Article

Reliability of technologies to measure the barbell velocity: Implications for monitoring resistance training

Abstract and Figures

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.
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RESEARCH ARTICLE
Reliability of technologies to measure the
barbell velocity: Implications for monitoring
resistance training
Alejandro Martı
´nez-Cava
1
, Alejandro Herna
´ndez-Belmonte
1
, Javier Courel-Iba
´ñez
1
,
Ricardo Mora
´n-Navarro
1
, Juan Jose
´Gonza
´lez-Badillo
2
, Jesu
´s G. Pallare
´sID
1
*
1Human Performance and Sports Science Laboratory, Faculty of Sport Sciences, University of Murcia,
Murcia, Spain, 2Faculty of Sport, Pablo de Olavide University, Seville, Spain
*jgpallares@um.es
Abstract
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 reli-
able, 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.
Introduction
The ability to develop force rapidly against a continuum of loads is a key factor in sport perfor-
mance. To be able to objectively quantify and monitor the actual training load undertaken by
athletes is a key issue in the design of effective, efficient and safer training programmes [1].
The use of the barbell movement velocity as the main variable, namely the velocity-based
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OPEN ACCESS
Citation: Martı
´nez-Cava A, Herna
´ndez-Belmonte A,
Courel-Iba
´ñez J, Mora
´n-Navarro R, Gonza
´lez-
Badillo JJ, Pallare
´s JG (2020) Reliability of
technologies to measure the barbell velocity:
Implications for monitoring resistance training.
PLoS ONE 15(6): e0232465. https://doi.org/
10.1371/journal.pone.0232465
Editor: Daniel Boullosa, Universidade Federal de
Mato Grosso do Sul, BRAZIL
Received: January 11, 2020
Accepted: April 15, 2020
Published: June 10, 2020
Copyright: ©2020 Martı
´nez-Cava et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All raw data files are
available on OSF via doi: 10.17605/OSF.IO/ACSQU.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
training (VBT), constitute a practical alternative to traditional percentage-based training using
the one repetition maximum (1RM) to estimate relative loads [24]. VBT relies on technology
to track the lifting velocity in real time and adjust the training load based on the resultant
velocity data [2]. The VBT has important practical implications for the design and implemen-
tation of individual training plans. On one hand, coaches are provided with quantitative out-
comes that can be used for multiple purposes, such as training autoregulation through the
warm-up loads’ velocity assessment [2,4,5], determination of individualised load-velocity pro-
files [6] and the real-time neuromuscular fatigue monitoring [4,7]. On the other hand, practi-
tioners receive instantaneous performance feedback about the actual velocity developed
during each lift, which has been shown to produce greater adaptation and larger training
effects [8]. Due to the number of advantages, the adoption of the VBT approach among profes-
sionals from different sport disciplines has been rising in recent years [9].
There is increasing evidences showing that VBT could be more effective than traditional
training methods to decrease training stress and improve velocity-specific adaptations [10,11].
An optimal VBT prescription needs the use of reliable devices to accurately measure the bar-
bell velocity for effectively managing the training load and maximize the adaptive responses.
This requirement constitutes one of the main drawbacks of VBT since very small changes in
velocity can represent decisive improvements or decrements in neuromuscular and functional
performance [1214]. As a consequence, there is an increasing number of available devices to
measure the barbell velocity using a wide variety of technologies. This technological develop-
ment has been accompanied by a parallel increase in studies attempting to examine the validity
and reliability of emerging devices, including linear velocity transducers [15], linear position
transducers [16,17] and optoelectronic systems [18]. While these technologies have been spe-
cifically designed to measure the barbell velocity, some authors have tested the validity of cam-
era-based tools such as smartphones apps [19,20], inertial measurement units [21] or 3D
motion analysis system (3DMA) [22] as alternatives.
Due to this increasing interest in testing the reliability of barbell velocity measurement
devices, there is a need to clarify some methods commonly used that may limit the data inter-
pretation and conclusions. Firstly, they wrongly used the Pearson’s correlation coefficient to
determine the level of agreement between devices. Pearson’s correlation quantifies the rela-
tionship between scores, but does not provide any insight into systematic errors inherent in
the measurement; thereby, an excellent correlation does not mean complete agreement
between scores [23]. 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 interpre-
tation of the magnitude of errors according to practical criteria and established acceptable lev-
els of disagreement [24,25], only a few studies have based their findings on these criteria [26
28]. An interesting approach has been presented based on the changes in performance (%
1RM) produced by increments in the barbell velocity [2628]. 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]. Based on these findings, to determine
these gains in performance, one would require a device accurate enough to ensure that the
changes are not produced by the error of the measurement but represent a real performance
improvement (i.e., error <0.05 or 0.10 m/s, at least). Hence, one could consider that a given
device with errors above this limit would not be reliable enough for VBT purposes. However,
only two studies have made recommendations on barbell velocity measurement devices based
on practical criteria [26,28], which encourage further research in this direction.
Finally, while all the available devices are apparently reliable to measure velocity in heavy-
load lifting (i.e., <0.50 m/s), the VBT requires the identification of measurement errors across
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a spectrum of relative loads, including fast movements against moderate and light loads
[21,2628]. In particular, monitoring high velocities are important to assess changes in neuro-
muscular and functional performance due to the higher specificity in relationship with most
sporting movements [29,30]. In this regard, there is no available data about the reliability of
the Speed4Lifts, the STT 3DMA camera system and My Lift app during actions >1.0 m/s for
the BP and SQ exercises. Because some devices could present greater errors when monitoring
lifts at higher velocities [27], it would be important to determine the magnitude of errors
throughout the whole load-velocity spectrum, for instance, heavy (80% 1RM), medium
(50% <1RM <80%) and light loads (50% 1RM). Furthermore, it would be of interest to
determine if the errors would allow the detection of changes in performance according to
practical acceptable criteria, such as the 5% 1RM approach [2628].
Altogether, the aforementioned drawbacks make it difficult to extrapolate the results to the
practice and may question the suitability of emerging technologies to provide objective and
reliable measurements for VBT. In addition, it is essential to inform about the magnitude of
errors from different velocity segments and across the entire loading spectrum (from heavy to
light loads) to help strength and conditioning practitioners in establishing velocity thresholds
according to the training plan and performance targets. Hence, the aim of this study was to
provide insights about the best use of each device by conducting a comprehensive reliability
and reproducibility analysis on four different technologies used in VBT to determine inherent
technical errors (i.e., the agreement between two devices from the same model and brand) and
compare their level of agreement/disagreement against a criterion device.
Materials and methods
Participants
Fifteen males volunteered to participate in this study (Mean ±SD: age 27.0 ±3.8 years old,
body mass 78.8 ±7.6 kg, height 178.0 ±6.3 cm). All participants were familiarised with the
testing protocols and had previously participated in similar studies. No physical limitations or
musculoskeletal injuries that could affect testing were reported. Participants signed a written
informed consent form. The study was conducted according to the Code of Ethics of the
World Medical Association (Declaration of Helsinki) and approved by the Bioethics Commis-
sion of the University of Murcia.
Study design
Participants underwent two experimental sessions in random order, one for the BP and one
for the SQ exercises, separated by 48 h of recovery. Participants completed a progressive load-
ing test in a Smith machine. This test consisted in performing one repetition against eight
increasing fixed loads ranging from 25 to 95 kg, with 10 kg increments and 5 min of recovery.
Seven devices based on four different technologies were used to simultaneously measure the
barbell velocity during the performance of each repetition. The magnitude of errors, levels of
agreement and linear relationships between two devices from the same brand and model
(intra-device agreement) as well as between any given device compared to a gold standard
(inter-device agreement) were calculated in overall and for particular loading ranges uses in
practical settings (50%, 50–80%, and 80% 1RM).
Methodology
A description of the BP and SQ testing protocols has been previously detailed [12,14]. After a
familiarization session. Participants performed one repetition against eight fixed loads (25, 35,
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45, 55, 65, 75, 85 and 95 kg) at maximal intended velocity with 5 min of rest between, in two
sessions separated by 48–72 h (one per exercise). To ensure that all participants were able to
complete the entire protocol, they performed a 1RM test in both exercises two weeks before
the experiment, achieving 99.9 ±3.2 kg in BP and 100.6 ±2.7 kg in SQ (1.28 ±0.11, 1.29 ±0.12
normalized per kg of body mass, respectively). The eccentric phase was performed at a con-
trolled velocity (0.50–0.70 m/s) for standardization and security reasons. This protocol was
implemented during the familiarization sessions with the aid of the real-time feedback pro-
vided by the T-Force System, so that the velocity could be adjusted to the required range dur-
ing the eccentric phase for all participants during actual testing procedures. Feet and grip
positions (shoulder width or slightly wider) were measured so that they could be reproduced
on every lift.
Measurement equipment and data acquisition
Seven single device units representatives of four different technologies (Table 1), were used to
simultaneously measure or estimate the barbell velocity during the upward part of the lifts (i.e.,
concentric phase) for each repetition, as previously explained [26]. In summary: two linear
velocity transducers, T-Force Dynamic Measurement System (Ergotech Consulting, Murcia,
Spain), two linear position transducers, Speed4Lifts (v2.0, Speed4Lifts, Madrid, Spain), two
smartphone video-based apps, My Lift (version 8.1 iOS), installed on two iPhone 5S units run-
ning iOS 12.2 (Apple Inc., California, USA) and a set of 3DMA with six cameras, STT (STT
Table 1. Technical characteristics of the devices under study.
Device T-Force System Speed4Lifts STT My Lift
Technology Linear velocity transducer Linear position transducer 3D Motion Analysis system Smartphone app
Software version 3.60 1.41 (Android) 6.10 8.1 (iOS)
Direct outcome
measures
Velocity; Time Position; Time Position; Time Position; Time
Indirect outcome
calculations
Distance; Acceleration; Force; Power Distance; Velocity; Power Distance; Velocity Distance; Velocity
Sampling
frequency
1000 Hz 100 Hz 100 Hz 60 Hz
Mechanic variables
displayed by the
software
Mean, peak and time to reach peak
values for all direct and indirect
outcomes, propulsive phase, estimated
load (%1RM), 1RM prediction,
number of repetitions, velocity loss
(%), velocity alerts (visual and audio
feedback)
Mean propulsive and peak velocity,
mean power, range of motion,
estimated load (%1RM), 1RM
prediction, number of repetitions,
velocity loss (%) inter and intra-set
(visual and audio feedback)
Position-time curve in axis: x
(lateral displacement), y
(vertical displacement) and z
(antero-posterior displacement)
Peak vertical and horizontal
displacement, peak and mean
vertical velocity,
instantaneous velocity and
time
External power
supply required
No No Yes No
Installation and
calibration time
before the first
execution
2.4 min 2.5 min 2.2 h 1.5 min
Time to obtain the
measure after
execution#
real time real time 130 s 45 s
Number of lost
repetitions per each
100 cases
0.8 0 1.7 0
Estimation of mean installation and equipment calibration time spent for the performance of three consecutive repetitions.
#Mean time required to obtain the MV, MPV or PV outcome value from three repetitions performed against medium to high loads (>50% 1RM).
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system, Basque Country, Spain) and specific software (v6.10, STT Systems, Paı
´s Vasco, Spain).
Three distinct velocity outcome measures were obtained from each device, when possible:
mean velocity (MV, mean concentric velocity); mean propulsive velocity (MPV, mean velocity
of the propulsive phase, defined as that portion of the concentric phase during which barbell
acceleration is greater than acceleration due to gravity) and peak velocity (PV, maximum
instantaneous velocity reached during the concentric phase).
A visual representation of the experiment set up is shown in Fig 1. To avoid the appearance
of errors due to the location of the devices [31], the retractable cables of all T-Force and
Speed4Lifts units were attached to the same right side of the Smith Machine, all of them placed
very close to the vertical displacement axis (3 cm to the right and left side of the axis), using a
purpose-built support (Fig 1). None of the participants felt difficult or uncomfortable when
lifting with the four retractable cables attached on one side of the barbell. The smartphones
running the My Lift app were placed on tripods, at a horizontal distance of 2.4 m, just in the
same lateral side where the other devices were located. The height of tripods was adapted in
each exercise (BP: 1.0 m and SQ: 1.35 m) to track the whole movement. An experienced exam-
iner used the automatic tracking mode available in the My Lift app following the developer´s
instructions. Intra-examiner reliability was conducted to ensure the consistency of the out-
comes. The STT camera-based system was synchronized to follow a retro-reflective marker
(14 mm; B&L Engineering, Santa Ana, CA) placed on the centre of the end-cap, at the end of
the barbell sleeves, in the same side of the barbell where the other devices were installed. Posi-
tion and time raw data were collected to obtain the outcome variables. The start (y
1
) and end
Fig 1. Experiment set up.
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(y
2
) positions of the concentric phase were located and displacement of this phase was calcu-
lated as y
2
–y
1
. Then, MV was obtained dividing the displacement of the concentric phase by
the time required to complete it (data time each 0.01 second; i.e. 100 Hz sampling). Instanta-
neous velocity was calculated by the differentiation of the displacement data with respect to
time. Finally, PV was defined as the highest value of instantaneous velocity during the concen-
tric phase. Each device was assembled and calibrated according to the manufacturer’s specifi-
cations before each session.
Intra-device reproducibility was assessed by comparing the velocity outcomes for the same
trial simultaneously obtained by each pair of the same brand and model devices. Since only
one set of STT was used, the intra-device analysis of this technology could not be examined.
The technology with the best intra-device agreement was taken as the reference to assess the
inter-device agreement of one representative unit of each technology.
Statistical analysis
Reliability analyses included the calculation of a set of statistics aimed at providing information
about the level of agreement and the magnitude of errors (both in absolute and relative values)
incurred when using the different technologies under study. To determine the magnitude of
errors at particular velocity ranges [27], data were then classified on three velocity segments
according to the velocity-load relationships for each exercise [12,13]: heavy loads (80%
1RM, MPV 0.50 m/s in BP and 0.70 m/s in SQ), medium loads (50% <1RM <80%, MPV
between 0.50 and 1.00 m/s in BP and between 0.70 and 1.15 m/s in SQ) and light loads (50%
1RM, MPV 1.00 m/s in BP and 1.15 m/s in SQ). A detailed explanation of the statistical
analyses conducted has been described elsewhere [26,27]. Correlation analyses included the
Pearson’s correlation coefficient (r), the intraclass correlation coefficient (ICC, one way-ran-
dom, absolute agreement) and the Lin’s concordance correlation coefficient (CCC), consider-
ing values over 0.99 as an almost perfect concordance, over 0.95 as moderate concordance and
below 0.90 as a poor concordance [32]. Linear regression analyses were used to provide predic-
tive equations for each device and calculate the standard error of the estimate (SEE). The stan-
dard error of measurement (SEM) was calculated from the square root of the mean square
error term in a repeated-measures ANOVA to determine the variability caused by measure-
ment error [33]. Data were presented in absolute (m/s) and relative terms as a coefficient of
variation (CV = 100 SEM/mean). The smallest detectable change (SDC) was calculated from
the SEM (SCD = p2×SEM×1.96) and considered as the change in the instrument score
beyond measurement error [34]. Bland-Altman plots were used to assess and display the agree-
ment along the entire spectrum of loads and at each velocity segment. Systematic difference
(bias) and its 95% limits of agreement (LoA = bias ±1.96 SD) were calculated. Maximum
errors (MaxError) were calculated from the SEE (Max Error
SEE
) and the bias (MaxError
bias
) as
the double of the upper bound of a 95% CI to represent the largest error expected from a given
measurement [26] and were expressed in absolute values (m/s) and as the corresponding rela-
tive load (% 1RM) for each velocity and exercise based on previous studies [2,12,14]. Criteria
for acceptable reliability were ICC >0.990 and SDC <0.07 m/s (~5% 1RM) according to pre-
vious recommendations [26,28,32] and based on the differences identified in MPV after short-
term resistance training interventions [3,35,36].
Results
Results from intra- and inter-device agreements for BP and SQ exercises are shown in Table 2.
Intra-device comparisons showed the T-Force (Figs 2and 3) as the most reproducible device
(ICC and CCC = 1.000, CV 0.62%, SEM 0.01 m/s, SDC 0.02 m/s). The second-best
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intra-device results were obtained with the Speed4Lifts (ICC 0.999, CCC 0.999, CV
1.80%, SEM 0.02 m/s, SDC 0.05 m/s). My Lift showed the greatest errors and the worst
reproducibility (ICC 0.972, CCC 0.945, CV 5.79%, SEM 0.08 m/s, SDC 0.24 m/s),
despite the high intra-examiner agreement observed in the BP (ICC 0.998, CCC 0.997,
SEM 0.04, CV 2.9%) and SQ (ICC 0.981, CCC 0.959, SEM 0.06 CV 3.9%).
Inter-device linear regression analyses (Fig 4) and Bland-Altman plots (Fig 5) for the three
different velocity segments identified the readings from STT and the Speed4Lifts as the most
similar to the criterion device (T-Force), especially against medium to heavy loads (i.e., mean
lifting velocities <1.0 m/s). However, both the STT and the Speed4Lifts showed greater errors
as the velocity increased. For instance, the SDC for Speed4Lifts at MPV 0.5 m/s were 0.01
and 0.02 m/s for BP and SQ respectively (Fig 4C and 4D) but increased up to 0.09 and 0.10 m/
s for MPV >1.0 m/s. The fact that a given device may produce greater errors when monitoring
higher velocities, even if it shows reliable measures at low velocities (e.g., 1RM), has been
Table 2. Intra- and inter-device agreement obtained for the velocity outcomes in the bench press and full squat exercise.
Bench press (BP) Full Squat (SQ)
Intra-device agreement Inter-device agreementIntra-device agreement Inter-device agreement
T-Force Speed4Lifts My Lift Speed4Lifts My Lift STT T-Force Speed4Lifts My Lift Speed4Lifts My Lift STT
Peak velocity (PV)
SEM m/s 0.01 0.02 0.08 0.06 0.10 0.08 0.01 0.01 0.08 0.02 0.12 0.07
SDC m/s 0.02 0.05 0.23 0.18 0.26 0.21 0.02 0.04 0.24 0.07 0.34 0.19
CV %0.45 1.54 5.79 4.94 7.04 5.57 0.46 0.86 5.02 1.60 7.59 4.22
Max Error % 1RM 1.8 4.4 25.0 15.7 19.4 10.4 2.2 4.3 28.1 7.0 24.3 9.7
ICC 1.000 1.000 0.993 0.995 0.991 0.994 1.000 0.999 0.972 0.997 0.937 0.981
CI-95% lower 1.000 0.999 0.990 0.993 0.987 0.991 1.000 0.999 0.959 0.996 0.910 0.973
CI-95% upper 1.000 1.000 0.995 0.997 0.993 0.996 1.000 0.999 0.980 0.998 0.956 0.987
CCC 1.000 0.999 0.986 0.991 0.981 0.988 1.000 0.998 0.945 0.994 0.890 0.963
Mean propulsive velocity (MPV)
SEM m/s 0.01 0.02 - 0.02 - - 0.01 0.01 - 0.03 - -
SDC m/s 0.01 0.04 - 0.06 - - 0.01 0.03 - 0.08 - -
CV %0.62 1.80 - 2.72 - - 0.58 1.24 - 3.09 - -
Max Error % 1RM 1.8 4.9 - 7.8 - - 1.8 4.3 - 9.5 - -
ICC 1.000 0.999 - 0.999 - - 1.000 0.999 - 0.995 - -
CI-95% lower 1.000 0.999 - 0.998 - - 1.000 0.999 - 0.994 - -
CI-95% upper 1.000 1.000 - 0.999 - - 1.000 0.999 - 0.997 - -
CCC 1.000 0.999 - 0.997 - - 1.000 0.999 - 0.991 - -
Mean velocity (MV)
SEM m/s <0.01 - - - - 0.03 <0.01 - - - - 0.01
SDC m/s 0.01 - - - - 0.08 0.01 - - - - 0.04
CV %0.55 - - - - 3.34 0.44 - - - - 1.61
Max Error % 1RM 1.4 - - - - 4.1 1.0 - - - - 4.5
ICC 1.000 - - - - 0.998 1.000 - - - - 0.999
CI-95% lower 1.000 - - - - 0.997 1.000 - - - - 0.998
CI-95% upper 1.000 - - - - 0.998 1.000 - - - - 0.999
CCC 1.000 - - - - 0.995 1.000 - - - - 0.995
The reference for assessing inter-device agreement was considered to be the device with the best intra-device agreement: T-Force (Figs 1and 2). SEM: standard error of
measurement; SDC: smallest detectable change; CV: SEM expressed as a coefficient of variation; Max Error: maximum error in %1RM calculated from the Bland-
Altman bias; ICC: intraclass correlation coefficient; CI: confidence interval; CCC: Lin’s concordance correlation coefficient.
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Fig 2. Intra-device agreement between two T-Force devices. Linear regressions for the velocity readings in bench
press (A, C and E panels) and full squat (B, D and F panels) exercises. Panels are ordered by velocity outcomes: mean
velocity (MV), mean propulsive velocity (MPV) and peak velocity (PV).
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Fig 3. Intra-device agreement between two T-Force devices. Bland–Altman plots for the velocity readings in bench
press (A, C and E panels) and full squat (B, D and F) exercises. Panels are ordered by velocity outcomes: mean velocity
(MV), mean propulsive velocity (MPV) and peak velocity (PV). The grey shaded area indicates an acceptable level of
agreement between devices, which results in differences in terms of load 5% 1RM [26,27].
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Fig 4. Linear regression analyses for the inter-device agreement in bench press (BP) and full squat (SQ) exercises.
Each technology is presented in a different colour and compared against the reference.
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Fig 5. Bland-Altman plots for the inter-device agreement in bench press (BP) and full squat (SQ). Each technology
is presented in a different colour and compared against the reference. The grey shaded area indicates an acceptable
level of agreement between devices, which results in differences in terms of load 5% 1RM [26,27].
https://doi.org/10.1371/journal.pone.0232465.g005
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previously suggested [27]. Moreover, the Speed4Lifts showed a changing trend, going from a
slight underestimation for slow velocities to an over-estimation of the MPV for high velocities
in the BP exercise compared to the T-Force (Fig 4C and 4D). My Lift showed the worst repro-
ducibility and the highest errors in both BP and SQ exercises, regardless of velocity (Fig 4E
and 4F). All the devices showed the smallest errors at slow velocities (<0.50 m/s).
Discussion
Based on the results of the present study, the linear velocity transducer T-Force stands as the
most reliable technology for VBT purposes, showing the finest readings among the tested
devices. The linear position transducer Speed4Lifts and the STT camera-based system were
found as suitable alternatives to the T-Force, especially when monitoring movements against
medium to heavy loads (Table 2). Nonetheless, our results suggest considering specific mar-
gins of errors for each exercise (BP or SQ), velocity parameter (MV, MPV and PV) and load
spectrum (from heavy loads at slow velocities to light loads at high velocities) according to the
SEM and SDC values obtained. Assuming the SDC as a change beyond measurement error
[34], coaches and practitioners using a particular device should take these values as a confi-
dence interval to make load adjustments, determine the number of repetitions and identify
training adaptations. Otherwise, it might be possible that the increments in the velocity come
from a measurement error and therefore the load adjustments could produce adverse effects
and increase the risk of injury, illness or overtraining [1]. Whereas this is the first time testing
the intra-device agreement of the Speed4Lifts, our findings support the only previous study
examining the reliability of this tool [17]. These authors observed a high agreement between
the Speed4Lifts readings and the Trio-OptiTrack 3DMA system when measuring BP and SQ
lifts from 0.38 to 0.88 m/s. Our study adds to this previous research by noting that Speed4Lifts
reliability decreases as the velocity movement increases. In particular, we identify increments
in MPV errors up to 0.05/0.07 m/s for BP/SQ at 50–80% 1RM (Fig 5C). This is an important
limitation for the Speed4Lifts to monitor resistance training against light-loads (high-veloci-
ties) and explosive movements. All in all, the Speed4Lifts is one of the most affordable devices
on the market and it may be considered as an adequate and practical tool for VBT, notwith-
standing the aforementioned observations.
The STT showed excellent results in the MV variable against medium to heavy loads, but
greater errors for the fastest movements (Fig 5A and 5B). One recent study has tested the reli-
ability of a similar 3DMA system (Qualisys Track Manager) to assess the barbell velocity with
similar findings [22]. The worse performance of the 3DMA system to monitor high-velocity
lifts could be attributable to the limited sampling rate of the cameras (i.e., the faster the move-
ment, the shorter the time and the lower the number of data points, resulting in greater errors).
Likewise, it was not possible to accurately estimate the MPV since the end of the propulsive
phase during resistance training exercises lasts less than 0.01 seconds [15]. Nonetheless,
assuming that technological advancements may solve this limitation in the future by develop-
ing faster cameras, several important disadvantages of 3DMA systems, such as expensiveness
of the equipment and time-consuming setup and data processing, makes it unpractical in real-
world settings.
The My Lift smartphone app (formerly PowerLift) has attracted much attention due to its
low cost, versatility and ease of implementation [17,19,20]. The new update of the app includes
an automatic tracking mode that estimates velocity from a side-view video by manually setting
the diameter of the weight discs as a reference. Despite the increasing popularity of smart-
phone apps for VBT, our results showed that the My Lift was the least reliable tool compared
to the other available devices (Fig 5E and 5F). These results are consistent with previous
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Technologies for velocity monitoring: A practical analysis
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research [19,27,37] suggesting that My Lift might be only reliable to track slow lifts with heavy
loads. Although this might be useful to conduct routinely submaximal loading tests at >80%
1RM and to avoid reaching the 100%, the use of the My Lift app for VBT is ill-advisable due to
its large measurement errors (SEM >0.10 m/s, SDC >0.23 m/s) when tracking lifts >0.30 m/
s, making it difficult to determine load adjustments or performance fluctuations with sufficient
accuracy. It must be noted that we found a very high and consistent intra-examiner reliability,
meaning that the app is user-friendly and confirming that the results were not affected by the
examiner’s handling but the device itself. At the time of this study, no previous research has
examined the reliability of this new update of the My Lift app, which encourages further
investigations.
The list of statistical calculations provided herein represents an added value compared to
the majority of studies testing the validity and reliability of velocity measurement devices for
resistance training. Practitioners should consider at least the SEM as the limit below which a
given device should be used, although the SDC would be advisable to identify meaningful
changes in performance and determine the real effort being incurred during training. In this
paper, we provide the SDC values for different segments (slow, medium and high velocities)
along a broad range of velocities (MV >0.2 to <2.8 m/s). Furthermore, it has been demon-
strated that traditional interpretation of correlations and linear relationship coefficients (i.e.,
values >0.90 as very high) failed to determine the reliability of a device [2426]. As could be
expected, all the devices in the present study showed high r and ICC values ~0.99, with 0.937
in the worst case. This just means that the faster the lift, the higher the measure of the device
but gives no information about the magnitude of errors in absolute values. Given that practi-
tioners make load adjustments in absolute values (i.e., m/s), the CCC appears to be a more
appropriate coefficient than the ICC and rto determine the reliability of VBT devices. Addi-
tionally, the use of Bland-Altman bias requires the interpretation of the results using accept-
able limits of agreement based on practical criteria. The recommendations provided in our
study are tailored to VBT practitioners and based on specific margins of errors previously
defined [26]. We encourage future researchers to follow this approach in order to assist
coaches and practitioners in the use of velocity measurements to decide which device to
choose and thus provide better training prescription.
Strength and conditioning practitioners, and particularly those using VBT, should consider
the magnitude of errors for their preferable device as a confidence interval to make load adjust-
ments, determine the number of repetitions and identify training adaptations with sufficient
accuracy. Otherwise, it might be possible that the increments in the velocity came from a mea-
surement error and therefore the adjustments could produce adverse training adaptations and
increase the risk of injury. Our findings suggest coaches and researchers to use the T-Force
device as preferable option for monitoring barbell velocity and identifying technical errors of
measurement for emerging devices. If T-force device is unavailable, both the Speed4Lifts and
the STT system can be used as a highly reliable option, especially against velocities 1.0 m/s.
Finally, practitioners are discouraged to use the automatic tracking mode of My Lift app for
assessing barbell velocity, since its high errors are well above the acceptable levels.
This work has some important strengths, such as the interpretation of the magnitudes of
errors according to practical criteria (i.e., 5% 1RM) and the identification of particular mea-
surement errors for light, medium or heavy loads. The current recommendations by segments
may serve as a practical guide to assist coaches and practitioners in the election of one device
or another depending on their practical interests. It also seems to be the first study examining
the reliability of the Speed4Lift, the My Lift app and the STT 3DMA system during the BP and
SQ at lifts >1.0 m/s. Although the present paper has not tested the biological variability pur-
posely but determine the technological error, there are available studies examining the changes
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Technologies for velocity monitoring: A practical analysis
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in the measures during repeated trials in common resistance training exercises [6,26,38]. The
choice of one technology over another should be taken according to the particular context and
depending on the accuracy required to identity true changes in performance.
Conclusions
Taken together, our findings suggest that the linear velocity transducer is an extremely reliable
technology for VBT purposes with the T-Force showing the finest readings among the tested
devices along the entire spectrum of velocities (MV >0.2 to <2.8 m/s). The linear position
transducer Speed4Lifts and the STT camera-based system are suitable alternatives to the
T-Force, especially to monitor barbell movements against medium to heavy loads (<1.0 m/s).
On the other hand, the My Lift smartphone app (formerly PowerLift) is ill-advisable for VBT
due to its large measurement errors when tracking lifts >0.30 m/s.
Acknowledgments
The authors would like to acknowledge the participants for their invaluable contribution to
the study.
Author Contributions
Conceptualization: Alejandro Martı
´nez-Cava, Juan Jose
´Gonza
´lez-Badillo, Jesu
´s G. Pallare
´s.
Data curation: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Javier Courel-
Iba
´ñez, Ricardo Mora
´n-Navarro, Jesu
´s G. Pallare
´s.
Formal analysis: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Javier Courel-
Iba
´ñez, Juan Jose
´Gonza
´lez-Badillo, Jesu
´s G. Pallare
´s.
Investigation: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Ricardo Mora
´n-
Navarro, Juan Jose
´Gonza
´lez-Badillo, Jesu
´s G. Pallare
´s.
Methodology: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Javier Courel-
Iba
´ñez, Juan Jose
´Gonza
´lez-Badillo, Jesu
´s G. Pallare
´s.
Project administration: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Jesu
´s G.
Pallare
´s.
Resources: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Jesu
´s G. Pallare
´s.
Software: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Ricardo Mora
´n-
Navarro.
Supervision: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Juan Jose
´Gonza
´lez-
Badillo, Jesu
´s G. Pallare
´s.
Validation: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Ricardo Mora
´n-
Navarro, Juan Jose
´Gonza
´lez-Badillo, Jesu
´s G. Pallare
´s.
Visualization: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Jesu
´s G. Pallare
´s.
Writing – original draft: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte, Javier
Courel-Iba
´ñez, Ricardo Mora
´n-Navarro, Juan Jose
´Gonza
´lez-Badillo, Jesu
´s G. Pallare
´s.
Writing – review & editing: Alejandro Martı
´nez-Cava, Alejandro Herna
´ndez-Belmonte,
Javier Courel-Iba
´ñez, Juan Jose
´Gonza
´lez-Badillo, Jesu
´s G. Pallare
´s.
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PLOS ONE | https://doi.org/10.1371/journal.pone.0232465 June 10, 2020 14 / 17
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Technologies for velocity monitoring: A practical analysis
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... However, to offer a safe and effective VBT prescription, those technologies should be valid and reliable (Martínez-Cava et al., 2020), making difficult the decision of what technology should be used among a wide range of devices. Between the different commercially available VBT technologies, the three-dimensional (3D) motion capture system and force platforms are usually considered as the most accurate systems (Giroux et al., 2015;Lorenzetti et al., 2017;Rahmani et al., 2001;Sato et al., 2015). ...
... The characteristics of the study intervention protocols are available in Table 3. Of the 21 articles included, eight investigated validity and reliability (Askow et al., 2018;Courel-Ibáñez et al., 2019;Dorrell et al., 2019;García-Ramos et al., 2016;Garnacho-Castaño et al., 2015;Lorenzetti et al., 2017;Pérez-Castilla et al., 2019;Thompson et al., 2020), while six tested only validity (Crewther et al., 2011;Fernandes et al., 2018;Gonzalez et al., 2019;, 2018;Mitter et al., 2019;Pérez-Castilla et al., 2017) and seven only tested reliability (Ferro et al., 2019;Grgic et al., 2020;Hansen et al., 2011aHansen et al., , 2011bJennings et al., 2005;Martínez-Cava et al., 2020;Orange et al., 2020). ...
... Additionally, five studies analysed LVTs such as T-Force (García-Ramos et al., 2016;Lorenzetti et al., 2017;Pérez-Castilla et al., 2017 and SmartCoach (Ferro et al., 2019). The validity and reliability of kinematic variables such as velocity (Askow et al., 2018;Courel-Ibáñez et al., 2019;Dorrell et al., 2019;Fernandes et al., 2018;Ferro et al., 2019;García-Ramos et al., 2016;Garnacho-Castaño et al., 2015;Gonzalez et al., 2019;Grgic et al., 2020;Lorenzetti et al., 2017;Martínez-Cava et al., 2020;, 2018;Mitter et al., 2019;Orange et al., 2020;Pérez-Castilla et al., 2017Thompson et al., 2020), power (Askow et al., 2018;Crewther et al., 2011;García-Ramos et al., 2016;Garnacho-Castaño et al., 2015;Grgic et al., 2020;Jennings et al., 2005;Orange et al., 2020;Pérez-Castilla et al., 2017) and force (Askow et al., 2018;Crewther et al., 2011;Dorrell et al., 2019;García-Ramos et al., 2016;Hansen et al., 2011b;Pérez-Castilla et al., 2017) were investigated using both peak and mean values. In a marginal way, some studies also evaluated acceleration variables (Courel-Ibáñez et al., 2019;Hansen et al., 2011aHansen et al., , 2011bLorenzetti et al., 2017;Martínez-Cava et al., 2020). ...
Article
Full-text available
This systematic review aimed to summarise and analyse the evidence on the reliability and validity of linear tranducers (LTs) in exercises of different nature and different modes of execution. This systematic review was carried out under PRISMA guidelines, and was carried out using three databases (PubMed, Web of Sciences, and Scopus). Of the 351 initially found, 21 were included in the qualitative synthesis. The results reflected that linear position transducers (LPTs) were valid and reliable in monitoring movement velocity in non-plyometric exercises. However, precision and reliability were lower in execution protocols without isometric phase and in the execution of exercises in multiple planes of movement, with greater measurement errors at higher sampling frequencies. On the other hand, linear velocity transducers (LVTs) proved to be valid and reliable in measuring velocity during plyometric and non-plyometric exercises performed on the Smith machine, with less variation in measurement in the latter. Finally, the use of peak values is recommended, since they are less dependent on the technological errors of LTs. Therefore, the performance of non-plyometric exercises, carried out in the Smith machine and with an isometric phase in the execution of the movement, will help to minimise the technological error of the LTs.
... Repetitions during the training sessions were recorded by using a linear velocity transducer with a sampling frequency of 1000 Hz (T-Force System, Ergotech, Murcia, Spain). This technology has been tested for barbell velocity measurement with excellent results of reproducibility and repeatability, as detailed elsewhere [25,26]. On the other hand, the CONTROL group was required to fully discontinue any kind of programmed resistance or endurance stimuli other than the normal physical activity of the active life of these young adults during the 10-week period. ...
... This superior predictive capability of the WAnT 20 could be due to the additional 5 s included in this proposal. Regarding the error generated by each model, a time-shortened WAnT could be considered useful if it has sufficient sensitivity to detect changes in the variable of interest (i.e., SDC ≤ change in MPO) [25,26]. For the first time, in order to practically interpret this sensitivity, the current research compared the SDC obtained by each time-shortened model in the cross-validation analysis with the changes in MPO after a resistance training or detraining period. ...
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This study aimed to analyze the validity and sensitivity of two time-shortened Wingate anaerobic tests (WAnTs), by means of three phases. In Phase A, 40 participants performed a traditional 30 s WAnT, whereas the first 15 s (WAnT15) and 20 s (WAnT20) were used to elaborate two predictive models. In Phase B, another 30 s WAnT was performed by 15 different volunteers to examine the error of these models (cross-validation). Finally, in Phase C, a 30 s WAnT was registered before and after a 10-week velocity-based training conducted by 22 different participants (training group, TRAIN = 11; control group that fully refrained from any type of training, CONTROL = 11). Power changes (in Watts, W) after this training intervention were used to interpret the sensitivity of the time-shortened WAnT. Adjusted coefficient of determination (R2) was reported for each regression model, whereas the cross-validation analysis included the smallest detectable change (SDC) and bias. Close relationships were found between the traditional 30 s WAnT and both the WAnT15 (R2 = 0.98) and WAnT20 (R2 = 0.99). Cross-validation analysis showed a lower error and bias for WAnT20 (SDC = 9.3 W, bias = −0.1 W) compared to WAnT15 (SDC = 22.2 W, bias = 1.8 W). Lastly, sensitivity to identify individual changes was higher for WAnT20 (TRAIN = 11/11 subjects, CONTROL = 9/11 subjects) than for WAnT15 (TRAIN = 4/11 subjects, CONTROL = 2/11 subjects). These findings suggest that the WAnT20 could become a valid and sensitive protocol to replace the traditional 30 s WAnT.
... For example, the lifting speed can be determined by measuring the extension of a string tied to the weight [7], [8]. However, this sort of instrumentation is usually not available, even though there are various commercial devices based on linear transducers, Inertial Measurement Units (IMUs), and more [9]. In contrast, measuring subjective exertion with an RPE scale implicitly contains all parameters relevant to the exhaustion, even in sessions or situations with various types of exertion. ...
... If the measurement errors exceed the expected changes, the device renders it completely useless for its intended purpose. 5,6 Therefore, it would be of great practical value to comprehensively quantify the reproducibility of the Rotor 2INpower, examining both its intradevice (errors generated when comparing several units of this powermeter 5 ) and interdevice (errors generated when comparing this powermeter against a gold standard 7 ) agreement. Moreover, examining this reproducibility when changing the cadence, pedaling position, or workloads would give practitioners the confidence to use them on a day-to-day basis. 2 Moreover, the fact that many cycling races take place on cobblestone roads or other rough terrains (eg, Paris-Roubaix or Tour of Flanders) makes it necessary to examine whether the accuracy of these technologies remains acceptable under vibration conditions. ...
Article
Purpose: To examine the reproducibility (intradevice and interdevice agreement) of the Rotor 2INpower device under a wide range of cycling conditions. Methods: Twelve highly trained male cyclists and triathletes completed 5 cycling tests, including graded exercise tests at different cadences (70-100 rpm), workloads (100-650 W), pedaling positions (seated and standing), and vibration conditions (20-40 Hz) and an 8-second maximal sprint (>1000 W). An intradevice analysis included a comparison between the power output registered by 3 units of Rotor 2INpower, whereas the power output provided by each one of these units and the gold-standard SRM crankset were compared for the interdevice analysis. Among others, statistical calculations included the standard error of measurement, expressed in absolute (in watts) and relative terms as the coefficient of variation (CV). Results: Except for the graded exercise test seated at 100 rpm/100 W (CV = 10.2%), the intradevice analysis showed an acceptable magnitude of error (CV ≤ 6.9%, standard error of measurement ≤ 12.3 W) between the 3 Rotor 2INpower. Similarly, these 3 units showed an acceptable agreement with the gold standard in all graded exercise test situations (CV ≤ 4.0%, standard error of measurement ≤ 13.1 W). On the other hand, both the intradevice and interdevice agreements proved to be slightly reduced under high cadences (intradevice: CV ≤ 10.2%; interdevice: CV ≤ 4.0%) and vibration (intradevice: CV ≤ 4.0%; interdevice: CV ≤ 3.6%), as well as during standing pedaling (intradevice: CV ≤ 4.1%; interdevice: CV ≤ 2.5%). Although within the limits of an acceptable agreement, measurement errors increased during the sprint tests (CV ≤ 7.4%). Conclusions: Based on these results, the Rotor 2INpower could be considered a reproducible tool to monitor power output in most cycling situations.
Article
In this study, we examined the load-velocity relationship in the hexagonal bar deadlift exercise in women. Twenty-seven resistance-trained women were recruited. Participants performed a progressive load test up to the one-repetition maximum (1RM) load for determining the individual load-velocity relationship in the hexagonal bar deadlift exercise. Bar velocity was measured in every repetition through a linear encoder. A very strong and negative relationship was found between the %1RM and bar velocity for the linear (R 2 = .94; standard error of the estimation = 5.43% 1RM) and second-order polynomial (R 2 = .95) regression models. The individual load-velocity relationship provided even better adjustments (R 2 = .98; coefficient of variation = 1.77%) than the general equation. High agreement level and low bias were found between actual and predicted 1RM for the general load-velocity relationship (intraclass correlation coefficient = .97 and 95% confidence interval [0.90, 0.99]; bias = −2.59 kg). In conclusion, bar velocity can be used to predict 1RM with high accuracy during hexagonal bar deadlift exercise in resistance-trained women.
Article
This study analyzed the predictive ability of movement velocity to estimate the relative load (i.e., % of one-repetition maximum [1RM]) during the horizontal leg-press exercise in older women and men. Twenty-four women and fourteen men living in community-dwelling centers volunteered to participate in this study. All participants performed a progressive loading test up to 1RM in the horizontal leg-press. The fastest peak velocity (PV) and mean velocity (MV) attained with each weight were collected for analysis. Linear regression equations were modeled for women and men. We observed very strong linear relationships between both velocity variables and the relative load in the horizontal leg-press in women (PV: r² = 0.93 and standard error of the estimate (SEE) = 5.96% 1RM; MV: r² = 0.94 and SEE = 5.59% 1RM) and men (PV: r² = 0.93 and SEE = 5.96% 1RM; MV: r² = 0.94 and SEE = 5.97% 1RM). The actual 1RM and the estimated 1RM using both the PV and MV presented trivial differences and very strong relationships (r = 0.98–0.99) in both sexes. Men presented significantly higher (p < 0.001–0.05) estimated PV and MV against all relative loads compared to women (average PV = 0.81 vs. 0.69 m·s⁻¹ and average MV = 0.44 vs. 0.38 m·s⁻¹). Our data suggest that movement velocity accurately estimates the relative load during the horizontal leg-press in older women and men. Coaches and researchers can use the proposed sex-specific regression equations in the horizontal leg-press to implement velocity-monitored resistance training with older adults.
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Musculoskeletal disorders such as tendinopathy are having an increasing burden on society and health systems. Tendinopathy is responsible for up to 30% of musculoskeletal disorders, having a high incidence in athletes and the general population. Although resistance training has shown short-term effectiveness for treating lower limb tendinopathy, more comprehensive exercise protocols and progression methods are required due to poor long-term outcomes. The most common resistance training protocols are pre-determined and standardised, which presents significant limitations. Current standardized protocols do not adhere to scientific resistance training principles and do not consider individual factors or take the importance of individualised training into account. Resistance training programs in tendinopathy are currently not achieving required intensity and dosage, leading to high recurrence rates. Therefore, better methods for individualising and progressing resistance training are required to improve outcomes. One potential method is autoregulation, which allows individuals to progress training at their own rate, taking individual factors into account. Despite being found effective for increasing strength in healthy athletes, autoregulation methods have not been investigated in tendinopathy. The purpose of this article was threefold: first to give an overview of individual factors in tendinopathy and current resistance training protocols in tendinopathy and their limitations. Secondly, to give an overview of the history, methods and application of autoregulation strategies both in sports performance and physiotherapy. Finally, a theoretical adaptation of a tendinopathy resistance training protocol with autoregulation methods is presented, providing an example of how the method could be implemented in clinical practice or future research.
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Purpose: To compare the effects of velocity-based training (VBT) vs percentage-based training (PBT) on strength, speed, and jump performance in academy rugby league players during a 7-wk in-season mesocycle. Methods: A total of 27 rugby league players competing in the Super League U19s Championship were randomized to VBT (n = 12) or PBT (n = 15). Both groups completed a 7-wk resistance-training intervention (2×/wk) that involved the back squat. The PBT group used a fixed load based on a percentage of 1-repetition maximum (1-RM), whereas the VBT group used a modifiable load based on individualized velocity thresholds. Biomechanical and perceptual data were collected during each training session. Back-squat 1-RM, countermovement jump, reactive strength index, sprint times, and back-squat velocity at 40–90% 1-RM were assessed pretraining and posttraining. Results: The PBT group showed likely to most likely improvements in 1-RM strength and reactive strength index, whereas the VBT group showed likely to very likely improvements in 1-RM strength, countermovement jump height, and back-squat velocity at 40% and 60% 1-RM. Sessional velocity and power were most likely greater during VBT compared with PBT (standardized mean differences = 1.8–2.4), while time under tension and perceptual training stress were likely lower (standardized mean differences = 0.49–0.66). The improvement in back-squat velocity at 60% 1-RM was likely greater following VBT compared with PBT (standardized mean difference = 0.50). Conclusion: VBT can be implemented during the competitive season, instead of traditional PBT, to improve training stimuli, decrease training stress, and promote velocity-specific adaptations.
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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
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The choice of the optimal squatting depth for resistance training (RT) has been a matter of debate for decades and is still controversial. In this study, fifty-three resistance-trained men were randomly assigned to one of four training groups: full squat (F-SQ), parallel squat (P-SQ), half squat (H-SQ), and Control (training cessation). Experimental groups completed a 10-week velocity-based RT programme using the same relative load (linear periodization from 60% to 80% 1RM), only differing in the depth of the squat trained. The individual range of motion and spinal curvatures for each squat variation were determined in the familiarization and subsequently replicated in every lift during the training and testing sessions. Neuromuscular adaptations were evaluated by one-repetition maximum strength (1RM) and mean propulsive velocity (MPV) at each squatting depth. Functional performance was assessed by countermovement jump, 20-m sprint and Wingate tests. Physical functional disability included pain and stiffness records. F-SQ was the only group that increased 1RM and MPV in the three squat variations (ES = 0.77–2.36), and achieved the highest functional performance (ES = 0.35–0.85). P-SQ group obtained the second best results (ES = 0.15–0.56). H-SQ produced no increments in neuromuscular and functional performance (ES = −0.11–0.28) and was the only group reporting significant increases in pain, stiffness and physical functional disability (ES = 1.21–0.87). Controls declined on all tests (ES = 0.02–1.32). We recommend using F-SQ or P-SQ exercises to improve strength and functional performance in well-trained athletes. In turn, the use of H-SQ is inadvisable due to the limited performance improvements and the increments in pain and discomfort after continued training.
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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
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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
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This study aims to identify the measurement error associated with the mean movement velocity when using high-speed camera-based methods and video analysis during resistance training. Eleven resistance-trained men (26.0 ± 3.4 years) completed a progressive loading test in bench press exercise. Measurements from concentric mean velocity (MV), distance and time were obtained from a linear velocity transducer (T-Force) and videos recorded with high speed cameras on readily available smartphones (Samsung S6, Xiaomi A1, and iPhone X) and digital photo cameras (Casio FH20). Videos were examined using video analysis software (Kinovea). Despite the high correlations detected, the Bland-Altman analyses revealed that all high speed cameras produced substantial overestimation of barbell MV against high loads >60% 1RM (MV error = 0.06 ± 0.03 m·s-1 to 0.08 ± 0.04 m·s-1), but mainly against low loads <60% 1RM (MV error = 0.13 ± 0.06 m·s-1 to 0.20 ± 0.09 m·s-1). The maximum estimation error of the load being lifted (%1RM) was considerable both for low (8.5% to 12.7% 1RM) and high loads (13.9% to 22.6% 1RM). Among other practical limitations, the video-based system using different high-speed cameras and smartphone devices presents severe limitations when estimating mean concentric velocity, especially when recording low loads at high velocity.
Article
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This study explored the effects of velocity-based training (VBT) on maximal strength and jump height. Sixteen trained males (22.8 ± 4.5 years) completed a countermovement jump test (CMJ), and one repetition maximum (1-RM) assessment on back squat, bench press, strict overhead press, and deadlift, before and after six weeks of resistance training. Participants were assigned to VBT, or percentage-based training (PBT) groups. The VBT group's load was dictated via real-time velocity monitoring, as opposed to pre-testing 1-RM data (PBT). No significant differences were present between groups for pre-testing data (p > 0.05). Training resulted in significant increases (p < 0.05) in maximal strength for back squat (VBT 9%, PBT 8%), bench press (VBT 8%, PBT 4%), strict overhead press (VBT 6%, PBT 6%), and deadlift (VBT 6%). Significant increases in CMJ were witnessed for the VBT group only (5%). A significant interaction effect was witnessed between training groups for bench press (p = 0.004) and CMJ (p = 0.018). Furthermore, for back squat (9%), bench press (6%), and strict overhead press (6%), a significant difference was present between the total volume lifted. The VBT intervention induced favourable adaptations in maximal strength and jump height in trained males when compared to a traditional PBT approach. Interestingly the VBT group achieved these positive outcomes despite a significant reduction in total training volume compared to the PBT group. This has potentially positive implications for the management of fatigue during resistance training.
Article
Velocity-based training (VBT) requires the monitoring of lift velocity plus the prescribed resistance weight. A validated and reliable device is needed to capture the velocity and power of several exercises. Objectives: The study objectives were to examine the validity and reliability of the Elite Form Training System® (EFTS) for measures of peak velocity (PV), average velocity (AV), peak power (PP), and average power (AP). Design: Validity of the EFTS was assessed by comparing measurements simultaneously obtained via the Qualisys Track Manager software (C-motion, version 3.90.21, Gothenburg, Sweden) utilizing 6 motion capture cameras (Oqus 400, 240 Hz, Gothenburg, Sweden). Methods: Six participants performed 6 resistance exercises in 2 sessions: power clean, dead lift, bench press, back squat, front squat, and jump squat. Results: Simple Pearson correlations indicated the validity of the device (0.982, 0.971, 0.973, and 0.982 for PV, AV, PP, and AP respectively) and ranged from 0.868 to 0.998 for the 6 exercises. The test-retest reliability of the EFTS was shown by lack of significant change in the Pearson correlation (<0.3% for each variable) between the 2 sessions. The multiple count error rate was 2.0% and the missed count error rate was 2.1%. Conclusions: The validity and reliability of the EFTS were classified as excellent across all variables and exercises with only one exercise showing a slight influence by the velocity of the movement.
Article
This study aimed to compare the load-velocity and load-power relationships of three common variations of the squat exercise. 52 strength-trained males performed a progressive loading test up to the one-repetition maximum (1RM) in the full (F-SQ), parallel (P-SQ) and half (H-SQ) squat, conducted in random order on separate days. Bar velocity and vertical force were measured by means of a linear velocity transducer time-synchronized with a force platform. The relative load that maximized power output (Pmax) was analyzed using three outcome measures: mean concentric (MP), mean propulsive (MPP) and peak power (PP), while also including or excluding body mass in force calculations. 1RM was significantly different between exercises. Load-velocity and load-power relationships were significantly different between the F-SQ, P-SQ and H-SQ variations. Close relationships (R² = 0.92–0.96) between load (%1RM) and bar velocity were found and they were specific for each squat variation, with faster velocities the greater the squat depth. Unlike the F-SQ and P-SQ, no sticking region was observed for the H-SQ when lifting high loads. The Pmax corresponded to a broad load range and was greatly influenced by how force output is calculated (including or excluding body mass) as well as the exact outcome variable used (MP, MPP, PP).
Article
Appleby, BB, Banyard, H, Cormie, P, Cormack, SJ, and Newton, RU. Validity and reliability of methods to determine barbell displacement in heavy back squats: Implications for velocity-based training. J Strength Cond Res XX(X): 000-000, 2018-The purpose of this study was to investigate the validity and reliability of methods for determining barbell displacement during heavy back squats. Twelve well-trained rugby union players (mean ± SD 1 repetition maximum [1RM] 90° squat = 196.3 ± 29.2 kg) completed 2 sets of 2 repetitions at 70, 80, and 90% of 1RM squats. Barbell displacement was derived from 3 methods across 4 load categories (120-129, 140-149, 160-169, and 180-189 kg) including: a (a) linear position transducer (LPT) attached 65 cm left of barbell center, (b) 3D motion analysis tracking of markers attached to either end of a barbell, and (c) cervical marker (C7) (criterion measurement). Validity was calculated using the typical error of the estimate as a coefficient of variation (CV%) ±90% confidence interval (CI), mean bias as a percentage, and the Pearson product moment correlation (r). Intraday reliability was calculated using the intraclass correlation coefficient and the typical error expressed as a percentage of CV% ±90% (CI). Mean displacement for C7, LPT, and the barbell ends was 520, 529, and 550-564 mm, respectively. Validity of the LPT compared with the criterion was acceptable (CV% = 2.1-3.0; bias = 0.9-1.5%; r = 0.96-0.98), whereas that of the barbell ends was less (CV% = 2.7-7.5; bias = 4.9-11.2%; r = 0.71-0.97). The CV% reliability of the C7 marker across the load categories was 6.6%, the LPT 6.6%, and the barbell ends between 5.9 and 7.2%. Despite reliable measures, overestimation of displacement occurs as the tracking location moves to the barbell ends in weighted back squats. The LPT demonstrated high validity to the criterion and high trial-to-trial reliability.