Increased oxygen uptake in
well-trained runners during uphill
high intensity running intervals: A
randomized crossover testing
Steffen Held1,2, Ludwig Rappelt 1,3, René Giesen1,
Tim Wiedenmann1, Jan-Philip Deutsch1, Pamela Wicker4* and
Lars Donath 1
1Department of Intervention Research in Exercise Training, German Sport University Cologne, Cologne,
Germany, 2Department of Fitness and Health, IST University of Applied Sciences, Duesseldorf, Germany,
3Department of Movement and Training Science, University of Wuppertal, Wuppertal, Germany,
4Department of Sports Science, Bielefeld University, Bielefeld, Germany
The time spent above 90% of maximal oxygen uptake ( _VO2max) during high-
intensity interval training (HIIT) sessions is intended to be maximized to improve
_VO2max. Since uphill running serves as a promising means to increase metabolic
cost, we compared even and moderately inclined running in terms of time ≥90%
_VO2max and its corresponding physiological surrogates. Seventeen well-trained
runners (8 females & 9 males; 25.8 ± 6.8yrs; 1.75 ± 0.08m; 63.2 ± 8.4kg; _VO2max:
63.3 ± 4.2 ml/min/kg) randomly completed both a horizontal (1% incline) and
uphill (8% incline) HIIT protocol (4-times 5min, with 90s rest). Mean oxygen uptake
( _VO2mean), peak oxygen uptake ( _VO2peak), lactate, heart rate (HR), and perceived
exertion (RPE) were measured. Uphill HIIT revealed higher (p ≤ 0.012; partial eta-
squared (pes) ≥ 0.351) _VO2mean (uphill: 3.3 ± 0.6 vs. horizontal: 3.2 ± 0.5 L/min;
standardized mean difference (SMD) = 0.15), _VO2peak (uphill: 4.0 ± 0.7 vs.
horizontal: 3.8 ± 0.7 L/min; SMD = 0.19), and accumulated time ≥90% _VO2max
(uphill: 9.1 ± 4.6 vs. horizontal: 6.4 ± 4.0 min; SMD = 0.62) compared to even HIIT.
Lactate, HR, and RPE responses did not show mode*time rANOVA interaction
effects (p ≥ 0.097; pes ≤0.14). Compared to horizontal HIIT, moderate uphill HIIT
revealed higher fractions of _VO2max at comparable perceived efforts, heartrate
and lactate response. Therefore, moderate uphill HiiT notably increased time
spent above 90% _VO2max.
KEYWORDS
incline, intervals, performance, injury, running
1 Introduction
High level endurance training requires large training volumes (Seiler, 2010). In elite
athletes, commonly, a high proportion of this training volume is performed at low training
intensities (Seiler, 2010). However, to achieve an optimal metabolic training stimulus on
maximal oxygen uptake ( _VO2max), it has been recommended to perform a certain amount
of high-intensity interval training (HIIT). This recommendation is especially relevant for
well-trained endurance athletes (Laursen and Jenkins, 2002). Thereby, HIIT involves
repeated bouts of high-intensity exercise interspersed with recovery periods (Laursen
and Jenkins, 2002; Buchheit and Laursen, 2013). This training method mainly focuses
OPEN ACCESS
EDITED BY
Andrea Nicolò,
Foro Italico University of Rome, Italy
REVIEWED BY
Marcel Lemire,
University of Upper Alsace, France
Stéphane P Dufour,
Université de Strasbourg, France
*CORRESPONDENCE
Pamela Wicker,
pamela.wicker@uni-bielefeld.de
SPECIALTY SECTION
This article was submitted to Exercise
Physiology,
a section of the journal
Frontiers in Physiology
RECEIVED 06 December 2022
ACCEPTED 06 February 2023
PUBLISHED 16 February 2023
CITATION
Held S, Rappelt L, Giesen R,
Wiedenmann T, Deutsch J-P, Wicker P
and Donath L (2023), Increased oxygen
uptake in well-trained runners during
uphill high intensity running intervals: A
randomized crossover testing.
Front. Physiol. 14:1117314.
doi: 10.3389/fphys.2023.1117314
COPYRIGHT
© 2023 Held, Rappelt, Giesen,
Wiedenmann, Deutsch, Wicker and
Donath. This is an open-access article
distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or
reproduction in other forums is
permitted, provided the original author(s)
and the copyright owner(s) are credited
and that the original publication in this
journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
Frontiers in Physiology
frontiersin.org
01
TYPE Original Research
PUBLISHED 16 February 2023
DOI 10.3389/fphys.2023.1117314
on _VO2max improvements (Midgley et al., 2006; Buchheit and
Laursen, 2013), as the upper limit to the aerobic metabolism and
a key determinant of endurance performance (Joyner and Coyle,
2008). In order to improve _VO2max in highly trained endurance
athletes, it has been suggested that a prolonged time at intensities
corresponding to a high percentage of maximal oxygen uptake is
important (Wenger and Bell, 1986; Midgley et al., 2006). Therefore,
the quality of a HIIT session can be deﬁned by mean oxygen uptake
( _VO2mean) or accumulated training time ≥90% _VO2max (Midgley
et al., 2006; Turnes et al., 2016). This adaptational potential has been
attributed
to
the
large
metabolic
stimulus
for
myocardial
morphological adaptations that increases maximal cardiac stroke
volume and also increased peripheral skeletal muscle adaptations
(Midgley et al., 2006).
In both prospective and cohort studies, a high weekly running
volume has been associated with running-related injuries (Macera et al.,
1989; Walter et al., 1989). Although the causes of running injuries are
multifactorial, in this context, the runner’s interaction with the ground
and the resulting reaction force has been considered to be one risk factor
(Zadpoor and Nikooyan, 2011; Daoud et al., 2012). Thus, higher
loading rates were associated with increased risk of sustaining an
injury (Crowell and Davis, 2011; Futrell et al., 2018). More recently,
however, in a prospective case control-study in recreational runners, the
vertical impact peak and loading rate were not associated with a higher
injury rate (Malisoux et al., 2022). Furthermore, in collegiate cross
country runners, an higher occurrence rate of bone stress injuries has
been linked to a higher step rate, but not higher ground reaction forces
(Kliethermes et al., 2021). Nevertheless, besides adequate periodization
and polarization models in endurance sports, reducing loading rates is
still recommended as an effective means to reduce the risk of developing
running injuries (Bowser et al., 2018). In this context, increasing the
slope might lead to a signiﬁcantly lower vertical loading rate during
uphill running compared to ﬂat level running (Gottschall and Kram,
2005; Lemire et al., 2022a). Also, increasing the slope from ﬂat level
running to 7% was found to reduce ﬂight time and increase ﬂoor
contact time, in turn resulting in highly signiﬁcant increases in step
frequency (Padulo et al., 2013). Apart from this, previous research
revealed an increased energy cost via uphill running compared to
horizontal running (Lemire et al., 2022b). Additionally, when running
at the same velocity, uphill running is more metabolically demanding
than horizontal running (Minetti et al., 2002; Vernillo et al., 2017),
hence allowing a similar training stimulus at a lower running velocity.
Against this background, this randomized crossover testing
examined the peak
_VO2, mean
_VO2 and accumulated time
spent ≥90% _VO2max during moderate slope uphill compared to
horizontal HIIT running. We assumed similar
_VO2 data and
reduced running speed during uphill HIIT. The ﬁndings of the
present study might be impactful for designing and integrating HIIT
session within polarization models and in terms of training variations to
minimize injury risks in runners with high training volumes.
2 Materials and methods
2.1 Participants
G*Power (Version 3.1.9.6) was employed to perform an a priori
power analysis. Based on increased metabolic costs via uphill
running (Minetti et al., 1994; 2002; Vernillo et al., 2017)
moderate effect sizes (standard mean differences (SMD) = 0.60)
between horizontal and uphill HIIT running were assumed. A
sample size of n = 13 was determined, using the following
statistical indicators (α = 0.05; study power (1-β-error) = 0.95;
one tail). Assuming moderate dropouts (15%–20%), n = 17 well-
trained runners were enrolled in this acute randomized controlled
crossover testing. These participants consisted of 8 female (age:
24.4 ± 3.7 yrs; height: 1.69 ± 0.07 m; body mass: 56.6 ± 5.8 kg; body
fat: 14.6 ± 4.8%; _VO2max: 60.5 ± 2.3 ml/min/kg; running volume:
58.1 ± 18.5 km/week) and 9 male (age: 27.1 ± 8.8 yrs; height: 1.80 ±
0.07 m; body mass: 69.1 ± 5.6 kg; body fat: 9.7 ± 3.1%; _VO2max:
65.7 ± 4.1 ml/min/kg; running volume: 65.0 ± 20.3 km/week)
trained runners. Inclusion criteria were running experience of at
least 3 years, running volume of at least 40 km/week, and no medical
condition that potentially impedes the completion of testing and
training. The study was approved by the local ethical committee
(153/2022), fulﬁlled the international ethical standards, and all
participants signed an informed written consent prior to the start
of the study.
2.2 Testing procedures
The measurements were conducted within four lab visits over
3 weeks for each participant. Thereby, horizontal and uphill
_VO2max tests (lab visit 1 & 2) as well as horizontal and uphill
HIIT protocols (lab visit 3 & 4) were performed. Adapted from
previous research (Rønnestad et al., 2019; 2022), the HIIT protocol
consisted of four 5-min intervals with 90 s passive rest in between.
During HIIT sessions, participants were instructed to run at their
maximal sustainable intensity during all four interval bouts
(isoeffort) (Seiler and Hetlelid, 2005). Therefore, participants
could
increase
or
decrease
the
velocity
individually.
All
measurements were conducted on a motorized treadmill (PPS
Med treadmill, Woodway, Waukesha, USA), with the horizontal
conditions being performed at 1% incline and the uphill conditions
being performed at 8% incline. To avoid sequencing effects, the ﬁrst
two and the last two lab visits were individually performed in a
randomized order. At least 96 h rest was ensured between each lab
visit. Participants were further instructed to avoid any strenuous
exercise 2 days before each testing session. To control for potential
circadian effects on performance, all measurements were conducted
at similar day times for each participant. A standardized 15-min
warm-up (easy running, including knee lift, heel lift, external
rotation
hip,
internal
rotation
hip,
10
lunges
alternating,
10 squats, individual dynamic stretching) was performed prior to
each lab session.
Spirometric data during all lab visits were collected using a
breath-by-breath system (Zan 600 Oxi USB, Zan Messgeräte,
Oberthulba, Germany). This spirometric system was calibrated
prior
to
each
test,
following
the
manufacturer’s
recommendations. To determine uphill and horizontal-running
_VO2max, an incremental ramp testing protocol was performed at
horizontal (1% incline) and uphill (8% incline) conditions (lab visit
1 & 2). Adapted from previous research with similar _VO2max values
(Baumgartner et al., 2021), the initial velocity for both ramp tests
was set based on prior running experience and estimated 10 km race
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time for each participant individually at 2, 2.5, or 3 m/s. The ramp
protocol then consisted of 0.2% increases every 30 s until the
participant
reached
exhaustion
(Midgley
et
al.,
2007).
All
participants were verbally encouraged and motivated in the same
way towards the end of each test. The highest consecutive oxygen
uptake values within 30 s during the ﬁnal part of the ramp tests were
considered as _VO2max. For both conditions, _VO2max and objective
exhaustion were veriﬁed for each participant following the
corresponding criteria (Midgley et al., 2007). All participants
fulﬁlled these objective exhaustion criteria (i.e., at least 4 out of
6 criteria). Adapted from previous research, the quality of both HIIT
sessions were deﬁned by mean _VO2 and accumulated training
time ≥90% _VO2max (Time90) (Midgley et al., 2006; Thevenet
et al., 2007; Turnes et al., 2016). Since both HIIT sessions were
time matched with the same work to rest ratio, mean _VO2 and
Time90 were determined based on the entire training session
(interval with pauses). Furthermore, to determine Time90, the
entire training session (interval with pauses) was normalized to
seconds, subsequently seconds with _VO2 value ≥ _VO2max were
summed up. Thereby, the highest _VO2max value of the horizontal or
incline ramp test was used as reference values. Furthermore, peak
oxygen consumption (highest oxygen uptake during the intervals
averaged over 30 s; _VO2peak) during both HIIT protocols was
additionally considered. Apart from this, total respiration per
minute
(minute
volume),
respiratory
frequency
(breath
frequency), and tidal volume were also used for further data
analysis. In addition, capillary blood samples were taken from
the earlobe of the participants for lactate analysis (EBIOplus;
EKF Diagnostic Sales, Magdeburg, Germany), heart rate (HR)
was measured using a heart rate strap (Polar, Kempele, Finland),
and perceived exertion levels were assessed based on RPE (CR-
10 scale) (Foster et al., 2001) prior to the ﬁrst interval and
immediately after each running interval.
2.3 Statistics
Data are presented as means ± standard deviations. Normal
distribution was initially tested using Shapiro-Wilk tests (p ≥ 0.1).
Variance homogeneity was visually conﬁrmed via plotting sampled
residuals vs. theoretical (ideal) residuals (Kozak and Piepho, 2018).
Sphericity was veriﬁed via Mauchly´s tests. To examine mode
differences
(horizontal
vs.
uphill)
for
the
respective
outcome
measures ( _VO2,
_VO2peak,
_VO2max, Time 90, minute volume,
breath frequency, and tidal volume), numerous separate two-way
(mode: horizontal vs. uphill) repeated measurement analysis of
variances (rANOVA) were conducted. 2 (mode: horizontal vs.
uphill) × 4 (time: pre vs. interval 1 vs. interval 2 vs. interval 3 vs.
interval 4) rANOVAs were calculated for lactate, HR, and RPE, and
running velocity data. rANOVA effect sizes are given as partial eta
squared (pes) with ≥0.01, ≥0.06, and ≥0.14 indicating small, moderate,
and large effects, respectively (Cohen, 1988). In case of signiﬁcant
mode × time interaction effects, Bonferroni post hoc tests were
subsequently
computed.
For
pairwise
effect
size
comparison,
standard mean differences (SMD) were additionally calculated as the
differences between means divided by the pooled standard deviations
(trivial: SMD <0.2; small: 0.2 ≤ SMD <0.5; moderate: 0.5 ≤ SMD <0.8;
large SMD ≥0.8) (Cohen, 1988). Furthermore, the smallest worthwhile
change was calculated as 30% of baseline standard deviation (Hopkins,
2004). Pearson correlation coefﬁcients were calculated in order to deﬁne
the relationships of the measured variables. A correlation coefﬁcient of |
r | ≈ 0.30 is interpreted as low/weak correlation, | r | ≈ 0.50 is interpreted
as mean/moderate correlation and | r | ≈ 0.80 is interpreted as large/
strong correlation (Cohen, 1988). Statistical analyses were conducted
using R (version 4.0.5) and RStudio (version 1.4.1106) software.
3 Results
3.1 Incremental ramp test
No signiﬁcant differences (p = 0.100; pes = 0.100; mean
difference (MD) = 0.2 ± 0.5 L/min; SMD = 0.28) were found
between horizontal (3.9 ± 0.7 L/min) and uphill _VO2max (4.1 ±
0.7 L/min) during the incremental ramp tests.
3.2 HIIT sessions
rANOVA revealed signiﬁcant effects (p ≤ 0.012; pes ≥0.351)
regarding
_VO2,
_VO2peak,
Time90,
minute
volume,
breath
frequency, and tidal volume (Figure 1). Thereby, uphill HIIT
showed higher values than horizontal HIIT for _VO2mean (3.3 ±
0.6 vs. 3.2 ± 0.5 L/min; MD = 0.1 ± 0.1 L/min; SMD = 0.15),
_VO2peak (4.0 ± 0.7 vs. 3.8 ± 0.7 L/min; MD = 0.1 ± 0.2 L/min;
SMD = 0.19), Time90 (9.1 ± 4.6 vs. 6.4 ± 4.0 min; MD = 2.7 ± 2.7 L/
min; SMD = 0.62), and tidal volume (2144 ± 511 vs. 2061 ± 502 ml;
MD = 83 ± 117 ml; SMD = 0.16). In contrast, uphill HIIT revealed
lower values than horizontal HIIT for minute volume (94.3 ± 15.1 vs.
101.2 ± 17.3 L/min; MD = 6.9 ± 8.4 L/min; SMD = 0.43) and breath
frequency (44.9 ± 6.0 vs. 50.5 ± 9.2 breaths/min, MD = 5.6 ±
5.9 breaths/min; SMD = 0.73). Furthermore, only for Time90, breath
FIGURE 1
Mean difference (MD ± standard deviation) between horizontal
and uphill high intensity training protocols for mean oxygen
consumption ( _VO2), peak oxygen consumption ( _VO2peak), and
accumulated time above 90% of maximal oxygen consumption
(Time90). Smallest worthwhile change (SWC) boundaries are marked
in grey. Signiﬁcance levels (p) and pairwise effect sizes as standard
mean differences (SMD) are presented.
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frequency and minute volume, the differences between conditions
exceeded
the
smallest
worthwhile
change.
Furthermore,
Time90 revealed high (r = 0.82) and signiﬁcant (p < 0.001)
correlations between horizontal and uphill HIIT.
No signiﬁcant mode × time rANOVA interaction effects (p ≥
0.097; pes ≤0.14) for lactate, HR, RPE and running velocity were
found (Figure 2). Nevertheless, running velocity revealed signiﬁcant
time effects (p ≤ 0.001). Subsequently performed post hoc tests (p ≤
0.001; SMD ≥3.53) revealed higher running velocity during
horizontal HIIT (4.47 ± 0.33 to 4.51 ± 0.35 m/s) compared to
uphill HIIT (3.17 ± 0.18 to 3.18 ± 0.21 m/s) during all intervals.
4 Discussion
To the best of our knowledge, this is the ﬁrst acute
randomized controlled crossover study that examined
_VO2,
lactate, HR, and RPE response of time- and effort-matched
horizontal vs. uphill HIIT running in well-trained runners.
Our key ﬁndings were increased mean
_VO2,
_VO2peak, and
accumulated training time ≥90%
_VO2max via uphill HIIT
compared to horizontal HIIT. In contrast, lactate, HR, and
RPE revealed no signiﬁcant differences between horizontal and
uphill HIIT protocols. Furthermore, horizontal and uphill ramp
tests yielded similar _VO2max values.
A higher acute oxygen consumption during uphill running is
commonly explained by the fact that the use of elastic energy may be
compromised, so that in turn more mechanical energy (i.e., greater
concentric muscle activity) needs to be generated, in order to lift the
body’s center of gravity upward and subsequently overcome the
slope (Snyder and Farley, 2011). Thus, in the present study, uphill
running during a HIIT session notably increased the mean
time ≥90% _VO2max by about 42%. Interestingly, this percentage
increase is quite similar to previous cycling-related research, which
used power-output variation within the work intervals (Bossi et al.,
2020). In this previous study, two different interval training sessions,
matched for duration and mean power output (6 × 5 min at a mean
intensity of 84% of maximal aerobic power (MAP), with 2.5 min of
rest between intervals), were performed. By performing several 30s
bouts at 100% MAP within these intervals to increase the power-
output variation within the work intervals, the mean time ≥90%
_VO2max increased by about 43% (Bossi et al., 2020). It thus seems
that variation of the power-output by performing short bouts of
sprinting or by employing inclination might be an important factor
to increase the time ≥90% _VO2max during HIIT sessions. In
addition, and in line with our ﬁndings, lactate, HR, and RPE
data reported by Bossi and colleagues (Bossi et al., 2020) were
similar for both interval training conditions. However, both studies
only focused on short-term effects. Therefore, Bossi and colleagues
(Bossi et al., 2020) emphasized the need for longitudinal studies
while speculating that performance adaptations will most likely be
superior to constant-intensity work intervals. Based on our data, a 6-
week period of uphill HIIT (2 sessions per week) would result in
about half an hour more accumulated time ≥90%
_VO2max
compared
to
horizontal
HIIT.
This
additional
accumulated
time ≥90% _VO2max via uphill HIIT is equivalent to 5 horizontal
FIGURE 2
Lactate (A), heart rate (B), RPE (C), and running velocity (D) data (mean ± standard deviation) of horizontal (grey) and uphill (black) high intensity
training protocols. Individual values are marked as points. In addition, p-values of time*mode interaction effects (p) of the repeated measurement
variance analyses (rANOVA) and corresponding effect sizes as partial eta squared (pes) are given.
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HIIT sessions. Therefore, superior performance adaptations could
be assumed via uphill HIIT. This assumption is supported by
increased _VO2max and power output at the lactate threshold
adaptations over a 4-week training period, if recreationally-
trained cyclists spent about 100s more time above 90% _VO2max
per training session (Turnes et al., 2016). In line with these ﬁndings,
the accumulated training time ≥90%
_VO2max is frequently
considered a highly important marker for efﬁcient HIIT sessions
designed to increase _VO2max (Midgley et al., 2006; Thevenet et al.,
2007; Turnes et al., 2016). Our ﬁndings of HIIT protocols performed
at the maximal sustainable intensity during all four interval bouts
(isoeffort) (Seiler and Hetlelid, 2005) revealed increased mean _VO2,
_VO2peak, and accumulated time above 90% _VO2max at a decreased
running velocity during the uphill HIIT condition and similar
lactate, HR, and RPE values. However, as at a given speed, uphill
running results in higher _VO2, lactate, HR, and RPE data compared
to horizontal running (Minetti et al., 1994; 2002; Vernillo et al.,
2017), it might be possible that the maximum oxygen uptake differs
between running uphill compared to level running conditions.
Nevertheless, we did not ﬁnd signiﬁcant differences in _VO2max
in the initial incremental ramp tests performed at horizontal
running condition and 8% slope. This is in line with results
reported by Lemire and colleagues (Lemire et al., 2020) who
reported similar
_VO2max values in well-trained trail runners
performing step tests on a treadmill in level and 15% uphill
running conditions. However, a different study conducted in
well-trained trail runners comparing the physiological responses
to step tests with increasing gradient reported signiﬁcantly higher
_VO2max values at gradients of 40% compared to level running
(Cassirame et al., 2022). This has also been described by Margaria
and colleagues (Margaria et al., 1963): According to their work,
when running on positive gradients up to 15% incline the minimum
energy cost of running increases as a function of the incline. At
slopes above 20%, however, the energy cost becomes equal to that of
concentric muscular work (Minetti et al., 2002). It therefore seems,
that at least in special populations (i.e., trail runners) and at very
steep inclination (i.e., above 15%) the maximal oxygen uptake might
signiﬁcantly and relevantly differ from level running. Hence, this
should be taken into account, when quantifying training load as a
percentage value of the maximal oxygen uptake.
Previous research revealed that 19%–79% of runners report
musculoskeletal injuries of the lower extremities annually (van
Gent et al., 2007). Thereby, loading rate and ground reaction
force were repeatedly named as relevant risk factors (Crowell
and Davis, 2011; Zadpoor and Nikooyan, 2011; Futrell et al.,
2018). These relationships, however, were often established
based on retrospective, cross-sectional data. More recently, in
prospective
case
control-studies
comprising
recreational
(Malisoux et al., 2022) and collegiate cross country runners
(Kliethermes et al., 2021), the vertical impact peak and
loading rate were not associated with a higher injury rate.
Nevertheless, reducing loading rates is still recommended as
an effective means to reduce the risk of developing running
injuries (Bowser et al., 2018). In this context, uphill running
revealed decreased ground reaction force data compared to
horizontal
running
(Gottschall
and
Kram,
2005).
Furthermore, we observed decreased running velocities during
uphill HIIT compared to horizontal HIIT, which additionally
decrease loading rate and ground reaction force (Keller et al.,
1996). In detail, previous research revealed a 22%–39% ground
reaction force decrease via an 6%–9% slope increase (Gottschall
and Kram, 2005; Kowalski and Li, 2016). Furthermore, slower
running resulted in reduced ground reaction force (Keller et al.,
1996). Based on our running velocity differences between
horizontal and uphill HIIT, this would result in a ground
reaction force reduction of 11%. For the present study a
possible
reduction
of
loading
rates
remains,
however,
speculative, as these loading rates and ground reaction forces
were not measured. Thus, more adequately powered prospective
studies
are
necessary
to
investigate
the
association
of
musculoskeletal injuries of the lower extremities and loading
rate as well as the potential prevention effect of uphill running.
Horizontal running has been linked to the stretch-shortening
cycle of the muscle-tendon unit of the lower limb (Schöfﬂ et al.,
2021), in which part of the mechanical energy of the center of
mass (COM) is absorbed during the negative work phase to be
restored during the next positive work phase (Nicol et al., 2006).
This storage and release of kinetic and potential energy
contributes to the acceleration of the body upwards during the
propulsive phase and to the reduction of the energy production
needed during the concentric phase (Snyder and Farley, 2011;
Snyder et al., 2012). In contrast, during uphill running, the center
of mass needs to be propelled vertically and does not oscillate
around an equilibrium (Dewolf et al., 2016). In detail, the center
of mass loses horizontal while simultaneously gaining vertical
velocity during the ﬁrst part of ground contact. Subsequently,
during the second part of the contact, a fraction of the energy
stored in the elastic elements of the muscle tendon unit is released
to increase the kinetic and potential of the center of mass (Dewolf
et al., 2016). Accordingly, differences in muscle activation
patterns of the lower extremities have been reported between
horizontal and uphill running (Yokozawa et al., 2007), with
concentric muscle work being dominant during uphill running
(Giandolini et al., 2016). Furthermore, to increase the running
velocity in ﬂat running conditions, athletes tend to increase their
stride length and frequency almost linearly (Ito et al., 1983;
Cavanagh and Kram, 1989; Brisswalter and Legros, 1995).
Simultaneously, the ﬂoor contact time and ﬂight time are
reduced
(Ito
et
al.,
1983;
Cavanagh
and
Kram,
1989;
Brisswalter and Legros, 1995). Even though this pattern is also
visible during uphill running compared to ﬂat running, stride
length and ﬂight time are signiﬁcantly reduced, since the foot
touches the belt or ground earlier (Padulo et al., 2012; 2013). As
the ﬂoor contact time does not seem to differ between ﬂat and
uphill running, this subsequently leads to a signiﬁcant reduction
in ﬂight time during the uphill running condition (Padulo et al.,
2012; 2013). Therefore, it seems possible, that prolonged training
sessions running uphill might change the athlete’s kinematics,
thus resulting in a reduction in running economy at horizontal
conditions. Nevertheless, at least for constant running velocities,
experienced athletes select an individual combination of stride
length and frequency resulting in the least energy cost (Cavanagh
and Kram, 1989; Cavagna et al., 1991), while providing the
greatest mechanical efﬁciency (Morgan et al., 1994). Even
though only a small fraction of the overall training time is
spent on high-intensity running (Stöggl and Sperlich, 2015), a
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potential longitudinal effect on running economy induced by
prolonged uphill running should be addressed in further research.
A limitation that needs to be addressed is the lack of
spatiotemporal running parameters including information on
stride length and frequency. Thus, further research should try to
disentangle the relationship between spatiotemporal running
parameters
and
oxygen
uptake
during
uphill
running.
In
addition, the potential long-term training effects mentioned
above
should
be
examined
in
appropriate
longitudinal
intervention studies.
In conclusion, this randomized crossover testing revealed
increased
mean
_VO2,
_VO2peak,
and
accumulated
training
time ≥90% _VO2max via uphill HIIT. Thus, uphill running during
HIIT sessions appears to be an effective alternative to traditional
horizontal HIIT sessions. Whether performance adaptations will be
superior to horizontal running work intervals remains to be
established by a longitudinal study, but similar lactate, HR, and
RPE data suggest that it is unlikely that negative training outcomes
occur. Nevertheless, future research should investigate whether
training-induced adaptations can be improved via uphill HIIT.
Furthermore,
such
further
studies
should
also
examine
if
different
muscle
activation
patterns
via
uphill
running
(Giandolini et al., 2016) lead to adverse effects in terms of
(horizontal) running economy.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed and
approved by Ethical committee of the German Sport University
Cologne
(approval
no.
153/2022).
The
patients/participants
provided their written informed consent to participate in this study.
Author contributions
SH, RG, and LD contributed to the conception and design of the
study. RG, TW, and JD led the intervention. LR, SH, and TW
performed the statistical analysis. SH wrote the ﬁrst draft of the
manuscript. LR, TW, PW, and LD wrote sections of the manuscript.
PW copyedited the draft for content, language, and format, and
organized the submission and revision/resubmission process. All
authors contributed to the article and approved the submitted version.
Funding
We acknowledge the ﬁnancial support of the German Research
Foundation (DFG) and the Open Access Publication Fund of
Bielefeld University for the article processing charge.
Acknowledgments
We appreciatively acknowledge Jonas Hochstrate for his support
during the data acquisition phase.
Conﬂict of interest
The authors declare that the research was conducted in the
absence of any commercial or ﬁnancial relationships that could be
construed as a potential conﬂict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and
do not necessarily represent those of their afﬁliated organizations, or
those of the publisher, the editors and the reviewers. Any product that
may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
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Increased oxygen uptake in well-trained runners during uphill high intensity running intervals: A randomized crossover testing.
Held, Steffen,Rappelt, Ludwig,Giesen, René,Wiedenmann, Tim,Deutsch, Jan-Philip,Wicker, Pamela,Donath, Lars