Sprint performance and mechanical outputs computed with an iPhone app: Comparison with existing reference methods

Abstract

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. https://doi.org/10.1111/sms.12490] 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.

Full text

ORIGINAL ARTICLE
Sprint performance and mechanical outputs computed with an iPhone
app: Comparison with existing reference methods
NATALIA ROMERO-FRANCO
1
, PEDRO JIMÉNEZ-REYES
2
, ADRIÁN CASTAÑO-
ZAMBUDIO
2
, FERNANDO CAPELO-RAMÍREZ
2
, JUAN JOSÉ RODRÍGUEZ-JUAN
3
,
JORGE GONZÁLEZ-HERNÁNDEZ
2
, FRANCISCO JAVIER TOSCANO-BENDALA
2
,
VÍCTOR CUADRADO-PEÑAFIEL
4
,&CARLOSBALSALOBRE-FERNÁNDEZ
5
1
Nursery and Physiotherapy Department, University of Balearic Islands, Palma de Mallorca, Spain;
2
Faculty of Sport,
Catholic University of San Antonio, Murcia, Spain;
3
Physiotherapy Department, Catholic University of San Antonio, Murcia,
Spain;
4
Alcalá de Henares University, Madrid, Spain &
5
Department of Sport Sciences, European University of Madrid,
Madrid, Spain
Abstract
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. https://doi.org/10.1111/sms.12490] 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 (F
0
), the maximal theoretical velocity (V
0
), the maximal power
(P
max
) 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.
Keywords: Acceleration, technology, biomechanics, testing
Introduction
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 force–velocity
(F–v) and power–velocity (P–v) 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:
carlos.balsalobre@icloud.com
European Journal of Sport Science, 2016
http://dx.doi.org/10.1080/17461391.2016.1249031

traditionally been performed utilising reference
methods which are measuring either the runner’s 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 athlete’s 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
0
),
velocity (V
0
), maximal power output (P
max
) 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 method’is based on the
measurement of either five split times or the vel-
ocity–time 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
standard’method 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 distance–time input) or a radar
system (for measuring speed–time 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 F–v 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 velocity–time curves) and
computed mechanical variables to the current two
reference methods in this context (Haugen & Buch-
heit, 2016): timing gates and radar.
Methods
Athletes
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 10.74–11.57 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.
Procedures
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
−1
for the radar). Split time
and velocity–time data were used along with subjects’
body-mass and body-height as inputs to calculate F
0
,
V
0
,Pmax and DRF according to Samozino’s 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 velocity–time 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 athletes’centre-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 iPhone’s 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 athletes’right 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
0
,V
0
,P
max
and DRF (radar method).
First, to analyse the concurrent validity of the
MySprint app in comparison with the other devices,
Pearson’s product–moment 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 Bland–Altman 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 Bland–Altman 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.
Results
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 = 0.007–0.015 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 = 1.00–1.00) and the Bland–Altman 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
flags.
Figure 2. Concurrent validity between the MySprint app and the
timing photocells for the measurement of the time to cover 0–5,
5–10, 10–15, 15–20, 20–30, 30–40 and 0–40 m. Each split time
is represented with different markers; however, the regression
equation and the value of R
2
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. Bland–Altman 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=
0.974–0.999, 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 = 0.987–1.00). 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 = 0.997–0.998).
Discussion
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 Pearson’s
product–moment correlation coefficient (r= 0.999)
and the very low SEE (0.013 s), with no statistically
significant differences. Moreover, the analysis of the
Bland–Altman 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 Bland–Altman plot ( ± 1.96 SD) were
just ± 0.028 s, with the majority of the points close
to 0. Moreover, the value of R
2
of the linear
regression on the Bland–Altman plot was very low
(R
2
= 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 (0–5, 5–10,
10–15, 15–20, 20–30 and 30–40 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
0
,V
0
,P
max
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)
F
0
(N/kg) 6.8 ± 0.6 6.9 ± 0.6 0.09 0.999 (0.998–0.999) 1.04 −0.22 0.999 (0.999–1.000)
V
0
(m/s) 8.28 ± 1.0 8.35 ± 1.0 0.03 0.988 (0.974–0.999) 0.992 0.14 0.987 (0.979–0.992)
P
max
(W/kg) 14.1 ± 2.4 14.5 ± 2.5 0.19 0.988 (0.974–0.999) 1.032 −0.09 0.998 (0.997–0.999)
DRF (%s/m) −0.077 ± 0.01 −0.078 ± 0.01 0.001 0.994 (0.990–0.9979 0.97 −0.001 0.994 (0.991–0.996)
Notes: F
0
, theoretical maximal force; V
0
, theoretical maximal velocity; P
max
, maximal power output; DRF, slope of the linear decrease on ratio
of force as sprint velocity increases; r, Pearson’s product–moment 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 Pearson’s
product–moment 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
0
,V
0
,P
max
and
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
0
,V
0
,P
max
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.
Acknowledgements
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.
ORCID
Carlos Balsalobre-Fernández http://orcid.org/0000-
0002-8329-1581
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Sprint performance and mechanical outputs computed with an iPhone app 7