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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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Department of Physical Education, Sport and Recreation, Universidad de La Frontera, Temuco, Chile;
Universidad San
Jorge, Campus Universitario, Zaragoza, Spain; an
AU1 d
Universidad de Jae´n, Campus de Las Lagunillas, Jaen, SpainAU2
´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$h
comparing data with another widely used device (the OptoGait
system). Eighteen trained male endurance runners performed an
incremental running test (8–20 km$h
with 3-minute stages) on
a treadmill. Spatiotemporal parameters (contact time [CT], flight
time [FT], step length [SL], and step frequency [SF]) were 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
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 ( 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
Journal of Strength and Conditioning Research
2018 National Strength and Conditioning Association
VOLUME 00 | NUMBER 00 | MONTH 2018 | 1
Copyright ª2018 National Strength and Conditioning Association
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$h
) by comparing
data with a widely used device for this purpose (i.e., the
OptoGait system).
TABLE 1. Coefficient of variation (%) of the spatiotemporal parameters (CT, FT, SL, and SF) at different running
velocities (8–20 km$h
) from OptoGait system and from Stryd system.
Speed (km$h
Contact time (CT) Flight time (FT) Step length (SL) Step frequency (SF)
Stryd OptoGait Stryd OptoGait Stryd OptoGait Stryd OptoGait
8 1.46 3.01 11.60 30.58 1.32 3.78 1.31 3.13
9 1.38 2.91 9.38 24.17 1.38 3.61 1.33 3.30
10 1.53 2.90 7.35 18.62 1.22 3.39 1.19 3.14
11 1.43 2.79 5.78 14.01 1.13 3.28 1.11 3.06
12 1.37 2.59 5.21 11.44 1.24 3.04 1.19 2.77
13 1.22 2.56 4.27 9.05 1.09 2.74 1.05 2.79
14 1.27 2.48 4.18 8.26 1.14 2.63 1.13 2.52
15 1.34 2.41 4.29 7.05 1.33 2.24 1.26 2.35
16 1.91 2.53 4.59 6.46 1.20 1.98 1.17 2.33
17 1.56 2.38 3.73 6.38 1.32 2.02 1.29 2.30
18 1.98 2.33 5.11 6.37 1.86 2.08 1.69 2.15
19 2.23 2.45 5.39 6.41 2.02 2.24 1.87 2.27
20 2.32 2.48 7.56 6.01 2.08 2.66 2.01 3.54
TABLE 2. SEM of the spatiotemporal parameters (CT, FT, SL, and SF) at different running velocities (8–20 km$h
from OptoGait system and from Stryd system.
Speed (km$h
Contact time (CT) Flight time (FT) Step length (SL) Step frequency (SF)
Stryd OptoGait Stryd OptoGait Stryd OptoGait Stryd OptoGait
8 0.005 0.005 0.008 0.009 1.345 1.259 2.483 2.269
9 0.004 0.005 0.007 0.009 1.228 1.179 2.138 2.068
10 0.003 0.004 0.005 0.007 1.071 1.032 1.746 1.777
11 0.003 0.003 0.005 0.007 1.479 1.539 2.213 2.234
12 0.003 0.003 0.005 0.007 1.572 1.539 2.227 2.229
13 0.003 0.002 0.004 0.005 1.583 1.497 2.108 2.103
14 0.003 0.002 0.004 0.005 1.704 1.757 2.198 2.179
15 0.002 0.002 0.003 0.004 1.794 1.730 2.207 2.164
16 0.002 0.002 0.003 0.004 1.930 1.881 2.318 2.355
17 0.002 0.002 0.003 0.004 2.146 2.151 2.507 2.529
18 0.001 0.002 0.004 0.004 2.412 2.484 2.771 2.787
19 0.001 0.002 0.003 0.003 2.252 2.278 2.535 2.591
20 0.001 0.003 0.003 0.003 2.013 2.079 2.211 2.406
Stryd System and Running Stride Kinematics
Journal of Strength and Conditioning Research
Copyright ª2018 National Strength and Conditioning Association
Experimental Approach to the Problem
With the introduction of new wireless devices, establishment
of their reliability and validity are essential before practical
use. In this study, the Stryd system was compared with the
OptoGait system for measuring spatiotemporal variables
during running at different velocities (8–20 km$h
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 .
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$h
and speed increased by 1 km$h
every 3 minutes until running speed reached 20 km$h
The slope was maintained at 1% (0.98). The treadmill pro-
tocol was preceded by a standardized 10-minute accommo-
dation programme (5 minutes walking at 5 km$h
, and
5 minutes running at 10 km$h
). Athletes were experienced
in running on a treadmill.
Materials and Testing. (a) Anthropometry: For descriptive
purposes, height (cm) and body mass (kg) were measured.
(b) Biomechanics: Spatiotemporal parameters were mea-
sured using 2 different devices:
TABLE 3. Pearson correlation between kinematics variables from Stryd vs. Optogait over an incremental running test
(8–20 km$h
) 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
AU12 .
TABLE 4. Intraclass correlation coefficients between kinematics variables from Stryd vs. Optogait over an incremental
running test (8–20 km$h
Speed (km$h
)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
Journal of Strength and Conditioning Research
VOLUME 00 | NUMBER 00 | MONTH 2018 | 3
Copyright ª2018 National Strength and Conditioning Association
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
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$min21.SFðsteps=minÞ:
Statistical Analyses
Descriptive statistics are represented as mean (SD). Tests of
normal distribution and homogeneity (Shapiro-Wilk and Lev-
ene’s test, respectively) were conducted on all data before anal-
ysis. Coefficient of variation (CV, %) and SEM were calculated
as a measure of absolute reliability (within-subject variation and
SD of a sampling distribution, respectively) (2,6). Intraclass cor-
relation coefficients were calculated between OptoGait and
Stryd data for each spatiotemporal variable analyzed (CT,
FT, SL, and SF). Values less than 0.5 are indicative of poor
reliability, values between 0.5 and 0.75 indicate moderate reli-
ability, values between 0.75 and 0.9 indicate good reliability,
and values greater than 0.90 indicate excellent reliability (8).
To determine concurrent validity, a Pearson correlation analysis
was also performed between OptoGait and Stryd data. The
following criteria were adopted to interpret the magnitude of
correlations between measure-
ment variables: ,0.1 (trivial),
0.1–0.3 (small), 0.3–0.5 (moder-
ate), 0.5–0.7 (large), 0.7–0.9 (very
large), and 0.9–1.0 (almost per-
fect) (7). Pairwise comparisons
of mean (t-test) were also con-
ducted between data (CT, FT,
SL, and SF) from the 2 devices
(OptoGait and Stryd) at different
running speeds (8–20 km$h
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
Stryd System and Running Stride Kinematics
Journal of Strength and Conditioning Research
Copyright ª2018 National Strength and Conditioning Association
performed using SPSS (version 21; SPSS, Inc., Chicago, IL,
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),
the SEM is provided in
T2Tab l e 2.
The Pearson correlation analy-
sis is shown in T3Table 3 (CT,
FT, SL, and SF or cadence at
8–20 km$h
running veloci-
ties). Contact time from both
devices showed large correla-
tions (0.5–0.7, p,0.05) at
low speeds (8–11 km$h
and race speeds (14–16
). 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$h
). Step
length and SF from both devices were nearly perfectly corre-
lated (r.0.9, p,0.001) at every running velocity tested.
The ICCs between kinematic variables from both Stryd
vs. OptoGait systems over the entire protocol (8–20
)areincludedin T4Table4.Contacttimeshowed
a low coefficient (,0.5), FT a moderate coefficient
(0.5–0.75), whereas SL and SF showed excellent coeffi-
cients (.0.9).
A paired t-test demonstrated some significant differences
(p,0.05) and large ES (.0.7) in the variables analyzed (CT,
FT, SL, and cadence) (Figures 1–4, respectively). Contact
time ( F1Figure 1) was underesti-
mated for Stryd compared
with OptoGait data (8–18
,p,0.001, and ES .
0.7; ;6–8%). Differences were
smaller at 19 km$h
and ES .0.7; ;4%), and no
differences were observed at 20
(p$0.05 and ES ,
0.1; ;0.5%).
Flight time ( F2Figure 2) was
overestimated for Stryd based
on OptoGait data at running
velocities between 8 and 19
(p,0.05, ES .0.7;
from ;65% at 8 km$h
;7% at 19 km$h
). No signif-
icant differences were found at
20 km$h
(p$0.05 and ES =
0.57; ;3%).
Step length from both devi-
ces is shown in F3Figure 3.
Figure 3. Step length (cm) during Running measured by Stryd and OptoGait systems. *p,0.05, **p,0.01,
Figure 2. Flight time (s) during running masured by Stryd and OptoGait systems. *p,0.05, **p,0.01, ***p,
Journal of Strength and Conditioning Research
VOLUME 00 | NUMBER 00 | MONTH 2018 | 5
Copyright ª2018 National Strength and Conditioning Association
pvalues show significant differences (p,0.05) between data
from Stryd and OptoGait at most analyzed velocities,
although Cohen’s dshowed a very small magnitude of
changes (ES ,0.1), with Stryd data overestimated com-
pared with OptoGait data (,1%). Likewise, significant dif-
ferences (p,0.05) were found in cadence between the 2
devices (
F4 Figure 4), but Cohen’s dreported a very small
change (ES ,0.1) with differences smaller than 1%.
This study aimed to determine the absolute reliability and to
evaluate the concurrent validity of the Stryd system for
measuring spatiotemporal variables during running at differ-
ent velocities (8–20 km$h
) 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
), 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
), 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
, 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
Journal of Strength and Conditioning Research
Copyright ª2018 National Strength and Conditioning Association
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
). However, SL and SF were
valid variables (,1%) over the entire range of running veloc-
ities, as compared with the OptoGait system.
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
VOLUME 00 | NUMBER 00 | MONTH 2018 | 7
Copyright ª2018 National Strength and Conditioning Association
... Contact time, flight time, step frequency, and step length are metrics used to profile the spatiotemporal characteristics of running gait (18,20). In addition, contact time and flight time can be used in equations to estimate vertical stiffness (K vert ) which represents the deformation of the center of mass during ground contact in running (27). ...
... Gait Characteristics. Contact time, step time, flight time, step frequency, and step length were determined from IC and TO events using previously described calculation methods (18). Contact time and flight time were then input into a spring-mass model equation to estimate K vert , defined as the ratio of vGRF peak and center of mass displacement (COM dis ) (27). ...
... The test-retest error in spatiotemporal variables derived from thoracic spine sensors has not been previously quantified. Irrespective of running velocity, this study showed between-day CV values of #8.9% for contact time, step time, flight time, step frequency, and step length from Blue Trident and Apex thoracic sites (Table 3), and this is consistent with work that concluded these same variables as reliable from foot-mounted IMUs (1,18). A finding of note from the reliability analysis was that the Apex devices (skin and vest) had a lower CV (#6.2%) for all spatiotemporal variables and were more reliable than the Blue Trident sensor (see Table 3). ...
Horsley, BJ, Tofari, PJ, Halson, SL, Kemp, JG, Chalkley, D, Cole, MH, Johnston, RD, and Cormack, SJ. Validity and reliability of thoracic-mounted inertial measurement units to derive gait characteristics during running. J Strength Cond Res XX(X): 000–000, 2023—Inertial measurement units (IMUs) attached to the tibia or lumbar spine can be used to analyze running gait but, with team-sports, are often contained in global navigation satellite system (GNSS) units worn on the thoracic spine. We assessed the validity and reliability of thoracic-mounted IMUs to derive gait characteristics, including peak vertical ground reaction force (vGRF peak ) and vertical stiffness (K vert ). Sixteen recreationally active subjects performed 40 m run throughs at 3–4, 5–6, and 7–8 m·s ⁻¹ . Inertial measurement units were attached to the tibia, lumbar, and thoracic spine, whereas 2 GNSS units were also worn on the thoracic spine. Initial contact (IC) from a validated algorithm was evaluated with F1 score and agreement (mean difference ± SD ) of gait data with the tibia and lumbar spine using nonparametric limits of agreement (LoA). Test-retest error {coefficient of variation, CV (95% confidence interval [CI])} established reliability. Thoracic IMUs detected a nearly perfect proportion (F1 ≥ 0.95) of IC events compared with tibia and lumbar sites. Step length had the strongest agreement (0 ± 0.04 m) at 3–4 m·s ⁻¹ , whereas contact time improved from 3 to 4 (−0.028 ± 0.018 second) to 7–8 m·s ⁻¹ (−0.004 ± 0.013 second). All values for K vert fell within the LoA at 7–8 m·s ⁻¹ . Test-retest error was ≤12.8% for all gait characteristics obtained from GNSS units, where K vert was most reliable at 3–4 m·s ⁻¹ (6.8% [5.2, 9.6]) and vGRF peak at 7–8 m·s ⁻¹ (3.7% [2.5, 5.2]). The thoracic-spine site is suitable to derive gait characteristics, including K vert , from IMUs within GNSS units, eliminating the need for additional sensors to analyze running gait.
... Based on a 6-axis inertial motion sensor (3-axis gyroscope and 3-axis accelerometer), this device provides 12 metrics to quantify performance: speed, distance, elevation, running power, form power, cadence, ground contact time, vertical oscillation, and leg stiffness. Previous studies have evidenced good reliability for spatiotemporal running characteristics 16 and power output. 17 Participants were encouraged to use Stryd in all training sessions and were reminded to regularly sync training data to the training platform. ...
... Nonetheless, participants were asked to run on flat route to account for this, and grade adjustment was applied to speed. Although previous studies have evidenced good reliability for spatiotemporal running characteristics 16 and power output 17 for the foot pod used in the current investigation, some error may occur when running style is changed. 31 Although this is unlikely to have occurred, differences in running style between trials may have affected the results. ...
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Purpose: This study aimed to compare estimations of critical speed (CS) and work completed above CS (D'), and their analogies for running power (critical power [CP] and W'), derived from raw data obtained from habitual training (HAB) and intentional maximal efforts in the form of time trials (TTs) and 3-minute all-out tests (3MTs) in recreational runners. The test-retest reliability of the 3MT was further analyzed. Methods: Twenty-three recreational runners (4 female) used a foot pod to record speed, altitude, and power output for 8 consecutive weeks. CS and D', and CP and W', were calculated from the best 3-, 7-, and 12-minute segments recorded in the first 6 weeks of their HAB and in random order in weeks 7 and 8 from 3 TTs (3, 7, and 12 min) and three 3MTs (to assess test-retest reliability). Results: There was no difference between estimations of CS or CP derived from HAB, TT, and 3MT (3.44 [0.63], 3.42 [0.53], and 3.76 [0.57] m · s-1 and 281 [41], 290 [45], and 305 [54] W, respectively), and strong agreement between HAB and TT for CS (r = .669) and CP (r = .916). Limited agreement existed between estimates of D'/W'. Moderate reliability of D'/W' was demonstrated between the first and second 3MTs, whereas excellent reliability was demonstrated for CS/CP. Conclusion: These data suggest that estimations of CS/CP can be derived remotely, from either HAB, TT, or 3MT, although the lower agreement between D'/W' warrants caution when using these measures interchangeably.
... Based on 6-axis inertial motion sensor with a 1-Hz sampling frequency, the device provides several metrics to characterize performance: pace, elevation, distance, cadence, ground contact time, vertical oscillation, leg stiffness, running power, and form power. The device has been validated previously at different running velocities (14,17). The power meter was firmly attached to the shoe according to the manufacturer recommendations. ...
Hingrand, C, Olivier, N, Combes, A, Bensaid, S, and Daussin, FN. Power is more relevant than ascensional speed to determine metabolic demand at different gradient slopes during running. J Strength Cond Res 37(11): 2298–2301, 2023—Trail running is characterized by successive uphill and downhill running sessions. To prescribe training intensity, an assessment of maximal running capacity is required. This study compared 2 uphill incremental tests using the same ascensional speed increment to identify the influence of the slope gradient on performance. Ten subjects (8 men and 2 women) performed 3 incremental exercises on various slope (1%: IT01, 10%: IT10, and 25%: IT25), and the ascensional speed increment was similar between IT10 and IT25 (100 m·h ⁻¹ every minute). Gas exchanges, heart rate, and power were monitored continuously during the tests. Similar V̇ o 2 max levels were observed in the 3 conditions: 68.7 ± 6.2 for IT01, 70.1 ± 7.3 for IT10, and 67.6 ± 7.0 for IT25. A greater maximal ascensional speed was reached in the IT25 (1760 ± 190 vs. 1,330 ± 106 for IT25 and IT10, respectively, p < 0.01). A significant relationship was observed between relative V̇ o 2 levels and relative power without any effect of slope. Power should be the parameter used for prescribing training intensity compared with ascensional speed in trail.
... Thus, Stryd running power can theoretically quantify training intensity in a manner analogous to cycling mechanical PO and could be superior to conventional measurement approaches using running speed. Despite evidence of repeatability [11], reliability [14,15], stability during prolonged running [16], and strong linear correlations with running speed [17,18], limited research has investigated the Stryd running metric at stable metabolic work rates relative to exercising thresholds. Thus, to determine the utility of Stryd power to indicate relative exercise intensity and assess running fitness and performance, the relationship between Stryd mechanical power and metabolic power needs to be established using an exercise intensity domain training approach (i.e., evaluating running power metrics during steady-state exercise relative to the gas exchange threshold (GET) and maximal metabolic steady state (MMSS)). ...
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We sought to determine the utility of Stryd, a commercially available inertial measurement unit, to quantify running intensity and aerobic fitness. Fifteen (eight male, seven female) runners (age = 30.2 [4.3] years; V·O2max = 54.5 [6.5] ml·kg−1·min−1) performed moderate- and heavy-intensity step transitions, an incremental exercise test, and constant-speed running trials to establish the maximal lactate steady state (MLSS). Stryd running power stability, sensitivity, and reliability were evaluated near the MLSS. Stryd running power was also compared to running speed, V·O2, and metabolic power measures to estimate running mechanical efficiency (EFF) and to determine the efficacy of using Stryd to delineate exercise intensities, quantify aerobic fitness, and estimate running economy (RE). Stryd running power was strongly associated with V·O2 (R2 = 0.84; p < 0.001) and running speed at the MLSS (R2 = 0.91; p < 0.001). Stryd running power measures were strongly correlated with RE at the MLSS when combined with metabolic data (R2 = 0.79; p < 0.001) but not in isolation from the metabolic data (R2 = 0.08; p = 0.313). Measures of running EFF near the MLSS were not different across intensities (~21%; p > 0.05). In conclusion, although Stryd could not quantify RE in isolation, it provided a stable, sensitive, and reliable metric that can estimate aerobic fitness, delineate exercise intensities, and approximate the metabolic requirements of running near the MLSS.
... Complementary, leg stiffness and vertical oscillation were estimated using a Stryd® device with a sampling frequency of 1000 Hz. The device uses a triaxial accelerometer that has shown an adequate reliability and excellent validity of these measures evaluated and reported by García-Pinillos et al. 28 The Stryd® device was paired with a Polar® watch (Vantage M GPS system; Polar, Kempele, Finland) and the information was analyzed using the Stryd Power Center program available on the web and mobile application. Data were recorded, and the final minute of each RE trial was averaged for subsequent analyses. ...
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Background Ethylene and vinyl acetate (EVA) and polyether block amide (PEBA) are recently the most widely used materials for advanced footwear technology (AFT) that has been shown to improve running economy (RE). This study investigated the effects of these midsole materials on RE and biomechanics, in both fresh and worn state (after 450 km). Methods Twenty‐two male trained runners participated in this study. Subjects ran four 4‐min trials at 13 km‧h⁻¹ with both fresh EVA and PEBA AFT and with the same models with 450 km of wear using a randomized crossover experimental design. We measured energy cost of running (W/kg), spatiotemporal, and neuromuscular parameters. Results There were significant differences in RE between conditions (p = 0.01; n² = 0.17). There was a significant increase in energy cost in the worn PEBA condition compared with new (15.21 ± 1.01 and 14.87 ± 0.99 W/kg; p < 0.05; ES = 0.54), without differences between worn EVA (15.13 ± 1.14 W/kg; p > 0.05), and new EVA (15.15 ± 1.13 w/kg; ES = 0.02). The increase in energy cost between new and worn was significantly higher for the PEBA shoes (0.32 ± 0.38 W/kg) but without significant increase for the EVA shoes (0.06 ± 0.58 W/kg) (p < 0.01; ES = 0.51) with changes in step frequency and step length. The new PEBA shoes had lower energy cost than the new EVA shoes (p < 0.05; ES = 0.27) with significant differences between conditions in contact time. Conclusion There is a clear RE advantage of incorporating PEBA versus EVA in an AFT when the models are new. However, after 450 km of use, the PEBA and EVA shoes had similar RE.
... The dependability coefficient's square root is the most incredible validity level [47]. A rating less than 0.5 is considered to have low validity, while a value more than 0.9 is deemed excellent validity [48]. ...
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BIM implementation is still low in construction, especially in developing countries. The study aims to identify the most effective strategies to improve BIM implementation in the construction industry. A quantitative approach was a means of data collection. There were no statistically significant differences in respondents’ opinions about the process of enhancing BIM implementation based on educational qualification, age bracket, or the number of BIM projects handled. However, there was based on the participants’ specialization and years of working experience. Multiple linear regression was conducted to investigate whether the strategies to improve BIM implementation in the construction industry could significantly predict the level of BIM knowledge/awareness. The findings indicate that three independent variables significantly predict the level of BIM knowledge, F (5, 61) = 8.795, p < 0.001. However, government support and national standard have an insignificant impact on the dependent variable. Also, the R ² = 0.419 depicts that the model explains 41.9% of the variance in the level of BIM knowledge. The research results have implications for enhancing BIM implementation in AEC projects and may increase industry effectiveness.
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Problem Statement: This systematic review focuses on the use of sensors to improve performance in endurance athletes by analyzing biomechanical parameters. Approach: The use of sensors in endurance sports has gained popularity in recent years, allowing athletes and coaches to measure and analyze different biomechanical parameters in real-time. Purpose: The main purpose of this systematic review is to answer the question of how sensors can be used and applied to improve performance in endurance runners by analyzing the biomechanical parameters they provide. Methods: Systematic review analyzing related keywords such as biomechanics, kinematics, kinetics, running, triathlon, ultra running, trail running, Stryd, SHFT, Runscribe, and performance, through scientific research articles from the database of the Electronic Library of the Isabel I University dated 02/2023 in English. A total of 192 investigations were found, of which 168 were excluded. After a detailed review, 15 relevant investigations were included. Results: Sensors can be useful to measure biomechanical parameters such as cadence, stride length, leg spring stiffness, ground contact time, and vertical oscillation, which can help to improve performance in endurance athletes. Conclusions: Sensors are a suitable tool to analyze performance improvement in endurance athletes by analyzing biomechanical parameters. However, it is important to highlight that not all sensors are similar, that it is necessary to carefully select the most suitable ones for each specific situation, and that biomechanics is also conditioned in each athlete, so universal rules cannot be established.
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Introduction/purpose: Previous results about longitudinal bending stiffness (LBS) and running economy (RE) show high variability. This study aimed to assess the effects of shoes with increased LBS on RE and performance in trained and national runners. Methods: Twenty-eight male runners were divided into two groups according to their 10-km performance times (trained: 38-45 min and national runners: <34 min). Subjects ran 2 x 3 min (at 9 and 13 km‧hr-1 for trained, and 13 and 17 km‧hr-1 for national runners) with an experimental shoe with carbon fiber plate to increase the LBS (Increased LBS) and a control shoe (without carbon fiber plate). We measured energy cost of running (W/kg) and spatiotemporal parameters in visit one and participants performed a 3,000 m time trial (TT) in two successive visits. Results: Increased LBS improved RE in the trained group at slow (11.41 ± 0.93 vs 11.86 ± 0.93 W·kg-1) and fast velocity (15.89 ± 1.24 vs 16.39 ± 1.24 W·kg-1) and only at the fast velocity in the national group (20.35 ± 1.45 vs 20.78 ± 1.18 W·kg-1). The improvements in RE were accompanied by different changes in biomechanical variables between groups. There was a similar improvement in the 3,000 m TT test in Increased LBS for trained (639 ± 59 vs 644 ± 61 s in control shoes) and national runners (569 ± 21 vs 574 ± 21 s in control shoes) with more constant pace in increased LBS compared to control shoes in both groups. Conclusions: Increasing shoe LBS improved RE at slow and fast velocities in trained and only at fast velocity in national runners. However, the 3,000 m TT test improved similarly in both levels of runners with increased LBS. The improvements in RE are accompanied by small modifications in running kinematics that could explain the difference between the different levels of runners.
This study aims compare the spatiotemporal and kinematic running parameters obtained by the WalkerView (Tecnobody, Bergamo, Italy) with those recorded by a optoelectronic 3D motion capture system. Seventeen participants were simultaneously recorded by the WalkerView and a motion capture system during running tests on the WalkerView at two different speeds (i.e., 8 km/h and 10 km/h). Per each parameter and speed the Root Mean Square Error (RMSE), the intraclass correlation coefficient (ICC), and the mean of the difference (MOD) and limits of agreement (LOAs) indexes obtained from Bland-Altman analysis were used to compare the two systems. ICCs show an excellent agreement for the mean step time and the cadence at both testing speeds (ICC=0.993 at 8 km/h; ICC=0.998 at 10 km/h); a lower agreement was found for all the kinematic variables. Small differences for some spatio-temporal parameters and greater differences for the kinematic variables were found. Therefore, WalkerView could represent a practical, accessible, and less expensive tool for clinicians, researchers, and sports trainers to assess the characteristics spatio-temporal parameters of running in non-laboratory settings.
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Background Interval training (IT) is influenced by several variables and its design. However, there is no consensus about the acute effects of this type of training on running kinematics and gait patterns due to the variety of session designs. Research question The aim of this systematic review was to determine the acute effects of IT on gait patterns and running kinematics in endurance runners depending on the characteristics of the training sessions. Methods A systematic search on four databases (Pubmed, WOS, Medline, and Scopus) was conducted on February 22, 2022. After analyzing 655 articles, studies were included if they met the inclusion criteria developed according to the PICO model. Nine studies were finally included. Results Only two of these studies measured kinematics changes during IT bouts while seven measured pre-post changes of these parameters. The quality scores of the included studies in the review averaged 5.44 (good quality) points using the modified PEDro scale. The observed changes in running kinematics during IT sessions were an increase in stride frequency, contact time and vertical displacement of center of mass. Significance Regarding the type of IT, anaerobic and short aerobic interval sessions (200–1000 m) should include long recovery periods (2–3 min) to avoid the increase of stride frequency, contact time and vertical oscillation of the center of mass as a results of muscle fatigue. For long aerobic interval sessions (>1000 m), a short recovery (1–2 min) between bouts do not induce a high level of muscle fatigue nor modifications in gait patterns. Coaches and athletes must consider the relative intensity and recovery periods of IT, and the type of IT, to prevent excessive fatigue which can negatively affect running kinematics.
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Vertical jump is one of the most prevalent acts performed in several sport activities. It is therefore important to ensure that the measurements of vertical jump height made as a part of research or athlete support work have adequate validity and reliability. The aim of this study was to evaluate concurrent validity and reliability of the Optojump photocell system (Microgate, Bolzano, Italy) with force plate measurements for estimating vertical jump height. Twenty subjects were asked to perform maximal squat jumps and countermovement jumps, and flight time-derived jump heights obtained by the force plate were compared with those provided by Optojump, to examine its concurrent (criterion-related) validity (study 1). Twenty other subjects completed the same jump series on 2 different occasions (separated by 1 week), and jump heights of session 1 were compared with session 2, to investigate test-retest reliability of the Optojump system (study 2). Intraclass correlation coefficients (ICCs) for validity were very high (0.997-0.998), even if a systematic difference was consistently observed between force plate and Optojump (-1.06 cm; p < 0.001). Test-retest reliability of the Optojump system was excellent, with ICCs ranging from 0.982 to 0.989, low coefficients of variation (2.7%), and low random errors (±2.81 cm). The Optojump photocell system demonstrated strong concurrent validity and excellent test-retest reliability for the estimation of vertical jump height. We propose the following equation that allows force plate and Optojump results to be used interchangeably: force plate jump height (cm) = 1.02 × Optojump jump height + 0.29. In conclusion, the use of Optojump photoelectric cells is legitimate for field-based assessments of vertical jump height.
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This study aimed to analyse the influence of muscular performance parameters on spatio-temporal gait characteristics during running when gradually increasing speed. 51 recreationally trained male endurance runners (age: 28 ± 8 years) voluntarily participated in this study. Subjects performed a battery of jumping tests (squat jump, countermovement jump, and 20 cm drop jump), and after that, the subjects performed an incremental running test (10 to 20 km/h) on a motorized treadmill. Spatio-temporal parameters were measured using the OptoGait system. Cluster k-means analysis grouped subjects according to the jumping test performance, by obtaining a group of good jumpers (GJ, n = 19) and a group of bad jumpers (BJ, n = 32). With increased running velocity, contact time was shorter, flight time and step length longer, whereas cadence and stride angle were greater (p < 0.001). No significant differences between groups (p ≥ 0.05) were found at any running speed. The results obtained indicate that increased running velocity produced no differences in spatio-temporal adaptations between those runners with good jumping ability and those with poor jumping ability. Based on that, it seems that muscular performance parameters do not play a key role in spatio-temporal adaptations experienced by recreational endurance runners with increased velocity. However, taken into consideration the well-known relationship between running performance and neuromuscular performance, the authors suggest that muscular performance parameters would be much more determinant in the presence of fatigue (exhausted condition), or in the case of considering other variables such as running economy or kinetic.
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Background Lower body positive pressure treadmills (LBPPTs) aim to reduce musculoskeletal loading during running. As LBPPTs have become more commercially available, they have become integrated into athletic performance and clinical rehabilitation settings. Consequentially, published research examining the biomechanical and physiological responses to unweighted running has increased. Objective The purpose of this systematic review was to synthesize the literature in an attempt to provide researchers and clinicians with a comprehensive review of physiologic and biomechanical responses to LBPPT running. Methods Through a generic search of PubMed, CINAHL, MEDLINE, and SPORTDiscus using a comprehensive list of search terms related to LBPPT, unweighting, and body weight support during running, we identified all peer-reviewed publications that included LBPPT running. Two reviewers independently evaluated the quality of studies using a modified Downs and Black checklist for non-randomized studies. Results A total of 15 articles met the inclusion criteria for this review. Peak and active vertical ground-reaction forces were consistently reduced with unweighting, but regional loading within the foot was also altered towards a forefoot strike. LBPPTs also provide some horizontal assistance. Neuromuscular activation is generally reduced with LBPPTs, but the stabilizer muscle groups may respond differently than the propulsive muscle groups. Submaximal heart rate and volume oxygen consumption are reduced with unweighting, but physiologic response remains generally unchanged at maximal intensities. Conclusions The current literature suggests that LBPPTs are effective in allowing individuals to achieve a given metabolic stimulus with reduced musculoskeletal loading. However, LBPPTs not only reduce impact but also change neuromuscular activation and biomechanics in a complex manner. Thus, clinicians must account for the specific biomechanical and physiological alterations induced by LBPPTs when designing training programs and rehabilitation protocols.
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The effects of footwear and inclination on running biomechanics over short intervals are well documented. Although recognized that exercise duration can impact running biomechanics, it remains unclear how biomechanics change over time when running in minimalist shoes and on slopes. Our aims were to describe these biomechanical changes during a 50-min run and compare them to those observed in standard shoes. Thirteen trained recreational male runners ran 50-min at 65% of their maximal aerobic velocity on a treadmill once in minimalist and once in standard shoes one-week apart in a random order. The 50-min trial was divided into 5-min segments of running at 0%, +5%, and -5% of treadmill incline sequentially. Data were collected using photocells, high-speed-video cameras, and plantar-pressure insoles. At 0% incline, runners exhibited reduced leg stiffness and plantar-flexion angles at footstrike and lower plantar pressure at the forefoot and toes in minimalist shoes from the 34th min of the protocol onward. However, only reduced plantar pressure at the toes was observed in standard shoes. Overall, similar biomechanical changes with increased exercise time were observed on the uphill and downhill inclines. The results might be due to the unfamiliarity of subjects to running in minimalist shoes.
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Accelerometer-based systems are often used to quantify human movement. This study’s aim was to assess the reliability and validity of the Myotest® accelerometer-based system for measuring running stride kinematics. Twenty habitual runners ran two 60 m trials at 12, 15, 18 and 21 km·h−1. Contact time, aerial time and step frequency parameters from six consecutive running steps of each trial were extracted using Myotest® data. Between-trial reproducibility of measures was determined by comparing kinematic parameters from the two runs performed at the same speed. Myotest® measures were compared against photocell-based (Optojump Next®) and high-frequency video data to establish concurrent validity. The Myotest®-derived parameters were highly reproducible between trials at all running speeds (intra-class correlation coefficient (ICC): 0.886 to 0.974). Compared to the photo-cell and high-speed video-based measures, the mean contact times from the Myotest® were 34% shorter and aerial times were 64% longer. Only step frequency was comparable between systems and demonstrated high between-system correlation (ICC ≥ 0.857). The Myotest® is a practical portable device that is reliable for measuring contact time, aerial time and step frequency during running. In terms of validity, it provides accurate step frequency measures but underestimates contact time and overestimates aerial time compared to photocell- and optical-based systems.
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.
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.
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.
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.
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.