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Background: The force- and power-velocity (F-V and P-V, respectively) relationships have been extensively studied in recent years. However, its use and application in endurance running events is limited. Research question: This study aimed to determine if the P-V relationship in endurance runners fits a linear model when running at submaximal velocities, as well as to examine the feasibility of the "two-point method" for estimating power values at different running velocities. Methods: Eighteen endurance runners performed, on a motorized treadmill, an incremental running protocol to exhaustion. Power output was obtained at each stage with the Stryd™ power meter. The P-V relationship was determined from a multiple-point method (10, 12, 14, and 17 km·h-1) as well as from three two-point methods based on proximal (10 and 12 km·h-1), intermediate (10 and 14 km·h-1) and distal (10 and 17 km·h-1) velocities. Results: The P-V relationship was highly linear ( r = 0.999). The ANOVAs revealed significant, although generally trivial (effect size < 0.20), differences between measured and estimated power values at all the velocities tested. Very high correlations ( r = 0.92) were observed between measured and estimated power values from the 4 methods, while only the multiple-point method ( r2 = 0.091) and two-point method distal ( r2 = 0.092) did not show heteroscedasticity of the error. Significance: The two-point method based on distant velocities (i.e., 10 and 17 km·h-1) is able to provide power output with the same accuracy than the multiple-point method. Therefore, since the two-point method is quicker and less prone to fatigue, we recommend the assessment of power output under only two distant velocities to obtain an accurate estimation of power under a wide range of submaximal running velocities.
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UNCORRECTED PROOF
Gait & Posture xxx (2018) xxx-xxx
Contents lists available at ScienceDirect
Gait & Posture
journal homepage: www.elsevier.com
Prediction of power output at di`erent running velocities through the two-point
method with the Strydpower meter
Felipe García-Pinillos⁠a⁠, ⁠, Pedro Á. Latorre-Román⁠b, Luis E. Roche-Seruendo⁠c, Amador García-Ramos⁠d⁠, ⁠e
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ARTICLE INFO
*=;36)7
Endurance runners
Linear regression
Power output
Two-Velocity method
ABSTRACT
'(/,6392) The force- and power-velocity (FV and PV, respectively) relationships have been extensively stud-
ied in recent years. However, its use and application in endurance running events is limited.
!*7*'6(- 59*78.32 This study aimed to determine if the PV relationship in endurance runners ^ts a linear model
when running at submaximal velocities, as well as to examine the feasibility of the two-point methodfor esti-
mating power values at different running velocities.
*8-3)7 Eighteen endurance runners performed, on a motorized treadmill, an incremental running protocol to
exhaustion. Power output was obtained at each stage with the Strydpower meter. The PV relationship was
determined from a multiple-point method (10, 12, 14, and 17 km·h1) as well as from three two-point methods
based on proximal (10 and 12 km·h1), intermediate (10 and 14 km·h1) and distal (10 and 17 km·h1) velocities.
!*79087 The PV relationship was highly linear ( 6= 0.999). The ANOVAs revealed significant, although gener-
ally trivial (effect size < 0.20), differences between measured and estimated power values at all the velocities
tested. Very high correlations ( 6= 0.92) were observed between measured and estimated power values from the
4 methods, while only the multiple-point method ( 6⁠2 = 0.091) and two-point method distal ( 6⁠2 = 0.092) did not
show heteroscedasticity of the error.
".,2.B('2(* The two-point method based on distant velocities (i.e., 10 and 17 km·h1) is able to provide power
output with the same accuracy than the multiple-point method. Therefore, since the two-point method is quicker
and less prone to fatigue, we recommend the assessment of power output under only two distant velocities to
obtain an accurate estimation of power under a wide range of submaximal running velocities.
1. Introduction
Testing endurance athletes is essential for determining how they
are adapting to their training program, understanding individual re-
sponses to training, assessing fatigue and the associated need for re-
covery, and minimizing the risk of nonfunctional overreaching, in-
jury, and illness [1,2]. The use of incremental tests for detecting adap-
tations to training, predicting performance and determining training
zones (i.e., thresholds) in endurance runners is quite ex
tended [2]. The term thresholdrefers to the level at which abrupt
changes in the dynamic of any parameter occur in response to a stim-
ulus. For instance, the blood lactate threshold concept has been used
to de^ne the exercise intensity at which there is a non-linear increase
in lactate concentration [3]. Likewise, the heart rate during incre-
mental exercise is sigmoidal, with a linear component in the mid-
dle and a plateau close to the maximal workloads [4]. The non-lin-
ear dynamic of these commonly used parameters makes dif^cult its
prediction and utilization for prescribing training intensity. The iden-
ti^cation of variables that change linearly together with the in
Corresponding author at: Department of Physical Education, Sports and Recreation, Universidad de La Frontera, Calle Uruguay, 1980, Temuco, Chile.
1'.0 '))6*77*7 fegarpi@gmail.com (F. García-Pinillos); platorre@ujaen.es (P.Á. Latorre-Román); leroche@usj.es (L.E. Roche-Seruendo); amagr@ugr.es (A. García-Ramos)
https://doi.org/10.1016/j.gaitpost.2018.11.037
Received 22 May 2018; Received in revised form 22 July 2018; Accepted 29 November 2018
Available online xxx
0966-6362/ © 2018.
Full length article
UNCORRECTED PROOF
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crease in intensity might facilitate the prescription of training intensity.
The force-velocity (FV) and load-V (LV) are two important rela-
tionships that follow a linear ^t (the higher the force and load, the lower
the velocity) [5,6]. The assessment of the FV relationship allows to de-
termine the athletes force and velocity de^cits [7,8], while the LV rela-
tionship enables to estimate the one-repetition maximum [9,10]. There-
fore, both relationships provide valuable information that can be used to
prescribe individualized resistance training programs. The typical pro-
cedures used to determine the FV and LV relationships are based on
the application of multiple loads (at least 4), which provide a wide
range of force and velocity data that can be modelled through a lin-
ear regression [11,12]. However, since this procedure is time consuming
and prone to fatigue, Jaric [13] proposed that, due to the high linear-
ity of the FV and LV relationships, the application of only two loads
could be enough to accurately determine these relationships. The name
two-point methodhas been proposed to describe the testing proce-
dure in which an individual linear relationship is modelled from only
two data points (e.g., two different loads or velocities), while the name
multiple-point methodis used when more than two loads or velocities
are applied [14].
Such a time-ef^cient method (i.e., two-point method') has been
proved to be valid and accurate for determining the FV and LV re-
lationships during a variety of resistance training exercises [9,15,16],
cycling [1719] and running [11,12,20]. Of note, in all the aforemen-
tioned studies subjects were required to exert maximum values of force
against all the tested loads, obtaining a linear FV relationship and a
parabolic power-V (PV) relationship. However, during an incremental
running test (i.e. submaximal intensities), the resistance (i.e. runners´
body mass) is constant and, therefore, a linear PV is expected. If this
rationale is con^rmed (i.e. the PV relationship obtained from differ-
ent treadmill velocities turns out to be approximately linear), a simpli-
^ed method for its assessment might be used (i.e., two-point method'
[20]), but no data is available regarding the feasibility of the two-point
method during submaximal efforts.
Once discussed the importance of the information provided by the
FV and PV relationships, now the point is how to measure them.
Traditionally, force data during running has been obtained using spe-
ci^c instrumented treadmills [21]. Despite the high accuracy of instru-
mented treadmills, most coaches do not have easy access to such expen-
sive equipment. In an attempt to provide an easier access to the FV
and PV relationships, Samozino et al. [12] proposed a method to es-
timate them from only anthropometric and spatiotemporal data during
an overground sprint acceleration, but this method is not applicable to
submaximal velocities. Fortunately, the development of inertial motion
units to quantify performance have been considerably developed in re-
cent years and, today, some devices provide power data during running
(e.g. Strydor Runscribe).
To ^ll the aforementioned gaps in the literature, an incremental run-
ning protocol to exhaustion was performed by trained endurance run-
ners and power output recorded with the Strydpower meter was av-
eraged at each stage to determine the PV relationship using a multi-
ple-point method (10, 12, 14, and 17 km·h1) as well as three two-point
methods based on proximal (10 and 12 km·h1), intermediate (10 and
14 km·h1) and distal (10 and 17 km·h1) velocities. This study aimed to
determine if the power-velocity (PV) relationship in endurance runners
^ts a linear model when running at submaximal velocities, as well as to
examine the feasibility of the two-point methodfor estimating power
values at different running velocities.
2. Methods
 '68.(.4'287
Eighteen recreationally trained male endurance runners (age range:
1946 years; age: 34 ± 7 years; height: 1.76 ± 0.05 m; body mass:
70.5 ± 6.2 kg) voluntarily participated in this study. All participants
met the inclusion criteria: (1) older than 18 years old, (2) able to run
10 km in less than 40 min, (3) training on a treadmill at least once per
week, (4) not suffering from any injury (points 3 and 4 related to the last
6 months before the data collection). After receiving detailed informa-
tion on the objectives and procedures of the study, each subject signed
an informed consent form in order to participate, which complied with
the ethical standards of the World Medical Associations Declaration of
Helsinki (2013). It was made clear that the participants were free to
leave the study if they saw ^t. The study was approved by the Institu-
tional Review Board.
 63(*)96*7
Subjects were individually tested on one day. The testing session
started with the collection of anthropometric data. Then, participants
performed an incremental running test on a motorized treadmill (HP
cosmos Pulsar 4 P, HP cosmos Sports & Medical, Gmbh, Germany). The
initial speed was set at 8 km.h1, and speed increased by 1 km.h1every
3 min until exhaustion [22]. The slope was maintained at 1% in order
to reproduce the effects of air resistance and try to obtain results as sim-
ilar as possible to ^eld conditions [23]. The treadmill protocol was pre-
ceded by a standardized 10-min accommodation program (5 min walk-
ing at 5 km.h1, and 5 min running at 10 km.h1). Participants were ex-
perienced in running on a treadmill but anyway, previous studies on hu-
man locomotion have shown that accommodation to a new condition
occurs in ~6-8 min [24,25].
 '8*6.'07 '2) 8*78.2,
For descriptive purposes, body height (cm) and body mass (kg) were
determined using a precision stadiometer and weighing scale (SECA 222
and 634, respectively, SECA Corp., Hamburg, Germany). All measure-
ments were taken with the participants wearing underwear. Body mass
index (BMI) was calculated from the subjects' body mass and height
(kg. m2).
Power output (in W) was estimated with the Strydpower me-
ter (Stryd Power meter, Stryd Inc. Boulder CO, USA). Strydis a rel-
atively new carbon ^bre-reinforced foot pod (attached to the shoe)
that weights 9.1 g. Based on a 6-axis inertial motion sensor (3-axis gy-
roscope, 3-axis accelerometer), this device provides twelve metrics to
quantify performance: pace, distance, elevation, running power, form
power, cadence, ground contact time, vertical oscillation, leg sti`ness.
To the best of the authors´ knowledge, just one study has examined
its validity and reliability (in this case, to measure spatiotemporal gait
characteristics [26]), with no data to demonstrate validity and relia-
bility of this device to measure power and related variables. For this
study, only two out of twelve metrics (running velocity and power out-
put) were used. Those variables were obtained from Stryd´s website
(https://www.stryd.com/powercenter/analysis) into the. ^t ^le. Then,
data were analyzed using the publically available software (Golden
Cheetah, version 3.4) and exported as. csl ^le. Those ^les were im-
ported from Excel® (2016, Microsoft, Inc., Redmond WA) and laps
were done every 3 min. Twenty seconds were removed from each stage
(10 s at the beginning and 10 s at the end) in order to avoid data
close to changes in running velocity. Mean and
2
UNCORRECTED PROOF
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standard deviation (SD) were calculated for those variables at each
stage. Therefore, power output was obtained at each stage from
8 km·h1to exhaustion (range: 16-20 km·h1). Four methods were con-
sidered in the present study to estimate power output at different run-
ning velocities: (1) multiple-point method (10, 12, 14, and 17 km·h1),
(2) two-point method proximal (10 and 12 km·h1), (3) two-point
method intermediate (10 and 14 km·h1), and (4) two-point method dis-
tal (10 and 17 km·h1) (Fig. 1). Velocities lower than 10 km·h⁠-1 were ex-
cluded from the models because resulted uncomfortable for trained en-
durance runners, while velocities higher than 17 km·h1were excluded
from the models because some runners did not reach those levels during
the protocol.
 "8'8.78.('0 '2'0=7.7
Descriptive data are presented as means and standard deviation,
while the Pearson's correlation coef^cient ( 6) are presented through
their median values and ranges. Normal distribution for all variables
(ShapiroWilk test) and the homogeneity of variances (Levene's test)
were con^rmed (4> 0.05). The 6coef^cient was used to evaluate the
strength of the individual PV relationships. A 1-way repeated-mea-
sures analysis of variance (ANOVA) with Bonferroni post hoc tests
was applied at each velocity condition to compare the measured val-
ues of power against the values of power obtained from the 4 es-
timation methods (i.e., multiple-point method, two-point method
Fig. 1. Power-velocity relationship obtained for a representative participant. The individ-
ual points represent the power values recorded against 10 different velocities. The black
points denote the velocities that were used for the 4 estimation methods (multiple-point
method: 10, 12, 14, and 17 km·h1, two-point method proximal: 10 and 12 km·h1,
two-point method intermediate: 10 and 14 km·h1, and two-point method distal: 10 and
17 km·h1). Note that the regression lines of the two-point method distal are not easily ap-
preciated due to their high overlap with the multiple-point method.
proximal, two-point method intermediate, and two-point method dis-
tal). To further explore the validity of the 4 estimation methods, the 6
coef^cient and the Cohen's )effect size (ES) were calculated between
the measured power and the values of power obtained from the 4 es-
timation methods. The scale used to interpret the magnitude of the ES
was speci^c to training research: negligible (<0.2), small (0.20.5),
moderate (0.50.8), and large (0.8) [27]. Finally, Bland-Altman plots
were constructed to examine the presence of systematic and propor-
tional bias between the measured and estimated values of power. Het-
eroscedasticity of error was de^ned as an 6⁠2 > 0.1 [28]. Significance
was accepted at 40.05. All statistical analyses were performed using
the software package SPSS (IBM SPSS version 22.0, Chicago, IL, USA).
3. Results
The strength of the PV relationship was very high (6= 0.999
[0.994, 1.000]). The ANOVAs revealed significant differences between
the measured and the estimated values of power at all the velocities
analysed (Table 1). However, most of the ES comparing the measured
and estimated values of power were trivial (ES < 0.2) with the only
exception of the two-point method proximal that overestimated power
outputs at high velocities (see Fig. 2; lower panel). The magnitude of
the correlations between the measured power and the power values es-
timated from the 4 methods was very high for the individual velocities
(Fig. 2) as well as when the data of all velocities were pooled (Fig. 3).
BlandAltman plots revealed heteroscedasticity of error for the
two-point method proximal ( 6⁠2 = 0.139) and for the two-point method
intermediate ( 6⁠2 = 0.128) with increasing differences in favour of the
estimated power at higher running velocities, while heteroscedasticity
of error was not observed for the multiple-point method ( 6⁠2 = 0.091)
and two-point method distal ( 6⁠2 = 0.092) (Fig. 4).
4. Discussion
This study explored the possibility of predicting power outputs at
different running velocities from the recording of power values un-
der only two velocity conditions ("two-point method"). The use of the
two-point method to estimate power output at different running veloc-
ities is justi^ed by the strong linearity observed in the current study
for the PV relationship. Despite that the three two-point methods ex-
amined in this study were able to provide valid estimations of power
output, the two-point method based on distant veloci
Table 1
Comparison of the measured values of power against the values of power obtained from the 4 estimation methods.
Velocity
(km·h1)
ANOVA
(Snedecor's
F)
Measured power
(W)
Multiple-point method
(W)
Two-point method proximal
(W)
Two-point method intermediate
(W)
Two-point method distal
(W)
11
(n = 18)
10.3⁠* 217.1 ± 18.8 216.1 ± 19.1 216.1 ± 19.0 215.7 ± 19.1⁠* 215.4 ± 19.1⁠*
13
(n = 18)
7.9⁠* 252.2 ± 22.3 250.4 ± 22.0⁠* 251.9 ± 22.0 250.7 ± 22.2⁠* 249.7 ± 22.1⁠*
15
(n = 18)
5.5⁠* 285.2 ± 25.6 284.6 ± 25.1 287.6 ± 25.1 285.7 ± 25.4 284.0 ± 25.2
16
(n = 18)
6.0⁠* 301.8 ± 27.0 301.7 ± 26.7 305.5 ± 26.7 303.2 ± 27.1 301.1 ± 26.7
18
(n = 17)
5.7⁠* 334.4 ± 30.5 334.9 ± 30.5 340.2 ± 30.5 337.0 ± 31.0 334.4 ± 30.6
19
(n = 11)
5.0⁠* 339.1 ± 31.4 340.5 ± 29.8 347.7 ± 30.6 344.2 ± 31.2 339.7 ± 29.7
20 (n =6) 3.6⁠* 343.3 ± 26.8 346.7 ± 29.0 349.4 ± 27.1 347.3 ± 27.8 346.7 ± 29.3
Mean ± standard deviation.
*denotes a signi^cant F value and signi^cant di`erences respect to the measured power (4< 0.05).
3
UNCORRECTED PROOF
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Fig. 2. Pearson's correlation coef^cients (upper panel) and Cohen's )effect size (lower
panel) between the measured and the estimated values of power from the multiple-point
method (^lled circle), two-point method proximal (empty circle), two-point method inter-
mediate (^lled triangle) and two-point method distal (empty triangle) at different running
velocities. Effect size = (estimated power mean measured power mean) / SDboth.
ties (i.e., 10 and 17 km·h1) provided the most accurate estimations,
especially at high running velocities. It is important to note that the
two-point method distal was able to provide power output with the
same accuracy than the multiple-point method.
As we earlier mentioned, the validity and reliability of the two-point
method has been tested during a wide variety of resistance training ex-
ercises [9,15,16]. Zivkovic et al. [15] tested twelve participants during
functional movement tasks against multiple loads, and an almost perfect
level of agreement between the routinely used multiple-point method
and a simple two-point methodwas reported. Some previous stud-
ies also used the two-point method during cycling [18,19,29]. In a re-
cent work, García-Ramos et al. [29] aimed to determine the two opti-
mal resistive forces for testing the FV relationship in cycling. The ex-
periment involved twenty-six men, who were tested on maximal sprints
performed on a leg cycle ergometer against 5 _ywheel resistive forces
(R1R5), and the authors concluded that the two-point method in cy-
cling should be based on 2 distant resistive forces (R1-R4). This ^nd-
ing, consistent with the current study, was reinforced by an interven-
tion study from the same research group [19]. In this case, the au-
thors reported that speci^c changes on the FV parameters during a cy-
cling-based training program can be accurately monitored by applying
just two distinctive resistances during routine testing. Despite method-
ological differences, these previous studies are in line with the current
^ndings, showing that the two-point method (with distant loads) accu-
rately predicts the FV and PV relationships in protocols and exercises
where variables are linearly, or close to, related.
Despite the bene^ts attributed to the two-point method, in terms
of time and effort [13,14], limited evidence has examined the possi-
bility to apply this method to running. Some previous studies have
applied the multiple-load method to determine the FV relationship
during maximal runs (i.e. sprints) [11,12]. Cross et al. [11] deter-
mined the FV relationship from the velocity recorded against a range
of sled-resisted sprints [11], whereas Samozino et al. [12] used
Fig. 3. Relationship between the measured and the estimated values of power from the multiple-point method (upper-left panel), two-point method proximal (upper-right panel),
two-point method intermediate (lower-left panel) and two-point method distal (lower-right panel). The regression equation and the Pearson's coef^cient of determination ( 6⁠2) are depicted.
4
UNCORRECTED PROOF
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Fig. 4. BlandAltman plots showing differences between the measured power and the values of power estimated from the multiple-point method (upper-left panel), two-point method
proximal (upper-right panel), two-point method intermediate (lower-left panel) and two-point method distal (lower-right panel). Each plot depicts the averaged difference and 95% limits
of agreement (dashed lines), along with the regression line (solid line) (n = 106). 6⁠2, Pearson's coef^cient of determination.
the anthropometric and spatiotemporal data recorded during an un-
loaded sprint [12]. Despite it seems well established that multi-joint
functional tasks typically reveal strong and approximately linear FV re-
lationship patterns [5], a mistake would be committed if results from
the aforementioned studies were compared to those reported by the cur-
rent work. With maximum values of force against a tested load (i.e.
maximal sprint), the higher the force the lower the velocity, obtaining
a linear FV relationship and a parabolic PV relationship. However, in
the current study performed at submaximal intensity, the resistance (i.e.
runners´ body mass in this case) is constant and, therefore, the PV re-
lationship is linear.
To the best of the authors´ knowledge, no previous studies have
tested the feasibility of using the two-point method to determine the
FV or PV relationship during running at submaximal intensities (com-
monly used for endurance runners in training and competition). Do-
brijevic et al. [20] tested 28 physically active subjects on their max-
imum pulling force exerted horizontally while walking or running on
a treadmill set to different velocities (512 km.h1), and concluded
that the FV relationship could be strong, linear, and reliable at ve-
locities tested, and the two-velocity methodcould provide reliable
and ecologically valid indices of force, velocity, and power. Before
comparing their ^ndings with the current study some points need to
be considered. First, based on the aforementioned rationale, the ap-
plication of their maximum pulling F while walking or running en-
sures a linear FV relationship and a parabolic PV relationship what
differs from our study, obtaining a linear PV relationship. Second,
the methodological differences according to the population involved
(physically active vs. trained endurance runners) and the velocities
tested (~5-12 km·h⁠-1 vs. ~8-21 km·h⁠-1) makes the comparison dif^cult.
Despite those differences, the results reported provide sup
port to the feasibility of using the two-point method to estimate the
power output during running at a wide range of velocities. The current
study is focused on endurance runners and suggests that the assessment
of power output, easy-to-obtain data with new devices such as Stryd
power meter, under only two distant velocities (i.e., 8 and 17 km·h⁠-1)
provided the most accurate estimations - with the same accuracy than
the multiple-point method. From a practical standpoint, this informa-
tion might be crucial for coaches. Con^rmed the linear PV relationship
during submaximal runs, any interval workout (including distant veloc-
ities) might be enough to update the PV pro^le during running and,
therefore, give coaches information about adaptations to the training
program (monitoring) and work capacity almost on a daily basis (peri-
odization and training design).
Finally, some limitations must be addressed. The validity and reli-
ability of the power output data from the Strydsystem is still un-
known. However, a recently published book [30] indicated that the
external mechanical power (W/kg) reported by this system is highly
correlated (R⁠2 = 0.96) with metabolic cost (VO⁠2 in ml/kg/min). It
is a relatively new device and more research is clearly needed to de-
termine its potential. Other point to consider, though not necessar-
ily a limitation, is related to the protocol itself. This is an incremen-
tal test to exhaustion, which means that high levels of fatigue are en-
sured at the end of the protocol. Since the duration that exercise can
be maintained decreases as the power requirements increase, and vice
versa [31], the fatigue induced might in_uence on the power out-
put if compared with data from just two-point methods (i.e., 10 and
17 km·h1as proposed in the current study). Notwithstanding these
limitations, the current work highlights the linear PV relationship
during running in a wide range of submaximal intensities (typically
performed in training and competition contexts), as well as con^rms
5
UNCORRECTED PROOF
'6(@' .2.0037*8'0 '.8 37896* <<<  <<<<<<
the effectiveness of the two-point method based on distant velocities
(i.e., 10 and 17 km·h1) for accurately estimating PV pro^le.
In conclusion, the results obtained in the current study show that the
two-point method based on distant velocities (i.e., 10 and 17 km·h1) is
able to provide power output with the same accuracy than the multi-
ple-point method. The data reported also indicate a strong linearity for
the PV relationship. Therefore, since the two-point method is quicker
and less prone to fatigue, we recommend the assessment of power out-
put under only two distant velocities to obtain an accurate estimation of
power under a wide range of submaximal running velocities.
Authors' contributions
FGP: analysis and interpretation of data and drafting the article;
PALR: conception and study design, acquisition data, revising the manu-
script critically; LERS: conception and study design, acquisition data, re-
vising the manuscript critically; AGR: conception and study design, ac-
quisition data, revising the manuscript critically. All authors have read
and approved the ^nal version of the manuscript, and agree with the or-
der of presentation of the authors.
Con#ict of interests
The authors declare that they have no con_ict of interests.
Declarations of interest
None.
Acknowledgements
The authors would like to thank to all the participants.
References
[1] P.C. Bourdon, M. Cardinale, A. Murray, P. Gastin, M. Kellmann, M.C. Varley, T.J.
Gabbett, A.J. Coutts, D.J. Burgess, W. Gregson, N.T. Cable, Monitoring athlete
training loads: consensus statement, Int. J. Sports Physiol. Perform. 12 (2017)
161170, https://doi.org/10.1123/IJSPP.2017-0208.
[2] L. Bosquet, L. Léger, P. Legros, Methods to determine aerobic endurance, Sport.
Med. 32 (2002) 675700.
[3] H. Stegmann, W. Kindermann, A. Schnabel, Lactate kinetics and individual anaero-
bic threshold, Int. J. Sports Med. 2 (1981) 160165, https://doi.org/10.1055/
s-2008-1034604.
[4] C.H. Wyndham, N.B. Strydom, J.S. Maritz, J.F. Morrison, J. Peter, Z.U. Potgieter,
Maximum oxygen intake and maximum heart rate during strenuous work, J. Appl.
Physiol. 14 (1959) 927936.
[5] S. Jaric, Force-velocity relationship of muscles performing multi-joint maximum
performance tasks, Int. J. Sports Med. 36 (2015) 699704, https://doi.org/10.
1055/s-0035-1547283.
[6] F. Pestaña-Melero, G. Ha`, F. Rojas, A. Pérez-Castilla, A. García-Ramos, Reliability
of the load-velocity relationship obtained through linear and polynomial regres-
sion models to predict the one-repetition maximum load, J. Appl. Biomech.
(2017).
[7] J.B. Morin, P. Samozino, Interpreting power-force-velocity pro^les for individual-
ized and speci^c training, Int. J. Sports Physiol. Perform. 11 (2016) 267272,
https://doi.org/10.1123/ijspp.2015-0638.
[8] P. Jiménez-Reyes, P. Samozino, M. Brughelli, J.-B. Morin, Effectiveness of an indi-
vidualized training based on force-velocity pro^ling during jumping, Front. Phys-
iol. 7 (2017) 677, https://doi.org/10.3389/fphys.2016.00677.
[9] A. García-Ramos, G. Ha`, F. Pestana-Melero, A. Perez-Castilla, F. Rojas, C. Balsalo-
bre-Fernandez, S. Jaric, Feasibility of the two-point method for deter
mining the one-repetition maximum in the bench press exercise, Int. J. Sports
Physiol. Perform. (2017) https://doi.org/10.1123/ijspp.2017-0374.
[10] H.G. Banyard, K. Nosaka, A.D. Vernon, G.G. Ha`, The reliability of individualized
load-velocity pro^les, Int. J. Sports Physiol. Perform. (2017) 122, https://doi.org/
10.1123/ijspp.2017-0610.
[11] M.R. Cross, M. Brughelli, P. Samozino, S.R. Brown, J.B. Morin, Optimal loading for
maximizing power during sled-resisted sprinting, Int. J. Sports Physiol. Perform.
12 (2017) 10691077, https://doi.org/10.1123/ijspp.2016-0362.
[12] P. Samozino, G. Rabita, S. Dorel, J. Slawinski, N. Peyrot, E. Saez de Villarreal, J.-B.
Morin, A simple method for measuring power, force, velocity properties, and me-
chanical effectiveness in sprint running, Scand. J. Med. Sci. Sports26 (2016)
648658, https://doi.org/10.1111/sms.12490.
[13] S. Jaric, Two-load method for distinguishing between muscle force, velocity, and
power-producing capacities, Sport Med. 46 (2016) 15851589, https://doi.org/10.
1007/s40279-016-0531-z.
[14] A. Garcia-Ramos, S. Jaric, Two-point method: a quick and fatigue-free procedure
for assessment of muscle mechanical capacities and the 1 repetition maximum,
Strength Cond. J. (2017) 1, https://doi.org/10.1519/SSC.0000000000000359.
[15] M.Z. Zivkovic, S. Djuric, I. Cuk, D. Suzovic, S. Jaric, A simple method for assess-
ment of muscle force, velocity, and power producing capacities from functional
movement tasks, J. Sports Sci. 35 (2017) 12871293, https://doi.org/10.1080/
02640414.2016.1221521.
[16] A. García-Ramos, S. Jaric, P. Padial, B. Feriche, Force-velocity relationship of up-
per body muscles: Traditional versus ballistic bench press, J. Appl. Biomech.
32 (2016) 178185, https://doi.org/10.1123/jab.2015-0162.
[17] T. Driss, H. Vandewalle, J.-M. Le Chevalier, H. Monod, Force-velocity relationship
on a cycle ergometer and knee-extensor strength indices, Can. J. Appl. Physiol.
27 (2002) 250262, https://doi.org/10.1139/h02-015.
[18] A. García-Ramos, A. Torrejón, A.J. Morales-Artacho, A. Pérez-Castilla, S. Jaric, Op-
timal resistive forces for maximizing the reliability of leg muscles capacities tested
on a cycle ergometer, J. Appl. Biomech. 34 (2018) 4752, https://doi.org/10.
1123/jab.2017-0056.
[19] A. García-Ramos, A. Torrejón, A. Pérez-Castilla, A.J. Morales-Artacho, S. Jaric, Se-
lective changes in the mechanical capacities of lower-body muscles after cycle-er-
gometer sprint training against heavy and light resistances, Int. J. Sports Physiol.
Perform. 13 (2018) 290297, https://doi.org/10.1123/ijspp.2017-0239.
[20] S. Dobrijevic, V. Ilic, S. Djuric, S. Jaric, Force-velocity relationship of leg muscles
assessed with motorized treadmill tests: two-velocity method, Gait Pos-
ture56 (2017) 6064, https://doi.org/10.1016/j.gaitpost.2017.04.033.
[21] R. Kram, T.M. Grif^n, J. Maxwell Donelan, Y. Hui Chang, Force treadmill for mea-
suring vertical and horizontal ground reaction forces, J. Appl. Physiol. 7 (1998)
764769, https://doi.org/10.1016/j.jacr.2010.07.010.
[22] F. Esfarjani, P.B. Laursen, Manipulating high-intensity interval training: Effects on,
the lactate threshold and 3000m running performance in moderately trained
males, J. Sci. Med. Sport10 (2007) 2735.
[23] A.M. Jones, J.H. Doust, A 1% treadmill grade most accurately re_ects the ener-
getic cost of outdoor running, J. Sports Sci. 14 (1996) 321327, https://doi.org/
10.1080/026404196367796.
[24] D.A. Schieb, Kinematic accommodation of novice treadmill runners, Res. Q. Exerc.
Sport 57 (1986) 17.
[25] V. Lavcanska, N.F. Taylor, A.G. Schache, Familiarization to treadmill running in
young unimpaired adults, Hum. Mov. Sci. 24 (2005) 544557, https://doi.org/10.
1016/j.humov.2005.08.001.
[26] F. García-Pinillos, L.E. Roche-Seruendo, N. Marcen-Cinca, L.A. Marco-Contreras,
P.Á.A. Latorre-Román, N. Marcén-Cinca, L.A. Marco-Contreras, P.Á.A. La-
torre-Román, Absolute reliability and concurrent validity of the Stryd system for
the assessment of running stride kinematics at different velocities, J. Strenght
Cond. Res (2018) 18, https://doi.org/10.1519/JSC.0000000000002595, in press.
[27] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 1988https://doi.
org/10.1234/12345678.
[28] G. Atkinson, A.M. Nevill, Statistical methods for assessing measurement error (reli-
ability) in variables relevant to sports medicine, Sport. Med. 26 (1998) 217238,
https://doi.org/10.2165/00007256-199826040-00002.
[29] A. García-Ramos, A. Torrejón, A.J. Morales-Artacho, A. Pérez-Castilla, S. Jaric, Op-
timal resistive forces for maximizing the reliability of leg musclescapacities tested
on a cycle ergometer, J. Appl. Biomech. 34 (2018) 4752, https://doi.org/10.
1123/jab.2017-0056.
[30] J.C. van Dijk, R. van Megen, The Secret of Running: Maximum Performance Gains
through Effective Power Metering and Training Analysis, in: https://
thesecretofrunning.com, 2017.
[31] M. Burnley, A.M. Jones, Powerduration relationship: physiology, fatigue, and the
limits of human performance, Eur. J. Sport Sci. 18 (2018) 112, https://doi.org/
10.1080/17461391.2016.1249524.
6
... Nevertheless, the development of novel technologies, such as running power meters, may help evaluate athletes' functional performance and monitor changes over time. A recent study confirmed a linear powervelocity relationship in running for maximal and submaximal protocols [13]. This enables the prediction of MPO at different submaximal running velocities using the two-point method, underlining the need to accurately determine the relationship between simplified FTP tests and FTP 60 method. ...
... Although the actual strategy Stryd TM use to isolate the sensor to avoid measurement noise, and the algorithmic computation to process raw data still undisclosed by the company as part of their knowhow, this system has demonstrated reliable to assess running spatiotemporal parameters in indoor setting compared to 3D motion analysis [33] and the OptoGait infrared system [13]. Furthermore, this sensor has shown moderate to excellent intra-system reliability for all measures through trail running bouts [34]. ...
... The average MPO obtained was 341.73 ± 27.19 W, the normalized MPO was 4.78 ± 0.15 W/kg, and the mean velocity of the test was 17.16 ± 0.56 km/h. These results slightly exceed those found in previous studies [13,15], although the speeds are not entirely comparable. Despite the results for FTP10 exhibiting the lowest association with FPT60 within the three TTs tested, previous studies confirmed a good association between short TTs (i.e., ≤10 min) and LT derivatives [22,38,39]. ...
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Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) values obtained during three submaximal running time trials (i.e., 10 min, 20 min, and 30 min) and the functional threshold power (FTP). Twenty-two recreationally trained male endurance runners completed four submaximal running time trials of 10, 20, 30, and 60 min, trying to cover the longest possible distance on a motorized treadmill. Absolute MPO (W), normalized MPO (W/kg) and standard deviation (SD) were calculated for each time trial with a power meter device attached to the shoelaces. All simplified FTP trials analyzed (i.e., FTP10, FTP20, and FTP30) showed a significant association with the calculated FTP (p < 0.001) for both MPO and normalized MPO, whereas stronger correlations were found with longer time trials. Individual correction factors (ICF% = FTP60/FTPn) of ~90% for FTP10, ~94% for FTP20, and ~96% for FTP30 were obtained. The present study procures important practical applications for coaches and athletes as it provides a more accurate estimation of FTP in endurance running through less fatiguing, reproducible tests.
... Thus, PW may not be directly related to running metabolic cost. Following the evidence-based use of wearable sensors, it has been found a linear power-velocity relationship(r = 0.999) at submaximal speed, and, the consequent used of the two-point method to predict PW in running at different speeds using the Stryd power meter [36]. The authors executed an incremental run-to-exhaustion protocol on a motorized treadmill at 0% slope gradient. ...
... The power-velocity relationship determined from three two-point methods at proximal (10 and 12 km·h −1 ), intermediate (10 and 14 km·h −1 ), and distal (10 and 17 km·h −1 ) speeds showed the same precision than the multiple-point method (used also by the authors to compare PWs through the study) to provide PW estimated by the Stryd power meter. As stated by the authors of the aforementioned study, since the two-point method can be developed faster and without developing fatigue in the athletes, it should be used when assessing PW to acquire accurate power estimations over a range of submaximal running speeds [36]. This might be an outstanding contribution to the strength and conditioning scene as the power-velocity relationship could be frequently updated influencing, therefore, on the quality of both running training and performance. ...
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Mechanical power may act as a key indicator for physiological and mechanical changes during running. In this scoping review, we examine the current evidences about the use of power output (PW) during endurance running and the different commercially available wearable sensors to assess PW. The Boolean phrases endurance OR submaximal NOT sprint AND running OR runner AND power OR power meter, were searched in PubMed, MEDLINE, and SCOPUS. Nineteen studies were finally selected for analysis. The current evidence about critical power and both power-time and power-duration relationships in running allow to provide coaches and practitioners a new promising setting for PW quantification with the use of wearable sensors. Some studies have assessed the validity and reliability of different available wearables for both kinematics parameters and PW when running but running power meters need further research before a definitive conclusion regarding its validity and reliability.
... This device estimates the forces generated based on temporal patterns in tri-axial accelerometry in combination with the wearer's body mass and tracked velocity to compute cadence, ground contact time, vertical oscillation, and estimated power output. The validity of the Stryd Tm is highly correlated (R 2 = 0.96) with metabolic costs and validated as a means to determine metrics of gait [26,28,29]. However, no data are currently available to demonstrate validity of this device to measure power against a gold standard. ...
... The validity of the Stryd Tm is highly correlated (R 2 = 0.96) with metabolic costs and validated as a means to determine metrics of gait [26,28,29], although no peer reviewed data are available regarding validity of power output measures. As such, we have referred to our data in this respect as estimated power output and even so, little is known surrounding inter-device reliability. ...
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Background Power output considers all movement aspects of the game of football and could have meaningful impact for teams. Purpose & methods To assess inter-reliability of ten power meters designed for running; and as a descriptor of individual and team performance during a five-a-side football match. The work aimed to assess inter-device reliability of running power-meters combined with data analysis from intermittent running, along with descriptives of player work rate, gait and team performance during a small-sided game of football. Methods 10 different running power meters inter-reliability were on a treadmill at 8, 10, 12, and 16 km h⁻¹ for 60 s in a random order. Football players (N = 10) performed the Yo-Yo ET1 with the running power meters to determine participants’ endurance capability, while assessing the ability to record metrics of gait and power output during intermittent running. Following a period of 7-days participants took part in a 20 min small-sided game of football wearing the running power meters to provide descriptors of work and gait. Results Good inter-device reliability for the power meters (CV 1.67, range 1.51–1.94 %) during continuous treadmill running were found. Overall mean ± SD results for Yo-Yo ET1 power output 263 ± 36W, power:weight 3.59 ± 0.34W∙kg⁻¹ significantly (p < 0.05) increased with successive stages, while ground-contact time 234 ± 17 ms, and vertical oscillation 90.7 ± 27 mm did not change (p > 0.05). Descriptive analysis of the small-sided game presented mean ± SD absolute and relative power outputs of 148 ± 44W and 1.98 ± 0.53W∙kg⁻¹, equating to 54 ± 21 %Wmax and 74 ± 5%HRmax. Characteristics of gait included cadence 125 ± 22 rpm, ground contact time 266 ± 19 ms, and vertical oscillation 76.7 ± 7 mm. The winning team worked relatively harder than the losing team (53.3 ± 0.7 %Wmax vs 46.7 ± 0.4 %Wmax, p < 0.0001) with more time (398 s vs 141 s) spent above 70 %Wmax. Significance As such, the use of a running power-meter is a useful tool for comparing work rate and aspects of gait between team members while more research is required to investigate relative work rate (%Wmax) within the field.
... Stryd has been evaluated during treadmill running [2][3][4][5], track running [3] trail running [6] and walking [6, 7]. Stryd provides reliable measures for power [2-4, 7], though a minimum sampling time of 10 seconds is required at a constant speed [8]. ...
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... These sensors may allow us to monitor and quantify workload from a fair and objective perspective with accurate replication, as they already do in cycling [12]. Velocity and both the body height and weight of a runner, as well as external conditions such as slope and wind, may influence power output in running [13,14]. Although the level of agreement between power meter systems in running and two theoretical models for power output analysis has been assessed [15], the lack of scientific evidence for the use and interpretation of such metrics in endurance runners may prevent sport practitioners from adopting them as a means to monitor and assess running performance. ...
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... respectively). 7,23 Relative to the CP location with respect VTs, few studies have determined a proximity to RCP in cycling. 24 However, due to the sensitivity of CP estimation and RCP location to the protocols used, there is a need to highlight the dependent essence of these results according to the predictive trials and GXT test used. ...
Purpose: The critical power (CP) concept has been extended from cycling to the running field with the development of wearable monitoring tools. Particularly, the Stryd running power meter and its 9/3-minute CP test is very popular in the running community. Locating this mechanical threshold according to the physiological landmarks would help to define each boundary and intensity domain in the running field. Thus, this study aimed to determine the CP location concerning anaerobic threshold, respiratory compensation point (RCP), and maximum oxygen uptake (VO2max). Method: A group of 15 high-caliber athletes performed the 9/3-minute Stryd CP test and a graded exercise test in 2 different testing sessions. Results: Anaerobic threshold, RCP, and CP were located at 73% (5.41%), 86.82% (3.85%), and 88.71% (5.84%) of VO2max, respectively, with a VO2max of 66.3 (7.20) mL/kg/min. No significant differences were obtained between CP and RCP in any of its units (ie, in watts per kilogram and milliliters per kilogram per minute; P ≥ .184). Conclusions: CP and RCP represent the same boundary in high-caliber athletes. These results suggest that coaches and athletes can determine the metabolic perturbance threshold that CP and RCP represent in an easy and accessible way.
... Commercially available technology for on-field gait analyses is growing dramatically. Inertial measurement units (IMUs), such as the Styrd Power Meter, are gaining special interest due to their low-cost and general availability [4,5]. Athletes, biomechanists, coaches and other sport and exercise related practitioners have begun using this device for measuring running and walking kinematics and kinetics [6,7]. ...
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... Thus, they may be useful for monitoring individuals and quantifying changes in functional performance over time. In this context, the running power data from Strydä had been successfully used to establish a linear power-velocity relationship to predict the power output at different submaximal running velocities, 16 showing the great potential of this portable equipment. In addition, a few studies found a positive correlation between Strydä power data and running economy 17 or metabolic demands. ...
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... In an effort to fill this innovative gap, the accelerometer-based Stryd TM foot pod attempts to provide this holistic view of the kinematic and kinetic information by estimating running power [18][19][20]. However, Stryd TM appears to have limitations when detecting temporal variables [20]. ...
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Linear regression models applied on force (F) and velocity (V) data obtained from loaded multi-joint functional movement tasks have often been used to assess mechanical capacities of the tested muscles. The present study aimed to explore the properties of the F-V relationship of leg muscles exerting the maximum pulling F at a wide range of V on a standard motorized treadmill. Young and physically active male and female subjects (N=13+15) were tested on their maximum pulling F exerted horizontally while walking or running on a treadmill set to 8 different velocities (1.4-3.3 m/s). Both the individual (median R=0.935) and averaged across the subjects F-V relationships (R=0.994) proved to be approximately linear and exceptionally strong, while their parameters depicting the leg muscle capacities for producing maximum F, V, and power (P; proportional to the product of F and V) were highly reliable (0.84<ICC<0.97). In addition, the same F-V relationship parameters obtained from only the highest and lowest treadmill V (i.e., the 'two-velocity method') revealed a strong relationship (0.89<R<0.99), and there were no meaningful differences regarding the magnitudes of the same parameters obtained from all 8 V’s of the treadmill. We conclude that the F-V relationship of leg muscles tested through a wide range of treadmill V could be strong, linear, and reliable. Moreover, the relatively quick and fatigue-free two-velocity method could provide reliable and ecologically valid indices of F, V, and P producing capacities of leg muscles and, therefore, should be considered for future routine testing.