ArticlePDF Available

Abstract and Figures

This is a respond to https://doi.org/10.1007/s10439-019-02304-2. This comment refers to the original article "Reproducibility and Repeatability of Five Different Technologies for Bar Velocity Measurement in Resistance Training" available at https://doi.org/10.1007/s10439-019-02265-6 Full text: https://www.researchgate.net/publication/334017166_Technical_Note_on_the_Reliability_of_the_PowerLift_App_for_Velocity-Based_Resistance_Training_Purposes_Response
Content may be subject to copyright.
Technical Note on the Reliability of the PowerLift App for Velocity-Based
Resistance Training Purposes: Response
JAVIER COUREL-IBA
´N
˜EZ ,
1
ALEJANDRO MARTI
´NEZ-CAVA,
1
ALEJANDRO HERNA
´NDEZ-BELMONTE,
1
JUAN JOSE
´GONZA
´LEZ-BADILLO,
2
and JESU
´SG. PALLARE
´S
1
1
Human Performance and Sports Science Laboratory, Faculty of Sport Sciences, University of Murcia, C/Argentina S/n.,
Santiago de la Ribera, Murcia, Spain; and
2
Faculty of Sport, Pablo de Olavide University, Seville, Spain
This comment refers to the article available at https://doi.org/10.1007/s10439-019-02265-6.
(Received 31 May 2019; accepted 7 June 2019)
Associate Editor Jane Grande-Allen oversaw the review of this article.
Dear Editor,
Thank you for the opportunity to respond to the
points raised in this letter from Mr. Balsalobre
regarding our recent article.
3
We appreciate the inter-
est shown in our study. However, it seems that this
author has misunderstood some points about the reli-
ability analyses that we conducted and, therefore, this
needs to be clarified. Our study
3
aimed to analyze the
agreement between five bar velocity monitoring de-
vices, currently used in resistance training, to deter-
mine which was the most reliable device based on the
results of reproducibility (between-device agreement
for a given trial) and repeatability (between-trial vari-
ation for each device) analyses. Among the five dif-
ferent technologies assessed, only the data regarding
the PowerLift smartphone app have been questioned
by the author. It should be noted that this author is the
main developer of the PowerLift app and the main
author of the paper in which it was allegedly vali-
dated.
2
The arguments put forward by the author to ques-
tion our results are based on the assumption that ‘‘the
observers who performed the video analysis did not use
the app properly, because, following their data, the raw
difference in frames would be higher that 20–30
frames’’. As clearly detailed in our manuscript,
3
‘‘Intra-
device reproducibility was assessed by comparing the
velocity outcomes for trial 1 simultaneously obtained
by each pair of the two (same brand and model) de-
vices’’. Thus, the results we presented here were
obtained using two same version PowerLift apps (v 4.0
iOS) installed on two iPhone 6 units running iOS 11.3,
and analyzed by the same examiner. Notwithstanding
the above, in the letter, the author noted ‘‘Specifically,
in powerlifting exercises like the bench-press, observers
need to manually select the frames in which the bar
takes-off the chest (beginning of the lift) and stops its
vertical ascent (end of the lift). Because of that, pre-
vious research analyzing the validity and reliability of
VBS [video-based systems] compared to scores of two
observers analyzing the same videos in order to detect
the inter-observer variability’’. Thus, it seems that the
author has confused the concept of ‘‘intra-device
agreement’’ (i.e., comparing the outcomes simultane-
ously obtained by two devices for a given trial) with the
concept ‘‘inter-observers’ agreement’’ (i.e., comparing
the outcomes obtained by two observers for a given
video file in the same smartphone). It should be noted
that we were fully aware of the need of counting both
with experienced examiners and reproducible proce-
dures to ensure the quality of the measures, particu-
larly in manually operated systems such as the
PowerLift app. In our study,
3
the examiner showed an
excellent reliability when he analysed the same video
file, in the same smartphone, in two separate occasions
(ICC >0.997, 95% ICC 0.984–1.000; CV <3.02%),
which are values higher than those reported in previous
investigations where the smartphone was hold in the
hands.
1,2
We therefore agree that the PowerLift is very
easy to use and reliable in terms of intra-observers’
agreement. However, this was clearly not the hypoth-
esis tested in our investigation, which leads us to think
that the author has not read the study carefully. The
two main goals of our work
3
were: (1) to identify the
errors arising from current velocity monitoring tech-
Address correspondence to Jesu´ s G. Pallare
´s, Human Perfor-
mance and Sports Science Laboratory, Faculty of Sport Sciences,
University of Murcia, C/Argentina S/n., Santiago de la Ribera,
Murcia, Spain. Electronic mails: alejandro.mcava@gmail.com,
jgpallares@um.es
Annals of Biomedical Engineering (2019)
https://doi.org/10.1007/s10439-019-02305-1
BIOMEDICAL
ENGINEERING
SOCIETY
2019 Biomedical Engineering Society
nologies (intra-device reproducibility and repeatabil-
ity) to determine which was the most reliable (i.e.,
which, in turn, could be taken as the gold standard or
reference device), and (2) to objectively quantify the
agreement between measurements from each device
against that gold standard.
3
This approach is inspired
by the need of comprehensive information to guaran-
tee the suitability of emerging devices (based on new
technologies) in order to identify whether the changes
observed in velocity against certain workloads are due
to changes in athletes’ performance or are due to
measurement error.
5
We believe this is invaluable
information to both coaches and practitioners in order
to be able to determine the real effort being incurred
during resistance exercise.
We would like to take this opportunity to clarify
that what our results show is a very poor reliability of
the PowerLift measurements in terms of intra-device
(i.e., between two smartphones for a given trial or
execution) and inter-device (i.e., between the T-Force
System linear velocity transducer and one smartphone
running the PowerLift app for a given trial) agreement
when monitoring the full load-velocity relationship
(i.e., broad spectrum of mean bar velocity values, from
<0.10 to >1.50 m s
21
). In this regard, the author
provided a list of studies examining other physical
skills such as the running mechanics or vertical jump
by using specific apps for these purposes. However, we
are not questioning the use of these apps but the
PowerLift as a monitoring tool for velocity-based
resistance training purposes. In a more appropriate
manner, the author cited three studies which used the
PowerLift
1,2,8
; in two of them, the main author was
himself, the developer of the app, Mr. Balsalobre.
1,2
While none of these studies provided data about the
intra-device agreement, the three of them examined the
inter-device agreement of the PowerLift against a
supposedly gold standard. There are two main points
to clarify here. Firstly, these three studies considered as
their gold standard a different device (Smartcoach
encoder
1,2
and a 3D motion analysis system
8
) than that
taken by us (T-Force System) to assess the concurrent
validity of the PowerLift app. To the best of our
knowledge, there is no available information about the
inherent technical errors, standard error of the mea-
surement or minimal detectable changes, associated to
any of those devices for bar velocity measurement
during resistance exercise, unlike the evidence reported
by us for the T-Force System device.
3
Consequently,
comparisons of our results with those reported earlier
must be done with extreme caution. Secondly, these
three studies
1,2,8
only included data from resistance
exercises performed at slow
2
(<0.50 m s
21
) or mod-
erate (<1.00 m s
21
) velocities,
1,8
when compared to a
much broader load-velocity spectrum analyzed in our
study
3
(from 0.10 to >1.50 m s
21
). With the above-
FIGURE 1. Bland–Altman plots showing between-device agreement (T-Force vs. PowerLift) in mean velocity (MV) for trial 1 in the
bench press exercise. Magnitude of errors correspond to MV £0.5 m s
21
(green lines), 0.5 m s
21
<MV £1.0 m s
21
(yellow line)
and >1.0 m s
21
(red line). LoA limits of agreement, SDC smallest detectable change. Area shaded in grey indicates an
acceptable level of agreement between devices (Data pertaining to the study by Courel-Iba
´n
˜ez et al.
3
).
BIOMEDICAL
ENGINEERING
SOCIETY
COUREL-IBA
´N
˜EZ et al.
mentioned caution, if we divide the full load-velocity
relationship into three segments or parts of 0.50 m s
21
(Fig. 1, example for the bench press exercise; data from
Courel-Iba
´n
˜ez et al.
3
), it can be observed that our
findings partially agree with those earlier reported
suggesting the use of the PowerLift for measuring high
loads at slow velocities (<0.50 m s
21
), with a modest
margin of error (e.g., bench press: SDC = 0.15 m s
21
,
~9% 1RM, Fig. 1; full squat: SDC = 0.12 m s
21
,
~8% 1RM). The main problem here is that mean
velocities <0.50 m s
21
are associated with extremely
high loads for the most common resistance training
exercises (>80% in bench press,
5,9
90% in full squat
10
or even >100% in prone bench pull
9
), which limits its
practical application. More importantly, we found that
the reliability of the app drastically decreased as mean
lifting velocity increased, with the highest errors
occurring for lifts performed at veloci-
ties >1.00 m s
21
(SDC 0.34 m s
21
, 21% 1RM;
Fig. 1). We consider this fact a limitation for velocity-
based resistance-training purposes as the range of
velocities that can be monitored with sufficient sensi-
tivity is greatly reduced. More importantly, monitoring
high velocities (1.00 m s
21
) should be preferable to
assess changes in neuromuscular and functional per-
formance due to the higher specificity in relationship
with most sporting movements.
4,6,7
Finally, the author of the letter mentioned our lack
of knowledge to use the PowerLift app because we did
not evaluate the prone bench pull exercise. We
respectfully disagree, and we must clarify that the pre-
customized mode and the specific algorithm for the
prone bench pull exercise was not available when the
data were collected (PowerLift v. 4.0 iOS).
Taken into account all of the above, and according
to what we detailed in the original paper,
3
we sustain
that the PowerLift smartphone app should be not
recommended as a monitoring tool for velocity-based
resistance-training, given the substantial errors in-
curred and uncertainty of the outcomes obtained.
There is no doubt that the intentions to develop
smartphone apps as cheaper methods to measure bar
velocity are laudable. In this same line, we have iden-
tified quite good alternatives to linear velocity trans-
ducers for ~550 e. But whatever the price, unless a
measurement device provides enough accurate and
reliable measurements, its use should be discouraged.
If a device lacks enough sensitivity and the errors in-
curred when using it exceed a certain magnitude (see
the concepts of smallest detectable change and maxi-
mum error in our original study
3
) the device renders
completely useless for its intended purpose. Now more
than ever, in this era where we are experiencing the
apps, gadgets and wearables booms, we must be very
critical when choosing our measurement instruments.
REFERENCES
1
Balsalobre-Ferna
´ndez, C., D. Marchante, E. Baz-Valle, I.
Alonso-Molero, S. L. Jime
´nez, and M. Mun
˜o
´z-Lo
´pez.
Analysis of wearable and smartphone-based technologies
for the measurement of barbell velocity in different resis-
tance training exercises. Front. Physiol. 8:649, 2017.
2
Balsalobre-Ferna
´ndez, C., D. Marchante, M. Mun
˜oz-Lo
´-
pez, and S. L. Jime
´nez. Validity and reliability of a novel
iPhone app for the measurement of barbell velocity and
1RM on the bench-press exercise. J. Sports Sci. 36:64–70,
2018.
3
Courel-Iba
´n
˜ez, J., A. Martinez-Cava, R. Moran-Navarro,
P. Escribano-Pen
˜as, J. Chavarren-Cabrero, and J. J.
Gonzalez-Badillo. Reproducibility and repeatability of five
different technologies for bar velocity measurement in
resistance training. Ann. Biomed. Eng. 47:1523–1538, 2019.
4
Franco-Ma
´rquez, F., D. Rodrı
´guez-Rosell, J. M. Gonza
´-
lez-Sua
´rez, F. Pareja-Blanco, R. Mora-Custodio, J. M.
Yan
˜ez-Garcı
´a, and J. J. Gonza
´lez-Badillo. Effects of
combined resistance training and plyometrics on physical
performance in young soccer players. Int. J. Sports Med.
36:906–914, 2015.
5
Gonza
´lez-Badillo, J. J., and L. Sa
´nchez-Medina. Move-
ment velocity as a measure of loading intensity in resistance
training. Int. J. Sports Med. 31:347–352, 2010.
6
Ortega-Becerra, M., F. Pareja-Blanco, P. Jime
´nez-Reyes,
V. Cuadrado-Pen
˜afiel, and J. J. Gonza
´lez-Badillo. Deter-
minant factors of physical performance and specific
throwing in handball players of different ages. J. Strength
Cond. Res. 32:1778–1786, 2018.
7
Pallare
´s, J. G., A
´.Lo
´pez-Samanes, V. E. Ferna
´ndez-Elı
´as,
R. Aguado-Jime
´nez, J. F. Ortega, C. Go
´mez, R. Ventura,
J. Segura, and R. Mora-Rodrı
´guez. Pseudoephedrine and
circadian rhythm interaction on neuromuscular perfor-
mance. Scand. J. Med. Sci. Sports 25:e603–e612, 2015.
8
Pe
´rez-Castilla, A., A. Piepoli, G. Delgado-Garcı
´a, G.
Garrido-Blanca, and A. Garcı
´a-Ramos. Reliability and
concurrent validity of seven commercially available devices
for the assessment of movement velocity at different
intensities during the bench press. J. Strength Cond. Res.
33:1258–1265, 2019.
9
Sa
´nchez-Medina, L., J. J. Gonza
´lez-Badillo, C. E. Pe
´rez,
and J. G. Pallare
´s. Velocity- and power-load relationships
of the bench pull vs bench press exercises. Int. J. Sports
Med. 35:209–216, 2014.
10
Sa
´nchez-Medina, L., R. Mora
´n-Navarro, C. Pe
´rez, J.
Gonza
´lez-Badillo, and J. G. Pallare
´s. Estimation of relative
load from bar velocity in the full back squat exercise. Sport.
Med. Int. Open 01:E80–E88, 2017.
Publisher’s Note Springer Nature remains neutral with re-
gard to jurisdictional claims in published maps and institu-
tional affiliations.
BIOMEDICAL
ENGINEERING
SOCIETY
Technical Note on the Reliability of the PowerLift App
... Secondly, most of the statements in favour to a given device reliability are based on Bland-Altman plots. Whereas the use of Bland-Altman analysis requires the interpretation of the magnitude of errors according to practical criteria and established acceptable levels of disagreement [24,25], only a few studies have based their findings on these criteria [26][27][28]. An interesting approach has been presented based on the changes in performance (% 1RM) produced by increments in the barbell velocity [26][27][28]. ...
... Whereas the use of Bland-Altman analysis requires the interpretation of the magnitude of errors according to practical criteria and established acceptable levels of disagreement [24,25], only a few studies have based their findings on these criteria [26][27][28]. An interesting approach has been presented based on the changes in performance (% 1RM) produced by increments in the barbell velocity [26][27][28]. Previous studies describing the load-velocity relationship for different resistance training exercises performed in a Smith machine observed that changes between 0.05 to 0.10 m/s in bench press (BP) and full squat (SQ) would represent 5% 1RM improvement [2,12,14]. ...
... Finally, while all the available devices are apparently reliable to measure velocity in heavyload lifting (i.e., < 0.50 m/s), the VBT requires the identification of measurement errors across a spectrum of relative loads, including fast movements against moderate and light loads [21,[26][27][28]. In particular, monitoring high velocities are important to assess changes in neuromuscular and functional performance due to the higher specificity in relationship with most sporting movements [29,30]. ...
Article
Full-text available
This study investigated the inter- and intra-device agreement of four new devices marketed for barbell velocity measurement. Mean, mean propulsive and peak velocity outcomes were obtained for bench press and full squat exercises along the whole load-velocity spectrum (from light to heavy loads). Measurements were simultaneously registered by two linear velocity transducers T-Force, two linear position transducers Speed4Lifts, two smartphone video-based systems My Lift, and one 3D motion analysis system STT. Calculations included infraclass correlation coefficient (ICC), Bland-Altman Limits of Agreement (LoA), standard error of measurement (SEM), smallest detectable change (SDC) and maximum errors (MaxError). Results were reported in absolute (m/s) and relative terms (%1RM). Three velocity segments were differentiated according to the velocity-load relationships for each exercise: heavy (≥ 80% 1RM), medium (50% < 1RM < 80%) and light loads (≤ 50% 1RM). Criteria for acceptable reliability were ICC > 0.990 and SDC < 0.07 m/s (~5% 1RM). The T-Force device shown the best intra-device agreement (SDC = 0.01–0.02 m/s, LoA <0.01m/s, MaxError = 1.3–2.2%1RM). The Speed4Lifts and STT were found as highly reliable, especially against lifting velocities ≤1.0 m/s (Speed4Lifts, SDC = 0.01–0.05 m/s; STT, SDC = 0.02–0.04 m/s), whereas the My Lift app showed the worst results with errors well above the acceptable levels (SDC = 0.26–0.34 m/s, MaxError = 18.9–24.8%1RM). T-Force stands as the preferable option to assess barbell velocity and to identify technical errors of measurement for emerging monitoring technologies. Both the Speed4Lifts and STT are fine alternatives to T-Force for measuring velocity against high-medium loads (velocities ≤ 1.0 m/s), while the excessive errors of the newly updated My Lift app advise against the use of this tool for velocity-based resistance training.
... Secondly, most of the statements in favour to a given device reliability are based on Bland-Altman plots. Whereas the use of Bland-Altman analysis requires the interpretation of the magnitude of errors according to practical criteria and established acceptable levels of disagreement [24,25], only a few studies have based their findings on these criteria [26][27][28]. An interesting approach has been presented based on the changes in performance (% 1RM) produced by increments in the barbell velocity [26][27][28]. ...
... Whereas the use of Bland-Altman analysis requires the interpretation of the magnitude of errors according to practical criteria and established acceptable levels of disagreement [24,25], only a few studies have based their findings on these criteria [26][27][28]. An interesting approach has been presented based on the changes in performance (% 1RM) produced by increments in the barbell velocity [26][27][28]. Previous studies describing the load-velocity relationship for different resistance training exercises performed in a Smith machine observed that changes between 0.05 to 0.10 m/s in bench press (BP) and full squat (SQ) would represent 5% 1RM improvement [2,12,14]. ...
... Finally, while all the available devices are apparently reliable to measure velocity in heavyload lifting (i.e., < 0.50 m/s), the VBT requires the identification of measurement errors across a spectrum of relative loads, including fast movements against moderate and light loads [21,[26][27][28]. In particular, monitoring high velocities are important to assess changes in neuromuscular and functional performance due to the higher specificity in relationship with most sporting movements [29,30]. ...
Article
Full-text available
This study investigated the inter- and intra-device agreement of four new devices marketed for barbell velocity measurement. Mean, mean propulsive and peak velocity outcomes were obtained for bench press and full squat exercises along the whole load-velocity spectrum (from light to heavy loads). Measurements were simultaneously registered by two linear velocity transducers T-Force, two linear position transducers Speed4Lifts, two smartphone video-based systems My Lift, and one 3D motion analysis system STT. Calculations included infraclass correlation coefficient (ICC), Bland-Altman Limits of Agreement (LoA), standard error of measurement (SEM), smallest detectable change (SDC) and maximum errors (MaxError). Results were reported in absolute (m/s) and relative terms (%1RM). Three velocity segments were differentiated according to the velocity-load relationships for each exercise: heavy (≥ 80% 1RM), medium (50% < 1RM < 80%) and light loads (≤ 50% 1RM). Criteria for acceptable reliability were ICC > 0.990 and SDC < 0.07 m/s (~5% 1RM). The T-Force device shown the best intra-device agreement (SDC = 0.01–0.02 m/s, LoA <0.01m/s, MaxError = 1.3–2.2%1RM). The Speed4Lifts and STT were found as highly reliable, especially against lifting velocities ≤1.0 m/s (Speed4Lifts, SDC = 0.01–0.05 m/s; STT, SDC = 0.02–0.04 m/s), whereas the My Lift app showed the worst results with errors well above the acceptable levels (SDC = 0.26–0.34 m/s, MaxError = 18.9–24.8%1RM). T-Force stands as the preferable option to assess barbell velocity and to identify technical errors of measurement for emerging monitoring technologies. Both the Speed4Lifts and STT are fine alternatives to T-Force for measuring velocity against high-medium loads (velocities ≤ 1.0 m/s), while the excessive errors of the newly updated My Lift app advise against the use of this tool for velocity-based resistance training.
Article
Some studies have reported considerable errors in the movement velocity measurement when using the My Lift app. This study aimed to investigate whether these errors may be related to the use of a range of movement (ROM) statically measured prior to the movement (ROMMYLIFT) instead of ROM dynamically monitored. Ten young adults performed two repetitions of the bench press exercise on a Smith machine with loads that allowed two velocity conditions (above and below 0.6 m s−1). The exercises were monitored by the My Lift app, a magnet and a rotary encoder. After, 15 older adults performed the same exercise at different percentages of 1RM, monitored by the My Lift app and a magnet. The results revealed that ROM dynamically obtained by encoder (reference method) with the mean velocity above (0.497 ± 0.069 m) and below (0.450 ± 0.056 m) 0.6 m s−1 were quite different (p < 0.05; large effect) from the ROMMYLIFT (0.385 ± 0.040 m). These errors provided highly biased and heteroscedastic mean velocity measurements (mean errors approximately 22%). The errors observed in adults were also observed in the older participants, except for loads equal to 85% of 1RM. The magnet method proved to be valid, presenting measurements very close to the encoder (mean errors approximately 1.7%; r > 0.99). In conclusion, the use of ROMMYLIFT is inadequate, as the higher the movement velocity, the higher the errors, both for young and older adults. Thus, to improve the measurement of the My Lift app, it is recommended that the magnet method be used in conjunction with the app to more accurately determine the ROM.
Article
Full-text available
This study aimed to analyze the agreement between five bar velocity monitoring devices, currently used in resistance training, to determine the most reliable device based on reproducibility (between-device agreement for a given trial) and repeatability (between-trial variation for each device). Seventeen resistance-trained men performed duplicate trials against seven increasing loads (20-30-40-50-60-70-80 kg) while obtaining mean, mean propulsive and peak velocity outcomes in the bench press, full squat and prone bench pull exercises. Measurements were simultaneously registered by two linear velocity transducers (LVT), two linear position transducers (LPT), two optoelectronic camera-based systems (OEC), two smartphone video-based systems (VBS) and one accelerometer (ACC). A comprehensive set of statistics for assessing reliability was used. Magnitude of errors was reported both in absolute (m s⁻¹) and relative terms (%1RM), and included the smallest detectable change (SDC) and maximum errors (MaxError). LVT was the most reliable and sensitive device (SDC 0.02–0.06 m s⁻¹, MaxError 3.4–7.1% 1RM) and the preferred reference to compare with other technologies. OEC and LPT were the second-best alternatives (SDC 0.06–0.11 m s⁻¹), always considering the particular margins of error for each exercise and velocity outcome. ACC and VBS are not recommended given their substantial errors and uncertainty of the measurements (SDC > 0.13 m s⁻¹).
Article
Full-text available
This aim of this study was to compare the reliability and validity of seven commercially available devices to measure movement velocity during the bench press exercise. Fourteen men completed two testing sessions. The bench press one-repetition maximum (1RM) was determined in the first session. The second testing session consisted of performing three repetitions against five loads (45-55-65-75-85% of 1RM). The mean velocity was simultaneously measured using an optical motion sensing system (Trio-OptiTrack™; “gold-standard”) and seven commercially available devices: 1 linear velocity transducer (T-Force™), 2 linear position transducers (Chronojump™ and Speed4Lift™), 1 camera-based optoelectronic system (Velowin™), 1 smartphone application (PowerLift™), and 2 inertial measurement units (PUSH™ band and Beast™ sensor). The devices were ranked from the most to the least reliable as follows: (I) Speed4Lift™ (coefficient of variation [CV] = 2.61%), (II) Velowin™ (CV = 3.99%), PowerLift™ (3.97%), Trio-OptiTrack™ (CV = 4.04%), T-Force™ (CV = 4.35%), Chronojump™ (CV = 4.53%), (III) PUSH™ band (CV = 9.34%), and (IV) Beast™ sensor (CV = 35.0%). A practically perfect association between the Trio-OptiTrack™ system and the different devices was observed (Pearson’s product-moment correlation coefficient (r) range = 0.947-0.995; P < 0.001) with the only exception of the Beast sensor (r = 0.765; P < 0.001). These results suggest that linear velocity/position transducers, camera-based optoelectronic systems and the smartphone application could be used to obtain accurate velocity measurements for restricted linear movements, while the inertial measurement units used in this study were less reliable and valid.
Article
Full-text available
The purpose of this study was to analyze the validity, reliability, and accuracy of new wearable and smartphone-based technology for the measurement of barbell velocity in resistance training exercises. To do this, 10 highly trained powerlifters (age = 26.1 ± 3.9 years) performed 11 repetitions with loads ranging 50–100% of the 1-Repetition maximum in the bench-press, full-squat, and hip-thrust exercises while barbell velocity was simultaneously measured using a linear transducer (LT), two Beast wearable devices (one placed on the subjects' wrist –BW–, and the other one directly attached to the barbell –BB–) and the iOS PowerLift app. Results showed a high correlation between the LT and BW (r = 0.94–0.98, SEE = 0.04–0.07 m • s −1), BB (r = 0.97–0.98, SEE = 0.04–0.05 m • s −1), and the PowerLift app (r = 0.97–0.98, SEE = 0.03–0.05 m • s −1) for the measurement of barbell velocity in the three exercises. Paired samples T-test revealed systematic biases between the LT and BW, BB and the app in the hip-thrust, between the LT and BW in the full-squat and between the LT and BB in the bench-press exercise (p < 0.001). Moreover, the analysis of the linear regression on the Bland-Altman plots showed that the differences between the LT and BW (R 2 = 0.004–0.03), BB (R 2 = 0.007–0.01), and the app (R 2 = 0.001–0.03) were similar across the whole range of velocities analyzed. Finally, the reliability of the BW (ICC = 0.910–0.988), BB (ICC = 0.922–0.990), and the app (ICC = 0.928–0.989) for the measurement of the two repetitions performed with each load were almost the same than that observed with the LT (ICC = 0.937–0.990). Both the Beast wearable device and the PowerLift app were highly valid, reliable, and accurate for the measurement of barbell velocity in the bench-press, full-squat, and hip-thrust exercises. These results could have potential practical applications for strength and conditioning coaches who wish to measure barbell velocity during resistance training.
Article
Full-text available
This study aimed to analyze various fitness qualities in handball players of different ages, and to determine the relationships between these parameters and throwing velocity. Forty-four handball players participated, pooled by age groups: ELITE (n = 13); under-18 (U18, n = 16); under-16 (U16, n = 15). The following tests were completed: 20-m running sprints; countermovement jumps (CMJ); jump squat to determine the load that elicited ~20 cm jump height (JSLOAD-20cm); a progressive loading test in full-squat and bench-press to determine the load that elicited ~1 m[middle dot]s-1 (SQ-V1-LOAD and BP-V1-LOAD), and handball throwing (Jump Throw and 3-Step Throw). ELITE showed greater performance in almost all sprint distances, CMJ, JSLOAD-20cm and bench-press strength than U18 and U16. The differences between U18 and U16 were unclear for these variables. ELITE also showed greater (P < 0.001) performance for squat strength and throwing than U18 and U16, and U18 attained greater performance (P < 0.05) for these variables than U16. Throwing performance correlated (P < 0.05) with sprint times (r = -.31; -.51) and jump ability (CMJ: r = .39; .56 and JSLOAD-20cm: r = .57; .60). Muscle strength was also associated (P < 0.001) with both types of throw (SQ-V1-LOAD: r = .66; .76; and BP-V1-LOAD: r = .33; .70). These results indicate handball throwing velocity is strongly associated with lower-limb strength, although upper-limb strength, jumping and sprint capacities also play a relevant role in throwing performance, suggesting the need for coaches to include proper strength programs to improve handball players' throwing velocity.
Article
Full-text available
The purpose of this study was to analyse the validity and reliability of a novel iPhone app (named: PowerLift) for the measurement of mean velocity on the bench-press exercise. Additionally, the accuracy of the estimation of the 1-Repetition maximum (1RM) using the load–velocity relationship was tested. To do this, 10 powerlifters (Mean (SD): age = 26.5 ± 6.5 years; bench press 1RM · kg−1 = 1.34 ± 0.25) completed an incremental test on the bench-press exercise with 5 different loads (75–100% 1RM), while the mean velocity of the barbell was registered using a linear transducer (LT) and Powerlift. Results showed a very high correlation between the LT and the app (r = 0.94, SEE = 0.028 m · s−1) for the measurement of mean velocity. Bland–Altman plots (R2 = 0.011) and intraclass correlation coefficient (ICC = 0.965) revealed a very high agreement between both devices. A systematic bias by which the app registered slightly higher values than the LT (P < 0.05; mean difference (SD) between instruments = 0.008 ± 0.03 m · s−1). Finally, actual and estimated 1RM using the app were highly correlated (r = 0.98, mean difference (SD) = 5.5 ± 9.6 kg, P < 0.05). The app was found to be highly valid and reliable in comparison with a LT. These findings could have valuable practical applications for strength and conditioning coaches who wish to measure barbell velocity in the bench-press exercise.
Article
Full-text available
This study aimed to determine the effects of combined resistance training and plyometrics on physical performance in under-15 soccer players. One team (n=20) followed a 6-week resistance training program combined with plyometrics plus a soccer training program (STG), whereas another team (n=18) followed only the soccer training (CG). Strength training consisted of full squats with low load (45-60% 1RM) and low-volume (2-3 sets and 4-8 repetitions per set) combined with jumps and sprints twice a week. Sprint time in 10 and 20 m (T10, T20, T10-20), CMJ height, estimated one-repetition maximum (1RMest), average velocity attained against all loads common to pre- and post-tests (AV) and velocity developed against different absolute loads (MPV20, 30, 40 and 50) in full squat were selected as testing variables to evaluate the effects of the training program. STG experienced greater gains (P<0.05) in T20, CMJ, 1RMest, AV and MPV20, 30, 40 and 50 than CG. In addition, STG showed likely greater effects in T10 and T10-20 compared to CG. These results indicate that only 6 weeks of resistance training combined with plyometrics in addition to soccer training produce greater gains in physical performance than typical soccer training alone in young soccer players. © Georg Thieme Verlag KG Stuttgart · New York.
Article
The use of bar velocity to estimate relative load in the back squat exercise was examined. Eighty strength-trained men performed a progressive loading test to determine their one-repetition maximum (1RM) and load-velocity relationship. Mean (MV), mean propulsive (MPV) and peak (PV) velocity measures of the concentric phase were analyzed. Both MV and MPV showed a very close relationship to %1RM (R2 = 0.96), whereas a weaker association (R2 = 0.79) and larger SEE (0.14 vs. 0.06 m•s-1) was found for PV. Prediction equations to estimate load from velocity were obtained. When dividing the sample into three groups of different relative strength (1RM/body mass), no differences were found between groups for the MPV attained against each %1RM. MV attained with the 1RM was 0.32 ± 0.03 m•s-1. The propulsive phase accounted for 82% of concentric duration at 40% 1RM, and progressively increased until reaching 100% at 1RM. Provided that repetitions are performed at maximal intended velocity, a good estimation of load (%1RM) can be obtained from mean velocity as soon as the first repetition is completed. This finding provides an alternative to the often demanding, time-consuming and interfering 1RM or nRM tests and allows to implement a velocity-based resistance training approach.
Article
This study analyzed the effects of pseudoephedrine (PSE) provided at different time of day on neuromuscular performance, side effects, and violation of the current doping cut-off threshold [World Anti-Doping Agency (WADA)]. Nine resistance-trained males carried out bench press and full squat exercises against four incremental loads (25%, 50%, 75%, and 90% one repetition maximum [1RM]), in a randomized, double-blind, cross-over design. Participants ingested either 180 mg of PSE (supra-therapeutic dose) or placebo in the morning (7:00 h; AMPLAC and AMPSE) and in the afternoon (17:00 h; PMPLAC and PMPSE). PSE enhanced muscle contraction velocity against 25% and 50% 1RM loads, only when it was ingested in the mornings, and only in the full squat exercise (4.4–8.7%; P < 0.05). PSE ingestion raised urine and plasma PSE concentrations (P < 0.05) regardless of time of day; however, cathine only increased in the urine samples. PSE ingestion resulted in positive tests occurring in 11% of samples, and it rose some adverse side effects such us tachycardia and heart palpitations. Ingestion of a single dose of 180 mg of PSE results in enhanced lower body muscle contraction velocity against low and moderate loads only in the mornings. These mild performance improvements are accompanied by undesirable side effects and an 11% risk of surpassing the doping threshold.
Article
This study compared the velocity- and power-load relationships of the antagonistic upper-body exercises of prone bench pull (PBP) and bench press (BP). 75 resistance-trained athletes performed a progressive loading test in each exercise up to the one-repetition maximum (1RM) in random order. Velocity and power output across the 30-100% 1RM were significantly higher for PBP, whereas 1RM strength was greater for BP. A very close relationship was observed between relative load and mean propulsive velocity for both BP (R2=0.97) and PBP (R2=0.94) which enables us to estimate %1RM from velocity using the obtained prediction equations. Important differences in the load that maximizes power output (Pmax) and the power profiles of both exercises were found according to the outcome variable used: mean (MP), peak (PP) or mean propulsive power (MPP). When MP was considered, the Pmax load was higher (56% BP, 70% PBP) than when PP (37% BP, 41% PBP) or MPP (37% BP, 46% PBP) were used. For each variable there was a broad range of loads at which power output was not significantly different. The differing velocity- and power-load relationships between PBP and BP seem attributable to the distinct muscle architecture and moment arm levers involved in these exercises.
Article
This study examined the possibility of using movement velocity as an indicator of relative load in the bench press (BP) exercise. One hundred and twenty strength-trained males performed a test (T1) with increasing loads for the individual determination of the one-repetition maximum (1RM) and full load-velocity profile. Fifty-six subjects performed the test on a second occasion (T2) following 6 weeks of training. A very close relationship between mean propulsive velocity (MPV) and load (%1RM) was observed (R (2)=0.98). Mean velocity attained with 1RM was 0.16+/-0.04 m x s(-1) and was found to influence the MPV attained with each %1RM. Despite a mean increase of 9.3% in 1RM from T1 to T2, MPV for each %1RM remained stable. Stability in the load-velocity relationship was also confirmed regardless of individual relative strength. These results confirm an inextricable relationship between relative load and MPV in the BP that makes it possible to: 1) evaluate maximal strength without the need to perform a 1RM test, or test of maximum number of repetitions to failure (XRM); 2) determine the %1RM that is being used as soon as the first repetition with any given load is performed; 3) prescribe and monitor training load according to velocity, instead of percentages of 1RM or XRM.