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Gross Efficiency During Flat and Uphill Cycling in Field Conditions

  • Carinthian Olympic Training Center


Whilst a number of studies investigated gross efficiency (GE) in laboratory conditions, few studies have analyzed GE in field conditions. Therefore, the aim of this study was to analyze the effect of gradient and cadence on GE in field conditions. Thirteen trained cyclists (mean ± SD age: 23.3 ± 4.1 years; stature: 177.0 ± 5.5 cm; body mass: 69.0 ± 7.2 kg; VO2max 68.4 ± 5.1 mL·min-1·kg-1) completed an incremental graded exercise test to determine the ventilatory threshold (VT) and 4 field trials of 6 min duration at 90% of VT on flat (1.1%) and uphill terrain (5.1%) with two different cadences (60 and 90 rev·min-1). Oxygen uptake was measured with a portable gas analyzer and power output was controlled with a mobile power crank, which was mounted on a 26-inch mountain bike. GE was significantly affected by cadence (20.6 ± 1.7% vs. 18.1 ± 1.3% at 60 and 90 rev·min-1, respectively; P<0.001) and terrain (20.0 ± 1.5% vs. 18.7 ± 1.7% at flat and uphill cycling, respectively; P=0.029). The end-exercise oxygen uptake was 2536 ± 352 mL·min-1 and 2594 ± 329 mL·min-1 for flat and uphill cycling, respectively (P=0.489). There was a significant difference in end-exercise oxygen uptake between the 60 (2352 ± 193 mL·min-1) and the 90 rev·min-1 (2778 ± 431 mL·min-1) (P<0.001). This findings support previous laboratory based studies demonstrating reductions in GE with increasing cadence and gradient that might be attributed to changes in muscle activity pattern.
Title: Gross efficiency during flat and uphill cycling in field conditions
Authors: Alfred Nimmerichter1, Bernhard Prinz1, Kevin Haselsberger1, Nina Novak1,
Dieter Simon1, and James G. Hopker2
1Sport and Exercise Sciences, University of Applied Sciences, Wiener Neustadt, Austria
2School of Sport and Exercise Sciences, University of Kent, England
Corresponding Author:
Alfred Nimmerichter
University of Applied Sciences Wiener Neustadt
Sport and Exercise Sciences
Johannes Gutenbergstrasse 3
A-2700 Wiener Neustadt, Austria
Phone: +43 (0) 2622 89084 615
Fax: +43 (0) 2622 89084 99
“This is a non-final version as accepted for publication in
the International Journal of Sports Physiology and Performance
Acceptance Date: January 8, 2015
Purpose: Whilst a number of studies
investigated gross efficiency (GE) in
laboratory conditions, few studies have
analyzed GE in field conditions. Therefore,
the aim of this study was to analyze the
effect of gradient and cadence on GE in
field conditions. Methods: Thirteen
trained cyclists (mean ± SD age: 23.3 ± 4.1
years; stature: 177.0 ± 5.5 cm; body mass:
69.0 ± 7.2 kg; VO2max 68.4 ± 5.1 mL·min-
1·kg-1) completed an incremental graded
exercise test to determine the ventilatory
threshold (VT) and 4 field trials of 6 min
duration at 90% of VT on flat (1.1%) and
uphill terrain (5.1%) with two different
cadences (60 and 90 rev.min-1). Oxygen
uptake was measured with a portable gas
analyzer and power output was controlled
with a mobile power crank, which was
mounted on a 26-inch mountain bike.
Results: GE was significantly affected by
cadence (20.6 ± 1.7% vs. 18.1 ± 1.3% at
60 and 90 rev.min-1, respectively; P<0.001)
and terrain (20.0 ± 1.5% vs. 18.7 ± 1.7% at
flat and uphill cycling, respectively;
P=0.029). The end-exercise oxygen uptake
was 2536 ± 352 mL.min-1 and 2594 ± 329
mL.min-1 for flat and uphill cycling,
respectively (P=0.489). There was a
significant difference in end-exercise
oxygen uptake between the 60 (2352 ± 193
mL.min-1) and the 90 rev.min-1 (2778 ± 431
mL.min-1) (P<0.001). Conclusion: This
findings support previous laboratory based
studies demonstrating reductions in GE
with increasing cadence and gradient that
might be attributed to changes in muscle
activity pattern.
Keywords: climbing, field cycling,
cadence, power output, SRM, cycling
Gross efficiency (GE) is an important
determinant of cycling performance as it
describes the ratio of mechanical work
accomplished to the metabolic energy
required to do that work.1 Moreover GE
has previously been shown to affect
cycling performance.2-4 Horowitz et al,5
found that cyclists with a high GE are able
to generate higher power outputs during a
1-hour cycle time-trial for the same
energetic cost as cyclists with a lower GE.
In addition, higher power outputs at similar
VO2max values (70-80 mL·min-1·kg-1) were
reported in professional cyclists compared
to elite but amateur cyclists, indicating a
higher GE.4 Jeukendrup et al,3 calculated
that a 1% enhancement in GE would result
in a 63-s benefit in a 40-km time-trial.
More recently it was demonstrated that a
higher GE at 60% of maximum minute
power at a cadence of 120 rev.min-1 was
significantly correlated with 1-h cycling
performance in trained cyclists.2 As GE is
a key determinant of cycling performance
it has been previously suggested that GE
should be routinely monitored during a
competitive cyclists training season.6
The most important variables, which affect
GE during cycling, are work rate and
cadence.1,7,8 Although, the most efficient
cadence is around 50 rev.min-1 at low work
rates,7,9 it has been shown that the most
efficient cadence increases with increasing
work rate, which is commensurate with the
observation that professional cyclists
prefer higher cadences (80-100 rev.min-1)
during most training sessions and
competitions.10 A previous study has
shown that power output is ~5% higher
during 20-min uphill compared to flat
time-trials.13 One possible explanation is
that the lower cadences observed during
uphill cycling could result in a higher GE
and consequently in higher power outputs
at the same oxygen consumption during
uphill compared to flat cycling. However,
in a recent study GE was significantly
lower at a gradient of 8% compared to
both, 4% and flat cycling on a motorized
Despite the high ecological validity of field
cycling13,15 only a few studies examined
GE in field conditions16,17. Millet et al,17
found no differences in GE between uphill
and flat cycling, in seated and standing
positions and Bertucci et al,16 reported GE
to be 10% lower in laboratory compared to
field conditions when cycling in seated
position. However, both studies used freely
chosen cadences, which might impact on
their results as variations in cadence affect
GE. The significantly lower freely chosen
cadence in the field could therefore explain
the higher GE reported by Bertucci et al.16
Currently the influence of standardized
work rates and cadences at different
gradients in field conditions remains to be
shown. Therefore, the aim of the present
study was to investigate the effect of
cadence during uphill and flat cycling on
GE in field conditions. It was hypothesized
that GE would decrease with increasing
gradient and cadence.
Thirteen trained male competitive cyclists
volunteered to participate in this study
(mean ± SD: age 23.3 ± 4.1 years; stature
177.0 ± 5.5 cm; body mass 69.0 ± 7.2 kg;
VO2max 68.4 ± 5.1 mL·min-1·kg-1). All
cyclists regularly participate in road races
and were not specially trained climbers or
time-trialists. The participants were
informed of the experimental procedures
and gave written informed consent before
entering the study, which was conducted in
accordance with the ethical principles of
the Declaration of Helsinki and approved
by the institutional review board.
Study design
Participants were asked to visit the
laboratory on two separate occasions.
During the first visit body mass and stature
was measured using an electronic scale and
stadiometer (Seca 813 and 213, Seca,
Germany) before completing an
incremental graded exercise test (GXT).
During the second visit two field trials on a
flat course and two uphill trials were
completed on a road near to the laboratory.
Participants were instructed to refrain from
alcohol and strenuous exercise the day
before the tests and to abstain from
caffeine, food and sports beverages for the
preceding 3 h.
Laboratory incremental graded exercise
The GXT was performed on an
electromagnetically braked ergometer
(Lode Excalibur, Groningen, The
Netherlands) at a cadence of 90-100
rev.min-1 to assess maximal measures of
power output (Pmax), oxygen uptake
(VO2max) and heart rate (HRmax). In
addition, the ventilatory threshold (VT)
was determined as sub-maximal
performance correlate. Achievement of
VO2max was taken as the highest 30 s value
attained before volitional exhaustion. To
determine the VT the criteria of an increase
of the ventilatory equivalent of O2
(VE/VO2), without a concomitant increase
of the ventilatory equivalent of CO2
(VE/VCO2) and the first loss of linearity in
pulmonary ventilation (VE) and carbon
dioxide ventilation (VCO2) were used.18
Pulmonary ventilation and gas exchange
was measured continuously throughout the
test via breath-by-breath open circuit
spirometry (MetaMax 3B, Cortex
Biophysik, Leipzig, Germany). The gas
analyzers were calibrated before each test
with gases of known concentrations (4.99
Vol% CO2, 15.99 Vol% O2, Cortex
Biophysik, Leipzig, Germany). Volume
and flow were calibrated with a 3-L
syringe (Type M 9474-C; Cortex
Biophysik, Leipzig, Germany). The
participants wore a facemask and breathed
through a low-resistance impeller turbine.
Heart rate was measured continuously
throughout the test using short-range radio-
telemetry (Polar Vantage NV; Polar
Electro, Kempele, Finland).
The ergometer was equipped with a drop
handlebar, a race saddle and the
participants’ own pedals. After a 3-min
standardized warm up at 40 W the work
rate was increased by steps of 20 W.min-1
until exhaustion. If the final work rate was
not completed, maximal power was
calculated according to the method of
Kuipers et al,19:
Pmax = PL + (t/60 x 20)
where PL is the last completed work rate
(W) and t is the time for the incomplete
work rate (s).
Field tests
The field tests consisted of two trials on a
flat course with an average gradient of 1.1
± 0.3% and two uphill trials with an
average gradient of 5.1 ± 0.2%. The road
surface of both courses was tarmac. The
flat course was a straight road whilst there
were two bends on the uphill course. After
the preliminary tests the uphill course was
chosen to allow all participants riding at
the required power output with a cadence
of 90 rev.min-1. Power output was
measured at a rate of 1 Hz with a SRM
professional power meter (Schoberer Rad-
Messtechnik, Juelich, Germany), which
was mounted on a 26-inch mountain bike.
To ensure accurate measures a static
calibration was applied before the study20
and the zero offset frequency was adjusted
according to the manufacturer’s
instructions. The bikes’ front suspension-
fork was locked and the slick tires were
inflated to 4 bar. The seatpost was
adjustable to allow a customized position,
which the riders were instructed to keep
constant during all trials, and participants
used their own pedals. Both trials were
conducted at a cadence of 60 and 90
rev.min-1 at a power output of 90% of VT
(90%VT) in a seated position. The tests
started with the flat trials at 60 and 90
rev.min-1 followed by the uphill trials in the
same order. All tests were interspersed by
30 min during which the riders were
allowed to drink water ad libitum.
After a 10-min warm up on the bike where
the riders were instructed to find the
optimal gearing for the required work rate
and cadence, the trials started with a 3-min
baseline exercise on a training roller (Tacx
Blue Motion, BV Terneuzen, Netherland)
at a power output of 50 W and 60 rev.min-1
followed by 6 min road cycling at the
criterion work rate and cadence. Both,
power output and cadence were self-
controlled by the athletes during the trials.
Immediately after the ride, the data were
downloaded from the SRM head unit to
verify the actual power output and
cadence. The accepted deviation was 5%
and/or 3 rev.min-1 over 10 consecutive
seconds for the target power output and
cadence, respectively.
Gas exchange, pulmonary ventilation and
heart rate were continuously measured
throughout the trials as described above. A
mouthpiece cover was used as a wind
protection to minimize the effects on
expired volume.21 A capillary blood
sample was taken from the hyperemic
earlobe for the measurement of blood
[lactate] 1 min post-exercise (Biosen S-
line, EKF Diagnostic, Barleben, Germany).
The analyzer was calibrated with a
standard solution of 12.0 mmol·L-1 and
accuracy was verified by using control
solutions with known concentrations of 1.6
mmol·L-1 and 3.6 mmol·L-1 (Precinorm-U,
Precipath-U, Roche Diagnostics,
Mannheim, Germany).
End-exercise oxygen uptake and heart rate
was determined over the last 60 s of each
trial. Gross efficiency was calculated from
measures of VO2 (mL.min-1), VCO2
(mL.min-1) and power output (W) averaged
over the last 60 s of each trial as GE (%) =
work rate / energy expenditure x 100.
Energy expenditure (EE) was calculated
using the formula of Brouwer22:
EE (J) = (3.869 x VO2 + 1.195 x
VCO2) x 4.186 (2)
Statistical analyses
All statistics were performed using the
software package SPSS statistics 20 (IBM
Corporation, Armonk, NY). Descriptive
data are presented as mean ± standard
deviation (SD). The assumption of
normality was verified using the Shapiro-
Wilk test. To determine differences across
the test variables a two-factorial mixed
ANOVA with cadence (60 vs. 90 rev.min-
1) and terrain (uphill vs. flat) as model
factors was used. Effect sizes are reported
as partial Eta-squared (𝜂!
!) and considered
as small (0.01), moderate (0.1) and large
(0.25) effects.23 The level of significance
was set at P < 0.05.
The results obtained during the GXT are
presented in Table 1. The mean power
output during the field tests was 163 ± 23
W and 165 ± 22 W for the uphill and flat
trial (F1,12 = 2.3; P = 0.153; 𝜂!
! = 0.162)
and 163 ± 22 W and 166 ± 23 W for the 60
and 90 rev.min-1 trial, respectively (F1,12 =
4.9; P = 0.046; 𝜂!
! = 0.292). The power
output was on average 41 ± 4 % of Pmax.
No significant interactions between terrain
and cadence have been observed for any
measure (P > 0.05). There was a
significant difference in GE between flat
(20.0 ± 1.5%) and uphill cycling (18.7 ±
1.7%) (F1,12 = 6.1; P = 0.029; 𝜂!
! = 0.338).
GE was significantly higher at 60 (20.6 ±
1.7%) compared to 90 rev.min-1 (18.1 ±
1.3%) (F1,12 = 34.4; P < 0.001; 𝜂!
! = 0.741)
(Figure 1).
The end-exercise oxygen uptake was 2536
± 352 mL.min-1 and 2594 ± 329 mL.min-1
for flat and uphill cycling, respectively
(F1,12 = 0.5; P = 0.489; 𝜂!
! = 0.041). There
was a significant difference in end-exercise
oxygen uptake between the 60 (2352 ± 193
mL.min-1) and the 90 rev.min-1 (2778 ± 431
mL.min-1) (F1,12 = 35.3; P < 0.001; 𝜂!
! =
The heart rate was not significantly
different between flat (125 ± 14 b.min-1)
and uphill cycling (126 ± 16 b.min-1) (F1,12
= 0.3; P = 0.617; 𝜂!
! = 0.021). There was a
significant difference in heart rate between
the 60 (121 ± 13 b.min-1) and the 90
rev.min-1 (130 ± 16 b.min-1) (F1,12 = 28.2; P
< 0.001; 𝜂!
! = 0.702).
There were no significant differences in
blood [lactate] between flat vs. uphill
cycling (1.1 ± 0.4 mmol.L-1 vs. 1.1 ± 0.3
mmol.L-1; F1,12 = 0.04; P = 0.850; 𝜂!
! =
0.003) and between 60 and 90 rev.min-1
(1.2 ± 0.4 mmol.L-1 vs. 1.1 ± 0.4 mmol.L-1;
F1,12 = 1.7; P = 0.219; 𝜂!
! = 0.123).
The main finding of the present study was
that GE during field cycling was higher
during flat compared to uphill cycling
(mean difference 1.3%). This was not
accompanied by differences in end-
exercise oxygen uptake, heart rate or blood
[lactate]. In addition, GE was higher at 60
compared to 90 rev.min-1 with a mean
difference of 2.5%, which was
accompanied by a higher end-exercise
oxygen uptake and heart rate during the
latter. However, there was no interaction
effect between gradient and cadence on
GE. Although there was a significant
difference in power output between the 60
and 90 rev.min-1 trials (3 W), the small
difference is unlikely to have a major
influence to this findings as GE would be
changed by ~0.4%.
The present study is the first field study
where power output and cadence was
controlled to investigate the effects on GE.
The difference found in GE between flat
and uphill cycling is partially supported by
a recent study14 where significant
differences were observed between 0% and
8% inclination and between 4% and 8%,
but not between 0% and 4% during
treadmill cycling. Other studies where no
significant differences in GE between flat
and uphill cycling in the field were
found17,24, used different work rates and
positions, as well as freely chosen
cadences, all of which have been shown to
affect efficiency.25 For example, Millet et
al,17 reported no significant differences in
GE during uphill and flat cycling, in seated
and standing positions at a work rate of
75% of Pmax. It has been shown that GE
increases with exercise intensity7 and
therefore, the higher GE (22.5 ± 1.9%)
shown by Millet et al,17 compared to the
results of the current study (19.4 ± 1.6%)
could be explained by the higher work rate
used. Although a work rate of 75% of Pmax
is well tolerated for a prolonged time in
trained cyclists, such heavy exercise
intensity is characterized by a sustained
metabolic acidosis.9 The delayed steady
state in 𝑉O2 (slow component) result in a
deviation from linearity between work rate
and 𝑉O2 and consequently, oxygen
consumption underestimates energy
expenditure, resulting in an artificially high
GE.26 The work rate chosen in the present
study (90% VT) corresponds to an exercise
intensity that trained cyclists used during
most of their basic endurance training
rides27 and therefore reflects a functional
work rate under steady state conditions.
In a previous study, power output was
found to be higher during uphill (8.5%)
compared to flat time-trials, with one of
the possible explanation being a higher GE
as a result of the lower cadence during
uphill cycling13. However, findings of both
the present study, and that of Arkesteijn et
al,14 do not support this notion. The latter
study found that during uphill cycling (8%)
the level of muscle activity increased, the
onset of muscle activity occurred earlier,
and torque was more evenly distributed
during the pedal revolution. Although
some studies28,29 found no effect of the
gradient on muscle activity and timing,
others reported modifications at very steep
gradients (20%).30 However, a change in
posture from seating to standing has been
shown to affect the EMG activity during
both, treadmill cycling at 4, 7 and 10%28
and stationary ergometer cycling at 0 and
8%.29 The higher muscle activity, together
with the alterations in muscle activity
pattern, could change the contraction and
relaxation dynamics during the pedal
revolution. This in turn could increase
energy expenditure and therefore could
possibly contribute to a decreased GE
during uphill cycling.31 Despite this
reduction in GE, the switch from seating to
standing pedaling, which frequently occurs
during uphill time-trials, increase muscle
activity and power production at the
bottom dead center of the pedal
revolution.28 This could contribute to
performance improvements during uphill
The lower GE found at 90 rev.min-1 is
consistent with the literature reporting GE
to be negatively correlated with cadence at
constant work rates.7,8,14 Lucia et al,32
reported a higher GE with increasing pedal
rates (60 vs. 100 rev.min-1) at a power
output of ~75% Pmax in professional
cyclists and concluded that increased
efficiency at higher cadences is one of the
main adaptations in professional cyclists.
However, this result might be explained by
the high power output used (as discussed
above). In accordance to our findings
Arkesteijn et al,14 reported no influence of
the gradient on the negative correlation
between GE and cadence. Therefore, it is
unlikely that a higher efficiency can
explain cyclist’s choice of lower cadences
during uphill cycling. In support of this
proposition, Emanuele and Denoth33 found
a leftward shift of the quadratic
power/cadence relationship during uphill
compared to flat cycling, concluding that
lowering the cadence is a performance
advantage during climbing. The authors
explained their findings with larger
oscillations in crank angular velocity
induced by the lower crank inertial load11
that could increase the dissipation of
kinetic energy when cycling uphill. In
addition, cycling in an upright position
during climbing has been shown to be
more powerful than the dropped position
usually chosen during flat cycling.33,34
The results indicate that the cardiovascular
and metabolic responses were similar for
flat and uphill cycling as no significant
effects were observed for heart rate (P =
0.617), end-exercise oxygen uptake (P =
0.489) and blood [lactate] (P = 0.850).
However, the heart rate as well as the end-
exercise oxygen uptake was significantly
higher at 90 rev.min-1 compared to 60
rev.min-1 (P < 0.001), which is supported
by previous studies.13,35,36 It has been
demonstrated35 that an increase in cadence
(50 vs. 100 rev.min-1) improves the action
of the skeletal-muscle pump, indicated by
increases in muscle blood flow and venous
return, which alters cardiac output via
increases in stroke volume and heart rate at
exercise intensities of 65-75% VO2max. At
lower exercise intensities (45-60% VO2max)
as used in the present study, the increased
oxygen demand in response to a higher
cadence was met by elevations in heart rate
and oxygen extraction whilst stroke
volume remains unchanged.36
Practical Applications and Conclusions
This study has demonstrated that GE is
lower during uphill compared to flat
cycling in field conditions at similar
cadences and functional work rates
frequently used during basic endurance
training. The inverse relationship between
GE and cadence was not affected by the
gradient of cycling. These findings support
previous laboratory based studies
demonstrating reductions in GE with
increasing gradient, attributed to changes
in muscle activity pattern. The assessment
of GE in field conditions is less time
disruptive for athletes and allows coaches
to monitor cycling performance more
easily. It remains to be shown whether the
reduction in GE due to increased gradient
could be altered in response to a specific
training intervention.
Conflicts of Interest and Source of
This research was not supported by
external funding. The authors declare no
conflict of interest. The results of the
current study do not constitute
endorsement of the product by the authors
or the journal.
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Figure 1: Gross efficiency during uphill and flat cycling at 60 and 90 rev.min-1. Error bars
represent 95% confidence limits
Table 1: Performance measures obtained from the incremental graded exercise test (mean ±
Group (N = 13)
Pmax (W)
406 ± 41
Pmax (
5.9 ± 0.4
VO2max (mL·min-1·kg-1 )
68.4 ± 5.1
VT (W)
178 ± 20
HRmax (b.min-1)
191 ± 8
Pmax = maximal power output; VO2max = maximal oxygen uptake; VT = ventilatory threshold;
HRmax = maximal heart rate
60 rev.min-1 90 rev.min-1
GE (%)
Terrain: F1,12 = 6.1; P = 0.029
Cadence: F1,12 = 34.4; P < 0.001
... Additionally, differences between level ground and uphill terrain have been investigated. [6][7][8] These previous studies focused on gross efficiency, cycling economy, cadence, and seated versus standing position. [6][7][8] Regarding cycling intensity, field-based studies traditionally describe it focusing on heart rate (HR), power output, and blood lactate concentration ([La − ]). ...
... [6][7][8] These previous studies focused on gross efficiency, cycling economy, cadence, and seated versus standing position. [6][7][8] Regarding cycling intensity, field-based studies traditionally describe it focusing on heart rate (HR), power output, and blood lactate concentration ([La − ]). 2,4,5 However, HR is influenced by cardiovascular drift, glycogen depletion, and environmental factors 1 and as such can result in underestimation of cycling intensity. ...
... While power-output pattern has been investigated extensively in road cycling, in both laboratory and field-based conditions, cardiovascular and respiratory responses have not been widely assessed in outdoor cycling conditions. Oxygen uptake (VO 2 ) has been measured in field-based trials using a portable system, 6,7,8 although investigated distances are shorter than in traditional roadcycling competitions. A recent study was the first to report the physiological demands of a real off-road cycling competition through the assessment of respiratory parameters. ...
Purpose: While a number of studies have researched road cycling performance, few studies have attempted to investigate the physiological response in field conditions. Therefore, the purpose of this study was to describe the physiological and performance profile of an uphill time-trial frequently used in cycling competitions. Methods: Fourteen elite road cyclists (mean±SD: age 25±6 years, height 174±4.2 cm, body mass 64.4±6.1 kg and fat mass 7.48±2.82%) performed a graded exercise test until exhaustion to determine maximal parameters. They then completed a field-based uphill time-trial in a 9.2 km first category mountain pass with a 7.1% slope. Oxygen uptake (VO2), power output, heart rate, lactate concentration and perceived exertion variables were measured throughout the field-based test. Results: During the uphill time-trial, mean power output and velocity were: 302±7 W (4.2±0.1 W·kg(-1)) and 18.7±1.6 km/h, respectively. Mean VO2 and heart rate were: 61.6±2.0 ml·kg(-1)·min(-1) and 178±2 bpm, respectively. Values were significantly affected by the first, second, sixth and final kilometers (p<0.05). Lactate concentration and perceived exertion were 10.87±1.12 mmol·l(-1) and 19.1±0.1, respectively, at the end of the test, being significantly difference from baseline measures. Conclusion: The studied uphill time-trial is performed at 90% of maximum heart rate and VO2 and at 70% of maximum power output. To our knowledge, this is the first study assessing cardiorespiratory parameters combined with measures of performance, perceived exertion and biochemical variables during a field-based uphill time-trial in elite cyclists.
... Arkesteijn et al. (2013) found that when cycling up an 8% slope, GE was 0.3 and 0.4% lower compared to cycling up a 4% slope or on level ground. Similarly, Nimmerichter et al. (2015) observed a 1.3% worse (lower) GE on a moderately steep 5.1% slope, as compared to a gentle uphill of 1.1%. In contrast, both Millet et al. (2002) and Arkesteijn et al. (2016) found on average slightly better GE on level compared to uphill, but these differences were not significant. ...
... There are several proposed mechanisms as to why cyclists do not optimize their cadence to obtain the highest cycling efficiency when riding on level ground surfaces (for a review, see Ettema and Lorås 2009). Yet, the effects of cadence on GE seem independent of slope (Leirdal and Ettema 2011;Arkesteijn et al. 2013;Nimmerichter et al. 2015) and reduce with increasing power output (Chavarren and Calbet 1999;Samozino et al. 2006). The cadence that a cyclist can adopt is determined by the cycling speed and gearing ratio of the bicycle (i.e., the ratio of the front sprocket to the rear sprocket). ...
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Purpose With few cycling races on the calendar in 2020 due to COVID-19, Everesting became a popular challenge: you select one hill and cycle up and down it until you reach the accumulated elevation of Mt. Everest (8,848 m or 29,029ft). With an almost infinite number of different hills across the world, the question arises what the optimal hill for Everesting would be. Here, we address the biomechanics and energetics of up- and downhill cycling to determine the characteristics of this optimal hill. Methods During uphill cycling, the mechanical power output equals the power necessary to overcome air resistance, rolling resistance, and work against gravity, and for a fast Everesting time, one should maximize this latter term. To determine the optimal section length (i.e., number of repetitions), we applied the critical power concept and assumed that the U-turn associated with an additional repetition comes with a 6 s time penalty. Results To use most mechanical power to overcoming gravity, slopes of at least 12% are most suitable, especially since gross efficiency seems only minimally diminished on steeper slopes. Next, we found 24 repetitions to be optimal, yet this number slightly depends on the assumptions made. Finally, we discuss other factors (fueling, altitude, fatigue) not incorporated in the model but also affecting Everesting performances. Conclusion For a fast Everesting time, our model suggests to select a hill climb which preferably starts at (or close to) sea level, with a slope of 12–20% and length of 2–3 km.
... It has been shown that field derived CP estimates may be considered as valid and reliable compared with laboratory estimates, whereas the reliability (and hence validity) of field derived W´ estimates is still debated [13,14]. However, previous research has shown that road gradient may partially affect biomechanical and physiological parameters like crank kinetics (e. g. crank inertial load, crank torque profile) [15][16][17], lower limb joint kinetics (e. g. joint moments, joint mechanical work) [18,19], lower limb neuromuscular activation (e. g. intensity and timing of EMG activity) [20][21][22] and gross efficiency [15,23] during cycling in a seated position. Furthermore, it has been shown that a certain metabolic rate (e. g. ...
... Road gradient partially affects biomechanical (e. g. joint moments) [15][16][17][18][19] and physiological (e. g. EMG activity) [15,[20][21][22][23] parameters during cycling exercise, indicating an effect of gradient on muscle recruitment patterns. Recent studies investigated the influence of changes in muscle activity, muscle contraction and/or muscle fibre type recruitment patterns on critical speed or CP estimates and their associated metabolic rate [24,25]. ...
The purpose of this study was to investigate the effects of flat and uphill cycling on critical power and the work available above critical power. Thirteen well-trained endurance athletes performed three prediction trials of 10-, 4- and 1-min in both flat (0.6%) and uphill (9.8%) cycling conditions on two separate days. Critical power and the work available above critical power were estimated using various mathematical models. The best individual fit was used for further statistical analyses. Paired t-tests and Bland-Altman plots with 95% limits of agreement were applied to compare power output and parameter estimates between cycling conditions. Power output during the 10- and 4-min prediction trial and power output at critical power were not significantly affected by test conditions (all at p>0.05), but the limits of agreement between flat and uphill cycling power output and critical power estimates are too large to consider both conditions as equivalent. However, power output during the 1-min prediction trial and the work available above critical power were significantly higher during uphill compared to flat cycling (p<0.05). The results of this investigation indicate that gradient affects cycling time-trial performance, power output at critical power, and the amount of work available above critical power.
... min − 1 , which are more commonly used by cyclists, end-exercise V O 2 was significantly higher whereas gross efficiency was significantly lower at 90 rev . min − 1 [24]. These studies have shown that cadence may affect parameters of O 2 -kinetics and should therefore be controlled. ...
... To this end, it was found, that trained cyclists are able to control cadence and power output in a narrow range when cycling in the field [25] which is a prerequisite to study O 2 -kinetics in standardised conditions, as both can affect the primary V O 2 response. In a recent study, trained cyclists were used to determine gross efficiency with a portable gas analyser during uphill and flat cycling in the field [24]. In addition, V O 2 was measured during simulated races to evaluate physiological demands in field cycling [26,27]. ...
The O2-kinetic response to constant work rate exercise provides an insight into the adjustment of systemic oxygen transport and muscle metabolism. Whether O2-kinetics measured in laboratory conditions reflect O2-kinetics in field conditions has not yet been analysed. The aim of this study was to compare O2-kinetics between field and laboratory conditions. Thirteen competitive male cyclists (mean±SD age 23.3±4.1 years; V̇O2peak 68.2±4.7 mL.min−−1) completed two 6-min severe-intensity trials at 60 and 90 rev.min−1 in both conditions. Power output was measured with an SRM power meter and V̇O2 was measured with a portable gas analyser. The time constant (τ), the time delay (TD) and the amplitude (Amp) were resolved by least square regression, and the V̇O2 slow component (SC) was calculated as the difference between the end-exercise V̇O2 and Amp. To determine differences between the trials, a repeated-measure ANOVA was conducted. The Amp and end-exercise V̇O2 were significantly higher during field cycling whereas the SC were significantly higher during laboratory cycling (all at p<0.001). No significant differences were found for τ (p=0.24). Laboratory measures tend to underestimate the oxygen demand in field cycling. A higher cadence leads to greater oxygen demand in laboratory and field cycling.
... In this regard, there is evidence suggesting that, even if greater PO values can be attained during climbing, gross efficiency might be reduced. Nimmerichter et al. 22 reported that when cadence was kept equal between conditions, cyclists showed a slightly lower (-1.3%) gross efficiency during a 6-minute trial performed at an intensity corresponding to 90% of the ventilatory threshold when performed uphill (5.1% slope) compared with a flat terrain (1.1%). ...
Objective To investigate the influence of road gradient on cycling power output in male professional cyclists, and to determine whether cyclist typology (i.e., flat or climbing specialist, respectively) moderates this influence. Design Observational study. Methods Ninety-eight professional cyclists (27 ± 6 years) were included in the analyses, of which 53 and 45 were considered flat and climbing specialists, respectively. We collected power output data during both training sessions and competitions over 10 years (2013–2022, 6 ± 5 seasons/cyclist). We determined the maximal mean power (MMP) values attained for efforts lasting 1, 5, 10 and 20 min, during both level and uphill cycling (average slope < or ≥ 5 %, respectively), as well as the average road gradients on which cyclists attained their MMP. Results MMP values were higher during uphill cycling than during level cycling for all effort durations (difference ranging between 0.4 and 3.6 %, all p < 0.003). This finding was confirmed for flat and uphill specialists separately (p < 0.003 for both), with a similar increase in MMP values between level and uphill cycling in the two typologies except for longer efforts (≥10 min), in which MMP values tended to increase more in climbers. Participants attained MMP at an average slope of 6.0–7.3 %, with no differences between effort durations or cyclist typologies. Conclusions Professional cyclists attain higher MMP values on steep than on level road gradients regardless of their typology, with an average gradient of 6–7 % appearing optimal (or at least the most common) for achieving the highest MMP values.
... The analyzed data were collected in the field, and therefore, multiple factors could have influenced the power output analyzed in this study. It is known that the position of the riders (upright vs time trial) (26,27) and terrain (flat vs uphill) (28) can influence the power output. Furthermore, the same amount of accumulating work done can be reached differently within a race or training; for example, large oscillations in the power output could result in different physiological stress compared with riding at a fixed power output (29). ...
Introduction: This study aimed to investigate if performance measures are related to success in professional cycling and to highlight the influence of work done on these performance measures and success. Methods: Power output data from 26 professional cyclists, in total 85 seasons, collected between 2012-2019, were analysed. The cyclists were classified as ‘climber’ or ‘sprinter’ and into category.1 (CAT.1) (≥400PSCpoints [successful]) and CAT.2 (<400PSCpoints [less successful]), based on the number of procyclingstats-points collected for that particular season (PSCpoints). Maximal mean power output (MMP) for 20min, 5min, 1min and 10sec relative to bodyweight for every season were determined. To investigate the influence of prior work done on these MMPs, six different work done levels were determined which are based on a certain amount of completed kJ∙kg-1 (0, 10, 20, 30, 40 and 50kJ∙kg-1). Subsequently, the decline in MMP for each duration (if any) after these work done levels was evaluated. Results: Repeated-measures ANOVA revealed that work done affects the performance of climbers and sprinters negatively. However, CAT.1 climbers have a smaller decline in 20min and 5min MMP after high amounts of work done compared to CAT.2 climbers. Similarly, CAT.1 sprinters have a smaller decline in 10sec and 1min MMP after high amounts of work done compared to CAT.2 sprinters. Conclusions: It seems that the ability to maintain high MMPs (corresponding with the specialization of a cyclist) after high amounts of work done (i.e. fatigue) is an important parameter for success in professional cyclists. These findings suggest that assessing changes in MMPs after different workloads might be highly relevant in professional cycling.
... The goal of pulse measure is to calculate the speed of rotation of the crank and as the magnet was located at 0°, reference of the angular position of the crank can be calculated taking into account the time period of the specific rotation. Errors related to hall sensor can occur if the speed of rotation reaches very high values, instead our system was tested at 110 rpm and no error cause data lost, since other researchers used for professional cyclist tests high cadence of 60-110 rpm with 120 rpm peaks [31][32][33]. Atmel ATmega2560/V microcontroller embedded on Arduino Mega 2650 board was used to manage the six channel (Force Projections) ADC conversion and digital channel (Cadence Pulse) acquisition. Those seven values per sample are putting into a package together with sample time and sent it to the Datalogger for storage. ...
This paper presents a new 3D instrumented crank prototype for characterization, analysis and validation in race bikes. System characterization throws a maximum linearity error of 0.30% for parallel projection transfer function. Uncertainty values for Monte Carlo method resulted smaller than classic method computations. An experiment was designed with four controlled factors where data obtained fallowed normal distribution. Symmetry and Cadence statistics (RMS, Mean and Variance) were used in ANOVA, showing that Symmetry on outdoor Environment was higher than indoor tests. Heaviest Subject presented greater symmetry, and the Gears increasing prove symmetry rising. The lightest Subject developed higher Cadence values, as also was developed on Indoor Environment. Greater speed was achieved for bigger gears. System variability was observed in ANOVA by Variance variable behavior. Routine programmed obtained useful graphs for sport training: Effective force, Torque and Power output symmetry analysis and Force 3D projection decomposition in crank arm analysis were done.
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There is little standardization of how to measure cycling gross efficiency (GE). Therefore, the purposes of these studies were to evaluate the effect of: i) stage duration, ii) relative exercise intensity, iii) work capacity and iv) a prior maximal incremental test on GE. Trained subjects (n=28) performed incremental tests with stage durations of 1-, 3-, and 6-min to establish the effect of stage duration and relative exercise intensity on GE. The effect of work capacity was evaluated by correlating GE with peak power output (PPO). In different subjects (n=9), GE was measured at 50% PPO with and without a prior maximal incremental test. GE was similar in 3- and 6-min stages (19.7±2.8% and 19.3±2.0%), but significantly higher during 1-min stages (21.1±2.7%), GE increased with relative exercise intensity, up to 50% PPO or the power output corresponding to the ventilatory threshold and then remained stable. No relationship between work capacity and GE was found. Prior maximal exercise had a small effect on GE measures; GE was lower after maximal exercise. In conclusion, GE can be determined robustly so long as steady state exercise is performed and RER ≤1.0.
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Many scientists and coaches are interested in mechanical power produced during cycling, and use Schoberer Rad Me\technik (SRM) bicycle power cranks to obtain this data. However, it has been expensive and difficult to calibrate SRM cranks, causing much of the collected data to be unreliable. We present a static method, derived from first principles, for obtaining a calibration factor for SRM cranks. A known mass and lever arm (chainring of a known diameter) are used to apply a known torque load to the instrument in four positions, and the output frequencies are used to calculate the calibration factor in Hz/Nm. The reproducibility of this method is ±0.01 Hz/Nm, which is acceptable for the application of the instrument, which is measurement of mechanical power application by cyclists at the crank. The method is reliable, inexpensive, and easy to set up, and will allow higher confidence in data collected using SRM power cranks. We recommend calibration of the power meter once every six months because of the measured drift of the calibration factor over time.
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Power output and heart rate were monitored for 11 months in one female (V(.)O(2max): 71.5 mL · kg⁻¹ · min⁻¹) and ten male (V(.)O(2max): 66.5 ± 7.1 mL · kg⁻¹ · min⁻¹) cyclists using SRM power-meters to quantify power output and heart rate distributions in an attempt to assess exercise intensity and to relate training variables to performance. In total, 1802 data sets were divided into workout categories according to training goals, and power output and heart rate intensity zones were calculated. The ratio of mean power output to respiratory compensation point power output was calculated as an intensity factor for each training session and for each interval during the training sessions. Variability of power output was calculated as a coefficient of variation. There was no difference in the distribution of power output and heart rate for the total season (P = 0.15). Significant differences were observed during high-intensity workouts (P < 0.001). Performance improvements across the season were related to low-cadence strength workouts (P < 0.05). The intensity factor for intervals was related to performance (P < 0.01). The variability in power output was inversely associated with performance (P < 0.01). Better performance by cyclists was characterized by lower variability in power output and higher exercise intensities during intervals.
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This study tested the effects of low-cadence (60 rev min−1) uphill (Int60) or high-cadence (100 rev min−1) level-ground (Int100) interval training on power output (PO) during 20-min uphill (TTup) and flat (TTflat) time-trials. Eighteen male cyclists (\( \dot{V}{\text{O}}_{2\max } \): 58.6 ± 5.4 mL min−1 kg−1) were randomly assigned to Int60, Int100 or a control group (Con). The interval training comprised two training sessions per week over 4 weeks, which consisted of six bouts of 5 min at the PO corresponding to the respiratory compensation point (RCP). For the control group, no interval training was conducted. A two-factor ANOVA revealed significant increases on performance measures obtained from a laboratory-graded exercise test (GXT) (P max: 2.8 ± 3.0%; p < 0.01; PO and \( \dot{V}{\text{O}}_{2} \) at RCP: 3.6 ± 6.3% and 4.7 ± 8.2%, respectively; p < 0.05; and \( \dot{V}{\text{O}}_{2} \) at ventilatory threshold: 4.9 ± 5.6%; p < 0.01), with no significant group effects. Significant interactions between group and uphill and flat time-trial, pre- versus post-training on PO were observed (p < 0.05). Int60 increased PO during both TTup (4.4 ± 5.3%) and TTflat (1.5 ± 4.5%). The changes were −1.3 ± 3.6, 2.6 ± 6.0% for Int100 and 4.0 ± 4.6%, −3.5 ± 5.4% for Con during TTup and TTflat, respectively. PO was significantly higher during TTup than TTflat (4.4 ± 6.0; 6.3 ± 5.6%; pre and post-training, respectively; p < 0.001). These findings suggest that higher forces during the low-cadence intervals are potentially beneficial to improve performance. In contrast to the GXT, the time-trials are ecologically valid to detect specific performance adaptations.
The purpose of this study was to assess the influence of age, training status and muscle fibre type distribution on cycling efficiency. Forty males were recruited into one of 4 groups: young and old trained cyclists, young and old untrained individuals. All participants completed an incremental ramp test to measure their VO2peak, maximal heart rate (HRmax) and maximal minute power output (MMP); a submaximal test of ratio corrected cycling gross efficiency at a series of absolute and relative work rates; and in trained participants only, a 1-hour cycling time trial. Finally, all participants underwent a muscle biopsy of their right vastus lateralis muscle. At relative work rates, a general linear model found significant main effects of age and training status on cycling efficiency (P<0.01). The percentage type I muscle fibres was higher in the trained groups (P<0.01), with no difference between age groups. There was no relationship between fibre type and cycling efficiency at any work rate or cadence combination. Stepwise multiple regression indicated that muscle fibre type did not influence cycling performance (P>0.05). Power output in the 1-h performance trial was predicted by average VO2 and GE, with standardised beta coefficients of 0.94 and 0.34 respectively, although some mathematical coupling is evident. These data demonstrate that muscle fibre type does not affect cycling efficiency and was not influenced by the ageing process. Cycling efficiency and the percentage of type I muscle fibres were influenced by training status, but only GE at 120 rev⋅min(-1) was seen to predict cycling performance.
This study was designed to examine the biomechanical and physiological responses between cycling on the Axiom stationary ergometer (Axiom, Elite, Fontaniva, Italy) vs. field conditions for both uphill and level ground cycling. Nine cyclists performed cycling bouts in the laboratory on an Axiom stationary ergometer and on their personal road bikes in actual road cycling conditions in the field with three pedaling cadences during uphill and level cycling. Gross efficiency and cycling economy were lower (-10%) for the Axiom stationary ergometer compared with the field. The preferred pedaling cadence was higher for the Axiom stationary ergometer conditions compared with the field conditions only for uphill cycling. Our data suggests that simulated cycling using the Axiom stationary ergometer differs from actual cycling in the field. These results should be taken into account notably for improving the precision of the model of cycling performance, and when it is necessary to compare two cycling test conditions (field/laboratory, using different ergometers).
Purpose: The purpose of this study was to determine the effect of gradient on cycling gross efficiency and pedaling technique. Methods: Eighteen trained cyclists were tested for efficiency, index of pedal force effectiveness (IFE), distribution of power production during the pedal revolution (dead center size [DC]), and timing and level of muscle activity of eight leg muscles. Cycling was performed on a treadmill at gradients of 0% (level), 4%, and 8%, each at three different cadences (60, 75, and 90 rev·min). Results: Efficiency was significantly decreased at a gradient of 8% compared with both 0% and 4% (P < 0.05). The relationship between cadence and efficiency was not changed by gradient (P > 0.05). At a gradient of 8%, there was a larger IFE between 45° and 225° and larger DC, compared with 0% and 4% (P < 0.05). The onset of muscle activity for vastus lateralis, vastus medialis, gastrocnemius lateralis, and gastrocnemius medialis occurred earlier with increasing gradient (all P < 0.05), whereas none of the muscles showed a change in offset (P > 0.05). Uphill cycling increased the overall muscle activity level (P < 0.05), mainly induced by increased calf muscle activity. Conclusions: These results suggest that uphill cycling decreases cycling gross efficiency and is associated with changes in pedaling technique.
In the present study, we quantitatively described and compared lower extremity neuromuscular patterns during level cycling (LC), 10 and 20% uphill cycling (UC). We hypothesized that both the timing and intensity of activity of selected lower extremity muscles will differ between steep (but not moderate slope) UC condition and LC. Twelve trained mountain bikers performed an experimental test with three different cycling conditions (level, 10% slope and 20% slope) with EMG monitoring of eight lower extremity muscles. Significant changes (p < 0.05) in muscle activation timing during 20% UC compared to LC (15° later onset and 39° earlier offset) were observed in m. rectus femoris (RF). Range of activity during 20% UC compared to LC was also significantly (p < 0.05) modified in m. vastus medialis, m. vastus lateralis (8° and 5° shorter) and m. biceps femoris (BF; 17° longer). Furthermore, a reduction of EMG activity level was observed for RF and m. tibialis anterior (TA) during 20% UC compared to LC (25 and 19%; p < 0.05), while the opposite effect was observed for m. gluteus maximus (GM; 12%; p < 0.05). Peak cross-correlation coefficients in all cycling conditions for all muscles were high (all coefficients ≥ 0.83). We have shown that altered body orientation during steep, but not moderate, slope UC significantly modified the timing and intensity of several lower extremity muscles, the most affected being those that cross the hip joint and TA. The observed modifications in neuromuscular patterns during 20% UC could have a significant effect on lower extremity joint kinetics and cycling efficiency.
In race cycling, the external power–cadence relationship at the performance level, that is sustainable for the given race distance, plays a key role. The two variables of interest from this relationship are the maximal external power output (P max) and the corresponding optimal cadence (C opt). Experimental studies and field observations of cyclists have revealed that when cycling uphill is compared to cycling on level ground, the freely chosen cadence is lower and a more upright body position seems to be advantageous. To date, no study has addressed whether P max or C opt is influenced by road incline or body position. Thus, the main aim of this study was to examine the effect of road incline (0 vs. 7%) and racing position (upright posture vs. dropped posture) on P max and C opt. Eighteen experienced cyclists participated in this study. Experiment I tested the hypothesis that road incline influenced P max and C opt at the second ventilatory threshold (\( P_{ \max }^{{{\text{VT}}_{ 2} }} \) and \( C_{\text{opt}}^{{{\text{VT}}_{ 2} }} \)). Experiment II tested the hypothesis that the racing position influenced \( P_{ \max }^{{{\text{VT}}_{ 2} }} \), but not \( C_{\text{opt}}^{{{\text{VT}}_{ 2} }} \). The results of experiment I showed that \( C_{\text{opt}}^{{{\text{VT}}_{ 2} }} \) and \( P_{ \max }^{{{\text{VT}}_{ 2} }} \) were significantly lower when cycling uphill compared to cycling on level ground (P < 0.01). Experiment II revealed that \( P_{ \max }^{{{\text{VT}}_{ 2} }} \) was significantly greater for the upright posture than for the dropped posture (P < 0.01) and that the racing position did not affect \( C_{\text{opt}}^{{{\text{VT}}_{ 2} }} \). The main conclusions of this study were that when cycling uphill, it is reasonable to choose (1) a lower cadence and (2) a more upright body position.