RESEARCH ARTICLE
The Dynamics of Speed Selection and
Psycho-Physiological Load during a Mountain
Ultramarathon
Hugo A. Kerhervé1,2*, Guillaume Y. Millet3,4, Colin Solomon1
1 School of Health and Sport Sciences, University of the Sunshine Coast, Sippy Downs, Australia,
2 Laboratoire de Physiologie de l’Exercice, EA-4338, Université Savoie Mont Blanc, Le Bourget-du-Lac,
France, 3 Human Performance Laboratory, University of Calgary, Calgary, Canada, 4 Laboratoire de
Physiologie de l’Exercice, Université de Lyon, F–42023, Saint–Etienne, France
* hugo-alain.kerherve@univ-smb.fr
Abstract
Background
Exercise intensity during ultramarathons (UM) is expected to be regulated as a result of the
development of psycho-physiological strain and in anticipation of perceived difficulties
(duration, topography). The aim of this study was to investigate the dynamics of speed,
heart rate and perceived exertion during a long trail UM in a mountainous setting.
Methods
Fifteen participants were recruited from competitors in a 106 km trail mountain UM with a
total elevation gain and loss of 5870 m. Speed and gradient, heart rate (HR) and ratings of
perceived exertion (dissociated between the general [RPEGEN] and knee extensor fatigue
[RPEKE] and collected using a voice recorder) were measured during the UM. Self-selected
speed at three gradients (level, negative, positive), HR, RPEGEN and RPEKE were deter-
mined for each 10% section of total event duration (TED).
Results
The participants completed the event in 18.3 ± 3.0 h, for a total calculated distance of 105.6 ±
1.8 km. Speed at all gradients decreased, and HR at all gradients significantly decreased
from 10% to 70%, 80% and 90%, but not 100% of TED. RPEGEN and RPEKE increased
throughout the event. Speed increased from 90% to 100% of TED at all gradients. Average
speed was significantly correlated with total time stopped (r = -.772; p = .001; 95% confidence
interval [CI] = -1.15, -0.39) and the magnitude of speed loss (r = .540; p = .038; 95% CI =
-1.04, -0.03), but not with the variability of speed (r = -.475; p = .073; 95% CI = -1.00, 0.05).
Conclusions
Participants in a mountain UM event combined positive pacing strategies (speed decreased
until 70–90% of TED), an increased speed in the last 10% of the event, a decrease in HR at
PLOS ONE | DOI:10.1371/journal.pone.0145482
December 21, 2015
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OPEN ACCESS
Citation: Kerhervé HA, Millet GY, Solomon C (2015)
The Dynamics of Speed Selection and Psycho-
Physiological Load during a Mountain Ultramarathon.
PLoS ONE 10(12): e0145482. doi:10.1371/journal.
pone.0145482
Editor: Pedro Tauler, University of the Balearic
Islands, SPAIN
Received: October 12, 2015
Accepted: December 6, 2015
Published: December 21, 2015
Copyright: © 2015 Kerhervé et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All relevant data are
within the paper.
Funding: No specific funding was used for this study.
The first author was supported by university research
funds and scholarship (USC and Queensland
Education and Training).
Competing Interests: The authors have declared
that no competing interests exist.
70–90% of TED, and an increase in RPEGEN and RPEKE in the last 30% of the event. A
greater speed loss and less total time stopped were the factors associated with increased
total performance. These results could be explained by theoretical perspectives of a com-
plex regulatory system modulating motor drive in anticipation of perceived difficulties such
as elevation changes.
Introduction
Exercise intensity during self-paced events is regulated by a complex protective system inte-
grating instantaneous somatosensory feed-back and anticipatory mechanisms in order to
maintain homeostasis and prevent from catastrophic failure [1–3]. As in shorter duration, self-
paced exercises [2, 4–8], the dynamics of exercise intensity during ultramarathons (UM) are
expected to be regulated as a result of the development of psycho-physiological strain and in
anticipation of perceived difficulties (duration, topography) [1]. Despite being sensitive to
environmental factors such as gradient and wind [9], speed is commonly used as an indicator
of exercise intensity in the study of athletic performance. Speed is predicted to decrease
throughout UM events [10], and systematic descriptions of pacing during field UM events
have been used to indicate that speed decreases overall and becomes more variable as a func-
tion of increased finishing times [11–14].
UM events performed on trails and in mountainous settings include changes in elevation,
surface, obstacles, altitude, remoteness, and adverse atmospheric conditions, which could alter
the dynamics of pacing, compared to flat events. For instance, significant peripheral fatigue
(low-frequency fatigue, indicative of the failure of excitation-contraction coupling typical of
exercises involving intense eccentric and stretch-shortening contractions [15, 16]) was
observed following a ~37 h mountain UM event [17], but not following a 24 h level, treadmill
run [14]. Therefore, it is possible that the dynamics of speed during a mountain UM would dif-
fer compared to a level UM.
Some measures of psycho-physiological load, such as the ratings of perceived exertion
(RPE) and heart rate (HR), are strong predictors of pacing during self-paced exercise [1, 18],
and could assist in characterising the psycho-physiological load pertaining to a runner at vari-
ous stages of an UM. HR is an indicator of the cumulative systemic physiological response to
variations in physiological and psychological load, and is routinely used to monitor exercise
intensity [19]. The RPE are scores of specifically-developed subjective scales [20] widely used
as indicators of psycho-physiological strain [2], and have been proposed to be a primary vari-
able involved in the selection of work rate [1, 2]. During a 68km UM lasting 9.8 ± 0.4 hr, HR
decreased in the second half, and RPE increased throughout the event without reaching maxi-
mal values (15.4 ± 0.4) [21]. RPE was also found to increase and to have slightly higher values
(14.1 ± 2.0, with a maximum under ~18) throughout a 73 km mountain UM lasting 11.5 ± 0.5
hr [22]. More recently, near-maximal RPE values (19.5 ± 1.5) were reported at the end of a 54
km mountain UM despite a moderate exercise intensity as evidenced by the relatively low aver-
age speed (3.83 km  h−1), and HR (111.7 ± 5.9 bpm) [23]. However, no description of psycho-
physiological load and pacing has been performed in long, mountain UM events.
Therefore, the aim of this study was to investigate the dynamics of speed, HR and perceived
exertion during a long trail UM in a mountainous setting. We hypothesised that a progressive
decrease in speed (positive pacing) and increased variability would be observed in participants.
Based on shorter duration events [11, 24], we also hypothesised that changes in speed would be
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correlated to changes in gradient, and would exhibit different dynamics at level gradients com-
pared to positive and negative gradients. In association with these changes, we hypothesised
that HR would decrease (reverse HR drift), and that RPE would increase throughout the event.
Together, these findings would indicate that pacing is regulated not only as a consequence of
the development of fatigue (simultaneous decrease in speed and HR, [25]), but also in an antic-
ipatory manner to prevent from reaching high levels of exertion prematurely.
Methods
Ethics statement and participants
Ethical approval for the study was granted by the research ethics committee of the University
of the Sunshine Coast (project code S/12/432). The participants were recruited from experi-
enced runners registered to compete in a ~167 km UM running event with ~10,000 m of eleva-
tion gain and loss, and held in Chamonix, France (The North Face1 Ultra-Trail du Mont-
Blanc, UTMB). Nineteen male participants provided written informed consent and were ini-
tially included in the study, fifteen of which completed the entire event and constitute the study
group (age: 43 ± 10 yr, height: 1.78 ± 0.60 m, weight: 74 ± 8 kg).
Study procedures
The participants were individually familiarised with the following study procedures in the 3
days preceding the event. Due to adverse meteorological conditions, the course was shortened
to ~106 km and the elevation gain and loss reduced to ~6,000 m on race day.
Measures of distance, speed and gradient.
The participants were equipped with a Non-
differential Global Positioning System (GPSND) device (BT-Q1000, Qstarz International, Tai-
wan) secured on top of their clothing or gear to record the distance, speed and elevation for the
entire event. The GPSND data were retrieved using the proprietary software, and exported in
columnar format for data analysis. The variables contained in the files were date and time (uni-
versal time constant, UTC), position (latitude, longitude, elevation) and speed (via Doppler
shift).
From the successive geographical coordinates recorded, we calculated the distance between
each data point using the Vincenty great-circle formulae [26], which are spherical trigonome-
try functions calculating the shortest distance between spatial coordinates at the surface of an
ellipsoid (earth dimensions used were the WGS-84 GPS model of reference with equatorial
radius  6,378.137 km, polar radius  6,356.752 314 245 km and flattening f  1=298:257223563).
An automated calculation of the Vincenty formulae can be obtained from an internet-based
utility (GPS Visualizer; www.gpsvisualizer.com), which we compared to our preliminary mea-
sures for 10 data sets and found to be in exact agreement (r = 1.00, p < .001). Therefore, we
used the automated formulae as a simple and generalisable procedure to obtain point-to-point
distances. Point-to-point speed was subsequently calculated using the ratio of the point-to-
point distances and of the GPS epoch time (one data point every 5 s in the current study).
To reduce the effect of signal errors in the analysis, a two-step treatment procedure was
then applied to the data. Preliminary calculations revealed that GPSND devices did not discrim-
inate speeds slower than 1 km  h−1 (0.28 m  s−1 or 1.39 m in 5 s) based on the typical error in
speed measured in a static position (drift, when a device will record speed values due to the
non geo-synchronous nature of the constellation of satellites). For the high end of the speed
range, it was considered that speeds higher than 20 km  h−1 (5.56 m  s−1 or 27.8 m in 5 s) were
not expected during a long UM and were likely due to signal jamming (which occurs mainly
when the signal from a satellite becomes too weak and forces the ground based receiver to pair
to another satellite). These erroneous distance and speed data were first assigned a value of
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zero, and all speed values were then smoothed in order to further increase the signal-to-noise
ratio. For smoothing, a 3, 9, and 15-pt weighted averages were graphically compared. The 9-pt
weighted average was considered satisfactory as it provided a balanced sensitivity to individual
observations of slow and high speeds. This two-step procedure facilitated the reduction of the
effect of signal drift and jamming, which both artificially increase the distance and speed mea-
sured using GPS devices, while remaining sensitive to periods of null speed values (Fig 1).
GPS-based elevation is considered to be inaccurate [27] due to differences between the
model of reference of the earth used for calculations and the actual shape of the earth, and
therefore an independent source was sought in order to increase the quality of the elevation
data. Due to the size of a typical file containing UM data at the relatively high recording rates
of GPS devices (12 h of data recording at 5 s equals 8640 observations), a digital elevation
model (DEM) was used in order to automate the treatment procedure. Elevation values were
reconstructed from the geographical positions using a DEM (in this study, the NASA SRTM3)
available from the same online utility (GPS Visualizer; gpsvisualizer.com). Data was smoothed
using a 9-pt weighted average. The gradient between two consecutive data points was then
Fig 1. Total time stopped. Total time stopped for each participant, including the relative position of official event checkpoints. “CP” are official event
checkpoints. CP 1 and 6: distance ~7 km (outbound) and ~69 km (inbound), altitude ~1015 m. CP 2: distance ~ 19 km, altitude ~815 m. CP 3 and 5: distance
~29 km (outbound) and ~52 km (inbound), elevation ~1160 m. CP 4: distance ~36.5 km, elevation ~1699 m. CP 7: distance ~93 km, elevation ~1263 m.
doi:10.1371/journal.pone.0145482.g001
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calculated as the change in elevation divided by the horizontal distance between two points
(the amount of vertical gain as a function of horizontal distance).
Measures of psycho-physiological load.
HR was measured continuously using a chest
strap and watch (RS800, RS800cx, RS400, or S810, Polar Electro, Kempele, Finland). Each
watch was set to record one data point every 15 s in order to optimise battery life and memory.
RPE scores were recorded using a portable voice recorder (ICD PX312, Sony, Tokyo,
Japan). We instructed the participants to record the time of observation, a general (RPEGEN)
and a local (muscular) RPE focused on the sensation of fatigue or pain of the knee extensor
muscles and excluding any psychological/psychic contribution to exertion (RPEKE) using
Borg’s 10 point category-ratio scale (CR-10) that the subjects carried over the entire race.
Variables and statistical analyses
We reported all variables as a function of total event distance (Figs 1 and 2) or duration (Figs 3
and 4) in order to represent all participants on a comparable scale (where 100% represents the
distance or duration at event completion for every participant). In order to ensure sufficient
data was used at each stage, data for each dependent variable was computed for every 10%
Fig 2. UTMB course outline and elevation profile. (A) Mean (±SD) group elevation data from the Digital Elevation Model values associated with measures
of geographical positions, and (B) group speed data associated with measures of geographical positions (CP, refer to legend of Fig 1 for description).
doi:10.1371/journal.pone.0145482.g002
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Fig 3. Dynamics of speed. Mean (±SD) group speed as a function of event duration in (A) level, (B) negative
(C) and positive gradients, respectively. Symbols denote significant differences to (*) 10%, ($) 20%, (#) 30%,
(θ) 40%, (&) 50%, (€) 60%, (ϕ) 70%, (Ω) 90% and (£) 100% of total event duration, at p < .05.
doi:10.1371/journal.pone.0145482.g003
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section of the total duration of the event. All statistical analyses were performed using SPSS
(version 21, IBM Corporation, Armonk NY, USA). Data are reported as mean ± SD, and the
level of significance was set at p < .05.
Dynamics of exercise intensity.
We determined the relationship between the variations of
speed and changes in elevation for the entire event using a quadratic regression of individual
speed and gradient. After confirming the assumption of the equality of variances were met, the
effect of exercise duration on speed at each gradient (level, negative and positive inclines) HR,
RPEGEN and RPEKE were determined using a multivariate ANOVA (MANOVA). Post-hoc
one-way, repeated measures ANOVAs with a Fisher’s LSD post-hoc test were used in order to
locate the differences in means for speed at all gradients. Due to incomplete data sets, the
dynamics of HR, RPEGEN and RPEKE were assessed using a one-way ANOVA on ranks (Krus-
kal-Wallis test) with a Student-Newman-Keuls post-hoc test.
As HR does not adjust to exercise intensity instantaneously, it is not possible to treat HR
data in the same way as speed. Instead, we investigated the dynamics of exercise intensity using
Fig 4. Dynamics of psycho-physiological load. (A) Mean (±SD) group heart rate (HR) as a function of total event duration, and (B) general and muscular
(knee extensors) ratings of perceived exertion (RPE) as a function of total event duration. Symbols denote significant differences to (*) 10% and ($) 20%, at p
< .05. Bpm: beats per minute. CR-10: 10-point category-ratio Borg scale.
doi:10.1371/journal.pone.0145482.g004
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sections of sustained uphill running, in order to maximise the contribution of metabolic work
compared to passive energy recovery (since the ability to perform eccentric contractions
decreases with the development of peripheral fatigue, refer to [15, 16]) in total work rate [28].
We identified 6 sections of sustained uphill combining at least 300 m of vertical gain at a 10%
average gradient (refer to Fig 2; the section from CP2 to CP3 was only 4.9% gradient and was
not included in the analysis). The effect of hill order (1 to 6) on HR was assessed using a
repeated-measures, one-way ANOVA and a Bonferroni post-hoc test.
However, during uphill running, overground speed is a less relevant metrics than the
amount of vertical gain (in m  h−1) to characterise exercise intensity, which is also dependent
on the gradient of the slope [28]. Therefore, to determine whether any drift in HR existed inde-
pendent of exercise intensity, we used a 1-factor principal component analysis to reduce the
dimension of these three variables (termed SVG for speed, vertical gain, gradient). After con-
firming the assumptions of normality using a Shapiro-Wilk test, the relationship between aver-
age HR and SVG in the 6 main ascents was assessed using Pearson’s product-moment
correlation. We further tested the effect of hill order (1–6) on HR using a repeated-measures
one-way ANCOVA, and a Bonferroni post-hoc test, using SVG as a covariate of HR.
Factors of performance. The relationship between final performance (using the individual
average speed, as it allows comparison of various UM distances) and 1) the variability of speed
(using the coefficient of variation of point-to-point speed values), 2) the magnitude of speed
loss (using the slope of the linear regression of speed over the entire event) and 3) the total time
stopped (assumed to correspond to resting, eating, clothing and gear change, toilet, other) were
tested using Pearson’s product-moment correlation after confirming assumptions of normality
were met (Shapiro-Wilk test). The 95% confidence intervals (CI) of the correlations were calcu-
lated using the unstandardised beta-weights of the linear regression of the Z-scores of each
variable.
Results
The following data sets were retrieved: 15 complete GPS traces, 9 HR data sets with at least
80% of event data (due to equipment issues and loss of signal), and 6 RPE data sets with at least
80% of data. The average distance for the event, calculated using filtered point-to-point ortho-
drome, was 105.6 ± 1.8 km (range: 103.0–107.5 km), and the total elevation gain and loss was
5871 ± 239 m. The 15 participants completed the event in 18.3 ± 3.0 h (range: 13.8–23.9 h) at
an average speed of 5.88 ± 0.9 km  h−1 (range: 4.58–7.58 km  h−1) and an average HR of
132 ± 10 bpm (range: 112–146 bpm). The mean group elevation and speed profiles are repre-
sented in Fig 2. There was a significant quadratic correlation between point-to-point speed and
elevation changes (linear factors model: r = .49, R2 = .24, F-linear = 316.76, p < .001; quadratic
factors model: r = .52, R2 = .27, F-change = 40.99, p < .001; Total factors: F-total = 185.22, p <
.001).
Dynamics of exercise intensity
The changes in speed as a function of total event duration are presented in Fig 3 (panels A, B
and C for level, negative and positive gradients, respectively). Positive pacing was observed on
level (speed loss: -2.91 ± 2.15%), negative (-2.61 ± 0.92%) and positive gradients (-1.31 ±
0.84%). The MANOVA indicated a difference in speed between the speed at level gradient, and
at the negative and positive gradients (p < .001). Speed was not significantly different between
the negative and positive gradients (p = .10). Post-hoc ANOVAs indicated that speed decreased
from 10% to all sections up to 70% of total duration at level inclines (except 60%, where it sig-
nificantly increased compared to 40% and 50%), and that speed increased at 90% compared to
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40%, 50%, 70%, 80%, and increased at 100% compared to all sections between 30% and 90%
(Fig 3A). For negative gradients, speed decreased from 10% to all other sections, and from 20%
and 30% to 70% and 90%. Speed then increased between 90% and 100% (Fig 3B). For positive
gradients, speed decreased at all sections until 80% of event duration except 60%. Speed
increased at 100% compared to all observations between 40% and 90% (Fig 3C).
Mean group HR averaged 132.6 ± 13.6 bpm, and decreased -34.2 ± 17.2 bpm over the UM.
HR decreased from 10% to 70%, 80% and 90%, but not 100% (Fig 4A). Mean group RPEGEN
and RPEKE averaged 4.3 ± 1.1 and 3.8 ± 1.3, respectively, and increased 6.9 ± 1.4 and 6.9 ± 2.2,
respectively during the event (Fig 4B). Mean group RPEGEN increased significantly from 10%
to 70%, 80%, 90% and 100%, and from 20% to 70%, 80% and 90% (Fig 4B). Mean group
RPEKE increased significantly from 10% to 70%, 80%, 90% and 100% (Fig 4B). Mean group
RPEGEN and RPEKE were significantly positively correlated (r = .980, p < .001).
There was no significant change in HR as a function of time in the 6 main climbs, as evi-
denced in the ANOVA (using HR alone) as well as in the ANCOVA (using HR and SVG as
covariates). HR was significantly positively correlated with SVG on all uphill sections (r = .663,
p < .001).
Factors of performance
Performance (average speed) was negatively correlated with total time stopped (r = -.772, p =
.001; 95% CI = -1.15, -0.39) and positively correlated with the magnitude of speed loss (r =
.540, p = .038; 95% CI = -1.04, -0.03) but not with the variability of speed (r = -.475, p = .073;
95% CI = -1.00, 0.05).
Discussion
There were three main findings in this study: (i) speed decreased overall at all inclines during
the event (positive pacing), but increased significantly in the last section at all inclines; (ii)
faster participants stopped less and decreased their speed more than slower participants
throughout the event; and (iii) the measures of psycho-physiological load indicated that despite
evidence of a reverse HR drift and increased RPE throughout the event, HR in sustained climbs
did not change, and maximal RPE values were relatively low, suggesting that participants
actively regulated (paced) their physiological and psychological load to complete the event and
avoid premature exhaustion.
During self-paced running exercise, the optimal locomotor speed is adjusted as a function
of environmental factors [10]. One of the main factors influencing speed selection is gradient,
where additional energy is required to run at high negative (to generate braking forces limiting
downward acceleration) and positive (to elevate a runner’s mass against gravity) gradients
compared to level or slightly negative gradients [28, 29]. The curvilinear relationship between
locomotor speed and gradient measured in this study (indicating that speed varies directly as a
function of gradient; Fig 2) had so far been assumed to exist [11] but not measured in UM
events due to limitations in the ability to measure speed and gradient of individual participants.
This relationship was measured despite the overall low speeds (especially at positive gradients)
and positive pacing strategies characteristic of mountain UM events, which could both have
affected the relationship.
The overall decrease in speed during the event indicates that the study participants used
positive pacing strategies (progressively slowing down), in agreement with previous research
findings specific to UM running [10–13, 25, 30]. Speed losses on level gradients were more pro-
nounced (the slope of the linear regression was greater, and the MANOVA indicated that sig-
nificant differences existed with both negative and positive gradients) and occurred at an
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earlier point in the event (reaching a minimum at 70% of event duration) compared to both
negative and positive gradients (minimum at 90% of event duration). The increase in speed at
60% of total event duration on level gradients could not be explained using the data we col-
lected. This section corresponded to a section of the course in the main valley of Chamonix,
and therefore we hypothesise that the terrain and surface were conducive for running (in con-
trast to walking), and that the course was accessible by spectators and crew members (which
could have provided support and motivation). The existence of an increase in speed at the end
of the event at all inclines in the current study is unique compared to other studies of long UM
on level ground [13]. Two combined factors could, in part, explain the presence of an increase
in speed in the last section: first, the mainly descending profile at the end of the event (after hill
6, refer to Fig 2), and second, a phenomenon termed cardio-pulmonary [7] or speed reserve [1]
predicting the increase of exercise intensity at the end of self-paced exercises. In this mountain
UM event, the marked elevation gain and loss may have favoured the use of conservative pac-
ing strategies decreasing the risk of premature exhaustion in anticipation of difficulties, when
compared to level or hilly UM events [1]. Together, these findings are indicative of mixed pac-
ing strategies, which is a subset of the three main types of pacing associating positive pacing for
the main part of the event and an increase in speed for the final section of the event.
The significant relationship between performance and speed loss in this project contrasts
with findings in other UM studies [11, 13], where participants with a higher performance level
had greater speed losses. Future studies are required to further investigate this unexpected
result, which could potentially originate from faster participants pacing their race less conser-
vatively from the start aiming to decrease overall time, compared to slower participants, for
whom finishing the event could have been the main goal. Future studies should investigate a
priori pacing strategies, and performance goals and attitudes toward risk taking as a function
of performance level. The inverse correlation between performance and time stopped was
novel in UM running, and extends findings in an ultra-endurance cycling event [31] where
faster athletes spent less time napping. While this result is expected in shorter duration events,
it is commonly believed among participants of ultra-long duration events that a bout of passive
rest can be beneficial to final performance. This finding indicates the marked differences in the
physiological demands of an UM event as a function of performance level due to the differ-
ences in time spent on course, where passive rest may be a relatively important feature of pac-
ing strategies for slower participants. Future studies are also required to determine whether a
threshold exists as a function of performance level in longer (> 300 km) UM events.
We reported that HR decreased from 10% to 70%, 80% and 90%, but not 100% of total
event duration. Although we could not determine the relationship between the dynamics of
speed and HR at each gradient over the entire event (due to the relatively low accuracy of
GPSND devices), it is likely that the variations of exercise intensity (speed) explained most of
the observed changes in HR (reverse HR drift), as this well-described physiological response is
typical of ultra-long duration exercise [1, 25]. Still, we reported that HR in sustained uphill sec-
tions distributed throughout the event (hills 1–6) did not change, including with the use of HR
scaled for exercise intensity (using the factorial component SVG), which indicates that bouts of
sustained uphill running may be regulated differentially given the high risk of exhaustion they
present.
The dissociated RPE (RPEGEN and RPEKE) had similar dynamics (the two measures of RPE
increased from 10% to all sections between 70% and 100% of event duration), and were highly
correlated, suggesting either (i) that the dynamics and magnitude of change of RPEGEN and
RPEKE are similar throughout the course of an UM (which would indicate that the measure of
one variable is sufficient), or (ii) that the two scales measured the same underlying construct (and
therefore, that RPE may be a general, but not location-specific indicator of psycho-physiological
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load). Future research is required to investigate the relative contribution of the perceived exertion
specific to a muscle group (knee extensors, plantar flexors) or physiological system (respiratory,
gastric) to the general RPE. The role of pain will also need to be investigated as it may alter the
perception of exertion and fatigue [1], and will be heightened following sustained downhill loco-
motion [32]. Further, although the highest values in RPE (both RPEGEN and RPEKE) were
recorded in the last section of the event, the maximal values were relatively low (6.3 and 6.7
group mean for RPEGEN and RPEKE, respectively). The relatively low maximum values distin-
guishes it from other studies in shorter UM [23], and could contribute to identify the protective
nature of fatigue in preventing the participants from attaining maximal values at the end of the
event [1, 3]. As such, the combined results of the dynamics of pacing and psycho-physiological
load may indicate that participants relied on relatively conservative pacing strategies and used a
functional reserve [1, 7] permitting an increase in speed observed in the last section of the event.
These findings are consistent with theoretical perspectives of a complex protective system regu-
lating work rate based on the interaction of the instantaneous and anticipated psycho-physiologi-
cal state of a participant, and of the environmental conditions in which the exercise is performed
[1–3]. Still, some questions remain regarding the regulation of speed at different gradients, since
some of our results (the pacing on level gradients differed to both downhill and uphill gradients)
were not expected. Previous research hypothesised that the changes in stride patterns were
altered differentially following a level [33] and a mountain UM [32] due to the increased reliance
on eccentric contractions typical of downhill running. Recently, Vernillo and colleagues [34]
measured an increase in the energy cost of running specifically at mild downhill (-5%) gradients
but not on level or uphill (5%) gradients as a function of the development of fatigue in an UM
event. Therefore, further studies are required to investigate the simultaneous variations of pacing
and psycho-physiological load as a function of gradient during an UM event with a greater
resolution.
Limitations
In this study, the main limitation was related to the resolution of measurements made using
non-differential GPS devices. We optimised the calculations to be able to define broad catego-
ries of total distance and duration (10% sections) and gradients (negative [-100 to -2.5%], level
[-2.5 to 2.5%], positive [2.5 to 100%]). Still, the temporal resolution of observations (10% of
event duration) was comparable to other UM studies [13], and was selected as a robust
approach ensuring a sufficient number of observations was available for each variable. Unfore-
seen issues with recording equipment reduced the numbers of data sets in HR, and therefore
limited the findings as we were unable to establish the relationship between HR and pacing at
each stage. In future studies, the use of GPS devices with higher temporal and spatial resolution
could also lead to the development of various indices of running performances in conditions of
trail running, such as the rate of ascent as a function of gradient which would be useful for ath-
letes and coaches, and in scientific research for the analysis of performance.
Conclusion
During a mountain UM, speed decreased over the first 90% of the event and at all gradients,
and speed increased in the last 10% section of the event. A greater speed loss and less total time
stopped were the factors associated with increased total performance. HR decreased overall,
but remained constant in the main ascents of the race, indicating the potential effect of conser-
vative pacing strategies to avoid premature exertion. Perceived exertion increased throughout
the event, but without reaching maximal values. These observations are supported by
Psycho-Physiological Load during a Mountain Ultramarathon
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December 21, 2015
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theoretical perspectives of a complex protective system regulating motor drive in anticipation
of remaining exercise duration and changes in elevation.
Acknowledgments
The authors would like to thank all the participants for their effort and valuable time. We
would also like to thank the organisers of the UTMB and other researchers who have permitted
this research effort. In particular, the authors would like to thank Dr Roger Ouillon and Dr
Pascal Edouard for conducting medical screenings, Mr Sylvain Battault for providing the voice
recording equipment, and Dr Léonard Féasson for organising and managing the testing facility.
We also thank Ms Benjie Bartos and Mr Dylan Astley for proof-reading and editing the
manuscript.
Author Contributions
Conceived and designed the experiments: HK CS. Performed the experiments: HK GM. Ana-
lyzed the data: HK CS. Wrote the paper: HK CS GM.
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The Dynamics of Speed Selection and Psycho-Physiological Load during a Mountain Ultramarathon.
Kerhervé, Hugo A,Millet, Guillaume Y,Solomon, Colin