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Effects of Altering Pedal Cadence on Cycling Time-Trial Performance

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Abstract

Our purpose was to examine the effects of altering cadence on 5-mile (8.045 km) time-trial (IT) performance in well-trained amateur male cyclists. Twelve cyclists (mean [SD] age: 24 [4] y; body mass: 70.9 [5.9] kg; and VO2max: 4.56 [0.52] L(.)min(-1)) rode three 5-mile TT. The first was at their freely chosen or preferred cadence (PC) the other two, high cadence (HC; PC + 10.8%) and low cadence (LC; PC - 9.2%), were randomly assigned and completed in a counterbalanced crossover design. Subjects rode their own bicycles, fitted with a power meter, and attached to a wind-load simulator. Practice sessions were completed 2 d prior to each TT. Cadences for PC, LC, and HC were 92 (2), 83 (6),101 (6) rpm, respectively; they were also significantly different from each other (p < 0.05). LC was 2.5% faster than HC and more economical than HC and PC (66 [3], 69 [2], 71 [4 (WL-1O2)-L-. (.) min(-1), respectively) (p <= 0.05). LC heart rate and ventilatory efficiency (V-E/VO2-ratio) were lower than PC counterparts, while LC and HC minute ventilation (V-E) were less than PC V-E (p < 0.05). LC may be the optimal cadence for 5 mile TT in well-trained amateur male cyclists because LC was the most economical, was faster than HC, resulted in the greatest proportion of fastest times (58% vs. 25% and 17% for PC and HC, respectively), and elicited less cardiorespiratory strain than PC.
Introduction
To maximize velocity, cyclists typically compete with a cadence
between 80 and 105 revolutions per minute (rpm) [9,11, 20]. At
various times in a race, they use different cadence strategies to
sustain maximal velocity, sometimes pedaling quickly with low
force or slowly with high force [16].
The various cadence strategies used by cyclists elicit different
physiological responses. Pedal force, for example, is lower at
105 rpm than 90 rpm in amateur cyclists at 200 W (70.7 to
75.8% V
˙
O
2max
) for 40 s and 5 min [23]. Similarly, myoelectrical
and EMG activity is lower at 100 rpm than 80 rpm in professional
cyclists at 366 W (92 % V
˙
O
2max
) for 6 min [14] or in subjects from
a mixture of athletic backgrounds at 400 W for 60 s [18], indicat-
ing less type II motor unit activation [17], or conversely, greater
utilization of the more economical and fatigue resistant type I
myofibers at these power outputs. As a consequence of reducing
pedal force and type II myofiber activation, high cadences lower
muscle stress [19, 23]. They also slow glycogen depletion [1], im-
prove cycling efficiency [4,14, 21], and enhance muscle blood
flow, which may increase O
2
and substrate delivery, as well as
lactate clearance [9]. Collectively, the aforementioned data sug-
gest that a high cadence-low force strategy could improve cy-
cling performance.
The adoption of a high cadence-low force strategy, however, is
not without potential physiological cost, as higher cadences are
generally less economical [5, 9,16]. For instance, V
˙
O
2
is lower at
80 rpm than 100 rpm in amateur and national elite level cyclists
Abstract
Our purpose was to examine the effects of altering cadence on 5-
mile (8.045 km) time-trial (TT) performance in well-trained
amateur male cyclists. Twelve cyclists (mean [SD] age: 24 [4] y;
body mass: 70.9 [5.9] kg; and V
˙
O
2max
: 4.56 [0.52] L · min
–1
) rode
three 5-mile TT. The first was at their freely chosen or preferred
cadence (PC); the other two, high cadence (HC; PC + 10.8%) and
low cadence (LC; PC 9.2%), were randomly assigned and com-
pleted in a counterbalanced crossover design. Subjects rode their
own bicycles, fitted with a power meter, and attached to a wind-
load simulator. Practice sessions were completed 2 d prior to
each TT. Cadences for PC, LC, and HC were 92 (2), 83 (6), 101 (6)
rpm, respectively; they were also significantly different from
each other (p < 0.05). LC was 2.5 % faster than HC and more eco-
nomical than HC and PC (66 [3], 69 [2], 71 [4 W · L
–1
O
2
·min
–1
, re-
spectively) (p 0.05). LC heart rate and ventilatory efficiency (V
˙
E
/
V
˙
O
2
-ratio) were lower than PC counterparts, while LC and HC mi-
nute ventilation (V
˙
E
) were less than PC V
˙
E
(p < 0.05). LC may be
the optimal cadence for 5 mile TT in well-trained amateur male
cyclists because LC was the most economical, was faster than HC,
resulted in the greatest proportion of fastest times (58% vs. 25%
and 17 % for PC and HC, respectively), and elicited less cardiore-
spiratory strain than PC.
Key words
Bicycling · pedaling · power · velocity · physiology
Training & Testing
1
Affiliation
Department of Exercise and Sport Sciences, Ithaca College, Ithaca, NY, USA
Correspondence
Greig Watson · Department of Kinesiology · University of Connecticut · Storrs · CT 06269 · USA ·
Phone: + 86 04 86 26 49 · E-mail: greig.watson@huskymail.uconn.edu
Accepted after revision: March 8, 2005
Bibliography
Int J Sports Med © Georg Thieme Verlag KG · Stuttgart · New York ·
DOI 10.1055/s-2005-865654 · Published online 2005 ·
ISSN 0172-4622
G. Watson
T. Swensen
Effects of Altering Pedal Cadence on
Cycling Time-Trial Performance
at 300 W for 3 min and 350 W for 8 min, respectively. Professio-
nal cyclists, however, produce higher V
˙
O
2
when using a low
(60 rpm) versus high cadence (100 rpm) [14], but have consider-
ably higher economy and efficiency during high power output
(> 350 W) compared to amateur and well trained cyclists
[12,13]; thus it is essential to differentiate between professional
and amateur cyclists. At best, for amateur cyclists, the increased
cost of cycling at higher cadences may limit or completely offset
the theoretical performance gains incurred by adopting this ped-
aling strategy. At worse, the increased cost of cycling at higher
cadences could decrease performance.
Whereas numerous studies have examined the physiological re-
sponses to various cadence strategies, none have determined if
these strategies alter performance. Our primary purpose, there-
fore, was to examine how altering pedal cadence from a cyclist’s
preferred cadence affects performance in well-trained amateur
cyclists during a 5-mile time-trial (TT).
Materials and Methods
Subjects
Twelve well-trained male amateur cyclists volunteered to partic-
ipate. The mean (SD) age, weight, and height of the subjects were
24 (4) y, 70.9 (5.9) kg, and 180 (6) cm, respectively. Subjects had
cycle trained regularly for 5.0 (2.1) y previous to the study and
were “in training” during the testing period. The study had uni-
versity ethical committee approval, and each subject gave writ-
ten consent after being informed about the nature of the experi-
ment and the possible risks.
Experimental design
Each subject reported to the laboratory on seven occasions over a
4-wk period. V
˙
O
2max
was measured during the first visit; each
subject then reported to the lab twice a wk for the next 3 wk.
These biweekly meetings included a familiarization session, and
48 h later, a 5-mile (8.045 km) TT. The TT were conducted at the
same time of day each week to avoid circadian fluctuations in
performance [18]. During the first week of the 3 biweekly meet-
ings, subjects rode at their freely chosen or preferred cadence
(PC). In a counterbalanced cross-over design, 6 subjects com-
pleted the low cadence treatment (LC: PC-10%) and 6 subjects
completed the high cadence treatment (HC: PC+ 10%) during
week 2; subjects were randomly assigned to these initial treat-
ments. In week 3, the opposite cadence treatment was com-
pleted by each subject.
V
˙
O
2max
test
On arriving at the laboratory, the subject’s nude body mass was
determined (Balance scale, Detecto, WEBB City, MO, U.S.A.). They
then completed a self-selected warm-up on a cycle ergometer
(Monark, Model 834 e, Varberg, Sweden) fitted with racing han-
dlebars and the subject’s pedal system. The initial test workload
was 160 W, which was increased by 40 W every two min until vo-
litional exhaustion or the prescribed cadence of 80 rpm was not
maintained. Peak power output (PPO) was calculated using the
following equation: PPO (W) = W
final
+ [(t/120 s) · 40 W], where
W
final
is the last workload in W reached but not necessarily com-
pleted for 120 seconds, t is the time the last workload was main-
tained, 120 is the duration of each workload, in seconds, and 40 is
the wattage difference between consecutive workloads. To re-
duce thermal stress, subjects were fan cooled during this and all
subsequent tests. V
˙
O
2
and V
˙
E
were measured throughout the test
(Parvo Medics, Salt Lake City, UT, USA); rating of perceived exer-
tion (RPE) using the category ratio scale [3] and heart rate via
telemetry (Polar Instruments Inc., Oulu, Finland) were measured
in the final 10 s of each stage. Immediately after volitional ex-
haustion had been reached, 3 successive 25-µl blood samples
were collected from one fingertip puncture for triplicate analysis
of blood lactate (YSI 1500 Sports Tester, Yellow Springs, OH, USA,
calibrated according to the manufacturer’s guidelines). Samples
were analyzed immediately, and in order of collection. Measure-
ment of the ambient temperature, humidity, and pressure was
made during all tests using an electronic weather station (Per-
ception II, Davis Instruments, Hayward, CA, USA).
Bike setup
During all familiarization sessions and TT, the subjects cycled in
the laboratory on their own bicycles, which were attached to a
Kreitler windload simulator that was equipped with a Killer
Headwind resistance unit (Kreitler Rollers Inc., Ottawa, KS,
USA). The headwind unit was set to one quarter open, which
closely approximates road conditions [22]. A rear wheel that
contained a power meter (Tune PowerTap, Cambridge, MA,
USA) and the subject’s cassette (cogs or gears) was fitted to the
bike prior to attaching it to the windload simulator. The subject’s
cassette was used to maintain gear ratio familiarity and avoid
chain/cog incompatibility. Rear tire pressure was standardized
to 100 psi, and the power meter torque was zeroed in accordance
to the manufacturer’s guidelines. PowerTap power measure-
ments have been shown to be approximately 2.5 ± 0.5% lower
compared to a dynamic calibration rig and are stable across an
11-month racing season [7].
Familiarization sessions
Approximately 48 h prior to each TT, subjects rode three 10-min
intervals at the cadence they were assigned for that week. These
intervals followed a standardized self-selected warm-up that
was used by the subject for all familiarization and TT sessions.
Intervals were separated by 10 min of active rest (pedaling at a
self-selected load below 200 W). Heart rate, V
˙
O
2
, and RPE were
measured as previously described, while the power meter re-
corded cadence and power output. These sessions were an op-
portunity for the subject to familiarize himself with each ca-
dence while riding on the windload simulator and to establish a
preferred gear ratio for the start of the upcoming TT.
Time-trials
On arriving at the laboratory the subjects’ nude body mass was
measured, as previously described. Subjects then completed
their standardized warm-up, which was followed by a “rollout
test”. The rollout test was performed to increase the reliability
of the power measurement at a set cadence by standardizing
average power recorded over 1 min to ± 4 W. It was previously
determined that error can result from differences in the tightness
of the power meter wheel against the roller of the windload sim-
ulator; this tightness is altered by a retaining bolt on the simula-
tor. If the desired power output was not achieved on the first roll-
out test, the tire/roller resistance was altered using the retaining
Watson G, Swensen T. Cadence and Cycling Performance Int J Sports Med
Training & Testing
2
bolt and the test was repeated until the desired power output
was achieved.
The TT began immediately after the rollout test. The clock was
started once the subject, using a rolling start, attained the target
cadence. The power meter measured velocity, power output, and
cadence. Heart rate and RPE were recorded every other minute,
while V
˙
O
2
and V
˙
E
were measured throughout the TT as previ-
ously described. Immediately after the TT, subjects remained sta-
tionary while blood samples were collected for blood lactate de-
termination as previously described.
To avoid the confounding effects of pacing, subjects had no
knowledge of velocity or time during the TT. However, they were
told when they had reached each mile, 4.5 miles, 4.75 miles, and
4.9 miles, or at any other time during the TT if they indicated the
need to know by raising a finger. Similarly the subjects had no
visible knowledge of cadence, but received constant verbal feed-
back as to their current cadence and average cadence. Subjects
were free to alter the gear ratio at anytime while riding the TT,
but had to maintain the required pedal cadence. They were also
informed not to alter their racing position during the study, be-
cause altering position affects power output, potentially biasing
the data. Subjects were instructed to rest the day before each TT
and to maintain a similar level of activity between weeks. Diet
was not controlled, but subjects were asked to keep a similar nu-
tritional regimen during the study.
Data analysis
Descriptive statistics (mean and standard deviation) were calcu-
lated for all variables. A 2 × 3 ANOVA (Group × Treatment) with
repeated measures on the 2nd factor was completed for: body
mass, laboratory atmospheric conditions, rollout test power out-
put and cadence, and for TT cadence, velocity, time, power out-
put, economy (calculated by dividing average power output [W]
by average V
˙
O
2
[L · min
–1
]), blood lactate, heart rate, V
˙
O
2
,V
˙
E
,V
˙
E
/
V
˙
O
2
ratio, power per pedal revolution (W · rev
–1
), breathing fre-
quency, and RPE. The between-subjects factor Group compared
results from the subjects that rode the HC TT first with those that
rode the LC TT first. The within-subjects factor Treatment com-
pared individual responses to the PC, LC, and HC TT. For all sta-
tistical tests, the alpha level was set at 0.05. The Tukey (HSD)
post hoc test was used to determine the location of any within-
subject differences.
Results
The subjects’ V
˙
O
2max
and peak power output (PPO) were 64.3
(5.0) ml· kg
–1
·min
–1
and 364 (33) W, respectively. There were
no differences in body mass and ambient temperature, pressure,
and humidity in the laboratory across the study. The power me-
ter was also reliably calibrated for each TT, as there was no differ-
ence in the cadence and power output maintained during the
rollout tests. The average TT cadences were significantly differ-
ent from each other (p 0.05). PC, LC, and HC averaged 92 (6),
83 (6), and 101 (6) rpm, respectively, so the actual difference in
cadence between PC and LC and PC and HC was 10.8% and 9.2 %,
respectively.
Time-trial test results
TT results are shown in Table 1. No order effects were observed
between HC and LC. On average, the LC TT was 2.5 % faster than
the HC TT (p 0.05); the mean reduction in time between LC
and HC was 17.8 (26.9) s. Seven subjects (58% of the subject co-
hort) were fastest using LC, compared to three (25%) using PC,
and two (17%) using HC. LC was the most economical (p 0.05)
because it produced 6.0 and 2.8 % more power per liter of oxygen
consumed each minute compared to HC and PC, respectively.
Heart rate was 3.4% lower during LC (p 0.05) than PC, and min-
ute ventilation (V
˙
E
) was lower during LC and HC compared to PC
(p 0.05) by 6 and 4.5 %, respectively. The V
˙
E
/V
˙
O
2
ratio was 5.8%
lower during the LC TT than HC TT (p 0.05). The LC produced
15% more power per pedal revolution (W · rev
–1
) than PC
(p 0.05), and PC produced 14% more W · rev
–1
than HC
(p 0.05). There were no other significant differences.
Discussion
Our purpose was to examine how altering pedal cadence from
preferred cadence (PC) affects 5-mile TT performance in well-
trained amateur cyclists. We found TT time was shortest with a
pedal rate that was 10% lower than the PC compared to one that
was 10% higher. Although low cadence (LC) did not signifi-
cantly improve TT time compared to PC, 9 out of the 12 cyclists
went faster during the LC than PC TT. In addition, LC was more
economical than PC and high cadence (HC), and LC elicited less
cardiorespiratory stress than PC.
At a low-medium power output (< 300350 W), lower cadences
are more economical than higher cadences, as V
˙
O
2
increases
Table 1 Mean and standard deviation for selected variables by Con-
dition
Condition
PC (92 ± 6) LC (83 ± 6) HC (101 ± 6)
TT time (s)*
b
747 ± 63 736 ± 63 754 ± 59
Velocity (miles · hr
–1
)*
b
39.05 ± 3.35 39.58 ± 3.33 38.62 ± 3.06
Power output (W)**
c
290 ± 33 297 ± 30 278 ± 28
W·rev
–1
*
a
3.1 ± 0.4 3.6 ± 0.5 2.7 ± 0.4
V
˙
O
2
(L ·min
–1
) 4.23 ± 0.49 4.21 ± 0.38 4.19 ± 0.37
V
˙
E
(L ·min
–1
)*
b
105 ± 15 99 ± 14 101 ± 14
V
˙
E
/V
˙
O
2
*
b
24.9 ± 2.4 23.5 ± 1.9 24.0 ± 2.4
Economy
(W ·LO
2
·min
–1
)*
c
69 ± 2 71 ± 4 67 ± 3
Heart rate (bpm)*
d
180.6 ± 8.4 175.8 ± 11.4 178.2 ± 9.4
Blood lactate
(mmol ·L
–1
)
13.2 ± 3.1 12.1 ± 3.2 12.3 ± 2.8
f (breaths · min
–1
) 44 ± 4 43 ± 4 45 ± 6
PC = preferred cadence, LC = low cadence, HC = high cadenceTT = time-trial,
W ·rev
–1
= average power per pedal revolution, V
˙
O
2
= rate of oxygen uptake, V
˙
E
=
minute ventilation, V
˙
E
/V
˙
O
2
= ventilatory efficiency, f = breathing frequency.
*p 0.05 and ** p < 0.01 for Treatment main effect.
a, b, c
and
d
are post-hoc anal-
ysis where
a
= difference between all conditions,
b
= between LC and HC,
c
= be-
tween PC and HC, and LC and HC, and
d
= between PC and LC. The alpha level was
set at 0.5
Watson G, Swensen T. Cadence and Cycling Performance Int J Sports Med
Training & Testing
3
with cadence in a linear [16], parabolic [2, 6], and exponential
[5, 9] fashion. The parabolic relationship lessens as power output
increases, but it is still significant at 300 W [2], which is similar
to the TT power output of our subject cohort. Although altering
cadence did not change V
˙
O
2
in this study, it did change power
output, which decreased as cadence increased; consequently, LC
was the most economical cadence, followed by PC and HC, re-
spectively. This finding conforms to observations that at 300 W,
in cyclists with a relative V
˙
O
2max
similar to the one in our subject
cohort ( 65 ml· kg
–1
·min
–1
), the most economical cadence is in
the range of 80 [6] to 90 rpm [9,11].
The reduced power output at the same V
˙
O
2
during the HC vs. LC
TT may be partially explained by the additional oxygen cost of
lifting the legs more often [8], the effort of which does not in-
crease propulsive power [24] and reduces the force effectiveness
index [19], thereby possibly lowering pedaling efficiency [21].
Since higher cadences require greater O
2
consumption for a given
power output below 300 350 W [5, 9], it is unlikely that our
subjects could have cycled faster during HC without first becom-
ing more fit or skilled at pedaling, as the average V
˙
O
2
during all
TT was approximately 93% of V
˙
O
2max
, which was sustained for
12.4 (0.1) min at 94% of HR
max
, and the subsequent post-TT lac-
tate concentration was 12.5 (2.7) mM. In short, our cyclists were
at or near their maximal sustainable V
˙
O
2
for that distance.
Our TT velocity data imply that the theoretical benefits of using
HC, such as reduced muscular stress [10], improved muscle
blood flow [8,11], or slowed glycogen utilization [1] were not
the primary determinates for short TT performance under our
experimental conditions. Instead, V
˙
O
2
or effective application of
pedal forces or some combination of these two factors was the
probable performance determinate. Perhaps only those cyclists
who have the aerobic power to sustain 400 W, that is, profession-
al cyclists can benefit from using a cadence higher than PC as
suggested by myoelectrical or EMG data. At 300 W, for example,
the EMG minimum for cycling specific muscles occurs at
86 ± 7.6 rpm, whereas at 366 to 400 W, the nadir occurs near
100 rpm [15].
A ± 10% change in cadence from PC did not alter whole body RPE.
Most subjects, however, remarked that LC caused the greatest
“leg fatigue”, probably reflecting the higher force produced per
pedal revolution at this cadence, a consequence of a high force-
low cadence strategy. Despite the difference in pedal force per
rev across the TT, there was no difference in blood lactate. In con-
trast to their perception of effort during LC, most subjects said
that HC produced the greatest “lung or central fatigue”, a conse-
quence of a low force-high cadence strategy. Although HC and LC
breathing frequency and minute ventilation (V
˙
E
) were similar,
the ventilatory equivalent of oxygen (V
˙
E
/V
˙
O
2
), an estimate of
ventilatory efficiency, was 5.8 % lower (p 0.05) during LC than
HC, which may partially explain the subjects’ feeling of “lung fa-
tigue”.
The manipulation of cadence in this study was modest 10% of
PC) and based on each subject’s PC. Changes in cadence were
based on PC so that we could be assured the variable was actually
manipulated. Had we decided that HC was 100 rpm, for example,
any subject whose PC was 100 rpm would not have completed a
HC TT. In short, changing cadence relative to PC meant that each
subject had a true LC and HC TT relative to a PC TT and that the
cadence manipulations were of similar magnitude. Altering ca-
dence from PC also required the subjects to complete the PC TT
first, a limitation of the study. There was, however, no order ef-
fect between the LC and HC TT that followed the PC TT, suggest-
ing that this limitation was minimal at best. The modest change
in cadence in this study was also purposeful. Larger changes
would have resulted in cadences well outside most subjects’
comfort zone, potentially confounding the data. To examine the
effects of larger changes in cadence on TT performance in well- to
highly-trained cyclists would probably require a training study
of sufficient duration to stimulate meaningful adaptations, a big-
ger sample size, and a significant alteration in the cyclists’ in-
season or late pre-season exercise regimen, factors that reduce
the practicality of such a study.
Conclusion
We found that TT time using a low cadence was significantly fast-
er than a high cadence. Furthermore, although the difference in
TT time between the low cadence and preferred cadence was
statistically insignificant, nine subjects rode faster during the
low cadence TT than the preferred cadence TT. Additionally, the
low cadence was more economical than the preferred cadence
and high cadence, elicited a lower heart rate and minute ventila-
tion than the preferred cadence, and resulted in the greatest pro-
portion of the fastest times. Collectively, these data show that a
lower cadence optimized 5-mile TT performance in well-trained
amateur cyclists under our experimental conditions.
Acknowledgements
A section of this data was presented at the American College of
Sports Medicine 2002 Conference (Med Sci Sports Exerc 2002;
34: S26).
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Training & Testing
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... The duration of the time trials is identical for all participants, which is essential because the sustainable PO _TT and therefore V O 2_TT as well as % V O 2_TT depends on exercise duration (Bosquet et al. 2002). 2. The cycling cadence is identical for all tests and participants, which is essential because BLC, PO, and V O 2 at MLSS as well as time trial performance are affected by cycling cadence (Denadai et al. 2006;Watson and Swensen 2006;Beneke and Leithäuser 2017;Graham et al. 2018). 3. The control of PO during the time trial is very easy and intuitional. ...
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Purpose There is no convincing evidence for the idea that a high power output at the maximal lactate steady state (PO_MLSS) and a high fraction of 𝑉˙O2max at MLSS (%𝑉˙O2_MLSS) are decisive for endurance performance. We tested the hypotheses that (1) %𝑉˙O2_MLSS is positively correlated with the ability to sustain a high fraction of 𝑉˙O2max for a given competition duration (%𝑉˙O2_TT); (2) %𝑉˙O2_MLSS improves the prediction of the average power output of a time trial (PO_TT) in addition to 𝑉˙O2max and gross efficiency (GE); (3) PO_MLSS improves the prediction of PO_TT in addition to 𝑉˙O2max and GE. Methods Twenty-one recreationally active participants performed stepwise incremental tests on the first and final testing day to measure GE and check for potential test-related training effects in terms of changes in the minimal lactate equivalent power output (∆PO_LEmin), 30-min constant load tests to determine MLSS, a ramp test and verification bout for 𝑉˙O2max, and 20-min time trials for %𝑉˙O2_TT and PO_TT. Hypothesis 1 was tested via bivariate and partial correlations between %𝑉˙O2_MLSS and %𝑉˙O2_TT. Multiple regression models with 𝑉˙O2max, GE, ∆PO_LEmin, and %𝑉˙O2_MLSS (Hypothesis 2) or PO_MLSS instead of %𝑉˙O2_MLSS (Hypothesis 3), respectively, as predictors, and PO_TT as the dependent variable were used to test the hypotheses. Results %𝑉˙O2_MLSS was not correlated with %𝑉˙O2_TT (r = 0.17, p = 0.583). Neither %𝑉˙O2_MLSS (p = 0.424) nor PO_MLSS (p = 0.208) did improve the prediction of PO_TT in addition to 𝑉˙O2max and GE. Conclusion These results challenge the assumption that PO_MLSS or %𝑉˙O2_MLSS are independent predictors of supra-MLSS PO_TT and %V˙O2_TT.
... This might be explained by the gear-shifting behaviors of professional cyclists in racing events. Professional cyclists attempt to ride with the optimum pedal torque and pedal rate to minimize the physiological and biomechanical load (Chavarren and Calbet, 1999;Watson and Swensen, 2006;Abbiss et al., 2009) through the gear ratio adjustment. A light gear ratio that leads to the lower pedal torque is chosen to avoid the use of the less fatigueresistance type II muscle fibers (Lucía et al., 2001). ...
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In recent decade, pedelec has become one of the most popular transportation modes due to its effectiveness in reducing physical effort. The effects of using pedelec as an alternative mode of exercise were explored in previous studies. However, the effects of pedelec parameters were not quantified for the self-selected gear ratio, random riding speed, and varied road slopes, which restricted its application. Hence, this study aimed to evaluate the effects of gear ratio and assistive torque and to determine the optimum riding condition regarding physiological, biomechanical, and subjective responses of the rider. The riding tests consisted of simulated slope (1.0 vs. 2.5% grade), gear ratio (light vs. heavy), and assistive levels (0.5, 1, 1.5, and 2), and the tests were conducted in a randomized order. A total of 19 non-athletes completed the riding tests to evaluate physiological [metabolic equivalent of task (MET), heart rate, and gross efficiency (GE)], biomechanical [muscle activity (expressed as reference voluntary contraction, RVC) and power output], and subjective responses [rating of perceived exertion (RPE) and sense of comfort (SC)]. The test conditions induced moderate to vigorous intensities (3.7–7.4 METs, 58.5–80.3% of maximal heart rate, 11.1–29.5% of RVC rectus femoris activity, and 9.4–14.2 RPEs). The effects of gear ratio and assistive level on the physiological responses were significant. Riding with the heavy gear ratio showed advantages in METs and GE. For the optimum assistive level selection, low GE and limited improvement in subjective responses suggested the impact of low-power output conditions. Overall, for the health pedelec commuters, riding with 0.75 W/kg power output with 50 rpm cadence is recommended to obtain the moderate intensity (4.7 METs) and the advantages in GE and subjective feelings. Moreover, the findings can be applied to exercise intensity control and save battery energy effectively in varying riding conditions.
... The bicycle simulator has been one of the more challenging simulators to develop because of the inherently unstable dynamics of the bicycle coupled with the dynamics of the human rider, and because of the difficulties associated with the real-time simulation of human-controlled and human-powered vehicles moving in a virtual environment (Kwon et al. 2001). Different forms of bicycle simulators have been utilized in medical science (Deutsch et al. 2012;Ranky et al. 2010;Vogt et al. 2015), sport science (Watson and Swensen 2006), video games (ElectronicSports 2008), and mechanical engineering (He et al. 2005;Hwan et al. 2006). However, very few studies have employed bicycling simulation in the context of transportation safety. ...
Technical Report
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There is little research on the behavioral interaction between bicycle lanes and commercial vehicle loading zones in the United States. These interactions are important to understand, to preempt increasing conflicts between truckers and bicyclists. In this study, a bicycling simulator experiment examined bicycle and truck interactions. The experiment was successfully completed by 48 participants. The bicycling simulator collected data regarding a participant’s velocity, lane position, and acceleration. Three independent variables were included in this experiment: pavement marking (white lane markings with no supplemental pavement color (white lane markings), white lane markings with solid green color applied to conflict areas (solid green), and white lane markings with dashed green color applied to conflict areas (dashed green)); signage (with and without a truck warning sign); and truck maneuver (no truck in the load zone, truck parked in the load zone, and truck pulling out of the load zone). The following bike-truck interactions were observed from the simulation. Bicyclists had the highest mean velocity when there was a white lane marking and no warning sign, and had the lowest mean velocity when there was a solid green pavement, no warning sign, and an exiting truck. Of the three independent variables, truck maneuvering had the greatest impact by decreasing mean bicyclist velocity. Bicyclists had the least lateral divergence when there was a white lane marking, a warning sign, and no truck. Of the three independent variables, truck maneuvering (parked and exiting) increased lateral movements, while solid green pavement markings decreased lateral variability. Bicyclists had the highest acceleration when there was a white lane marking, no truck, and a warning sign. Of the three independent variables, truck maneuvering had the greatest impact by increasing bicyclist acceleration. The results showed that truck presence does have an effect on bicyclist’s performance, and this effect varies on the basis of the engineering and design treatments employed. The findings of the current study showed that when a truck is present in a loading zone, solid green pavement causes bicyclists to have a lower velocity and lower divergence from the right edge of the bike lane, and employment of a warning sign causes a higher divergence from the right edge of the bike lane.
... Different forms of bicycle simulators have been utilized in medical science (Deutsch et al., 2012;Ranky et al., 2010;Vogt et al., 2015), sport science (Watson and Swensen, 2006), video games (ElectronicSports, 2008), and mechanical engineering (He, et al., 2005;Jeong et al., 2006). ...
Technical Report
Full-text available
There is little research on the behavioral interaction between bicycle lanes and commercial vehicle loading zones in the United States. These interactions are important to understand, to preempt increasing conflicts between truckers and bicyclists. In this study, a bicycling simulator experiment examined bicycle and truck interactions. The experiment was successfully completed by 48 participants. The bicycling simulator collected data regarding a participant’s velocity, lane position, and acceleration. Three independent variables were included in this experiment: pavement marking (white lane markings with no supplemental pavement color (white lane markings), white lane markings with solid green color applied to conflict areas (solid green), and white lane markings with dashed green color applied to conflict areas (dashed green)); signage (with and without a truck warning sign); and truck maneuver (no truck in the load zone, truck parked in the load zone, and truck pulling out of the load zone). The following bike-truck interactions were observed from the simulation. Bicyclists had the highest mean velocity when there was a white lane marking and no warning sign, and had the lowest mean velocity when there was a solid green pavement, no warning sign, and an exiting truck. Of the three independent variables, truck maneuvering had the greatest impact by decreasing mean bicyclist velocity. Bicyclists had the least lateral divergence when there was a white lane marking, a warning sign, and no truck. Of the three independent variables, truck maneuvering (parked and exiting) increased lateral movements, while solid green pavement markings decreased lateral variability. Bicyclists had the highest acceleration when there was a white lane marking, no truck, and a warning sign. Of the three independent variables, truck maneuvering had the greatest impact by increasing bicyclist acceleration. The results showed that truck presence does have an effect on bicyclist’s performance, and this effect varies on the basis of the engineering and design treatments employed. The findings of the current study showed that when a truck is present in a loading zone, solid green pavement causes bicyclists to have a lower velocity and lower divergence from the right edge of the bike lane, and employment of a warning sign causes a higher divergence from the right edge of the bike lane.
... The major elements of a typical bicycling simulator include: cueing systems (visual, auditory, proprioceptive, and motion), bicycle dynamics, computers and electronics, bicycle frame and control, measurement algorithms, and data processing and storage (Fisher et al., 2011). Different forms of bicycling simulators have been utilized in medical science (Deutsch et al., 2012;Ranky et al., 2010;Vogt et al., 2015), sport science (Watson andSwensen, 2006), video games (ElectronicSports, 2008), and mechanical engineering (He et al., 2005;Jeong et al., 2006). However, very few studies have employed full-scale bicycling simulators in the context of transportation safety. ...
Article
There is little research on the behavioral interaction between bicycle lanes and commercial vehicle loading zones (CVLZ) in the United States. These interactions are important to understand, to preempt increasing conflicts between truckers and bicyclists. In this study, a bicycling simulator experiment examined bicycle and truck interactions. The experiment was successfully completed by 48 participants. The bicycling simulator collected data regarding a participant's velocity and lateral position. Three independent variables reflecting common engineering approaches were included in this experiment: pavement marking (L1: white lane markings with no supplemental pavement color, termed white lane markings, L2: white lane markings with solid green pavement applied on the conflict area, termed solid green, and L3: white lane markings with dashed green pavement applied on the conflict area, termed dashed green), signage (L1: No sign and L2: a truck warning sign), and truck maneuver (L1: no truck in CVLZ, L2: truck parked in CVLZ, and L3: truck pulling out of CVLZ). The results showed that truck presence does have an effect on bicyclist's performance, and this effect varies based on the engineering and design treatments employed. Of the three independent variables, truck maneuvering had the greatest impact by decreasing mean bicyclist velocity and increasing mean lateral position. It was also observed that when a truck was present in a CVLZ, bicyclists had a lower velocity and lower divergence from right-edge of bike lane on solid green pavement, and a higher divergence from the right-edge of bike lane was observed when a warning sign was present.
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Conflict between bicycles and right-turning vehicles on the approach to signalized intersections is a critical safety concern. To understand the operational implications of protected-permitted right-turn signal indications in conjunction with pavement markings on bicyclist performance, a full-scale bicycling simulator experiment was performed. Velocity and lateral position of bicyclists were evaluated during conflicts between bicycles and right-turning vehicles. A mixed factorial design was considered. Two within-subject factors were analyzed: the signal indication for right-turning vehicles with five levels (circular red, circular green, solid red arrow, solid green arrow, and flashing yellow arrow), and the pavement markings in the conflict area with two levels (white lane markings with no supplemental pavement color and white lane markings with solid green pavement applied in the conflict area). Additionally, the influence of gender as a between-subject variable was considered. Forty-eight participants (24 female) completed the experiment. Signal indications and pavement markings had statistically significant effects on bicyclist velocity and lateral position, but these effects varied at different factor levels. Additionally, during the conflicts, male participants were found to have higher velocity than female participants. This difference was not influenced by engineering treatments. The results provide guidance to transportation professionals about how traffic control devices could be applied to conflict areas on the approach to signalized intersections.
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Cycling is scientifically an interesting activity because it is particularly suitable for muscle-mechanical and physiological research. Numerous publications on cycling document this interest. A practical focus of this research is the examination of cadence and pedaling technique from different points of view. Focusing on competition the optimal cadence and pedaling technique to gene-rate maximal power for an endurance activity are of great interest. With one question cycling research has not argued enough yet: why do professional cyclists adopt a lower cadence when they are cycling uphill compared to cycling on level ground? Currently no scientific evidence is available to explain this choice of a lower cadence. This review article shows the current research state in the field of optimal cadence and pedaling technique and lists some potential factors that could affect the optimal cadence and give an answer to the mentioned question. Conclusion: A general optimal cadence does not exist. With a holistic mechano-physiological model the variations of optimal cadence could be explained.
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Our objective was to investigate the influence of pedaling technique on gross efficiency (GE) at various exercise intensities in twelve elite cyclists ((V)over dotO(2)max = 75.7 +/- 6.2 ml.kg(-1).min(-1)). Each cyclist completed a (V)over dotO(2)max assessment, skinfold measurements, and an incremental test to determine their lactate threshold (LT) and onset of blood lactate accumulation (OBLA) values. The GE was determined during a three-phase incremental exercise test (below LT, at LT, and at OBLA). We did not find a significant relationship between pedaling technique and GE just below the LT. However, at the LT, there was a significant correlation between GE and mean torque and evenness of torque distribution (r = 0.65 and r = 0.66, respectively; p < 0.05). At OBLA, as the cadence frequency increased, the GE declined (r = -0.81, p < 0.05). These results suggest that exercise intensity plays an important role in the relationship between pedaling technique and GE.
Article
We sought to determine whether pedaling strategy during a 30 minute simulated time trial affected caloric requirements. The simulation consisted of a 30 minute bout divided into three successive 10 minute workloads (250W, 200W and 300W) ridden with three different cadence protocols. Protocol 1 matched the cadence to the power output of each 10 minute period, protocol 2 matched cadence to the average power output over bout and protocol 3 used a constant 95 rpm (common cadence in competition). Heart rate (HR), oxygen consumption (VO2), and perceived exertion (RPE) were measured. The average total Kcal requirement for each protocol (1-3) was 470.1, 469.8 and 494.3, respectively. The average HR for each protocol was 151.5, 149.3 and 153.0 and the average RPE for each protocol was 12.8, 12.3 and 12.4. Protocol 3 required the greatest amount of energy (P0.05). The results show energy expenditure was significantly elevated during the constant 95 rpm bout and that perceived exertion was not an accurate indicator of energy expenditure.
Article
We determined if high cadences, during a prolonged cycling protocol with varying intensities (similar to race situations) decrease performance compared to cycling at a lower, more energetically optimal, cadence. Eight healthy, competitive male road cyclists (35 ± 2 yr) cycled for 180 min at either 80 or 100 rpm (randomized) with varying intensities of power outputs corresponding to 50, 65 and 80% of VO2max. At the end of this cycling period, participants completed a ramped exercise test to exhaustion at their preferred cadence (90 ± 7 rpm). There were no cadence differences in blood glucose, respiratory exchange ratio or rate of perceived exertion. Heart Rate, VO2 and blood lactate were higher at 100 rpm vs. 80 rpm. The total energy cost while cycling during the 65% and 80% VO2max intervals at 100 rpm (15.2 ± 2.7 and 19.1 ± 2.5 kcal·min-1, respectively) were higher than at 80 rpm (14.3 ± 2.7 and 18.3± 2.2 kcal·min-1, respectively) (p < 0.05). Gross efficiency was higher at 80 rpm vs. 100 rpm during both the 65% (22.8 ± 1.0 vs. 21.3 ± 4.5%) and the 80% (23.1 vs. 22.1 ± 0.9%) exercise intensities (P< 0.05). Maximal power during the performance test (362 ± 38 watts) was greater at 80 rpm than 100 rpm (327 ± 27 watts) (p < 0.05). Findings suggest that in conditions simulating those seen during prolonged competitive cycling, higher cadences (i.e., 100 vs. 80 rpm) are less efficient, resulting in greater energy expenditure and reduced peak power output during maximal performance.
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Relying on a five-bar linkage model of the lower limb/bicycle system, intersegmental forces and moments are computed over a full crank cycle. Experimental data enabling the solution of intersegmental loads consist of measured crank arm and pedal angles together with the driving pedal force components. Intersegmental loads are computed as a function of pedaling rate while holding the average power over a crank cycle constant. Using an algorithm that avoids redundant equations, stresses are computed in 12 lower limb muscles. Stress computations serve to evaluate a muscle stress-based objective function. The pedaling rate that minimizes the objective function is found to be in the range of 95–100 rpm. In solving for optimal pedaling rate, the muscle stresses are examined over a complete crank cycle. This examination provides insight into the functional roles of individual muscles in cycling.
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The purpose of this study was to clarify the reason for the difference in the preferred cadence between cyclists and noncyclists. Male cyclists and noncyclists were evaluated in terms of pedal force, neuromuscular activity for lower extremities, and oxygen consumption among the cadence manipulation (45, 60, 75, 90, and 105 rpm) during pedaling at 150 and 200 W. Noncyclists having the same levels of aerobic and anaerobic capacity as cyclists were chosen from athletes of different sports to avoid any confounding effect from similar kinetic properties of cyclists for lower extremities (i.e., high speed contraction and high repetitions in prolonged exercise) on both pedaling performance and preferred cadence. The peak pedal force significantly decreased with increasing of cadence in both groups, and the value for noncyclists was significantly higher than that for cyclists at each cadence despite the same power output. The normalized iEMG for vastus lateralis and vastus medialis muscles increased in noncyclists with rising cadence; however, cyclists did not show such a significant increase of the normalized iEMG for the muscles. On the other hand, the normalized iEMG for biceps femoris muscle showed a significant increase in cyclists while there was no increase for noncyclists. Oxygen consumption for cyclists was significantly lower than that for noncyclists at 105 rpm for 150 W work and at 75, 90, and 105 rpm for 200 W work. We conclude that cyclists have a certain pedaling skill regarding the positive utilization for knee flexors up to the higher cadences, which would contribute to a decrease in peak pedal force and which would alleviate muscle activity for the knee extensors. We speculated that pedaling skills that decrease muscle stress influence the preferred cadence selection, contributing to recruitment of ST muscle fibers with fatigue resistance and high mechanical efficiency despite increased oxygen consumption caused by increased repetitions of leg movements.
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The effects of three different cadences and five different work rates on Gross (GE) and Delta Efficiency (DE) during cycle ergometry were studied. Fifteen well-trained cyclists exercised for 30 minutes at 60, 80, or 100 RPM on three different occasions. On each occasion, the load was increased every five minutes and corresponded to approximately 50, 60, 70, 80 and 90% of VO2max. During the last three minutes of each stage, steady-state energy expenditure was calculated while work rate was recorded. In addition, the oxygen cost of unloaded cycling (CUC) was also measured. GE was calculated as the ratio of work rate to the rate of energy expenditure, whereas DE was calculated as the reciprocal of the slope of this relationship at work rates between 50 and 90% of VO2max. The CUC corresponded to 0.66 +/- 0.03 l/min, 0.77 +/- 0.04 l/min and 1.04 +/- 0.04 l/min at 60 RPM, 80 RPM and 100 RPM, respectively (p less than 0.01 for all comparisons). GE was similar at all cadences when cycling at 80 and 90% VO2max. DE increased with increasing rpm and corresponded to 20.6 +/- 0.4%, 21.8 +/- 0.6%, and 23.8 +/- 0.4% at 60 RPM, 80 RPM and 100 RPM, respectively (p less than 0.01 for all comparisons). Therefore, when trained cyclists exercise intensely (80-90% VO2max), GE is similar at cadences of 60, 80 and 100 RPM, despite the significant increase in the CUC. Thus, it is possible that delta efficiency increases with increasing cadence.
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The purpose of this investigation was to examine the ability of a windload simulator reproduce the resistive forces faced by a cyclist during on-road training. The simulator consisted of a multivaned fan enclosed in a ported housing. The level of resistance could be augmented by increasing the port opening. The oxygen consumption ([latin capital V with dot above]O2) of five healthy male subjects was measured under steady state condition at speeds of 16.1, 24.1 and 32.2 km per hour at three intake port openings (1/4, 1/2 and 3/4 open) for each speed. The results were compared to previously reported predicted and actual on-road oxygen consumption values. Two-way ANOVA showed that increaes in speed and opening elicited significant increases in VO2; and a singnificant interaction existed between speed and opening. Post hoc comparisons of [latin capital V with dot above]O2 showed all speeds and openings to be different (p < 0.05). When compared with previously reported on-road values, the resistance settings on the windload simulator showed the ability to produce differing levels of [latin capital V with dot above]O2 within the ranges of those encountered during on-road traning. The results indicate that the windload trainer can accurately reproduce resistive forces within the range of actual on-road training. (C) 1990 National Strength and Conditioning Association
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Nine of the best road-racing cyclists in the United States were tested to evaluate selected psychological characteristics using the Eysenck Personality Inventory (EPI) and the Profile of Mood States (POMS). Their V̇O2max and other factors possibly involved in high-level cycling performance were measured and compared to similar data on East German cyclists in an attempt to determine the performance factors affecting the success of American cyclists in international events. The cyclists were found to be more introverted than normal adults. This is in contrast to what has been found for elite marathon runners but agrees with the trait of introversion found in marathon runners at other competitive levels. The POMS profile of the cyclists was similar to those of elite marathoners, oarsmen and wrestlers. The POMS scales reveal cyclists to be less tense, confused, depressed or angry than college age normals. They also scored higher than normals on the vigor scale. The cyclists were able to average 52.8±4.9 (mean ±SE) seconds at a load in excess of 3780 kmp x min-1 on the bicycle ergometer indicating that, in addition to highly developed aerobic systems, these cyclists also possess the capacity for extremely high power outputs for short periods of time. The V̇O2max for the group averaged 70.3±2.0 ml x kg-1 x min-1 which is very similar to a number of previous reports on European cyclists; their age, height, weight and years in training were also virtually the same. Therefore, it is suggested that other factors, including tactics and technique, must contribute to the performance differences seen between American and European cyclists.
Article
After review of previous studies, it seemed desirable to investigate further the interrelationships between pedalling rate, power output, and energy expenditure, using bicycle ergometry as a model for recreational bicycling. Three young adult male subjects rode a Monark ergometer at eight pedalling rates (30-120 rev min ) and four power outputs (‘ 0 ’ 81-7. 163-4. and 1961 W) [vdot] o2 determinations were made, and using measured R, gross energy expenditure was derived. When these values were combined with the results of other researchers using similar protocol but different power outputs, it was found that: (I) a ‘ most efficient’ pedalling rate exists for each power output studied: (2) the ( most efficient ) pedalling rate increases with power output from 42 rev min at 40-8 W to 62 rev min at 326-8 W: and (3) the increase in energy expenditure observed when pedalling slower than‘ most efficient’ is more pronounced at high power outputs than at low outputs, while the increase in response to pedalling faster than “lsquo; most efficient’ is less pronounced at high power outputs than at low outputs. Thus, there is appreciable interaction between pedalling rate and power output in achieving the ‘ most efficient ’ rate in bicycle ergometry. The ‘ most efficient’ pedalling rate observed at high power outputs in the present study is considerably lower than that reported for racing cyclists by others. This discrepancy may well be related to the difference in swing weights between the ergomeler' s heavy steel flywheel and crankset, and that of the lightweight wheel and crankset used on racing bicycles.
This study was conducted to determine whether the pedaling frequency of cycling at a constant metabolic cost contributes to the pattern of fiber-type glycogen depletion. On 2 separate days, eight men cycled for 30 min at approximately 85% of individual aerobic capacity at pedaling frequencies of either 50 or 100 rev.min-1. Muscle biopsy samples (vastus lateralis) were taken immediately prior to and after exercise. Individual fibers were classified as type I (slow twitch), or type II (fast twitch), using a myosin adenosine triphosphatase stain, and their glycogen content immediately prior to and after exercise quantified via microphotometry of periodic acid-Schiff stain. The 30-min exercise bout resulted in a 46% decrease in the mean optical density (D) of type I fibers during the 50 rev.min-1 condition [0.52 (0.07) to 0.28 (0.04) D units; mean (SEM)] which was not different (P > 0.05) from the 35% decrease during the 100 rev.min-1 condition [0.48 (0.04) to 0.31 (0.05) D units]. In contrast, the mean D in type II fibers decreased 49% during the 50 rev.min-1 condition [0.53 (0.06) to 0.27 (0.04) units]. This decrease was greater (P < 0.05) than the 33% decrease observed in the 100 rev.min-1 condition [0.48 (0.04) to 0.32 (0.06) units). In conclusion, cycling at the same metabolic cost at 50 rather than 100 rev.min-1 results in greater type II fiber glycogen depletion. This is attributed to the increased muscle force required to meet the higher resistance per cycle at the lower pedal frequency.(ABSTRACT TRUNCATED AT 250 WORDS)
Article
Eleven men with recreational bicycling experience rode a bicycle ergometer with instrumented force pedals to determine the effects of pedalling rate and power output on the total resultant pedal force, Fr, and the component of the force perpendicular to the crank arm. The force patterns were obtained at power outputs of 100 W and 200 W for pedalling rates of 40-120 rpm in intervals of 10 rpm. Data were not obtained at 40 rpm at the 200 W power output. The Fr averaged over a crank cycle (Far) was lowest at 90 rpm and 100 W, a value statically different (P less than 0.05) from those at 40, 50, and 120 rpm. At 200 W, the Fr was lowest at 100 rpm, a value statistically different (P less than 0.05) from those at 50, 60, and 70 rpm. The Far varied widely (range of 30% of mean for all subjects) for individuals at a given power output. The results suggest that pedalling at 90-100 rpm may minimize peripheral forces and therefore peripheral muscle fatigue even though this rate may result in higher oxygen uptake.