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The Science of Cycling: Factors Affecting Performance ??? Part 2

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Abstract

This review presents information that is useful to athletes, coaches and exercise scientists in the adoption of exercise protocols, prescription of training regimens and creation of research designs. Part 2 focuses on the factors that affect cycling performance. Among those factors, aerodynamic resistance is the major resistance force the racing cyclist must overcome. This challenge can be dealt with through equipment technological modifications and body position configuration adjustments. To successfully achieve efficient transfer of power from the body to the drive train of the bicycle the major concern is bicycle configuration and cycling body position. Peak power output appears to be highly correlated with cycling success. Likewise, gear ratio and pedalling cadence directly influence cycling economy/efficiency. Knowledge of muscle recruitment throughout the crank cycle has important implications for training and body position adjustments while climbing. A review of pacing models suggests that while there appears to be some evidence in favour of one technique over another, there remains the need for further field research to validate the findings. Nevertheless, performance modelling has important implications for the establishment of performance standards and consequent recommendations for training.
Sports Med 2005; 35 (4): 313-337
R
EVIEW
A
RTICLE
0112-1642/05/0004-0313/$34.95/0
2005 Adis Data Information BV. All rights reserved.
The Science of Cycling
Factors Affecting Performance – Part 2
Erik W. Faria,
1
Daryl L. Parker
2
and Irvin E. Faria
2
1 Exercise Physiology Laboratories, University of New Mexico, Albuquerque, New
Mexico, USA
2 Department of Kinesiology and Health Science, California State University, Sacramento,
California, USA
Contents
Abstract ....................................................................................313
1. Aerodynamics ..........................................................................314
2. Drafting ................................................................................315
3. Rolling Resistance .......................................................................316
4. Equipment Configuration ................................................................317
5. Gear Ratios ............................................................................318
6. Peak Power Output .....................................................................319
7. Pedalling Cadence .....................................................................320
8. Cycling Economy .......................................................................322
9. Cycling Intensity ........................................................................323
10. Muscle Recruitment .....................................................................325
11. Pacing Strategy.........................................................................327
12. Altitude Acclimatisation .................................................................328
13. Performance Modelling .................................................................330
14. Conclusion .............................................................................333
This review presents information that is useful to athletes, coaches and
Abstract
exercise scientists in the adoption of exercise protocols, prescription of training
regimens and creation of research designs. Part 2 focuses on the factors that affect
cycling performance. Among those factors, aerodynamic resistance is the major
resistance force the racing cyclist must overcome. This challenge can be dealt
with through equipment technological modifications and body position configura-
tion adjustments. To successfully achieve efficient transfer of power from the
body to the drive train of the bicycle the major concern is bicycle configuration
and cycling body position. Peak power output appears to be highly correlated with
cycling success. Likewise, gear ratio and pedalling cadence directly influence
cycling economy/efficiency. Knowledge of muscle recruitment throughout the
crank cycle has important implications for training and body position adjustments
while climbing. A review of pacing models suggests that while there appears to be
some evidence in favour of one technique over another, there remains the need for
further field research to validate the findings. Nevertheless, performance model-
314 Faria et al.
ling has important implications for the establishment of performance standards
and consequent recommendations for training.
When writing this review, we have been aware configuration is changed from the upright to the aero
that advances are being made at an exponential rate
position there is a significant increase in oxygen
in the areas of human biology and exercise biochem-
consumption (
˙
VO
2
), heart rate (HR) and respiratory
istry. Consequently, some of the beliefs that are held
exchange ratio (RER).
[6]
today might fail to be supported in the future. How-
In that regard, riding a bicycle in an extreme aero
ever, our goal is to establish a foundation, for the
position increases the metabolic cost of cycling
science of cycling, upon which new information can
when wind resistance is not taken into account.
easily be incorporated and applied. This review arti-
More specifically, the aero position requires a
cle attempts to provide an overview of physiologi-
higher metabolic cost of approximately 37W and
cal, biomechanical and technological factors that
decrease in net mechanical efficiency of approxi-
determine cycling performance. A better under-
mately 3%.
[6,9]
Mechanical efficiency is defined as
standing of the mechanisms and their interactions
the ratio between power output and energy turnover
that underlie the notion of optimisation in the dy-
above resting, expressed in
˙
VO
2
.
[6]
Nonetheless,
namics of cycling will enable more educated ap-
there occurs a 20% decrease in air drag when chang-
proaches to testing, training and research.
ing from the upright sitting and straight arm position
to the hands on the drop bars and another 10–17%
1. Aerodynamics
decrease from hands on the drop bars to the full
The air resistance component while cycling is
crouched aero position.
[7,8,10]
Taken in total, a
proportional to the cube of speed; consequently, it is
30–35% reduction in drag occurs when moving
the primary energy cost factor at high speeds. This
from the upright into the aero position.
aerodynamic resistance represents >90% of the total
The FA of the cyclist combined with that of the
resistance the cyclist encounters at speeds >30 km/
bicycle play a significant part in the creation of
hour.
[1]
At speeds >50 km/hour, aerodynamic resis-
cycling resistance. To estimate the FA, photographs
tance is the most performance-determining varia-
of the cyclist in a specific riding position and of a
ble.
[1,2]
For instance, the drag force at 50 km/hour,
reference rectangle of known area are taken. The
computed in a wind tunnel prior to an attempt at the
contour of the ensemble cyclist-bicycle and that of
1-hour cycling world record, was 12g (where g =
the rectangle are then cut out and weighed. The
acceleration of gravity 9.807 m/sec
2
at sea level).
[3]
cyclist’s FA is then estimated by comparing the
In an effort to reduce this drag force, the configura-
masses of the pictures of the ensemble cyclist-bicy-
tion of the bicycle and its components and cyclist’s
cle and that of the reference area.
[7,11]
Alternatively,
body position have received much attention.
the FA may also be measured by planimetry from
In particular, the aerodynamic effect of the clip-
scaled photographs of the cyclist sitting on the bicy-
on aero-handlebar was first most effectively demon-
cle.
[12]
Bassett et al.
[13]
describe another method used
strated by Greg Lemond during his spectacular Tour
to estimate a cyclist’s FA utilising body surface area
de France win in 1989.
[4]
Moreover, aside from
(SA) and data from the work of Wright et al.
[14]
and
equipment, the cyclists riding position has an impor-
Kyle.
[1]
Knowledge of the cyclist’s FA is especially
tant consequence for both speed and metabolic
important since it generally represents ~18% of the
cost.
[4-6]
The aerodynamic advantage from a reduced
cyclist’s body SA.
[13]
Bassett et al.
[13]
present an
frontal area (FA) when the cyclist assumes a for-
equation, utilising the cyclist’s height and weight
ward crouched upper body position is well estab-
that will estimate the cyclist’s total FA in terms of
lished.
[7,8]
However, when the cyclist’s upper body
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 315
height and mass while in the aero-racing position reveals that the weight of the larger cyclist’s bi-
utilising aero bars: cycles represents a small proportion of their weight
than do the bicycles of smaller cyclists (12% vs
FA = 0.0293H
0.725
M
0.425
+ 0.0604
17%, respectively).
[11]
That being true, the larger
where: FA = frontal area in m
2
; H = height in m; M =
cyclists should once again have a relative advantage
mass in kg.
in energy cost. However, the smaller cyclists have
While there is little effect of body size on the
the advantage in hill climbing because their relative
energy cost of stationary cycling, body size does
maximum oxygen consumption (
˙
VO
2max
) is greater
influence road-racing energy cost. Wind tunnel data
compared with larger cyclists than their disadvan-
and anthropometrical measurements reveal that
tage in energy cost, again supported by race re-
when expressed relative to body mass, the frontal
sults.
[15]
drag of the small cyclists is considerably greater
Large cyclists have the advantage during de-
than that of larger cyclists.
[15]
Moreover, the energy
scents since gravity propels them downward with a
expended to overcome air resistance while cycling is
force that is proportional to the total mass.
[15]
Never-
proportional to the cyclist’s SA.
[15]
Accordingly, the
theless, they often find it difficult to make up the
larger cyclists have an advantage in terms of relative
time lost in ascending mountainous terrain. Taken
energy cost of cycling on level ground because
together, the smaller cyclists should be favoured in
smaller cyclists have a ratio of body SA to FA,
mountainous stage races.
which is larger, thereby creating a greater relative air
In summary, the FA of the cyclist, ~18% of the
resistance.
[7,15]
Furthermore, because the bicycle
cyclist’s body SA, and bicycle is responsible for the
comprises a smaller fraction of the bicycle and cy-
majority of drag force created while cycling. Energy
clist combination for the larger cyclist compared
expended to overcome air resistance is proportional
with the smaller cyclist, the total FA (bicycle and
to the cyclist’s SA, assuming a full crouched aero
cyclist) of the larger cyclist is favoured.
[15]
position reduces drag by 30–35%. Expressed rela-
For example, in a study of large and small cy-
tive to body SA, the FA of the small cyclist is greater
clists, while larger cyclists had a 22% lower
˙
VO
2
/
than the large cyclist. In terms of SA/BW, the larger
bodyweight (BW) than small cyclists at speeds of
cyclist, on flat roads, has an advantage over the
16, 24 and 32 km/hour the SA/BW ratio of the large
smaller cyclist; however, because of the relative
cyclists was only 11% lower than that of the small
˙
VO
2max
to BW the small cyclist has an advantage in
cyclists.
[15]
However, in a racing posture, the FA of
climbing.
the large cyclists had a 16% lower FA/BW ratio than
the small cyclists.
[15]
2. Drafting
Swain
[15]
points out that the difference in frontal
drag (energy cost) is not compensated for by the The ability to avoid crashes and efficiently draft
advantage to small cyclists in relative
˙
VO
2max
(ener- appear to be the two most important factors enabling
gy supply), since the mass exponent for drag (1/3) is cyclists of the major tours to finish within the same
closer to zero than for
˙
VO
2max
(2/3). Consequently, time; however, they are not determinants of a win-
the small cyclists would be at a disadvantage in flat ning performance. Several studies investigated the
time trials where air resistance is the primary force beneficial effects of the drafting component during
to overcome, which is supported by time trial race cycling and triathlon performance.
[16-19]
Drafting
data. The question that remains is who is favoured was found to reduce air resistance, the primary force
on mountainous ascents? Slowing down while as- opposing cyclists, and reduces energy utilisation by
cending reduces air resistance, therefore, the major as much as 40%.
[16]
Furthermore, it has been demon-
force to overcome is gravity. In this instance, the strated while drafting a cyclist outdoors at 39.5 km/
force of gravity is directly proportional to the com- hour compared with cycling alone results in reduc-
bined mass of the cyclist and bicycle. Research tions of ~14% for
˙
VO
2
, 7.5% in HR, and ~31% for
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
316 Faria et al.
expiratory volume (V
E
).
[17]
In a study of triathletes, loading on the wheel. Furthermore, major determi-
it was shown that cycling at 40.9 km/hour continu-
nants of the rolling resistance during cycling are
ously behind the lead cyclist lowers the
˙
VO
2
~16%,
wheel diameter, type of tyre, inflation pressure,
HR ~11%, expiratory flow rate ~10% and blood
ground surface and the friction in the machinery of
lactate ~44% compared with alternate drafting.
[18]
the bicycle.
However, alternate drafting, where the cyclist takes
Rolling resistance can influence power output
the lead every 500m, was not as efficient as continu-
more than drag at low riding speeds in still air.
[5]
ous drafting.
[18]
These data were collected while
Moreover, the rolling resistance is inversely propor-
cycling on an indoor cycling track.
tional to the radius of the wheel.
[5]
Therefore, the
McCole et al.
[19]
reported that the metabolic (e.g.
small-wheel bicycle will have more resistance to
˙
VO
2
) benefit of drafting was higher with increasing
motion than the large wheel under the same condi-
cycling velocity. In this study, the observed reduc-
tions. In terms of road surface, there is a 5% differ-
tion in
˙
VO
2
was independent of the cyclist’s posi-
ence between the rolling resistance of concrete and
tion in a line situation, whereas the adoption of a
blacktop. Tyre pressure can also have an effect on
drafting position at the back of the group of eight
rolling resistance. The higher the operating tyre
cyclists has been reported to further reduce
˙
VO
2
.
[19]
pressure, the greater the reduction in rolling resis-
Therefore, there appears to be a great interest for
tance, which contributes to increased cycling effi-
favourite winners of the Tour de France such as
ciency. The type of tyre (clinchers vs sew-ups)
Lance Armstrong or Jan Ulrich to use drafting stra-
influences the power output to speed relationship.
tegically in the middle of the pack to conserve
For example, at 0.2 horsepower the increase in
energy for subsequent hard stages (e.g. climbing
speed using sew-ups would be almost 6 km/hour
stages). Toward that end, the air resistance in the
faster than clinchers.
[22]
middle of the pack is reduced by as much as 40%.
[19]
The simplest approach for obtaining air- and
Briefly, drafting reduces energy cost of cycling
rolling-resistance values is the coasting-down
by as much as 40%. Likewise, there is a 40% reduc-
method.
[23]
It has been demonstrated that this
tion of air resistance when drafting in the middle of
method provides accurate measurements of velocity
the pack. This reduced drag force has the effect of
under conditions similar to those that prevail during
lowering the
˙
VO
2
, HR, VE and lactate values while
cycling.
[12]
Candau et al.
[24]
developed a simplified
cycling when compared with remaining on the front.
deceleration method (coasting-down method) for
Alternate drafting, taking the lead every 500m, is
assessment of resistance forces in cycling. This
not as effective as continuous drafting.
technique has the practical advantage of allowing
testing of both aerodynamic and rolling resistances
3. Rolling Resistance
over real road conditions. Moreover, the method is
less time consuming and more affordable compared
The third major resistance that must be overcome
with wind-tunnel testing. Additionally, it affords
while cycling is rolling resistance.
[20,21]
Rolling re-
opportunities for energy cost evaluation. The sub-
sistance is the result of the compression of either the
ject’s personal bicycle is used and is generally
wheel or the ground or both. As the bicycle wheel
equipped with aero-handlebars. Cycling attire in-
rolls, the same total area of the tyre and road remain
cludes a classical helmet and racing clothing.
in contact. The greater the area of this ‘patch’ of the
Development of the coast-down test took place in
tyre and road that are in contact, the greater the
an 80m long indoor hallway (2.5m width and 3.5m
resistance will be, since the contact region will
height) with linoleum flooring.
[24]
Trials were per-
extend further in front of the wheel. Hence, the units
formed at initial velocities ranging from 2.5 to 12.8
of rolling resistance are the pound-force per pound-
m/sec. The cyclist initiated the given speed then
force, the first unit being the resistance to rolling in
ceased to pedal before coasting over three timing
pounds of force and the second being the vertical
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 317
switches located at 1m and 20m. While coasting, the The STA is measured at the position of the seat
cyclist continued to pedal without transmitting force
relative to the crank axis of the bicycle. Road racing
to the rear wheel. This leg action provided turbu-
cyclists prefer a STA between 72° and 76°, whereas
lence common during cycling. Newton’s second law
triathletes prefer a STA between 76° and 78°.
[39,40]
served as the basis for the equation of motion for a
The STA can easily be altered for any bicycle frame
cyclist moving on level ground and for a given
by sliding the seat forward or backward on the seat’s
effective FA (AC
D
, in m
2
) and rolling coefficient
rails. Generally, cyclists move their seat as far back-
(C
R
, unitless):
[24]
ward as possible on its rails. In this regard, road
ΣF = M dv/dt =
C
R
M g –
1
/
2
p AC
D
v
2
cyclists choose a shallow (~76°) STA. Their reason
where: F = force; d = distance; v = speed (in m/sec);
for doing so appears to be because most road races
t = time (in seconds); p = air density (in kg/m
3
); M
emphasise hill-climbing road stages and not time-
= mass; g = acceleration due to gravity (in m/sec
2
).
trial stages. In contrast, time-trial cyclists using aer-
The equation assumes that AC
D
and C
R
are not
obars prefer a steeper (>76°) STA.
[39]
Optimal STAs
affected by velocity.
[7,15,20,25]
These researchers
[24]
range from 78.5° for short cyclists to 73.2° for the
found that slight changes in total mass induced
tallest cyclists.
significant differences in rolling resistance. From
Nevertheless, cycling test results regarding the
these observations, they predicted that in a 60km
effect of STA on metabolic parameters are conflict-
competition using racing aero position with the head
ing in the literature. Heil et al.
[39]
found that mean
in the upright position, the time could be improved
values of
˙
VO
2
, HR and rating of perceived exertion
by 191 seconds through assuming an intermediate
(RPE) for STA at 83° and 90° were significantly
head position and by 224 seconds through the head
lower than for STA of 69°, whereas more recently,
in line with the trunk. Caution is advised that the
Garside and Doran
[40]
indicated the lack of signifi-
coast-down test could result in a slight underestima-
cant variations in
˙
VO
2
and HR between STA at 81°
tion of rolling resistance and slight overestimation
and 73°. A STA of 81° was found to improve
of the aerodynamic drag.
[24]
triathlete 10km running and combined cycling plus
In summary, the major determinants of the roll-
running performance,
[40]
whereas STAs of 76°, 83°
ing resistance, in terms of pound-force per pound-
force, during cycling are wheel diameter, type of and 90° elicited similar cardiorespiratory responses
tyre, inflation pressure, ground surface and the fric-
in cyclists.
[39]
Sound scientific rationale for STA
tion in the machinery of the bicycle. Rolling resis-
preference differences between cyclists and triath-
tance can influence power output more than drag at
letes remain to be determined.
low riding speeds in still air. The coasting-down
When the STA is shifted from 69° to 90°, a
method is a cost-effective means of measuring roll-
greater flexion at the ankle occurs and there is a
ing resistance; however, it may underestimate the
change in lower-limb orientation that places the
resistance.
rotation of the legs more directly over the crank
axis.
[39]
The impact of these changes on power out-
4. Equipment Configuration
put is not yet known. However, as hip angle is
increased with an increase in STA, the length of
muscles crossing the hip joint (i.e. biarticulate rectus
The transfer of power from the human body to
femoris, hamstring muscles and gluteus maximus)
the drive train of the bicycle depends upon the crank
are systematically altered. Applying a forward dy-
length
[26-28]
longitudinal foot position on the
namical musculoskeletal model, Raasch et al.
[41]
pedal,
[27,29,30]
pedal cadence,
[26,29-32]
seat
showed that the gluteus maximus provided 55% of
height
[26,29,30,33-35]
and seat-tube angle (STA).
[26,36-38]
all energy delivered to the crank to overcome the
Moreover, the cyclist’s performance is influenced
by the bicycle profile and cyclist’s attire. resistive load and hence, to the power production.
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
318 Faria et al.
Accordingly, it appears that the power phase of cardiovascular response differences.
[43]
Power out-
pedalling may be dependent upon the hip angle. put on- and off-road is significantly lower for the
front suspension system. These data suggest that the
Handlebar designs combined with steeper STAs
dual suspension bicycle may not be as efficient
now allow the cyclist to assume a more crouched
compared with the front suspension bicycle in cross-
upper body position resulting in lower wind resis-
country racing where ~70% of the time spent in-
tance. Consequently, STA can greatly affect the
volves uphill riding.
[43]
cyclists’ efficiency. Road cyclists try to move the
The use of an eccentric chainring was examined
hip joints forward relative to the bottom bracket
during an all-out 1km laboratory sprint test.
[44]
In
allowing them to sit more crouched and in so doing
this study, although no differences between the cir-
reduce wind drag. Steeper STAs will extend the hip,
cular and eccentric chainring were observed for
thereby allowing a more forward and more crouched
cardiorespiratory variables, performance was signif-
upper body position resulting in a decrease of drag
icantly improved utilising the eccentric chainring.
and increase in cycling velocity.
[6,39]
These authors hypothesise that the improved per-
The effect of an aerodynamic frame and wheels
formance is due to the higher torque during the
on the outcome of a 40km individual time trial (ITT)
downstroke resulting from the greater crank length
was modelled by Jeukendrup and Martin.
[42]
An
during that cycling phase.
[44]
Moreover, the variable
aerodynamic frame with the cyclist in an aero posi-
crank arm length used with the eccentric chainring
tion lowered the time by 1:17 minutes for elite-level
may be better adapted for velodrome cycling events.
cyclists, while aerodynamic wheels lowered the
However, at this time, further research with indoor
time by 1 minute for elite-level cyclists. These find-
cyclists employing tests in a velodrome is needed to
ings suggest that at the elite level, cyclists can lower
examine the maximal potential of an eccentric
their 40km ITT time by >2 minutes using aero
chainring.
wheels and frame. It should also be noted that im-
In summary, the bicycle profile, cyclist’s riding
provements in time were larger for lesser trained
position and cycling attire greatly influence per-
cyclists since they spend a greater amount of time on
formance. An STA between 72° and 76° is preferred
the course.
[42]
by road-racing cyclists, whereas triathletes use an
The importance of cycling equipment configura-
STA range of 76–78°. The change in hip angle with
tion and weight is evident in world record attempts.
increased STA allows greater power production and
Accordingly, the bicycle used in the 1-hour world
a more favourable aerodynamic body position. In
record ride weighed 7.280kg. Front and rear disk
cross-country racing, the front suspension bicycle
wheels were made of Kevlar, the diameters being
construction appears to be superior to the dual-
66cm for the front and 71.2cm for the rear. The front
suspension system. For the moment, the value of the
and rear tubeless tyres were 19 and 20mm wide,
eccentric chainring remains equivocal.
respectively. A 180mm crank was used. The cyclist
wore an aerodynamically designed suit (85% Cool-
5. Gear Ratios
max, 15% elastane) and a helmet (Rudy Project,
Italy).
[3]
To meet the requirements of competition in pro-
In contrast to road bicycles, mountain bikes are fessional racing, i.e. Tour de France, Giro, Vuelta a
typically equipped with either a front suspension or Espa
˜
na, cyclists usually adopt hard gears. During
a front and rear (four-bar linkage rear) suspension long, flat stages, gears of 53 × 13–14 are employed
system. Oxygen cost and HR of the cyclist do not to reach an average speed of 43.8 km/hour.
[15]
Time-
differ when riding uphill on- or off-road utilising trial gears of 54 × 13–14 are used to obtain speeds
front and dual suspension uphill.
[43]
However, power averaging 47.3 km/hour.
[15]
Top performance time-
output is significantly higher on the dual suspen- trial cyclists who must perform at an average veloc-
sions system for uphill cycling without concomitant ity of ~50 km/hour use pedal rates >90 revolutions
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 319
per minute (rpm).
[15]
Lower pedal cadences would tion in competition. Furthermore, it is recommended
require use of 58–60 × 11 at 70 rpm in order to
that cyclists with a power index of <40% have to
achieve winning times. The gear ratio used by the
improve their aerobic power.
[52]
Cyclists with a
cyclist who set the new 1-hour world record was 59
power index of >45% have to improve their anaer-
× 14 and the average pedal rate was 101 rpm using a
obic power.
[52]
Consequently, the power index ap-
180mm crank length.
[3]
pears useful when recommending training proto-
cols.
6. Peak Power Output
The importance of the cyclist’s ability to produce
power is best illustrated by the work of Luc
´
ia et
al.
[16]
who collected data during professional stage
One of the newest used measuring devices to
measure power is the SRM Training Systems
races. The mean absolute power output generated
(Schoberer Rad Messtechnik SRM, Julich, Germa-
during the Giro d’Italia, Tour de France and Vuelta a
ny). The SRM system calculates power output from
Espa
˜
na stage races has been estimated to be approxi-
the torque and the angular velocity. To accomplish
mately 400W during a 60-minute period.
[16]
Data
this, strain-gauges are located between the crank
further suggest that a high power output to body
axle and the chainring, and their deformation is
mass ratio (6 W/kg) at maximal or close-to-maxi-
proportional to the torque generated by each pedal
mal intensity is a prerequisite for racing cyclists.
[53]
revolution. This SRM system, previously validated
In support of this finding, during the 1-hour world
from a comparison with a motor-driven friction
record ride, the average power output was
brake
[45]
may be used during both laboratory and
509.53W.
[3]
This cyclist had a power/BW ratio of
field-based studies.
[46]
The system is able to record
6.29 W/kg, which lends credence to the concept of a
power and store the data in its memory. Additional-
power/BW ratio of 6 W/kg or more as a prerequisite
ly, it records and stores information regarding speed,
for racing success.
distance covered, cadence and HR.
Elite male off-road cyclists, National Off-Road
More importantly, to accurately assess power
requires an assessment of the relationships between
Bicycle Association, have demonstrated maximal
force and velocity. Furthermore, to accurately assess
power output values of 5.9 to 5.4 W/kg.
[52,54]
Peak
peak and average power output research reveals that
power output, during a Wingate test, for the United
the inertial-load method, which uses both frictional
States Cycling Federation road cyclists in categories
resistance and flywheel inertia, and the isokinetic
II, III and IV is reported as 13.9, 13.6 and 12.8 W/kg
method are valid.
[47-51]
When measuring power out-
(load 0.095 kg/BW), respectively.
[55]
put, it is recommended that maximal power output,
In summary, the SRM system calculates power
averaged over a complete revolution, is best ob-
output from the torque and the angular velocity. To
tained at ~100 rpm and calculated as follows:
[51,52]
accurately assess power requires an assessment of
W
max
= W
E
+ (40w/t × t
E
)
the relationships between force and velocity. When
where: W
max
= maximal power output (W); W
E
=
measuring power output, it is recommended that
power output of last complete stage (W); 40w =
maximal power output, averaged over a complete
work-load increment; t = work-load duration
revolution, is best obtained at ~100 rpm. Power
(seconds); t
E
= duration final stage (seconds).
output information is useful when establishing train-
To determine the maximal power index, the mean
ing protocols. The cyclist with a power index of
of W
max
is divided by the mean of highest peak
<40% suggests a need to improve aerobic power
power then multiplied by 100.
while an index of >45% indicates attention be given
Baron
[52]
suggests that the power index provides
to anaerobic power training. For road racing suc-
information on the proportion of aerobic to anaerob-
cess, a power/BW ratio of 6 W/kg is suggested.
ic energy contribution that is related to specialisa-
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
320 Faria et al.
7. Pedalling Cadence and percentage of type I muscle fibres. Nonetheless,
the efficiency of muscle contraction is reduced after
Pedalling rate is widely accepted as an important
prolonged cycling and is attributable to both central
factor affecting cycling performance, although there
and peripheral factors but is not influenced by the
does not exist any consensus regarding criteria that
pedalling rate.
[62,63]
The freely chosen cadence ap-
determine the selection of cadence. Previous studies
pears to be related to the ability of the cyclists to
have indicated that pedalling rate could influence
effectively generate force in the quadriceps muscle.
the neuromuscular fatigue in working muscles.
[56,57]
Lepers et al.
[63]
observed that the decrease in muscle
Moreover, pedalling rate may influence the fibre
capacities after cycling exercise was independent of
type recruitment pattern.
[58]
Fewer fast-twitch (type
pedalling rates.
II) muscle fibres, compared with slow-twitch (type
In this regard, several factors influence the effi-
I) muscle fibres, are recruited when pedal cadence is
ciency of pedalling rate. These factors include crank
increased from 50 to 100 rpm.
[58]
This recruitment is
length, body position, linear and angular displace-
in response to the reduced muscle force required per
ments, velocities and accelerations of body seg-
pedal revolution at the higher cadence. Clearly, the
ments and forces in joints and muscles.
[5]
Further-
force demands of pedalling, rather than the velocity
more, cycling experience appears to exert an influ-
of contraction, determines the type of muscle fibres
ence on the metabolic effect of various pedal speeds
recruited.
[58]
Consequently, a cadence of 100 rpm is
and on cycling efficiency.
[16]
Luc
´
ia et al.
[64]
ex-
not too high for type I muscle fibres to effectively
amined the effect of pedal cadence of 60, 80 and 100
contribute to cycling velocity. Other than at maxi-
rpm on the gross efficiency (GE) and other physio-
mal cycling speed, the cyclist will minimise the
logical variables (i.e.
˙
VO
2
, HR, lactate, pH, V
E
,
recruitment of type II fibres by maintaining a high
motor unit recruitment estimated by an electromy-
cadence with low resistance. Moreover, consistent
ogram [EMG]) of professional cyclists while gener-
with the metabolic acidosis discussion in part 1 of
ating high power outputs. It was shown that GE
this review, with less reliance on type II fibres, there
averaged 22.4 ± 1.7, 23.6 ± 1.8 and 24.2 ± 2.0% at
is a decreased likelihood for the onset of premature
60, 80 and 100 rpm, respectively. Mean GE at 100
metabolic acidosis.
[59]
rpm was significantly higher than at 60 rpm. Simi-
Fibre-type recruitment and cycling efficiency ap-
larly, mean values of
˙
VO
2,
HR, RPE, lactate and
pear to be linked with muscle contraction velocity.
normalised root-mean square EMG (rms-EMG) in
At 80 rpm, type I muscle fibres of the vastus lateralis
both vastus lateralis and gluteus maximum muscles
are contracting closer to their peak efficiency con-
decreased at increasing cadences.
[64]
These findings
traction velocity than type II muscle fibres.
[60]
When
confirm that efficiency/economy are very high in
type I muscle fibres are contracting at a speed of
professional cyclists and thus corroborate previous
approximately one-third of the maximal speed of
findings.
[16]
Moreover, these results partly answer
shortening in individual muscle fibres they are at
the question of which pedal cadence is more effi-
their maximised muscular efficiency.
[61]
Conse-
cient/economical in very well trained cyclists and
quently, the cyclist with a higher percentage of type
may help understand why the unusually high ca-
I compared with type II fibres will be more efficient
dence adopted by Lance Armstrong is so beneficial.
as reflected by a lower
˙
VO
2
for a given power
It has been reported that muscle efficiency is
output. Accordingly, the single best predictor of a
40km time-trial performance is the average amount highest at velocities that are slightly lower than
of power output that can be maintained in 1 hour.
[60]
optimal velocity for peak power output regardless of
This predictor represents 89% of the variance in
their fibre type.
[65,66]
These authors conclude that
time-trial performance.
[15]
there appears to be no relation between fibre type
recruitment pattern and neuromuscular fatigue dur-
Coyle et al.
[60]
found a strong relationship (r =
0.75; p < 0.001) between years of endurance training ing cycling exercise. However, these findings have
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 321
to be viewed with caution as animal models were cadences (105 rpm), which contribute to a decrease
employed. in peak pedal force and thereby alleviate muscle
activity for the knee extensors.
[75]
It appears that a
Racing cyclists tend to acquire the skill to ride at
pedalling cadence that decreases muscle stress influ-
a cadence >90 rpm, whereas recreational or novice
ences the preferred cadence selection. Furthermore,
cyclists tend to prefer lower pedal rates.
[67,68]
the preferred higher cadence contributes to the re-
Research has shown that the cadence of trained
cruitment of type I fibres with fatigue resistance and
cyclists during laboratory testing is usually 90–100
high mechanical efficiency despite increased
˙
VO
2
rpm.
[53,68]
Furthermore, the pedalling cadence adopt-
caused by increased repetitions of leg move-
ed with various gear ratios generally varies between
ments.
[73]
It has been observed that optimal cadence,
70–100 rpm.
relative to
˙
VO
2
, changes linearly, increasing from
In this respect, there is considerable discrepancy
>40 rpm at 100W to nearly 80 rpm at 300W.
[76]
in the literature with reference to preferred ca-
While the vast majority of research on pedal rate
dence.
[27,49,69,70]
A popular rationale for the use of
has been conducted in the laboratory, Luc
´
ia et al.
[16]
higher cadences is that they are more efficient. In
investigated the preferred cycling cadence of profes-
general, it appears that the GE, defined as the ratio
sional cyclists during competition. Data obtained
of work accomplished to energy expended,
[71]
dur-
during the Giro d’Italia, Tour de France or Vuelta a
ing pedalling may be influenced by speed of limb
Espa
˜
na stage races reveal that optimal pedalling rate
movement. However, Faria et al.
[72]
reported that at
was characterised as that eliciting the lowest
˙
VO
2
,
high power output a decrease in efficiency was not
lactate and V
E
. The means of cadence and speed
evident when pedal rate was increased while holding
were significantly lower during high mountain as-
power output constant. In this study, at a pedal rate
cents of 7.2% (~70 rpm) than both long, flat stages
of 130 rpm efficiency remained at 22%. Results
and ITT (~90 rpm).
[16]
The individual value of ca-
from the studies of Sidossis and Horowitz
[61]
con-
dence observed in each flat stage averaged 126 rpm.
firm and extend the findings of Faria et al.
[72]
in
The larger, more powerful cyclists recorded pedal
showing that delta efficiency (DE) increased from
rates of 80–90 rpm, whereas lighter cyclists adopted
21% to 24.5% as cadence increased from 60 to 100
faster pedal speeds of 90–100 rpm.
[16]
Break-aways
rpm. Furthermore, both Faria et al.
[72]
and Sidossis
during the last kilometres of the stage and sprints
and Horowitz
[61]
employed power outputs that were
resulted in a pedal rate of ~95 rpm.
[16]
considerably higher than adopted in previous inves-
tigations. Consequently, their efficiency findings Maximum cadence, observed during long, flat
appear to be more characteristic of cycling competi- stages at average speeds of >40 km/hour, averaged
tion. In support of these findings, other investigators 126 rpm.
[16]
The best climbers recorded a pedal rate
report that experienced cyclists are more efficient at of ~80 rpm during ascents of <10%. Maximum
higher pedal rates.
[32,73]
One factor that may contrib- individual value in each high mountain averaged 92
ute to the higher efficiency at higher pedal rates is rpm.
[16]
The best time-trial specialist reached a pedal
that the working muscles are contracting closer to rate of >90 rpm. More to the point, Luc
´
ia et al.
[16]
the speed of shortening that maximised their effi- found the best time trialist recorded a mean pedal
ciency. cadence of 96 rpm.
Chavarren and Calbet
[74]
demonstrated that re- It is well documented that high cadences reduce
gardless of pedalling rate, GE improved with in- the force used per pedal stroke;
[53,77]
consequently,
creasing exercise intensity; however, GE decreased muscle fatigue is reduced in type II fibres. However,
as a linear function of power output. In support of high pedal rates require greater
˙
VO
2
for a given
this finding, Takaishi et al.
[73]
found that trained power output because of an increase in internal work
cyclist’s posses a certain pedalling skill regarding for repetitive limb movements.
[31,68,71,76,78-81]
Never-
the positive utilisation for knee flexors up to high theless, high pedalling rates, which reduce force
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
322 Faria et al.
demands required per pedal revolution and shorten er, these factors favour the cyclist with a higher
percentage of type I muscle fibres. The force de-
contraction time, encourage enhanced blood flow to
mands of the pedal stroke, rather than the velocity of
type I muscle fibres, which in turn is associated with
contraction, determines the muscle fibre type re-
greater recruitment of the type I fibres.
[57,58,82]
It is
cruitment. It is concluded that the optimal pedalling
interesting to note that the world 1-hour cycling
rate is not uniquely specified by the power-velocity
record has been consistently set with average ca-
relationship of muscle.
dence just over 100 rpm.
[3,83]
It is now widely accepted that a pedalling rate
8. Cycling Economy
exists at which significantly smaller neuromuscular
fatigue may be realised. The feelings of strain in the
There exist several calculations that relate to
working muscles and joints appear to dictate pedal
cycling economy (CE)/efficiency. The most often
rate rather than oxygen cost or HR.
[84]
However,
used is GE, while work efficiency (WE), net effi-
sensation of muscular effort as measured by RPE, is
ciency (NE) and DE are less often addressed in the
controversial.
[57]
Marsh et al.
[85]
reported that RPE
literature. For baseline, NE and WE have the resting
may be a critical variable in cadence selection dur-
metabolic rate and the energy required by the un-
ing submaximal power output cycling.
loaded cycling, respectively.
[71]
Nevertheless, one of
Although higher pedalling cadence demands a
the principle determinants of cycling performance is
greater energy expenditure, it has the advantage of
economy/efficiency.
decreasing both actual pedalling force to turn the
CE is defined as the submaximal
˙
VO
2
per unit of
crank and the ratio of maximal peak tension on the
BW required to perform a given task.
[88]
According-
pedal to maximal leg force for dynamic contraction
ly, enhanced CE is reflected by a decrease in the
at a given pedal rate.
[75,86]
Moreover, it has been
percentage of
˙
VO
2max
required to sustain a given
reported that the RER ratio for 90 rpm is significant-
mechanical work.
[89]
It is thought to be the physio-
ly lower than for 60 rpm.
[87]
This finding suggests
logical criterion for ‘efficient’ performance. From
that many type I muscle fibres, characterised by
both a technical and operational point of view, CE
their high oxidative capacity and adapted for pro-
offers a conceptually clear and useful measure for
longed exercise and mechanical efficiency for con-
evaluation of cycling performance. Professional cy-
traction, are recruited at higher pedalling
clists display considerably higher cycling CE and
rates.
[60,80,82,87]
The extent to which these mecha-
efficiency than amateur cyclists despite similar
nisms are advantageous to the cyclists appear to be
˙
VO
2max
values.
[89,90]
related to the cyclist’s pedalling skill.
[57,75]
Economy is seldom used in cycling, although it
In summary, pedalling cadence, which decreases
has provided valuable information in running.
[91]
muscle stress, influences the preferred rate of pedal-
Research has demonstrated that variations in econo-
ling. Nonetheless, experienced cyclists do not al-
my can explain 65.4% of the variation in perform-
ways select their most economical or efficient ca-
ance among a group of elite runners similar in
dence relative to
˙
VO
2
for a given power output.
˙
VO
2max
.
[92]
This same relationship might be true for
Although, terrain and strategy dictate cadence, most
cyclists since
˙
VO
2max
values, provided a minimum
professional cyclists select pedalling between 80
of >65 mL/kg/min is obtained, do not distinguish
and 126 rpm. Increasing pedal cadence, while reduc-
professional from amateur cyclists.
[89,90]
Because of
ing muscle force, encourages recruitment of type I
its value to cyclists and coaches, Luc
´
ia et al.
[89]
muscle fibres, thereby minimising type II fibre in-
recommend that in the future, constant-load exercise
volvement. Reduced muscle force per pedal revolu-
protocols, used to measure CE, be included in the
tion during high cadence results in increased circula-
‘routine’ tests that most competitive cyclists per-
tion and a more effective skeletal muscle pump,
form several times during the season. CE and GE
thereby enhancement of venous turn. Taken togeth-
information made available from constant-load
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 323
bouts has applicability complementary to that ob- It is generally agreed that increased GE is an
essential determinant of cycling performance. Per-
tained from other evaluation measures.
[89]
formance improvement, at least in part, is related to
Efficiency is a measure of effective work and is
the submaximal variables of GE at the lactate
expressed as the percentage of total energy expend-
threshold (LT) or at the respiratory compensation
ed that produces external work. Cycling efficiency
point rather than gains in
˙
VO
2max
. Moseley and
has been reported to be in the range of 10–25%.
[71]
Jeukendrup
[94]
found that 35W increments and
That being the case, then 75–90% of the total energy
3-minute workload stages provided reproducible
used to maintain metabolic equilibrium is obtained
measures of both GE and CE. It was noted that while
from adenosine triphosphate hydrolysis and released
DE is suggested to be the most valid estimate of
as heat.
[71]
muscle efficiency
[88]
it is not the most reliable mea-
There is a mounting interest in measures of WE
sure. DE is defined as the ratio of the change in work
expressed as GE. Toward that end, the formula of
performed to the change in energy expended.
[71]
Brouwer
[93]
is useful. When measures of
˙
VCO
2
and
Moreover, smaller changes in efficiency can be de-
˙
VO
2
are known, energy expenditure may be deter-
tected in GE compared with DE.
mined:
While GE and DE have their limitations, GE is
Energy expenditure (J/sec) = ([3.869 ×
˙
VO
2
] +
recognised as a poor measure of muscle WE
[71,88,99]
[1.195 ×
˙
VCO
2
]) × (4.186/60) × 1000
but is a better measure of whole-body efficien-
cy;
[71,99,100]
consequently, it may serve a more rele-
In this instance, GE is calculated as the mean of all
vant purpose for the cyclist. Until more data are
data collected in the last 2 minutes of every work
available, it is recommended that both GE and DE
rate over and including 95W and until the RER
be used to assess the cyclist’s efficiency.
exceeds 1.00.
[94]
In summary, enhanced CE is reflected by a de-
GE (%) = [work rate (J/sec)]/energy expended (J/
crease in the percentage of
˙
VO
2max
required to
sec) × 100%
sustain a given mechanical work. CE is recommend-
Luc
´
ia et al.
[89]
found that, in professional world-
ed as part of the ‘routine’ test series of cyclists
class cyclists, both CE and GE were inversely corre-
throughout the year. World-class cyclists have
lated to
˙
VO
2max
. In this regard, it has been shown
demonstrated an inverse correlation to
˙
VO
2max
for
that in professional cyclists, the rate of the
˙
VO
2
both CE and GE. In professional cyclists, the rate of
elicited by gradual increases in exercise workload
the
˙
VO
2
elicited by gradual increases in exercise
decreases at moderate to high workloads to the
workload decreases at moderate to high workloads
maximum attainable power output. Additionally,
to the maximum attainable power output. Addition-
mechanical efficiency appears to increase with ris-
ally, mechanical efficiency appears to increase with
ing cycling intensity.
[95]
Luc
´
ia et al.
[89]
suggest that a
rising cycling intensity. Improvement in cycling
high CE/GE might compensate for a relatively low
performance is best related to submaximal variables
˙
VO
2max
. GE for the professional cyclists tested was
of GE at the LT or respiratory compensation point
~24%. CE was calculated as W/L/min, while GE
rather than gains in
˙
VO
2max
. Both GE and DE are
was expressed as the ratio of work accomplished per
recommended in the assessment of the cyclist’s effi-
minute (W converted to kcal/min) to energy expend-
ciency.
ed per minute (in kcal/min). CE of professional
cyclists was found to average 85 W/L/min. A two-
9. Cycling Intensity
time world champion cyclist with a
˙
VO
2max
of 70
mL/kg/min recorded a CE >90 W/L/min and a GE
Knowledge of the exercise intensity required dur-
of 25%. Factors that may influence GE include
ing cycling competition provides valuable informa-
pedalling cadence,
[78]
diet,
[96]
overtraining,
[97]
genet-
tion for the prescription of training regimens. It is
ics
[98]
or fibre-type distribution.
[88]
interesting to note that at 3 and 5 minutes after
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
324 Faria et al.
competition of the 1-hour world record, the cyclist’s beats/min for a stage time of ~288 minutes.
[101]
lactate levels were 5.2 and 5.1 mmol/L, respective-
Interestingly, during the mountain stages, for the
ly,
[3]
while the peak HR reached 190 beats/min.
[3]
combined tours, the mean stage HR was ~132 beats/
Collectively, the work of Fern
´
andez-Garc
´
ia et al.
[101]
min for a mean stage time of ~328 minutes.
[101]
The
and Padilla et al.
[102]
have provided the most com-
low cycling intensity (HR) recorded for the com-
prehensive cycling intensity data collected during
bined tour during mountain stages may be attributa-
the multi-stage road races. Employing laboratory
ble to methodological constraints where the authors
metabolic data collected 2 weeks prior to races,
were unable to differentiate climbing versus decent
Fern
´
andez-Garc
´
ia et al.,
[101]
characterised the cy-
HR. Nevertheless, the combined tour mean HR for
cling intensity utilising HR data recorded every 15
ITT was ~168 beats/min during a mean time of 45
seconds via telemetry. The HR values were then
minutes.
[101]
used to establish four heart zones that corresponded
Two lactate criterion measures of exercise inten-
to intensities of exercise relative to:
sity were applied during the tour observations. The
anaerobic (over the individual anaerobic thresh-
LT was identified as the exercise intensity eliciting 1
old; around 90% of
˙
VO
2max
);
mmol/L increase in lactate concentration above av-
intense aerobic (70–90% of
˙
VO
2max
);
erage baseline lactate values measured when exer-
moderate aerobic (50–70% of
˙
VO
2max
);
cising at 40–60% of the maximal aerobic power
output.
[104]
Based on pre-race laboratory tests, the
recovery (<50% of
˙
VO
2max
).
LT value established for each cyclist was linked to
Clearly, professional multi-stage cycling races
the heart rate (HR
LT
) and power output (W
LT
), then
demand long-duration, high-intensity exercise. Fer-
termed the LT
zone
[102,105]
Secondly, again based on
n
´
andez-Garc
´
ia et al.
[101]
observed that cyclists were
pre-race laboratory tests, the onset of blood lactate
involved in intense aerobic work of ~75 and ~79
accumulation (OBLA = 4 mmol/L)
[102,105]
was estab-
min/day and moderate aerobic work represented ~97
lished for each cyclist then linked to the HR and
and ~89 min/day during the Vuelta a Espa
˜
na and the
termed the OBLA
zone
.
[102]
It is noteworthy that the
Tour de France, respectively. During both races, the
total time spent at and above the OBLA
zone
during
cyclist spent ~20 min/day over the individual anaer-
high-mountain stages was similar to that reported
obic threshold. About 93 min/day were spent in flat
for short individual and team time trials (16 min-
stages and 123 min/day in mountain stages riding at
utes). Some cyclists have been observed to spend 80
an intensity >70% of
˙
VO
2max
and 18–27 of these
minutes in the OBLA
zone.
[102]
High mountain stages
minutes were at an intensity >90% of
˙
VO
2max
. Tak-
accounted for the longest time spent in the LT
zone
.
en together, ~75% of each stage is spent >50% of
These data serve a useful purpose for the develop-
˙
VO
2max
. In contrast, during ITT, cyclists spend a
ment of training protocol for stage-racing competi-
mean of 20 minutes >90%
˙
VO
2max
. Luc
´
ia et al.
[103]
tion.
reported that the professional winner of the short
road-cycling time trial during the Tour de France
Padilla et al.
[102]
demonstrated that when exercise
sustained 70 minutes at an intensity >90% of
intensity recorded during racing is estimated from
˙
VO
2max.
HR reserve
[106]
instead of HR
max
, values are ~8%
lower but close to %W
max
values. This closeness is
HR data reveal that the HR is related to the
good reason to choose the HR reserve method along
course profile rather than being stochastic. In ITT
with individual laboratory HR-power output rela-
stages, cyclists reached a mean HR of ~171 beats/
tionships.
[102]
With regard to W
max
values, approxi-
min and during flat stages a HR mean of ~125 beats/
mately 2 weeks prior to each stage race, the cyclists
min.
[101]
The mean overall HR for the combined
underwent an incremental maximal cycling test. Ex-
tours was observed to be ~134 beats/min for a mean
ercise began with an initial resistance of 100W, with
stage time of ~254 minutes.
[101]
During flat stage
further increments of 35W every 4 minutes, inter-
cycling, the combined tour mean HR was ~126
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 325
spersed with 1-minute recovery intervals. A 75 rpm ~254 minutes. Total time spent at and above OBLA
cadence was required.
[102]
In this case, W
max
was during high mountain stages is 16 minutes, while
measured as the highest work-load the cyclist can some cyclists remain at OBLA for 80 minutes. Off-
maintain for a complete 4-minute period. When the road cross-country racing demands a starting HR of
last work-load cannot be maintained for the com- ~90% HR
max
or 84%
˙
VO
2max
. The mean intensity
plete 4-minute period, W
max
is computed as fol- of off-road cross-country racing is similar to road-
lows:
[107]
racing time trials and a longer time is spent above
LT than road stage-racing cyclists.
W
max
= W
f
+ ([t/240] × 35)
where: W
f
= value of the last complete work-load; t
10. Muscle Recruitment
= time the last uncompleted work-load was main-
tained (in seconds); 35 = the power output differ-
Road and off-road bicycle racing call for hill-
ence between the last two work-loads.
climbing ability. High mountain stages require up-
Racing strategy has a significant influence on the
hill cycling during several periods lasting 30–60
HR response. Off-road cross-country cycling com-
minutes. While climbing, the cyclist is confronted
petition is shown to demand near maximal effort at
with and must overcome the force of gravity and
the start of the race in order to establish an advanta-
gravity-induced resistance of the body mass.
[109]
In
geous starting position. Accordingly, the average
response to increased resistance, cyclists frequently
HR during such starts has been reported as ~90% of
switch from the conventional sitting position to a
HR
max
corresponding to ~84% of
˙
VO
2max
.
[108]
less economical standing posture. Consequently, the
Moreover, the average exercise intensity of cross-
cyclist can then exert more force on the pedals but is
country competitions lasting ~147 minutes is similar
it economical to do so? Accordingly, Millet et al.
[110]
to that reported by Padilla et al.
[3]
for short road-
demonstrated that the technical features of standing
cycling time trials lasting ~10 and ~39 minutes (89 ±
versus seated position may affect metabolic re-
3% and 85 ± 5% of HR
max
, respectively). When the
sponses. Toward that end, these investigators dis-
intensity profile is expressed as a percentage of LT,
covered that level-seated, uphill-seated, or standing-
cross-country cyclists have been observed to spend
cycling positions exhibit similar external efficiency
longer time periods at and above LT than road stage-
and economy in trained cyclists at a submaximal
racing cyclists.
[108]
intensity.
[110]
In contrast, HR and V
E
were found to
The higher intensity and longer duration at high
be higher in a standing as opposed to a seated
intensity by off-road cyclists may be partially ex-
position. In addition, during a maximal 30-second
plained by the demand placed on the cyclist for
sprint, the power output was reported to be 25–30%
bicycle control and stabilisation on very rough ter-
greater in a standing position than when seated.
[110]
rain. Repeated isometric muscle contractions of the
In the standing position, a higher power output
arms and legs may influence HR values during off-
would be expected since the cyclist’s BW can con-
road racing. However, there is evidence that use of
tribute to a greater force on the pedal per pedal
front suspension cycles serve to minimise such a
revolution. Furthermore, pedalling cadence was ~60
response.
[43]
rpm, for both seated and standing while climbing,
compared with ~90 rpm in level cycling. This study
In summary, professional multi-stage races and
was limited to a single gradient, short duration and
off-road cross-country races demand long-duration,
different pedal cadences, therefore, the combination
high-intensity exercise. In stage races, approximate-
of speed, hill gradient and duration that would fa-
ly 75% of each stage is spent at >50%
˙
VO
2max
. ITT
vour climbing in the seated or standing position
cyclists spend 20 minutes at >90%
˙
VO
2max
, while
remains to be investigated.
the winner of the Tour de France sustained 70 min-
utes at >90%
˙
VO
2max
. Mean HR for the combined The cyclist can generate a high climbing speed of
tours is ~134 beats/min for a mean stage time of 20 km/hour by using light gears (39 × 23–25) and
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
326 Faria et al.
high cadences (90 rpm); however, that is generally pedal reaction force in relation to the hip joint axis.
not the case. Rather, most cyclists elect to rise off Calwell et al.
[111]
observed this vertical force to be
the saddle and push harder gears (39 × 17–21) at the major component of the pedal reaction force
relatively low cadences while others remain on the while standing. The vastus lateralis, a one-joint knee
saddle and push hard gears. Standing on the pedals, extensor, and the rectus femoris, a biarticular exten-
however, changes the range of motion of the hip sor, increase their duration of activity with a change
joint substantially during the crank revolution. More to the standing posture. It appears that the single-
importantly, the centre of body mass is modified by joint plantar flexor soleus increases ankle plantar
standing on the pedals.
[111,112]
Consequently, stand- flexor moment when standing on the pedals. The
ing and seated postures produce different pedal rectus femoris, a knee extensor and hip flexor, be-
force, crank torque and joint movement comes active for a longer duration in the standing
profiles.
[111,112]
A change in cycling posture suggests versus seated posture.
[111,112]
It is interesting to note
that there will be modifications in the patterns of that three muscles, the biceps femoris, gastrocnemi-
muscle activity. Additionally, alteration of grade us and tibialis anterior, appear to display similar
accompanied by a change in cycling posture will activity for both saddle and standing posi-
result in changes in the direction of the force applied tions.
[114,115]
to the pedal.
[111]
The activity patterns of mono- and bi-articular
When climbing, the magnitude and activity of the muscles have been shown to have different activity
gluteus maximus, whose function is hip extension, is patterns with respect to seated and standing uphill
higher in the standing position than when seated on conditions.
[113,116]
This variance is believed to be due
the saddle. Its activity begins just before the top to the possibility that monoarticular muscles con-
dead centre of crank position and continues well into tribute to positive work, whereas biarticular muscles
the later part of the downstroke, which is not differ- control the direction of force applied to the
ent from the saddle position.
[5]
However, the gluteus pedal.
[117]
The involvement of the biceps femoris
maximus does not appear to increase extensor mo- appears to be related to the specific pedalling tech-
ment but rather serves to enhance pelvis stabilisa- nique of the cyclist. The use of a fixed rather than
tion.
[113]
The vastus lateralis, a powerful knee exten- flexing ankle joint throughout the crank cycle results
sor, is observed to be activated earlier in the upward in different muscle recruitment and coordina-
recovery phase and is active longer into the subse- tion.
[117]
It should be noted that a limitation of this
quent downward power phase when moving from type of research, if performed in a laboratory where
sitting on the saddle to standing during a climb. the bicycle ergometer is fixed in place, is the ab-
During the down-stroke of seated cycling, extensor sence of the confounding factor of lateral sway of
moments are needed at all three lower extremity the bicycle, which occurs in standing while climbing
joints, except for the knee joint moment that changes and is commonly observed among cyclists when
from extensor to flexor in the middle of the down ascending and sprinting. This sway action could
stroke.
[114,115]
introduce modified muscle activity patterns.
With a change from the seated to standing posi- In summary, shifting from a seated to standing
tion, a distinct alteration in the pelvic angle occurs posture while cycling changes the magnitude and
accompanied by a dramatic increase in muscle activ- activity of several key muscle groups whose func-
ity of the gluteus maximus.
[113]
The more forward tion is to provide maximum force to the pedals.
position of the hip joint in relation to the crank When standing, the magnitude and activity of the
spindle, in the standing posture, reduces the horizon- gluteus maximus is higher. Activation of the vastus
tal distance between the hip joint and the point of lateralis is earlier and its duration of activity is
force application on the pedal. Consequently, this longer. Likewise, the rectus femoris increases its
position reduces the moment arm of the vertical duration of activity and the soleus increases ankle
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 327
plantar flexion. However, similar activity is ob- race, which results in a ‘negative split’.
[121]
In con-
served for the biceps femoris, gastrocnemius and
trast, maintenance of a variable pace throughout a
tibialis anterior for the saddle and standing position.
race means that a cyclist will be accelerating and
Nonetheless, in well trained cyclists and within the
decelerating. This mode of pacing is common
limitations of current investigations, economy and
among road and off-road racing cyclists. Variable
gross-external efficiency in seated uphill cycling are
pacing, however, is said to lead to excessive glyco-
not different than in uphill standing cycling. Further
gen depletion and premature onset of fatigue. Com-
research is needed in order to expand our knowledge
pared with a variable pace during a 20km time-trial,
of those variables that affect uphill cycling perform-
steady pace riding was shown to be 6% better.
[118]
ance.
Subsequently, these authors found that time-trial
performance was similar when well trained subjects
11. Pacing Strategy
performed prolonged variable interval exercise or
constant-load exercise of the same average intensity.
Whether racing against the clock or other com-
Only small differences were observed in skeletal
petitors, finishing in the absolute quickest time pos-
muscle carbohydrate metabolism and muscle re-
sible is the cyclist’s goal. Similarly, the objective of
cruitment and such differences did not affect the
time trialing is to maintain the highest sustainable
performance of a subsequent bout of high-intensity
speed for the period. Toward that end, knowledge of
cycling.
[53]
It was concluded that type I muscle
the effects of systematic variations in pacing strate-
fibres were more depleted by constant rather than
gy on performance allows the athlete and coach to
variable pacing while type II muscle fibres were
plan optimal strategy for energy expenditure. Palm-
more depleted of glycogen by variable pacing. Faria
er et al.
[118]
suggested that varying power may im-
et al.
[122]
examined the use of HR as variable for
pede time trial performance. Moreover, Foster et
pacing strategy. Pacing with HR did not produce
al.
[119]
reported that the best performance in simulat-
significant decreases in time to completion of a
ed 2km time trials was attained when the cyclist
given amount of work compared with no pacing.
maintained an even pacing strategy for the duration
A basic question raised concerning pacing strate-
of the ride. Despite this finding, there were no
gy is whether the lower power output during the
systematic differences in serial
˙
VO
2
, accumulated
downhill/downwind race section will allow suffi-
oxygen deficit, or post-exercise lactate that could
cient recovery for the cyclist to successfully accom-
account for the even pacing advantage.
plish a variable power strategy. Swain
[123]
demon-
Starting strategy may also play an important role
strated that modestly increasing power output dur-
for the racing cyclist. Bishop et al.
[120]
reported that
ing uphill/up-wind sections (by as little as 5%) and
an all-out start strategy for 10 seconds followed by a
decreasing power during downhill/downwind sec-
transition to even pacing resulted in a significantly
tions resulted in faster times as compared with those
greater 2-minute, all-out kayak ergometer perform-
during a constant power effort. In his computer
ance when compared with an even pacing strategy.
model, Swain
[123]
confirmed that the fastest time
These authors found that the average power was
when riding in winds or on hills should occur when
significantly greater during the first half of the test
the cyclist varies power to counter the conditions.
using the all-out start strategy, and significantly
This variable power strategy resulted in significant
lower in the second half of the tests when compared
time savings, even when the magnitude of variation
with the even strategy. The all-out start strategy
was only 5% above and below mean power.
resulted in significantly greater
˙
VO
2
at 30 and 45
seconds and a significantly greater total
˙
VO
2
.
In support of this finding, Liedl et al.
[121]
found,
using a pacing strategy that deviated ±5% every 5
A small body of research tends to support the
minutes from the cyclist’s mean power output dur-
strategy of even pacing albeit with a slightly faster
steady speed employed during the second half of the ing a 1-hour time trial, imposed no additional physi-
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
328 Faria et al.
ological stress compared with constant power out- ble race time may it may be best not to vary >5% for
either the slow or fast portion of the race.
put. Moreover, Atkinson and Brunskill
[124]
tested
Swain’s computer model with a computer-generated
12. Altitude Acclimatisation
16.1km course in which the cyclists encounter a
headwind on the first half of the course and a
Competitive cycling events are often staged at
tailwind in the second half of the course. These
moderate altitude (1500–2500m). If not given spe-
authors
[124]
found a significant decrease in time to
cial adaptation consideration, for the lowland native
complete the course when paced by power output.
cyclist, altitude may be the nemesis. Consequently,
They further found a small but non-significant in-
a considerable amount of research has been conduct-
crease in performance when varying power by +5%
ed on short- and long-term altitude acclimatisation
into the headwind and
5% in the tailwind. These
and its effects on the performance of endurance
data suggest that a small variance in pacing will
athletes at altitude and sea level; however, the
significantly increase ITT performance.
[124]
weight of scientific evidence remains equivocal.
Counter to intuitive practice, it appears wise to
The positive effects of living and/or training in an
speed up slightly when the cyclist begins to feel
environment with decreased partial pressure of oxy-
heavy legs, rather than to slow down. In this in-
gen appear to be mediated primarily through in-
stance, type II muscle fibres begin to work while
creases in haemoglobin concentration, buffering ca-
giving type I muscle fibres a chance to provide more
pacity and adaptations in skeletal muscle.
[126]
Theo-
pyruvate to the mitochondria. This practice is fol-
retically, these changes should result in an
lowed by Kenyan runners who are the best endur-
improvement in endurance exercise performance at
ance runners in the world. Along this same line of
both sea level and altitude. Nonetheless, it is appar-
thinking, Billat et al.
[125]
suggest that it is normal for
ent from a review of the literature, that methodologi-
cal technicalities have often been responsible for the
athletes to vary their pace. However, to produce the
lack of consistent evidence in favour of altitude
best possible time it is suggested that the pace
residence for performance at sea level.
[127]
Research
should not vary >5% for either the slow or fast
has been limited by small sample sizes, insufficient
side.
[123]
or varying durations of altitude exposure, varying
The majority of time-trialing studies have been
altitudes, different types of exposure (hypobaric vs
conducted in the laboratory; however, in reality
normobaric), unequal training status and lack of
cycling time trialists must consider which form of
adequate controls.
[128,129]
Furthermore, past altitude
pacing might best fit the environment and terrain.
research has been confounded by the use of training
Consequently, future field studies are required to
as a variable, which elicits similar physiological
confirm the best time-trial protocol.
changes to acclimatisation alone.
[130]
In summary, common among racing cyclists is a
Sea-level endurance athletes that are acutely ex-
variable pace strategy throughout the race. Howev-
posed to even moderate altitudes, 900m
[131]
and
er, steady pace riding compared with variable pac-
580m
[132]
have demonstrated significant drops in
ing has been shown to be 6% better for 20km time
˙
VO
2max
and performance. The decline in
˙
VO
2max
trialing. Yet, time-trial performance was shown to
has been documented in athletes
[132]
and trained
be similar when well trained cyclists performed
subjects
[133]
to begin as low as ~600m. For every
prolonged variable interval exercise or constant-
1000m increase above 1050m, Robergs and Rob-
load exercise of the same average intensity. Re-
erts
[133]
reported an 8.7% decrement in
˙
VO
2max
. A
search suggests that varying power by +5% into the
decrement in aerobic exercise performance at an
headwind and
5% in the tailwind may be the most
altitude of 2240m above sea level may approach
effective race pace strategy. In close agreement,
7%.
[134]
Furthermore, this decrement is increased to
other findings suggest that to produce the best possi- 12% at 2286m.
[135]
In trained subjects, the reduction
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 329
in oxygen saturation (SaO
2
) with increasing hypoxia possible prior to competing at altitude to minimise
the drop in
˙
VO
2max
at altitude.
accounts for around 86% of the variance in the
decrement in
˙
VO
2max
.
[136]
It is interesting to note
The other option presented by Daniels
[139]
was to
that athletes with a SaO
2
<90% during maximal sea-
ascend to the competition site early to undergo accli-
level exercise display a significant decline in
matisation to the altitude. Indeed, Fulco et al.
[140]
˙
VO
2max
during acute exposure to a mild altitude of
have pooled submaximal and maximal data and
~1000m.
[137]
However, trained athletes who main-
shown significant increases in submaximal perform-
tain a SaO
2
>92% during maximal sea-level exer-
ance following acclimatisation, with very little
cise do not display a significant reduction in
˙
VO
2max
change in
˙
VO
2max
following acclimatisation. How-
at 1000m.
[138]
Trained cyclists residing at moderate
ever, full acclimatisation can take several weeks and
altitude (1800–1900m) have been shown to have an
is therefore not practical for most athletes. Although
SaO
2max
significantly higher by 5% during hyper-
it is often more inconvenient, many athletes sleep at
oxia versus normoxia.
[138]
Furthermore, moderate
a low altitude until the morning of the event and then
altitude-acclimatised athletes appear to preserve
are transported to the competition site early in the
oxyhaemoglobin saturation at sea level.
[138]
morning. This practice may not be necessary and
may be unwise.
Consequently, to meet the challenge of racing
above sea level, cyclists have used various novel
Weston et al.
[141]
examined performance after
approaches and regimens for altitude training. The
arrival at 1700m following 6, 18 and 47 hours of
method called ‘live high/train low’ has recently re- exposure. These authors reported that the greatest
drop in performance was following 6 hours with
ceived much attention. Toward that end, a 2- to 4-
improvement in performance following 18 hours
week stay at moderate altitude has afforded accli-
and no further improvement following 47 hours.
matisation resulting in a decreased production or
These data suggest that an overnight stay at the
increased clearance of lactate and moderate im-
altitude competition venue may be of value rather
provement of muscle buffering capacity.
[130]
Fur-
than detriment. Similarly, in an unpublished obser-
thermore, it appears that acclimatisation to moderate
vation, Parker et al. examined changes in
˙
VO
2max
altitude does not include increased red cell produc-
following an overnight stay in a hypobaric chamber
tion sufficiently to increase red cell volume and
decompressed to 440 Torr (~4300m). Following a
haemoglobin mass.
[130]
Nevertheless, hypoxia does
13-hour exposure, these authors reported no signifi-
increase serum erthyropoietin levels but only weak
cant changes in
˙
VO
2max
and arterial oxygen satura-
evidence exists of an increase in young red blood
tion. Parker et al. did report significant decreases in
cells (reticulocytes).
[130]
Living and training at mod-
BW, leg muscle cross-sectional area and plasma
erate altitude does not appear to improve sea-level
volume due to increased diuresis. Despite the decre-
performance of highly trained athletes. Living at
ment in plasma volume,
˙
VO
2max
was retained.
altitude and training near sea level may be effective
Robergs et al.
[142]
previously found that maintaining
for some cyclists; however; more research is re-
arterial oxygen saturation and decreasing leg muscle
quired to confirm its efficacy.
size would minimise the drop in
˙
VO
2max
at altitude
With acute exposure to altitude, subjects demon-
and may serve as an explanation for how the acute
strate increased ventilation and increased diuresis.
responses of hyperventilation and increased diuresis
Both of these responses increase fluid losses and
observed by Parker et al. maintained
˙
VO
2max
(un-
subsequently decrease plasma volume. The more
published observation). The increased diuresis and
prolonged the exposure, the greater the decrement in
hyperventilation as a result of altitude exposure that
fluids and presumably the greater the decrement in
Daniels
[139]
was trying to avoid may in fact be ad-
performance. This response has lead Daniels
[139]
to
vantageous rather than compromising to perform-
ance at altitude. The decline in fluid volume at
suggest that lowland athletes stay low for as long as
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
330 Faria et al.
altitude decreases muscle volume, thus diffusion In summary, adaptations to altitude are mediated
primarily through increases in haemoglobin concen-
distance in the muscle to help maintain oxygen
tration, buffering capacity and adaptations in skele-
delivery. Furthermore, the hyperventilation ob-
tal muscle. For every 1000m increase above 1050m
served at altitude has the advantage of shifting the
there is an 8.7% decrease in
˙
VO
2max
. Reduction in
oxyhaemoglobin curve to the left and protecting
S
a
O
2
with increasing altitude accounts for around
SaO
2
during intense exercise.
[143]
Therefore, it ap-
86% variance in
˙
VO
2max
decrement. However,
pears that the more appropriate recommendation for
˙
VO
2max
alone is not valid indicator of submaximal
cyclists that are going to compete at altitude is
exercise performance. Nonetheless, trained athletes
ascend to the competition the day before to undergo
who maintain a SaO
2
>92% during maximal sea-
partial acclimatisation.
level exercise do not display a significant reduction
Past literature on altitude acclimatisation reveals
in
˙
VO
2max
at 1000m. Because full acclimatisation to
that following 10 days of exposure to moderate
altitude can take several weeks, it is not practical for
altitude, a significant improvement is seen in physi-
most athletes. It appears that cyclists who are going
cal work capacity at altitude
[144]
and a reduction in
to compete at altitude, but who are limited in altitude
plasma lactate occurs when subjects exercise at the
acclimatisation resources, should ascend to the com-
same power output.
[145]
More importantly, these
petition the day before to undergo partial accli-
changes occur without a significant change in
˙
VO
2
.
matisation.
Cycling at altitude has special circumstances in
relation to effects on performance. For example,
13. Performance Modelling
there has been a definite drop in
˙
VO
2max
demon-
strated at altitude, but performance at altitude may
Performance modelling is employed to estimate
actually improve despite this drop in
˙
VO
2max
. Since
the power required to perform individual and team
the primary source of resistance in cycling is air
pursuits under a variety of cycling conditions. Addi-
moving over the body of the cyclists, the lower
tionally, the power required to cycle at a certain
barometric pressure at altitude leads to a decrease in
speed can be estimated by adjusting the model for
air resistance. Using the equations of motion of Di
important factors affecting cycling performance.
Prampero et al.
[20]
for a theoretical drop in
˙
VO
2max
While recent development of the SRM bicycle
of 5% at an altitude of ~1500m would actually lead
crank dynamometer has made it possible to accu-
to an increase in speed of 4%. This response, howev-
rately measure cycling power output, theoretical
er, is only true for level ground cycling, the oxygen
modelling serves as an alternative method. As such,
cost of hill climbing stays very close to the same at
modelling remains useful to athletes, coaches and
altitude. These equations suggest that cycling per-
researchers who might want to estimate the power
formance at moderate altitude is improved on flat
required to establish new cycling records under vari-
terrain despite drops in
˙
VO
2max
, while hilly terrain
ous conditions. Since 1876, the distance for the
performance will drop proportionately with the drop
prestigious 1-hour cycling record has been doubled.
in
˙
VO
2max
. Moreover, during most aerobic competi-
Mathematical models have been used to predict the
tive cycling events, athletes perform at intensities
power requirements needed to better the 1-hour re-
below that which elicits
˙
VO
2max
. Fulco et al.
[140]
cord, 4000m team and individual pursuit races, and
have commented that submaximal exercise perform-
other competitive events. Physiological, aerody-
ance decrements are difficult to predict based on the
namic and equipment-related modelling has proven
decline in
˙
VO
2max
, since the measurement of
to be a valuable guide for the prediction of success-
˙
VO
2max
represents only the maximal aerobic contri-
ful performance.
[13]
Since 1967, ~60% of the im-
bution, whereas differing proportions of aerobic and
provement in the 1-hour record has come from aero-
anaerobic processes are involved in exercise of vari-
dynamic advances and ~40% for higher power out-
ous intensities and durations. puts.
[13]
The world 1-hour record of 56.375km set by
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 331
Chris Boardman in 1996 was accomplished employ- sion for the average team pursuit power as a function
of velocity:
[147]
ing a power output of 442W.
[13]
P = 0.7697 K(0.00953 M
t
V + 0.0075V
2
+ K
1
Factors such as body mass, height, saddle posi-
(A
f
) 0.007551V
3
)
tion of the cyclist, type and aerodynamic character-
where: P = power (W); K = constant describing
istics of the bicycle and cyclist’s apparel, rolling
track characteristics and rolling resistance; M
t
=
resistance of the surface, wind, temperature, air den-
mass of cyclist and bike (kg); V = speed (km/hour);
sity, humidity and many more have proven valuable
K
1
= constant describing aerodynamic factors; A
f
=
in the prediction of performance.
[13]
More impor-
frontal area of the cyclist, calculated as A
f
= 0.0293
tantly, modelling is useful to further improve train-
× height
0.725
× weight
0.425
+ 0.0604; and 0.7697 =
ing protocol and testing for potential racing success.
correction factor for team pursuit.
Practically, theoretical calculations applied during
pre-competition allow for effective strategy modifi- The constant K, which is usually around 1, quan-
tifies the influence of aerodynamic factors and is
cation.
calculated as follows:
The validation of various models is generally
K
1
= K
d
× K
po
× K
b
× K
c
× K
h
accomplished utilising metabolic data, power output
where: K
d
= density ratio (1 at sea level, 0.78 at
variables and practical testing.
[146]
Models enable an
2500m altitude); K
po
= position of the cyclist on the
estimate of performance impact of changes in physi-
cycle (1.08–1.18 for standard position, 1 for stan-
ological, biophysical, biomechanical, anthropomet-
dard aero position); K
b
= bicycle-related factor (1
ric and environmental parameters. Numerous re-
for standard cycle, 0.93 for aerodynamically op-
searchers have demonstrated reasonable accuracy of
timised track cycle); K
c
= clothing (1 for aerody-
performance modelling.
[7,13,114,146-152]
Moreover,
namic skin suit, 1.09 for long sleeve jersey); K
h
=
modelling software predicts that, for a trained cy-
helmet (1 for aero time-trial helmet, 1.025 for con-
clist (riding an average of 300W over 40km), a 1%
ventional cycle helmet.
improvement in efficiency will result in a 63-second
improvement in a 40km time-trial time.
[94]
Related factors include bicycle weight, disk
wheels, front and real wheel diameter, tyre construc-
Pursuit and time-trial racing requires high aero-
tion, inflation pressure, gear ratio and rpm.
bic and anaerobic power and special aerodynamic
Padilla et al.
[3]
have demonstrated the validity of
characteristics. Modelling affords the opportunity to
mathematical models that integrate the main cycling
evaluate in advance the cyclist’s potential for top
performance-determining variables that predict ve-
performance. Toward that end, the work of several
lodrome cycling performance. It is interesting to
investigators has provided important basics for the
note that the FA value of 1-hour cycling record
modelling of cycling performance.
[146-150]
For in-
holders represent ~18.1% of their BSA. Conse-
stance, the energy cost factor for the interchange of
quently, some may find it useful to apply this FA to
front cyclist in the 4000m team pursuit, when ex-
BSA relationship when selecting potential 1-hour
posed unshielded from team-mates (one-fifth of a
record competitors.
lap) has been estimated to be 0.7697.
[147]
Research
evidence reveals that power (W) at increasing speed
A model of cycling performance based on equat-
is dependant on the track characteristics and rolling
ing two expressions for the total amount of work
resistance (K) and A
f
of the cyclist.
[147]
Taking into
performed was designed by Olds et al.
[150]
One
consideration these dependent variables, a theoreti-
expression was deduced from biomechanical princi-
cal model was designed to predict individual and
ples deriving energy requirements from total resis-
team pursuit times by utilising either direct field or
tance. The other models the energy available from
laboratory power measurements or by estimating
aerobic and anaerobic energy systems, including the
power from actual individual pursuit perform-
effect of
˙
VO
2
kinetics at the onset of exercise. Like
ances.
[147]
The following equation yields an expres-
other models, the equation can then be solved for
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
332 Faria et al.
any of the variables. Empirically derived field and an impact on the optimal pedal rate. First, from the
laboratory data were used to assess the accuracy of
perspective of the power-velocity relationship, opti-
the model.
[150]
Model estimates of 4000m individual
mal pedalling rate is one that allows as many mus-
pursuit performance times showed a correlation of
cles as possible to actively contract close to the
0.803 (p 0.0001) with times measured in 18 high-
velocity at which power production is maxi-
performance track cyclists, with a mean difference
mal.
[76,153]
A second factor that influences optimal
(predicted-measured) of 4.6 seconds (1.3% of mean
pedalling rate is the process of calcium release and
performance time).
[150]
This model enables esti-
reuptake from the sarcoplasmic reticulum.
[153]
The
mates of the performance impact of alterations in
third factor is pedal forces that are so high that the
physiological, biomechanical, anthropometric and
upper body loses contact with the saddle, resulting
environmental parameters. Additionally, Olds et
in a change of the mechanical system.
[152]
Accord-
al.
[151]
presented a cycling efficiency model for road-
ingly, the authors elected to fix the pelvis to the
cycling time-trial performance. It includes correc-
saddle to circumvent this issue. These investigators
tions for the effect of winds, tyre pressure and wheel
discovered that activation dynamics was a major
radius, altitude, relative humidity, rotational kinetic
determinant of the pedalling rate that maximises
energy, drafting and changed drag. Relevant physio-
mechanical power output of the model during sprint
logical, biophysical and environmental variables
cycling.
[153]
The results of the study showed that
were measured in 41 experienced cyclists complet-
activation dynamics is a major determinant of the
ing a 26km road time trial. The model yielded 95%
pedalling rate that maximises mechanical power
confidence limits for the predicted times and sug-
output of the model used during sprint cycling. The
gested that the main physiological factors contribut-
authors comment that this study presents a strong
ing to road-cycling performance are
˙
VO
2max
, frac-
case, in that both model structure and parameter
tional utilisation of
˙
VO
2max
, mechanical efficiency
values were taken from previous work on vertical
and projected FA. The model was applied to some of
jumping
.
[155]
The model structure and parameter val-
the following practical problems in road cycling:
ues used in this study provide a description of the
the effect of drafting;
musculoskeletal system involved in the task of
the advantage of using smaller front wheels;
sprint cycling. Moreover, it was shown that the
the effects of added mass;
muscle spends a large fraction of total metabolic
the importance of rotational kinetic energy;
work on pumping calcium irons back into the
the effect of changes in drag as a result of
sarcoplasmic reticulum. Accordingly, fast sar-
changes in bicycle configuration;
coplasmic reticulum calcium is a prerequisite for
the normalisation of performances under differ-
high mechanical power output.
[153]
It was concluded
ent conditions;
that the optimal pedalling rate is not uniquely speci-
the limits of human performance.
fied by the power-velocity relationship of muscle.
This model predicted a 3% improvement in 26km
Heil
[156]
developed a generalised allometric
time-trial time with a 1 standard deviation improve-
model (GMA) of endurance time-trialing cycling
ment in GE.
performance:
Van Soest and Casius
[153]
present a modelling/
S
MAX
= (R
NET
) × (W
S(MAX)
)
simulation approach to pedalling rate in sprint cy-
where: W
S(MAX)
= maximal metabolic steady-state
cling in which the movement is calculated from the
power supply capable of directly resisting R
NET
neural input to muscles. Furthermore, others
[154]
during a time-trial performance; R
NET
= net resis-
have reported that maximal power output has been
tance to forward motion (N) and the sum of aerody-
found to be sustainable for only approximately 5
namic drag (R
D
, N) and gravitational (R
G
, N) resis-
seconds at a pedalling rate of 120–130 rpm. Van
Soest and Casius
[153]
identify three factors as having tance.
2005 Adis Data Information BV. All rights reserved. Sports Med 2005; 35 (4)
Science of Cycling: Part 2 333
The GMA predicted that larger cyclists are Acknowledgements
favoured to win endurance time-trial races when the
road incline is downhill, flat or slightly uphill (grade The authors wish to express their sincere gratitude and
appreciation to those fellow scientists whose works are dis-
1.5°), while lighter cyclists were favoured during
cussed and cited in this paper. Without their research the
steep uphill races (grades >2°).
[156]
These findings
scientific knowledge reviewed herein would not exist. We are
are compatible with the work of Capelli et al.
[7]
and
greatly indebted to the individuals who willingly physically
Swain.
[15]
At slight incline, however, the GAM pre-
participated in this research. Further, we want to acknowl-
dicted that cycling time-trial performance would be edge the reviews whose numerous constructive comments
contributed to the comprehensiveness of this manuscript and
independent of body mass. The author
[156]
com-
for sharing their expertise and valuable input.
ments that the GMA is useful to coaches, athletes
No sources of funding were used to assist in the prepara-
and sport sciences in evaluation of the optimal body
tion of this review. The authors have no conflicts of interest
mass for the 1-hour record and for development of
that are directly relevant to the content of this review.
laboratory protocol that accurately mimics the phys-
iological demands of uphill time-trial cycling. Fur-
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... Cycling performance appears to be largely determined by the cyclist's ability to produce high power outputs with minimal metabolic cost. Since pedaling speed (cadence) can affect both the power generation ability and the rate of energy consumption, it is thought that the choice of cadence may have a significant impact on cycling performance (Faria et al., 2005). ...
... In the research, the performance related to the professional road bike; It is believed to be related to maximum oxygen consumption (Max Vo2), cycling efficiency (Maximum efficiency with low metabolic load) and lactate threshold. Together, these factors have been shown to enable elite cyclists to establish and maintain high workloads and relatively fast cadence (Majerczac et al., 2006;Faria et al., 2005). ...
... The most measured responses include heart rate (HR), perceived exertion (RPE), oxygen uptake (VO2), Lactate Threshold (LT), and blood lactate (BLa) ratings. The effectiveness of cadence selection has been shown to play a role in metabolic responses by influencing the onset of fatigue and sustained maximum strength (Faria et al., 2005;Brisswalter et al., 2000). 5 Formenti et al. (2019) stated that high cadence may cause an increase in metabolic demand on the skeletal muscle system. ...
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The aim of this research was to investigate the effects of low and high cadence data of the riders on performance and success ranking in the Tour de France 2021, one of the most important and difficult bicycle races in the world. In this research, the cadence data obtained during the 21 stages of the riders who could complete the Tour de France 2021 were obtained from the Strava program, and the general classification rankings were obtained from the official website of the Tour de France 2021. The riders included in the study were determined according to their general classification. The data of six riders in the first twenty and the last twenty of the general classification were analyzed. Descriptive statistics and independent sample t-test applied in the study were performed with SPSS 22.0 statistical package program. Literature data of the study were obtained by using content analysis method with academic publications that can shed light on the subject. The findings we obtained the analysis of the data show that high cadence brings better performance and success. It is seen that new studies to be carried out in this direction are valuable in terms of the importance and clarification of the subject.
... Additionally, it has been considered as a marker of reduced muscle efficiency (Jones et al., 2011;Poole and Jones, 2012;Rossiter, 2011). Hence, higher oxygen cost has often been related to lower endurance performance (Faria et al., 2005;Hausswirth and Brisswalter, 2008;Zamparo et al., 2011). Therefore, understanding the VO 2 excess phenomenon underpinning mechanisms is paramount for improved exercise guidance. ...
Thesis
L'objectif de ce travail était d'étudier l'oxygénation globale et musculaire au cours de l'exercice intense tout en vérifiant les hypothèses explicatives de l'excès (Δ) de VO2 et la composante lente (CL) de VO2 observés respectivement au cours de l'exercice incrémental et à charge constante. La première étude a renforcé le lien entre ΔVO2 et le travail respiratoire. La deuxième étude a montré les importantes sollicitations des muscles respiratoires au cours de l'exercice à charge constante corrélées avec CL VO2 comparées à l'exercice incrémental. De plus, cette étude a permis à travers de l'enregistrement de l'oxygénation au niveau du vaste externe de confirmer l'existence CL VO2 et l'absence ΔVO2 local. Malgré les origines communes entre CL VO2 et ΔVO2, ces deux phénomènes ne sont pas de corrélés. La corrélation entre l'oxygénation globale et locale au cours de l'exercice à charge constante nous a conduits à la troisième étude s'intéressant à investiguer cette relation en fonction de l'intensité de l'exercice. En effet, cette corrélation a été impactée par l'intensité avec une composante lente locale qui n'était pas différente entre les deux intensités. Associé à ceci, le taux total d'hémoglobine a diminué à l'exercice sévère avec des sollicitations ventilatoires plus importantes comparées à l'exercice intense. Dans ce sens, la dernière étude révèle que l'endurance des muscles respiratoire est corrélée à la baisse de saturation tissulaire en oxygène au niveau du vaste externe à la fin de l'exercice ainsi qu'à la performance.Ces résultats contribuent à la compréhension de l'oxygénation globale et musculaire et en particulier ΔVO2 et CL VO2 et leurs origines
... Monitoring training load and response to training is crucial to control athletes' adaptation [1]. Currently, the popularization of different wearable devices allows access to knowledge of different parameters of internal and external load during the exercise, providing access to information in real-time [2]. Some examples of internal load are heart rate, muscle glucose, or muscle oxygen saturation (SmO 2 ), while external load could be power or speed [1,3]. ...
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A new method to monitor internal training load from muscle oxygen saturation using near-infrared spectroscopy could be of practical application for research and training purposes. This technology has been validated in different scientific fields, including sport science, and Humon Hex and Moxy are two leading brands. However, its relationship with hemoglobin has not been studied. Forty-eight professional cyclists, 19 men and 29 women, underwent a blood test to measure hemoglobin in the early morning. Immediately afterwards, hemoglobin and muscle oxygenation were monitored at rest by Moxy and Humon Hex on their right quadriceps (where the skinfold was measured). Venous blood hemoglobin was higher than the measurement for both devices (p < 0.001). Both hemoglobin (p < 0.001) and muscle oxygen saturation measurements (p < 0.05) were higher in Humon Hex than for Moxy, and there was a reasonable reproducibility (ICC=0.35 for hemoglobin and 0.26 for muscle oxygen saturation). Skinfold had an inverse relationship with hemoglobin measurement (r = –0.85 p < 0.001 for Humon Hex and r = –0.75 p < 0.001 for Moxy). These findings suggest that resting hemoglobin data provided by these devices are not coincident with those of blood sample, and skinfold has an inverse relationship with blood hemoglobin measurement.
... From a sport science perspective, the physiological and performance attributes for each cycling discipline are very different. While road cycling events require a high level of aerobic capacity, BMX and some of the track cycling events involve a high anaerobic contribution (Faria et al. 2009;Faria et al. 2005). Muscle typology in world class cyclists competing in BMX, cyclo cross, cross country, road and track cycling was recently investigated and the findings revealed that differences in muscle typology could inform talent identification and talent transfer (Lievens et al. 2020). ...
Thesis
Monitoring and evaluating the physiological and performance characteristics of endurance athletes provides relevant information about the long-time athletic development, training process and talent identification. While there is growing evidence for the physiological and performance attributes in junior and professional cyclists, limited information is available about the U23 category. Therefore, the aim of this thesis was to examine the longitudinal physiological and performance characteristics of U23 elite cyclists, with a special focus on the application of the power profile and the power-duration relationship. Study 1 involved a critical evaluation of the current literature on power profiling methodologies and the application of the power-duration relationship. In order to improve the predictive ability of the power profile and the power-duration relationship across exercise intensity domains, it is recommended to ensure a high ecological validity (e.g. rider specialization, race demands) during standardized field testing. For this reason, single effort prediction trials outside the severe exercise intensity domain should be avoided, due to a high measurement bias and a low predictive ability regarding the power-duration relationship. Standardized field testing for power profiling should be conducted at least two times per season to obtain an accurate fingerprint of a cyclist’s performance capacity in the field. In addition, future research is required to better understand the fatigue mechanisms and downward-shift of the power profile and power-duration relationship in the moderate and heavy exercise intensity domains following prior heavy exercise. In Studies 2 and 3 the power profile and power-duration relationship were investigated throughout a competitive season in U23 elite cyclists. Study 2 examined the changes in maximal mean power output (MMP) and derived critical power (CP) and work capacity above critical power (W´) obtained during training and racing. The results revealed that the absolute power profile was not significantly different during a competitive season, except changes in the relative power profile due to a reduction in body mass. Study 3 investigated the differences in the power profile derived from training and racing, the training characteristics across a competitive season, and the relationships between the training characteristics and the power profile in U23 elite cyclists. Higher absolute and relative power profiles were recorded during racing than training. Training characteristics were lowest in pre-season followed by late-season. Changes in training characteristics correlated with changes in the power profile in early- and mid-season, but not in late-season. Practitioners should consider the influence of racing on the derived power profile and adequately balance training programs throughout a competitive season. Studies 4 and 5 analysed the power profile, workload characteristics and race performance in U23 and professional cyclists during a five-day multi-stage race. Study 4 compared the power profile, internal and external workloads, and racing performance between U23 and professional cyclists and between varying rider types, including allrounders, domestiques and general classification (GC) riders. This study demonstrated that the power profile after 1.000-3.000 kJ of total work could be used to evaluate the readiness of U23 cyclists to move into the professional ranks, as well as differentiate between rider types during racing. Study 5 specifically analysed climbing performance in a professional multistage race, and assessed the influence of climb category, prior workload, and intensity measures on climbing performance in U23 and professional cyclists. The findings indicated that climbing performance in professional road cycling is influenced by climb categorization as well as prior workload and intensity measures. Professional cyclists displayed better climbing performance than U23 cyclists, while the workload and intensity measures were higher in U23 than professional cyclists. Collectively the studies within this thesis have contributed to an improved understanding of the physiological and performance attributes of U23 elite cyclists in their maturation to the professional level. These studies have confirmed the practical application of the power profile and power-duration relationship for performance evaluation and prediction during training and racing. This thesis has enabled detailed insights about factors affecting the power profile and the power-duration relationship, and it has provided a concise applied strategy for the inclusion of power profiling in the longitudinal athletic development pathway to maximize cycling performance.
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El objetivo de este estudio fue analizar la relación entre la producción de potencia en ciclismo, y variables analíticas de core, como la fuerza, la estabilidad y la cinemática durante el pedaleo. En este estudio participaron 30 ciclistas de tres disciplinas, los cuales realizaron un test de estabilidad sedente, un test de fuerza de extensores y flexores de tronco y un test de potencia en ciclismo, donde se analizó la cinemática del tronco. Se encontró una mayor estabilidad sedente en los ciclistas de mountain bike, una mayor inclinación anterior torácica en ciclistas de carretera y un mayor rango de inclinación lateral en triatletas. Solo se encontraron correlaciones significativas entre la inclinación anterior torácica y la potencia crítica. En conclusión, tanto la fuerza como la estabilidad de tronco tienen una baja influencia en la producción de potencia, aunque sí existen diferencias entre las distintas disciplinas en las dimensiones del core.
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Background: Mixed-reality sports are increasingly reaching the highest level of sport, exemplified by the first Virtual Tour de France, held in 2020. In road races, power output data are only sporadically available, which is why the effect of power output on race results is largely unknown. However, in mixed-reality competitions, measuring and comparing the power output data of all participants is a fundamental prerequisite for evaluating the athlete’s performance. Objective: This study investigates the influence of different power output parameters (absolute and relative peak power output) as well as body mass and height on the results in mixed-reality competitions. Methods: We scrape data from all six stages of the 2020 Virtual Tour de France of women and men and analyze it using regression analysis. Third-order polynomial regressions are performed as a cubic relationship between power output and competition result can be assumed. Results: Across all stages, relative power output over the entire distance explains most of the variance in the results, with maximum explanatory power between 77% and 98% for women and between 84% and 99% for men. Thus, power output is the most powerful predictor of success in mixed-reality sports. However, the identified performance-result gap reveals that other determinants have a subordinate role in success. Body mass and height can explain the results only in a few stages. The explanatory power of the determinants considered depends in particular on the stage profile and the progression of the race. Conclusion: By identifying this performance-result gap that needs to be addressed by considering additional factors like competition strategy or the specific use of equipment, important implications for the future of sports science and mixed-reality sports emerge.
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With the emergence of digital sensors in sports, all cyclists can now measure many parameters during their effort, such as speed, slope, altitude, heart rate or pedalling cadence. The present work studies the effect of these parameters on the average developed power, which is the best indicator of cyclist performance. For this, a cumulative logistic model for ordinal response with functional covariate is proposed. This model is shown to outperform competitors on a benchmark study, and its application on cyclist data confirms that pedalling cadence is a key performance indicator. However, maintaining a high cadence during long effort is a typical characteristic of high‐level cyclists, which is something on which amateur cyclists can work to increase their performance.
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Purpose Rowing instrumentation systems provide measures of stroke power, stroke rate, and boat velocity during rowing races, but how well these measures predict race performance has not been reported previously. Methods Data was collected per stroke from 45 2000-m races using Peach PowerLine and OptimEye S5 GPS units. The boat classes assessed were nine male singles, eight female singles, three male pairs, and six female pairs. Random effects and residuals from general linear mixed modelling of stroke velocity adjusted for stroke power, stroke rate, and mean headwind provided measures interpreted as technical efficiency, race conditions, and stroke-velocity variability. These measures, along with mean race power, mean stroke rate, and mean headwind were then included in multiple linear regressions to predict race velocity from official race times. Effects were assessed for 2-SD changes in predictors and interpreted using interval-hypothesis tests. Results Effects of mean race power, mean stroke rate, and mean headwind on race velocity ranged from small to extremely large and were mostly decisively substantial. Effects of technical efficiency and race conditions ranged from trivial to extremely large but were generally unclear, while stroke-velocity variability had trivial-small and mostly unclear effects. Prediction error was small to moderate and decisively substantial. Men’s pairs lacked sufficient data for analysis. Conclusion On-water rowing race performance can be predicted with mean race values of power, stroke rate and headwind. Estimates from stroke data are potentially useful predictors but require impractical numbers of boats and races to reduce their uncertainty.
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The practice of physical activity in a variable climate during the same competition is becoming more and more common due to climate change and increasingly frequent climate disturbances. The main aim of this pilot study was to understand the impact of cold ambient temperature on performance factors during a professional cycling race. Six professional athletes (age = 27 ± 2.7 years; height = 180.86 ± 5.81 cm; weight = 74.09 ± 9.11 kg; % fat mass = 8.01 ± 2.47%; maximum aerobic power (MAP) = 473 ± 26.28 W, undertook ~20 h training each week at the time of the study) participated in the Tour de la Provence under cold environmental conditions (the ambient temperature was 15.6 ± 1.4 °C with a relative humidity of 41 ± 8.5% and the normalized ambient temperature (Tawc) was 7.77 ± 2.04 °C). Body core temperature (Tco) was measured with an ingestible capsule. Heart rate (HR), power, speed, cadence and the elevation gradient were read from the cyclists’ onboard performance monitors. The interaction (multivariate analysis of variance) of the Tawc and the elevation gradient has a significant impact (F(1.5) = 32.2; p < 0.001) on the variables (cadence, power, velocity, core temperature, heart rate) and on each individual. Thus, this pilot study shows that in cold environmental conditions, the athlete’s performance was limited by weather parameters (ambient temperature associated with air velocity) and race characteristics. The interaction of Tawc and elevation gradient significantly influences thermal (Tco), physiological (HR) and performance (power, speed and cadence) factors. Therefore, it is advisable to develop warm-up, hydration and clothing strategies for competitive cycling under cold ambient conditions and to acclimatize to the cold by training in the same conditions to those that may be encountered in competition.
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Time-relaxed sports timetables utilize (many) more time slots than there are games per team, and therefore offer the flexibility to take into account player and venue availability. However, time-relaxed tournaments also have the drawback that the difference in games played per team and the rest period between teams’ consecutive games can vary considerably. In addition, organisers may want to avoid consecutive home and away games (i.e. breaks). To construct fair timetables, we propose relax-and-fix (R&F) and fix-and-optimize (F&O) heuristics that make use of team- and time-based variable partitioning schemes. While the team-based R&F constructs a timetable by gradually taking into account the integrality constraints related to all home games of a subset of teams, the time-based R&F maintains a rolling horizon of time intervals in which the integrality constraints of all games scheduled within the time interval are activated. The F&O heuristics use the same variable partitioning schemes, but they never relax the integrality constraints and allow to recover from mistakes by making a small number of changes with respect to the variables optimized in previous iterations. For numerous real-life instances, our heuristics generate high-quality timetables using only a fraction of the computational resources used by monolithic integer programming solvers.
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LUCÍA, A., J. HOYOS, M. PÉREZ, A. SANTALLA, and J. L. CHICHARRO. Inverse relationship between V̇O2max and economy/efficiency in world-class cyclists. Med. Sci. Sports Exerc., Vol. 34, No. 12, pp. 2079–2084, 2002. Purpose: To determine the relationship that exists between V̇O2max and cycling economy/efficiency during intense, submaximal exercise in world-class road professional cyclists. Methods: Each of 11 male cyclists (26 ± 1 yr (mean ± SEM); V̇O2max: 72.0 ± 1.8 mL·kg−1·min−1) performed: 1) a ramp test for V̇O2max determination and 2) a constant-load test of 20-min duration at the power output eliciting 80% of subjects’ V̇O2max during the previous ramp test (mean power output of 385 ± 7 W). Cycling economy (CE) and gross mechanical efficiency (GE) were calculated during the constant-load tests. Results: CE and GE averaged 85.2 ± 2.3 W·L−1·min−1 and 24.5 ± 0.7%, respectively. An inverse, significant correlation was found between 1) V̇O2max (mL·kg−0.32·min−1) and both CE (r = −0.71;P = 0.01) and GE (−0.72;P = 0.01), and 2) V̇O2max (mL·kg−1·min−1) and both CE (r = −0.65;P = 0.03) and GE (−0.64;P = 0.03). Conclusions: A high CE/GE seems to compensate for a relatively low V̇O2max in professional cyclists.
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Purpose: The purposes of this study were to estimate noninvasively the maximal lactate steady state (MLSS) in trained cyclists on a windload simulator with a velocity based technique and to determine whether the HR at MLSS (HR MLSS ) elicited a similar blood lactate concentration (BLC) during field testing. Methods: To determine and verify MLSS, 10 male cyclists performed five to seven laboratory trials on separate days, including a VO 2max test; a 5-km time trial (TT); and two or more 30-min trials at specific percentages of each subject's average 5-km TT speed (AVS 5km ). Mean ± SD for the following variables were obtained at MLSS: velocity was 90.3 ± 2.7% of the AVS 5km , BLC was 5.4 ± 1.6 mM, RPE was 15 ± 2.1, VO 2 was 80 ± 6.3% of VO 2max , and HR was 167 ± 9.5 beats.min -1 , which was 88 ± 3.8% of the mean maximum HR. Field tests included three laps of an 8-km road circuit at HR MLSS ± 3 beats.min -1 and one lap at maximum sustainable velocity (a road TT). Results: There were no significant differences in BLC, HR, and RPE between the three steady-state road laps and the lab MLSS trial. There was also good agreement between the road and lab MLSS velocity/TT velocity ratios. Conclusions: Our data suggest that 5-km TT cycling velocity, as measured on a windload simulator, may be used to estimate MLSS and the HR at MLSS for training purposes.
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A bicycle and its rider are strongly impeded by their resistance to the flow of air. Aerodynamic stratagems have brought vehicles that can go 60 miles per hour on a level road without assistance. This article discusses techniques for drag reduction and reviews aerodynamic data for various human powered vehicles.