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Validity, Sensitivity, Reproducibility, and Robustness of the PowerTap, Stages, and Garmin Vector Power Meters in Comparison With the SRM Device

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  • University of Champagne-Ardenne (France)

Abstract and Figures

Purpose: This study aimed to determine the validity, sensitivity, reproducibility and robustness of the Powertap (PWT), Stages (STG) and Garmin Vector (VCT) power meters in comparison with the SRM device. Methods: A national-level male competitive cyclist was required to complete three laboratory cycling tests that included a sub-maximal incremental test, a sub-maximal 30-min continuous test and a sprint test. Two additional tests were performed: the first on vibration exposures in the laboratory and the second in the field. Results: The VCT provided a significantly lower 5 s power output (PO) during the sprint test with a low gear ratio compared with the POSRM (-36.9%). The POSTG was significantly lower than the POSRM within the heavy exercise intensity zone (zone 2, -5.1%) and the low part of the severe intensity zone (zone 3, -4.9%). The POVCT was significantly lower than the POSRM only within zone 2 (-4.5%). The POSTG was significantly lower in standing position than in the seated position (-4.4%). The reproducibility of the PWT, STG and VCT was similar to that of the SRM system. The POSTG and POVCT were significantly decreased from a vibration frequency of 48 Hz and 52 Hz, respectively. Conclusions: The PWT, STG and the VCT systems appear to be reproducible, but the validity, sensitivity and robustness of the STG and VCT systems should be treated with some caution according to the conditions of measurement.
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Note. This article will be published in a forthcoming issue of the
International Journal of Sports Physiology and Performance. The
article appears here in its accepted, peer-reviewed form, as it was
provided by the submitting author. It has not been copyedited,
proofread, or formatted by the publisher.
Section: Original Investigation
Article Title: Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages
and Garmin Vector Power Meters in Comparison With the SRM Device
Authors: Anthony Bouillod1,2,4, Julien Pinot1,3, Georges Soto-Romero4,5, William Bertucci6
and Frederic Grappe1,3
Affiliations: 1 EA4660, C3S Health - Sport Department, Sports University, Besancon,
France. 2 French Cycling Federation, Saint Quentin en Yvelines, France. 3 Professional
Cycling Team FDJ, Moussy le Vieux, France. 4 LAAS-CNRS, Université de Toulouse,
CNRS, Toulouse, France. 5 ISIFC - Génie Biomédical, 23 Rue Alain Savary, Besançon,
France. 6 EA 4694, GRESPI / UFR STAPS, URCA, Reims, France.
Journal: International Journal of Sports Physiology and Performance
Acceptance Date: November 21, 2016
©2016 Human Kinetics, Inc.
DOI: http://dx.doi.org/10.1123/ijspp.2016-0436
Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Validity, sensitivity, reproducibility and robustness of the Powertap, Stages and
Garmin Vector power meters in comparison with the SRM device
Original investigation
Authors:
Anthony Bouillod1,2,4, Julien Pinot1,3, Georges Soto-Romero4,5, William Bertucci6 & Frederic
Grappe1,3
Affiliations:
1 EA4660, C3S Health - Sport Department, Sports University, Besancon, France
2 French Cycling Federation, Saint Quentin en Yvelines, France
3 Professional Cycling Team FDJ, Moussy le Vieux, France
4 LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
5 ISIFC - Génie Biomédical, 23 Rue Alain Savary, Besançon, France
6 EA 4694, GRESPI / UFR STAPS, URCA, Reims, France
Anthony Bouillod
Département Santé et Sports
Equipe Culture - Sport - Santé - Société (C3S)
Sports University of Besancon
31 chemin de l'épitaphe
25000 Besançon, France
anthonybouillod@gmail.com
Tel.: +33/644/892 702
Fax: +33/384/537 838
Running Head: Validity of the Powertap, Stages and Garmin Vector
Abstract word count: 250
Text-only word count: 3927
Number of Figures: 6
Number of Tables: 1
Downloaded by University of Calgary on 12/14/16, Volume 0, Article Number 0
Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Abstract
A large number of power meters were produced on the market for nearly 20 years and
according to user requirements. Purpose: This study aimed to determine the validity,
sensitivity, reproducibility and robustness of the Powertap (PWT), Stages (STG) and Garmin
Vector (VCT) power meters in comparison with the SRM device. Methods: A national-level
male competitive cyclist was required to complete three laboratory cycling tests that included
a sub-maximal incremental test, a sub-maximal 30-min continuous test and a sprint test. Two
additional tests were performed: the first on vibration exposures in the laboratory and the
second in the field. Results: The VCT provided a significantly lower 5 s power output (PO)
during the sprint test with a low gear ratio compared with the POSRM (-36.9%). The POSTG
was significantly lower than the POSRM within the heavy exercise intensity zone (zone 2, -
5.1%) and the low part of the severe intensity zone (zone 3, -4.9%). The POVCT was
significantly lower than the POSRM only within zone 2 (-4.5%). The POSTG was significantly
lower in standing position than in the seated position (-4.4%). The reproducibility of the
PWT, STG and VCT was similar to that of the SRM system. The POSTG and POVCT were
significantly decreased from a vibration frequency of 48 Hz and 52 Hz, respectively.
Conclusions: The PWT, STG and the VCT systems appear to be reproducible, but the
validity, sensitivity and robustness of the STG and VCT systems should be treated with some
caution according to the conditions of measurement.
Keywords: Mobile power meter, power output, comparison, laboratory, field, cycling.
Downloaded by University of Calgary on 12/14/16, Volume 0, Article Number 0
Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Introduction
A large number of power meters were produced on the market for nearly 20 years and
according to user requirements. The use of power meters enables the assessment of cyclists
training1 and racing2 intensity zones according to their skills and thus to their race
performance profile3. These data enable the coach and athlete to have measurements of
intensity in real cycling locomotion in the field, thus allowing training programs to be
optimised using power output (PO). To be used, power meters should provide a valid,
sensitive, reproducible and robust PO4. Validity is the ability of power meters to reflect what
it is designed to measure5. Sensitivity is the smallest measurement change that can be
detected by power meters. Reproducibility refers to the variation in measurements made on
power meters under changing conditions6. Finally, robustness is the ability of power meters
to remain unaffected by small variations of experimental factors.
The SRM power meter (SRM, Schoberer Rad Messtechnich, Julich, Germany) is the
most commonly used system in cycling, particularly in professional and amateur racing.
Indeed, eight of the 2016 UCI WorldTeams use the SRM power meter while seven different
power meters (Pioneer, Rotor, Quarq, 4iiii, Power2Max, Stages and Shimano) are used in the
remaining ten WorldTeams. The SRM system is a crankset that includes a number of strain
gauges (4-20 depending on the model used) located between the crank axle and the chainring.
The SRM is considered as a gold standard measurement system due to its high validity,
reliability and sensitivity during the measure (± 1% average error after calibration procedure
performed under standard environmental conditions)7,8. This 1% average error represents
changes in PO measurements of 2 W in endurance (200 W) and 20 W during sprints (2000
W). Additionally, the SRM is one of the few power meters that can be calibrated by the user
to increase the accuracy of the PO measurement. However, the high cost of the SRM has led
manufacturers to develop less expensive mobile cycling power meters. Some of them have
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
been studied for their validity and reliability (Powertap4, Max one9, Polar S71010, Ergomo11
and Look Keo Power12), but other newer power meters have not yet been studied. As a valid
and reliable device, the SRM has been used as a reference system to validate previous mobile
power meters and stationary ergometers, such as the Kingcycle cycle ergometer13 and the
Axiom Powertrain cycle ergometer14.
The Powertap (PWT, Saris Cycling Group, Madison, USA) is also considered a valid
and reliable power meter when compared with the SRM4 or a dynamic calibration rig7. The
PWT device measures the PO with strain gauges located in the hub of the rear wheel. The
new Stages (STG, Stages Cycling, Saddleback Ltd., UK) and Garmin Vector (VCT, Olathe,
USA) power meters are less known power meters. The STG power meter uses only the left
crank arm for the PO measurement. The strain gauges are integrated into a small plastic case
bonded to the rear side of the left crank arm. As the crank measures the PO on the left side
only, the algorithm for power calculation doubles this value to obtain a complete reading for
both the left and right sides15. In the VCT power meter, PO is measured at the pedals where
force is applied. The VCT measures the slight deflection of the pedal spindle though the
entire pedal stroke as well as the 2D force vectors; these data are used to calculate power.
The force sensors are housed in both pedals, so that they can independently measure power
from each leg and report the total PO considering the balance between both left and right
legs.
This study aimed to assess the validity, sensitivity, reproducibility and robustness of
the PWT, STG and VCT power meters during both laboratory and field cycling tests in
comparison with the SRM device considered to be the gold standard. We hypothesised that
the PO measured by the four systems would be different considering that the force was
measured at different locations on the bicycle (pedals, crank, crankset and rear hub). The
highest PO would be measured at the pedals, whereas the lowest PO would be measured at
Downloaded by University of Calgary on 12/14/16, Volume 0, Article Number 0
Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
the rear wheel hub, which does not take into account the mechanical loss in the chain drive
system. Moreover, the four power meters would be considered as sensitive, reproducible and
robust.
Methods
Participant
A national-level male competitive cyclist (age: 23 years old, height: 1.88 m, body
mass: 80 kg) with a very low asymmetry index (4%)16, measured by combining intensity and
duration with the SRM torque analysis system in our laboratory (personal data), volunteered
as the subject for this study. Prior to testing and after having received a full explanation of the
nature and purpose of the study, the subject gave his written informed consent. The study was
approved by the ethics committee of the institute. Before experimenting, the subject
underwent several habituation sessions to get used to the testing procedure.
Experimental design
All testing sessions were performed with the same road-racing bicycle. The bicycle
tire pressure was inflated to 700 kPa. The bicycle was fitted with an SRM crankset, a rear
wheel composed of the PWT G3 hub, the STG power crank and the VCT pedals. The SRM
power meter was paired with a SRM power control whereas PWT, STG and VCT power
meters were paired with Garmin power controls. To ensure accurate measures of the SRM
power meter, a static calibration was applied before the study17 and the zero offset frequency
was adjusted according to the manufacturer's instructions. The PO was measured
continuously at a frequency of 1 Hz for all power meters. A short acceleration (six pedalling
cycles) was realised at the beginning of each testing session to facilitate data synchronisation.
The validity of the PWT, STG and VCT power meters was investigated in the field
and in the laboratory at submaximal and maximal intensities from three experimental
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
protocols: 1) a sub-maximal incremental test, 2) a sub-maximal 30-min continuous test and 3)
a sprint test. The incremental and continuous sub-maximal tests were performed on a
motorised treadmill (S 1930, HEF Techmachine, Andrezieux-Boutheon, France) of 3.8 m
length and 1.8 m width, and the sprint test was performed on a Cateye ergometer (CS-1000,
Cateye, Osaka, Japan). The subject performed the three protocols on the same day and
repeated each protocol three times on three different days to assess reproducibility.
Sensitivity was studied in the laboratory during the sub-maximal incremental test by using
three different pedalling cadences (60, 80 and 100 rpm) and by measuring the difference in
PO between seated and standing positions18. Robustness was investigated in the laboratory by
using twelve different vibration frequencies (vibration test).
Sub-maximal incremental test
A sub-maximal incremental test was performed on a motorised treadmill at speeds of
19.5, 21, 22.5, 24 and 25.5 km.h-1 (150-350 W). The mass of the system (subject + bicycle)
contributes to the PO required to ride on a treadmill at a given velocity. We controlled this
parameter by adding or removing water from two bottles in the bottle cages of the bicycle4.
At each velocity, both the pedalling cadence (60, 80 and 100 rpm) and the position (seated
and standing) effects on the PO were tested in randomized order. The combinations of the
different velocities, pedalling cadences and positions resulted in 30 different data sets (5
velocities, 3 pedalling cadences and 2 positions), each data set lasting 30 seconds.
Sub-maximal continuous test
A 30-min continuous exercise test was performed in a seated position at 21 km.h-1 on
a 3% slope with a pedalling cadence of 80 rpm.
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Sprint test
The sprint test consisted of three 8 s sprints in a seated position to determine the
maximal 1 s PO (POmax) and 5 s PO. The magnetic resistance of the Cateye ergometer was set
to a simulated grade of 7%. Three different gear ratios were used (53/15, 53/17 and 53/19) to
determine the three different maximal pedalling cadences and the levels of applied force.
Sprints were separated by 5 min of active recovery at low intensity (<150 W).
Vibration test
The frequencies used during the vibration test corresponded to vibrations measured in
road cycling19. The test consisted of a 30 sec ramp exercise bouts on vibrating plates (Globus,
Physioplate FIT, Italy) by mean steps of 4 Hz increasing from 12 to 56 Hz (amplitude of 4
mm) and decreasing from 56 to 12 Hz. The exercise was performed at 200 W with a
pedalling cadence of 80 rpm. The bicycle was fixed on an ergo-trainer (Tacx, Netherlands).
Field test
The field test consisted of a 2 h road cycling session on a hilly terrain including the
different laboratorial experimental conditions (seated and standing positions, different
pedalling cadences and different velocities) at an average temperature of 20.9 °C. The Record
Power Profile (RPP) was used to compare the four power meters according to the exercise
intensity zones determined by Pinot and Grappe3 and defined as follows: zone 1 (between 1
and 4 h; moderate exercise intensity), zone 2 (between 20 and 60 min; heavy exercise
intensity), zone 3 (between 5 and 20 min; low part of the severe intensity zone), zone 4
(between 30 and 5 min; high part of the severe intensity zone) and zone 5 (between 1 and 30
s; force velocity zone).
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Statistical analysis
Descriptive statistics were used, and all data were expressed as mean ± standard
deviation. Statistical analysis was performed using SigmaPlot 12.0 software (Systat Inc. San
Jose, USA). Bland-Altman plots and 95% limits of agreement20 were applied to assess the
agreement among POPWT, POSTG, POVCT and POSRM during the sub-maximal incremental test.
The data of the submaximal incremental tests were checked for heteroscedasticity by
calculating the heteroscedasticity correlation between 1) the absolute differences between
POPWT, POSTG, POVCT and POSRM and 2) the mean PO as described by Atkinson and Nevill5.
Although this analysis showed that heteroscedasticity was not present, the data were
logarithmically transformed according to the recommendations of Nevill and Atkinson21. The
data of the sub-maximal incremental test, sub-maximal continuous test and sprint test were
not normally distributed. Thus, the analysis of the differences among the POPWT, POSTG,
POVCT and POSRM of each protocol was conducted with the non-parametric Kruskal-Wallis
test. An initial two-way ANOVA (power meters vs. exercise intensity zones) was used to
analyse the influence of the exercise intensity zones on the power meters. The pedalling
cadence and cycling position effects on POPWT, POSTG, POVCT and POSRM during the sub-
maximal incremental test were evaluated with the non-parametric Kruskal-Wallis test. To
assess reproducibility, the non-parametric Kruskal-Wallis test was also used for the sub-
maximal incremental test, and the mean coefficient of variation (CV) was calculated for all
conditions. A second two-way ANOVA (power meters vs. vibration frequency) was used to
analyse the influence of the vibration frequency on the power meters. Significance was set to
p < 0.05 in all statistical tests.
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Results
Validity
During the sub-maximal incremental test (150-350 W), strong correlations were found
between POSRM and POPWT (0.997, p < 0.001), POSTG (r = 0.985, p < 0.001) and POVCT (r =
0.996, p< 0.001). The mean PO from 19.5 to 25.5 km.h-1 was not significantly different
among the four systems. The ratio limits of agreement of the PO differences were 1.007 × ÷
1.056 between POSRM and POPWT, 0.945 × ÷ 1.110 between POSRM and POSTG and 1.004 × ÷
1.051 between POSRM and POVCT. The Bland-Altman analysis (Figure 1) shows that the mean
bias between POSRM and POPWT was 1.3 ± 6.0 W (95% CI: -10.4 and 13.0 W). Additionally,
the mean bias between POSRM and POSTG was -13.7 ± 12.4 W (95% CI: -37.9 and 10.6 W)
and 0.6 ± 6.2 W (95% CI: -11.6 and 12.7 W) with the POVCT.
No significant difference was measured among the mean POs during the 30 min
continuous test, and the mean CVs were 2.8%, 3.6%, 3.6% and 2.0% for POSRM, POPWT,
POSTG and POVCT, respectively.
No significant difference was found in POmax among the four power meters. However,
the 5 s POVCT was lower than that of POSRM (-36.9%, p < 0.05) during the sprint test with a
low gear ratio (Figure 2).
Significant differences were found within certain intensity zones among the power
meters. Figure 3 shows the RPP according to the different power meters and the exercise
intensity zones. The two-way ANOVA indicated that POSTG was lower (p < 0.05) than POSRM
within zones 2 (-5.1%) and 3 (-4.9%). POVCT was lower (p < 0.05) than POSRM within zone 2
(-4.5%).
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Sensitivity
The pedalling cadence had no effect on PO among the different power meters.
However, cycling position had an effect (p < 0.001) on POSRM, POPWT and POSTG. For the
same velocity on the treadmill, the PO was higher in the standing position for both the SRM
(+2.1%, p < 0.001) and the PWT (+2.4%, p < 0.001) power meters. By contrast, POSTG was
lower in the standing position (-4.4%, p < 0.001).
Reproducibility
No significant difference was detected in all the incremental tests with the Kruskal-
Wallis analysis. The mean CVs (Table 1) for all the cycling conditions (5 velocities, 3
pedalling cadences and 2 pedalling postures) were 1.9 ± 1.3% for POSRM, 2.6 ± 1.5% for
POPWT, 3.0 ± 1.9% for POSTG and 2.5 ± 1.3% for POVCT.
Robustness
Figure 4 shows that both STG and VCT power meters are sensitive to high vibration
frequencies. POSTG and POVCT were decreased from a vibration frequency of 48 Hz (p <
0.001) and 52 Hz (p < 0.001), respectively, whereas vibrations did not influence the SRM and
PWT power meters.
Discussion
This study is the first to analyse the validity, sensitivity, reproducibility and
robustness of the PWT, STG and VCT power meters in comparison with the SRM device
during laboratory and field cycling exercises.
The results of the PWT power meter demonstrated a narrow CI (23.4 W) and non-
significant differences compared with the results of the SRM device. The PWT device was
considered valid. However, the CI reported by Bertucci et al.4 was narrower (12.9 W) during
the sub-maximal incremental test.
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
A large CI (48.5 W) was reported for the STG power meter, thus rendering this
system invalid during the sub-maximal incremental test. This power meter also significantly
underestimated the PO during the field test within zone 2 (-5.1%) and zone 3 (-4.9%)
compared with the SRM device. Despite the fact that no significant difference was detected,
POSTG was markedly lower within zones 4 (-10.2%) and 5 (-5.0%) than POSRM. Our results
are in accordance with those of Hurst et al.15, who reported that the STG device did not show
any agreement with the SRM power meter and underestimated the PO by an average of 8%
during off-road cycling tasks. Miller et al.22 also reported that the STG device underestimated
the PO in downhill and flat field sections in comparison with the PWT device.
The VCT power meter had a valid PO during the sub-maximal exercise in laboratory.
However, it underestimated the PO during the sprints with a low gear ratio (-36.9%) and the
field test within zone 2 (-4.5%) compared with the SRM device. Even if no significant
difference was detected, POVCT was markedly lower within zones 4 (-12.7%) and 5 (-6.3%)
than POSRM. Furthermore, a poor reproducibility of the VCT system was demonstrated by a
high standard deviation during the sprints with a low gear ratio. A recent study23 showed that
the VCT device slightly overestimated the PO during laboratory efforts when compared with
the SRM system.
POSTG was significantly lower in the standing position than in the seated position
probably because of the left-crank-only measurement. The algorithm used to determine
power for the STG system simply doubles the value determined at the left crank and then
creates an average. This process may create problems in situations in which a contralateral
force production imbalance is present. Bilateral asymmetries have been studied among
cyclists24-26, and the results show that asymmetries are reduced with an increase in workload.
As asymmetries depend on the subject, further studies must be conducted on several cyclists
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
controlling this parameter with the STG device in both laboratory and field cycling
conditions.
The VCT system did not measure the PO change between the seated and standing
positions. This could be due to some technological limits of the system that will be discussed
below. Both STG and VCT power meters were not considered sensitive because POSRM and
POPWT were significantly higher in standing position than in the seated position. These results
are in accordance with those of Bouillod et al.18 who measured a systematic increase in PO in
the standing position because of the mechanical deformation of the tires and the bike frame.
The importance of reproducible power meters to detect small changes in performance
has been emphasised in a review27. The detectable change in performance represents a
magnitude of less than 2% in elite athletes. The mean CVs obtained with the PWT, STG and
VCT devices are slightly higher than 2%, but the statistical analysis indicates that the four
power meters provide reproducible PO during submaximal tests in the laboratory.
Additionally, Van Praagh et al.28 proposed a 5% margin of error to consider power meters as
reproducible, but this margin is too wide to detect a small change in performance.
Considering an elite athlete with a maximal aerobic power of 400 W, the margin of error
represents 20 W. Supposing that the claimed accuracy of the SRM is correct, our results
indicate that the PWT, STG and VCT have an accuracy of ± 2-3% for PO between 150 and
350 W. This value slightly exceeds the manufacturers’ claimed accuracies of ± 1.5% for
PWT and 2.0% for STG and VCT. In comparing the results of the present study with those of
previous research in the agreement between cycling power meters, the CVs were similar to
those previously reported for the Powertap hub system (CV = 2.1%), Polar S710 (CV =
2.2%) and Ergomo Pro (CV = 4.1%)4,10,11. Hurst et al.15 reported higher CV for both the STG
(CV = 5.5%) and SRM (CV = 5.1%) power meters, but this result was biased by trail
vibrations and a small variation in pacing in the different trials. Higher CVs were also
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
reported for the G-Cog BMX29,30 and the Look Keo Power12 power meters than those of the
power meters measured in the present study.
The effect of vibration frequencies on the four power meters demonstrated that POSTG
and POVCT significantly stalled with high vibrations (48 Hz and 52 Hz, respectively), whereas
POSRM and POPWT were not affected by vibrations. It is important to specify that the actual
cycling condition on cobblestones induce successive shocks that cover a great range of
frequencies and enlarge the density of the frequency spectrum principally from 0 to 200 Hz
(personal data). This condition is different than in the laboratory vibrating platform where the
vibration plate generate excitation without shocks at specific frequencies designed by the
experimenter. For a cyclist riding on a granular rough road (26 to 36 km/h), 88% of the road
excitation power falls within a 1050 Hz frequency bandwidth19. According to Chiementin et
al.31, the mean excitations frequencies on cobblestones (120 ± 11 mm of length) for speeds
from 20 to 35 km/h were from 35 to 65 Hz, respectively. These results suggest that the
robustness of the STG and VCT could alter the validity of the PO measurement on roads with
cobblestones or with high macrotexture surfaces. Also, POSTG and POVCT stall could be due
to the use of accelerometers to measure PO. The accelerometers could be out of range of
measurement especially when the road conditions involve severe vibratory exposure from 48
Hz. These results indicate that the STG and VCT power meters are not suitable for practice
with strong vibrations, such as mountain biking and cobblestone roads.
Note that this study is limited to only one participant. Nevertheless, the study design
provided a large number of measurement over a variety of exercise intensities and conditions
of cycling. This variety enabled the assessment of PO typically generated by elite athletes3
(PO ranging between 1223 and 1454 W for the sprint test and between 150 and 350 W for the
sub-maximal incremental tests).
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Main sources of error encountered in power meters
Our results show that the theoretical mechanical losses from the PO measurements
between the pedals and the rear hub are not verified. These results can be explained by 1) the
strain gauges’ sensitivity and their number included in each system, 2) the environmental
temperature, 3) the fatigue of certain components, 4) the signal processing (amplification,
filtering, analog to digital conversion and data analysis), 5) the calibration methods and 6) the
elapsed time since the last calibration (PO measurement drift). Thus, it appears that the
mechanical properties of the strain gauges inside the system are very important to consider.
Indeed, all the power meters include force measurement which is obtained using
semiconductor strain gauge rosettes as sensors. After signal conditioning (impedance bridge
and amplification/filtering), the resultant voltage is converted 1) into a digital signal (by an
Analog to Digital converter) and processed by a microcontroller (Figure 5) and 2) into a
frequency signal by Pulse Width Modulation (PWM) before sending it to a power control
(SRM), in which the microcontroller processes data after the demodulation step (Figure 6).
Operating principles of the STG and VCT power meters have not been presented because we
had not the opportunity to disassemble the two systems. In both detailed cases (PWT and
SRM), the angular velocity of the wheel and of the crankset (obtained by reed switches) is
necessary to compute power data. Temperature effect, mechanical fatigue, misalignment
errors, Wheatstone Bridge Nonlinearity, electronic components noise (Amplifier, A/D
converter) and testing cell fatigue (long-time effect) were identified as the main sources of
error in strain gauges measurements. To compensate for some of these errors, it is necessary
to calibrate the system.
According to standard instructions of calibration recommended by the manufacturers,
POVCT and POSTG should be higher than POSRM, whereas POPWT should be lower than POSRM
considering the measurement location and the mechanical losses in the bicycle components.
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Practical Applications and Conclusions
This study confirms that the PWT power meter can be considered a suitable and
valuable device for PO measurement during cycling. However, both the STG and VCT
systems should be treated with some caution given the presence of significant differences
when they are compared with the SRM device. The use of accurate devices such as SRM and
PWT is required for coaches and scientists to enable the assessment of cyclists intensity
zones and to establish a long-term power profile of individual performance. Among the four
power meters tested in this study, only the SRM device can be calibrated by the user, thus
making it a useful system for coaches and scientists.
Our study demonstrates that the PWT, STG and VCT systems are reproducible
mobile power meters compared with SRM device. However, the validity, sensitivity and
robustness of the STG and VCT systems should be treated with some caution as they may
limit the potential application of the crank and pedal systems for researchers. To date, SRM
and PWT remain the most reliable systems for sport scientists and coaches. To be as reliable
as the SRM and PWT power meters, the STG and VCT systems should improve their signal
processing in some assessment conditions.
Acknowledgments
The authors would like to thank the participating cyclist for his cooperation as well as the
Matsport Company, the University of Reims-Champagne-Ardenne and Frederic Puel for their
support.
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Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Figure 1. Bland-Altman plots of the differences between a) POSRM and POPWT, b)
POSRM and POSTG and c) POSRM and POVCT power meters during the sub-maximal
incremental test. The dashed line represents the bias whereas the solid lines represent the high
and low 95% confidence interval (CI).
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Figure 2. SRM, PWT, STG and VCT 5-sec PO during sprint test with low, middle and high
gear ratios. *significant difference between VCT and SRM (p < 0.05).
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Figure 3. Record Power Profile according to the power meters and exercise intensity zones.
a significant differences between sTG and SRM (p < 0.05)
b significant difference between VCT and SRM (p < 0.05)
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Figure 4. SRM, PWT, STG and VCT PO obtained during the vibration test.
a significant difference between STG and SRM (p < 0.001)
b significant difference between VCT and SRM (p < 0.001)
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Figure 5. Powertap Operating Principle. Image of a Powertap rear hub (left). Measurement
chain includes strain gauges bridge, amplification/filtering step (AMP), analog to digital
conversion (ADC), data analysis in microcontroller and wireless transmission by ANT +
protocol (2.4 GHz) (right).
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Validity, Sensitivity, Reproducibility and Robustness of the Powertap, Stages and Garmin Vector Power Meters in
Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Figure 6. SRM Operating Principle. Image of a SRM crankset (left). Measurement chain
includes strain gauges bridge, amplification/filtering step (AP), Pulse Width Modulation step
(PWM) and wireless transmission ANT + protocol (2.4 GHz) (right).
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Comparison With the SRM Device” by Bouillod A et al.
International Journal of Sports Physiology and Performance
© 2016 Human Kinetics, Inc.
Table 1. Mean PO (W) and CV (%) for SRM, PWT, STG and VCT power meters at different
velocities, pedalling cadences and positions during the sub-maximal incremental test.
Velocities
Pedalling
cadences
Positions
Mean
POSRM
Mean
POSTG
Mean
POVCT
SRM
CV
PWT
CV
STG
CV
VCT
CV
(km.h-1)
(rpm)
(W)
(%)
19.5
60
Seated
149.7
151.7
155.7
2.5
1.6
3.6
0.4
19.5
60
Standing
154.0
143.3
153.3
4.5
1.3
5.6
5.6
19.5
80
Seated
152.7
155.3
157.0
2.5
0.6
1.6
2.9
19.5
80
Standing
154.3
136.3
154.0
5.4
3.2
5.0
4.9
19.5
100
Seated
149.7
143.7
155.7
4.9
2.3
6.9
3.5
19.5
100
Standing
155.7
130.0
157.0
4.1
1.0
9.4
2.9
21.0
60
Seated
195.7
195.0
197.3
1.1
5.1
1.0
2.0
21.0
60
Standing
198.3
190.0
196.3
1.0
3.3
2.8
1.5
21.0
80
Seated
196.7
196.3
200.7
0.8
4.8
2.9
2.1
21.0
80
Standing
200.3
185.7
200.3
0.8
4.9
0.3
1.8
21.0
100
Seated
196.3
188.3
201.7
0.6
4.0
2.6
2.1
21.0
100
Standing
201.3
173.7
201.3
1.6
3.3
3.2
1.7
22.5
60
Seated
252.7
253.7
254.3
2.9
4.2
3.6
4.4
22.5
60
Standing
253.7
241.3
254.3
2.0
4.8
1.9
1.3
22.5
80
Seated
251.3
246.7
256.7
2.4
3.5
2.8
1.8
22.5
80
Standing
256.7
236.3
254.0
1.6
2.2
2.8
1.0
22.5
100
Seated
252.7
245.0
257.0
2.5
5.3
4.7
2.7
22.5
100
Standing
260.7
230.3
258.3
2.2
1.1
2.4
0.2
24.0
60
Seated
303.3
303.3
308.7
2.1
1.9
2.0
3.1
24.0
60
Standing
305.3
291.3
303.0
0.7
0.5
0.5
2.0
24.0
80
Seated
302.3
293.0
304.7
1.6
3.0
0.7
1.9
24.0
80
Standing
307.7
288.3
302.7
1.7
2.8
3.7
4.0
24.0
100
Seated
304.7
289.7
309.0
1.7
2.1
1.4
3.7
24.0
100
Standing
314.7
278.0
307.3
0.7
1.4
3.4
3.3
25.5
60
Seated
344.3
335.7
342.0
1.0
2.1
0.6
1.5
25.5
60
Standing
346.0
330.0
342.3
0.3
1.0
2.9
1.9
25.5
80
Seated
344.7
331.3
348.7
1.9
3.1
2.5
3.4
25.5
80
Standing
351.7
324.3
341.7
0.7
0.9
2.2
3.3
25.5
100
Seated
342.3
324.0
348.0
1.0
2.0
3.6
2.3
25.5
100
Standing
355.7
313.7
348.7
1.0
0.9
2.1
2.1
Downloaded by University of Calgary on 12/14/16, Volume 0, Article Number 0
... Cycling, like any other time-based sport, is strongly dependent on speed where athletes aim to cover a given distance as fast as possible. From a cycling performance perspective, the power output is one of the most important factors for athletes and coaches [1,2]. This provides an objective and quantifiable measure of a cyclist's performance. ...
... It must be highlighted that the aim of the present study was not to validate the TN2T smart trainer. For such purpose, i.e., validity (which is the ability of a given device to reproduce what it is designed to measure [1,31]), the comparison should be performed with a gold standard device such as the Lode Excalibur ergometer [15] or other cycle-mounted power meter devices that are considered gold standard such as the SRM power meter [8,32]. Once again, the aim of this study was to investigate the metrological proprieties of the TN2T (accuracy and reliability) and its agreement with the GV3 pedals. ...
... As aforementioned, the GV pedals showed to be a reliable and accurate measure of power output and cadence during submaximal cycling [24,30]. Notwithstanding, other studies noted somewhat contradictory findings [1,15]. For instance, Lanferdini and co-workers [15] aimed to evaluate and compare the power output reliability, based on two different pedaling protocols (incremental and variable tests), using the GV pedals and a Lode Excalibur ergometer. ...
Article
Full-text available
The power output in cycling is one of the most important factors for athletes and coaches. The cycling community has several commercial gears that can be used. One of the most used is the TACX Neo 2T (TN2T) smart trainer. The objective of this study was to investigate the metrological proprieties of the TN2T (accuracy and reliability), as well as its agreement with the Garmin Vector 3 (GV3) pedals at different power stages. The sample consisted of ten regional-level cyclists with a mean age of 45.6 ± 6.4 years, who regularly participated in regional and national competitions. Residual relative differences were found between the two devices. Both devices showed good reliability with coefficients of variation and intraclass correlation coefficients ranging from 0.03% to 0.15% and from 0.731 to 0.968, respectively. Independent samples t-test comparison between devices showed no significant differences in all power stages (p > 0.05). Bland–Altman plots showed that more than 80% of the plots were within the 95% confidence intervals in all power stages. The present data showed that there were non-significant differences between the two devices at power stages between 100W and 270W, with a strong agreement. Therefore, they can be used simultaneously.
... Nowadays, numerous authors have assessed the validity and reliability of the power output measurements of several powermeters (e.g., Stages, Garmin Vector, Quarq, Keo Power, etc.) [2]. However, this is not the case for two widely used devices in the scientific literature [1,[3][4][5]: Power2Max (strain gauges in the chainring) and PowerTap G3 (strain gauges in the rear hub). While their validity and reliability during submaximal pedaling in a seated position has been proven [3,5], the validity and reliability of the PowerTap during supramaximal pedaling (i.e., 5 s sprints) and under different pedaling conditions (i.e., seated vs. standing cyclists' positions, low vs. high gear ratios used) has been questioned [3,4]. ...
... However, this is not the case for two widely used devices in the scientific literature [1,[3][4][5]: Power2Max (strain gauges in the chainring) and PowerTap G3 (strain gauges in the rear hub). While their validity and reliability during submaximal pedaling in a seated position has been proven [3,5], the validity and reliability of the PowerTap during supramaximal pedaling (i.e., 5 s sprints) and under different pedaling conditions (i.e., seated vs. standing cyclists' positions, low vs. high gear ratios used) has been questioned [3,4]. To the best of our knowledge, no previous study has analyzed Power2Max validity and reliability under these types of efforts and conditions. ...
... The agreement found between PowerTap and Power2Max power measurements during both submaximal and incremental maximal tests (r = 0.992 y 0.997, respectively) is similar to that obtained in previous studies (r = 0.997, p < 0.001) in which PowerTap and SRM devices were compared [4]. PowerTap's underestimation of power relative to Power2Max (Tables 1 and 2) is justified by their different placement (i.e., rear hub and chainring, respectively). ...
Article
Full-text available
The purpose was to assess the concurrent validity and reliability of two portable powermeters (PowerTap vs. Power2Max) in different types of cycling efforts. Ten cyclists performed two submaximal, one incremental maximal and two supramaximal sprint tests on an ergometer, while pedaling power and cadence were registered by both powermeters and a cadence sensor (GarminGSC10). During the submaximal and incremental maximal tests, significant correlations were found for power and cadence data (r = 0.992–0.997 and 0.996–0.998, respectively, p < 0.001), with a slight power underestimation by PowerTap (0.7–1.8%, p < 0.01) and a high reliability of both powermeters (p < 0.001) for measurement of power (ICC = 0.926 and 0.936, respectively) and cadence (ICC = 0.969 and 0.970, respectively). However, during the supramaximal sprint test, their agreement to measure power and cadence was weak (r = 0.850 and −0.253, p < 0.05) due to the low reliability of the cadence measurements (ICC between 0.496 and 0.736, and 0.574 and 0.664, respectively; p < 0.05) in contrast to the high reliability of the cadence sensor (ICC = 0.987–0.994). In conclusion, both powermeters are valid and reliable for measuring power and cadence during continuous cycling efforts (~100–450 W), but questionable during sprint efforts (>500 W), where they are affected by the gear ratio used (PowerTap) and by their low accuracy in cadence recording (PowerTap and Power2Max).
... (accessed on 15 September 2022)). Nevertheless, two recent studies have questioned the interchangeability of the registry of different portable power meters in field conditions [3,5]. Consequently, Maier et al. [3] observed that the power output registered by different power meters is highly variable (even when they have been designed by the same manufacturer) and recommend further studies in field conditions with changes in ambient temperature, vibrations, or gear shifts. ...
... Shute et al. [6] observed that environmental temperature affected the registry of various power meters. Furthermore, Bouillod et al. [5] demonstrated that vibration and field conditions affect the power output measured. This latter could condition the analysis and interpretation of both exercise intensity zones and power output profile of the cyclists [2,5,7], and their critical power [8,9]. ...
... Furthermore, Bouillod et al. [5] demonstrated that vibration and field conditions affect the power output measured. This latter could condition the analysis and interpretation of both exercise intensity zones and power output profile of the cyclists [2,5,7], and their critical power [8,9]. These variables are widely used to quantify the competition load and to plan training [2]. ...
Article
Full-text available
Various power meters are used to assess road-cycling performance in training and competition, but no previous study has analyzed their interchangeability in these conditions. Therefore, the purpose was to compare the data obtained from two different power meters (PowerTap vs. Power2Max) during cycling road races. A national-level under-23 male competitive cyclist completed six road-cycling official competitions (five road races and one individual time trial), in which power output was simultaneously registered with the two power meters. After this, the main power output variables were analyzed with the same software. The average and critical power obtained from the PowerTap power meter were slightly lower than from the Power2Max power meter (3.56 ± 0.68 and 3.62 ± 0.74 W·kg−1, 5.06 and 5.11 W·kg−1, respectively), and the correlations between both devices were very high (r ≥ 0.996 and p < 0.001). In contrast, the PowerTap power meter registered a significantly higher (p < 0.05) percentage of time at <0.75 and >7.50 W·kg−1 and power profile at 1, 5 and 10 s. In conclusion, the data obtained in competitions by the two power meters were interchangeable. Nevertheless, the Power2Max power meter underestimated the pedaling power during short and high-intensity intervals (≤10.0 s and >7.50 W·kg−1) compared to the PowerTap power meter. Therefore, the analysis of these efforts should be treated with caution.
... The HR analysis also monitors the session load and quantifies the physiological demands (19,21,22). Therefore, the HR analysis between competitions provides a better understanding of the load and intensity of the exercise and racing strategies (19,20,23). ...
... In the last 6 h (>18-24 h) the race intensity between the two WRs remains similar with a moderate effect and higher HR mean values for the new WR. This may be the result of the cyclist's strategy to attain the new WR, where the cyclist monitors HR variations to regulate the physical and physiological demands (19,20,23). However, the literature lacks individual racing strategies based on HR variations. ...
Article
Full-text available
A plethora of factors determine elite cycling performance. Those include training characteristics, pacing strategy, aerodynamics, nutritional habits, psychological traits, physical fitness level, body mass composition, and contextual features; even the slightest changes in any of these factors can be associated with performance improvement or deterioration. The aim of the present case report is to compare the performances of the same ultra-cyclist in achieving two world records (WR) in 24 h cycling. We have analyzed and compared the distance covered and speed for each WR. The 24 h period was split into four-time intervals (0–6 h; > 6–12 h; > 12–18 h; > 18–24 h), and we compared the differences in the distance covered and speed between the two WRs. For both WRs, a strong negative correlation between distance and speed was confirmed (r = –0.85; r = –0.89, for old and new WR, respectively). Differences in speed (km/h) were shown between the two WRs, with the most significant differences in 12–18 h (1 = 6.50 km/h). For the covered distance in each block, the most significant differences were observed in the last part of the cycling (1 = 38.54 km). The cyclist effective surface area (ACd) was 0.25 m2 less and 20% more drag in the new WR. Additionally, the mechanical power was 8%, the power to overcome drag was 31%, and the powerweight ratio was 8% higher in the new WR. The mechanical efficiency of the cyclist was 1% higher in the new WR. Finally, the heart rate (HR) presented significant differences for the first 6 h (Old WR: 145.80 ± 5.88 bpm; New WR: 139.45 ± 5.82 bpm) and between the 12 and 18 h time interval (OldWR: 133.19 ± 3.53 bpm; New WR: 137.63 ± 2.80 bpm). The marginal gains concept can explain the performance improvement in the new WR, given that the athlete made some improvements in technical specifications after the old WR.
... High quality power meters have been validated against a calibrated ergometer, and against other brands of power meter [111,[113][114][115][116][117][118], and allow the user to calibrate the meter, ensuring valid and reliable data [113,[119][120][121]. Riders, coaches, and sport-scientists use this data to improve decision-making around the preparation of riders for future events. ...
Thesis
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Track cycling events, both sprint and endurance, are primarily focused on performance of high and medium power durations, and it is suggested, measures of peak power govern performance in the sprint and pursuit cycling events. Various tests and metrics in the laboratory have been used to try and model track cycling. With the advent of power meters cyclists have been able to record power output in the field and several basic tests have evolved to use as a means to get started with training and racing with power. This thesis proposes a linear model based on total least squares regression, to evaluate these models and provide an option for coaches to see what durations are key for performance, and for sprint cyclists what types of training should be performed at a given part of a training build up. This analysis is applied to sprint cycling, male and female sprint cyclists, and pursuit cyclists to evaluate field-based data compared to lab and model derived metrics. The key conclusions from this thesis are: 1. For each specific power duration along the hyperbolic power-duration curve shows field-based data offers a better model for both sprint and pursuit durations. The linear model has a parabolic relationship the closer the inputs get to the specific duration assessed. 2. This disproves the contention of a linear process governed by peak power being the key metric of sprint cycling. The data in this thesis shows not only is this relationship incorrect, but strong relationships with sprint cycling durations hold for durations as long as 20-min. 3. This thesis finds there are sex differences for the model showing women have a higher variation of sprint power than men. 4. The linear model is applied to track endurance cycling to show, again, how a peak power (or maximal sprinting power or 𝑉̇O2max) does not govern performance, more a broad base of capacity reflected by a high lactate threshold, ventilatory threshold, critical power or other estimates of the maximal metabolic steady state. 5. Based on an understanding of the importance of capacity as well as peak power Chapter 6 shows this information can successfully be applied to the performance of sprint cyclists training towards peak performance.
... This was possible with the participants using their cycling power meter connected to an online training platform. Due to the expansion of power meters through reduced cost and improvements in their reproducibility (Bouillod et al., 2017), the implementation of powerbased training prescription has become increasingly popular among cyclists over the last several years. Using this approach, coaches can consult, analyse, and monitor various physiological (HR, power, pace/speed, energy expenditure) and perceptual (RPE, overall feeling and wellness) training metrics for multiple athletes simultaneously. ...
Thesis
Endurance athletes have traditionally been advised to consume high carbohydrate intake before, during and after exercise to support high training loads and facilitate recovery. Accumulating evidence suggests periodically training with low carbohydrate availability, termed “train-low”, augments skeletal oxidative adaptations. Comparably, to account for increased carbohydrate utilisation during exercise in hot environmental conditions, nutritional guidelines advocate high carbohydrate intake. Recent evidence suggests heat stress induces oxidative adaptation in skeletal muscle, augmenting mitochondrial adaptation during endurance training. This thesis aimed to assess the efficacy of training with reduced carbohydrate and the impact of elevated ambient temperatures on performance and metabolism. Chapter 4 demonstrated 3 weeks of Sleep Low-Train Low (SL-TL) improves performance when prescribed and completed remotely. Chapter 5 implemented SL-TL in hot and temperate conditions, confirming SL-TL improves performance and substrate metabolism, whilst additional heat stress failed to enhance performance in hot and temperate conditions following the intervention. Chapters 6 and 7 optimised and implemented a novel in vitro skeletal muscle exercise model combining electrical pulse stimulation and heat stress. Metabolomics analysis revealed an ‘exercise-induced metabolic response, with no direct metabolomic impact of heat stress. Chapter 8 characterised the systemic metabolomic response to acute exercise in the heat following SL-TL and heat stress intervention revealing distinct metabolic signatures associated with exercise under heat stress. In summary, this thesis provides data supporting the application of the SL-TL strategy during endurance training to augment adaptation. Data also highlights the impact of exercise, environmental temperature and substrate availability on skeletal muscle metabolism and the systemic metabolome. Together, these data provide practical support for the efficacy of the SL-TL strategy to improve performance and adaptation whilst casting doubt on the utility of this approach in hot environments in endurance-trained athletes.
... The accuracy of the latest SRM cycle ergometer (power meter in the science system) is reported to be ± 0.5-1% according to the manufacturer and previous studies (Bouillod et al., 2017;Nimmerichter et al., 2017;Montalvo-Pérez et al., 2021). Therefore, a representation of t PCr−peak was determined as the time until W peak during the 15-s ASCT because energy was influenced by the ATP-PCr system until peak power during the initial seconds of a maximal short-term exercise (Serresse et al., 1988;Beneke et al., 2002). ...
Article
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Purpose: This study aimed at comparing previous calculating formulas of maximal lactate accumulation rate ( ν La.max) and a modified formula of pure ν La.max (P ν La.max) during a 15-s all-out sprint cycling test (ASCT) to analyze their relationships. Methods: Thirty male national-level track cyclists participated in this study (n = 30) and performed a 15-s ASCT. The anaerobic power output (Wpeak and Wmean), oxygen uptake, and blood lactate concentrations (La⁻) were measured. These parameters were used for different calculations of ν La.max and three energy contributions (phosphagen, W PCr; glycolytic, W Gly; and oxidative, W Oxi). The P ν La.max calculation considered delta La⁻, time until Wpeak (tPCr−peak), and the time contributed by the oxidative system (tOxi). Other ν La.max levels without tOxi were calculated using decreasing time by 3.5% from Wpeak (tPCr −3.5%) and tPCr−peak. Results: The absolute and relative W PCr were higher than W Gly and W Oxi (p < 0.0001, respectively), and the absolute and relative W Gly were significantly higher than W Oxi (p < 0.0001, respectively); ν La.max (tPCr −3.5%) was significantly higher than P ν La.max and ν La.max (tPCr−peak), while ν La.max (tPCr−peak) was lower than P ν La.max (p < 0.0001, respectively). P ν La.max and ν La.max (tPCr−peak) were highly correlated (r = 0.99; R 2 = 0.98). This correlation was higher than the relationship between P ν La.max and ν La.max (tPCr −3.5%) (r = 0.87; R 2 = 0.77). ν La.max (tPCr−peak), P ν La.max, and ν La.max (tPCr −3.5%) were found to correlate with absolute Wmean and W Gly. Conclusion: P ν La.max as a modified calculation of ν La.max provides more detailed insights into the inter-individual differences in energy and glycolytic metabolism than ν La.max (tPCr−peak) and ν La.max (tPCr −3.5%). Because W Oxi and W PCr can differ remarkably between athletes, implementing their values in P ν La.max can establish more optimized individual profiling for elite track cyclists.
... [8,9] The speciality literature includes numerous studies that determine the validity, sensitivity, reproducibility and robustness of PowerTap (PWT), Stages (STG) and Garmin Vector (VCT) technologies compared to the SRM Training System. [10,11] The Garmin device is a valid alternative for training which provides an overview of the athlete's level of performance, so that this level can be maintained or improved continuously. [12] The pedaling power (power-endurance) in cycling events, is part of the strength-endurance axis, respectively long-term muscular endurance (R-M), which requires the ability to apply force against a standard endurance for a long period of time. ...
Conference Paper
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In recent years, specialists have paid particular attention to the improvement of the pedaling technique. ,,Spinning" or circular pedaling (road test) involves rotating the pedals around the entire circumference, not pressing them when they move downward. A good pedaling technique will always be supported by adequate physical training. Road cycling overloads the aerobic system. The anaerobic energy system is used by cyclists at the end of the race or during the ascent. Thus, junior cyclists must be prepared to make a prolonged effort over long distances and pedaling should generate rotations that maintain speed and power according to the environment and the terrain. The purpose of this paper is to implement and carry out a physical training program that will lead to the acceleration of the evolution of pedaling power in junior cyclists. It is considered that the use of specific means for winter training, taking into account age-specific somato-functional features, will lead to increased pedaling power of junior cyclists. A strength training program for road cycling requires the use of some means with a load that reflects the endurance that the athlete must overcome in competitive conditions. The experiment took place between 2019-2020, on a sample of 16 cadet cyclists. To measure the effects of the variables, progress was monitored using Garmin technology. The target indicators were ,,power expressed in watts"(P), ,,power/kg body weight, expressed in watts/kg" and heart rate. The obtained results show that the means of force development had a positive effect on the pedaling power of cadet cyclists.
... Also, there might be opportunities associated with establishing collaborations with the virtual racing community and individual researchers when developing such products. Furthermore, as a recent review shows (Bouillod et al., 2017), the research literature that has evaluated different power meters shows varying results and uses methodologies that have limited generalizability to the context of virtual racing. Manufacturers of trainers and power meters should consider these new expectations of their products while also realizing that producing equipment that is not very accurate will directly hurt the credibility of the sport (and their brands). ...
Article
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Competitive racing through virtual cycling has established itself as an entirely new discipline within cycling. This study explores what equipment racers use and examines important power metrics for racing. Data were collected from three different races from the current ranking of the most highly regulated and professionally organized race series on the virtual cycling platform Zwift. Power output data from 116 race participants, over five power durations (5 s-20 min), and two separate power measuring sources were collected and analyzed using the Bland-Altman method. The findings indicate that the physiological efforts of these races are comparable to those found in traditional competitive cycling. Furthermore, findings also support that the equipment typically used produces similar power outputs with good agreement between different power meters for most measurement points. Finally, the implications of these results for the status of virtual racing are discussed.
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This study compared the impact of two polarized training programs (POL) on aerobic capacity in well-trained (based on maximal oxygen uptake and training experience) female cyclists. Each 8-week POL program consisted of sprint interval training (SIT) consisting of 8–12 repetitions, each lasting 30 seconds at maximal intensity, high-intensity interval training (HIIT) consisting of 4–6 repetitions, each lasting 4 minutes at an intensity of 90–100% maximal aerobic power, and low-intensity endurance training (LIT) lasting 150–180 minutes with intensity at the first ventilatory threshold. Training sessions were organized into 4-day microcycles (1st day—SIT, 2nd day—HIIT, 3rd day—LIT, and 4th day—active rest), that were repeated throughout the experiment. In the first POL program, exercise repetitions during SIT and HIIT training were performed with freely chosen cadence above 80 RPM (POLFC group, n = 12), while in the second POL program with low cadence 50–70 RPM (POLLC group, n = 12). Immediately before and after the 8-week POL intervention, participants performed an incremental test to measure maximal aerobic power (Pmax), power achieved at the second ventilatory threshold (VT2), maximal oxygen uptake (VO2max), maximal pulmonary ventilation (VEmax), and gross efficiency (GE). Moreover, participants performed VO2max verification test. Analysis of variance showed a repeated measures effect for Pmax (F = 21.62; η² = 0.5; p = 0.00), VO2max (F = 39.39; η² = 0.64; p = 0.00) and VEmax (F = 5.99; η² = 0.21; p = 0.02). A repeated measures x group mixed effect was demonstrated for Pmax (F = 4.99; η² = 0.18; p = 0.03) and VO2max (F = 6.67; η² = 0.23; p = 0.02). Post-hoc Scheffe analysis showed that increase in Pmax were statistically significant only in POLLC group. The Friedman test showed that VT2 differed between repeated measures only in the POLLC group (χ² = 11; p = 0.001; W = 0.917). In conclusion, it was found that POL program where SIT and HIIT were performed at low cadence was more effective in improving aerobic capacity in well-trained female cyclists, than POL with SIT and HIIT performed at freely chosen cadence. This finding is a practical application for athletes and coaches in cycling, to consider not only the intensity and duration but also the cadence used during various interval training sessions.
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This study aimed to determine if the Garmin Vector (Schaffhausen, Switzerland) power meter produced acceptable measures when compared with the Schoberer Rad Messetechnik (SRM; Julich, Germany) power meter across a range of high-intensity efforts. Twenty-one well-trained cyclists completed power profiles (seven maximal mean efforts between 5 and 600 s) using Vector and SRM power meters. Data were compared using assessments of heteroscedasticity, t tests, linear regression, and typical error of estimate (TEE). The data were heteroscedastic, whereby the Vector pedals increasingly overestimated values at higher power outputs; however, t tests did not identify any significant differences between power meters (p > .05). Using linear regression, Vector data were fit to an SRM equivalent (slope = .99; intercept = −9.87) and TEE produced by this equation was 3.3% (3.0%–3.8%). While the data shows slight heteroscedasticity due to differing strain-gauge placement and resultant torque measurement variance, the Vector appears acceptable for measures of power output across various cycling efforts.
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AbstractAdvances in technology have made the use of a variety of power meters ubiquitous in road cycling along with an ever-increasing popularity during mountain biking. This study compared data from one bicycle using three power meters: Stages (non-driveside crank arm); Quarq (chainring spider); and Powertap (rear-wheel hub). While no differences (p?>?.05) between power meters were present during treadmill riding at high or low cadences, dissimilarities for both power (W) and cadence (rpm) were apparent during actual cross-country mountain bike riding. Frequency distribution and analysis of coasting indicate that the Stages records more time (p?<?.001) at zero watts (6.9?±?3.3 s) and zero cadence (6.9?±?3.3 s) compared with Quarq (W?=?3.3?±?1.5 s, rpm?=?.8?±?.7 s) and Powertap (W?=?1.1?±?.8 s, rpm?=?3.0?±?1.2 s). Consequently, significant interactions (power meter???terrain, p?=?.0351) and main effects (power meter p?<?.0001, and terrain p?<?.0001) for power output were present and include
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Aim. It remains unclear if cyclists with better performance have less asymmetry. Therefore, this study aimed at assessing the relationship between cycling time trial performance and bilateral asymmetries in pedal forces. Methods. Ten cyclists/triathletes performed an incremental cycling test to exhaustion to measure maximal oxygen uptake and power output. In a second session, bilateral pedal forces were acquired during a 4-km cycling time trial on the stationary cycle ergometer. Resultant and effective forces were computed along with the index of effectiveness at 500 m sections of the time trial using instrumented pedals. Intra-limb variability and the asymmetry index were calculated for each force variable. Results. Multivariate analysis assessed bilateral differences in pedal forces accounting for power output, pedalling cadence and oxygen uptake of each cyclist. Force variables did not change throughout the test (effective – P=0.98, resultant P=0.90 and index of effectiveness – P=0.99) with larger force applied by the dominant limb (11-21%). The relationship between asymmetries and performances was strong for the effective force (r=-0.72) but weak for the resultant force (r = 0.01) and for the index of effectiveness (r=-0.29). Substantial asymmetries were observed for the effective force (36-54%), resultant force (11-21%) and for the index of effectiveness (21-32%) at greater range than intra-limb variability (effective force =8-22%, resultant force =5-10% and index of effectiveness =1-3%). Conclusion. Larger asymmetries in effective force were related to better performances during the 4-km time trial with low intra-limb variability for force measures suggesting consistence in asymmetries for individual cyclists.
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The aim of this study was to determine the agreement between two portable cycling powermeters for use doing field based mountain biking. A single participant performed 15 timed ascents of an off-road climb. The participants’ bicycle was instrumented with Stages Cycling and SRM powermeters. Mean and peak power output and cadence were recorded at 1 s intervals by both systems. Significant differences were determined using paired t-tests, whilst agreement was determined by calculating the bias and random error and the associated 95% limits of agreement (LoA). Significant differences were found between the two systems for mean power output (p<0.001), with the Stages powermeter under reporting power by 8 ± 1 % compared to the SRM. Bias and random error for mean power output were -18 ± 7 W (95 % LoA = 12 - 25 W above and below the mean). CV was 5.5 % and 5.2 %, for the Stages and SRM respectively. Peak power output was significantly lower with the Stages powermeter (p=0.02) by 6 ± 1 % when compared to the SRM powermeter. Bias and random error for peak power output were -25 ± 74 W (95 % LoA = 49 – 99 W above and below the mean), whilst CV was 13.7 % and 13.1 %, for Stages and SRM respectively. No significant differences were found for mean or peak cadence, whilst CV were <3 % for mean cadence for both systems and <6 % for peak cadence for both systems. This study found that both powermeters provided a reliable means of recording mean power output and cadence, though peak power values were less reliable. However, the Stages system significantly underestimated mean and peak power output when compared with the SRM system. This may in part be due to differences in strain gauges configuration and the subsequent algorithms used for the calculation of power output and the potential bilateral influences on power output production.
Conference Paper
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Background: Gross efficiency (GE) has been shown to be one of the most relevant parameter influencing cycling performance but to the best of our knowledge, just one study focused on measuring GE in seated and standing positions during field conditions (Millet et al., 2002: Medicine & Science in Sports & Exercise, 34(10), 1645-52). Other authors investigated the effects of change in body position (seated vs. standing) on energy expenditure, heart rate (HR) or pulmonary ventilation (VE) but there were large changes in the results (Ryschon due to the different protocols. Indeed, the cross effect of slope and intensity on GE in standing compared to seated position has not been studied. Thus, we found interesting to measure GE in real locomotion with elite cyclists at different intensities and slopes.
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Numerous workers are exposed to vibrations which can turn out to be fatal for the health. Athletes can be included in this population, in particular cyclists who are exposed to vibration due to the irregularity of the road. This nuisance depends of the duration of exposure and the range of vibrations. While the worker is mostly directly excited by a vibrating system, the cyclist is indirectly subjected to it. He undergoes the vibrations of an excited sub-structure which is the bicycle. So the bicycle plays the role of a vibration filter or amplifier. In this paper we propose to (i) study the transmission of vibrations to the cyclist after excitation on a paving road, (ii) calculate the limit time of exposure to this type of excitation rate by the use of the standard ISO 5349 and the European directive 2002/44/EC, and (iii) compare the weighting curve of the standard with a vibrations transmissibility curve obtained between the collarbone and the stem. For this particular case of an excited sub-structure, a weighting curve is proposed by considering the first modal frequency of the bicycle.
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This paper describes a technique designed to measure the in-situ acceleration signals that will be used to drive a road simulator in the study of road bike vibration transmission in a laboratory setting. To measure the signals, a bike mounted by a cyclist and towed by a motor vehicle is used. A road simulator using actuators driven by a digital signal is described. The impulse response of the bike used to measure road data is convoluted with the road acceleration in order to obtain the required actuator signal. The reproduction capacity of the simulator is evaluated by comparing the frequency content as well as the time statistical parameters of the acceleration signal measurement with road to the acceleration obtained on the simulator. On a granular road with a broadband excitation spectrum, the vertical excitation obtained with the simulator adequately mimics the measured road acceleration. This technique can be used to compare vibration transmission characteristics among different road bikes.
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Unlabelled: Power meters have traditionally been integrated into the crank set, but several manufacturers have designed new systems located elsewhere on the bike, such as inside the pedals. Purpose: This study aimed to determine the validity and reliability of the Keo power pedals during several laboratory cycling tasks. Methods: Ten active male participants (mean ± SD age 34.0 ± 10.6 y, height 1.77 ± 0.04 m, body mass 76.5 ± 10.7 kg) familiar with laboratory cycling protocols completed this study. Each participant was required to complete 2 laboratory cycling trials on an SRM ergometer (SRM, Germany) that was also fitted with the Keo power pedals (Look, France). The trials consisted of an incremental test to exhaustion followed by 10 min rest and then three 10-s sprint tests separated by 3 min of cycling at 100 W. Results: Over power ranges of 75 to 1147 W, the Keo power-pedal system produced typical error values of 0.40, 0.21, and 0.21 for the incremental, sprint, and combined trials, respectively, compared with the SRM. Mean differences of 21.0 and 18.6 W were observed between trials 1 and 2 with the Keo system in the incremental and combined protocols, respectively. In contrast, the SRM produced differences of 1.3 and 0.6 W for the same protocols. Conclusions: The power data from the Keo power pedals should be treated with some caution given the presence of mean differences between them and the SRM. Furthermore, this is exacerbated by poorer reliability than that of the SRM power meter.
Article
The aim of this study was to test the validity and the reliability of the G-Cog which is a new BMX powermeter allowing for the measurements of the acceleration on X-Y-Z axis (250 Hz) at the BMX rear wheel. These measurements allow computing lateral, angular, linear acceleration, angular, linear velocity and the distance. Mechanical measurements at submaximal intensities in standardized laboratory conditions and during maximal exercises in the field conditions were performed to analyse the reliability of the G-Cog accelerometers. The performances were evaluated in comparison with an industrial accelerometer and with 2 powermeters, the SRM and PowerTap. Our results in laboratory conditions show that the G-Cog measurements have low value of variation coefficient (CV=2.35%). These results suggest that the G-cog accelerometers measurements are reproducible. The ratio limits of agreement of the rear hub angular velocity differences between the SRM and the G-Cog were 1.010 × ÷ 1.024 (95%CI=0.986-1.034) and between PowerTap and G-Cog were 0.993 × ÷ 1.019 (95%CI=0.974-1.012). In conclusion, our results suggest that the G-Cog angular velocity measurements are valid and reliable compared with SRM and PowerTap and could be used to analyse the kinematics during BMX actual conditions.
Article
The aim of this study was to test the validity and reliability of the G-Cog which is a new BMX power meter allowing for the measurements of the power output (250 Hz) at the BMX rear wheel during actual cycling and laboratory conditions. Sprints in road cycling (6-8 s) from static start and incremental tests in the laboratory (100-400 W) have been performed to analyse the validity and reliability of the power output values by comparison with 2 devices: The PowerTap and the SRM which are considered as the gold standard. The most important finding of this study is that the G-Cog power output data were not valid and reliable during sprint and standardised laboratory tests compared with the SRM and the PowerTap devices. During the sprint and the laboratory tests the ratio limits of agreement of the power output differences between the SRM and G-Cog were 1.884×÷1.970 (95%CI=0.956-3.711) and 12.126×÷16.281 (95%CI=0.745-197.430), respectively. In conclusion, the G-Cog must be used with caution regarding the power output measurements. Nevertheless, the G-Cog could be used for the first time to analyse the determinants of the BMX performance from the pedalling profile.