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García-Pinillos, F, Roche-Seruendo, LE, Marcen-Cinca, N, Marco-Contreras, LA, and Latorre-Román, PA. Absolute reliability and concurrent validity of the Stryd system for the assessment of running stride kinematics at different velocities. J Strength Cond Res XX(X): 000-000, 2018-This study aimed to determine the absolute reliability and to evaluate the concurrent validity of the Stryd system for measuring spatiotemporal variables during running at different velocities (8-20 km·h) by comparing data with another widely used device (the OptoGait system). Eighteen trained male endurance runners performed an incremental running test (8-20 km·h with 3-minute stages) on a treadmill. Spatiotemporal parameters (contact time [CT], flight time [FT], step length [SL], and step frequency [SF]) were measured using 2 different devices (Stryd and OptoGait systems). The Stryd system showed a coefficient of variation (CV) <3%, except for FT (3.7-11.6%). The OptoGait achieved CV <4%, except for FT (6.0-30.6%). Pearson correlation analysis showed large correlations for CT and FT, and almost perfect for SL and SF over the entire protocol. The intraclass correlation coefficients partially support those results. Paired t-tests showed that CT was underestimated (p < 0.05, effect size [ES] > 0.7; ∼4-8%), FT overestimated (p < 0.05, ES > 0.7; ∼7-65%), whereas SL and SF were very similar between systems (ES < 0.1, with differences <1%). The Stryd is a practical portable device that is reliable for measuring CT, FT, SL, and SF during running. It provides accurate SL and SF measures but underestimates CT (0.5-8%) and overestimates FT (3-67%) compared with a photocell-based system.
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ABSOLUTE RELIABILITY AND CONCURRENT VALIDITY OF
THE STRYD SYSTEM FOR THE ASSESSMENT OF RUNNING
STRIDE KINEMATICS AT DIFFERENT VELOCITIES
FELIPE GARCI
´A-PINILLOS,
1
LUIS E. ROCHE-SERUENDO,
2
NEOL MARCEN-CINCA,
2
LUIS A. MARCO-
CONTRERAS,
2
AND PEDRO A. LATORRE-ROMA
´N
3
1
Department of Physical Education, Sport and Recreation, Universidad de La Frontera, Temuco, Chile;
2
Universidad San
Jorge, Campus Universitario, Zaragoza, Spain; an
AU1 d
3
Universidad de Jae´n, Campus de Las Lagunillas, Jaen, SpainAU2
ABSTRACT
Garcı
´a-Pinillos, F, Roche-Seruendo, LE, Marcen-Cinca, N,
Marco-Contreras, LA, and Latorre-Roma
´n, PA. Absolute reliabil-
ity and concurrent validity of the Stryd system for the assess-
ment of running stride kinematics at different velocities. J
Strength Cond Res XX(X): 000–000, 2018—This study aimed
to determine the absolute reliability and to evaluate the concur-
rent validity of the Stryd system for measuring spatiotemporal
variables during running at different velocities (8–20 km$h
21
)by
comparing data with another widely used device (the OptoGait
system). Eighteen trained male endurance runners performed an
incremental running test (8–20 km$h
21
with 3-minute stages) on
a treadmill. Spatiotemporal parameters (contact time [CT], flight
time [FT], step length [SL], and step frequency [SF]) were mea-
sured using 2 different devices (Stryd and OptoGait systems).
The Stryd system showed a coefficient of variation (CV) ,3%,
except for FT (3.7–11.6%). The OptoGait achieved CV ,4%,
except for FT (6.0–30.6%). Pearson correlation analysis showed
large correlations for CT and FT, and almost perfect for SL and
SF over the entire protocol. The intraclass correlation coeffi-
cients partially support those results. Paired t-tests showed that
CT was underestimated (p,0.05, effect size [ES] .0.7; ;4–
8%), FT overestimated (p,0.05, ES .0.7; ;7–65%),
whereas SL and SF were very similar between systems (ES ,
0.1, with differences ,1%). The Stryd is a practical portable
device that is reliable for measuring CT, FT, SL, and SF during
running. It provides accurate SL and SF measures but under-
estimates CT (0.5–8%) and overestimates FT (3–67%) com-
pared with a photocell-based system.
KEY WORDS biomechanics, technology
AU3
INTRODUCTION
Interest in running gait analysis is appropriate in both
an injury prevention (11,17) and an athletic perfor-
mance context (1,3,13,18). Although previous meth-
ods of analysis have generally required well-equipped
research laboratories, recently, there has been a move to
produce low-cost, portable gait analysis equipment. This
has allowed researchers to remove participants from an arti-
ficial laboratory environment and measure participants in
a more natural environment (14).
In the current study, the authors compared Stryd data
with a widely used device for assessing spatiotemporal
variables during locomotion. The OptoGait system is
composed of photoelectric cells positioned along
transmitting-receiving bars of 1 m in length with a maxi-
mum distance of 6 m between bars. The transmitting-
receiving bars contain infrared light-emitting diodes
(LEDs), enabling communication between the 2 bars.
When a subject passes between the transmitting bar and
the receiving bar, the system automatically calculates
spatiotemporal parameters by sensing interruptions in
communication. The assessment results of this gait analysis
system have been previously validated in healthy adults
walking at a comfortable speed (9), and the system has
been used to examine spatiotemporal parameters of ath-
letes when running at different velocities and under differ-
ent conditions (12,16).
Stryd system (www.stryd.com) is a pioneer in
manufacturing wearable power meters for running. Power
meters have helped performance-focused cyclists revolu-
tionize their training and racing (15), and the same may
soon be accomplished for runners. This power meter for
runners is a foot pod that attaches to a running shoe to
measure 12 metrics to quantify performance: pace, dis-
tance, elevation, running power, form power, cadence,
ground contact time (CT), vertical oscillation, and leg stiff-
ness. This is a relativel AU4
y new tool, and yet, there are no data
to demonstrate validity and reliability of this device, mak-
ing this type of study beneficial.
Address correspondence to Felipe Garcı
´a-Pinillos, fegarpi@gmail.com.
00(00)/1–7
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The variety of available technologies for gait analysis
(e.g., accelerometers, gyroscopes, force plates, pressure
plates, and photoelectric cells) implies that a variety of
devices should exist for analyzing stride characteristics.
However, some of these devices have not yet been
validated. The validity and reliability of a gait analysis
system are essential to determine whether results are due
to changes in gait pattern or are simply systematic
measurement errors. Therefore, the aim of the current
study is to determine the absolute reliability (within-subject
variation) and to evaluate the concurrent validity of the
Stryd system for measuring spatiotemporal variables during
running at different velocities (usual for endurance runners
at training and competing, 8–20 km$h
21
) by comparing
data with a widely used device for this purpose (i.e., the
OptoGait system).
TABLE 1. Coefficient of variation (%) of the spatiotemporal parameters (CT, FT, SL, and SF) at different running
velocities (8–20 km$h
21
) from OptoGait system and from Stryd system.
Speed (km$h
21
)
Contact time (CT) Flight time (FT) Step length (SL) Step frequency (SF)
Stryd OptoGait Stryd OptoGait Stryd OptoGait Stryd OptoGait
8 1.46 3.01 11.60 30.58 1.32 3.78 1.31 3.13
9 1.38 2.91 9.38 24.17 1.38 3.61 1.33 3.30
10 1.53 2.90 7.35 18.62 1.22 3.39 1.19 3.14
11 1.43 2.79 5.78 14.01 1.13 3.28 1.11 3.06
12 1.37 2.59 5.21 11.44 1.24 3.04 1.19 2.77
13 1.22 2.56 4.27 9.05 1.09 2.74 1.05 2.79
14 1.27 2.48 4.18 8.26 1.14 2.63 1.13 2.52
15 1.34 2.41 4.29 7.05 1.33 2.24 1.26 2.35
16 1.91 2.53 4.59 6.46 1.20 1.98 1.17 2.33
17 1.56 2.38 3.73 6.38 1.32 2.02 1.29 2.30
18 1.98 2.33 5.11 6.37 1.86 2.08 1.69 2.15
19 2.23 2.45 5.39 6.41 2.02 2.24 1.87 2.27
20 2.32 2.48 7.56 6.01 2.08 2.66 2.01 3.54
TABLE 2. SEM of the spatiotemporal parameters (CT, FT, SL, and SF) at different running velocities (8–20 km$h
21
)
from OptoGait system and from Stryd system.
Speed (km$h
21
)
Contact time (CT) Flight time (FT) Step length (SL) Step frequency (SF)
Stryd OptoGait Stryd OptoGait Stryd OptoGait Stryd OptoGait
8 0.005 0.005 0.008 0.009 1.345 1.259 2.483 2.269
9 0.004 0.005 0.007 0.009 1.228 1.179 2.138 2.068
10 0.003 0.004 0.005 0.007 1.071 1.032 1.746 1.777
11 0.003 0.003 0.005 0.007 1.479 1.539 2.213 2.234
12 0.003 0.003 0.005 0.007 1.572 1.539 2.227 2.229
13 0.003 0.002 0.004 0.005 1.583 1.497 2.108 2.103
14 0.003 0.002 0.004 0.005 1.704 1.757 2.198 2.179
15 0.002 0.002 0.003 0.004 1.794 1.730 2.207 2.164
16 0.002 0.002 0.003 0.004 1.930 1.881 2.318 2.355
17 0.002 0.002 0.003 0.004 2.146 2.151 2.507 2.529
18 0.001 0.002 0.004 0.004 2.412 2.484 2.771 2.787
19 0.001 0.002 0.003 0.003 2.252 2.278 2.535 2.591
20 0.001 0.003 0.003 0.003 2.013 2.079 2.211 2.406
Stryd System and Running Stride Kinematics
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METHODS
Experimental Approach to the Problem
With the introduction of new wireless devices, establishment
of their reliability and validity are essential before practical
use. In this study, the Stryd system was compared with the
OptoGait system for measuring spatiotemporal variables
during running at different velocities (8–20 km$h
21
).
Subject
AU5 s
A grou
AU6 p of 18 recreationally trained male endurance runners
(age range: 19–46 years; age: 34 67 years; height: 1.76 6
0.05 m; body mass: 70.5 66.2 k
AU7 g) voluntarily participated
in this study. All participants met the inclusion criteria: (a)
older than 18 years, (b) able to run 10 km in less than 40 mi-
nutes, (c) training on a treadmill at least once per week, and
(d) not suffering from any injury (points 3 and 4 related to
the last 6 months before the data collection). After receiving
detailed information on the objectives and procedures of the
study, each subject signed an informed consent form to par-
ticipate, which complied with the ethical standards of the
World Medical Association’s Declaration of Helsinki (2013).
It was made clear that the participants were free to leave the
study if they saw fit. The study was approved by the Ethics
Committee of the San Jorge University (Zaragoza, Spain)
AU8 .
Procedures
The study was conducted in June 2017. At the time of these
observations, the subjects had completed between 6 and 7
months of training. Subjects were individually tested on one
day (between 16:00 and 21:00 hours). Before all testing,
subjects refrained from severe physical activity for at least 48
hours and all testing was at least 3 hours after eating. Tests
were performed with the subjects’ usual training shoes to
measure their typical performance.
Subjects performed an incremental running test on
a motorized treadmill (HP cosmos Pulsar 4P; HP cosmos
Sports & Medical, Gmbh, Nußdorf, Germany). The initial
speed was set at 8 km$h
21
and speed increased by 1 km$h
21
every 3 minutes until running speed reached 20 km$h
21
.
The slope was maintained at 1% (0.98). The treadmill pro-
tocol was preceded by a standardized 10-minute accommo-
dation programme (5 minutes walking at 5 km$h
21
, and
5 minutes running at 10 km$h
21
). Athletes were experienced
in running on a treadmill.
Materials and Testing. (a) Anthropometry: For descriptive
purposes, height (cm) and body mass (kg) were measured.
(b) Biomechanics: Spatiotemporal parameters were mea-
sured using 2 different devices:
TABLE 3. Pearson correlation between kinematics variables from Stryd vs. Optogait over an incremental running test
(8–20 km$h
21
).
Speed
(km$h
21
) 8 9 1011121314151617181920
Contact time 0.657* 0.636* 0.5740.5250.433 0.435 0.5070.5040.5030.453 0.415 0.429 0.078
Flight time 0.602* 0.656* 0.685* 0.703* 0.722* 0.739z0.722z0.782z0.811z0.800z0.775z0.6800.834
Step length 0.934z0.999z0.999z0.999z0.999z0.998z0.997z0.998z0.999z0.999z0.999z0.997z0.991z
Step frequency 0.959z0.996z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z
*p,0.01.
p,0.05.
zp,0.001
AU12 .
TABLE 4. Intraclass correlation coefficients between kinematics variables from Stryd vs. Optogait over an incremental
running test (8–20 km$h
21
).
Speed (km$h
21
)8 9 1011121314151617181920
Contact time 0.457 0.463 0.416 0.386 0.303 0.330 0.407 0.400 0.380 0.329 0.294 0.381 0.063
Flight time 0.555 0.599 0.655 0.679 0.702 0.726 0.758 0.768 0.799 0.778 0.744 0.635 0.806
Step length 0.934 0.998 0.999 0.999 0.999 0.998 0.997 0.998 0.999 0.999 0.999 0.997 0.991
Step frequency 0.956 0.995 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.997 0.983
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The OptoGait system (Optogait; Microgate, Bolzano,
Italy) was previously validated for the assessment of
spatiotemporal parameters of the gait of young adults
(9). As indicated by Lee et al. (9), the OptoGait
achieved a high level of correlation with all spatiotem-
poral parameters by intraclass correlation coefficients
(ICCs) (0.785–0.952), coefficients of variation (1.66–
4.06%), SEM (2.17–5.96%), and minimum detectable
change (6.01–16.52%). The system detects any inter-
ruptions and therefore measures both CT and flight
time (FT) with a precision of 1/1,000 seconds. The 2
parallel bars of the device system were placed on the
side edges of the treadmill at the same level as the
contact surface. Contact time, FT, step length (SL),
and step frequency (SF or cadence) were measured
for every step during the treadmill test and were
defined as follows:
(a) CT (second): time from when the foot contacts the
ground to when the toes lift off the ground.
(b) FT (second): time from toe-off to initial ground
contact of consecutive footfalls (e.g., right-left).
(c) SL (meter): length the treadmill belt moves from
toe-off to initial ground contact in successive steps.
(d) SF or cadence (steps per minutes): number of
ground contact events per minute.
Stryd (Stryd Powermeter; Stryd, Inc., Boulder, CO,
USA): a relatively new device, which estimates power
in watts. Stryd is carbon fiber–reinforced foot pod
(attached to your shoe) that weights 9.1 g. Based on a 6-
axis inertial motion sensor (3-axis gyroscope and 3-axis
accelerometer), this device provides spatiotemporal
data including CT and SF. From CTand SF, in addition
to running velocity, the authors calculated FT and SL as
follows:
FT ðsÞ¼step time ðsÞ2CT ðsÞ;(1)
where step time is the time from the beginning of the step
cycle (take-off ) to the end (previous frame to take-off ).
step time ðsÞ¼60=SF ðsteps=minÞ:
SL ðmÞ¼running velocitym$min21.SFðsteps=minÞ:
(2)
Statistical Analyses
Descriptive statistics are represented as mean (SD). Tests of
normal distribution and homogeneity (Shapiro-Wilk and Lev-
ene’s test, respectively) were conducted on all data before anal-
ysis. Coefficient of variation (CV, %) and SEM were calculated
as a measure of absolute reliability (within-subject variation and
SD of a sampling distribution, respectively) (2,6). Intraclass cor-
relation coefficients were calculated between OptoGait and
Stryd data for each spatiotemporal variable analyzed (CT,
FT, SL, and SF). Values less than 0.5 are indicative of poor
reliability, values between 0.5 and 0.75 indicate moderate reli-
ability, values between 0.75 and 0.9 indicate good reliability,
and values greater than 0.90 indicate excellent reliability (8).
To determine concurrent validity, a Pearson correlation analysis
was also performed between OptoGait and Stryd data. The
following criteria were adopted to interpret the magnitude of
correlations between measure-
ment variables: ,0.1 (trivial),
0.1–0.3 (small), 0.3–0.5 (moder-
ate), 0.5–0.7 (large), 0.7–0.9 (very
large), and 0.9–1.0 (almost per-
fect) (7). Pairwise comparisons
of mean (t-test) were also con-
ducted between data (CT, FT,
SL, and SF) from the 2 devices
(OptoGait and Stryd) at different
running speeds (8–20 km$h
21
).
In addition, the magnitude of the
differences between values was
also interpreted using the
Cohen’s deffect size (ES) (19).
Effect sizes of less than 0.4 rep-
resented a small magnitude of
change, whereas 0.41–0.7 and
greater than 0.7 represented
moderate and large magnitudes
of change, respectively (19). The
level of significance used was
p,0.05. Data analysis was
Figure 1. Contact time (s) during running measured by Stryd and OptoGait systems. *p,0.05, **p,0.01, ***p
,0.01.
Stryd System and Running Stride Kinematics
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performed using SPSS (version 21; SPSS, Inc., Chicago, IL,
USA).
RESULTS
Reliability
T1 Table 1 shows the CV (as a measure of absolute reliability)
of spatiotemporal parameters at different running velocities
from both Stryd and OptoGait. For the Stryd system, CV
ranged between 1.2–2.3% (CT), 3.7–11.6% (FT), 1.1–2.1%
(SL), and 1.1–2.0% (SF), whereas for the OptoGait system,
CV was 2.3–3.0% (CT), 6.0–
30.6% (FT), 2.0–3.8% (SL),
and2.23.6%(SF).Inaddition,
the SEM is provided in
T2Tab l e 2.
Validity
The Pearson correlation analy-
sis is shown in T3Table 3 (CT,
FT, SL, and SF or cadence at
8–20 km$h
21
running veloci-
ties). Contact time from both
devices showed large correla-
tions (0.5–0.7, p,0.05) at
low speeds (8–11 km$h
21
)
and race speeds (14–16
km$h
21
). Flight time from Op-
toGait and Stryd showed large
and very large correlations,
respectively (0.602 ,r.
0.834, p,0.05), over the veloc-
ities tested (8–20 km$h
21
). Step
length and SF from both devices were nearly perfectly corre-
lated (r.0.9, p,0.001) at every running velocity tested.
The ICCs between kinematic variables from both Stryd
vs. OptoGait systems over the entire protocol (8–20
km$h
21
)areincludedin T4Table4.Contacttimeshowed
a low coefficient (,0.5), FT a moderate coefficient
(0.5–0.75), whereas SL and SF showed excellent coeffi-
cients (.0.9).
A paired t-test demonstrated some significant differences
(p,0.05) and large ES (.0.7) in the variables analyzed (CT,
FT, SL, and cadence) (Figures 1–4, respectively). Contact
time ( F1Figure 1) was underesti-
mated for Stryd compared
with OptoGait data (8–18
km$h
21
,p,0.001, and ES .
0.7; ;6–8%). Differences were
smaller at 19 km$h
21
(p,0.05
and ES .0.7; ;4%), and no
differences were observed at 20
km$h
21
(p$0.05 and ES ,
0.1; ;0.5%).
Flight time ( F2Figure 2) was
overestimated for Stryd based
on OptoGait data at running
velocities between 8 and 19
km$h
21
(p,0.05, ES .0.7;
from ;65% at 8 km$h
21
to
;7% at 19 km$h
21
). No signif-
icant differences were found at
20 km$h
21
(p$0.05 and ES =
0.57; ;3%).
Step length from both devi-
ces is shown in F3Figure 3.
Figure 3. Step length (cm) during Running measured by Stryd and OptoGait systems. *p,0.05, **p,0.01,
***p,0.01.
Figure 2. Flight time (s) during running masured by Stryd and OptoGait systems. *p,0.05, **p,0.01, ***p,
0.01.
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pvalues show significant differences (p,0.05) between data
from Stryd and OptoGait at most analyzed velocities,
although Cohen’s dshowed a very small magnitude of
changes (ES ,0.1), with Stryd data overestimated com-
pared with OptoGait data (,1%). Likewise, significant dif-
ferences (p,0.05) were found in cadence between the 2
devices (
F4 Figure 4), but Cohen’s dreported a very small
change (ES ,0.1) with differences smaller than 1%.
DISCUSSION
This study aimed to determine the absolute reliability and to
evaluate the concurrent validity of the Stryd system for
measuring spatiotemporal variables during running at differ-
ent velocities (8–20 km$h
21
) by comparing data with
a device widely used for this purpose (OptoGait system).
The major findings of this study were (a) CV, as a measure
of reliability, was lower in all analyzed variables for the Stryd
system than for the OptoGait system (,5% in all cases,
except for FT), whereas SEM was almost identical for every
variable over the entire protocol (8–20 km$h
21
), and (b)
concurrent validity of the Stryd and OptoGait systems
regarding spatiotemporal variables is not yet settled: moder-
ate for CT, low for FT, and very high for SL and SF. Results
from Pearson correlation analysis indicated a strong concur-
rent validity over the entire range of running velocities (8–20
km$h
21
), with large correlations in CT, very large correla-
tions in FT, and almost perfect correlations in SL and SF.
The ICCs partially provide support to those results with
excellent coefficients for SL and SF and moderate for FT,
but poor coefficients for CT (over the entire protocol). In
addition, the paired t-test let us improve our comparison and
some interesting findings are worth noting: (a) The Stryd
system underestimated CT (up to ;8% at low velocities)
and overestimated FT (up to
;65% at low velocities) com-
pared with the OptoGait sys-
tem, with reduced differences
at high running velocities, and
(b) despite differences in pval-
ues, the very small magnitude
of changes reported suggests
that SL and SF (from the Stryd
system) are valid variables over
running velocities of 8–20
km$h
21
, compared with the
OptoGait system.
As mentioned earlier, scien-
tists have discovered the
potential of accelerometers
(and inertial measurement
units [IMUs]) in assessing gait
analysis without the restric-
tions of laboratory technology.
Having the chance to measure
athletes or clients in a natural
environment and using less expensive and more time-
efficient equipment is a huge step forward for coaches and
clinicians. Nevertheless, this advantage would be worthless if
the data were not valid. The Stryd system (based on a 6-axis
inertial motion sensor: 3-axis gyroscope and 3-axis acceler-
ometer) is mainly a running power meter, but it also provides
spatiotemporal variables that are used by coaches and
clinicians (information easily accessible to users) as a feed-
back, necessitating confirmation of the validity of these data.
Comparing between devices and technologies (i.e.,
photoelectric cells vs. IMUs), the authors hypothesize that
differences in temporal variables might be at least partially
explained by the height of the OptoGait system’s LEDs. As
described by Lienhard et al. (10), the LEDs of the OptoGait
system are positioned 3 mm above ground, and thereby,
sensing of heel contact occurs earlier, whereas sensing of
toe lift-off occurs later in the gait cycle (timing differences).
In a similar previously published study (4), the authors
assessed the reliability and validity of an accelerometer-
based system (Myotest) against a photocell-based system
(OptoJump) for measuring running stride kinematics. In
line with our data, the authors reported CT 34% shorter
and FT 64% longer than the photocell-based system. That
work (4) also found a good validity in SF. Therefore, the
data obtained in the current study agree with those re-
ported by previous studies that compared accelerometer-
based systems to photocell-based systems, and our results
support the explanation for this discrepancy given by
Lienhard et al. (10).
Some final limitations need to be taken into consideration.
First, the use of photocell-based systems as the gold standard
reference for establishing concurrent validity should be
evaluated, instead of instruments that measure ground
Figure 4. Step frequency (cadence, step per minute) during running measured by Stryd and OptoGait systems.
*p,0.05, **p,0.01, ***p,0.01.
Stryd System and Running Stride Kinematics
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reaction force, such as a force platform. Because we do not
possess such equipment in our laboratory, the use of the
OptoGait system was considered to be an adequate proxy
system, given its demonstrated good validity compared with
GAITRite system—pressure platform (9) or compared with
force platform during jumping tests (5). Furthermore, the
OptoGait system is more practical and portable for record-
ing several consecutive steps than force or pressure platforms
imbedded into the ground in series where participants often
have to adjust SL and target platforms to obtain clearly
defined foot contact data. A second consideration is that
validation data were obtained from an analysis based on
within-subject variation (CV) rather than on different days.
Although the number of steps analyzed in 3-minutes of run-
ning at these velocities is high (400–500 steps in 3 minutes),
our current reliability statistics might not generalize to runs
performed several days apart.
To s u m u
AU9 p, based on traditional thresholds, the absolute
(i.e., CV) reliability of CT, FT, SL, and SF derived using the
Stryd device were classified as adequate for running assess-
ments, and this suggests that the Stryd is useful for monitoring
individuals and quantifying changes in functional performance
over time. However, the concurrent validity of Stryd as com-
pared to OptoGait was low-moderate for CT and FT, and
excellent for SL and SF. The paired comparisons added to
those correlations showed that the Stryd system underesti-
mated CT (0.5–8%) and overestimated FT (3–67%) compared
with OptoGait system, with reduced differences at elevated
running velocities (8–20 km$h
21
). However, SL and SF were
valid variables (,1%) over the entire range of running veloc-
ities, as compared with the OptoGait system.
PRACTICAL APPLICATIONS
From a practical point of view and considering that both
systems are widely used, scientists and clinicians should
know that both devices showed an adequate reliability for
running assessments, and thereby, spatiotemporal parame-
ters reported from these devices can be compared over time
(if using the same device). However, the clients also should
be aware about the limitations of comparing data reported
from these 2 devices.
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Journal of Strength and Conditioning Research
the
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|
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VOLUME 00 | NUMBER 00 | MONTH 2018 | 7
Copyright ª2018 National Strength and Conditioning Association
... 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]. ...
... Knowledge of the reliability and validity of these IMU devices is of paramount importance to collect and interpret data accurately. Some researchers have analyzed the Stryd's reliability and validity during running [2,5,[8][9][10][11]. However, the reliability and validity have been less investigated during walking [12,13], and never during walking on positive slopes using different backpack loads. ...
... Validity and reliability were determined for cadence and GCT, power was assessed for reliability only. Two different Stryd devices were simultaneously used for reliability testing, while validity testing was performed against a gold standard device (Optojump) [5,16]. ...
Article
Background: The Styrd Power Meter is gaining special interest for on-field gait analyses due to its low-cost and general availability. However, the reliability and validity of the Stryd during walking on positive slopes using different backpack loads have never been investigated. Research Question: Is the Stryd Power Meter reliable and valid to quantify gait mechanics during walking on positive inclines and during level walking incorporating load carriage? Methods: Seventeen participants from a police force rescue team performed 8 submaximal walking trials for 5-min at 3.6 km•h-1 during different positive slope (1, 10 and 20%) and backpack load (0, 10, 20, 30 and 40% of body mass) conditions. Two Stryd devices were utilized for reliability analyses. Validity of cadence and ground contact time (GCT) were analyzed against a gold standard device (Optojump). Results: The Stryd demonstrated acceptable reliability [mean bias: <2.5%; effect size (ES): <0.25; standard error of the mean: <1.7%; r: >0.76] for power, cadence, and GCT. Validity measures (mean bias: <0.8%; ES: <0.07; r: >0.96; Lin’s Concordance Coefficient: 0.96; Mean Absolute Percent Error: <1%) for cadence were also found to be acceptable. The Stryd overestimated (P < 0.001; ES: >5.1) GCT in all the walking conditions. A significant systematic positive bias (P < 0.022; r = 0.56 to 0.76) was found in 7 conditions. Significance: The Stryd Power Meter appears to produce reliable measurements for power output, cadence and GCT. The Stryd produced valid measurements for cadence during walking on positive slopes and during level walking with a loaded backpack. However, the Stryd is not valid for measuring GCT during these walking conditions. This study adds novel data regarding the reliability and validity of this device and might be of particular interest for scientists, practitioners, and first responders seeking reliable devices to quantify gait mechanics during walking.
... While several studies have already analysed power output in running [18,19] and others have investigated the relation between VO2max and power production [16,20], to the best of the authors' knowledge, there are no studies assessing the difference in power output between shod and barefoot running. In order to bridge this gap, this study aims to identify the effect of footwear on power output in endurance runners. ...
... Additionally, the running spatiotemporal parameters of contact time (time the foot spends in contact with the ground (CT)), flight time (time from toes-off to initial contact of the same foot (FT)), step length (distance covered between initial contact of one foot and the initial contact of the other foot (SL)) and step frequency (number of ground contacts that occurred in a minute (SF)) were also measured utilising the Stryd™ system, which has been previously validated for such purposes [18]. ...
Article
Full-text available
Several studies have already analysed power output in running or the relation between VO2max and power production as factors related to running economy; however, there are no studies assessing the difference in power output between shod and barefoot running. This study aims to identify the effect of footwear on the power output endurance runner. Forty-one endurance runners (16 female) were evaluated at shod and barefoot running over a one-session running protocol at their preferred comfortable velocity (11.71 ± 1.07 km·h−1). The mean power output (MPO) and normalized MPO (MPOnorm), form power, vertical oscillation, leg stiffness, running effectiveness and spatiotemporal parameters were obtained using the Stryd™ foot pod system. Additionally, footstrike patterns were measured using high-speed video at 240 Hz. No differences were noted in MPO (p = 0.582) and MPOnorm (p = 0.568), whereas significant differences were found in form power, in both absolute (p = 0.001) and relative values (p < 0.001), running effectiveness (p = 0.006), stiffness (p = 0.002) and vertical oscillation (p < 0.001). By running barefoot, lower values for contact time (p < 0.001) and step length (p = 0.003) were obtained with greater step frequency (p < 0.001), compared to shod running. The prevalence of footstrike pattern significantly differs between conditions, with 19.5% of runners showing a rearfoot strike, whereas no runners showed a rearfoot strike during barefoot running. Running barefoot showed greater running effectiveness in comparison with shod running, and was consistent with lower values in form power and lower vertical oscillation. From a practical perspective, the long-term effect of barefoot running drills might lead to increased running efficiency and leg stiffness in endurance runners, affecting running economy.
... As RunScribe outcomes have generally shown good validity in previous studies, we hypothesised that the majority of the outcomes will also show acceptable intra-session and intersession reliability. Given the fact that previous studies have mostly reported similar validity and reliability values for wearable sensors across different running speeds (DeJong & Hertel, 2020;García-Pinillos et al., 2021;Gindre et al., 2016), we hypothesised that the reliability will not be affected by speed in our study. We also hypothesised that the surface will affect reliability, considering that the surface can affect means and between-participant variability scores . ...
... In contrast, a similar wearable sensor (Stryd TM ) was reported to provide very reliable intra-session estimates of contact time (CV < 2.3 %) during treadmill running at velocities from 8 to 20 km/h (2.22-5.56 m/ s; García-Pinillos et al., 2021). Another study reported good reliability (ICC = 0.88-0.99; ...
Article
The aim of this study was to investigate the reliability of running biomechanics assessment with a wearable commercial sensor (RunScribeTM). Participants performed multiple 200-m runs over sand, grass and asphalt ground at the estimated 5-km tempo, with an additional trial with 21-km tempo at the asphalt. Intra-session reliability was excellent for all variables at 5-km pace (intra-class coefficient correlation (ICC) asphalt: 0.90–0.99; macadam: 0.94–1.00; grass: 0.92–1.00), except for shock (good; ICC = 0.83), and contact time and total power output (moderate; ICC = 0.68–0.71). Coefficient of variation (CV) were mostly acceptable in all conditions, except for horizontal ground reaction force (GRF) rate in asphalt 5-km pace trial (CV = 24.5 %), power (CV = 14.3 %) and foot strike type (CV = 30.9 %) in 21-km pace trial, and horizontal GRF rate grass trial (CV = 15.7 %). Inter-session reliability was high or excellent for the majority of the outcomes (ICC≥0.85). Total power output (ICC = 0.56–0.65) and shock (ICC = 0.67–0.75) showed only moderate reliability across all conditions. Power (CV = 12.5–13.8 %), foot strike type (CV = 14.9–29.4 %) and horizontal ground reaction force rate (CV = 12.4–36.4 %) showed unacceptable CV.
... The time component tstance is in itself relevant since a runner can only produce forces to propel the BCoM during the stance phase. For running speeds between 8 to 20 km/h, tstance has a strong negative curvilinear relation with speed with values typically ranging between 0.34 to 0.18 seconds 86,95,96,100,179,182,[188][189][190][191][192][193][194][195] (Figure 6.3). Due to the reduction in tstance with increasing speed, the GRF curves are compressed along the time axis, with increased force amplitudes to attain a sufficient impulse to maintain speed (I = ∫ (F • ∆t)) 95,96,169,179,193 . ...
... The manner in which tflight increases with running speed is described by a positive curvilinear relationship (Figure 6.3) 188,189,191,195 . Typically, tflight ranges between ~100 to 150 ms. ...
Thesis
Full-text available
The large-scale usage of smartphone applications and sports watches in running provides the potential to lower injury risk and improve performance. To achieve these common goals, contextual factors need to be taken into account to provide users with accurate and personal feedback. This thesis aims to develop methods to improve the quality of wearable feedback and its interpretation. Within the data process from parameter detection to feedback to the user, the are several ways to improve the quality of the feedback. The studies in this thesis demonstrate various possibilities. The thesis projects concern an improved algorithm for cycle detection; a method to cross-validate speed; an approach to determine an energetic optimal running technique; highlight the importance of individual differences; and a concise, yet comprehensive description of the full spectrum of running styles. It is concluded that to further improve the quality of wearable feedback, cross-validation, self-optimization, biomechanical dependencies, and individual differences should be considered as demonstrated in the thesis.
... Mean power output (normalized by body mass), step frequency, form power and running effectiveness were calculated using the Stryd ™ power meter (Stryd Power meter, Stryd Inc. Boulder CO, USA) attached on the upper part of the running shoes. This sensor provides accurate kinematic [31,32] and consistent power output metrics [15]. Data from Stryd ™ power meter were obtained into the fit file via the manufacturer's website (https:// www. ...
Article
Full-text available
Background The advent of power meters for running has raised the interest of athletes and coaches in new ways of assessing changes in running performance. The aim of this study is to determine the changes in power-related variables during and after a strenuous endurance running time trial. Methods Twenty-one healthy male endurance runners, with a personal record of 37.2 ± 1.2 min in a 10-km race, completed a 1-h run on a motorized treadmill trying to cover as much distance as they could. Before and after the time trial the athletes were asked to perform a 3-min run at 12 km h ⁻¹ . Normalized mean power output, step frequency, form power and running effectiveness were calculated using the Stryd™ power meter. Heart rate (HR) and rating of perceived exertion (RPE) were monitored, and data averaged every 5 min. Results Despite high levels of exhaustion were reached during the time trial (HRpeak = 176.5 ± 9.8 bpm; RPE = 19.2 ± 0.8), the repeated measures ANOVA resulted in no significant differences ( p ≥ 0.05), between each pair of periods for any of the power-related variables. The pairwise comparison ( T test) between the non-fatigued and fatigued constant 3-min runs showed an increase in step frequency ( p = 0.012) and a decrease in form power ( p < 0.001) under fatigue conditions, with no meaningful changes in normalized mean power output and running effectiveness. Conclusions Trained athletes are able to maintain power output and running effectiveness during a high demanding extended run. However, they preferred to reduce the intensity of vertical impacts under fatigue conditions by increasing their step frequency.
... Aunque la última actualización del dispositivo estima la fuerza y la resistencia que ofrece viento expresada en potencia (https://blog.stryd.com/tag/validation-white-papers/). Este sensor inercial ha demostrado validez y fiabilidad al comparar sus análisis de parámetros espacio-temporales con sistemas validos cómo OptoGait (García-Pinillos et al., 2018). Además, Aubry (2018) analizó la relación entre consumo de oxígeno (V02) y PW de Stryd en corredores de elite y recreativos, encontrando relación significativa, pero débil entre estas variables ( r = 0,29, p = 0,02). ...
... This implies that CP can be derived from the estimated internal work, whose predictive parameters can be easily obtained in a valid and reliable way from inertial sensors (ie, ground contact time, stride frequency, and duty factor) and, specifically, from the Stryd device. 21 Since its market launch, knowing which type of power Stryd reports has been of great interest among the running community. Cerezuela-Espejo et al 7,22 determined the relationship between the power output reported from 5 commercial power meters and 2 theoretical power models varying in speed, weight, and slope. ...
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.
... In the last decade, foot-worn sensors to assess and meaningfully analyze running metrics (e.g., step frequency, stride length, ground contact time) have gained increased attention and popularity [1][2][3][4][5]. These sensors are meant to improve laboratory and in-field testing and training by delivering key performance data. ...
Article
Full-text available
Running power as measured by foot-worn sensors is considered to be associated with the metabolic cost of running. In this study, we show that running economy needs to be taken into account when deriving metabolic cost from accelerometer data. We administered an experiment in which 32 experienced participants (age = 28 ± 7 years, weekly running distance = 51 ± 24 km) ran at a constant speed with modified spatiotemporal gait characteristics (stride length, ground contact time, use of arms). We recorded both their metabolic costs of transportation, as well as running power, as measured by a Stryd sensor. Purposely varying the running style impacts the running economy and leads to significant differences in the metabolic cost of running (p < 0.01). At the same time, the expected rise in running power does not follow this change, and there is a significant difference in the relation between metabolic cost and power (p < 0.001). These results stand in contrast to the previously reported link between metabolic and mechanical running characteristics estimated by foot-worn sensors. This casts doubt on the feasibility of measuring running power in the field, as well as using it as a training signal.
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El deporte ecuestre sigue creciendo en popularidad, pero las investigaciones anteriores se refieren, por un lado, a las barreras relacionadas con las lesiones asociadas a este deporte, y por otro, a los beneficios de la equitación terapéutica para personas con discapacidad (MacKinnon & Laliberte, 1995). No existen muchos estudios acerca los beneficios para la salud y las barreras para la participación de las personas que no padecen estos trastornos (Koca, 2016). El propósito de analizar las relaciones causa-efecto de las mujeres que practican equitación, es comprender los niveles en los que la salud se ve afectada, abarcando el cuerpo y la mente de las participantes. 2540 mujeres jinetes (1827 amateurs y 713 profesionales) completaron el EBBS (Exercise Benefits/Barriers Scale) (Sechrist et al., 1987) el cual consta de 43 ítems, 29 ítems del constructo de beneficios y 14 ítems bajo el constructo barreras. Los beneficios para la salud con mayor puntuación media son: mejora de vida, rendimiento físico, interacción social y salud preventiva. En lo referido a las barreras: gasto de tiempo, esfuerzo físico, entorno de ejercicio y desánimo familiar. Existen fuertes correlaciones positivas entre casi todos los beneficios estando menos conectados rendimiento físico e interacción social. A diferencia de los beneficios, las barreras tienen muy poca correlación entre sí, y son casi independientes unas de otras. Entre las barreras y los beneficios hay correlaciones muy pequeñas o nulas. Respecto a la diferencia entre jinetes profesionales y amateurs, en los beneficios los profesionales obtienen mayores puntuaciones en esfuerzo físico y los amateurs en salud preventiva. En lo que respecta a las barreras, las jinetes profesionales puntúan más alto en gasto de tiempo y las amateurs en entorno de ejercicio y desánimo familiar. Una conclusión importante es que las participantes obtuvieron impacto positivo en el constructo beneficios para la salud.
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Recreational running, or jogging has increased in recent years, becoming a global phenomenon (Scheerder et al., 2015). However, due to busy schedules and personal reasons, some of the participants tend to practice physical activity in the evening (Saleh et al., 2019). Despite amateur runners’ reason to participate has been widely analyzed, night runners’ motivational aspects remain unclear. Therefore, the aim of this research was to analyze night amateur athletes’ motivation to run. Data from 233 participants (52.8% male and 47.2% female) was collected, and to assess night runners’ motivations, the Polish translation of the motivations of marathoners’ scales (MOMS) created by Masters and Ogles was used (Dybała, 2013). Descriptive and relational statistics were carried out and results showed that five out of nine dimensions of MONS scale showed statistical differences gender wise (p < 0.001). Female scored higher in motivations related to weight concern, while male scored significantly higher in personal goal achievement, competition, recognition, and affiliation reasons to participate. Partially in concordance with previous research, we conclude that male night runners’ motivation is related to performance aspects (personal goal achievement and competition), and social aspects (recognition and affiliation), while female night runners show a higher motivation to run related to weight concern than male athletes. In conclusion, these results should be taken into account when promoting active lifestyles and night running participation.
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In this study, we analyzed the relationship between running economy (RE) and biomechanical parameters in a group running at the same relative intensity and same absolute velocity. Sixteen homogeneous male long-distance runners performed a test to determine RE at 4.4 m.s⁻¹, corresponding to 11.1% below velocity at the ventilatory threshold. We found significant correlations between RE and biomechanical variables (vertical oscillation of the center of mass, stride frequency, stride length, balance time, relative stride length, range of elbow motion, internal knee, ankle angles at foot strike, and electromyographic activity of the semitendinosus and rectus femoris muscles). In conclusion, changes in running technique can influence RE and lead to improved running performance. © 2012 by the American Alliance for Health, Physical Education, Recreation and Dance.
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Mobile power meters provide a valid means of measuring cyclists’ power output in the field. These field measurements can be performed with very good accuracy and reliability making the power meter a useful tool for monitoring and evaluating training and race demands. This review presents power meter data from a Grand Tour cyclist’s training and racing and explores the inherent complications created by its stochastic nature. Simple summary methods cannot reflect a session’s variable distribution of power output or indicate its likely metabolic stress. Binning power output data, into training zones for example, provides information on the detail but not the length of efforts within a session. An alternative approach is to track changes in cyclists’ modelled training and racing performances. Both critical power and record power profiles have been used for monitoring training-induced changes in this manner. Due to the inadequacy of current methods, the review highlights the need for new methods to be established which quantify the effects of training loads and models their implications for performance.
Article
Objective To evaluate the role of bodyweight-supported treadmill training (BWSTT) for chronic stroke survivors. Design Prospective, randomized controlled study. Methods Patients with a first episode of supratentorial arterial stroke of more than 3 months’ duration were randomly allocated to 3 groups: overground gait training, treadmill training without bodyweight support, and BWSTT (20 sessions, 30 min/day, 5 days/week for 4 weeks). The primary outcome was overground walking speed and endurance and secondary outcome was improvement by the Scandinavian Stroke Scale (SSS) and locomotion by the Functional Ambulation Category (FAC). We analyzed data within groups (pre-training vs post-training and pre-training vs 3-month follow-up) and between groups (at post-training and 3-month follow-up). Results We included 45 patients (36 males, mean post-stroke duration 16.51 ± 15.14 months); 40 (89.9%) completed training and 34 (75.5%) were followed up at 3 months. All primary and secondary outcome measures showed significant improvement (P < 0.05) in the 3 groups at the end of training, which was sustained at 3-month follow-up (other than walking endurance in group I). Outcomes were better with BWSTT but not significantly (P > 0.05). Conclusion BWSTT offers improvement in gait but has no significant advantage over conventional gait-training strategies for chronic stroke survivors.
Article
Objective: Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis. Discussion for researchers: There are 10 forms of ICCs. Because each form involves distinct assumptions in their calculation and will lead to different interpretations, researchers should explicitly specify the ICC form they used in their calculation. A thorough review of the research design is needed in selecting the appropriate form of ICC to evaluate reliability. The best practice of reporting ICC should include software information, "model," "type," and "definition" selections. Discussion for readers: When coming across an article that includes ICC, readers should first check whether information about the ICC form has been reported and if an appropriate ICC form was used. Based on the 95% confident interval of the ICC estimate, values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 are indicative of poor, moderate, good, and excellent reliability, respectively. Conclusion: This article provides a practical guideline for clinical researchers to choose the correct form of ICC and suggests the best practice of reporting ICC parameters in scientific publications. This article also gives readers an appreciation for what to look for when coming across ICC while reading an article.
Article
Purpose: High school cross country runners have a high incidence of injury, particularly at the shin and knee. An increased step rate during running has been shown to reduce impact forces and loading of the lower extremity joints. The purpose of this prospective study was to examine step rate as a risk factor for injury occurrence. Materials/methods: Running step rates of 68 healthy high school cross country runners (47 females; 21 males; mean age 16.2±1.3 yrs) were assessed at a fixed speed (3.3±0.0 m/s) and self-selected speed (mean 3.8±0.5 m/s). Runners were prospectively followed during the interscholastic season to determine athletic exposures, occurrences of shin injury and anterior knee pain, and days lost to injury. Results: During the season, 19.1% of runners experienced a shin injury and 4.4% experienced anterior knee pain. Most injuries (63.6%) were classified as minor (1-7 days lost). At the fixed speed, runners in the lowest tertile of step rate (≤164 steps/min) were more likely (OR=6.67; 95% CI, 1.2-36.7; p=0.03) to experience a shin injury compared to runners in the highest tertile (≥174 steps/min). Similarly, for self-selected speed, runners in the lowest tertile (≤166 steps/min) (OR=5.85; 95% CI, 1.1-32.1; p<0.04) were more likely to experience a shin injury than runners in the highest tertile (≥178 steps/min). Anterior knee pain incidence was not significantly influenced by step rate. Conclusion: A lower running step rate was associated with a greater likelihood of shin injury at both self-selected and fixed running speeds. Future studies evaluating whether increasing running step rate reduces shin injury risk and time lost during a high-school cross country season should be considered.