mann–Whitney 
Figure 3. Evolution of (A) speed, (B) percentage of average change speed, (C) relative energy consumed
per minute, (D) relative energy consumed per kilometer, (E) percentage of maximum oxygen uptake,
and (F) cost running above the standing level. Dots represent the average values for males (orange)
and females (blue), and error bars represent the standard deviation of the mean. Bars represent the
percentage of sex diﬀerence (Gap). Mean values (±standard deviation) of each variable analyzed for
the whole marathon distance are showed in the corresponding panel. A Mann–Whitney U test was
used for testing sex diﬀerences. Bold denotes signiﬁcant diﬀerences in values obtained for the whole
marathon distance between females (F) and males (M).
Int. J. Environ. Res. Public Health 2020, 17, 6196
9 of 14
Additionally, we evaluated the evolution of ACS (Figure 3B). Results denoted higher changes of
speed in females compared to males (6.29 ± 2.58 versus 5.40 ± 2.62, respectively). This observation
is mainly caused by the signiﬁcant increase of running speed made by females in the last section of
the race. In fact, ACS values were lower in females compared to males (4.24 ± 1.75 versus 4.37 ± 2.68,
respectively) when the ACS was evaluated without taking into account the last race section. That is,
running pace of females was more stable than males in the ﬁrst 40 km of the race and, for that reason,
they were able to sprint in the last section of the marathon. Our results denoted signiﬁcant sex
diﬀerences in the ACS only in this last section of the race (the unique race section without signiﬁcantly
sex diﬀerences in the absolute running speed; Figure 3A).
Energy consumption was also compared between females and males. Males consumed 12% to
18% more energy (kilocalories normalized per body mass) per minute than females at all marathon
sections (Figure 3C) and in the entire race distance (Table 3).
Accelerometry-derived data was also used to estimate the %VO2max sustained throughout the
race by each runner, following the methodology previously published by our research group [38].
Females and males consumed a similar %VO2max at the diﬀerent race sections analyzed (Figure 3E).
Besides, very similar overall values were observed in males (80.76 ± 11.51% of VO2max) and females
(81.57 ± 7.59% of VO2max) (Table 3). However, it is noted that females needed to maintain a slightly
higher %VO2max for running at a lower running intensity level in comparison to males (at very vigorous
and at extremely vigorous intensity for females and males, respectively).
Running economy was measured by estimating both the energy (kilocalories normalized by body
mass) consumed per kilometer and the Cost running above standing (Crnet). As expected for the
last section of the race, no diﬀerences were observed in running economy between male and female
runners, independently of the method used (Figure 3D,F, and Table 3). Females seemed to have better
running economy than males in the last race section (from 40 km to the ﬁnish line), which is denoted
by: (i) females consumed less energy per kilometer than males (0.822 ± 0.187 kcal·kg−1·km−1 versus
0.977 ± 0.210 kcal·kg−1·km−1; p-value = 0.017; Cohen’s d = 0.53), and (ii) females presented a lower
Crnet than males (3248 ± 0.794 j·kg−1·m−1 versus 3860 ± 0.875 j·kg−1·m−1; p-value = 0.022; Cohen’s
d = 0.51). Maximum Gap values were observed in the last race section for both variables (−18.89%
for kcal·kg−1·km−1; and −18.85% for Crnet). In this race section, the negative values of Gap denoted
that males consumed more energy per distance (and therefore presented a lower running economy)
than females.
4. Discussion
This observational study aimed at increasing our understanding on how females and males achieve
their marathon goal. In this regard, we focused on analyzing the evolution of several accelerometry-
estimated parameters (energy consumption, running intensity distribution, running economy, oxygen
uptake), as well as pace-related variables (running speed, average change in speed), throughout a
marathon race taking into account runner’s sex. For this purpose, we directly monitored female and
male recreational runners throughout the entire marathon distance by using accelerometer-based
devices. Moreover, we also collected split times of each runner (provided by the organizers of the
Valencia Marathon).
Similar to previous studies [4,53], the average running speed was signiﬁcantly higher in males
compared to females. In this study, the diﬀerence rate in running speed between males and females
was 11.24%. This percentage matched with values obtained in previous studies, which ranged from 8%
to 14% [4–6,8,10,17]. However, this percentage of sex diﬀerence seems to be lower in elite compared to
amateur marathoners. In fact, the female marathon world record (2:14:09, Brigid Kosgei, Chicago 2019)
is only 9.32% slower than the male marathon world record (2:01:39, Eliud Kipchoge, Berlin 2018).
Sex variability in marathon performance may be explained by the well-known diﬀerences in
several physiological traits [4,53–55]. Descriptive analyses showed that males presented higher
maximum oxygen uptake, body mass index, muscle strength, and lower percentage of body fat than
Int. J. Environ. Res. Public Health 2020, 17, 6196
10 of 14
females. Muscle strength has been shown to determine the runner’s ability of displacement and thus
running speed. In fact, a lower percentage of sex diﬀerence has been observed for swimming speed
(6–7%), a physical activity in which the muscle strength component is considerably less crucial for
succeeding compared to running [56,57].
However, according to the split times collected of each runner, males were more likely to slow
their pace in the last part of the marathon race than females. Males started the marathon running at a
15.53% faster speed than females in the ﬁrst race section (from start line to 5 km), and this diﬀerence
rate was decreasing throughout the marathon. In fact, females run at 4.5% faster speed than males in
the last marathon section (from 40 km to ﬁnish line). Additionally, by analyzing the ACS [9], we were
able to conﬁrm that females raced at a more constant pace from the start line to the 40 km as compared
to males. This pace strategy may allow females to signiﬁcantly increase their running speed in the last
section of the race, while males were more likely to “hit the wall” in the last kilometers.
At this point, we would like to highlight the notably diﬀerence in the running speed maintained
by males and females in the ﬁrst section of the race (Gap of −15.53%). This diﬀerence may be attributed
to the fact that, in races with more than 20,000 participants, runners cannot run the ﬁrst kilometers
freely without diﬃculty due to the large number of participants and the limited space. This notable sex
diﬀerence may thus indicate that males started the race being more ambitious, while females adopted
a more cautious attitude [54,55,58]. This observation was not previously seen by Nikolaidis and cols
(2019) [9], probably because they split the race distance into 10 km sectors and not in 5 km sectors
as we did in this work. Having shorter sections allowed us to observe changes in running pace and
intensity in greater detail.
To further explore sex diﬀerences in marathon performance, and taking into account that there is
a lack of gold standard for measuring energy consumption in free-living conditions (as a marathon
race) [40], we used accelerometer-based devises for estimating the distribution of physical eﬀort
throughout the marathon according to runner’s sex [39]. Speciﬁcally, we estimated the time running
at each of the six-relative intensity levels (sedentary, light, moderate, vigorous, very vigorous and
extremely vigorous) in each one of the nine race sections and in the entire marathon. The analysis of
physical eﬀort distribution denoted that females tended to race at a lower intensity level than males
(females signiﬁcantly ran more percentage of time at very vigorous intensity than males, who mainly
ran at extremely vigorous intensity).
In addition, accelerometry-based data allowed us to estimate the energy consumed and the
%VO2max sustained per each runner, and afterwards his/her running economy. According to our
results, females reported a better eﬃciency of movement than males in the last section of the marathon
(from 40 km to the ﬁnish line). That is, a superior energy was demanded by males for running at a
given speed the last 2.195 km of the marathon. This may be a consequence of the high physical eﬀort
sustained by males in the ﬁrst part of the marathon, pointing out the importance of controlling physical
eﬀort distribution in a marathon race to avoid “hitting the wall”. Running at high intensities has been
shown to accelerate glycolytic depletion [55], which may contribute to the decrease of running pace
observed in males in the last part of the marathon. Females, however, may use fats as principal energy
source maintaining their glycogen stores in muscles thanks to running at less demanding intensities.
As stated in lab-based conditions [13], females may present lower respiratory exchange ratio (RER)
compared to males, indicating in turn that fat may be the principal fuel source used by females. Future
work may be focused on validating accelerometry for RER estimations.
Two limitations are noteworthy in the present study. Firstly, we are aware about the low number
of females included in our population (15.91%). However, this percentage is even higher than the rate
of females, aged 30 to 45 years, ﬁnishing the Valencia Marathon in 2016 (13.16%). Higher eﬀort should
be done in future studies for increasing the number of females collected. The second limitation is that
values of accelerometer-based parameters analyzed in this study were merely estimations. No gold
standard method is available yet to perform a direct measurement of VO2 consumed by a runner in
free-living conditions. We may assume a plausible maximum error of 10% in our estimations.
Int. J. Environ. Res. Public Health 2020, 17, 6196
11 of 14
In summary, thanks to the accelerometry-based and pace-based data collected, this study reveals
how female and male middle-aged amateur marathoners face a marathon in terms of pacing, running
strategy, running intensity distribution, energy consumption and running economy. The use of
accelerometer devices for monitoring runners allowed us to perform an individualized assessment
in the context of free-living movement. In general, females showed a good control of physical eﬀort
throughout the marathon, while the running intensity distribution and pacing of males were not so
well-balanced. Subsequently, an increased decay of running pace in the last part of the marathon was
observed for males. Results may reﬂect well-known sex diﬀerences in physiology (i.e., muscle strength,
fat metabolism, VO2max), and in running strategy approach (i.e., females run at a more conservative
intensity level in the ﬁrst part of the marathon compared to males).
5. Conclusions
Compared to males, females maintained a more stable pace and ran at less demanding running
intensities throughout the marathon, limiting the decay of running pace in the last part of the race.
Together with previous studies, the results obtained after analyzing a huge number of variables suggest
that the steady pacing of females may be because of the following reasons:
•
Females may manage energy during the race more eﬃciently than males [7,55].
•
Females may make better decisions in terms of pacing strategy than males [6,48,54].
•
Females typically use more fat than carbohydrates during endurance exercise compared to
males [14,59,60].
•
Females tend to preserve muscle strength and have less neuromuscular fatigue than males at the
end of the marathon [61].
Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/17/6196/s1.
Table S1: Evaluation of running intensity distribution and estimation of calories consumed by male runners based
on accelerometry data. Table S2: Evaluation of running intensity distribution and estimation of calories consumed
by male runners based on accelerometry data. File S1: Raw data of the study.
Data Availability: All data generated or analyzed during this study are included in this published article (and its
Supplementary Materials).
Author Contributions: C.H. (Carlos Hernando) and B.H. contributed to conception and design of the study,
article drafting and critical revision of the article. C.H. (Carlos Hernando) and C.H. (Carla Hernando) contributed
to data curation, analysis and interpretation. C.H. (Carlos Hernando), I.M.-N., E.C.-B. and N.P. contributed to data
collection and critical revision of the article. C.H. (Carlos Hernando), I.M.-N. and E.C.-B. contributed to funding
acquisition. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Fundacion Trinidad Alfonso, the Vithas-Nisa Hospitals group and the
Sociedad Deportiva Correcaminos.
Acknowledgments: We are grateful to all the stuﬀ involved in the organization of the Valencia Marathon
Fundacion Trinidad Alfonso EDP 2016, and all marathoners and volunteers participating in this study.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Using Accelerometry for Evaluating Energy Consumption and Running Intensity Distribution Throughout a Marathon According to Sex.
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