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To validate the new PowerTap P1® pedals power meter (PP1), thirty-three cyclists performed 12 randomized and counterbalanced graded exercise tests (100–500 W), at 70, 85 and 100 rev·min-1 cadences, in seated and standing positions. A scientific SRM system and a pair of PP1 pedals continuously recorded cadence and power output data. Significantly lower power output values were detected for the PP1 compared to the SRM for all workloads, cadences, and pedalling conditions (2–10 W, p < 0.05), except for the workloads ranged between 150 W to 350 W at 70 rev·min-1 in seated position (p > 0.05). Strong Spearman’s correlation coefficients were found between the power output values recorded by both power meters in a seated position, independently from the cadence condition (rho ≥ 0.987), although slightly lower concordance was found for the standing position (rho = 0.927). The mean error for power output values were 1.2%, 2.7%, 3.5% for 70, 85 and 100 rev·min-1, respectively. Bland-Altman analysis revealed that PP1 pedals underestimate the power output data obtained by the SRM device in a directly proportional manner to the cyclist’s cadence (from -2.4 W to -7.3 W, rho = 0.999). High absolute reliability values were detected in the PP1 pedals (150–500 W; CV = 2.3%; SEM < 1.0 W). This new portable power meter is a valid and reliable device to measure power output in cyclists and triathletes for the assessment, training and competition using their own bicycle, although caution should be exercised in the interpretation of the results due to the slight power output underestimation of the PP1 pedals when compared to the SRM system and its dependence on both pedalling cadence and cyclist’s position (standing vs. seated).
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©Journal of Sports Science and Medicine (2018) 17, 305-311
http://www.jssm.org
Received: 20 November 2017 / Accepted: 16 April 2018 / Published (online): 14 May 2018
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Validity and Reliability of the PowerTap P1 Pedals Power Meter
Jesús G. Pallarés and José Ramón Lillo-Bevia
Human Performance and Sports Science Laboratory, University of Murcia, Murcia, Spain
Abstract
To validate the new PowerTap P1® pedals power meter (PP1),
thirty-three cyclists performed 12 randomized and counterbal-
anced graded exercise tests (100–500 W), at 70, 85 and 100
revꞏmin-1 cadences, in seated and standing positions. A scientific
SRM system and a pair of PP1 pedals continuously recorded ca-
dence and power output data. Significantly lower power output
values were detected for the PP1 compared to the SRM for all
workloads, cadences, and pedalling conditions (2–10 W, p <
0.05), except for the workloads ranged between 150 W to 350 W
at 70 revꞏmin-1 in seated position (p > 0.05). Strong Spearman’s
correlation coefficients were found between the power output val-
ues recorded by both power meters in a seated position, inde-
pendently from the cadence condition (rho ≥ 0.987), although
slightly lower concordance was found for the standing position
(rho = 0.927). The mean error for power output values were 1.2%,
2.7%, 3.5% for 70, 85 and 100 revꞏmin-1, respectively. Bland-Alt-
man analysis revealed that PP1 pedals underestimate the power
output data obtained by the SRM device in a directly proportional
manner to the cyclist’s cadence (from -2.4 W to -7.3 W, rho =
0.999). High absolute reliability values were detected in the PP1
pedals (150–500 W; CV = 2.3%; SEM < 1.0 W). This new port-
able power meter is a valid and reliable device to measure power
output in cyclists and triathletes for the assessment, training and
competition using their own bicycle, although caution should be
exercised in the interpretation of the results due to the slight
power output underestimation of the PP1 pedals when compared
to the SRM system and its dependence on both pedalling cadence
and cyclist’s position (standing vs. seated).
Key words: Cycling, mobile power meter, testing, cycle ergom-
eter, power output.
Introduction
Mobile power meters became commercially available in
the 1980’s, allowing direct measurement of power output
in field conditions (Nimmerichter et al., 2017). Since then,
scientists, coaches and cyclists have been able to measure
bicycle power output during cycling training and competi-
tion, as traditionally performed in a laboratory setting. The
SRM power meter soon became the gold standard of the
mobile power meters. It consists of a crankset that allows
the measurement of torque via strain gauges, located be-
tween the crank and the chain rings, and angular velocity
from the cadence. Therefore, power output is calculated as
the product of torque and angular velocity.
Several valid and reliable laboratory-specialized er-
gometers and power meters have been developed so far to
monitor exercise performance while cycling: Lode
(Earnest et al., 2005; Reiser et al., 2000), Ergoline
(Maxwell et al., 1998), Monark (MacIntosh et al., 2001;
Maxwell et al., 1998), Velotron (Abbiss et al., 2009;
Astorino and Cottrell, 2012), Wattbike (Hopker et al.,
2010; Wainwright et al., 2017). It should be noted that it is
not possible to use them for field testing, moreover their
size, weight and especially their price, could make difficult
their use in laboratories with low financial resources
(Peiffer and Losco, 2011). In addition, even if the cyclist
can customize the position of the ergometer´s handlebars,
saddle and pedals (not always possible), there would be
considerable variations with their own bicycles in some de-
cisive metrics such as the crank width (Q-factor), crank
length, and other differences related to the specific geome-
try of the bicycle itself, which could affect comfort, pedal-
ling performance and might even increase injury incidence
(Disley and Li, 2014).
Currently, there are some mobile power meters
whose validity and reliability have been confirmed, such as
Garmin Vector (Bouillod et al., 2016; Nimmerichter et al.,
2017; Novak and Dascombe, 2016), or PowerTap Hub
(Bertucci et al., 2005b; Bouillod et al., 2016; Gardner et al.,
2004). Nevertheless, there are others whose results are re-
producible, but whose validity remains in question, such as
Stages (Bouillod et al., 2016; Granier et al., 2017). Finally,
there are others considered unreliable power meters, for ex-
ample, Look Keo Power pedal (Sparks et al., 2015).
The validity and reliability of power meters is
linked to the usefulness of the information obtained, since
it is well known that poor reliability in power output meas-
urement does not allow for optimisation of the training pro-
gram, in comparison with previous or future tests, nor an
accurate analysis of the data (Jeukendrup et al., 2000).
Changes in performance and training status cannot be de-
termined without a high level of reliability for the measure-
ment of power output (Garcia-Lopez et al., 2016; Hopkins
et al., 2001; Jeukendrup et al., 2000; Pallares et al., 2016;
Paton and Hopkins, 2001). For the evaluation of the effect
of training or detraining with power output measurement,
it is important to know the variation due to the technical
error of the power meter (Bertucci et al., 2005a). Specifi-
cally, Vanpraagh et al. (1992) suggested that the range of
the technical error for workload recorded using ergometers
should be within 5%. When using a power meter to test
high-level athletes, it would be advisable for this technical
error to be closer to 2%, due to the fact that elite male cy-
clists have typical variation of ~1% for time trials lasting
~1 hour (Paton and Hopkins, 2001).
The recent development of the PowerTap P1 pedals
(PP1, CycleOps, Madisson, USA) has introduced another
mobile power measuring tool to the market with a reduced
price (~$999.99). In a similar way to others, this manufac-
turer claims that the PP1 pedals are accurate to within
Research article
Validity of PowerTap P1 pedals power meter
306
1.5%, with a very limited extra weight (~150 g) compared
with mid or top range clipless road pedals. They are built
with eight strain gauges, which work with a ‘Multipole
Ring’, a sensor made of 20 small magnets around the pedal
spindle. It allows cyclists to use their own bicycle in tests
or training sessions carried out on laboratory ergometers,
indoor trainers, rollers or in the field, by just replacing the
pedals. The power measurement comes directly from the
point of contact with the bicycle, reducing the loss of
power output due to mechanical connections (Jones, 1998).
As far as we know, the PowerTap P1 pedals have
not been previously validated. For this reason, the purpose
of this study is to examine the validity, reliability and ac-
curacy of a new powermeter placed in the pedals of the
bike under laboratory cycling conditions.
Methods
Experimental approach to the problem
A descriptive, cross-sectional, quantitative study was con-
ducted. During a period of three weeks, each participant
performed several tests, conducted on separate days, in the
same exercise laboratory, under standardized conditions
(22.9 ± 2.0 oC; 39.3 ± 3.0 % humidity). The study, which
was conducted according to the declaration of Helsinki,
was approved by the Bioethics Commission of the Univer-
sity of the University of Murcia, and written informed con-
sent was obtained from all participants prior to participa-
tion.
Participants
Thirty-three well-trained male cyclists and triathletes vol-
unteered to take part in this study (age 32.4 ± 9.0 yr; height
1.86 ± 0.08 m; body mass 78.6 ± 12.9 kg; VO2max 57.7 ±
6.6 mlꞏkg-1ꞏmin-1; maximal aerobic power (MAP) 399 ± 31
W; cycling training experience 11.2 ± 2.7 years). All par-
tici pants train ed for 6 h ours or mor e per w eek du ring a min-
imum of twelve months preceding the study. Participants
were asked to avoid strenuous exercise, caffeine and alco-
hol for at least 24 hours prior to each testing session.
Testing procedures
A brand new PowerTap P1 power meter (CycleOps, Mad-
ison, USA) was compared against an SRM crank-based
power meter (scientific model with adjustable 7075 Alu-
minium crank length; Schoberer Rad Messtechnik, Julich,
Germany, ±1% accuracy). For all testing sessions, PP1
were mounted on the SRM cranks with the manufacturer-
recommended torque. Additionally, a medium size road bi-
cycle (Giant Defy 3, 2010 Giant Bicycles, Taiwan; Alu-
minium alloy frame with carbon fibre fork) was fitted with
the SRM 172.5 mm crank power meter. This precision
strain gauge-based crank and sprocket dynamometer trans-
mitted data to a unit display fixed on the handlebars.
The relationship between the frequency output and
the strain gauges and torque is determined during manufac-
ture and considered constant. The validity of this SRM sys-
tem has been previously demonstrated and therefore taken
as the gold standard power meter device (Jones, 1998;
Martin et al., 1998; Passfield and Doust, 2000). To mini-
mize the possible influence in the validity and reliability
values of the PP1 data, the same bicycle and SRM power
meter were used in all testing conditions. A dy-
namic calibration of the SRM crankset was performed by
the manufacturer prior to the beginning of the study.
The rear wheel of the bicycle was removed and at-
tached to a direct drive pedalling unit Cycleops Hammer
(Cycleops, Wisconsin, EEUU) (Lillo-Bevia and Pallares,
2017) with 10 speed (11–25 tooth) rear gear ratio and 39–
53 tooth front gear ratio. For all tests, the gear ratio 39:15
was selected, and cyclists were not allowed to change it to
prevent a potential effect of this variable on pedalling tech-
nique. Prior to each testing session, the calibration of the
Hammer ergometer was carried out according to the man-
ufacturer’s recommendations. In this way, the Hammer can
accurately determine the power required to overcome bear-
ing and belt friction, and set the zero offset of strain gauges.
Furthermore, the zero offset of the PP1 pedals was set be-
fore each testing session. Likewise, the front fork of the
bicycle was attached to the accompanying steering appa-
ratus for stability purposes. The bicycle seat height position
was matched to the cyclist’s own training geometry. Cy-
clists used their own cycling shoes fitted with Look cleats.
The absolute and relative validity of this direct drive device
has been recently confirmed (Lillo-Bevia and Pallares,
2017).
Testing protocol
All testing protocols began with a standardized warm-up of
5 minutes at 100 W with a freely chosen cadence. Follow-
ing this period, the validity and reliability of the devices
were assessed in the laboratory during three different test-
ing protocols:
All participants performed three randomized and
counterbalanced graded exercises tests, one for each se-
lected fixed cadence (70, 85 and 100 revꞏmin-1), at six sub-
maximal workloads (100, 150, 200, 250, 300 and 350 W)
of 75 seconds duration (Jones, 1998). The three graded ex-
ercise tests were separated by 5 min of recovery at 75 W,
performed in seated position and with freely chosen ca-
dence. The order of the three cadence levels was random-
ized to ensure that the validity of the results was not af-
fected by increments on the ergometer break temperature
or by the cyclists’ fatigue. After 5 min of recovery at 75 W,
cyclists performed a 75-second seated free cadence 500 W
workload. Finally, they performed a graded exercise test at
three sub-maximal workloads (250, 350 and 450 W) of 75
seconds with a freely chosen cadence, in a standing pedal-
ling position. Two minutes of recovery at 75 W with freely
chosen cadence were kept between the three workloads
tested. The pedalling power output was registered by the
PP1 and SRM simultaneously.
Following the recommendation of Jones (1998),
only power outputs and cadence values from the 10th to the
70th second of each 75-second step were analysed, to allow
the Cycleops Hammer enough time to stabilise the assigned
breaking workload. During each test, power output (W)
and cadence (revꞏmin-1) of PP1 were recorded at a fre-
quency of 1 Hz using a Garmin 1000 cycling computer
(Garmin International Inc., Olathe, KS, USA). Addition-
ally, power output and cadence of the SRM crankset were
recorded at a frequency of 1 Hz using the Power Control
V.
Pallarés and Lillo-Bevia
307
Statistical analysis
Standard statistical methods were used for the calculation
of means, standard deviations (SD), coefficient of variation
(CV) and standard error of the mean (SEM). Data were as-
sessed for heteroscedasticity by plotting the predicted vs.
the residual values for power and cadence measurements.
The Kolmogorov-Smirnov test and complementary anal-
yses of normality were used. The SRM and PP1 power out-
put and cadence data were not normally distributed. Thus,
the analysis of differences between the mean of power out-
puts and cadences values of each device were assessed with
a non-parametric Mann-Whitney U test. Spearman's rank
order correlation coefficients were calculated comparing
the power outputs values of the SRM and the PP1 power
meters during every graded exercise test. Additionally,
given the fact that a high correlation does not necessarily
imply that there is good agreement between any two meth-
ods, Bland-Altman plots were used to assess and display
the agreement and systematic difference among the SRM
and PP1 power outputs values (Bland and Altman, 1999).
The power outputs differences were drawn in relation to
the mean values and 95% of the differences, which were
expected to lie between the two limits of agreement (LoA).
LoA was defined as mean bias ± 2 standard deviation (SD)
(Atkinson and Nevill, 1998). Statistical significance for all
tests was regarded as p < 0.05. The recorded data were
downloaded from the previously described units and
further analysed using publicly available software (Golden
Cheetah, version 3.4) and Microsoft Excel 2016 (Microsoft
Software). Analyses were performed using GraphPad
Prism 6.0 (GraphPad Software, Inc., CA, USA), SPSS soft-
ware version 19.0 (SPSS, Chicago, IL) and Microsoft Ex-
cel 2016 (Microsoft Corp, Redmond, WA, USA).
Results
Validity
No significant differences were detected in power output
values between SRM scientific model and PP1 pedals at 70
revꞏmin-1 in seated position for workloads ranged between
150 W to 300 W (p > 0.05). However, in the rest of the
workloads, cadences, and pedalling positions assessed, sig-
nificantly lower values were detected in the PP1 compared
to the SRM power meter (p < 0.05) (Table 1). Nevertheless,
high levels of Spearman’s correlation coefficients were de-
tected between the power output values recorded by the
PP1 and the SRM devices in seated position (rho ≥ 0.987;
p < 0.001), independently from the cadence condition (70,
85 and 100 revꞏmin-1). However, for standing pedalling a
slightly weaker correlation coefficient was found (rho =
0.927; p < 0.001) (Table 1 and Figure 1). Confirming the
means difference data analysis, the Bland-Altman analysis
revealed low bias, but not negligible, between the power
output values of the SRM power meter and PP1 pedals for
all seated tests. Specifically, the PP1 pedals underestimated
the power output data obtained by the SRM device in a
directly proportional manner to the cyclist’s pedalling
cadence (bias = -2.4 W (LoA -12.1 to 7.3) at 70 revꞏmin-1,
Table 1. Results from the validity and reliability analysis.
POWER OUTPUT CADENCE
SRM (W) PT P1 (W) SEM
(W)
Rho
Spearman
value
Bland
Altman SRM (rpm) PT P1 (rpm)
Mean
±SD CV Mean
±SD CV Bias
(W)
SD Bias
(W)
Mean
±SD CV Mean
±SD CV
70
CAD
SITTING
100 W 99±6 5.6% 97±4* 4.2% 0.7
0.989#
-2.4
4.8
LoA
(-12.1 to 7.3)
70.4±1.0 1.4% 71.7±1.1 1.5%
150 W 150±5 3.4% 148±5 3.0% 0.8 70.7±0.9 1.3% 70.7±1.0 1.5%
200 W 200±5 2.4% 198±4 2.1% 0.7 70.6±1.1 1.5% 70.9±1.1 1.5%
250 W 251±5 2.0% 248±5 1.9% 0.8 70.7±1.0 1.4% 70.8±1.0 1.3%
300 W 303±5 1.5% 300±5 1.6% 0.8 70.4±0.9 1.3% 70.9±0.9 1.3%
350 W 356±4 1.2% 352±5* 1.4% 0.9 70.0±1.0 1.5% 70.6±1.0 1.5%
85
CAD
SITTING
100 W 101±6 5.9% 96±6* 5.7% 1.0
0.987#
-5.3
6.1
LoA
(-17.6 to 7.0)
84.7±0.8 0.9% 85.0±0.8 0.9%
150 W 149±6 4.0% 145±5* 3.7% 0.9 84.7±0.8 0.9% 84.8±0.8 0.9%
200 W 201±6 2.7% 196±5* 2.7% 0.9 84.8±0.9 1.1% 85.0±0.9 1.1%
250 W 252±5 1.9% 246±5* 2.2% 0.9 84.8±1.1 1.3% 85.0±1.1 1.8%
300 W 303±6 1.8% 298±6* 2.0% 1.1 84.9±1.2 1.4% 85.1±1.2 1.4%
350 W 355±5 1.5% 349±6* 1.7% 1.0 84.9±1.0 1.2% 85.1±1.0 1.7%
100
CAD
SITTING
100 W 96±8 8.6% 91±7* 7.2% 1.1
0.999#
-7.3
7.9
LoA
(-23.1 to 8.4)
98.9±1.3 1.3% 99.7±1.2 1.3%
150 W 145±7 4.9% 139±5* 3.9% 0.9 98.9±1.4 1.4% 99.2±1.5 1.5%
200 W 197±8 4.1% 191±7* 3.7% 1.2 99.6±1.2 1.2% 99.1±1.2 1.3%
250 W 248±7 2.9% 241±7* 2.8% 1.2 99.6±1.3 1.3% 99.7±1.3 1.3%
300 W 298±7 2.4% 291±7* 2.4% 1.2 99.5±1.5 1.6% 99.8±1.6 1.6%
350 W 352±5 1.9% 342±8* 2.3% 1.3 99.8±1.9 1.9% 99.7±1.9 1.9%
FC
S
TAND
250 W 253±7 2.6% 241±5* 2.2% 0.9
0.927# -9.0
5.3
LoA
(-19.7 to 1.7)
75.9±6.1 8.0% 74.9±11.0 14.7%
350 W 352±6 1.8% 345±5* 1.5% 0.9 74.8±9.1 12.1% 73.5±12.9 17.6%
450 W 455±8 1.7% 446±6* 1.2% 1.0 69.6±7.7 11.1% 68.5±10.7 15.7%
FC
SIT
500 W 499±9 1.8% 492±11* 2.2% 1.9
-7.0
3.5
LoA
(-14.1 to 0.0)
90.0±10.1 11.2% 89.8±10.5 11.7%
STAND = Standing; SIT= Sitting; CAD = Cadence; FC = Free cadence; SD = Standard Deviation; CV = Coefficient of variation; rho Spearman = Spearman
correlation coefficient; LoA = Limits of Agreement; * Significant differences compared to the SRM device; # significant Spearman correlation coefficient;
(p < 0.05).
Validity of PowerTap P1 pedals power meter
308
-5.3 W (LoA -17.6 to 7.0) at 85 revꞏmin-1 and -7.3 W (LoA
-23.1 to 8.4) at 100 revꞏmin-1, rho = 0.999). A slightly
greater underestimation was found for standing tests (bias
= -9.0 W (LoA -19.7 to 1.7)) (Table 1 and Figure 2).
Figure 1. Spearman´s Correlation Coefficient of the
PowerTap PP1 pedals under three different cadences, during
the submaximal graded exercises tests, compared to the sci-
entific SRM power meter at 70, 85 and 100 revꞏmin-1.
Reliability
The mean CV for the sitting graded exercise tests were
2.7% vs. 2.4%, 3.0% vs. 3.0% and 4.1% vs. 3.7% for the
SRM compared with the PP1 at 70, 85 and 100 revꞏmin-1,
respectively. These values were considerably lower if the
100 W workload was excluded (2.1 vs. 2.0%, 2.4 vs. 2.5%
and 3.2 vs. 3.0%). The mean CV for standing pedalling
tests of both devices (SRM vs. PP1) were 2.0% vs. 1.6%,
respectively, while CV for the high workload (i.e., 500 W)
in seated position remained very low (1.8% vs. 2.2%). The
SEM for the PP1 remained at very low values for all testing
conditions (ranging between 0.7 W and 1.9 W) (Table 1).
Discussion
The main finding of this study is that the PP1 is a highly
valid and reliable tool for testing and training purposes in
cycling under all assessed workloads (100 W to 500 W),
cadences (70, 85 and 100 revꞏmin-1) and pedalling posi-
tions (seated and standing). To our knowledge, this is the
first study that validates the PP1, which is a portable power
meter with some important advantages with respect to
other portable devices such as the use of the cyclist's own
bicycle, maintaining the usual riding position and the
wheelset and the crankset of the bicycle, the reduced extra
weight compared to other high performance portable
power meters (installed at crankset or hub), and finally the
ease of installation, which allows exchanging it between
various bicycles.
Nevertheless, it is important to be conscious that
this portable power meter slightly underestimated the
power output data in a directly proportional manner to the
pedalling cadence (from -2.4 W at 70 revꞏmin-1 to -7.3 W
at 100 revꞏmin-1), independently of the cycling workload
or pedalling position. This fact could be due to the strain
gauges’ sensitivity, or due to the signal processing (ampli-
fication, filtering, analog to digital conversion and data
analysis).
Figure 2. Bland-Altman plots of the PowerTap PP1 pedals,
assessed during the submaximal graded exercises tests, com-
pared to the scientific SRM power meter at 70 (A), 85 (B) and
100 (C) revꞏmin-1.
Laboratory based ergometers (e.g., SRM, Lode, Ve-
lotron, Wattbike) are still considered the “gold standard”
power meters due to their high levels of validity and relia-
bility (Abbiss et al., 2009; Earnest et al., 2005; Hopker et
al., 2010; Hopkins et al., 1999; Jones, 1998; Paton and
Hopkins, 2001; Reiser et al., 2000; Wainwright et al.,
2017). Thus, for a cycle trainer or power meter to be useful
in a research setting it must have similar qualities of meas-
urement. Different researchers have tested the validity of
other mobile ergometers such as Tacx Fortius (Peiffer and
Losco, 2011), KICKR Power Trainer (Zadow et al., 2017;
Zadow et al., 2016), LeMond Revolution (Novak et al.,
2015), and Elite Axiom Powertrain (Bertucci et al., 2005a),
as well as other mobile power meters, including PowerTap
Hub (Bertucci et al., 2005b; Bouillod et al., 2016; Gardner
Pallarés and Lillo-Bevia
309
et al., 2004) and Garmin Vector (Bouillod et al., 2016;
Nimmerichter et al., 2017; Novak and Dascombe, 2016). It
should be noted that the SRM, as the reference power me-
ter, is also affected by some measurement error. Jones
(1998) reported extremely low variability 0.3% and ±
1.0% for two different 20 strain gauge, and ± 1.8% for a 4
strain gauge models), while the accuracy claimed by the
manufacturer of these devices is also very high 0.5 %
and ± 2.5 %, for the 20 and 4 strain gauge, respectively).
Additionally, most of these validation studies have used the
SRM scientific model comprising 20 strain gauges (Ber-
tucci et al., 2005a, Duc et al., 2007; Jones, 1998) or the
SRM professional model (4 strain gauge) (Gardner et al.,
2004; Hurst and Atkins, 2006) as the gold standard devices.
Despite the fact that, according to the data collected
in the present study, there are small but significant differ-
ences between the mean power output values obtained by
the PP1 pedals and the SRM scientific model, there are
highly significant, “near perfect”, relationships
(rho ≥ 0.987; p < 0.001) from 100 W to 350 W with seated
position at low, medium and high cadences. The previous
concordance is reduced for standing, freely chosen cadence
pedalling (rho = 0.927; p < 0.001).
It is also important to note that this study has found
very small bias and SD of bias in the agreement between
the SRM and PP1 power output data, as well as between
SRM and PP1 cadence (from -2.4 ± 4.8 W to -9.0 ± 5.3 W),
both for the standing and seated pedalling positions, even
though it is known that standing pedalling causes lateral
sways and affects the biomechanics of pedalling (Stone and
Hull, 1993). These results are consistent but progressive.
When used in the laboratory and compared to the SRM
crankset, similar mean and SD biases, as well as the 95%
limits of agreement data, were reported for other mobile
power meters, such as Garmin Vector Pedals (Bouillod et
al., 2016; Nimmerichter et al., 2017) (0.6 ± 6.2 W, 11.6 to
12.7 W; -11.6 to 12.7 W, -3.7 to 9.5 W), PowerTap Hub
(Bertucci et al., 2005b) (2.9 ± 3.3 W; -3.7 to 9.5 W), and
Look Keo Power Pedal (Sparks et al., 2015) (4.6 ± 0.4 W;
-15.9 to 13.9 W). Bouillod, et al. (2016) found higher mean
and SD biases when the SRM crankset was compared with
the Stages (-13.7 ± 12.4 W, -37.9 W to 10.6 W).
Paton and Hopkins (2001) suggested that in elite
athletes, a magnitude lower than 2% is required to detect
changes in performance from an ergogenic or training in-
tervention. Besides, Hopkins (2000) suggested that an 84%
confidence interval is a more reasonable threshold than the
traditional 95% interval when attempting to detect changes
in athletic performance. Based on a workload of 350 W,
changes of ≥ 2% (7.0 W) and ≥ 1% (3.5 W) would be re-
quired to be confident (84%) that a trained cyclist had
changed power output because of a training intervention.
When compared to the SRM, the mean error of the PP1
shows that, in the present data, it falls within this range.
Based on the current study’s evaluation of the PP1, a mean
error of ~2% compared to the SRM would be acceptable
for talent identification purposes. These results suggest that
the PP1 power meter is sufficiently accurate to track per-
formance changes over time, and thus would serve as an
acceptable training tool. Regarding reliability (Table 1),
when we compare the PP1 with other mobile power meters
from previous studies, mean CVs are similar to these find-
ings. Bertucci et al. (2005b) reported a CV from 1.7 to
2.7% for the PowerTap Hub and from 1.2 to 2.0% for the
SRM crankset over a workload range of 100 W to 420 W.
The mean CVs reported in laboratory and field trials by
Nimmerichter et al. (2017) were 0.95 vs. 1.00% and 2.82
vs. 3.05%, for the SRM and a Garmin device, respectively.
These results mean that CV for the SRM and the PP1 in the
current study concur with the reliability data from previous
studies.
Cycling technique and type of ergometer can affect
cycling efficiency (Arkesteijn et al., 2013). In our opinion
the inclusion of cyclists as participants adds more ecologi-
cal validity to the real use of the pedals. From this point of
view, the reliable results of the current research confirm
that this biological variability does not affect the validity
of the power output data, nor the cadence, of this power
meter. What is more, the pedals and cyclists were tested
with three different and representative cadences to analyse
if the cadence affects the reliability of the power output and
cadence. Besides, the number of participants and their fit-
ness level (i.e., well-trained cyclists) are consistent with
other published research studies assessing the reliability
and validity of cycle ergometers (Pallares et al., 2016;
Passfield and Doust, 2000; Wainwright et al., 2017).
It is important to note that PP1 pedals have some
limitations in their use, in spite of the practical advantages
they offer. As the SRM power meters are checked for va-
lidity and reliability against a first principles dynamic cal-
ibration rig, the PP1 pedals cannot be easily checked by
this method because of the difficulty of applying a known
force dynamically to the pedals. The application of the
torque at the bottom bracket will not cause any deflection
within pedals axles. On the other hand, a static calibration
cannot be performed either due to the fact that the PP1 ped-
als will not transmit any data to a recording device if a ca-
dence reading is not available (Bini and Hume, 2014). As
stated earlier, the best current method to assess the varia-
bility of the PP1 pedals was to compare them with a scien-
tific model SRM crankset, which has been shown to be ac-
curate and reliable. Additionally, the slope of the power
curve cannot be adjusted, meaning that PP1 will always be
limited by the factory calibration. Accordingly, PP1 pedals
should be checked regularly against a calibrated scientific
SRM crankset. If this process is done regularly, PP1 pedals
provide an acceptable method of power output measure-
ment and their use in detecting changes in performance and
monitoring external training power output is supported.
Since the tests were developed with workloads up to 500
W, additional research must be done to test the reliability
and validity of the PP1 for sprint cycling tests above 500
W. Also, further research is needed to evaluate this power
meter system in field conditions (Bouillod et al., 2016).
Conclusions
This study confirms that, despite the slight but consistent
underestimation found at 85 and 100 revꞏmin-1 (2–7 W),
which slightly depends on both pedalling cadence and cy-
clists’ position (i.e., seated vs. standing), this new PP1 is a
valid, reliable and accurate mobile power meter, compared
Validity of PowerTap P1 pedals power meter
310
with the recognized gold standard scientific SRM, to meas-
ure power output and cadence in cyclists. This new porta-
ble power meter provides an alternative to more expensive
laboratory ergometers, allowing cyclists to use their own
bicycle for testing, training or competition purposes. Cur-
rent results demonstrate that the PP1 provides valid read-
ings of power output from 100 to 500 W, in either seated
or standing positions, at cadences of 70, 85 and 100
revꞏmin-1, or even at freely chosen cadences.
Acknowledgements
The authors wish to thank the participants for their invaluable contribution
to the study. The experiments comply with the current laws of Spain.
There are no conflicts of interest.
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Key points
PP1 pedals slightly underestimate power output at
medium to high cadences (2 to 7 W).
PP1 pedals provide valid readings of power output
from 100 to 500 W, in either seated or standing po-
sitions, at fixed cadences of 70, 85 and 100 revꞏmin-
1, or even at freely chosen cadences.
These results suggest that the new PP1 pedals is a
valid, reliable and accurate mobile power meter to
measure power output and cadence in cyclists using
their own bicycles.
AUTHOR BIOGRAPHY
Jesús G PALLARÉS
Employment
Professor of Exercise Physiology (Human
Performance and Sport Science Labora-
tory, University of Murcia, Spain)
Degree
PhD
Research interest
Exercise physiology and training; perfor-
mance analysis; ergogenic aids.
E-mail: jgpallares@um.es
Jose Ramon LILLO-BEVIA
Employment
PhD student at Faculty of Sport Sciences,
University of Murcia, Murcia, Spain
Degree
MSc, PhD candidate
Research interests
Exercise physiology and training, perfor-
mance analysis
E-mail: joseramon.lillo@um.es
Jesús García Pallarés
Faculty of Sport Science, Argentina S/N, Santiago de la Ribera,
Murcia, Spain
... The first approach was the SRM (professional model; Schoberer Rad Messtechnik, Julich, Germany) crankset (strain gauges), which remains as the Gold-Standard to measure the bicycle power output (PO) outside the laboratory [10][11][12]. Since then, emerging alternatives have been demonstrated to be valid and reliable, such as the wheels Powertap Hub [13][14][15] or the pedals Garmin Vector [1,[15][16][17][18] and Powertap P1 [19][20][21]. In particular, due to their quick installation and use [1,[15][16][17][18][19][20][21], the pedal power meters would represent a high practical technology to be used interchangeably in different bicycles (e.g., track, road, and time trials). ...
... Since then, emerging alternatives have been demonstrated to be valid and reliable, such as the wheels Powertap Hub [13][14][15] or the pedals Garmin Vector [1,[15][16][17][18] and Powertap P1 [19][20][21]. In particular, due to their quick installation and use [1,[15][16][17][18][19][20][21], the pedal power meters would represent a high practical technology to be used interchangeably in different bicycles (e.g., track, road, and time trials). Additionally, pedals are likely to reduce the loss of PO due to mechanical connections [12]. ...
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A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature on the validity of cycling power meters. PubMed, SPORTDiscus, and Google Scholar have been explored with PRISMA methodology. A total of 74 studies have been extracted for the reviewing process. Validity is a general quality of the measurement determined by the assessment of different metrological properties: Accuracy, sensitivity, repeatability, reproducibility, and robustness. Accuracy was most often studied from the metrological property (74 studies). Reproducibility was the second most studied (40 studies) property. Finally, repeatability, sensitivity, and robustness were considerably less studied with only 7, 5, and 5 studies, respectively. The SRM power meter is the most used as a gold standard in the studies. Moreover, the number of participants was very different among them, from 0 (when using a calibration rig) to 56 participants. The PO tested was up to 1700 W, whereas the pedalling cadence ranged between 40 and 180 rpm, including submaximal and maximal exercises. Other exercise conditions were tested, such as torque, position, temperature, and vibrations. This review provides some caveats and recommendations when testing the validity of a cycling power meter, including all of the metrological properties (accuracy, sensitivity, repeatability, reproducibility, and robustness) and some exercise conditions (PO range, sprint, pedalling cadence, torque, position, participant, temperature, vibration, and field test).
... The advent of the power meter, allows rider, coach and sport scientist to assess performance in the field with physiological responses, as well as in exercise in the laboratory [3][4][5][6]. High quality power meters have been validated against a calibrated ergometer, and against other brands of power meter [5,[7][8][9][10][11][12], and allow the user to calibrate the meter, ensuring valid and reliable data [7,[13][14][15]. Riders, coaches and sport-scientists use these data to improve decision-making around the preparation of riders for future events. ...
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Cycling performance models are used to study rider and sport characteristics to better understand performance determinants and optimise competition outcomes. Performance requirements cover the demands of competition a cyclist may encounter, whilst rider attributes are physical, technical and psychological characteristics contributing to performance. Several current models of endurance-cycling enhance understanding of performance in road cycling and track endurance, relying on a supply and demand perspective. However, they have yet to be developed for sprint-cycling, with current athlete preparation, instead relying on measures of peak-power, speed and strength to assess performance and guide training. Peak-power models do not adequately explain the demands of actual competition in events over 15-60 s, let alone, in World-Championship sprint cycling events comprising several rounds to medal finals. Whilst there are no descriptive studies of track-sprint cycling events, we present data from physiological interventions using track cycling and repeated sprint exercise research in multiple sports, to elucidate the demands of performance requiring several maximal sprints over a competition. This review will show physiological and power meter data, illustrating the role of all energy pathways in sprint performance. This understanding highlights the need to focus on the capacity required for a given race and over an event, and therefore the recovery needed for each subsequent race, within and between races, and how optimal pacing can be used to enhance performance. We propose a shift in sprint-cyclist preparation away from training just for peak power, to a more comprehensive model of the actual event demands.
... The power Tap p1 pedals (pp1, cycleops, fitchburg, Ma, uSa) were used for the assessment of power and cadence. 29 on the second assessment, the participants performed the testing protocol suggested by coggan et al., 15 again in the laboratory setting. They could view their progress on a computer monitor and were provided with information on the time completed and gear selected; all other information was blinded; verbal encouragement was not provided, and water was allowed ad libitum. ...
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Use of muscle oxygen saturation (SmO2) has been validated as a performance factor during incremental exercise with portable near-infrared stereoscopy (NIRS) technology. However, there is little knowledge about the use of SmO2 to identify training zones. The objective of this study was to evaluate the metabolic zones by SmO2: maximum lipid oxidation zone (Fatmax), ventilatory thresholds (VT1 and VT2) and maximum aerobic power (MAP) during a graded exercise test (GXT). Forty trained cyclists and triathletes performed a GXT. Output power (W), heart rate (HR), oxygen consumption (VO2), energy expenditure (kcal/min) and SmO2 were measured. Data were analysed using the ANOVA test, ROC curves and multiple linear regressions. Significance was established at p ≤ 0.05. SmO2 decreases were observed from baseline (LB) to Fatmax (Δ = -16% p < 0.05), Fatmax to VT1 (Δ = -16% p < 0.05) and VT1 to VT2 (Δ = -45% p < 0.01). Furthermore, SmO2 together with weight, HR and output power have the ability to predict VO2 and energy expenditure by 89% and 90%, respectively. We conclude that VO2 and energy expenditure values can be approximated using SmO2 together with other physiological parameters and SmO2 measurements can be a complementary parameter to discriminate aerobic workload and anaerobic workload in athletes.
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Background"Sleep Low-Train Low" is a training-nutrition strategy intended to purposefully reduce muscle glycogen availability around specific exercise sessions, potentially amplifying the training stimulus via augmented cell signalling. The aim of this study was to assess the feasibility of a 3-week home-based "sleep low-train low" programme and its effects on cycling performance in trained athletes.Methods Fifty-five trained athletes (Functional Threshold Power [FTP]: 258 ± 52W) completed a home-based cycling training program consisting of evening high-intensity training (6 × 5 min at 105% FTP), followed by low-intensity training (1 hr at 75% FTP) the next morning, three times weekly for three consecutive weeks. Participant's daily carbohydrate (CHO) intake (6 g·kg-1·d-1) was matched but timed differently to manipulate CHO availability around exercise: no CHO consumption post- HIT until post-LIT sessions [Sleep Low (SL), n = 28] or CHO consumption evenly distributed throughout the day [Control (CON), n = 27]. Sessions were monitored remotely via power data uploaded to an online training platform, with performance tests conducted pre-, post-intervention.ResultsLIT exercise intensity reduced by 3% across week 1, 3 and 2% in week 2 (P < 0.01) with elevated RPE in SL vs. CON (P < 0.01). SL enhanced FTP by +5.5% vs. +1.2% in CON (P < 0.01). Comparable increases in 5-min peak power output (PPO) were observed between groups (P < 0.01) with +2.3% and +2.7% in SL and CON, respectively (P = 0.77). SL 1-min PPO was unchanged (+0.8%) whilst CON improved by +3.9% (P = 0.0144).Conclusion Despite reduced relative training intensity, our data demonstrate short-term "sleep low-train low" intervention improves FTP compared with typically "normal" CHO availability during exercise. Importantly, training was completed unsupervised at home (during the COVID-19 pandemic), thus demonstrating the feasibility of completing a "sleep low-train low" protocol under non-laboratory conditions.
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Purpose: The purpose of this study was to assess the reliability of power output measurements of a Wahoo KICKR Power Trainer (KICKR) on two separate occasions, separated by fourteen months of regular use (~1 h per week). Methods: Using the KICKR to set power outputs, powers of 100-600W in increments of 50W were assessed at cadences of 80, 90 and 100rev.min(-1) which were controlled and validated by a dynamic calibration rig (CALRIG). Results: A small ratio bias of 1.002 (95%rLoA: 0.992-1.011) was observed over 100-600W at 80-100rev.min(-1) between Trial 1 and Trial 2. Similar ratio biases with acceptable limits of agreement were observed at 80rev.min(-1) (1.003 (95% 0.987-1.018)), 90rev.min(-1) (1.000 (95%rLoA: 0.996-1.005)) and 100rev.min(-1) (1.002 (95%rLoA: 0.997-1.007)). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI) for mean power (W) between trials was 1.00 (95%CI: 1.00-1.00) with a typical error (TE) of 3.1W and 1.6% observed between Trial 1 and Trial 2. Conclusion: When assessed at two separate time points fourteen months apart, the KICKR has acceptable reliability for combined power outputs of 100-600W at 80-100rev.min(-1), reporting overall small ratio biases with acceptable limits of agreement and low TE. Coaches and sports scientists should feel confident in the measured power output by the KICKR over an extended period of time when performing laboratory training and performance assessments.
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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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Purpose The purpose of this study was to determine, i) the reliability of blood lactate and ventilatory-based thresholds, ii) the lactate threshold that corresponds with each ventilatory threshold (VT1 and VT2) and with maximal lactate steady state test (MLSS) as a proxy of cycling performance. Methods Fourteen aerobically-trained male cyclists (V˙O2max 62.1±4.6 ml·kg⁻¹·min⁻¹) performed two graded exercise tests (50 W warm-up followed by 25 W·min⁻¹) to exhaustion. Blood lactate, V˙O2 and V˙CO2 data were collected at every stage. Workloads at VT1 (rise in V˙E/V˙O2;) and VT2 (rise in V˙E/V˙CO2) were compared with workloads at lactate thresholds. Several continuous tests were needed to detect the MLSS workload. Agreement and differences among tests were assessed with ANOVA, ICC and Bland-Altman. Reliability of each test was evaluated using ICC, CV and Bland-Altman plots. Results Workloads at lactate threshold (LT) and LT+2.0 mMol·L⁻¹ matched the ones for VT1 and VT2, respectively (p = 0.147 and 0.539; r = 0.72 and 0.80; Bias = -13.6 and 2.8, respectively). Furthermore, workload at LT+0.5 mMol·L⁻¹ coincided with MLSS workload (p = 0.449; r = 0.78; Bias = -4.5). Lactate threshold tests had high reliability (CV = 3.4–3.7%; r = 0.85–0.89; Bias = -2.1–3.0) except for DMAX method (CV = 10.3%; r = 0.57; Bias = 15.4). Ventilatory thresholds show high reliability (CV = 1.6%–3.5%; r = 0.90–0.96; Bias = -1.8–2.9) except for RER = 1 and V-Slope (CV = 5.0–6.4%; r = 0.79; Bias = -5.6–12.4). Conclusions Lactate threshold tests can be a valid and reliable alternative to ventilatory thresholds to identify the workloads at the transition from aerobic to anaerobic metabolism.
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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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This study aimed to evaluate the agreement in cycling power output measurements between the LeMond Revolution cycle ergometer and SRM power meter. The LeMond Revolution measures power output via removal of the rear bicycle wheel and attaching it using a quick-release system, estimating power output through a head-unit that processes drive-train resistance and atmospheric conditions. Fourteen well-trained cyclists completed incremental protocols and power profile assessments on a bicycle fitted with SRM scientific power meter and attached to a LeMond Revolution cycle ergometer. Power output was measured by both devices at 1 Hz. Data from each device were compared using Pearson's correlations, paired t-tests, assessments of heteroscedasticity, Bland-Altman plots and 95% limits of agreement. During incremental tests, errors in power measurement of the LeMond Revolution progressively increased at greater power outputs when compared with SRM (bias: 2-34 W; CV 1.5-6.7%). During power profile assessments, errors in mean power measurement of the LeMond Revolution were also slightly overestimated for all efforts from a rolling start (+3 ± 8%; CV = 5.1%). Conversely, the LeMond Revolution underestimated peak power output during five second sprint efforts and the greatest error was observed between measurements for mean power output during a five second sprint from a stationary start (-7 ± 24%; CV = 10.6%). Overall, the LeMond Revolution is a practical, cost-effective alternative to more expensive ergometers for detecting large changes in mean power output. However, high level of error during high-intensity sprint efforts from a stationary start is a limitation for well-trained sprint cyclists.
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Purpose: To validate the new drive indoor trainer Hammer designed by Cycleops®. Methods: Eleven cyclists performed 44 randomized and counterbalanced graded exercise tests (100-500W), at 70, 85 and 100 rev.min-1 cadences, in seated and standing positions, on 3 different Hammer units, while a scientific SRM system continuously recorded cadence and power output data. Results: No significant differences were detected between the three Hammer devices and the SRM for any workload, cadence, or pedalling condition (P value between 1.00 and 0.350), except for some minor differences (P 0.03 and 0.04) found in the Hammer 1 at low workloads, and for Hammer 2 and 3 at high workloads, all in seated position. Strong ICCs were found between the power output values recorded by the Hammers and the SRM (≥0.996; P=0.001), independently from the cadence condition and seated position. Bland-Altman analysis revealed low Bias (-5.5-3.8) and low SD of Bias (2.5-5.3) for all testing conditions, except marginal values found for the Hammer 1 at high cadences and seated position (9.6±6.6). High absolute reliability values were detected for the 3 Hammers (150-500W; CV<1.2%; SEM<2.1). Conclusions: This new Cycleops trainer is a valid and reliable device to drive and measure power output in cyclists, providing an alternative to larger and more expensive laboratory ergometers, and allowing cyclists to use their own bicycle.
Article
Purpose: This study aimed to determine the validity and the reliability of the Stages power meter crank system (Boulder, United States) during several laboratory cycling tasks. Methods: Eleven trained participants completed laboratory cycling trials on an indoor cycle fitted with SRM Professional and Stages systems. The trials consisted of an incremental test at 100W, 200W, 300W, 400W and four 7s sprints. The level of pedaling asymmetry was determined for each cycling intensity during a similar protocol completed on a Lode Excalibur Sport ergometer. The reliability of Stages and SRM power meters was compared by repeating the incremental test during a test-retest protocol on a Cyclus 2 ergometer. Results: Over power ranges of 100-1250W the Stages system produced trivial to small differences compared to the SRM (standardized typical error values of 0.06, 0.24 and 0.08 for the incremental, sprint and combined trials, respectively). A large correlation was reported between the difference in power output (PO) between the two systems and the level of pedaling asymmetry (r=0.58, p < 0.001). Recalculating PO of the Stages system according to the level of pedaling asymmetry provided only marginal improvements in PO measures. The reliability of the Stages power meter at the sub-maximal intensities was similar to the SRM Professional model (coefficient of variation: 2.1 and 1.3% for Stages and SRM, respectively). Conclusions: The Stages system is a suitable device for PO measurements, except when a typical error of measurement <3.0% over power ranges of 100-1250W is expected.
Article
To assess the validity and reliability of the Garmin Vector against the SRM power meter, 6 cyclists completed 3 continuous trials at power outputs from 100-300 W at 50-90 rev·min(-1) and a 5-min time trial in laboratory and field conditions. In field conditions only, a 30-s sprint was performed. Data were compared with paired samples t-tests, with the 95% limits of agreement (LoA) and the typical error. Reliability was calculated as the coefficient of variation (CV). There was no significant difference between the devices in power output in laboratory (p=0.245) and field conditions (p=0.312). 1-s peak power was significantly different between the devices (p=0.043). The LoA were ~1.0±5.0 W and ~0.5±0.5 rev·min(-1) in both conditions. The LoA during the 30-s sprint was 6.3±38.9 W and for 1-s peak power it was 18.8±17.1 W. The typical error for power output was 2.9%, while during sprint cycling it was 7.4% for 30-s and 2.7% for 1-s peak power. For cadence, the typical error was below 1.0%. The mean CVs were ~1.0% and ~3.0% for the SRM and Garmin, respectively. These findings suggest, that the Garmin Vector is a valid alternative for training. However, during sprint cycling there is lower agreement with the SRM power meter. Both devices provide good reliability (CV<3.0%).
Article
The purpose of the study was to assess the validity and inter-bike reliability of 10 Wattbike cycle ergometers, and to assess the test-retest reliability of one Wattbike. Power outputs from 100 to 1000 W were applied using a motorised calibration rig (LODE) at cadences of 70, 90, 110 and 130 rev · min(-1), which created nineteen different intensities for comparison. Significant relationships (P < 0.01, r(2) = 0.99) were found between each of the Wattbikes and the LODE. Each Wattbike was found to be valid and reliable and had good inter-bike agreement. Within-bike mean differences ranged from 0.0 W to 8.1 W at 300 W and 3.3 W to 19.3 W at 600 W. When taking into account the manufacturers stated measurement error for the LODE (2%), the mean differences were less than 2%. Comparisons between Wattbikes at each of the nineteen intensities gave differences from 0.6 to 25.5 W at intensities of 152 W and 983 W, respectively. There was no significant difference (P > 0.05) between the measures of power recorded in the test-retest condition. The data suggest that the Wattbike is an accurate and reliable tool for training and performance assessments, with data between Wattbikes being able to be used interchangeably.
Purpose: The purpose of this study was to assess the validity of power output settings of the Wahoo KICKR Power Trainer (KICKR) using a dynamic calibration rig (CALRIG) over a range of power outputs and cadences. Methods: Using the KICKR to set power outputs, powers of 100-999W were assessed at cadences (controlled by the CALRIG) of 80, 90, 100, 110 and 120rpm. Results: The KICKR displayed accurate measurements of power between 250-700W at cadences of 80-120rpm with a bias of -1.1% (95%LoA: -3.6-1.4%). A larger mean bias in power were observed across the full range of power tested, 100-999W 4.2% (95%LoA: -20.1-28.6%), due to larger biases between 100-200W and 750-999W (4.5%, 95%LoA:-2.3-11.3% and 13.0%, 95%LoA: -24.4-50.3%), respectively. Conclusion: When compared to a CALRIG, the Wahoo KICKR Power Trainer has acceptable accuracy reporting a small mean bias and narrow limits of agreement in the measurement of power output between 250-700W at cadences of 80-120rpm. Caution should be applied by coaches and sports scientists when using the KICKR at power outputs of <200W and >750W due to the greater variability in recorded power.