ArticlePDF Available

Abstract and Figures

Technique and energy saving are two variables often considered as important for performance in cycling and related to each other. Theoretically, excellent pedalling technique should give high gross efficiency (GE). The purpose of the present study was to examine the relationship between pedalling technique and GE. 10 well-trained cyclists were measured for GE, force effectiveness (FE) and dead centre size (DC) at a work rate corresponding to ~75% of VO(2)max during level and inclined cycling, seat adjusted forward and backward, at three different cadences around their own freely chosen cadence (FCC) on an ergometer. Within subjects, FE, DC and GE decreased as cadence increased (p < 0.001). A strong relationship between FE and GE was found, which was to great extent explained by FCC. The relationship between cadence and both FE and GE, within and between subjects, was very similar, irrespective of FCC. There was no difference between level and inclined cycling position. The seat adjustments did not affect FE, DC and GE or the relationship between them. Energy expenditure is strongly coupled to cadence, but force effectiveness, as a measure for pedalling technique, is not likely the cause of this relationship. FE, DC and GE are not affected by body orientation or seat adjustments, indicating that these parameters and the relationship between them are robust to coordinative challenges within a range of cadence, body orientation and seat position that is used in regular cycling.
Content may be subject to copyright.
Eur J Appl Physiol (2011) 111:2885–2893
DOI 10.1007/s00421-011-1914-3
The relationship between cadence, pedalling technique and gross
eYciency in cycling
Stig Leirdal · Gertjan Ettema
Received: 20 October 2010 / Accepted: 7 March 2011 / Published online: 25 March 2011
© The Author(s) 2011. This article is published with open access at
Abstract Technique and energy saving are two variables
often considered as important for performance in cycling
and related to each other. Theoretically, excellent pedalling
technique should give high gross eYciency (GE). The pur-
pose of the present study was to examine the relationship
between pedalling technique and GE. 10 well-trained
cyclists were measured for GE, force eVectiveness (FE) and
dead centre size (DC) at a work rate corresponding to
»75% of VO2max during level and inclined cycling, seat
adjusted forward and backward, at three diVerent cadences
around their own freely chosen cadence (FCC) on an
ergometer. Within subjects, FE, DC and GE decreased as
cadence increased (p< 0.001). A strong relationship
between FE and GE was found, which was to great extent
explained by FCC. The relationship between cadence and
both FE and GE, within and between subjects, was very
similar, irrespective of FCC. There was no diVerence
between level and inclined cycling position. The seat
adjustments did not aVect FE, DC and GE or the relation-
ship between them. Energy expenditure is strongly coupled
to cadence, but force eVectiveness, as a measure for pedalling
technique, is not likely the cause of this relationship. FE,
DC and GE are not aVected by body orientation or seat
adjustments, indicating that these parameters and the
relationship between them are robust to coordinative
challenges within a range of cadence, body orientation and
seat position that is used in regular cycling.
Keywords Cadence · Inclined · Level · Pedalling ·
In cycling, not only the work capacity but also a proper
technical execution of the propulsive movements is often
considered to be important for performance. In cycling,
force eVectiveness (FE) is often used as a parameter to indi-
cate the quality of pedalling technique (e.g., Patterson and
Pearson 1983; Ericson and Nisell 1988; Coyle et al. 1991;
Sanderson 1991; Sanderson and Black 2003; Zameziati
et al. 2006; Candotti et al. 2007; KorV et al. 2007). FE is
the ratio between the force directed 90° on the crank arm
and the total resultant force on the pedal. Furthermore, it is
generally believed that high gross eYciency (GE) is related
to good technique in general and high FE speciWcally (e.g.,
Zameziati et al. 2006; Candotti et al. 2007). In a mechani-
cally eVective pedalling technique with high FE a large
component of the generated force is directed perpendicu-
larly on the crank arm. Forces directed otherwise, i.e., radially
to the crank, do not contribute to work rate and the associ-
ated energy cost is wasted. Thus, in principle, FE aVects
GE in a direct manner. A number of studies have demon-
strated a moderate to strong relationship between FE and
GE (e.g., Zameziati et al. 2006; Candotti et al. 2007).
On the other hand, various studies have shown that for
an eVective and powerful crank cycle, one must generate
considerable radial forces. This is due to the mechanical
constraints within the rider–bicycle system (Kautz and
Hull 1993). Because of the constraints, it is not a priori so
that the most eVective force is generated with the least
muscular eVort. Furthermore, inertial and gravitational
forces add to the ineVective component of pedal forces
Communicated by Jean-René Lacour.
S. Leirdal (&) · G. Ettema
Department of Human Movement Science,
Norwegian University of Science and Technology (NTNU),
Dragvoll Idrettssenter 3 etg, 7491 Trondheim, Norway
2886 Eur J Appl Physiol (2011) 111:2885–2893
(Kautz and Hull 1993), which are of no physiological
consequence. This raises questions on both the origin as
well as the signiWcance of the apparent relationship
between FE and GE.
GE indicates the total metabolic rate, including muscle
work rate, for a given external work rate, and FE is the
resultant outcome of all muscle activation. Thus, an FE–GE
relationship that is unaVected by other factors would indi-
cate that the total amount of muscle work done at a given
external work rate (via GE) and the net coordinative out-
come (via FE) are tightly coupled.
Cadence is shown to aVect both FE (Patterson and Pear-
son 1983; Sanderson 1991; Candotti et al. 2007; Lorås et al.
2009) and GE (Seabury et al. 1977; Coast and Welch 1985;
Belli and Hintzy 2002; Foss and Hallen 2004, 2005; Sam-
ozino et al. 2006; Hansen and Sjøgaard 2007). Body orien-
tation (e.g., inclination as in inclined cycling) and seating
position (e.g., diVerences between road cycling, time trial
and triathlon cycling, see Faria et al. 2005) are factors that
possibly aVect both FE and/or GE (e.g., Bertucci et al.
2005; Brown et al. 1996; Caldwell et al. 1998, 1999; Millet
et al. 2002; Faria et al. 2005; Heil et al. 1997; Price and
Donne 1997; Ricard et al. 2006; Umberger et al. 1998;
Welbergen and Clijsen 1990). While both FE and GE are
studied quite extensively related to these factors, relatively
little is done on the relationship between FE and GE, i.e., if
the relationship is independent of other factors. Particu-
larly, the role of cadence is of importance and may explain
any relationship between FE and GE if it aVects both in a
similar fashion.
Recently, Leirdal and Ettema (2010) introduced a new
pedalling technique parameter, which described the dead
centre (DC) and was deWned as the minimum power
divided by the average power during the pedal stroke. It
had a stronger relationship with GE than FE and it was,
unlike FE, not aVected by inertial forces that increase with
cadence. Thus, it could be hypothesised that DC is not
aVected by cadence in the way that FE and GE are. The
inXuence of cadence on DC has not been investigated
before. By studying, in detail, how the relationship between
on the one hand FE and DC (technique) and on the other
GE (energy expenditure) is aVected by cadence, more
insight may be obtained in how cycling technique and
energy expenditure are related.
In the present study, we therefore investigated the rela-
tionship between pedalling technique (FE and DC) and GE,
over three cadences, for level and inclined cycling position,
and for three seating positions. We took the freely chosen
cadence (FCC) of the cyclists as a departure point to set the
range of cadences. This increased the total range of
cadences and allowed for studying the eVect of absolute
(inter-individual diVerences) as well as relative cadence
(intra-individual diVerences).
The study was approved by the local ethics committee and
all participants signed an informed written consent before
participating in the study. Ten well-trained cyclists, at a
national and regional level, participated in the study. The
participant’s physical characteristics are presented in
Table 1.
Protocol and analysis
All participants met in the lab on two occasions. On the Wrst
occasion, they performed an incremental test at freely
chosen cadence (FCC) on a Velotron ergometer with a
computer-controlled electro-magnetic brake mechanism
(Velotron, Racermate inc., Washington). This ergometer
generates a constant power condition, independent of
cadence. The participants wore cycling shoes and the seat
and handle bar position on the ergometer was adjusted to
the preferred sitting position for each participant. During
the test, the participants did not receive any information
about their pedal rate. After a 10-min warm-up at 100 W,
the participants performed an increasing work rate protocol
that started at 100 W and had a 50 W increment every
2 min until exhaustion. Exhaustion was deWned as meeting
three of the four following criteria: (1) within 5 BPM from
the participants self-reported maximal heart rate (HRmax),
(2) above 7.5 mmol l¡1 in blood lactate concentration, (3)
the respiratory exchange ratio (RER) >1.1, and (4) a VO2
which stops increasing or starts decreasing with increased
work rate. Pedal rate, oxygen consumption, and heart rate
were measured continuously.
Gas exchange values were measured by open-circuit
indirect calorimetry using an Oxycon Pro apparatus (Jaeger
GmbH, Hoechberg, Germany). Before each measurement,
the VO2 and VCO2 gas analyzers were calibrated using
high-precision gases (16.00 §0.04% O2 and 5.00 §0.1%
CO2, Riessner-Gase GmbH & co, Lichtenfels, Germany).
The Xow meter was calibrated with a 3-L volume syringe
(Hans Rudolph Inc., Kansas City, MO). Heart rate (HR)
was measured with a heart rate monitor (Polar S610, Polar
Electro OY, Kempele, Finland), using a 5-s interval for
data storage. VO2max was deWned as the highest 1-min
Table 1 Physical characteristics of the participants in study
mass (kg)
(ml kg min¡1)
power (watt)
Avg 23.4 183.2 77.3 58.1 370
Std 11.7 6.3 9.5 3.3 42
Eur J Appl Physiol (2011) 111:2885–2893 2887
average VO2 during the test. Maximal heart rate was deW-
ned as the highest value that was attained, in average over a
5-s period at the Wnal stage of the protocol. Blood lactate
concentration was measured 2 min after completion of the
VO2max test (10.6 mmol l¡1§1.9) by taking 5 L samples
from the Wngertip by a Lactate Pro LT-1710t(ArkRay Inc,
Kyoto, Japan). This system was validated in literature
(Medbø et al. 2000; Pyne et al. 2000; Baldari et al. 2009).
The second occasion, 3 days after the incremental test,
all participants performed a protocol consisting of eight
repetitions of 5 min cycling at a work rate that was esti-
mated to elicit »80% of their VO2max as was determined in
the Wrst test. Also during this test, the participants wore
cycling shoes. The eVorts were done at level and in tilted
(11%, i.e., 6.3° inclined) position (Fig. 1b), all at preferred
seat position. In the tilted position, the entire ergometer was
tilted by elevating the front. Both positions (level and
tilted) were performed at three cadences (FCC, FCC ¡10
and FCC + 10 rpm), giving six conditions. In addition, the
level position was also performed with the seat moved for-
ward and backward from the preferred position, giving two
additional conditions. Corresponding seat adjustments were
made in height as well, such that the angle of the line
between crank centre and (rock point of the) seat was
rotated by approximately 3° in both directions. This led to a
similar total angle change of about 6° as in levels versus
inclined position. For all forward and backward positions
the distance between hip (major trochanter) and crank cen-
tre was unaltered in comparison with preferred seat position
(Fig. 1c). This was done using a tape measure. The handle-
bars were moved in the same manner. Thus, in these condi-
tions, the orientation of the upper body was not altered.
During all tests on the second occasion, the participants
received continuous feedback about their cadence and were
asked to keep it at a preset level. The FCC was set as the
average cadence that was used the last minute during the
incremental test at the work rate increment nearest 80% of
VO2max. To avoid any eVect of fatigue, learning eVect, or
drift of energy expenditure in the statistical analysis, all
conditions were done in a diVerent order for each partici-
pant. Oxygen consumption and heart rate were measured
continuously. GE was calculated as the ratio of work rate
over metabolic cost rate as calculated from VO2 and RER.
All measurements on »80% VO2max showed RER values
below 1.0 (RER was 0.89 §0.03) indicating no signiWcant
anaerobic contribution. Kinetics was sampled for 5 times
for 10 s at the end of each minute during the 5-min work
Crank and pedal kinematics were recorded using a Pro-
ReXex (Qualisys, Sweden) 3D motion capture system with
Fig. 1 Conditions in present
study. aNormal condition.
bInclined condition. cSeat
preferred backward and forward
= α11%
2888 Eur J Appl Physiol (2011) 111:2885–2893
8 cameras in the same way as described by Ettema et al.
(2009). Two spherical reXective markers were placed on
extensions of both pedals in the sagittal plane of cyclist and
bicycle. The positions of these markers were used to deter-
mine pedal orientation and crank angle. Both pedals were
equipped with two force cells (Model 9363, Revere, capacity
250 kg per cell, The Netherlands), detecting pedal normal
and shear forces (Ettema et al. 2009). The pedals were
calibrated by applying full normal forces and full shear
forces of known magnitude. A constant proportional cross-
talk between the normal and shear forces of a single pedal
was detected (<3%) and taken into account by building a
gain matrix.
All data were recorded using the QTM software (Quali-
sys, Sweden) at a sample rate of 500 Hz and further
processed in Matlab (Mathworks, US). All data were low-
pass Wltered (10 Hz, 8th order, zero lag Butterworth). After
correction for acceleration artefacts (Ettema and Huijing
1994), pedal normal and shear forces were transformed to
crank shear and normal forces by rotation of the coordina-
tion system from pedal to crank using the angle between
pedal and crank as calculated from the kinematical data.
The vector sum of right and left pedal forces (in the crank
coordinate system) was used for further analysis (Lorås
et al. 2009). This leads to higher FE values than consider-
ing the pedals separately, mainly because of the elimination
of the negative eVect of gravity during the up-stroke.
Normal crank force was considered to be the eVective
force component. Thus, the ratio of normal force over
total force was deWned as FE. FE was calculated as aver-
age of the 5 £10 s measurements from each 5-min work
DC was deWned as the lowest work rate (average of
top and bottom dead centre) divided by the average work
rate (Leirdal and Ettema 2010). Thus, this is a parameter
describing the evenness of work rate generation; 100%
indicates a perfect circular work rate generation,
whereas 0% indicates that the work rate at the DC equals
Power was calculated as the product of crank moment
(i.e., eVective (normal) crank force £crank arm) and crank
angular velocity. Continuous crank angular velocity was
calculated from crank angles using a 5-point diVerentiating
Wlter. The average crank cycle (for all variables) was calcu-
lated by interpolation of the crank angle—variable data to
360 samples, i.e., 1 sample per degree crank angle (Ettema
et al. 2009).
To investigate how technique (FE, DC) and eYciency of
energy consumption (GE) relate to each other, and how
cadence may aVect this relationship, we performed correla-
tion matrix analysis as well as multiple regression analysis
for GE with FE, DC, FCC and work rate as independent
All statistics were performed using Statistical Package for
Social Sciences 15.0 (SPSS). The analysis consisted of two
parts. Firstly, to conWrm or refute Wndings in the literature,
the general eVect of cadence (in the range of 20 rpm around
FCC) and position on the variables of interest was exam-
ined: the intra-individual eVects of position and cadence on
GE, FE, and DC was analysed using a 2-way ANOVA
(cadence and body position) and a 1-way ANOVA (seat
position). FCC and absolute work rate were implemented
as covariates. The second and main part of the analysis
regarded the eVect of cadence on the relationship between
technique and energy expenditure: the inter-individual rela-
tionships between GE (dependent) and FE, DC, FCC, and
work rate (independent variables) was performed by multi-
ple regression analysis at the three cadences. Furthermore,
Pearson’s correlations between variables were compared.
This approach could not only indicate if, but also how FE
and GE are related. Normality of the data distribution was
checked with the one-sample Kolmogorov–Smirnov test.
All data were considered normally distributed (all pvalues
>0.337). The signiWcance level was set at p<0.05.
The FCC in the main experiment (at the predicted work rate
of 80% VO2max, averaging 210 W) was 96 §9.1 rpm
(range 75–107). Thus, the FCC ¡10 and FCC + 10 condi-
tions were performed on 86 §9.1 and 106 §9.1 rpm,
respectively. The load in this test elicited 75% VO2max
instead of the predicted 80% during the steady-state
The 2-way ANOVA showed the following results for GE,
FE and DC (average results are presented in Fig. 2). GE, FE
and DC declined signiWcantly (p< 0.001) with each increase
in cadence in a similar way. Body orientation did not seem to
have any eVect on either FE (p= 0.307), GE (p= 0.823) or DC
(p= 0.166). Seat position had no eVect on GE (p= 0.58) or
DC (p= 0.978). The eVect on FE was just not signiWcant
(p= 0.058). No signiWcant cadence–orientation interaction was
detected (FE, p= 0.090; GE, p= 0.794; and DC, p= 0.382).
The weak interaction trend for FE was localized between FCC
and FCC + 10, which was diVerent between the level and
inclined orientation (p= 0.036). Both work rate (mean 210 W,
§40 W) and FCC diVered between participants in present
study. Absolute work rate aVects GE (Leirdal and Ettema
2009) and possibly FE and DC directly as well as being depen-
dent on cadence. Therefore, we also treated absolute work rate
and FCC as a covariate and examined its eVect. The statistical
Wndings using work rate as a covariate remained unaltered
except for DC: cadence on FE, p= 0.001; on DC, p= 0.164;
Eur J Appl Physiol (2011) 111:2885–2893 2889
on GE, p= 0.027; orientation on FE, p= 0.308; on DC,
p= 0.553; on GE, p= 0.172; interaction on FE, p= 0.981; on
DC, p= 0.424; on GE, p= 0.708. Thus, the weak interaction
trend on FE is explained by work rate diVerences. When using
FCC as a covariate, all signiWcant eVects on FE, DC and GE
disappeared (all p> 0.175). In summary, within a subject,
cadence was the main variable inXuencing FE, DC and GE in a
similar way. Yet, these eVects seemed to be related to the sub-
ject’s FCC such that cyclists with a high FCC tend to show a
cadence eVect and those with low FCC did not.
Body orientation and seating position did not seem to have
any eVect on any of FE, DC, and GE. We double checked this
by comparing the regression lines for the various conditions
with the line of identity. In all cases, the regression estimate for
FE, DC, and GE between any of the comparable conditions (4
at FCC, 2 at each other cadence) did not signiWcantly diVer
from the line of identity (i.e., the intercept = 0 and slope = 1).
Thus, we could reduce the data by comparing the mean data
for all conditions at each cadence. Table 2 shows a correlation
matrix of all variables of interest. It appears that over the three
cadences, inter-individual diVerences in FE and GE, and to a
lesser extent also DC, are very consistent. FCC is strongly cor-
related with FE at all cadences, but not with DC. DC and FE
are not related. Work rate correlates well with FCC, FE and
GE. In the multiple regression analysis, for all three cadences,
FCC was the only signiWcant variable that remained, indepen-
dent of variable selection method, signiWcantly explaining the
variance in GE. Still, in isolation, also FE and work rate
showed signiWcant correlations with GE (Table 2). In other
words, FCC correlated strongest with GE (see Table 2), and
FE and work rate did not signiWcantly improve the prediction
of GE, likely because they overlap in explaining the variance
in FCC. Figure 3 shows FE and GE for all subjects and
cadence against absolute cadence and indicates that FE and
GE are tightly coupled to absolute cadence, irrespective of
FCC. It is important to note that the intra-individual relation-
ships (three points per subject, not shown in Wgure, but slopes
are presented in the caption) were very similar to the overall
relationship shown in the Wgure.
Although the mean values of both GE and FE are clearly
aVected by cadence (see ANOVA results above and Fig. 3),
the changes are very consistent as indicated by the FE–FE and
GE–GE correlations between cadences and the intra-class cor-
relation (ICC) values (Table 2). Therefore, we also used the
grand mean data of all conditions to estimate the average rela-
tionship between GE, FE and FCC. This led to a correlation
between FE–GE of 0.660, which was just signiWcant
(p= 0.037), between FCC and GE of ¡0.812 (p= 0.004), and
between FCC and FE of ¡0.914 (p< 0.001).
The present study showed that cadence has a strong nega-
tive and similar eVect on both FE and DC, as well as GE,
which is in line with the literature for both FE (Patterson
and Pearson 1983; Sanderson 1991; Candotti et al. 2007;
Lorås et al. 2009) and GE (Seabury et al. 1977; Coast and
Welch 1985; Belli and Hintzy 2002; Foss and Hallen 2004,
2005; Samozino et al. 2006; Hansen and Sjøgaard 2007).
The same eVect for DC has, to our knowledge, not been
reported before. The multiple regression analysis (plus cor-
relation matrix) showed that both FE and GE are strongly
aVected by absolute cadence and thus by FCC. This Wnding
is important when interpreting the relationship between FE
and GE, which is probably not a causal one.
Within the range of frequencies used by this group of
cyclists, there is clear and linear (negative) relationship
between absolute cadence and GE, and even more so
between cadence and FE (Fig. 3). It may be tempting to
Fig. 2 The eVect of cadence and body orientation on FE (top), DC
(middle) and GE (bottom). FE, DC, and GE declined with increasing
cadence (p< 0.001). Body orientation or seat adjustments did not have
any impact. Vertical bars indicate SEM
DC ( -)
FE ( -)
FCC -10 FCC FCC + 10
GE (%)
2890 Eur J Appl Physiol (2011) 111:2885–2893
conclude that the reduced FE with increasing cadence
causes the cadence–GE relationship, in other words, that
GE is directly aVected by FE. However, this is unlikely
because the cadence-induced FE reduction is explained by
inertial mechanisms (Lorås et al. 2009) that have no bear-
ing on energy consumption. The increased energy cost for
moving the lower extremities is a more likely explanation.
The Xuctuations in internal kinetic energy (rotation of the
lower extremities) increase with cadence. Although this
energy Xow can be utilised as external work (see Kautz and
Neptune 2002; Ettema and Lorås 2009), it is likely associ-
ated with an increased energy cost and thus aVects
eYciency negatively. There are no studies, however, that
have properly investigated the amount of this cost. (Note:
These Xuctuations are often referred to as internal work and
considered fully as energy loss; various biomechanical
analyses have shown this to be a Xaw; for discussion, see
e.g., Kautz and Neptune 2002 and Ettema and Lorås 2009.)
A higher cadence will also increase the inertial, non-muscu-
lar component of the pedal forces (Kautz and Hull 1993;
Ettema et al. 2009; Lorås et al. 2009), which are closely
related to the kinetic energy Xuctuations. An increase in
inertial forces increases the radial force component in par-
ticular, and thereby aVects FE in a negative way (e.g.,
Kautz and Hull 1993; Kautz and Neptune 2002; Lorås et al.
2009). Kautz and Neptune (2002) even argue that “eVective
force” is a misnomer. Thus, the similarity in cadence eVect
on eYciency and FE may be explained by two separate
aspects of a common mechanism. However, this does not
mean that force eVectiveness is aVecting eYciency. The
inertial forces that aVect the non-propulsive force compo-
nent have, by deWnition, no associated metabolic cost.
Table 2 Correlation matrix of signiWcant relationships between FCC, work rate, and DE, FE, and GE at three cadences
Correlations with a signiWcance <0.001 are shown in bold. pvalues are given in parentheses. These are generally very high, except for DC between
FCC ¡10 and FCC + 10. The (average measure) ICC for DC, FE and GE are shown in the left bottom corner
* The correlations between the same variables but diVerent cadence
FCC 10 FCC FC + 10
FCC 10
FCC + 10
0.905 0.987 0.976
Eur J Appl Physiol (2011) 111:2885–2893 2891
Lorås et al. (2009) showed that FE of the muscular force
component is almost independent of cadence and relatively
high (>0.8) (Lorås et al. 2009). Thus, the changes in eVec-
tive crank forces are likely mainly caused by the inertial
force component, which was also indicated by Kautz and
Hull (1993) and Kautz and Neptune (2002). The present
results conWrm this notion that the cadence–FE relationship
is caused by a mechanism that is extremely consistent,
within and between subjects. Not only do all cyclists show
the same trend when changing form FCC ¡10 to FCC +
10 rpm, but this trend is identical with the inter-individual
diVerence that is created by choice of cadence (FCC)
(Fig. 3a). Furthermore, the multiple regression analysis
demonstrates that cadence (or FCC) rather than the associ-
ated FE determines GE. The increase of metabolic cost
(decrease in eYciency) can therefore not be linked to the
decrease in FE, at least not in a direct causal manner.
Beside the assumed metabolic cost of rotating the legs,
extra costs may occur because high cadence requires addi-
tional muscle activity for coordination. Zameziati et al.
(2006) reported a signiWcant FE–GE relationship, deter-
mined over a range of work rates at 80 rpm cadence. This
may be explained in a similar way. When increasing work
rate at one cadence, the ineVective inertial forces will
remain constant while the propulsive force must increase to
increase power. This will automatically lead to an enhanced
FE which is not necessarily indicating an improved tech-
nique (work rate has a diminishing eVect on the inertial
force contribution). Work rate also has a positive eVect on
eYciency but via a diVerent mechanism (see Ettema and
Lorås 2009).
Absolute work rate co-varies negatively with FCC and
may be a partial explanation for the cadence–GE relationship.
Because of the general work rate–eYciency relationship
(Ettema and Lorås 2009), a higher work rate (i.e., lower
FCC) will result in a higher GE. However, the intra-
individual eVect of cadence on GE is not aVected by the
work rate that was applied. Thus, the work rate eVect does
not explain the entire relationship between cadence and GE.
Leirdal and Ettema (2010) found that inertial eVects not
to aVect DC. Still, within each subject, cadence negatively
aVects DC when using a high FCC. Furthermore, Leirdal
and Ettema (2010) reported an inter-individual relationship
between DC and GE. Thus, it seems reasonable to suggest
that the diminishing DC with increasing cadence explains
the relationship between cadence and GE. However, the
Wnding by Leirdal and Ettema (2010), i.e., the DC–GE rela-
tionship, was not reproduced in the present study, which
leaves this proposed explanation open for debate. A reason
for the contradicting results of this study and Leirdal and
Ettema (2010) may be the type of bicycle–ergometer
system that was used. Leirdal and Ettema (2010) used a
racer bicycle with regular gears on resisting rollers,
whereas in the present study a computer-controlled electro-
magnetic brake system was used. Leirdal and Ettema
(2009) showed that these systems have a diVerent outcome
on the choice of cadence in relation to work rate. Thus,
cycling technique (e.g., DC) may also have been aVected by
the choice of ergometer system. This may also explain the
relative low FE values as compared with other studies (e.g.,
Dorel et al. 2009; Lorås et al. 2009; Sanderson and Black
2003; Hug et al. 2008). Our lower FE values cannot be
explained by the method of calculation; Lorås et al. (2009)
showed that this method leads to higher values rather than
Fig. 3 FE (a) and GE (b) plotted against absolute cadence. Data are
all subjects for all three cadences each subject performed at. The over-
all linear regression lines are indicated in the diagrams. The correla-
tions were ¡0.935 (FE) and ¡0.825 (GE), p< 0.001. The regression
for each subject (3 data points) are not shown, but the slopes of these
were not diVerent from the slope of the regression of all data; FE: indi-
vidual slopes ¡0.0051 §0.0009 versus all data ¡0.0056; GE: indi-
vidual slopes ¡0.103 §0.042 versus all data ¡0.128
60 70 80 90 100 110 120
60 70 80 90 100 110 120
FE (-)
GE (%)
Cadence (rpm)
2892 Eur J Appl Physiol (2011) 111:2885–2893
lower. The data by Lorås et al. (2009) were collected in the
same laboratory with identical measurement equipment and
calculation algorithms and a similar subject group. The
only diVerence was the type of ergometer/bicycle that was
used. This supports the notion that the type of ergometer
may aVect these technique values considerably.
There was no diVerence on any parameter between level
and tilted cycling or between preferred, forward, and back-
ward seat position. FE, DC and GE showed almost identical
values and eVects of cadence in both level and tilted cycling
and for the three seat positions. This is quite a noteworthy
Wnding as it suggests that the individual cyclist has his own
pedalling characteristic that is unaVected by (upper) body
orientation. The present results are in disagreement with the
notion that cycling technique and thereby power production
and energy consumption is aVected by relatively small
changes in body orientation as occur in practice (e.g., Cald-
well et al. 1998; Heil et al. 1997; Price and Donne 1997).
The high ICC values for FE, DC and GE between condi-
tions conWrm the notion that FE, DC and GE are very sub-
ject speciWc. The changes between the various orientation
and seating conditions (about 6° rotation) may appear mar-
ginal. This could explain the lack of any eVect of these
parameters. However, from a practical standing, these
changes are quite large: to obtain a change of 3° in the seat–
crank angle, the seat was shifted approximately 4 cm. Fur-
thermore, changes in other technique variables caused by
such position changes have been detected: unpublished
results from our laboratory indicate that the 6° rotation of
the cyclist (inclined position) or the lower extremities (by
seat position) cause a phase shift of the crank cycle (see
also McGhie and Ettema 2011) of the same amount (i.e., 6
degrees); Umberger et al. (1998) reported relatively small
but signiWcant changes in power at maximal eVort (about
4 W per degree seat–crank angle) and hip angles (about
1 degree degree¡1). Thus, the relatively small range of
body orientation used in this study should not be considered
as a limiting factor for detection of its eVect on technique
and energy consumption.
There are some limitations in the present study. The ped-
alling rates investigated (86–106 rpm) are a relatively small
range around the FCC for competitive cyclists that covers
most cadences used in mass starts competitive cycling.
However, the Wndings of this study cannot be generalised to
a wider range of cadences that is used regularly in experi-
mental studies and in other cycling disciplines. Further-
more, the ergometer used in present study may have
inXuenced the choice of cadence (Leirdal and Ettema 2009)
and might also aVect pedalling dynamics.
In conclusion, energy expenditure is strongly coupled to
cadence, but force eVectiveness, as a measure for pedalling
technique, is not likely the cause of this relationship. Along
with other studies (Kautz and Hull 1993; Ettema et al.
2009; Lorås et al. 2009), we are inclined to conclude that
FE is mostly aVected by inertial forces, and thus the value
of this parameter as a measure for technique should be
questioned. Contrary to Leirdal and Ettema (2010), we do
not Wnd a signiWcant relationship between DC and GE.
Thus, the present study provides no indication for the
notion that technique aVects energy consumption. There
was no signiWcant eVect of body orientation or seat position
on GE, FE or DC, or on the relationship between them,
indicating that these parameters and the relationship
between them are robust to coordinative challenges within
a range of cadence, body orientation and seat position that
is used in regular cycling.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution Noncommercial License which permits any
noncommercial use, distribution, and reproduction in any medium,
provided the original author(s) and source are credited.
Baldari C, Bonavolonta V, Emerenziani GP, Gallotta MC, Silva AJ,
Gudetti L (2009) Accuracy, reliability, linearity of accutrend
and Lactate Pro versus EBIO plus analyzer. Eur J Appl Physiol
Belli A, Hintzy F (2002) InXuence of pedalling rate on the energy cost
of cycling in humans. Eur J Appl Physiol 88:158–162
Bertucci W, Grappe F, Girard A, Betik A, Rouillon JD (2005) EVects
of the crank torque proWle when changing pedalling cadence in
level ground and inclined road cycling. J Biomech 38:1003–1010
Brown DA, Kautz SA, Dairaghi CA (1996) Muscle activity patterns al-
tered during pedalling at diVerent body orientations. J Biomech
Caldwell GE, Li L, McCole SD, Hagberg JM (1998) Pedal and crank
kinetics in inclined cycling. J Appl Biomech 14:245–259
Caldwell GE, Hagberg JM, McCole SD, Li L (1999) Lower extremity
joint moments during inclined cycling. J Appl Biomech 15:166–181
Candotti CT, Ribeiro J, Soares DP, DeOliveira AR, Loss JF, Guimar-
aes ACS (2007) EVective force and economy of triathletes and cy-
clists. Sport Biomech 6(1):31–43
Coast JR, Welch HG (1985) Linear increase in optimal pedal rate with
increased power output in cycle ergometry. Eur J Appl Physiol
Coyle EF, Feltner ME, Kautz SA, Hamilto MT, Montain SJ, Baylor
AM, Abraham LD, Petrek GW (1991) Physiological and biome-
chanical factors associated with elite endurance cycling perfor-
mance. Med Sci Sports Exerc 23(1):93–107
Dorel S, Drouet J M, Couturier A, Champoux Y, Hug F (2009) Changes
of pedalling technique and muscle coordination during an exhaus-
tive exercise. Med Sci Sports Exerc 41(6):1277–1286
Ericson MO, Nisell R (1988) EYciency of pedal forces during ergom-
eter cycling. Int J Sports Med 9:118–122
Ettema G, Huijing PA (1994) Frequency response of rat gastrocnemius
medialis in small amplitude vibrations. J Biomech 27:1015–1022
Ettema G, Lorås H (2009) EYciency in cycling: a review. Eur J Appl
Physiol 106(1):1–14
Ettema G, Lorås H, Leirdal S (2009) The eVects of cycling cadence on
the phases of joint power, crank power, force and FE.
J Electromyogr Kinesiol 19(2):94–101
Faria EW, Parkel DL, Faria IE (2005) The science of cycling: factors
aVecting performance—Part 1. Sport Med 35:285–312
Eur J Appl Physiol (2011) 111:2885–2893 2893
Foss Ø, Hallen J (2004) The most economical cadence increases with
increasing workload. Eur J Appl Physiol 92:443–451
Foss Ø, Hallen J (2005) Cadence and performance in elite cyclists. Eur
J Appl Physiol 93:453–462
Hansen EA, Sjøgaard G (2007) Relationship between eYciency and
pedal rate in cycling: signiWcance of internal power and muscle
Wber composition. Scand J Med Sci Sports 17:408–414
Heil DP, Derrick TR, Whittlesey S (1997) The relationship between
preVered and optimal positioning during submaximal cycle
ergometry. Eur J Appl Physiol 75:160–165
Hug F, Drouet JM, Champoux Y, Couturier A, Dorel S (2008) Interin-
dividual variability of electromyographic patterns and pedal force
proWles in trained cyclists. Eur J Appl Physiol 104(4):667–678
Kautz SA, Hull ML (1993) A theoretical basis for interpreting the force
applied to the pedal in cycling. J Biomech 26(2):155–165
Kautz SA, Neptune RR (2002) Biomechanical determinants of pedal-
ling energetics: internal and external work are not independent.
Exerc Sport Sci Rev 30(4):159–165
KorV T, Romer LM, Mayhew I, Martin JC (2007) EVect of pedalling
technique on mechanical eVectiveness and eYciency in cyclists.
Med Sci Sports Exerc 39(6):991–995
Leirdal S, Ettema G (2009) Freely chosen pedal rate during free cycling on
a roller and ergometer cycling. Eur J Appl Physiol 106:799–805
Leirdal S, Ettema G (2010) Pedalling technique and energy cost in
cycling. Med Sci Sports Exerc. doi:10.1249/MSS.0b013e3181
f6b7ea (accepted 23 Aug 2010)
Lorås H, Leirdal S, Ettema G (2009) The muscle force component in
pedalling retains constant direction across pedalling rates. J Appl
Biomech 25:1–9
McGhie D, Ettema G (2011) The eVect of cadence on timing of muscle
activation and mechanical output in cycling: On the activation
dynamics hypothesis. J Electromyogr Kinesiol 21:18–24
Medbø JI, Mamen A, Holth Olsen O, Evertsen F (2000) Examination
of four diVerent instruments for measuring blood lactate concen-
tration. Scand J Clin Lab Invest 60:367–380
Millet GP, Tronche C, Fuster N, Candau R (2002) Level ground and
inclined cycling eYciency in seated and standing positions. Med
Sci Sport Exer 34:(10)1645–1652
Patterson RP, Pearson JL (1983) The inXuence of Xywheel weight and
pedalling frequency on the biomechanics and physiological
response to bicycle exercise. Ergonomics 26(7):659–668
Price D, Donne B (1997) EVect of variation in seat tube angle at diVer-
ent seat heights on submaximal cycling performance in man.
J Sport Sci 15:395–402
Pyne DB, Boston T, martin DT, Logan A (2000) Evaluation of the lactate
Pro blood lactate analyser. Eur J Appl Physiol 82:112–116
Ricard MD, Hills-Meyer P, Miller MG, Michael TJ (2006) The eVects
of bicycle frame geometry on muscle activation and power during
a Wingate anaerobic test. J. Sport Sci Med 5:25–32
Samozino P, Nicolas H, Hintzy F (2006) Interactions between cadence
and power output eVects on mechanical eYciency during sub
maximal cycling exercises. Eur J Appl Physiol 97:133–139
Sanderson DJ (1991) The inXuence of cadence and power output on the
biomechanics of force application during steady-rate cycling in
competitive and recreational cyclists. J Sport Sci 9:191–203
Sanderson DJ, Black A (2003) The eVect of prolonged cycling on ped-
al forces. J Sport Sci 21:191–199
Seabury JJ, Adams WC, Ramey MR (1977) InXuence of pedalling rate
and power output on energy expenditure during bicycle ergome-
try. Ergonomics 20(5):491–498
Umberger BR, Scheuchenzuber HJ, Manos TM (1998) DiVerences in
power output during cycling at diVerent seat tube angles. J Hum
Move Stud 35:21–36
Welbergen E, Clijsen LPVM (1990) The inXuence of body position
on maximal performance in cycling. Eur J Appl Physiol
Zameziati K, Mornieux DR, Belli A (2006) Relationship between the
increase of eVectiveness indexes and the increase of muscular eY-
ciency with cycling power. Eur J Appl Physiol 96:274–281
... In road cycling, cadence is a metric of high interest [1,2]. Over recent decades, this metric has been studied widely to relate it to cycling performance and efficiency [3][4][5][6]. The state-of-the-art approach [7] in most studies and in commercial cadence-measurement systems uses a Hall-effect sensor [8,9] in combination with a permanent magnet. ...
... AccX σ 4 31.00 MagZ σ 5 26.95 AccY σ 6 8.33 MagY σ 7 5.09 GyroX/GyroZ σ 8 3.26 GyroZ/GyroX σ 9 2.10 GyroY ...
Full-text available
Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This work proposes an alternative cadence-measurement system with less of the mentioned restrictions, without the need for distinct cadence-measurement apparatus attached to the pedal and shaft of the road bicycle. The proposed design applies an inertial measurement unit (IMU) to the seating pole of the bike. In an experiment, the motion data were gathered. A total of four different road cyclists participated in this study to collect different datasets for neural network training and evaluation. In total, over 10 h of road cycling data were recorded and used to train the neural network. The network’s aim was to detect each revolution of the crank within the data. The evaluation of the data has shown that using pure accelerometer data from all three axes led to the best result in combination with the proposed network architecture. A working proof of concept was achieved with an accuracy of approximately 95% on test data. As the proof of concept can also be seen as a new method for measuring cadence, the method was compared with the ground truth. Comparing the ground truth and the predicted cadence, it can be stated that for the relevant range of 50 rpm and above, the prediction over-predicts the cadence with approximately 0.9 rpm with a standard deviation of 2.05 rpm. The results indicate that the proposed design is fully functioning and can be seen as an alternative method to detect the cadence of a road cyclist.
... While physiological responses at maximal effort represent the strength and power of the respective bodily systems, physiological responses during submaximal effort express the degree of efficiency and physiological stress to which they are subjected (13) when a similar mechano-metabolic load is encountered, a decrease in the above parameters indicates a more economical and less demanding physiological load. In most cases, physiological improvement in the latter (reduced submaximal responses) should result in increased maximal exercise capacity (22,23), but this was not the case in our study. So, what could explain the above changes, which were only observed during the second exercise test's submaximal phase but not during the second resting PFT (PFTpost) or at the height of the second CPET (CPETpeak post)? ...
Full-text available
Objective: The current study is only the second known empirical study of Rebirthing, a holistic self-improvement therapy. The study looked at fitness-related physiological outcomes following a series of rebirthing sessions. Methods and materials: Ten healthy young women (mean age, weight, and height: 372.7 years, 54.16.4 kg, and 161.24.9 cm, respectively) underwent two identical resting pulmonary function tests (PFTs) and two two-stage all-out graded cardiopulmonary exercise tests (CPETs) before (pre) and after (post) a series of 10 weekly Rebirthing treatments. The rebirthing sessions were held at the Israeli Rebirthing Center in Tel Aviv. All rebirthing treatments were performed by a single qualified Rebirthing therapist and lasted approximately 40-50 minutes each. Results: There were no significant changes (p˃0.05) in resting lung functions (PFTpost) or peak values at maximal effort (CPETpost) after the rebirthing program (except for a decrease in HRpeak). Nonetheless, the results show a significant reduction (p˂0.05) in several cardiopulmonary attributes measured during the submaximal phase of the second CPET (HRsub, VO2sub, RERsub, VEsub, BRsub; Bfsub and an increase in Vd/Vtsub). Conclusions: As the first study to investigate the effect of a series of rebirthing treatments on responses of selected fitness-related physiological measures at rest and during exercise, it is not surprising that no unambiguous answers to the research questions were found. Further studies are needed to provide reliable support and explanations for the study findings.
... This could also explain the lack of V : O 2 max improvement observed in athletes [27]. The observed increase of the mechanical performance (i.e maximal power output) at the end of the event could be explained by a better pedaling efficiency [28] although we cannot measure it. Sex could also play a role in the lack of systolic bi-ventricular and LV diastolic dysfunction related to exercise observed in this study. ...
Full-text available
Purpose Effects of intense and/or prolonged exercise have been studied extensively in male athletes. Nevertheless, data are scare on the effect of long duration events on cardiac function in female athletes. Our aim was to investigate the effect of a long-lasting moderate-intensity stage cycling event on cardiac function of young female athletes. Methods Seven well-trained female cyclists were included. They completed a cycling event of 3529 km on 23 days. All underwent an echocardiography on 6 time-points (baseline and at the arrival of day (D) 3, 7, 12, 13 and 23). Cardiac function was assessed by conventional echocardiography, tissue Doppler imaging and speckle tracking techniques. Daily exercise load was determined by heart rate (HR), power output and rate of perceived exertion data (RPE, Borg scale). Results All stages were mainly done at moderate intensity (average HR: 65% of maximal, average aerobic power output: 36% of maximal, average RPE: 4). Resting HR measured at the time of echocardiography did not vary during the event. Resting cardiac dimensions did not significantly change during the 23 days of cycling. No significant modification of cardiac function, whatever the studied cavity, were observed all along the event. Conclusion The results suggest that, in the context of our case study, the long-lasting moderate-intensity stage cycling event was not associated with cardiac function alteration. Nevertheless, we must be careful in interpreting them due to the limits of an underpowered study.
... There are several proposed mechanisms as to why cyclists do not optimize their cadence to obtain the highest cycling efficiency when riding on level ground surfaces (for a review, see Ettema and Lorås 2009). Yet, the effects of cadence on GE seem independent of slope (Leirdal and Ettema 2011;Arkesteijn et al. 2013;Nimmerichter et al. 2015) and reduce with increasing power output (Chavarren and Calbet 1999;Samozino et al. 2006). The cadence that a cyclist can adopt is determined by the cycling speed and gearing ratio of the bicycle (i.e., the ratio of the front sprocket to the rear sprocket). ...
Full-text available
Purpose With few cycling races on the calendar in 2020 due to COVID-19, Everesting became a popular challenge: you select one hill and cycle up and down it until you reach the accumulated elevation of Mt. Everest (8,848 m or 29,029ft). With an almost infinite number of different hills across the world, the question arises what the optimal hill for Everesting would be. Here, we address the biomechanics and energetics of up- and downhill cycling to determine the characteristics of this optimal hill. Methods During uphill cycling, the mechanical power output equals the power necessary to overcome air resistance, rolling resistance, and work against gravity, and for a fast Everesting time, one should maximize this latter term. To determine the optimal section length (i.e., number of repetitions), we applied the critical power concept and assumed that the U-turn associated with an additional repetition comes with a 6 s time penalty. Results To use most mechanical power to overcoming gravity, slopes of at least 12% are most suitable, especially since gross efficiency seems only minimally diminished on steeper slopes. Next, we found 24 repetitions to be optimal, yet this number slightly depends on the assumptions made. Finally, we discuss other factors (fueling, altitude, fatigue) not incorporated in the model but also affecting Everesting performances. Conclusion For a fast Everesting time, our model suggests to select a hill climb which preferably starts at (or close to) sea level, with a slope of 12–20% and length of 2–3 km.
... The work is focused on a specific component of the cycling shoe, the sole. In fact, the sole is the structural component which is subject to the most significant stresses during the pedalling action [13]. For this reason, the presented system consists of two main modules used to monitor deformations and stresses on the cycling shoes insole. ...
... This intensity was selected to minimise anaerobic energy contribution (Rodríguez-Marroyo et al., 2009). In order to avoid possible effects of fatigue, learning, or drift of energy expenditure, the three saddle heights were randomised (Leirdal & Ettema, 2011). The three positions (preferred, 2% higher and 2% lower than the preferred position) implied six different combinations, which were randomly assigned to the cyclists. ...
Modifications in saddle height affect the range of movement of the lower limb's joints during pedalling. Although its effect on movement patterns is poorly understood. The purpose of this study was to analyse the acute effects of small changes in bicycle saddle height on pedalling coordination and its variability. Lower extremity kinematic data were collected in random order for ten well-trained cyclists while pedalling at three different saddle heights: preferred, 2% higher and 2% lower than preferred position. A dynamical systems approach was used to quantify the coordination and its variability for selected joint couplings. Modifications in saddle height produced large changes in the frequency of movement patterns, although they were not enough to alter the coordination classification. Lowering the saddle height increased the frequency of the proximal coordinative hip-ankle pattern (F = 11.77, p < .01) and knee-ankle couplings (F = 14.39, p < .01), while decreasing inphase coordination (F > 11.03, p < .01) during the propulsive phase. Pedalling coordination variability was not affected, being greatest during the movement transitions and when the ankle joint was included in the coupling. This study demonstrated that pedalling pattern coordination and coordination variability were generally stable to acute small changes in saddle height in well-trained cyclists.
... Such prospect has allowed the relationship between internal and external training load to be quantified, with links to performance having previously been investigated. [22][23][24][25][26][27] As such, it appears that rowing as a sport has several tools to effectively capture and analyse on-water rowing training, but these devices don't offer a practical or holistic approach for monitoring total training load on a day-to-day basis. ...
Performance tracking devices in the form of wrist-worn watches are common in rowing; however, the accuracy of relevant output variables (i.e. stroke rate [SR] and velocity) during on-water training is unknown. To assess the quality of wrist-watch data output, 16 rowing athletes recorded 118 on-water rowing sessions using a Garmin Forerunner 735XT, which was compared to a Catapult Optimeye R4 tracking device. Garmin recording function was set to ‘Every Second’ ( N = 68 sessions) or ‘Smart’ ( N = 50 sessions). Catapult velocity was calculated as the average velocity per stroke, while a 15 s velocity moving average was determined for Garmin data. Catapult and Garmin were filtered for training-specific data (SR = 14–50 strokes per minute [spm]; velocity = 2.1–7.0 m/s ⁻¹ ). Efficacy and reliability of the Garmin was assessed via the difference between devices (% error), intra-class correlation coefficient (ICC ± 95% confidence interval (CI)) and coefficient of variation (CV%). Error in 15 s smoothed velocity was 3.8% (‘Every Second’) and 8.2% (‘Smart’). Both recording functions demonstrated ‘good’ reliability (ICC = 0.75–0.9, CV < 10%) for SR and velocity; the exception was SR using ‘Smart’ recording. Our data suggests that when using the ‘Every Second’ recording function, data is filtered and velocity is smoothed over 15 s, the Garmin device can be reliable for SR and velocity measurement within 1 spm and <0.20 m/s ⁻¹ respectively.
Cyclic motion variability reflects the movement error correction. Since movement motor control generally worsens with impaired biomechanical setups, we assessed whether the pedaling cadence variability (PCV) increases by worsening the bike fitting across multiple workloads. Sixteen cyclists performed multiple 5-min bouts of constant load cycling exercise at 0, 20, 40, 55, 70, 85% of their maximum workload (MWL) capacity at 60 rpm with proper (PROPER) and worsened (WORSENED; 15 cm saddle height drop) bike fittings. Cycle-by-cycle duration series were collected. PCV was calculated as the standard deviation of each series. In both PROPER and WORSENED, PCV showed a U-shaped feature by increasing workload (minimum PCV values at 55% MWL). PCV was higher in WORSENED than PROPER, except at 55% MWL (0% MWL: 36.69 ± 10.06 vs. 42.21 ± 11.3, p < 0.01; 55% MWL: 18.87 ± 3.51 vs. 19.74 ± 4.73, p = 0.3; 85% MWL: 34.93 ± 10.51 vs. 39.52 ± 11.84, p < 0.01; ms; PROPER vs. WORSENED, respectively). PCV seems to be a workload-dependent variable, being greater at low and high workloads. At intermediate workloads, the moderate force expression to continue the movement, along with the effect of the workload itself in counteracting the natural extension of the leg, might explain a lower need for continuous motion adjustments and, consequently, a lower PCV in both bike setups.
Full-text available
Voor een goede voorbereiding op La Marmotte (een fietstocht in de Franse Alpen met 5221 hoogtemeters) lijkt het verstandig om de nodige klimkilometers te maken. Dit is echter lang niet voor alle (Nederlandse) deelnemers mogelijk, gezien de vlakke omgeving waarin zij wonen. Hoe nadelig is dit eigenlijk? Gebruik je andere spieren tijdens het bergop fietsen vergeleken met op vlak terrein fietsen? Of gebruik je je spieren anders? Op deze en andere aanverwante vragen probeer ik in dit artikel een antwoord te geven.
Full-text available
Marko, D, Bahenský, P, Snarr, RL, and Malátová, R. V̇ o2 peak Comparison of a treadmill vs. cycling protocol in elite teenage competitive runners, cyclists, and swimmers. J Strength Cond Res 36(10): 2875-2882, 2022-The purpose of this study was to compare the cardiorespiratory and metabolic responses of a maximal graded exercise test (GXT) on a treadmill and cycle ergometer in elite-level, youth competitive athletes. Thirty-one athletes (11 distance runners, 11 mountain-bike cyclists, and 9 long-distance swimmers) were randomly selected to complete either a running or cycling GXT on the first day, followed by the alternative 72 hours apart. The initial work rate for each GXT was set at 50% of the individuals' previously established V̇ o2 peak to elicit fatigue within 8-12 minutes. For the treadmill protocol, speed was increased by 1 km·h -1 each minute, with a constant 5% grade, until volitional fatigue. Cycle ergometer work rate was increased by 30 W every minute until volitional fatigue or the inability to maintain proper cadence (i.e., 100 ± 5 rev·min -1 ). Throughout both testing sessions, V̇ o2 peak, heart rate [HR] peak, breathing frequency (BF), tidal volume (V T ), and minute ventilation (V E ) were assessed and used to compare within-sport differences. Runners displayed a higher V̇ o2 peak (∼7%; d = 0.92), HRpeak (4%; d = 0.77), V E (6%; d = 0.66), and BF (12%; d = 0.62) on the treadmill vs. cycle. However, the cycling group demonstrated a greater V̇ o2 peak (∼8%; d = 0.92), V T (∼14%; d = 0.99), and V E (∼9%; d = 0.78) on the cycle, despite no change in HRpeak. For swimmers, the treadmill GXT elicited higher values in V̇ o2 peak (∼5%; d = 0.75), BF (∼11.5%; d = 0.78), and HRpeak (3%; d = 0.69). Collectively, these findings indicate that exercise mode may greatly affect physiological outcome variables and should be considered before exercise prescription and athletic monitoring.
Full-text available
The aim of this review is to provide greater insight and understanding regarding the scientific nature of cycling. Research findings are presented in a practical manner for their direct application to cycling. The two parts of this review provide information that is useful to athletes, coaches and exercise scientists in the prescription of training regimens, adoption of exercise protocols and creation of research designs. Here for the first time, we present rationale to dispute prevailing myths linked to erroneous concepts and terminology surrounding the sport of cycling. In some studies, a review of the cycling literature revealed incomplete characterisation of athletic performance, lack of appropriate controls and small subject numbers, thereby complicating the understanding of the cycling research. Moreover, a mixture of cycling testing equipment coupled with a multitude of exercise protocols stresses the reliability and validity of the findings. Our scrutiny of the literature revealed key cycling performance-determining variables and their training-induced metabolic responses. The review of training strategies provides guidelines that will assist in the design of aerobic and anaerobic training protocols. Paradoxically, while maximal oxygen uptake (VO2max) is generally not considered a valid indicator of cycling performance when it is coupled with other markers of exercise performance (e.g. blood lactate, power output, metabolic thresholds and efficiency/economy), it is found to gain predictive credibility. The positive facets of lactate metabolism dispel the ‘lactic acid myth’. Lactate is shown to lower hydrogen ion concentrations rather than raise them, thereby retarding acidosis. Every aspect of lactate production is shown to be advantageous to cycling performance. To minimise the effects of muscle fatigue, the efficacy of employing a combination of different high cycling cadences is evident. The subconscious fatigue avoidance mechanism ‘teleoanticipation’ system serves to set the tolerable upper limits of competitive effort in order to assure the athlete completion of the physical challenge. Physiological markers found to be predictive of cycling performance include: (i) power output at the lactate threshold (LT2); (ii) peak power output (Wpeak) indicating a power/weight ratio of ≥5.5 W/kg; (iii) the percentage of type I fibres in the vastus lateralis; (iv) maximal lactate steady-state, representing the highest exercise intensity at which blood lactate concentration remains stable; (v) Wpeak at LT2; and (vi) Wpeak during a maximal cycling test. Furthermore, the unique breathing pattern, characterised by a lack of tachypnoeic shift, found in professional cyclists may enhance the efficiency and metabolic cost of breathing. The training impulse is useful to characterise exercise intensity and load during training and competition. It serves to enable the cyclist or coach to evaluate the effects of training strategies and may well serve to predict the cyclist’s performance. Findings indicate that peripheral adaptations in working muscles play a more important role for enhanced submaximal cycling capacity than central adaptations. Clearly, relatively brief but intense sprint training can enhance both glycolytic and oxidative enzyme activity, maximum short-term power output and VO2max. To that end, it is suggested to replace ~15% of normal training with one of the interval exercise protocols. Tapering, through reduction in duration of training sessions or the frequency of sessions per week while maintaining intensity, is extremely effective for improvement of cycling time-trial performance. Overuse and over-training disabilities common to the competitive cyclist, if untreated, can lead to delayed recovery.
Full-text available
The purpose of this study was to compare the effects of bicycle seat tube angles (STA) of (72° and 82°) on power production and EMG of the vastus laeralis (VL), vastus medialis (VM), semimembranous (SM), biceps femoris (BF) during a Wingate test (WAT). Twelve experienced cyclists performed a WAT at each STA. Repeated measures ANOVA was used to identify differences in muscular activation by STA. EMG variables were normalized to isometric maximum voluntary contraction (MVC). Paired t-tests were used to test the effects of STA on: peak power, average power, minimum power and percent power drop. Results indicated BF activation was significantly lower at STA 82° (482.9 ± 166.6 %MVC·s) compared to STA 72° (712.6 ± 265.6 %MVC·s). There were no differences in the power variables between STAs. The primary finding was that increasing the STA from 72° to 82° enabled triathletes' to maintain power production, while significantly reducing the muscular activation of the biceps femoris muscle. Key PointsRoad cyclists claim that bicycle seat tube angles between 72° and 76° are most effective for optimal performance in racing.Triathletes typically use seat tube angles greater than 76°. It is thought that a seat tube angle greater than 76° facilitates a smoother bike to run transition in the triathlon.Increasing the seat tube angle from 72 to 82 enabled triathletes' to maintain power production, while significantly reducing the muscular activation of the biceps femoris muscle.Reduced hamstring muscular activation in the triathlon frame (82 seat tube angle) may serve to reduce hamstring tightness following the bike phase of the triathlon, allowing the runner to use a longer stride length.
Full-text available
The aim of this study was to determine whether high inter-individual variability of the electromyographic (EMG) patterns during pedaling is accompanied by variability in the pedal force application patterns. Eleven male experienced cyclists were tested at two submaximal power outputs (150 and 250 W). Pedal force components (effective and total forces) and index of mechanical effectiveness were measured continuously using instrumented pedals and were synchronized with surface electromyography signals measured in ten lower limb muscles. The intersubject variability of EMG and mechanical patterns was assessed using standard deviation, mean deviation, variance ratio and coefficient of cross-correlation (_R(0), with lag time = 0). The results demonstrated a high intersubject variability of EMG patterns at both exercise intensities for biarticular muscles as a whole (and especially for Gastrocnemius lateralis and Rectus femoris) and for one monoarticular muscle (Tibialis anterior). However, this heterogeneity of EMG patterns is not accompanied by a so high intersubject variability in pedal force application patterns. A very low variability in the three mechanical profiles (effective force, total force and index of mechanical effectiveness) was obtained in the propulsive downstroke phase, although a greater variability in these mechanical patterns was found during upstroke and around the top dead center, and at 250 W when compared to 150 W. Overall, these results provide additional evidence for redundancy in the neuromuscular system.
The purpose of this study was to investigate the interactions between cadence and power output effects on cycling efficiency. Fourteen healthy subjects performed four constant power output-tests (40, 80, 120 and 160 W) in which the cadence varied in five bouts from 40 to 120 rpm. Gross efficiency (GE) was determined over the last ten respiratory cycles of each bout and was calculated as the ratio of mechanical energy to energy expenditure. Results showed that (1) GE-cadence relationships reached a maximum at each power output corresponding to the cadence maximising efficiency (CAeff) and (2) GE increased with power output whatever the cadence until a maximal theoretical value. Moreover, interactions were found between these two factors: the cadence effect decreased linearly with power output and the power output effect increased exponentially with cadence. Consequently, cycling efficiency decreased more when cadence differed from CAeff at low than at high power output, and increased more with power output at high cadence than at low cadence. These interactions between cadence and power output effects on GE were mainly due to cadence and power output effects on the energy expenditure shares not contributing to power production.
Alterations in kinetic patterns of pedal force and crank torque due to changes in surface grade (level vs. 8% uphill) and postuer (seated vs. standing) were investigated during cycling on a computerized ergometer. Kinematic data from a planar cine analysis and force data from a pedal instrumented with piezoelectric crystals were recorded from multiple trials of 8 elite cyclists. These measures were used to calculate pedal force, pedal orientation, and crank torque profiles as a function of crank angle in three conditioned: seated level, seated uphill, and standing uphill. The change in surface grade from level to 8% uphill resulted in a shift in pedal angle (toe up) and a moderately higher peak crank torque, due at least in part to a reduction in the cycling cadence. However, the overall patterns of pedal and crank kinetics were similar in the two seated conditions. In contrast, the alteration in posture from sitting to standing on the hill permitted the subjects to produce different patterns of pedal and crank kinetics, characterized by significantly higher peak pedal force and crank torque that occurred much later in the downstroke. These kinetic changes were associated with modified pedal orientation (toe down) throughout the crank cycle. Further, the kinetic changes were linked to altered nonmuscular (gravitational and inertial) contributions to the applied pedal force, caused by the removal of the saddle as a base of support.
Lower extremity joint moments were investigated in three cycling conditions: level seated, uphill seated and uphill standing. Based on a previous study (Caldwell, Li, McCole, and Hagberg, 1998), it was hypothesized that joint moments in the uphill standing condition would be altered in both magnitude and pattern. Eight national caliber cyclists were filmed while riding their own bicycles mounted to a computerized ergometer. Applied forces were measured with an instrumented pedal, and inverse dynamics were used to calculate joint moments. In the uphill seated condition the joint moments were similar in profile to the level seated but with a modest increase in magnitude. In the uphill standing condition the peak ankle plantarflexor moment was much larger and occurred later in the downstroke than in the seated conditions. The extensor knee moment that marked the first portion of the downstroke for the seated trials was extended much further into the downstroke while standing, and the subsequent knee flexor moment period was of lower magnitude and shorter duration. These moment changes in the standing condition can be explained by a combination of more forward hip and knee positions, increased magnitude of pedal force, and an altered pedal force vector direction. The data support the notion of an altered contribution of both muscular and non-muscular sources to the applied pedal force. Muscle length estimates and muscle activity data from an earlier study (Li and Caldwell, 1996) support the unique roles of mono-articular muscles for energy generation and bi-articular muscles for balancing of adjacent joint moments in the control of pedal force vector direction.
To determine if differences exist in power output during cycling at different seat tube angles (STAs), 12 male, physically active, non-cyclists were analyzed for power output and lower extremity joint kinematics during a 15sec, maximal effort, cycle ergometer test. STA was defined relative to the horizontal and subjects were tested at 69°, 76°, 83° and 90°. All tests were performed on an instrumented, computer interfaced, cycle ergometer, with a modified seat that allowed for variation in the STA. Power output values increased systematically and hip angle decreased systematically, as STA was decreased from 90° to 69°. Other joint kinematic factors remained stable as STA was decreased from 90° to 69°. Based on these results, cycling power output is maximized at shallow STAs and decreased at steeper STAs, within the range of 69° to 90°. The differences in power output may be due to altered muscle lengths and moment arms associated with the changes in hip joint angle.
Because cycling is an extreme endurance sport, energy saving and therefore efficiency is of importance for performance. It is generally believed that gross efficiency (GE) is affected by pedaling technique. A measurement of pedaling technique has traditionally been done using force effectiveness ratio (FE; ratio of effective force and total force). The aim of the present study was to investigate the relationship among GE, FE, and a new technique parameter, dead center (DC) size in competitive cyclists. Twenty-one competitive cyclists cycled for 10 min at approximately 80% VO(2max) at a freely chosen cadence (FCC). GE, FE ratio, and DC size were calculated from oxygen consumption and propulsive force recordings. Mean work rate was 279 W, mean FCC was 93.1 rpm, and mean GE was 21.7%. FE was 0.47 and 0.79 after correction for inertial forces; DC was 27.3% and 25.7%, respectively. DC size correlated better with GE (r = 0.75) than with the FE ratio (r = 0.50). Multiple regressions revealed that DC size was the only significant (P = 0.001) predictor for GE. Interestingly, DC size and FE ratio did not correlate with each other. DC size is a pedaling technique parameter that is closely related to energy consumption. To generate power evenly around the whole pedal, revolution may be an important energy-saving trait.