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The purpose of this study was to assess validity and reliability of sprint performance outcomes measured with an iPhone application (named: MySprint) and existing field methods (i.e. timing photocells and radar gun). To do this, 12 highly trained male sprinters performed 6 maximal 40-m sprints during a single session which were simultaneously timed using 7 pairs of timing photocells, a radar gun and a newly developed iPhone app based on high-speed video recording. Several split times as well as mechanical outputs computed from the model proposed by Samozino et al. [(2015). A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running. Scandinavian Journal of Medicine & Science in Sports.] were then measured by each system, and values were compared for validity and reliability purposes. First, there was an almost perfect correlation between the values of time for each split of the 40-m sprint measured with MySprint and the timing photocells (r=0.989–0.999, standard error of estimate=0.007–0.015 s, intraclass correlation coefficient (ICC)=1.0). Second, almost perfect associations were observed for the maximal theoretical horizontal force (F0), the maximal theoretical velocity (V0), the maximal power (Pmax) and the mechanical effectiveness (DRF – decrease in the ratio of force over acceleration) measured with the app and the radar gun (r= 0.974–0.999, ICC=0.987–1.00). Finally, when analysing the performance outputs of the six different sprints of each athlete, almost identical levels of reliability were observed as revealed by the coefficient of variation (MySprint: CV=0.027–0.14%; reference systems: CV=0.028–0.11%). Results on the present study showed that sprint performance can be evaluated in a valid and reliable way using a novel iPhone app.
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Sprint performance and mechanical outputs computed with an iPhone
app: Comparison with existing reference methods
Nursery and Physiotherapy Department, University of Balearic Islands, Palma de Mallorca, Spain;
Faculty of Sport,
Catholic University of San Antonio, Murcia, Spain;
Physiotherapy Department, Catholic University of San Antonio, Murcia,
Alcalá de Henares University, Madrid, Spain &
Department of Sport Sciences, European University of Madrid,
Madrid, Spain
The purpose of this study was to assess validity and reliability of sprint performance outcomes measured with an iPhone
application (named: MySprint) and existing field methods (i.e. timing photocells and radar gun). To do this, 12 highly
trained male sprinters performed 6 maximal 40-m sprints during a single session which were simultaneously timed using 7
pairs of timing photocells, a radar gun and a newly developed iPhone app based on high-speed video recording. Several
split times as well as mechanical outputs computed from the model proposed by Samozino et al. [(2015). A simple
method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running. Scandinavian
Journal of Medicine & Science in Sports.] were then measured by each system, and values
were compared for validity and reliability purposes. First, there was an almost perfect correlation between the values of
time for each split of the 40-m sprint measured with MySprint and the timing photocells (r= 0.9890.999, standard error
of estimate = 0.0070.015 s, intraclass correlation coefficient (ICC) = 1.0). Second, almost perfect associations were
observed for the maximal theoretical horizontal force (F
), the maximal theoretical velocity (V
), the maximal power
) and the mechanical effectiveness (DRF decrease in the ratio of force over acceleration) measured with the app
and the radar gun (r= 0.9740.999, ICC = 0.9871.00). Finally, when analysing the performance outputs of the six
different sprints of each athlete, almost identical levels of reliability were observed as revealed by the coefficient of
variation (MySprint: CV = 0.0270.14%; reference systems: CV = 0.0280.11%). Results on the present study showed that
sprint performance can be evaluated in a valid and reliable way using a novel iPhone app.
Keywords: Acceleration, technology, biomechanics, testing
Sprint speed, power output and forward acceleration
are key physical determinants of performance in
many activities in sport (Cronin & Sleivert, 2005;
Faude, Koch, & Meyer, 2012; Morin et al., 2012).
The entire spectrum of linear force, velocity and
power output capabilities of an athlete may be
described and studied through the forcevelocity
(Fv) and powervelocity (Pv) relationships
(Morin & Samozino, 2016; Rabita et al., 2015). Fur-
thermore, the importance of the mechanical
effectiveness of ground force application has been
shown as paramount for a horizontally oriented
force production during sprint acceleration
(Hunter, Marshall, & McNair, 2005; Kawamori,
Nosaka, & Newton, 2013; Morin et al., 2012;
Rabita et al., 2015). These mechanical factors of
sprint acceleration performance have been the focus
of a recently published review (Haugen & Buchheit,
2016), and they are of interest to sport practitioners
in designing effective training programmes.
The analysis of sprint performance (i.e. mainly split
times and average speed over sprint intervals) has
© 2016 European College of Sport Science
Correspondence: Carlos Balsalobre-Fernández, European University of Madrid, C/ Tajo, Villaviciosa de Odón, Madrid, Spain, E-mail:
European Journal of Sport Science, 2016
traditionally been performed utilising reference
methods which are measuring either the runners dis-
placement (e.g. timing gates) or velocity (e.g. radar
and laser systems) as a function of time (Haugen &
Buchheit, 2016). However, the underlying mechan-
ical determinants of performance (sprint kinetics;
i.e. external forces acting upon the athletes body)
are usually measured and computed by means of con-
siderably more costly and complex devices (e.g. force
platforms or instrumented treadmills (Morin, Samo-
zino, Bonnefoy, Edouard, & Belli, 2010; Rabita et al.,
2015)). Therefore, the possibility for many coaches
and/or sports clubs to measure sprint kinetics
becomes unavailable or impractical. Thus, a detailed
analysis of sprint performance remains exclusive to
research laboratories or high-performance centres.
In order to analyse sprint kinetics in field con-
ditions, a simple method has been designed to accu-
rately estimate the theoretical maximal force (F
velocity (V
), maximal power output (P
) and
mechanical effectiveness of ground force application
(ratio of force, RF, and decrease in the RF over accel-
eration, DRF) during sprint acceleration (Samozino
et al., 2015). This simple methodis based on the
measurement of either five split times or the vel-
ocitytime data and basic laws of motion applied to
the centre-of-mass and has been shown valid in com-
parison to force platform measurements the gold
standardmethod for running kinetics (Samozino
et al., 2015). The main advantage of this method is
that it allows an accurate estimation of sprint per-
formance and mechanics in a more affordable way
than using several force platforms or instrumented
treadmills. It has also been used in the sport science
research context in several recent studies (Buchheit
et al., 2014; Cross et al., 2015; Marrier et al., 2016;
Mendiguchia et al., 2014; Pantoja, Saez de Villarreal,
Brisswalter, Peyré-Tartaruga, & Morin, 2016). This
method, although simple, still requires at least
seven pairs of timing gates (for measuring split
times and the distancetime input) or a radar
system (for measuring speedtime input). Although
considered as reference methods for sprint perform-
ance monitoring (Haugen & Buchheit, 2016),
timing gates and radar guns are still costly and not
available for the majority strength and conditioning
professionals. In addition, and most importantly,
the computations required to process data with this
simple method are substantial and require significant
skills. Therefore, a low-cost, user-friendly and accu-
rate device or application could have significant prac-
tical applications for coaches with no advanced
instrumental or biomechanics education required.
Such an application (MyJump) has recently been
developed and validated to measure flight time
during vertical jumping from slow-motion (120 fps)
video recordings using an iPhone 5s (Balsalobre-Fer-
nández, Glaister, & Lockey, 2015; Gallardo-Fuentes
et al., 2016); providing easy and accurate compu-
tation of the jumping Fv profile and other complex
mechanical determinants of jumping performance
(Morin & Samozino, 2016; Samozino et al., 2014;
Samozino, Morin, Hintzy, & Belli, 2008; Samozino,
Rejc, Di Prampero, Belli, & Morin, 2012). Using
the same approach, now with a higher frame rate
recording (240 fps for the iPhone 6 or newer and
iPad Air or newer), an Apple application called
MySprint has been designed for the measurement of
40-m sprint acceleration performance and the com-
putation of all the aforementioned sprint mechanical
outputs in field conditions and for a much lower cost
(iPhone around $500 + $9 of the App compared to
about $4000 for photocells or radar). In order to
test the validity of MySprint, the aim of this study
was to assess validity and reliability of performance
inputs (split times and velocitytime curves) and
computed mechanical variables to the current two
reference methods in this context (Haugen & Buch-
heit, 2016): timing gates and radar.
Twelve trained male sprinters (age, 21.4 ± 3.9 years;
body-mass, 71.5 ± 4.5 kg; body-height 1.80 ±
0.05 m; body-fat, 7.2 ± 3.3%) voluntarily partici-
pated in the study. Their best performances over
100 m were in the range 10.7411.57 s and each
athlete had participated in national or regional level
before this study, thus, all of them were highly
trained and familiarised with the testing exercise.
None of the athletes had suffered any lower-extremity
injury during the six months preceding the study.
Before commencing of the study, all athletes signed
an informed consent according the Declaration of
Helsinki and approved by the ethical committee of
the Catholic University of San Antonio.
All athletes performed a 15-min warm-up consisting
of 5 min of jogging, 5 min of lower limb dynamic
stretching and 5 min of progressive sprints (i.e. 40-
m at 50%, 70% and 90% effort). Following the
warm-up, athletes performed 6 maximal effort 40-m
sprints, with 5-min rest between trials, on a synthetic
outdoor track. Athletes started from a crouching pos-
ition (staggered-stance) with the right hand on the
track. The six trials were assessed by recording each
sprint using an iPhone 6 and MySprint app (Apple
2N. Romero-Franco et al.
Inc., USA), a radar gun (Stalker ATS ProII; Applied
Concepts, Plano, TX, USA) and seven pairs of
timing photocells (Microgate, Bolzano, Italy) simul-
taneously. In order to synchronise the three devices,
the start of the sprint was determined as the
moment in which the right thumb of the athlete
took off the ground (this was detected by visual
inspection with MySprint, pressure pad for timing
gates and the centre-of-mass velocity above an arbi-
trary speed of 0.2 m s
for the radar). Split time
and velocitytime data were used along with subjects
body-mass and body-height as inputs to calculate F
,Pmax and DRF according to Samozinos method
(Samozino et al., 2015). Data were compared
between the MySprint app, radar gun and timing
photocells. This comparison to the two reference
methods included the inputs of the method (i.e.
split times or velocitytime data) and the outputs
(i.e. sprint mechanics) of the sprint effort.
Seven pairs of timing photocells were placed at 0,
5, 10, 15, 20, 30 and 40 m to measure the 6 different
split times during the six 40-m trials of each athlete. A
radar gun measured instantaneous velocity at a
sampling rate of 46.875 Hz. The radar gun was
placed on a tripod 10 m behind the athletes at a
height of 1 m, corresponding approximately to the
height of athletescentre-of-mass (Morin et al.,
2012). The MySprint app was developed using the
software XCode 5.0.5 for Mac OSX 10.9.2 and was
installed on an iPhone 6 running iOS 9.3.2; filmed
with the iPhones built in 240 fps high-speed
camera at a quality of 720p.
The MySprint app was specifically designed for
analysing multiple split times from a high-speed
video of a maximal 40-m sprint by registering the
time-stamp for the beginning and different points
where athlete is crossing the six different markers.
To record the video of each sprint, the iPhone 6
was mounted to a tripod (in the frontal plane) in
order to film the sprint from the side, at the 20 m
marker and at 18 m from the track, in order to reg-
ister the entire sprint. Since the iPhone 6 was in a
fixed position, video parallax was corrected to
ensure 5-, 10-, 15-, 20-, 30- and 40-m split times
were measured properly (Figure 1). Since the
iPhone 6 was in a fixed position, video parallax
was corrected to ensure 5-, 10-, 15-, 20-, 30- and
40-m split times were measured properly (Figure
1). The correction of the parallax was done by posi-
tioning the different markers not exactly at the
associated distances (i.e. 5, 10, 15, 20, 30 and
40 m from the starting line), but at adjusted pos-
itions so that the subjects were viewed by the
iPhone camera to cross the markers with their hip
when they were exactly at these targeted distances
(5, 10, 15, 20, 30 and 40 m, Figure 1).
Two independent observers, were asked to select
the first frame in which athletesright thumb left
the ground (start of the sprint) and, subsequently,
the frame in which the pelvis was aligned with each
of the 5 different markers for each of the 72 recorded
sprints using the MySprint app. After this detection
procedure, the MySprint app automatically calculated
each split time in milliseconds and sprint mechanical
outputs by implementing the equations developed by
Samozino et al. (2015).
Statistical analyses
All data for the 72 sprints compared in total (6 trials,
12 athletes) are presented as mean ± standard devi-
ation (SD). An independent t-test analysis was used
in order to compare the MySprint app to the radar
gun and the photocells for the measurement of (i)
each split time (timing gate methods) and (ii) the
values of F
and DRF (radar method).
First, to analyse the concurrent validity of the
MySprint app in comparison with the other devices,
Pearsons productmoment correlation coefficient
with 95% confidence intervals (CI), the analysis of
the slope and y-intercept of the resultant regression
lines and the standard error of estimate (SEE) were
used. Second, to test the level of agreement
between devices on the measurement of the afore-
mentioned variables, the intraclass correlation coeffi-
cient (ICC-2,1-) (mean values of the six trials of each
individual) and BlandAltman plots were analysed.
Also, the coefficient of variation (CV) was used to
analyse the level of reliability of each instrument on
the measurement of the six different sprints of each
athlete. In order to analyse the inter-observer
reliability, the ICC (2,1) and BlandAltman plots
were used. All statistical analyses were performed
using IBM SPSS statistics 22 (IBM Co, Armonk,
NY, USA) and Microsoft Excel 2010 (Microsoft
Corp., Redmont, WA, USA) with a level of signifi-
cance of p< .05.
MySprint app vs. timing photocells for the
measurement of sprint time
An almost perfect correlation between the MySprint
app and the photocells for the measurement of the
different sprint times was observed (r= 0.989
0.999, SEE = 0.0070.015 s, p< .001) (Figure 2).
Also, a perfect agreement between the values of
time was obtained with both the MySprint app and
the photocells as revealed by the ICC (ICC = 1.00,
CI = 1.001.00) and the BlandAltman plot
Sprint performance and mechanical outputs computed with an iPhone app 3
(Figure 3). No statistically significant differences
between the two instruments on the measurement of
sprint time were observed, as revealed by the indepen-
dent measures t-test (mean difference = 0.002 ±
0.01 s, p= .952). Also, root mean square of error
was calculated for comparison between two devices
(RMSE = 0.003). When analysing the reliability of
the MySprint app for the measurement ofthe six differ-
ent trials, very low CV (average values of the different
split times), almost identical to those obtained with the
timing photocells, were observed (MySprint:CV=
0.027%; timing photocells: CV = 0.028%).
Figure 1. Proposed reference system used during the validation protocol of MySprint. Distances of 5, 10, 15, 20, 30 and 40 m are marked by
Figure 2. Concurrent validity between the MySprint app and the
timing photocells for the measurement of the time to cover 05,
510, 1015, 1520, 2030, 3040 and 040 m. Each split time
is represented with different markers; however, the regression
equation and the value of R
correspond to the whole dataset.
The identity line is represented by the discontinuous line, while
the regression line is represented by the continuous black line.
Figure 3. BlandAltman plots for the timing photocells and
MySprint app split time data. The central line represents the absol-
ute average difference between instruments, while the upper and
the lower lines represent ± 1.96 s.
4N. Romero-Franco et al.
MySprint app vs. radar gun for the calculation of
sprint mechanics
An almost perfect correlation between the mechan-
ical variables computed with the MySprint app and
the radar gun time data was also observed (r=
0.9740.999, p< .001). Also, the ICC revealed very
high to perfect agreements between the mechanical
variables computed with the MySprint app and the
radar gun (ICC = 0.9871.00). Furthermore, no stat-
istically significant differences between the two
instruments on the measurement of the different
mechanical variables were observed, as revealed by
the independent measures t-test (p> .05). Moreover,
when analysing the reliability of the MySprint app for
the measurement of mechanical variables computed
from the six different trials, very low CV, almost iden-
tical to those obtained with the radar gun, were
observed (MySprint: CV = 0.14%; radar gun: CV =
0.11%). See Table I for more details.
Inter-observer reliability
Finally, when analysing the values of sprint time
measured by the two independent observers, an
almost perfect agreement, with no significant differ-
ences between raters was observed, as revealed by
the independent measures t-test (mean difference =
0.004 ± 0.03, p= .999) and the ICC (ICC = 0.998,
CI = 0.9970.998).
The present investigation was aimed to analyse the
validity and reliability of a new iPhone app
(MySprint) for the measurement of sprint mechanics
under field conditions. Results showed that the
agreement of the MySprint app with reference
methods for the measurement of sprint time (i.e.
timing photocells) revealed almost perfect Pearsons
productmoment correlation coefficient (r= 0.999)
and the very low SEE (0.013 s), with no statistically
significant differences. Moreover, the analysis of the
BlandAltman plot provided valuable information
about the agreement between MySprint and the
timing photocells which, as revealed by the ICC,
was perfect (ICC = 1.0). First, the limits of agree-
ment of the BlandAltman plot ( ± 1.96 SD) were
just ± 0.028 s, with the majority of the points close
to 0. Moreover, the value of R
of the linear
regression on the BlandAltman plot was very low
= 0.119) meaning that the differences between
devices were the same at all ranges of speeds
measured (i.e. there was no proportional bias).
Finally, when analysing the six sprints of each
athlete with both the MySprint app and the timing
photocells, almost identical CV was observed,
meaning that the measures obtained with MySprint
were as reliable as those measured with the timing
photocells. In conclusion, MySprint can be used to
time 40 m sprints and its different splits (05, 510,
1015, 1520, 2030 and 3040 m) in a very valid,
reliable and accurate way. These results are in line
with a recent study that demonstrated that video-
analysis is indeed the most stable timing system
when analysing the variability of different sprints of
the same subject, with lower SDs than photocells or
laser systems (Bond, Willaert, & Noonan, 2016).
Although sprint time is an important variable
related to other measures of physical performance,
analysing other sprint-related mechanical variables
(e.g. F
and DRF) has been highlighted
as paramount over the last decade (Samozino et al.,
2015). Specifically, it has been shown that the
ability to produce horizontal force via a high mechan-
ical effectiveness is key to understand the success of
highly trained sprinters (Morin et al., 2012; Rabita
et al., 2015) and, moreover, it was observed that
measuring horizontal forces during acceleration
sprints could provide relevant information in the
context of hamstring injury prevention (Mendiguchia
et al., 2014,2016). To date, measuring these mech-
anical variables requires advanced equipment such
as force platforms, radar guns, laser system or
timing photocells and a significant knowledge of
Table I. Concurrent validity and reliability of the radar gun vs. MySprint on the measurement of sprint mechanics
Radar method MySprint method SEE rsiICC (95% CI)
(N/kg) 6.8 ± 0.6 6.9 ± 0.6 0.09 0.999 (0.9980.999) 1.04 0.22 0.999 (0.9991.000)
(m/s) 8.28 ± 1.0 8.35 ± 1.0 0.03 0.988 (0.9740.999) 0.992 0.14 0.987 (0.9790.992)
(W/kg) 14.1 ± 2.4 14.5 ± 2.5 0.19 0.988 (0.9740.999) 1.032 0.09 0.998 (0.9970.999)
DRF (%s/m) 0.077 ± 0.01 0.078 ± 0.01 0.001 0.994 (0.9900.9979 0.97 0.001 0.994 (0.9910.996)
Notes: F
, theoretical maximal force; V
, theoretical maximal velocity; P
, maximal power output; DRF, slope of the linear decrease on ratio
of force as sprint velocity increases; r, Pearsons productmoment correlation coefficient; s, slope of the regression line; i,y-intercept of the
regression line; ICC, intraclass correlation coefficient; CI, confidence interval; SEE, standard error of estimate.
Sprint performance and mechanical outputs computed with an iPhone app 5
biomechanics and data processing (Samozino et al.,
2015). However, since the validated reference
method by Samozino et al. (2015) for measuring
sprint mechanics uses 6 splits times of a 40-m
sprint, it was expected that the MySprint app (which
implements Samozino et al.s equations) would
provide valid and reliable estimations of those mech-
anical variables provided that the main input of the
model (sprint time) was successfully validated. This
was confirmed by the almost perfect Pearsons
productmoment correlation coefficients (r= 0.974
0.999), the almost perfect agreement (ICC = 0.979
1.0) or the small bias (< 2.5%) between the MySprint
app and the radar gun for the measurement of the
main sprint mechanical outputs of F
mechanical effectiveness. Therefore, this study
demonstrates that MySprint is highly valid and
reliable for measuring both sprint time and key accel-
eration mechanics.
The main limitation of the methodology used by
the MySprint app to measure the sprint performance
is the fact that the user has to manually select the
frames in which the athlete passes for different
markers, making the measurement process subjective
which may produce some error. To address this issue,
we analysed the values of sprint times measured by
two independent observers using the MySprint app,
in order to test if significant differences occurred.
Our results showed that the difference between
both observers was negligible, with no statistically sig-
nificant differences between the values of time
measured with MySprint by two observers indepen-
dently and an almost perfect ICC. These results are
in line with another study that analysed the validity
of the MyJump app which measures flight time of ver-
tical jumps by the manual selection of the take-off and
landing frames (Balsalobre-Fernández et al., 2015;
Stanton, Kean, & Scanlan, 2015). These small differ-
ences between observers are due to the fact that the
distance between one frame and the next or previous
one is just 0.004 s thanks to the 240 fps video record-
ing of the iPhone 6; therefore, taking into consider-
ation that observers typically differed on one or two
frames, the absolute difference in seconds between
observers could not be high. In fact, our results
showed that the mean difference between observers
was 0.004 ± 0.03 s, that is, one frame.
Summarising, the present study demonstrated that
the MySprint app shows a very high agreement with
the current main reference methods for sprint per-
formance analysis (Haugen & Buchheit, 2016) for
the measurement of 40-m sprint times and its differ-
ent splits. Therefore, in addition to its low cost com-
pared with the aforementioned reference methods, it
can be considered valid to calculate mechanical vari-
ables such as F
and DRF while keeping in
mind that the measurement process is based on the
manual selection of different frames in which the ath-
letes pass several markers. To the best of our knowl-
edge, this is the first study analysing the validity and
reliability of an iPhone application for measuring
sprint performance. Physiotherapists, coaches and
athletes may benefit from this easy-to-use, portable
and affordable tool that provides advanced sprint
mechanics measurements under field conditions.
Therefore, the development of this app could be of
great interest in the context of sport performance
and injuries management.
We are grateful to Dr Pierre Samozino (Laboratory of
Exercise Physiology, University of Savoy, France)
and Dr Jean-Benoit Morin (Laboratory of Human
Motricity, Education Sport and Health, University
of Nice, France) for their valuable participation in
this project and their enthusiastic and friendly collab-
oration, as well as for their helpful and stimulating
comments on the present article. Also we would
like to thank Víctor Cuadrado-Peñafiel (Complu-
tense University of Madrid, Spain) and Juan
Párraga-Montilla (University of Jaen, Spain) for
their participation in the measurement sessions and
valuable participation during the process.
Disclosure statement
The second author is the main designer of the app
covered in this manuscript. To guarantee the inde-
pendency of the results, two independent observers
collected and analyse the data.
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Sprint performance and mechanical outputs computed with an iPhone app 7
... After an individualized sprint-specific warm-up, lasting ~30 minutes, including jogging and dynamic stretching followed by three progressive sprints of 40-m, participants performed two maximal 30-m sprints from a 3-point standing position, separated by 5 minutes of rest (Romero-Franco et al., 2017;Samozino et al., 2016). The testing procedures were conducted in an indoor stadium with a synthetic track. ...
... Eight marking poles were positioned along the 30-m distance to determine the 5-m split times. The marking poles placed at adjusted positions to avoid parallax error (Romero-Franco et al., 2017). ...
... The selection criterion for the definition of the split times was the moment in which the right hip crossed the corresponding marking pole. Moreover, 5-m split time and running velocity per 5-m interval were calculated from the modelled spatiotemporal data of each camera (Romero-Franco et al., 2017;Samozino et al., 2016). The intraclass correlation coefficient (ICC) between trial 1 and trial 2, based on 30-m sprint time, was very high (0.998, with 95% confidence interval (CI) = 0.996 -0.999). ...
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The aim of this study was to explore the sprint mechanical and kinematic characteristics of sub-elite and recreational male sprinters during the acceleration phase of a linear sprint running section. Eighteen sprinters (nine sub-elite, nine recreational) performed two all-out 30-m sprints. Three high speed panning cameras were used to record the entire sprint distance continuously. The sprint velocity-time data of each camera were determined by temporal analysis of the video recording. These values were used to determine the variables of the horizontal F-v profile (theoretical maximal values of horizontal force [F0], velocity [v0], power [Pmax], the maximal ratio of horizontal to resultant force [RFmax], the decline in the ratio of horizontal force production as the running speed increases [DRF]) and key kinematic characteristics. Significantdifferences were observed between the groups for v0 (0.79 ± 0.24 m∙s-1, p = 0.005), Pmax (3 ± 1.17 W∙kg-1, p = 0.020) and RFmax (3.1 ± 1.2 %, p = 0.021). No statistical differences were found for F0 (0.55 ± 0.46 N∙kg-1, p = 0.25) and DRF (0.2 ± 0.5 %∙s∙m, p = 0.67). The mean running velocity and mean step rate were higher, whereas mean ground contact time was shorter in sub-elite sprinters. There were no differences in mean step length and mean flight time. The sub-elite sprinters in our study demonstrated the capacity to generate higher amounts of horizontal forces at higher running speeds, apply horizontal force to the ground more efficiently and achieve higher step rates during sprint acceleration than recreational sprinters.
... The MySp app videos were filmed with an iPad (7th generation; iOS 14.4.2 with built in slow-motion video support at 120 fps at a quality of 720p) according to previousl validated methodologies [20]. Notably, Romero et al. [20] used a 40-m track, whereas th current study implemented a 30-m track, consequently slightly altering our paralla measurements ( Figure 2). ...
... The MySp app videos were filmed with an iPad (7th generation; iOS 14.4.2 with built in slow-motion video support at 120 fps at a quality of 720p) according to previousl validated methodologies [20]. Notably, Romero et al. [20] used a 40-m track, whereas th current study implemented a 30-m track, consequently slightly altering our paralla measurements ( Figure 2). The iPad was mounted to a tripod (height, 1.46 m) to recor each sprint, assessing the frontal plane to film the sprint from the side. ...
... The video paralla was corrected to ensure that the 5, 10, 15, 20, 25, and 30 m split times were measure accurately. As per [20], the marking poles were not exactly at the associated distances bu rather, at the adjusted positions. The iPad camera filmed the participants' hips as the crossed the markers when they were precisely at the targeted distances ( Figure 3). ...
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This study examined the level of agreement (Pearson product-moment correlation [rP]), within- and between-day reliability (intraclass correlation coefficient [ICC]), and minimal detectable change of the MusclelabTM Laser Speed (MLS) device on sprint time and force–velocity–power profiles in Division II Collegiate athletes. Twenty-two athletes (soccer = 17, basketball = 2, volleyball = 3; 20.1 ± 1.5 y; 1.71 ± 0.11 m; 70.7 ± 12.5 kg) performed three 30-m (m) sprints on two separate occasions (seven days apart). Six time splits (5, 10, 15, 20, 25, and 30 m), horizontal force (HZT F0; N∙kg−1), peak velocity (VMAX; m∙s−1), horizontal power (HZT P0; W∙kg−1), and force–velocity slope (SFV; N·s·m−1·kg−1) were measured. Sprint data for the MLS were compared to the previously validated MySprint (MySp) app to assess for level of agreement. The MLS reported good to excellent reliability for within- and between-day trials (ICC = 0.69–0.98, ICC = 0.77–0.98, respectively). Despite a low level of agreement with HZT F0 (rP = 0.44), the MLS had moderate to excellent agreement across nine variables (rp = 0.68–0.98). Bland–Altman plots displayed significant proportional bias for VMAX (mean difference = 0.31 m∙s−1, MLS < MySp). Overall, the MLS is in agreement with the MySp app and is a reliable device for assessing sprint times, VMAX, HZT P0, and SFV. Proportional bias should be considered for VMAX when comparing the MLS to the MySp app.
... Se trataría de grabar un sprint recorriendo una distancia determinada conociendo la masa y altura del atleta. Se puede realizar mediante el uso de células fotoeléctricas o la app móvil MySprint igualmente validada (Romero-Franco et al., 2017). En función de los resultados, podremos observar si el atleta es mejor en la fase de aceleración o en la de velocidad máxima, etc.; y así priorizar unos métodos de entrenamiento u otros. ...
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The subject of study on which this work focuses is related to high-performance track and field, specifically sprinting. The aim is to analyse a weakness in the entity where the university internships have been carried out in order to be able to implement proposals for improvement that optimise, in this case, sport performance. To do this, a group of sprint training belonging to the Club Escuela Atletismo Majadahonda were analysed, using various techniques such as direct observation, monitoring of training sessions or informal conversations with the coach and athletes. The main weakness observed was a high training load in the form of plyometric and strength work, of which the magnitude of metabolic and mechanical stress they can cause is unknown. These aspects are of great interest, as the existence of a relationship between jumping capacity, strength production and performance in high-intensity sprints is being considered. In order to establish a proposal for improvement, a broad theoretical framework will be described in which the determining aspects of the training process on sprinting performance will be presented. Finally, a series of practical tests with their respective technological tools are presented. In addition, recommendations are established in terms of the periodisation of the tests, the programming of the protocol and of these tests throughout the season. Once the effects of the training load are known with greater accuracy, a strength training programming proposal is described, prioritising certain exercises with a greater transfer to sprint performance. By way of conclusion, we reflect on the traditional training programmes with high fatiguing volumes, dismantling this old conception through evidence of the same effects with a reduction in training.
... Anterior-posterior force production capacities during sprinting were obtained using a validated method based on a macroscopic inverse dynamics approach applied to the players center of mass and requiring only split times (at 5, 10, 20, and 30 m) and anthropometrical input data (31). Split times were obtained by filming each sprint with MySprint app on an iPhone 7 (240 fps; Apple, Inc., Cupertino, CA), of which the validity and reliability for these measurement have been previously published (29). All instructions recommended by application's developers were respected except for the starting procedure. ...
Le Scouarnec, J, Samozino, P, Andrieu, B, Thubin, T, Morin, JB, and Favier, FB. Effects of repeated sprint training with progressive elastic resistance on sprint performance and anterior-posterior force production in elite young soccer players. J Strength Cond Res 36(6): 1675-1681, 2022-This study aimed to determine whether repeated sprint training with progressive high elastic resistance could improve sprint performance and anterior-posterior (AP) force production capacities of elite young soccer players. Seven elite U19 soccer players underwent 10 sessions of elastic-resisted repeated sprints on 8 weeks, whereas 8 U17 players from the same academy (control group) followed the same protocol without elastic bands. Sprint performance and mechanical parameters were recorded on a 30-m sprint before and after training. The control group did not show change for any of the measured variables. In contrast, the elastic-resisted training resulted in a significant improvement of the sprint time (-2.1 ± 1.3%; p = 0.026; Hedges' g = -0.49) and maximal velocity (Vmax; +3.9 ± 2%; p = 0.029; Hedges' g = 0.61) reached during the 30-m sprint. These enhancements were concurrent with an increase in the maximal power output related to AP force (Pmax; +4.9 ± 5.1%%; p = 0.026; Hedges' g = 0.42). Although the theoretical maximal AP force (F0) remained unchanged in both groups, there was a medium but nonsignificant increase in theoretical maximal velocity (V0; +3.7 ± 2.5%; p = 0.13; Hedges' g = 0.5) only in the elastic group. Therefore, the present results show that sprint capacity of elite young soccer players can be further improved by adding incremental resistance against runner displacement to raise the ability to produce AP force, rather at high velocity in the final phase of the acceleration.
... Cameras were [31]. Split times of 5, 10, 15, 20, 25 and 30 m were determined using marking poles which were placed along the distance of 30 m sprint, ensuring the correction of video parallax error [34]. Additionally, in order to measure step length, 0.05 m × 0.05 m custom markers were placed on both sides of the running lanes across the entire runway [35]. ...
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The aim of this study was to investigate the effects of heavy sled towing using a load corresponding to a 50% reduction of the individual theoretical maximal velocity (ranged 57–73% body mass) on subsequent 30 m sprint performance, velocity, mechanical variables (theoretical maximal horizontal force, theoretical maximal horizontal velocity, maximal mechanical power output, slope of the linear force–velocity relationship, maximal ratio of horizontal to total force and decrease in the ratio of horizontal to total force) and kinematics (step length and rate, contact and flight time). Twelve (n = 5 males and n = 7 females) junior running sprinters performed an exercise under two intervention conditions in random order. The experimental condition (EXP) consisted of two repetitions of 20 m resisted sprints, while in the control condition (CON), an active recovery was performed. Before (baseline) and after (post) the interventions, the 30 m sprint tests were analyzed. Participants showed faster 30 m sprint times following sled towing (p = 0.005). Running velocity was significantly higher in EXP at 5–10 m (p = 0.032), 10–15 m (p = 0.006), 15–20 m (p = 0.004), 20–25 m (p = 0.015) and 25–30 m (p = 0.014). No significant changes in sprint mechanical variables and kinematics were observed. Heavy sled towing appeared to be an effective post-activation potentiation stimulus to acutely enhance sprint acceleration performance with no effect on the athlete’s running technique.
... Electronic timing provides an accurate and instantaneous result, which makes it easy to quickly compare a large group of skiers or athletes. Because of these characteristics, this is the timing equipment used in official competitions governed by FIS regulations [16] and it is also one of the reference systems used as gold standard in sport research [12,13,[17][18][19]. However, some of the limitations include: the time taken for correct positioning and alignment, the high economic cost, the weather conditions that can affect time measurements, e.g., extreme temperatures [16], the fact that each athlete can cut the light beam with a different part of the body [20], and the reduced number of times that could be taken along a course in relation to the number of gates, as in the case of skiing. ...
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Citation: Buxadé, C.P.-C.; Fernández-Valdés, B.; Morral-Yepes, M.; Viñas, S.T.; Riu, J.M.P.; Moras Feliu, G. Validity of a Magnet-Based Timing System Using the Magnetometer Built into an IMU. Abstract: Inertial measurement units (IMUs) represent a technology that is booming in sports right now. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically to evaluate a magnet-based timing system (M-BTS) for timing short-duration sports actions using the magnetometer built into an IMU in different sporting contexts. Forty-eight athletes (22.7 ± 3.3 years, 72.2 ± 10.3 kg, 176.9 ± 8.5 cm) and eight skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg) performed a 60-m linear sprint running test and a ski slalom, respectively. The M-BTS consisted of placing several magnets along the course in both contexts. The magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. The time between peaks was calculated. The system was validated with photocells. The 95% error intervals for the total times were less than 0.077 s for the running test and 0.050 s for the ski slalom. With the M-BTS, future studies could select and cut the signals belonging to the other sensors that are integrated in the IMU, such as the accelerometer and the gyroscope.
... To this aim, some researchers developed simple methods for field applications in order to measure or estimate several aspects of running biomechanics, such as lower limb stiffness, foot strike patterns, force-velocity profiles and joint angle estimations [51]. Such measurements or estimations are now possible by only using smartphone applications [52][53][54], which makes evaluation of some aspects of the running biomechanics easily accessible for the end-users. ...
The purpose of this chapter is to describe the interdisciplinary field of biomechanics and its importance in the evaluation of running. We will shortly describe the basic principles and origins of this scientific field and go into further details of new technical innovations. Then, a comprehensive overview of laboratory and field-based biomechanical analyses will be discussed in the light of implication for injury aetiology and prevention. Finally, we will end with a discussion on new research areas and implications for future research.
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La presente tesis busca demostrar la veracidad de la teoría del vector de fuerza, ¿importa la fuerza neta o con qué dirección la manifestamos? Estudio realizado con 15 deportistas activos, a los cuales se testeó con diferentes saltos (verticales y horizontales) y sprint. Se utilizan los coeficientes de correlación de Pearson y Spearman, observándose mayores fuerzas de correlaciones para los saltos horizontales.
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This research aimed to develop an android-based speed test instrument called SprintA. SprintA can measure acceleration, length, and the number of steps. This research is development research. The research phase consists of a feasibility test and trial. Participants in the feasibility test consisted of 3 athletic trainers and 1 electrical doctor lecturer. To increase the feasibility, the researcher enlisted the help of four athletic trainers who were not subject to expert judgment. Meanwhile, 15 sprint athletes competed in the trial. The trial was conducted by comparing sprintA with a stopwatch. The root means square error correlation value is a data analysis technique. The results of four experts' evaluations yielded an average score of 86.08, placing them in the "very good" category. In the usability aspect, the results of the assessment from 4 experts show the product helps athletes and coaches to increase scores (33.4%); the product has a real team nature (13.3%); the product can know the speed, acceleration, number of steps (20%); the product is effective for measuring sprints by percentage (20%); and other answers (13.3%). The scores on the safety aspect are the product safe and comfortable to set up (26.70%), the product is easy to use (40%), the product does not interfere with exercise (20%), and other answers (20%). Furthermore, the test results of the sprintA vs. stopwatch correlation score found a value of 0.99 and an RMSE score of 0.47. In conclusion, SprintA is feasible to measure speed, acceleration, and the number of steps. The comparison test for SprintA vs. stopwatch got an RMSE value that was not much different. The creation of this tool is said to facilitate the evaluation of short-distance running athletes.
Fernández-Galván, LM, Casado, A, García-Ramos, A, and Haff, GG. Effects of vest and sled resisted sprint training on sprint performance in young soccer players: A systematic review and meta-analysis. J Strength Cond Res XX(X): 000-000, 2022-The aim of the meta-analysis was to determine the effect of resisted sprint training (RST) on sprint performance in young (<20 years) soccer players and to analyze whether the training equipment (sled or vest) and magnitude of the resistive load (above or below 20% of body mass [BM]) influences the long-term adaptations in sprint performance. Resisted sprint training reduced the acceleration phase time [standardized mean difference (SMD) = -0.41], with greater reduction in sprint time occurring in response to applying resistance with a vest (SMD = -0.70) when compared with a sled (SMD = -0.27). Similar reductions were determined for resistive loads <20% (SMD = -0.55) and ≥20% of BM (SMD = -0.31). Full sprint time showed a small reduction after RST (SMD = -0.36), regardless of the training equipment (sled: SMD = -0.44; vest: SMD = -0.26) and resistive load (<20% of BM: SMD = -0.40 ≥ 20% of BM: SMD = -0.21). There was a small and nonsignificant reduction in the maximum-velocity phase after RST (SMD = -0.25), which was comparable when the training was performed with vest (SMD = -0.34) or sled (SMD = -0.22). No significant differences in the changes of the acceleration phase time (SMD = 0.05) or full sprint time (SMD = 0.08) were observed between the experimental (sled or vest RST) and control groups (only soccer or unresisted sprint training). In conclusion, RST is effective to improve sprint performance in young soccer players, but the improvements are not superior to unresisted sprint training.
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PURPOSE: To compare the sensitivity of a sprint versus a countermovement (CMJ) test after an intense training session in international rugby Sevens players, as well as to analyze the effects of fatigue on sprint acceleration. METHODS: Thirteen international rugby sevens players completed two 30-meter sprints and a set of four repetitions of CMJ before (Pre) and after (Post) a highly demanding rugby Sevens training session. RESULTS: Change in CMJ height was unclear (-3.6%; ±90% confidence limits 11.9%. Chances of a true positive/trivial/negative change: 24/10/66%), while a very likely small increase in 30-m sprint time was observed (1.0% ±0.7%, 96/3/1%). A very likely small decrease in the maximum horizontal theoretical velocity V0 (-2.4; ±1.8%, 1/4/95%) was observed. A very large correlation (r = -0.79 ±0.23) between the variations of V0 and the 30-m sprint performance was also observed. Changes in 30-m sprint time were negatively and very largely correlated with the distance covered above the maximal aerobic speed (r = -0.71 ±0.32). CONCLUSIONS: The CMJ test appears to be less sensitive than the sprint test, which casts doubts on the usefulness of a vertical jump test, in sports such as rugby, that mainly involve horizontal motions. The decline in sprint performance relates more to a decrease in velocity than in force capability, and is correlated with the distance covered at high-intensity.
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Recent studies have brought new insights into the evaluation of power-force-velocity profiles in both ballistic push-offs (e.g. jumps) and sprint movements. These are major physical components of performance in many sports, and the methods we developed and validated are based on data that are now rather simple to obtain in field conditions (e.g. body mass, jump height, sprint times or velocity). The promising aspect of these approaches is that they allow for a more individualized and accurate evaluation, monitoring, and training practices; the success of which are highly dependent on the correct collection, generation and interpretation of athletes' mechanical outputs. We therefore wanted to provide a practical vade mecum to sports practitioners interested in implementing these power-force-velocity profiling approaches. After providing a summary of theoretical and practical definitions for the main variables, we have first detailed how vertical profiling can be used to manage ballistic push-off performance with emphasis on the concept of optimal force-velocity profile and the associated force-velocity imbalance. Further, we have discussed these same concepts with regards to horizontal profiling in the management of sprinting performance. These sections have been illustrated by typical examples from our own practice. Finally, we have provided a practical and operational synthesis, and outlined future challenges that will help in further developing these approaches.
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Very little is currently known about the effects of acute hamstring injury on over-ground sprinting mechanics. The aim of this research was to describe changes in power-force-velocity properties of sprinting in two injury case studies related to hamstring strain management: Case 1: during a repeated sprint task (10 sprints of 40 m) when an injury occurred (5th sprint) in a professional rugby player; and Case 2: prior to (8 days) and after (33 days) an acute hamstring injury in a professional soccer player. A sports radar system was used to measure instantaneous velocity-time data, from which individual mechanical profiles were derived using a recently validated method based on a macroscopic biomechanical model. Variables of interest included: maximum theoretical velocity (V0) and horizontal force (FH0), slope of the force-velocity (F-v) relationship, maximal power, and split times over 5 and 20 m. For Case 1, during the injury sprint (sprint 5), there was a clear change in the F-v profile with a 14% greater value of FH0 (7.6-8.7 N/kg) and a 6% decrease in V0 (10.1 to 9.5 m/s). For Case 2, at return to sport, the F-v profile clearly changed with a 20.5% lower value of FH0 (8.3 vs. 6.6 N/kg) and no change in V0. The results suggest that the capability to produce horizontal force at low speed (FH0) (i.e. first metres of the acceleration phase) is altered both before and after return to sport from a hamstring injury in these two elite athletes with little or no change of maximal velocity capabilities (V0), as evidenced in on-field conditions. Practitioners should consider regularly monitoring horizontal force production during sprint running both from a performance and injury prevention perspective.
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The purpose of this study is to analyze the concurrent validity and reliability of the iPhone app named My Jump for measuring jump height in 40 cm drop jumps (DJ), countermovement jumps (CMJ) and squat jumps (SJ). To do this, 21 male and female athletes (age, 22.1 ± 3.6 y) completed five maximal DJ, CMJ and SJ in two separate days, which were evaluated using a contact platform and the app My Jump, developed to calculate jump height from flight time using the high-speed video recording facility on the iPhone. A total of 630 jumps were compared using the intraclass correlation coefficient -ICC (2,1)-, Bland-Altman plots, Pearson product moment correlation coefficient (r), Cronbach’s alpha (α) and coefficient of variation (CV). There was almost perfect agreement between measurement instruments for all jump height values (ICC = 0.97 – 0.99), with no differences between instruments (P>0.05; mean difference of 0.2 cm). Almost perfect correlation was observed between measurement instruments for SJ, CMJ and DJ (r = 0.96 – 0.99). My Jump showed very good within-subject reliability (α = 0.94 – 0.99; CV = 3.8 – 7.6) and inter-day reliability (r = 0.86 to 0.95) for SJ, CMJ and DJ in all subjects. Therefore, the iPhone app named My Jump provides reliable inter and intra-session data, as well as valid measurements for maximal jump height during fast (i.e., DJ) and slow (i.e., CMJ) stretch shortening cycle muscle actions, and during concentric only explosive muscle actions (i.e., SJ), in both male and female athletes in comparison with a professional contact platform.
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The aim of this review is to investigate methodological concerns associated with sprint performance monitoring, more specifically the influence and magnitude of varying external conditions, technology and monitoring methodologies not directly related to human physiology. The combination of different starting procedures and triggering devices can cause up to very large time differences, which may be many times greater than performance changes caused by years of conditioning. Wind, altitude, temperature, barometric pressure and humidity can all combine to yield moderate time differences over short sprints. Sprint performance can also be affected by the athlete’s clothing, principally by its weight rather than its aerodynamic properties. On level surfaces, the track compliance must change dramatically before performance changes larger than typical variation can be detected. An optimal shoe bending stiffness can enhance performance by a small margin. Fully-automatic timing systems, dual-beamed photocells, laser guns and high-speed video are the most accurate tools for sprint performance monitoring. Manual timing and single-beamed photocells should be avoided over short sprint distances (10-20 m) due to large absolute errors. The validity of today’s GPS technology is satisfactory for long distances (>30 m) and maximal velocity in team sports, but multiple observations are still needed due to questionable reliability. Based on different approaches used to estimate the smallest worthwhile performance change and the typical error of sprint measures, we have provided an assessment of the usefulness of speed evaluation from 5 to 40 m. Finally, we provide statistical guidelines to accurately assess changes in individual performance; i.e., considering both the smallest worthwhile change in performance and the typical error of measurement, which can be reduced while repeating the number of trials.
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The objective of this study was to characterize the mechanics of maximal running sprint acceleration in high-level athletes. Four elite (100-m best time 9.95–10.29 s) and five sub-elite (10.40–10.60 s) sprinters performed seven sprints in overground conditions. A single virtual 40-m sprint was reconstructed and kinetics parameters were calculated for each step using a force platform system and video analyses. Anteroposterior force (FY), power (PY), and the ratio of the horizontal force component to the resultant (total) force (RF, which reflects the orientation of the resultant ground reaction force for each support phase) were computed as a function of velocity (V). FY-V, RF-V, and PY-V relationships were well described by significant linear (mean R2 of 0.892 ± 0.049 and 0.950 ± 0.023) and quadratic (mean R2 = 0.732 ± 0.114) models, respectively. The current study allows a better understanding of the mechanics of the sprint acceleration notably by modeling the relationships between the forward velocity and the main mechanical key variables of the sprint. As these findings partly concern world-class sprinters tested in overground conditions, they give new insights into some aspects of the biomechanical limits of human locomotion.
Purpose: The best sprint performances are usually reached between the ages of 20 and 30; however even in well-trained individuals, performance continues to decrease with age. While this inevitable decrease in performance has been related to reductions in muscular force, velocity and power capabilities, these measures have not been assessed in the specific context of sprinting. The aim of this study was to investigate the mechanical outputs of sprinting acceleration among Masters sprinters to better understand the mechanical underpinnings of the age-related decrease in sprint performance. Methods: The study took place during an international Masters competition, with testing performed at the end of the warm-up for official sprint races. Horizontal ground reaction force, velocity, mechanical power outputs and mechanical effectiveness of force application were estimated from running velocity-time data during a 30-m sprint acceleration in twenty-seven male sprinters (39 to 96 yrs). Data were presented in the form of age-related changes and compared to elite young sprinters data. Results: Maximal force, velocity and power outputs decreased linearly with age (all r>0.84; P<0.001), at a rate of ~1% per year. Maximal power of the oldest subject tested was about one ninth of that of younger world-class sprinters (3.57 vs. 32.1 W·kg). While the maximal effectiveness of horizontal force application also decreased with age, its decrease with increasing velocity within the sprint acceleration was not age-dependent. Conclusions: In addition to lower neuromuscular force, velocity and power outputs, Master sprinters had a comparatively lower effectiveness of force application, especially at the beginning of the sprint.
With the importance placed on athletic speed, it is important to utilize a valid and reliable timing system, particularly in sprints of short duration. Unfortunately, many of the commonly used timing systems have not been rigorously evaluated. This study aimed to compare results from a single-beam infrared photocell (PC), single-beam laser with a microprocessor (LA), and a previously validated video camera (VC) sacrum based timing system; and in doing so, determine these systems' reliabilities, and establish best practices for increasing reliability. It was hypothesized that PC and LA times would be different from VC times and show reduced reliability compared to VC. Fifteen athletes performed five repetitions of a 60 foot maximal effort sprint with split times recorded for the first and second half. PC and LA full time and first half split times were significantly slower than VC (P < 0.001), but almost identical for the second half split (P = 0.08). Repeated sprint analysis showed VC tended to have smaller standard deviations compared to PC and LA for first half split (0.05 s vs. 0.08 s vs. 0.09 s, respectively) and total time (0.09 s vs. 0.10 s vs. 0.11 s, respectively). Time differences were more dependent upon initial forward lean and varying body segments triggering the beam, than a systematic instrument error. The increased variability of PC and LA systems dampen the ability to determine if meaningful change has occurred. The VC system allows for very valid and reliable measurements of an athlete's sprint time, especially in distances < 30 feet.
My Jump Health and Fitness iOS 7.0 or later; Optimised for iPhone 5, iPhone 6 and iPhone 6 Plus. Compatible with iPhone, iPad and iPod touch. $A7.49 Current version is V.2.1 which has iPhone 6 and iPhone 6 Plus support, and iOS8 support. No trial version is available. Vertical jump is a widely used measure of functional performance in athletic and non-athletic populations.1 My Jump is a low-cost, easy-to-use application which integrates with the video camera to assess vertical jump performance (figure 1). The in-app settings allow slow-motion playback for easy identification of the video frame in which jump take-off and landing occurs. The app determines the number of …
This study aimed to validate a simple field method for determining force- and power-velocity relationships and mechanical effectiveness of force application during sprint running. The proposed method, based on an inverse dynamic approach applied to the body center of mass, estimates the step-averaged ground reaction forces in runner's sagittal plane of motion during overground sprint acceleration from only anthropometric and spatiotemporal data. Force- and power-velocity relationships, the associated variables, and mechanical effectiveness were determined (a) on nine sprinters using both the proposed method and force plate measurements and (b) on six other sprinters using the proposed method during several consecutive trials to assess the inter-trial reliability. The low bias (<5%) and narrow limits of agreement between both methods for maximal horizontal force (638 ± 84 N), velocity (10.5 ± 0.74 m/s), and power output (1680 ± 280 W); for the slope of the force-velocity relationships; and for the mechanical effectiveness of force application showed high concurrent validity of the proposed method. The low standard errors of measurements between trials (<5%) highlighted the high reliability of the method. These findings support the validity of the proposed simple method, convenient for field use, to determine power, force, velocity properties, and mechanical effectiveness in sprint running. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.