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Power Responses of a Rowing Ergometer: Mechanical Sensors vs. Concept2® Measurement System

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The aim of this study was to compare the power provided by a recent ergometer with the power developed by the rower determined using mechanical sensors set on the same apparatus. Six rowers and six non-rowers performed a power graded test and an all-out start on an instrumented ergometer (Concept2 system, model D, Morrisville, VT, USA). Power values displayed by the ergometer were recorded with a specific software. A strain gauge placed near the handle and a position sensor installed on the chain allowed the calculation of the power developed by the rower. Power values provided by the ergometer were strongly correlated to those determined with a direct measurement and calculation of power. However, power values given by the Concept2 system were lower (- 17.4 to - 72.4 W) than those calculated using mechanical sensors. This difference in power measurements was lower at a steady pace and for rowers. The Concept2 system underestimates the power produced by the rower by approximately 25 W. This difference in power seems to be independent of the level of power developed but increases with variations in intensity and pace. The deletion of the first strokes following changes in power production allows to limit this phenomenon. According to the use of the power parameter in the experimental design, it could be appropriate to correct values provided by the Concept2 ergometer.
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Introduction
Rowing is an Olympic sport requiring a high level of training. The
training volume of rowers participating in the World Champion-
ships has been estimated to be 3 hours a day [9]. Ergometers play
an important role in rowers’ training and are used to select na-
tional crews [7]. The power produced by the rower at the handle
is a decisive factor in the performance [1]. Thus, ergometers have
been instrumented with mechanical sensors in order to record
the power developed by the rower [4, 6]. Today, most commonly
used ergometers are those using wind-resistance made by Con-
cept2
®
. These ergometers are equipped with monitors and dis-
play stroke parameters such as speed, pace and power. The
power displayed by monitors is used to carry out progressive
and maximal tests and to study physiological responses induced
by rowing [2]. However, only one study [5] presented results
about the reliability of the power measurements of ergometers
and reported a strong correlation between the power displayed
by the ergometer and the power measured with mechanical sen-
sors. Authors also indicated that mean values obtained from the
ergometer display were 6.8 % lower. These results need to be con-
firmed as authors used an old ergometer model (Concept2
®
,
model A) which calculated the power developed by the rower
(P
C2A
) as depicted in equation 1 (E
1
) [3], whereas recent ergo-
meters (Concept2
®
, models C and D) determine power (P
ergo
)
with a different formula provided by Concept2
®
(equation 2, E
2
).
Abstract
The aim of this study was to compare the power provided by a
recent ergometer with the power developed by the rower deter-
mined using mechanical sensors set on the same apparatus. Six
rowers and six non-rowers performed a power graded test and
an all-out start on an instrumented ergometer (Concept2
®
sys-
tem, model D, Morrisville, VT, USA). Power values displayed by
the ergometer were recorded with a specific software. A strain
gauge placed near the handle and a position sensor installed on
the chain allowed the calculation of the power developed by the
rower. Power values provided by the ergometer were strongly
correlated to those determined with a direct measurement and
calculation of power. However, power values given by the Con-
cept2
®
system were lower (– 17.4 to 72.4 W) than those calcu-
lated using mechanical sensors. This difference in power meas-
urements was lower at a steady pace and for rowers. The Con-
cept2
®
system underestimates the power produced by the rower
by approximately 25 W. This difference in power seems to be in-
dependent of the level of power developed but increases with
variations in intensity and pace. The deletion of the first strokes
following changes in power production allows to limit this phe-
nomenon. According to the use of the power parameter in the ex-
perimental design, it could be appropriate to correct values
provided by the Concept2
®
ergometer.
Key words
Instrumentation · expertise level · rower · exercise intensity
Training & Testing
830
Affiliation
Laboratory “Motricité, Interactions, Performance” JE 2438, UFR STAPS, Université de Nantes, Nantes, France
Correspondence
Sébastien Boyas · UFR STAPS Laboratoire “Motricité, Interactions, Performance” ·
25 bis Boulevard Guy Mollet · 44322 Nantes Cedex 3 · France · Phone: + 332 51837217 ·
Fax: + 332 51837210 · E-mail: sebastien.boyas@univ-nantes.fr
Accepted after revision: November 28, 2005
Bibliography
Int J Sports Med 2006; 27: 830 833 © Georg Thieme Verlag KG · Stuttgart · New York ·
DOI 10.1055/s-2006-923774 · Published online April 11, 2006 ·
ISSN 0172-4622
S. Boyas
A. Nordez
C. Cornu
A. Guével
Power Responses of a Rowing Ergometer:
Mechanical Sensors vs. Concept2
®
Measurement System
Downloaded by: University of Ottawa. Copyrighted material.
P
C2A
R
t
3
t
1
J
_
dt
t
3
t
1
E
1
P
ergo
R
t
2
t
1
C
1
_
3
dt
1
2
J
_
2
3
_
2
1
t
3
t
1
E
2
θ: angular position of the flywheel (rad), α: deceleration of the
wheel assessed during a calibration protocol (rad ·s
–2
), J: moment
of inertia of the flywheel (kg ·m
2
), t
1
: starting time of the rowing
cycle i.e., the catch (s), t
2
: end of the pull, i.e., the finish (s), t
3
: end
of the rowing cycle, i.e., the next catch, C
1
: constant calculated for
each stroke on the previous recovery (kg ·m
2
):
C
1

R
t
3
t
2
J
_
2
t
3
t
2
Using E
1
, the power was calculated as the product of the torque
applied to the flywheel (T) by its angular velocity (θ
.
) [3]. The ab-
sence of torque sensors in rowing ergometers made it necessary
to determine T using an indirect method. With this method, con-
sidering that the flywheel had a nearly constant velocity be-
tween two successive strokes, the torque was assessed by calcu-
lating the flywheel deceleration (α) using a calibration protocol
and applying the equation of motion (T = Jα). Thus, E
1
did not
take into account factors such as changes in friction on the fly-
wheel bearings with time or changes in air properties. Moreover,
E
1
considered that the power was close stroke to stroke and the
assessment of α seemed to be less reliable at low and high angu-
lar velocities [3]. Using E
2
, these problems are solved. Indeed, the
power at the level of the flywheel is considered to be the sum of
the power dissipated by air resistance and the power developed
to accelerate the flywheel between two successive strokes.
Therefore, it could be hypothesized that the use of a more recent
ergometer allows to obtain a better accuracy of power measure-
ments. Considering E
2
, no calibration is required since the power
dissipated is calculated using a constant (C
1
) assessed for each
stroke with the flywheel deceleration measured during the pre-
vious recovery. Nevertheless, to our knowledge, the accuracy of
this power assessment model has not been tested in the litera-
ture. Moreover, both E
1
and E
2
calculate the power at the level of
the flywheel and then do not take into account the energy dissi-
pated in the chain. Consequently, differences between the power
assessed by the Concept2
®
model D (C2D) ergometer and the
power produced by the rower could remain and has to be deter-
mined. The influence of rowing at an expertise level on differ-
ences between power measurements has not been studied yet.
As Smith et al. [8] observed that trained rowers developed a bet-
ter stroke to stroke consistency compared to novices, it can be in-
teresting to test if this ability has an incidence on the accuracy of
power measurements.
The aim of this study was to compare the power displayed by the
last Concept2
®
ergometer with the power developed by the row-
er determined using mechanical sensors installed on the same
apparatus. This work also focused on the potential influence of
the expertise level on these power measurements.
Methods
Twelve subjects distributed into two populations volunteered for
this study. Six studying physical education, non-specialists in
rowing, formed the “novices” population (22.0 ± 2.3 years,
181.3 ± 10.9 cm, 77.3 ± 11.5 kg). Six rowers practicing since 9.3
2.7) years composed the “experts” population (22.2 ± 2.2
years, 185 ± 5,7 cm, 78.2 ± 6.9 kg). All subjects signed informed
consent documents.
After a specific warm-up, subjects realized an all-out start of 15
strokes and a power graded test (in reference to power values
displayed by the ergometer). Initial power was 100 W for both
populations and was increased every 30 seconds, by 25 W for
novices and by 50 W for experts, until subjects were unable to
maintain the requested power during 5 consecutive strokes.
Tests were carried out on an instrumented wind braked rowing
ergometer C2D (Concept2
®
, Morrisville, VT, USA) equipped with
a strain gauge placed at the handle (DPSystèmes
®
, 2 kN, Cournon,
France) and a position sensor installed on the chain (PT1
Scaime
®
, Annemasse, France). These mechanical sensors, previ-
ously calibrated, allowed the measure of the force at the handle
and its position variations. Hence, the power developed by the
rower was calculated by multiplying the force produced at the
handle by its velocity (determined by derivation of the position).
This power was averaged on the whole rowing cycle (i.e., be-
tween two successive catches) and called P
sensors
. Power values
displayed by the ergometer for each stroke (P
ergo
) were calculat-
ed by the C2D system as presented in E
2
and were recorded using
the RowPro™ 1.7 software (Digital Rowing Inc., Boston, MA,
USA).
Changes in P
ergo
and P
sensors
were studied with Bravais Pearson
correlations coefficients (r). Differences in power measurements
(DIPM) were determined for each stroke and calculated as the
differences between P
sensors
and P
ergo
. Student’s t-tests were used
to compare differences between P
ergo
and P
sensors
and differences
in DIPM between novices and experts. A second set of the same
statistical analysis was carried out after the removal of data from
the first three strokes of each grade of the graded test and of the
first three strokes of the start. This deletion was realized in order
to study strokes achieved at a relative steady pace. The level of
statistical significance was set at p < 0.05.
Results
Results are presented as means and ranges (Table 1). Correlations
between P
ergo
and P
sensors
, considering all the subjects were strong
for the graded test (r = 0.96, p < 0.001, Fig. 1A) and good for the
start (r = 0.75, p < 0.001). However, P
ergo
were significantly lower
than P
sensors
(– 26.0 W for the graded test and 68.2 W for the
start, p < 0.001).
After the deletion of initial strokes, correlations between P
ergo
and P
sensors
increased (r = 0.97, p < 0.0 01; r = 0.93, p < 0.001, for
the graded test [Fig.1B] and the start respectively), and differ-
ences between P
ergo
and P
sensors
fell to 22.7 W (p < 0.05) for the
graded test and to 31.6 W (p < 0.001) for the start. Highly signifi-
cant differences between P
ergo
and P
sensors
were observed, except
Boyas S et al. Power Responses of a Rowing Ergometer Int J Sports Med 2006; 27: 830833
Training & Testing
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after the removal of the first three strokes of the start. Differen-
ces in power measurements were always higher for novices than
for experts (p < 0.001), except when considering all strokes of the
start (72.4 W vs. 62.9 W, for novices and experts respectively,
p > 0.05).
Discussion
The purpose of this study was to compare the power displayed by
the C2D ergometer with the power developed by the rower de-
termined by the mechanical sensors. The major finding of this
study showed that the ergometer underestimates the power de-
veloped by the rower. Results also indicated that this difference
in power measurements was enhanced by the variability in
power production and this was higher for novices than for ex-
perts.
Differences in power measurements
As Lormes et al. [5] used a different apparatus (model A) and
Concept2
®
upgraded the power calculation method of ergo-
meters, it was hypothesized that the C2D system would provide
more accurately the power developed by the rower. In our study,
despite a strong correlation between power measurements
(r = 0.98, p < 0.001), the power developed by the rower during a
graded test realized by trained subjects was 7.4% higher than
the power provided by the ergometer. These two results are close
to those reported by Lormes et al. [5]. Consequently, our hypoth-
esis of a better accuracy of power measurements is rejected. It
can be supposed that the manufacturer has modified the C2D
with the main objective to reproduce the speed of the boat rather
than to assess the actual power developed by the rower. More
precise information from the manufacturer (Concept2
®
) and
more methodological indications in the article of Lormes et al.
[5], such as the way of recording power values from the ergo-
meter and the number of data used to establish the linear corre-
lations, could have helped us to compare results of this previous
study with ours whereas two different power calculation meth-
ods were used by ergometers. The remaining difference in power
measurements could be due to phenomena which are not still
taken into account in E
2
, i.e., the power used to stretch the shock
cord chain return, the energy dissipated at the level of the chain,
the power stored in the flywheel and some limitations in the
model of power dissipation.
A constant shift in power measurement
Despite differences between P
ergo
and P
sensors
, these two power
measurements evolved in the same way as illustrated by high
correlations coefficients values (0.67 < r < 0.99, p < 0.001). The
low coefficient obtained for the start of the experts (r = 0.27,
p < 0.05) could be explained by the ability of experts to rapidly
produce high power values which induced differences in power
measurements. The strong correlations between P
ergo
and P
sensors
added to the parallelism of the linear regression and the P
sensors
=
P
ergo
line (Fig. 1) would indicate that the difference in power
measurements was relatively constant. This is in line with the re-
sults exhibited by Lormes et al. [5] (linear regression: P
sen-
sors
= 1.01 × P
ergo
+ 13.70). Furthermore, considering differences in
power measurements calculated during the graded test at steady
pace, the ergometer underestimated the power by 22.7 W. This
finding is close to the y-intercept value presented in Fig. 1B,
which strengthens the idea of a constant shift in power measure-
ments by the C2D ergometer.
DIPM variability and expertise level
The deletion of initial strokes in both experimental situations
improved correlations between P
ergo
and P
sensors
and reduced
DIPM. During the first strokes of a new grade or of the start, row-
ers have to change the intensity and the pace of the rowing
strokes. So, it can be hypothesized that differences in power
Table 1 Averaged ± standard deviation of P
ergo
and P
sensors
(W), mean differences in power measurements (DIPM) and ranges (min-max) for
novices, experts and both populations (n = 12) for both experimental situations and the two statistical analyses
Graded test Start
All
strokes
P
ergo
P
sensors
DIPM n r p P
ergo
P
sensors
DIPM n r p
Novices 210.7 ±
72.6
228.5 ±
77.5 #
29.2
18.3 35.4
848 0.90 *** 451.5 ±
130.7
503.1 ±
103.9 Ø
72.4
48.1 103.2
84 0.67 ***
Experts 308.8 ±
113.6
327.1 ±
113.9
21.6
14.4 30.6
610 0.98 *** 634.8 ±
98.5
683.3 ±
55.8
62.9
58.0 72.0
83 0.27 ***
n=12 252.7 ±
103.9
269.7 ±
106.2
26.0
14.4 35.4
1458 0.96 *** 539.0 ±
151.2
588.4 ±
127.4
68.2
48.1 103.2
167 0.75 ***
Except first 3 strokes
Novices 212.0 ±
72.1
227.9 ±
76.2 #
26.3
14.9 33.5
683 0.92 *** 492.8 ±
96.5
505.9 ±
100.2 #
39.7
15.0 61.1
66 0.86 *
Experts 320.0 ±
111.4
334.9 ±
111.4
17.4
10.5 25.0
466 0.99 *** 673.8 ±
48.7
677.1 ±
51.1
21.8
14.2 25.8
65 0.82 n.s.
n=12 255.8 ±
104.5
271.3 ±
106.0
22.7
10.5 33.5
1149 0.97 *** 578.7 ±
123.1
586.3 ±
121.1
31.6
14.2 61.1
131 0.93 *
All strokes all the strokes were analyzed; Except firs t 3 strokes the first three strokes were deleted; “n” number of studied strokes; “r” Bravais Pearson correlation coeffi-
cient between P
ergo
and P
sensors
; “p”: level of significant difference between P
ergo
and P
sensors
; ns: p > 0.05, * p < 0.05, *** p < 0.001; # p < 0.001 significant difference in DIPM
between novices and exper ts; “Ø”: no significant difference between novices and experts
Boyas S et al. Power Responses of a Rowing Ergometer Int J Sports Med 2006; 27: 830 833
Training & Testing
832
Downloaded by: University of Ottawa. Copyrighted material.
measurements are enhanced by the variability of the power pro-
duced by the subject between two successive strokes. Smith [8]
indicated that novices rowed with a lower stroke to stroke con-
sistency than trained subjects, and so with more variations in
power production. Power values displayed by the C2D ergometer
do not take into account the additional power due to an uneven
pace. These elements associated with a higher DIPM for novices
than for experts confirm that rowing at an uneven pace induces
higher differences in power measurements than rowing with
consistency.
Conclusions
The Concept2
®
system (model D) underestimates the power de-
veloped by the rower by approximately 25 W. This difference in
power measurements seems to be independent of the level of the
power produced, but increases with variations in intensity and
pace. The removal of the first strokes following changes in power
production allows to limit this phenomenon. According to the
use of the power parameter in the experimental design, it could
be appropriate to correct power values provided by the ergo-
meter.
Acknowledgements
The authors are grateful to Pr. Marinus van Holst for information
about power measurements of Concept2
®
ergometers available
on the web (http://home.hccnet.nl/m.holst/ErgoDisp.html).
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Fig.1A and B Correlations between P
ergo
and P
sensors
for all the subjects. A graded test.
B graded test, except the first three strokes
of each grade. P
sensors
=a×P
ergo
+ b: equation
of the linear regression (solid line). n = num-
ber of studied strokes. r = Bravais Pearson
correlation coefficient. P
sensors
=P
ergo
: dotted
line.
Boyas S et al. Power Responses of a Rowing Ergometer Int J Sports Med 2006; 27: 830833
Training & Testing
833
Downloaded by: University of Ottawa. Copyrighted material.
... Furthermore, the C2 underestimates P by approximately 14-25 W (~ 6.8-7.4%) (7,14). This error depends, amongst other things, on the variability of stroke-to-stroke power in individual rowers. ...
... However, this study was limited by a nowadays outdated C2 ergometer model (model C) and the lack of a reference system. Smith and Spinks (19) applied an external reference system, but used an unusual wheeled rowing ergometer and they did not report variability within steps, which is also a limitation of the study conducted by Boyas et al. (7). In addition, none of the aforementioned studies evaluated whether variability differs between male and female elite rowers. ...
... It has already been reported here and elsewhere (7), that the first three to five strokes play a "special role" in the C2-calculation of P. They almost inevitably affect variability. Unsurprisingly Table 2 shows that all average CVs are significantly lower when the first three strokes are excluded from the analysis -irrespective of measurement system and sex. ...
Article
Full-text available
https://www.germanjournalsportsmedicine.com/archiv/archive-2022/issue-2/variability-of-mechanical-power-output-in-elite-rowers-during-ergometer-testing/
... Rowing ergometers play a crucial role in rowers' training and testing procedure (Boyas et al., 2006;Hopkins et al., 2001;Smith and Hopkins, 2012). Thereby, the windbraked Concept 2 rowing ergometer (Concept 2/Type D, Morrisville, NC, United States) are regarded as the most commonly used devices (Boyas et al., 2006). ...
... Rowing ergometers play a crucial role in rowers' training and testing procedure (Boyas et al., 2006;Hopkins et al., 2001;Smith and Hopkins, 2012). Thereby, the windbraked Concept 2 rowing ergometer (Concept 2/Type D, Morrisville, NC, United States) are regarded as the most commonly used devices (Boyas et al., 2006). This Concept 2 rowing ergometer is considered as the gold standard, which enables a valid and reliable (standard error of measurement of about 0.5%) testing device for longer test duration like the most common 2000m time trials (Hopkins et al., 2001;Smith and Hopkins, 2012). ...
... This Concept 2 rowing ergometer is considered as the gold standard, which enables a valid and reliable (standard error of measurement of about 0.5%) testing device for longer test duration like the most common 2000m time trials (Hopkins et al., 2001;Smith and Hopkins, 2012). Since, peak power (highest power output during one rowing stroke) is highly correlated (r = 0.92; p ≤ 0.001) to the 2000m time trial performance (Bourdin et al., 2004), the concept 2 rowing ergometer is frequently used for shorter test durations of 30s (Mikulić et al., 2009), 20s (Cataldo et al., 2015, 15s (Boyas et al., 2006) or 5 to 7 stroke peak power tests (Ingham et al., 2002;Metikos et al., 2015;Nugent et al., 2019;Sprague et al., 2007). Unfortunately, power measurement of the first (about 5) strokes are particularly inaccurate on the Concept 2 ergometer (Boyas et al., 2006;Holt et al., 2021). ...
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Accurate assessment of peak rowing power is crucial for rowing-specific performance testing. Therefore, within and between day reliability of a non-modified rowing ergometer was examined. 52 trained male rowers (21.0 ± 2.9 years; 1.89 ± 0.05 m; 83.2 ± 8.2 kg; 2,000-m ergometer Time Trial mean power: 369 ± 57 W) performed (two times 4) isolated concentric rowing strokes (DRIVE) and single flexion–extension cycle (FEC-type) rowing strokes (SLIDE-DRIVE) on two separate days (1 week apart). Good to excellent intraclass correlation coefficients (0.94 ≤ ICC ≤ 1.00), low standard error of measurement (≤ 2.7%), low coefficient of variation (≤ 4.9%), and suitable level of agreements (≤ 30W) for DRIVE and SLIDE-DRIVE indicated a high level of (within and between day) reliability. In addition, SLIDE-DRIVE (423 ± 157 W) revealed remarkably higher rowing power (p ≤ 0.001; ηp2 = 0.601; SMD = 0.34) compared to DRIVE (370 ± 154 W). The non-modified rowing ergometer is considered to be a reliable tool for the peak power assessment during isolated concentric contraction and FEC-type rowing strokes. Notably higher power outputs (compared to an isolated concentric contraction) during FEC rowing may refer to an underlying stretch shortening cycle.
... Two validation studies compared the mechanical power output of the C2 vs. a criterion measure (i.e., external forceand displacement-sensors) during rowing. Lormes et al. (1993) postulated a systematic underestimation of approximately 14 W (6.8%) of the C2 (Model C) and Boyas et al. (2006) reported a systematic underestimation of approximately 25 W (7.4%) (Model D). The latter study showed in fact differences in the error's magnitude between novice and trained rowers due to stroke-to-stroke variability. ...
... Hence, the in accuracy of the C2 ergometer is positively associated with stroke to stroke inconsistency. Boyas and colleagues already reported in 2006 a higher accuracy of the C2 in trained rowers (Boyas et al., 2006), who performed with a higher stroke to stroke "consistency" than untrained rowers (Smith and Spinks, 1995). Our results add the information that inconsistency in the drive:recovery ratio has a higher impact in accuracy than variations in stroke force and they also demonstrate a magnitude dependence associated with inconsistency. ...
... Vice versa, the positive association between consistency and accuracy of the power calculation also became evident in the C2's relatively small underestimation of the mechanical power output of only ∼13-17 W found in our study during steady simulated rowing. This range is considerably smaller than 14-25 W reported previously in human rowers (Lormes et al., 1993;Boyas et al., 2006). The main reason is likely that our test rig's reliability is much higher than human reliability. ...
Article
Full-text available
Introduction The Concept 2 (C2) rowing ergometer is used worldwide for home-based training, official competitions, and performance assessment in sports and science. Previous studies reported a disparate underestimation of mechanical power output positively related to an unclearly defined stroke variability. The aim of this study was to quantify the accuracy of the C2 while controlling for the potentially influencing variables of the rowing stroke by using a test rig for air-braked rowing ergometers and thus excluding biological variability. Methods A unique motorized test rig for rowing ergometers was employed. Accuracy was assessed as the difference in mechanical power output between C2 and a reference system during steady (i.e., minimal variations of stroke power within a series of 50 spacemark, no -strokes) and unsteady simulated rowing (i.e., persistent variations during measurement series) while manipulating the stroke variables shape, force, or rate. Results During steady simulated rowing, differences between C2 and the reference system ranged 2.9–4.3%. Differences were not significantly affected by stroke shapes ( P = 0.153), but by stroke rates ranging 22–28 min ⁻¹ ( P < 0.001). During unsteady simulated rowing with alterations of stroke force and rate, mean differences of 2.5–3.9% were similar as during steady simulated rowing, but the random error increased up to 18-fold. C2 underestimated mechanical power output of the first five strokes by 10–70%. Their exclusion reduced mean differences to 0.2–1.9%. Conclusion Due to the enormous underestimation of the start strokes, the nominal accuracy of the C2 depends on the total number of strokes considered. It ranges 0.2–1.9%, once the flywheel has been sufficiently accelerated. Inaccuracy increases with uneven rowing, but the stroke shape has a marginal impact. Hence, rowers should row as even as possible and prefer higher stroke rates to optimize C2 readings. We recommend external reference systems for scientific and high-performance assessments, especially for short tests designs where the start strokes will have a major impact.
... The validity of power measurement off-water has been investigated using mechanical sensors attached to Concept2 ergometer models A and D, with negative systematic error estimates of 5-8% reported (Lormes et al., 1993;Boyas et al., 2006). However, the validity of power from on-water instrumentation systems is yet to be established. ...
... The Concept2 drag factor was set to 80 units for females and 100 units for males, which were lowered by 30 units from typical settings to account for the greater resistance of the Swingulator-Concept2 system. Mechanical sensors (hereafter referred to as the Reference System) were attached to the Swingulator, similar to that used previously on Concept2 ergometers (Macfarlane et al., 1997;Boyas et al., 2006), and included a quadrature optical encoder (HEDS-5500 Optical Encoder) coupled inline to the Concept2's chain, allowing finite linear displacement to be measured with regard to a fixed reference mark. A force transducer (DACELL UMMA-K200) was housed in a custom attachment (Küsel Dësign, Melbourne, VIC, Australia) at Pulley 4 on the Swingulator assembly (Figure 2), enabling the measurement of force applied through the Swingulator cord when a participant pulled on the oar handle. ...
... The consistent ∼10% difference in mean power for Peach, Weba, and the Concept2 from the Reference System may therefore reflect positive systematic error for the Reference System whereby true power is closer to that of Peach, Weba, and the Concept2. Only the Concept2 has been investigated previously for its validity of power output, where negative systematic error of ∼7% was found in comparison to instrumentation similar to that used in the current study (Boyas et al., 2006). Most of the apparent systematic error in the current study may therefore be coming from the devices rather than the Reference System. ...
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Purpose: Instrumentation systems are increasingly used in rowing to measure training intensity and performance but have not been validated for measures of power. In this study, the concurrent validity of Peach PowerLine (six units), Nielsen-Kellerman EmPower (five units), Weba OarPowerMeter (three units), Concept2 model D ergometer (one unit), and a custom-built reference instrumentation system (Reference System; one unit) were investigated. Methods: Eight female and seven male rowers [age, 21 ± 2.5 years; rowing experience, 7.1 ± 2.6 years, mean ± standard deviation (SD)] performed a 30-s maximal test and a 7 × 4-min incremental test once per week for 5 weeks. Power per stroke was extracted concurrently from the Reference System ( via chain force and velocity), the Concept2 itself, Weba (oar shaft-based), and either Peach or EmPower (oarlock-based). Differences from the Reference System in the mean (representing potential error) and the stroke-to-stroke variability (represented by its SD) of power per stroke for each stage and device, and between-unit differences, were estimated using general linear mixed modeling and interpreted using rejection of non-substantial and substantial hypotheses. Results: Potential error in mean power was decisively substantial for all devices (Concept2, –11 to –15%; Peach, −7.9 to −17%; EmPower, −32 to −48%; and Weba, −7.9 to −16%). Between-unit differences (as SD) in mean power lacked statistical precision but were substantial and consistent across stages (Peach, ∼5%; EmPower, ∼7%; and Weba, ∼2%). Most differences from the Reference System in stroke-to-stroke variability of power were possibly or likely trivial or small for Peach (−3.0 to −16%), and likely or decisively substantial for EmPower (9.7–57%), and mostly decisively substantial for Weba (61–139%) and the Concept2 (−28 to 177%). Conclusion: Potential negative error in mean power was evident for all devices and units, particularly EmPower. Stroke-to-stroke variation in power showed a lack of measurement sensitivity (apparent smoothing) that was minor for Peach but larger for the Concept2, whereas EmPower and Weba added random error. Peach is therefore recommended for measurement of mean and stroke power.
... For RUN, PO is estimated only from the elevation of the center of gravity, neglecting the horizontal component of propulsion (Kram and Taylor, 1990), thereby slightly underestimating PO. For UP, on a similar Concept2 rowing ergometer, PO was shown to be underestimated by about 25 W (Boyas et al., 2006). Furthermore, the baseline metabolic cost in UP (performing the movement at 0 W) is relatively low, while in RUN, and this cost is considerably larger (as in running on the flat) (Kram and Taylor, 1990). ...
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Purpose: To investigate the interaction between exercise modality (i.e., upper- and lower-body exercise) and sex in physiological responses and power output (PO) across the entire intensity spectrum (i.e., from low to maximal intensity). Methods: Ten male and 10 female cross-country (XC) skiers performed a stepwise incremental test to exhaustion consisting of 5 min stages with increasing workload employing upper-body poling (UP) and running (RUN) on two separate days. Mixed measures ANOVA were performed to investigate the interactions between exercise modalities (i.e., UP and RUN) and sex in physiological responses and PO across the entire exercise intensity spectrum. Results: The difference between UP and RUN (Δ UP−RUN ), was not different in the female compared with the male XC skiers for peak oxygen uptake (18 ± 6 vs. 18 ± 6 mL·kg ⁻¹ ·min ⁻¹ , p = 0.843) and peak PO (84 ± 18 vs. 91 ± 22 W, p = 0.207). At most given blood lactate and rating of perceived exertion values, Δ UP−RUN was larger in the male compared with the female skiers for oxygen uptake and PO, but these differences disappeared when the responses were expressed as % of the modality-specific peak. Conclusion: Modality-differences (i.e., Δ UP−RUN ) in peak physiological responses and PO did not differ between the female and male XC skiers. This indicates that increased focus on upper-body strength and endurance training in female skiers in recent years may have closed the gap between upper- and lower-body endurance capacity compared with male XC skiers. In addition, no sex-related considerations need to be made when using relative physiological responses for intensity regulation within a specific exercise modality.
... It is worth mentioning, that the reliability and validity of the frequently used rowing ergometers was not validated in similar quality as is the case with e.g., cycle ergometers. That is surprising, because the few studies published suggest a limited validity (7,35) and furthermore a high stroke-by-stroke variability in ergometer testing has been observed (77). This gap in knowledge may be due to the lack of appropriate testing devices, but since these have been recently developed (43), it is likely that the international rowing community will soon receive such results. ...
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Olympic rowing in its current form is a high-intensity boat race covering a distance of 2000 m with fastest race times ranging ~5.5-7.5 min, depending on boat class, sex, and environmental factors. To realize such race times, rowers need strength and endurance, which is physiologically evident in an oxidative Adaption of the skeletal muscles, a high aerobic capacity, and the ability to contribute and sustain a relatively high percentage of anaerobic energy for several minutes. Anthropometrically, male and female rowers are characterized by relatively large body measurements. Biomechanics & Physiology: The sitting position of the rower, the involvement of a large muscle mass and the structure of the rowing cycle, consisting of drive and recovery phase where the rower slides back and forth on a sliding seat, affect the cardiovascular and the respiratory system in a unique manner. In Addition to these physiological and anthropometric characteristics, this brief review outlines the extreme metabolic implications of the sport during racing and training and mentions rarely-discussed topics such as established testing procedures, summarizes data on training intensity distribution in elite rowing and includes a short section on heat stress during training and racing in hot and humid conditions expected for the Olympic Games 2021 in Tokyo.
... Reliable and valid power output measures were previously reported using nearly identical methods during similar steady-state rowing protocols with Concept 2 rowing ergometers. 15,16 Stroke length (in meters) and stroke rate (in strokes per minute) were used as indicators of movement execution and were provided by the RP3 computer. Stroke length was defined as the distance between minimal and maximal handle distance to the flywheel for each stroke cycle. ...
Article
Purpose: To investigate how resistance training (RT) in a regular training program affects neuromuscular fatigue (NMF) and gross efficiency (EGROSS) in elite rowers. Methods: Twenty-six elite male rowers performed 4 RT sessions within 10 days. At baseline and after the first and fourth RT, EGROSS and NMF were established. From breathing gas, EGROSS was determined during submaximal rowing tests. Using a countermovement jump test, NMF was assessed by jump height, flight time, flight-to-contraction-time ratio, peak power, and time to peak power. Muscle soreness was assessed using a 10-cm-long visual analog scale. Results: No significant differences were found for EGROSS (P = .565, ω2 = .032). Muscle soreness (P = .00, ω2 = .500) and time to peak power (P = .08, ω2 = 0.238) were higher compared with baseline at all test moments. Flight-to-contraction-time ratio, jump height, and peak power after the fourth RT differed from baseline (P < .05, ω2 = .36, ω2 = .38, and ω2 = .31) and from results obtained after the first RT (P < .05, ω2 = .36, ω2 = .47, and ω2 = .22). Conclusions: RT in general does not influence EGROSS, but large individual differences (4.1%-14.8%) were observed. NMF is affected by RT, particularly after multiple sessions. During periods of intensified RT, imposed external load for low-intensity endurance training need not be altered, but rowers are recommended to abstain from intensive endurance training. Individual monitoring is strongly recommended.
Chapter
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The aim of this study was to develop a portable data-acquisition system to measure the stroke-by-stroke power output and the force developed at the feet during simulated rowing, and to use the system to investigate the reliability of selected variables used to describe rowing performance. Using a Concept II rowing ergometer, the instantaneous power output was calculated as the product of the force at the handle, measured using a small transducer mounted near the handle, and the velocity of the handle, measured using an infra-red emitter-receiver to detect the passage of each vane of the flywheel. The cumulative force at the feet was measured using two force-plates, one mounted under each foot. The outputs from all transducers were sampled at 30 Hz using an 80386SX computer running Asyst data-acquisition software. Excellent linearity in all transducers was established and a calibration of the system revealed measurement errors of less than 3%. The reliability of the variables used to describe rowing performance was studied using a repeated 90 s maximal test on seven experienced oarsmen. Statistical analysis indicated that, of the 14 variables used, only two failed to meet the set criterion. In conclusion, it was found that a rower's performance during simulated rowing was very reliable and that the selected variables used in this study could be used to objectively describe performance on a rowing ergometer.
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This review analyses rowing by linking the biological and mechanical systems that comprise the rowing system. Blade force was found to be the only propulsive force to counter the drag forces, consisting of both air drag and hydrodynamic drag, acting on the system. Vertical oscillations of the shell are shown to have minimal impact on system dynamics. The oar acts as the link between the force generated by the rower and the blade force and transmits this force to the rowing shell through the oarlock. Blade dynamics consist of both lift and drag mechanisms. The force on the oar handle is the result of a phased muscular activation of the rower. Oar handle force and movement are affected by the joint strength and torque-velocity characteristics of the rower. Maximising sustainable power requires a matching of the rigging setup and blade design to the rower's joint torque-velocity characteristics. Coordination and synchrony between rowers in a multiple rower shell affects overall system velocity. Force-time profiles should be better understood to identify specific components of a rower's biomechanics that can be modified to achieve greater force generation.
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A dry-land rowing system was developed to provide the coach and/or athlete with quantitative information about the athlete's kinetics and kinematics while the athlete trains. This system consists of a Concept II rowing ergometer instrumented with a force transducer and potentiometer, four electrogoniometers attached to the athlete's ankle, knee, hip, and elbow, and a data acquisition computer. The force transducer is used to quantify the athlete's pulling force. The potentiometer signal is used to locate the position of the handle. The electrogoniometers provide signals proportional to joint angles. A link segment model of the human body is used to locate joint centers based on limb lengths and joint angles. The computer is used to collect and process all the transducer signals, perform the link segment calculations and provide feedback to the coach or athlete in the form of a stick figure animation overlaid with kinematic and kinetic information. This system allows the coach and athlete to quickly study a rower's mechanics, to evaluate the effects that technique changes have on the power produced by the athlete, and to identify technique differences between athletes.
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This review analyses rowing by linking the biological and mechanical systems that comprise the rowing system. Blade force was found to be the only propulsive force to counter the drag forces, consisting of both air drag and hydrodynamic drag, acting on the system. Vertical oscillations of the shell are shown to have minimal impact on system dynamics. The oar acts as the link between the force generated by the rower and the blade force and transmits this force to the rowing shell through the oarlock. Blade dynamics consist of both lift and drag mechanisms. The force on the oar handle is the result of a phased muscular activation of the rower. Oar handle force and movement are affected by the joint strength and torque-velocity characteristics of the rower. Maximising sustainable power requires a matching of the rigging setup and blade design to the rower's joint torque-velocity characteristics. Coordination and synchrony between rowers in a multiple rower shell affects overall system velocity. Force-time profiles should be better understood to identify specific components of a rower's biomechanics that can be modified to achieve greater force generation.
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The aim of the present study was to test the hypothesis that peak power output (Ppeak) sustained during maximal incremental testing would be an overall index of rowing ergometer performance over 2000 m (P2000), and to study the influence of selected physiological variables on Ppeak. A group of 54 highly trained rowers (31 heavyweight [HW] and 23 lightweight [LW] rowers) was studied. Body mass, maximal oxygen uptake ((.-)VO(2max)), oxygen consumption corresponding to a blood lactate of 4 mmol. l (-1) expressed in percentage of (.-)VO(2max) (V.O (2)La4 %), and rowing gross efficiency (RGE) were also determined during the incremental test. In the whole group Ppeak was the best predictor of P2000 (r = 0.92, p < 0.0001). Body mass (r = 0.65, p < 0.0001), V.O (2max) (r = 0.84, p < 0.0001), (.-)VO 2)La4 % (r = 0.49, p < 0.0001) and RGE (r = 0.35, p < 0.01) were significantly correlated with P2000 as well. To take the influence of body mass into account, (.-)VO(2max) was related to kg (0.57). Ppeak was significantly related to body mass (r = 0.56, p < 0.0001), (.-)VO(2max) x kg (-0.57) (r = 0.63, p < 0.0001), (.-)VO(2)La4 % (r = 0.45, p < 0.001) and RGE (r = 0.34, p < 0.05). Multiple regression analysis indicated that the above parameters taken together explained 82.8 % of Ppeak variation in the whole group. It was also demonstrated that Ppeak was the best predictor of P2000 when LW and HW groups were considered separately. It was concluded that, by integrating the main physiological factors of performance, Ppeak is an overall index of physiological rowing capacity and rowing efficiency in heterogeneous as well as in homogeneous groups. It presents the further advantage of being easily measured in the field.