ArticlePDF Available

Absolute Reliability and Concurrent Validity of the Stryd System for the Assessment of Running Stride Kinematics at Different Velocities

Authors:
Felipe García Pinillos at University of Granada
Felipe García Pinillos
Luis Enrique Roche Seruendo at Universidad San Jorge
Luis Enrique Roche Seruendo
Noel Marcen-Cinca at Universidad San Jorge
Noel Marcen-Cinca
Luis Alberto Marco-Contreras at Universidad San Jorge
Luis Alberto Marco-Contreras
Show all 5 authorsHide
Download full-text PDF
Read full-text
Download citation

Abstract and Figures

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.
Figures - uploaded by Felipe García Pinillos
Author content
All figure content in this area was uploaded by Felipe García Pinillos
Content may be subject to copyright.
Content uploaded by Felipe García Pinillos
Author content
All content in this area was uploaded by Felipe García Pinillos on Dec 01, 2018
Content may be subject to copyright.
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
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$h
21
) by comparing
data with a widely used device for this purpose (i.e., the
OptoGait system).
TABLE 1. Coefficient of variation (%) of the spatiotemporal parameters (CT, FT, SL, and SF) at different running
velocities (8–20 km$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$h
21
)
Contact time (CT) Flight time (FT) Step length (SL) Step frequency (SF)
Stryd OptoGait Stryd OptoGait Stryd OptoGait Stryd OptoGait
8 0.005 0.005 0.008 0.009 1.345 1.259 2.483 2.269
9 0.004 0.005 0.007 0.009 1.228 1.179 2.138 2.068
10 0.003 0.004 0.005 0.007 1.071 1.032 1.746 1.777
11 0.003 0.003 0.005 0.007 1.479 1.539 2.213 2.234
12 0.003 0.003 0.005 0.007 1.572 1.539 2.227 2.229
13 0.003 0.002 0.004 0.005 1.583 1.497 2.108 2.103
14 0.003 0.002 0.004 0.005 1.704 1.757 2.198 2.179
15 0.002 0.002 0.003 0.004 1.794 1.730 2.207 2.164
16 0.002 0.002 0.003 0.004 1.930 1.881 2.318 2.355
17 0.002 0.002 0.003 0.004 2.146 2.151 2.507 2.529
18 0.001 0.002 0.004 0.004 2.412 2.484 2.771 2.787
19 0.001 0.002 0.003 0.003 2.252 2.278 2.535 2.591
20 0.001 0.003 0.003 0.003 2.013 2.079 2.211 2.406
Stryd System and Running Stride Kinematics
2
Journal of Strength and Conditioning Research
the
TM
Copyright ª2018 National Strength and Conditioning Association
METHODS
Experimental Approach to the Problem
With the introduction of new wireless devices, establishment
of their reliability and validity are essential before practical
use. In this study, the Stryd system was compared with the
OptoGait system for measuring spatiotemporal variables
during running at different velocities (8–20 km$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$h
21
) 8 9 1011121314151617181920
Contact time 0.657* 0.636* 0.574†0.525†0.433 0.435 0.507†0.504†0.503†0.453 0.415 0.429 0.078
Flight time 0.602* 0.656* 0.685* 0.703* 0.722* 0.739z0.722z0.782z0.811z0.800z0.775z0.680†0.834†
Step length 0.934z0.999z0.999z0.999z0.999z0.998z0.997z0.998z0.999z0.999z0.999z0.997z0.991z
Step frequency 0.959z0.996z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z0.999z
*p,0.01.
†p,0.05.
zp,0.001
AU12 .
TABLE 4. Intraclass correlation coefficients between kinematics variables from Stryd vs. Optogait over an incremental
running test (8–20 km$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
Journal of Strength and Conditioning Research
the
TM
|
www.nsca.com
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
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$h
21
).
In addition, the magnitude of the
differences between values was
also interpreted using the
Cohen’s deffect size (ES) (19).
Effect sizes of less than 0.4 rep-
resented a small magnitude of
change, whereas 0.41–0.7 and
greater than 0.7 represented
moderate and large magnitudes
of change, respectively (19). The
level of significance used was
p,0.05. Data analysis was
Figure 1. Contact time (s) during running measured by Stryd and OptoGait systems. *p,0.05, **p,0.01, ***p
,0.01.
Stryd System and Running Stride Kinematics
4
Journal of Strength and Conditioning Research
the
TM
Copyright ª2018 National Strength and Conditioning Association
performed using SPSS (version 21; SPSS, Inc., Chicago, IL,
USA).
RESULTS
Reliability
T1 Table 1 shows the CV (as a measure of absolute reliability)
of spatiotemporal parameters at different running velocities
from both Stryd and OptoGait. For the Stryd system, CV
ranged between 1.2–2.3% (CT), 3.7–11.6% (FT), 1.1–2.1%
(SL), and 1.1–2.0% (SF), whereas for the OptoGait system,
CV was 2.3–3.0% (CT), 6.0–
30.6% (FT), 2.0–3.8% (SL),
and2.2–3.6%(SF).Inaddition,
the SEM is provided in
T2Tab l e 2.
Validity
The Pearson correlation analy-
sis is shown in T3Table 3 (CT,
FT, SL, and SF or cadence at
8–20 km$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$0.05 and ES =
0.57; ;3%).
Step length from both devi-
ces is shown in F3Figure 3.
Figure 3. Step length (cm) during Running measured by Stryd and OptoGait systems. *p,0.05, **p,0.01,
***p,0.01.
Figure 2. Flight time (s) during running masured by Stryd and OptoGait systems. *p,0.05, **p,0.01, ***p,
0.01.
Journal of Strength and Conditioning Research
the
TM
|
www.nsca.com
VOLUME 00 | NUMBER 00 | MONTH 2018 | 5
Copyright ª2018 National Strength and Conditioning Association
pvalues show significant differences (p,0.05) between data
from Stryd and OptoGait at most analyzed velocities,
although Cohen’s dshowed a very small magnitude of
changes (ES ,0.1), with Stryd data overestimated com-
pared with OptoGait data (,1%). Likewise, significant dif-
ferences (p,0.05) were found in cadence between the 2
devices (
F4 Figure 4), but Cohen’s dreported a very small
change (ES ,0.1) with differences smaller than 1%.
DISCUSSION
This study aimed to determine the absolute reliability and to
evaluate the concurrent validity of the Stryd system for
measuring spatiotemporal variables during running at differ-
ent velocities (8–20 km$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
6
Journal of Strength and Conditioning Research
the
TM
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
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.
REFERENCES
1. Anderson, T. Biomechanics and running economy. Sport Med 22:
76–89, 1996.
2. Atkinson, G and Nevill, AM. Statistical methods for assessing
measurement error (reliability) in variables relevant to sports
medicine. Sport Med 26: 217–238, 1998.
3. Barnes, KR and Kilding, AE. Running economy: Measurement,
norms, and determining factors. Sport Med Open 1: 8, 2015.
4. Gindre, C, Lussiana, T, Hebert-Losier, K, and Morin, JB. Reliability
and validity of the Myotest for measuring running stride kinematics.
J Sport Sci: 1–7, 2015. Available at: https://www.researchgate.net/
profile/Thibault_Lussiana2/publication/280089781_Reliability_
and_validity_of_the_MyotestR_for_measuring_running_stride_
kinematics/links/55a8083e08ae815a04212eb1/Reliability-and-
validity-of-the-MyotestR-for-measuring-running-st AU10.
5. Glatthorn, JF, Gouge, S, Nussbaumer, S, Stauffacher, S, Impellizzeri,
FM, and Maffiuletti, NA. Validity and reliability of Optojump
photoelectric cells for estimating vertical jump height. J Strength
Cond Res 25: 556–560, 2011.
6. Hopkins, WG. Measures of reliability in sports medicine and
science. Sport Med 30: 1–15, 2000.
7. Hopkins, WG, Marshall, SW, Batterham, AM, and Hanin, J.
Progressive statistics for studies in sports medicine and exercise
science. Med Sci Sports Exerc 41: 3–13, 2009.
8. Koo, TK and Li, MY. A guideline of selecting and reporting
intraclass correlation coefficients for reliability research. J Chiropr
Med 15: 155–163, 2016.
9. Lee, MM, Song, CH, Lee, KJ, Jung, SW, Shin, DC, and Shin, SH.
Concurrent validity and test-retest reliability of the OPTOGait
photoelectric cell system for the assessment of spatio-temporal
parameters of the gait of young adults. J Phys Ther Sci 26: 81–85,
2014.
10. Lienhard, K, Schneider, D, and Maffiuletti, NA. Validity of the
Optogait photoelectric system for the assessment of spatiotemporal
gait parameters. Med Eng Phys 35: 500–504, 2013.
11. Luedke, LE, Heiderscheit, BC, Williams, DSB, and Rauh, MJ.
Influence of step rate on shin injury and anterior knee pain in high
school runners. Med Sci Sports Exerc 48: 1244–1250, 2016.
12. Lussiana, T, He
´bert-Losier, K, Millet, GP, and Mourot, L.
Biomechanical changes during a 50-minute run in different footwear
and on various slopes. J Appl Biomech 32: 40–49, 2016.
13. Mooses, M, Mooses, K, Haile, DW, Durussel, J, Kaasik, P, and
Pitsiladis, YP. Dissociation between running economy and running
performance in elite Kenyan distance runners. J Sports Sci 33:
136–144, 2015.
14. Norris, M, Anderson, R, and Kenny, IC. Method analysis of
accelerometers and gyroscopes in running gait: A systematic review.
Proc Inst Mech Eng P J Sports Eng Technol 228: 3–15, 2014.
15. Passfield, L, Hopker, J, Jobson, S, Friel, D, and Zabala, M.
Knowledge is power: Issues of measuring training and performance
in cycling. J Sports Sci 35: 1426–1434, 2017.
16. Roche-Seruendo, LE, Garcı
´a-Pinillos, F, Haicaguerre, J, Bataller-
Cervero, AV, Soto-Hermoso, VM, and Latorre-Roma
´n, PA
´. Lack
of influence of muscular performance parameters on spatio-
temporal adaptations with increased running velocity. JStrength
Cond Res,2017 AU11.
17. Schubert, AG, Kempf, J, and Heiderscheit, BC. Influence of stride
frequency and length on running mechanics: A systematic review.
Sports Health 6: 210–217, 2014.
18. Tartaruga, MP, Brisswalter, J, Peyre
´-Tar t a ruga, LA , A
´vila, AOV,
Alberton,CL,Coertjens,M,Tiggemann,CL,Silva,EM,andKruel,
LF. The relationship between running economy and
biomechanical variables in distance runners. Res Q Exerc Sport 83:
367–375, 2012.
19. Thomas, JR, Silverman, SS, Nelson, JK. Research Methods in Physical
Activity. (7th ed.). Champaign, IL: Human Kinetics, 2015.
Journal of Strength and Conditioning Research
the
TM
|
www.nsca.com
VOLUME 00 | NUMBER 00 | MONTH 2018 | 7
Copyright ª2018 National Strength and Conditioning Association
... Several commercial RP meter devices have been investigated (Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a;Taboga et al., 2021), but so far the shoe-mounted Stryd footpod ( Figure 1) has shown the strongest validity and reliability across its reported metrics: RP (Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a and2020b;Garcia-Pinillos et al., 2019;García-Pinillos et al., 2019;Navalta et al., 2019), stride frequency (SF) (Garcia-Pinillos et al., 2018), stride length (SL) (Garcia-Pinillos et al., 2018), ground contact time (GCT) (Garcia-Pinillos et al., 2018;Imbach et al., 2020), vertical oscillation (VO) (Navalta et al., 2019;Smith et al., 2022), and leg spring stiffness (LSS) (Imbach et al., 2020). Furthermore, when examining trained to highly trained endurance runners (McKay et al., 2021), Stryd RP has demonstrated the strongest linear relationship with measures of metabolic workload (oxygen consumption or metabolic power), at least during variable speeds and treadmill inclines (Baumgartner et al., 2021;Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a;Garcia-Pinillos et al., 2019;García-Pinillos et al., 2019;Imbach et al., 2020;Taboga et al., 2021;van Rassel et al., 2023). ...
... Several commercial RP meter devices have been investigated (Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a;Taboga et al., 2021), but so far the shoe-mounted Stryd footpod ( Figure 1) has shown the strongest validity and reliability across its reported metrics: RP (Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a and2020b;Garcia-Pinillos et al., 2019;García-Pinillos et al., 2019;Navalta et al., 2019), stride frequency (SF) (Garcia-Pinillos et al., 2018), stride length (SL) (Garcia-Pinillos et al., 2018), ground contact time (GCT) (Garcia-Pinillos et al., 2018;Imbach et al., 2020), vertical oscillation (VO) (Navalta et al., 2019;Smith et al., 2022), and leg spring stiffness (LSS) (Imbach et al., 2020). Furthermore, when examining trained to highly trained endurance runners (McKay et al., 2021), Stryd RP has demonstrated the strongest linear relationship with measures of metabolic workload (oxygen consumption or metabolic power), at least during variable speeds and treadmill inclines (Baumgartner et al., 2021;Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a;Garcia-Pinillos et al., 2019;García-Pinillos et al., 2019;Imbach et al., 2020;Taboga et al., 2021;van Rassel et al., 2023). ...
... Several commercial RP meter devices have been investigated (Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a;Taboga et al., 2021), but so far the shoe-mounted Stryd footpod ( Figure 1) has shown the strongest validity and reliability across its reported metrics: RP (Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a and2020b;Garcia-Pinillos et al., 2019;García-Pinillos et al., 2019;Navalta et al., 2019), stride frequency (SF) (Garcia-Pinillos et al., 2018), stride length (SL) (Garcia-Pinillos et al., 2018), ground contact time (GCT) (Garcia-Pinillos et al., 2018;Imbach et al., 2020), vertical oscillation (VO) (Navalta et al., 2019;Smith et al., 2022), and leg spring stiffness (LSS) (Imbach et al., 2020). Furthermore, when examining trained to highly trained endurance runners (McKay et al., 2021), Stryd RP has demonstrated the strongest linear relationship with measures of metabolic workload (oxygen consumption or metabolic power), at least during variable speeds and treadmill inclines (Baumgartner et al., 2021;Cerezuela-Espejo, Hernández-Belmonte, et al., 2020a;Garcia-Pinillos et al., 2019;García-Pinillos et al., 2019;Imbach et al., 2020;Taboga et al., 2021;van Rassel et al., 2023). ...
The Ability of Stryd Footpod Metrics to Reflect Changes in Metabolic Power Between Running Shoe Types
Article
It is unclear if running power (RP) estimated by the Stryd footpod device maintains its linear relationship to metabolic power (WMET) when switching between training and racing shoe types. This study determined if RP estimated by the Stryd footpod and its other spatiotemporal metrics reflect the improvement (decrease) in WMET when wearing high-performance racing shoes (HPRS; Nike AlphaFly Next%) compared to control training shoes (CTS; Nike Revolution 5). Fourteen well-trained runners completed two treadmill tests: Absolute Velocity Running Test (AVRT; 11.3–14.5 km·hr−1) and Relative Velocity Running Test (RVRT; 55–75% VO2MAX). WMET was determined with indirect calorimetry. RP was not significantly different between shoe types (p > 0.432) during the AVRT, but WMET was ~5% lower in HPRS (p < 0.001). During the RVRT, participants ran ~6% faster and at ~6% higher RP (both, p < 0.001) in HPRS for the same WMET (p = 0.869). Linear mixed models confirmed WMET was ~5% lower in HPRS for a given RP (p < 0.001). Still, RP and WMET were strongly related within shoe types (p < 0.001, conditional-R2 = 0.982, SEE = 2.57%). Form power ratio and ground contact time correlated with energetic cost (p < 0.011) but did not fully reflect the influence of shoe type. Therefore, runners should account for their shoe type when using RP to indicate WMET between training and racing.
View
... Recently, there has been an effort to produce low-cost, portable gait and running analysis equipment. This has allowed researchers to remove participants from an artificial laboratory environment and measure participants in a more natural environment [9] Commercially available wearable technology, and the biofeedback provide by them, has been welcomed by coaches and runners, including those in the trail running community. The commercial availability of such devices in the market is extensive, including popular brands such as GARMIN RP , Stryd TM , RunScribe, Polar, and Suunto, among others. ...
... In the market, there are two standout wearable devices designed for measuring running biomechanics on the market. On one hand, there is the Stryd Power Meter, which is a foot-pod device that has been used recently to measure variables related to trail running performance [13] and this device is capable of measuring power, contact time, flight time, step length, vertical oscillation, and cadence [6,7,9]. On the other hand, there are devices from the GARMIN RP brand. ...
... In light of intra-and inter-device reliability considerations, our findings are consistent with the results reported in previous scientific studies [6][7][8][9][13][14][15][16]. There is a large amount of evidence about determining validity through comparing these devices to gold standards, but there is limited research on intra-device reliability. ...
Assessing Trail Running Biomechanics: A Comparative Analysis of the Reliability of StrydTM and GARMINRP Wearable Devices
Article
Full-text available
This study investigated biomechanical assessments in trail running, comparing two wearable devices—Stryd Power Meter and GARMINRP. With the growing popularity of trail running and the complexities of varied terrains, there is a heightened interest in understanding metabolic pathways, biomechanics, and performance factors. The research aimed to assess the inter- and intra-device agreement for biomechanics under ecological conditions, focusing on power, speed, cadence, vertical oscillation, and contact time. The participants engaged in trail running sessions while wearing two Stryd and two Garmin devices. The intra-device reliability demonstrated high consistency for both GARMINRP and StrydTM, with strong correlations and minimal variability. However, distinctions emerged in inter-device agreement, particularly in power and contact time uphill, and vertical oscillation downhill, suggesting potential variations between GARMINRP and StrydTM measurements for specific running metrics. The study underscores that caution should be taken in interpreting device data, highlighting the importance of measuring with the same device, considering contextual and individual factors, and acknowledging the limited research under real-world trail conditions. While the small sample size and participant variations were limitations, the strength of this study lies in conducting this investigation under ecological conditions, significantly contributing to the field of biomechanical measurements in trail running.
View
... In particular, the OptoGait™ system has gained popularity among sports scientists and clinicians for its ability to capture and analyze spatiotemporal gait parameters. The validity and reliability of this gait analysis system have been previously analyzed while walking [13][14][15][16][17] and running [18,19]. ...
... Notwithstanding variations in the experimental design (i.e., running vs. walking), the authors found that the accelerometer recorded shorter contact times and longer flight times compared to the photoelectric system. In the same way, a recent study [19] assessed the validity and reliability of another wearable IMU (Stryd) against the OptoGait system, presenting comparable discrepancies between the two systems. Therefore, the data obtained in the current study agree with those reported by previous studies that compared accelerometers to photoelectric systems, further supporting the explanation provided by Lienhard et al. [15] for these observed differences. ...
Concurrent Validity and Relative Reliability of the RunScribe™ System for the Assessment of Spatiotemporal Gait Parameters During Walking
Article
Full-text available
The evaluation of gait biomechanics using portable inertial measurement units (IMUs) offers real-time feedback and has become a crucial tool for detecting gait disorders. However, many of these devices have not yet been fully validated. The aim of this study was to assess the concurrent validity and relative reliability of the RunScribe™ system for measuring spatiotemporal gait parameters during walking. A total of 460 participants (age: 36 ± 13 years; height: 173 ± 9 cm; body mass: 70 ± 13 kg) were asked to walk on a treadmill at 5 km·h−1. Spatiotemporal parameters of step frequency (SF), step length (SL), step time (ST), contact time (CT), swing time (SwT), stride time (StT), stride length (StL) and normalized stride length (StL%) were measured through RunScribe™ and OptoGait™ systems. Bland–Altman analysis indicated small systematic biases and random errors for all variables. Pearson correlation analysis showed strong correlations (0.70–0.94) between systems. The intraclass correlation coefficient supports these results, except for contact time (ICC = 0.64) and swing time (ICC = 0.34). The paired t-test showed small differences in SL, StL and StL% (≤0.25) and large in CT and SwT (1.2 and 2.2, respectively), with no differences for the rest of the variables. This study confirms the accuracy of the RunScribe™ system for assessing spatiotemporal parameters during walking, potentially reducing the barriers to continuous gait monitoring and early detection of gait issues.
View
... This alternative highlights the potential of these devices for gait analysis but without the corresponding limitations of traditional laboratory technology [19]. For assessing spatiotemporal parameters, the Stryd Running Power Meter has been widely studied in athletes to analyze the absolute reliability and concurrent validity of the Stryd system for the assessment of running stride kinematics at different velocities [20], as well as the agreement between spatiotemporal gait parameters from two different wearable devices and high-speed video analysis [21]. Additionally, the influence of biomechanical parameters on performance in elite triathletes has been analyzed [22]. ...
... The Stryd Summit Power Meter has been recognized as a reliable and valid instrument for the measurement of spatiotemporal biomechanical parameters during running [30], having been extensively utilized in research for this purpose [20][21][22]. The device, a carbon fiber-reinforced foot pod, is affixed to the toe cap of the right shoe. ...
Stability of Running Stride Biomechanical Parameters during Half-Marathon Race
Article
Full-text available
  • Jun 2024
This study explores the stability of biomechanical parameters of the running stride of male trained athletes during a half-marathon competition. Using a field-based descriptive design, eight male athletes from a local training group were monitored throughout an official half-marathon race under identical conditions, assessing biomechanical parameters including ground contact time (GCT), leg spring stiffness (LSS), vertical oscillation (VO), and stride length (SL) recorded via the Stryd Summit Power Meter. A repeated measures analysis of variance (RM ANOVA) was conducted to detect significant changes in biomechanical parameters as the race progressed. Results demonstrated minimal changes in all parameters, with no significant differences observed for GCT (F = 0.96, p = 0.38), VO (F = 0.23, p = 0.87), and SL (F = 1.07, p = 0.35), and a small (η 2 = 0.004) yet statistically significant difference in LSS (F = 5.52, p = 0.03) between the first and second segments, indicating that athletes were able to maintain stable biomechanical parameters throughout the race. The conclusion highlights the need for personalized training programs tailored to the unique biomechanical adaptations and demands of endurance running.
View
... reliability in maximal efforts (Ruiz-Alias et al., 2024b). Similarly, the spatiotemporal parameters reported by the Stryd IMU determining running speed (i.e., cadence and step length) have shown a high level of agreement with respect to gold standards (García-Pinillos et al., 2021;Imbach et al., 2020), which provides the opportunity to record distance in contexts where GNSS is not feasible (i.e., treadmill and indoor track). On the other hand, it is well-known that the distance captured by the GNSS signal of current sport watches is susceptible to different sources of error (i.e., satellite signal obstruction, satellite availability, weather conditions, and gaps in the data) (Scott et al., 2016), which could condition the proximity of the predicting trials to the best-fit regression line. ...
Power or speed: Which metric is more accurate for modelling endurance running performance on track?
Article
Full-text available
This study aimed to compare the accuracy of the power output, measured by a power meter, with respect to the speed, measured by an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) sport watch to determine the critical power (CP) and speed (CS), work over CP (W') and CS (D'), and long‐duration performance (i.e., 60 min). Fifteen highly trained athletes randomly performed seven time trials on a 400 m track. The CP/CS and W'/D' were defined through the inverse of time model using the 3, 4, 5, 10, and 20 min trials. The 60 min performance was estimated through the power law model using the 1, 3, and 10 min trials and compared with the actual performance. A lower standard error of the estimate was obtained when using the power meter (CP: 2.7 [2.1–3.3] % and W': 13.8 [10.4–17.3] %) compared to the speed reported by the IMU (CS: 3.4 [2.5–4.3] %) and D': 20.7 [16.6–24.7] %) and GNSS sport watch (CS: 3.4 [2.5–4.3] % and D': 20.6 [16.7–24.7] %). A lower coefficient of variation was also observed for the power meter (4.9 [3.7–6.1] %) Regarding the speed reported by the IMU (10.9 [7.1–14.8] %) and GNSS sport watch (10.9 [7.0–14.7] %) in the 60 min performance estimation, the power meter offered lower errors than the IMU and GNSS sport watch for modelling endurance performance on the track.
View
... Most studies investigating running power data derived by Stryd featured a laboratory setting [19,47,58]. Here we present a longitudinal training dataset of running power in a "real-world" setting, with outdoor and indoor training sessions. ...
Training Intensity Distribution of a 7-Day HIIT Shock Microcycle: Is Time in the “Red Zone” Crucial for Maximizing Endurance Performance? A Randomized Controlled Trial
Article
Full-text available
  • Sep 2024
Background Various studies have shown that the type of intensity measure affects training intensity distribution (TID) computation. These conclusions arise from studies presenting data from meso- and macrocycles, while microcycles, e.g., high-intensity interval training shock microcycles (HIIT-SM) have been neglected so far. Previous literature has suggested that the time spent in the high-intensity zone, i.e., zone 3 (Z3) or the “red zone”, during HIIT may be important to achieve improvements in endurance performance parameters. Therefore, this randomized controlled trial aimed to compare the TID based on running velocity (TIDV), running power (TIDP) and heart rate (TIDHR) during a 7-day HIIT-SM. Twenty-nine endurance-trained participant were allocated to a HIIT-SM consisting of 10 HIIT sessions without (HSM, n = 9) or with (HSM + LIT, n = 9) additional low-intensity training or a control group (n = 11). Moreover, we explored relationships between time spent in Z3 determined by running velocity (Z3V), running power (Z3P), heart rate (Z3HR), oxygen uptake (Z3V˙O2Z3V˙O2{\text{Z}}{3_{\dot{\text{V}}\text{O}_2}}) and changes in endurance performance. Results Both intervention groups revealed a polarized pattern for TIDV (HSM: Z1: 38 ± 17, Z2: 16 ± 17, Z3: 46 ± 2%; HSM + LIT: Z1: 59 ± 18, Z2: 14 ± 18, Z3: 27 ± 2%) and TIDP (Z1: 50 ± 8, Z2: 14 ± 11, Z3: 36 ± 7%; Z1: 62 ± 15, Z2: 12 ± 16, Z3: 26 ± 2%), while TIDHR (Z1: 48 ± 13, Z2: 26 ± 11, Z3: 26 ± 7%; Z1: 65 ± 17, Z2: 22 ± 18, Z3: 13 ± 4%) showed a pyramidal pattern. Time in Z3HR was significantly less compared to Z3V and Z3P in both intervention groups (all p < 0.01). There was a time x intensity measure interaction for time in Z3 across the 10 HIIT sessions for HSM + LIT (p < 0.001, pη² = 0.30). Time in Z3V and Z3P within each single HIIT session remained stable over the training period for both intervention groups. Time in Z3HR declined in HSM from the first (47%) to the last (28%) session, which was more pronounced in HSM + LIT (45% to 16%). A moderate dose–response relationship was found for time in Z3V and changes in peak power output (rs = 0.52, p = 0.028) as well as time trial performance (rs = − 0.47, p = 0.049) with no such associations regarding time in Z3P, Z3HR, and Z3V˙O2Z3V˙O2{\text{Z}}{3_{\dot{\text{V}}\text{O}_2}}. Conclusion The present study reveals that the type of intensity measure strongly affects TID computation during a HIIT-SM. As heart rate tends to underestimate the intensity during HIIT-SM, heart rate-based training decisions should be made cautiously. In addition, time in Z3V was most closely associated with changes in endurance performance. Thus, for evaluating a HIIT-SM, we suggest integrating a comprehensive set of intensity measures. Trial Registration Trial register: Clinicaltrials.gov, registration number: NCT05067426.
View
... To the best of our knowledge, this is the first study that investigated shoe properties and clustering running pattern responses in the field with a commercially available sensor. The reliability of the STRYD sensor has been proven accurate for ground contact time, leg spring stiffness, flight time, stride length, and cadence [21,22], but it is yet to be validated for vertical oscillation and peak ground reaction force. Having the possibility to use the individual's leg length could give a more individualized approach in the results for the end user and would be recommended as an option in further software development of wearable sensors. ...
Clustering Runners’ Response to Different Midsole Stack Heights: A Field Study
Article
Full-text available
Advanced footwear technology featuring stack heights higher than 30 mm has been proven to improve running economy in elite and recreational runners. While it is understood that the physiological benefit is highly individual, the individual biomechanical response to different stack heights remains unclear. Thirty-one runners performed running trials with three different shoe conditions of 25 mm, 35 mm, and 45 mm stack height on an outdoor running course wearing a STRYD sensor. The STRYD running variables for each participant were normalized to the 25 mm shoe condition and used to cluster participants into three distinct groups. Each cluster showed unique running patterns, with leg spring stiffness and vertical oscillation contributing most to the variance. No significant differences were found between clusters in terms of body height, body weight, leg length, and running speed. This study indicates that runners change running patterns individually when running with footwear featuring different stack heights. Clustering these patterns can help understand subgroups of runners and potentially support running shoe recommendations.
View
... The main spatiotemporal parameters of the gait cycle (contact time and step frequency) were measured for every step during treadmill running using a Stryd Power Meter device (V2; Stryd, Boulder, CO, USA), sampling at 1000 Hz, and the information was analyzed using the web-based Stryd Power Center application. The device uses a triaxial accelerometer with adequate reliability and excellent validity for the variables evaluated [30,31]. Data were analyzed for the final minute of each trial and were averaged for subsequent analyses. ...
Relationship Between Advanced Footwear Technology Longitudinal Bending Stiffness and Energy Cost of Running
Article
Full-text available
  • Jun 2024
  • SCAND J MED SCI SPOR
Introduction/Purpose Shoe longitudinal bending stiffness (LBS) is often considered to influence running economy (RE) and thus, running performance. However, previous results are mixed and LBS levels have not been studied in advanced footwear technology (AFT). The purpose of this study was to evaluate the effects of increased LBS from curved carbon fiber plates embedded within an AFT midsole compared to a traditional running shoe on RE and spatiotemporal parameters. Methods Twenty‐one male trained runners completed three times 4 min at 13 km/h with two experimental shoe models with a curved carbon fiber plate embedded in an AFT midsole with different LBS values (Stiff: 35.5 N/mm and Stiffest: 43.1 N/mm), and a Control condition (no carbon fiber plate: 20.1 N/mm). We measured energy cost of running (W/kg) and spatiotemporal parameters in one visit. Results RE improved for the Stiff shoe condition (15.71 ± 0.95 W/kg; p < 0.001; n² = 0.374) compared to the Control condition (16.13 ± 1.08 W/kg; 2.56%) and Stiffest condition (16.03 ± 1.19 W/kg; 1.98%). However, we found no significant differences between the Stiffest and Control conditions. Moreover, there were no spatiotemporal differences between shoe conditions. Conclusion Changes in LBS in AFT influences RE suggesting that moderately stiff shoes have the most effective LBS to improve RE in AFT compared to very stiff shoes and traditional, flexible shoe conditions while running at 13 km/h.
View
Acute effects of long interval training sessions with different recovery durations in well-trained runners
Article
  • Oct 2024
  • SCI SPORT
Objectives. — During interval training, the combination of several training components (stimulus duration, intensity, and recovery) determines the athlete’s acute fatigue, and manipulating these components elicits different acute fatigue responses. We analyzed the effects of manipulating the duration of the recovery period during isoeffort interval training sessions on biomechanical, physiological, perceptual, and neuromuscular parameters. Equipment and methods. — Twelve well-trained runners completed three interval sessions at the highest possible running speed (4 × 4 min) on a treadmill with 1-min, 2-min, or self-selected (SSrecovery) passive recovery periods (standing quietly on the treadmill). Results. — Overall speed and distance after 4 × 4 min were 2.5% higher with a SSrecovery (∼190s) when they were compared to 1-min (P < 0.05) and 2-min recoveries (P < 0.05). Blood lactate concentration (P < 0.05) was 15.6% lower in SSrecovery when compared to 1-min recovery. Rating of perceived exertion was 6.7% higher in 1-min vs. 2-min and SSrecovery (P < 0.05). Vertical oscillation was 3.4% lower in SSrecovery vs. 1-min (P < 0.05), and step frequency was also 1.3 and 1.4% lower in 1-min and 2-min recovery periods when compared to SSrecovery (P < 0.01; P < 0.05). Conclusions. — These results suggest that self-selected recovery with a mean duration of 190 s involves less fatigue than 1- or 2-min recoveries after 3 long interval training sessions (4 × 4 min). In fact, self-selected recovery showed lower blood lactate concentration, vertical oscillation, RPE and step frequency than 1- or 2-min recovery.
View
From lab to field: validity and reliability of inertial measurement unit-derived gait parameters during a standardised run
Article
  • Sep 2024
The aim was to assess concurrent validity and test–retest reliability of spatiotemporal gait parameters from a thoracic-placed inertial measurement unit (IMU) in lab- (Phase One) and field-based (Phase Two) conditions. Spatiotemporal gait parameters were compared (target speeds 3, 5 and 7.5 m·s−1) between a 100 Hz IMU and an optical measurement system (OptoJump Next, 1000 Hz) in 14 trained individuals (Phase One). Additionally, 29 English Premier League football players performed weekly 3 × 60 m runs (5 m·s−1; observations = 1227; Phase Two). Mixed effects modelling assessed the effect of speed on agreement between systems (Phase One) and test–retest reliability (Phase Two). IMU step time showed strong agreement (<0.3%) regardless of individual or running speed. Direction of mean biases up to 40 ms for contact and flight time depended on the running speed and individual. Step time, length and frequency were most reliable (coefficient of variation = 1.3-1.4%) but confounded by running speed. Step time, length and frequency derived from a thoracic-placed IMU can be used confidently. Contact time could be used if bias is corrected for each individual. To optimise test–retest reliability, a minimum running distance of 40 m is needed to ensure 10 constant-speed steps is gathered.
View
Validity and Reliability of Optojump Photoelectric Cells for Estimating Vertical Jump Height
Article
Full-text available
  • Feb 2011
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.
View
Lack of Influence of Muscular Performance Parameters on Spatiotemporal Adaptations With Increased Running Velocity
Article
Full-text available
  • Feb 2017
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.
View
Physiological and Biomechanical Responses to Running on Lower Body Positive Pressure Treadmills in Healthy Populations
Article
Full-text available
  • Feb 2017
  • SPORTS MED
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.
View
Biomechanical Changes During a 50-min Run in Different Footwear and on Various Slopes
Article
Full-text available
  • Sep 2015
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.
View
Reliability and validity of the Myotest® for measuring running stride kinematics
Article
Full-text available
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.
View
The relationship between running economy and biomechanical variables in distance runners
Article
  • Sep 2012
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.
View
Knowledge is power: Issues of measuring training and performance in cycling
Article
  • Aug 2016
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.
View
Bodyweight-supported treadmill training for retraining gait among chronic stroke survivors: A randomized controlled study
Article
  • Apr 2016
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.
View
A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research
Article
  • Mar 2016
  • J Chiropr Med
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.
View
Influence of Step Rate on Shin Injury and Anterior Knee Pain in High School Runners
Article
  • Jan 2016
  • MED SCI SPORT EXER
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.
View