Abstract

The effect of load on time-series data has yet to be investigated during weightlifting derivatives. This study compared the effect of load on the force-time and velocity-time curves during the countermovement shrug (CMS). Twenty-nine males performed the CMS at relative loads of 40%, 60%, 80%, 100%, 120%, and 140% one repetition maximum (1RM) power clean (PC). A force plate measured the vertical ground reaction force (VGRF), which was used to calculate the barbell-lifter system velocity. Time-series data were normalized to 100% of the movement duration and assessed via statistical parametric mapping (SPM). SPM analysis showed greater negative velocity at heavier loads early in the unweighting phase (12-38% of the movement), and greater positive velocity at lower loads during the last 16% of the movement. Relative loads of 40% 1RM PC maximised propulsion velocity, whilst 140% 1RM maximized force. At higher loads, the braking and propulsive phases commence at an earlier percentage of the time-normalized movement, and the total absolute durations increase with load. It may be more appropriate to prescribe the CMS during a maximal strength mesocycle given the ability to use supramaximal loads. Future research should assess training at different loads on the effects of performance.
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Comparing biomechanical time series data across
countermovement shrug loads
David Meechan, Stuart A McErlain-Naylor, John J McMahon, Timothy J
Suchomel & Paul Comfort
To cite this article: David Meechan, Stuart A McErlain-Naylor, John J McMahon, Timothy
J Suchomel & Paul Comfort (2022): Comparing biomechanical time series data across
countermovement shrug loads, Journal of Sports Sciences, DOI: 10.1080/02640414.2022.2091351
To link to this article: https://doi.org/10.1080/02640414.2022.2091351
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 11 Aug 2022.
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SPORTS PERFORMANCE
Comparing biomechanical time series data across countermovement shrug loads
David Meechan
a,b
, Stuart A McErlain-Naylor
c
, John J McMahon
a
, Timothy J Suchomel
d
and Paul Comfort
a,e,f
a
The Salford Institute of Human Movement and Rehabilitation, University of Salford, Salford, UK;
b
Department of Elite Training Science and
Technology Division, Hong Kong Sports Institute, Hong Kong, China;
c
School of Sport, Exercise and Health Sciences, Loughborough University,
Loughborough, UK;
d
Department of Human Movement Sciences, Carroll University, Waukesha, Wisconsin, US;
e
School of Medical and Health
Sciences, Edith Cowan University, Joondalup, Australia;
f
Institute for Sport, Physical Activity and Leisure, Carnegie School of Sport, Leeds Beckett
University, Leeds, UK
ABSTRACT
The eect of load on time-series data has yet to be investigated during weightlifting derivatives. This
study compared the eect of load on the force–time and velocity–time curves during the counter-
movement shrug (CMS). Twenty-nine males performed the CMS at relative loads of 40%, 60%, 80%, 100%,
120%, and 140% one repetition maximum (1RM) power clean (PC). A force plate measured the vertical
ground reaction force (VGRF), which was used to calculate the barbell-lifter system velocity. Time-series
data were normalized to 100% of the movement duration and assessed via statistical parametric mapping
(SPM). SPM analysis showed greater negative velocity at heavier loads early in the unweighting phase
(12–38% of the movement), and greater positive velocity at lower loads during the last 16% of the
movement. Relative loads of 40% 1RM PC maximised propulsion velocity, whilst 140% 1RM maximized
force. At higher loads, the braking and propulsive phases commence at an earlier percentage of the time-
normalized movement, and the total absolute durations increase with load. It may be more appropriate
to prescribe the CMS during a maximal strength mesocycle given the ability to use supramaximal loads.
Future research should assess training at dierent loads on the eects of performance.
ARTICLE HISTORY
Accepted 14 June 2022
KEYWORDS
Weightlifting; performance;
resistance training; statistical
parametric mapping
Introduction
Numerous researchers have investigated gross kinetic and
kinematic dierences in weightlifting derivatives. These
have included the power clean [PC] (Comfort et al., 2011a,
2011b, 2018), hang power clean (Kipp et al., 2021, 2016;
Suchomel et al., 2014), countermovement shrug (CMS;
Meechan, Suchomel et al., 2020), mid-thigh pull (Comfort
et al., 2015; Meechan, Suchomel et al., 2020), snatch pull
(James et al., 2020), hang pull (Meechan, McMahon et al.,
2020), hang high pull (Suchomel et al., 2018; Suchomel,
Lake et al., 2017), pull from the knee (Comfort et al., 2017;
Meechan, McMahon et al., 2020) and jump shrug (Kipp et
al., 2021, 2016; Suchomel et al., 2013, 2018; Suchomel, Lake
et al., 2017; Suchomel et al., 2014). Researchers have inves-
tigated the kinetic and kinematic characteristics of the sec-
ond pull, commencing from the mid-thigh (“power”)
position (DeWeese & Scruggs, 2012), and have reported
that this phase produces the greatest force and power in
experienced weightlifters during the clean, snatch and PC
(Enoka, 1979; Souza et al., 2002). Additionally, the result of
previous cross-sectional research indicates that weightlifting
pulling derivatives (i.e., those that exclude the catch phase)
may provide a comparable (Comfort et al., 2011a, 2011b,
2018) or greater (Comfort et al., 2017; Kipp et al., 2016;
Suchomel et al., 2015; Suchomel, Lake et al., 2017;
Suchomel & Sole, 2017a, 2017b; Suchomel et al., 2014)
training stimulus to catching derivatives, and may be easier
to coach and implement (Comfort et al., 2018; Suchomel et
al., 2015).
Recently, investigators have reported greater kinetic and
kinematic parameter values (peak and mean force, power,
velocity, net impulse and barbell velocity) during the propul-
sion phase of the CMS compared to the mid-thigh pull
(Meechan, Suchomel et al., 2020), highlighting the potential
superiority of the CMS as a training stimulus to enhance
force–time characteristics. Although valuable, these gross mea-
surements only represent instantaneous (i.e., peak) or mean
values, usually during the concentric (propulsion) phase
(Comfort et al., 2011a, 2018; Suchomel, Comfort et al., 2017).
It would be benecial to further understand the kinetics and
kinematics of such exercises throughout the entire movement,
including any changes in the specic phase durations (i.e.,
unweighting [where relevant], braking, propulsion). A detailed
analysis of phases with respect to time may provide a greater
mechanistic understanding of biomechanical dierences
between relative loads during the CMS and how this could be
implemented to inform load selection, given that appropriate
force production (e.g., maximal force vs. rate of force develop-
ment) for sporting tasks is considered a primary training con-
sideration when developing a training programme (Suchomel
& Sole, 2017a).
CONTACT David Meechan D.Meechan@edu.salford.ac.uk The Salford Institute of Human Movement and Rehabilitation, School of Health and Society,
University of Salford, Greater Manchester, UK, M5 4WT
JOURNAL OF SPORTS SCIENCES
https://doi.org/10.1080/02640414.2022.2091351
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Whilst mean and peak kinetic and kinematic variables have
been extensively reported, a more sophisticated and detailed
analysis of the force–time data may provide additional insight
into where the dierences occur between loading conditions,
and how practitioners can appropriately implement these exer-
cises. It is recommended that when testing non-directed
hypotheses involving biomechanical vector elds, researchers
should implement statistical parametric mapping analysis
(SPM) as it is generally biased to test one-dimensional data
(1D) using zero-dimensional methods, and SPM may reduce
such bias (Pataky et al., 2013, 2015, 2016). Researchers have
utilized time-normalized curve analysis (sometimes termed
waveform or temporal phase analysis) to assess force–, velo-
city–, power– and displacement–time data during weightlifting
derivatives (Kipp et al., 2021; Suchomel & Sole, 2017a, 2017b)
and jumps (Cormie et al., 2008, 2009; McMahon, Murphy et al.,
2017; McMahon, Rej et al., 2017). A variety of statistical techni-
ques have been used for these comparisons, including SPM
and a continuous band of 95% condence intervals (curve
analysis), which creates upper and lower condence limits
and identies non-overlapping areas (McMahon, Jones et al.,
2017). Briey, SPM uses random-eld theory to construct
probability distributions based on continuous curve or time-
series data (Pataky, 2010), whilst 95% condence intervals
utilize pair-wise comparison across data time points (Kipp et
al., 2021).
Kipp et al. (2021) performed both SPM and curve analysis to
compare dierences in the force–, velocity–, power–, and dis-
placement–time curves during the hang power clean and the
jump shrug at 70% one repetition maximum (1RM). Curve
analysis indicated that the jump shrug exhibited greater
ground reaction force from ~46% to 50% of the movement
and lower vertical velocities and power from ~72% to 76% and
~70% to 76% of the movement, when compared to the hang
power clean. However, these dierences were not observed
with the SPM analysis, highlighting that the dierences
observed in the curve analysis may be related to an increase
in type one error (Pataky et al., 2016). Statistical parametric
mapping has been previously used to compare performances
in jumping (Hughes et al., 2021) and weightlifting derivatives
(Kipp et al., 2021), and may be a more appropriate analysis of
time-series data compared to a temporal phase analysis (Kipp
et al., 2021). The SPM algorithm calculates the test statistic eld
across the entire waveform and retains a family-wise type I
error rate of α = 0.05 by calculating the critical test statistic
threshold by using the smoothness and size of data, based on
random eld theory (Pataky et al., 2013).
Suchomel and Sole (2017a) investigated dierences in time-
normalized force characteristics between the jump shrug, hang
high pull and hang power clean at relative loads of 30%, 45%,
65% and 80% of 1RM hang power clean, demonstrating that the
jump shrug produced greater force, impulse, and rate of force
development, and a dierent force–time prole compared the
other exercises, particularly in the last 20–25% of movement
time. This is likely due to biomechanical dierences later in the
movement, with no deceleration until around the point that
plantar exion occurs during the jump shrug, to ensure that
the participant jumps, highlighting the potential superiority of
the jump shrug when focusing on movement velocity. Such
ndings help the practitioner make informed decisions regard-
ing exercise and load selection, which may be most benecial to
developing specic muscular attributes. Although researchers
have previously compared force–time, velocity–time and
power–time curves during the jump shrug, hang power clean
and hang high pull (Kipp et al., 2021; Suchomel & Sole, 2017a,
2017b), no study to date has investigated curve analysis during
the CMS or across a spectrum of loads. It could be surmised that
an increase in load alters the relative phase duration
(unweighting, braking, and propulsion) and the shape of
the waveform, therefore further investigations of the eect
of load on the resulting waveforms are needed. The limita-
tions of prescribing training loads based on acute evalua-
tions of power output have been previously discussed and
are evident in the fact that power can be maintained across
a spectrum of loads due to the interaction between load-
related changes in force and velocity (Meechan, McMahon
et al., 2020; Meechan, Suchomel et al., 2020). Additionally,
training at the loads that elicit the maximal power does not
appear to be more benecial than heavy load training for
developing power (Ha & Nimphius, 2012; Harris et al.,
2007). Therefore, the primary purpose of this study was to
investigate dierences in the force–time and velocity–time
curves during the CMS across loads of 40%, 60%, 80%,
100%, 120% and 140% 1RM PC. It was hypothesized that
an increase in load would result in greater values in the
time-normalized force and lower time-normalized velocity
values with an increase in load. Due to the lack of prior
literature, no a priori hypotheses were made pertaining the
timings of any dierences between loads; however, we
hypothesized that the total CMS absolute durations would
increase with an increase in load.
Materials and methods
Experimental approach
A within-participant repeated-measures experimental research
design was used to examine the eect of load on vertical ground
reaction force (VGRF), barbell-lifter system centre of mass vertical
velocity throughout the entire movement of the CMS. These
variables were measured with participants performing all lifts
on a force platform using progressively increasing relative loads
of 40, 60, 80, 100, 120, 140% 1RM PC. Progressive loads were
used to ensure ecological validity (as this is how they would be
implemented in a training session). Prior to the experimental
trials, participants visited the strength and conditioning facility
on two occasions, at the same time of day (5–7 days apart), to
establish 1RM PC reliability, following the protocol previously
used in similar research (Comfort et al., 2015; Meechan,
Suchomel et al., 2020), and were all familiar with the exercises
based on their recent training programmes. All lifts were
increased with a minimum of 2.5 kg increments. Participants
were encouraged to use a consistent technique between condi-
tions, with no change in countermovement depth. A Friedman’s
2D. MEECHAN ET AL.
test was performed comparing the eect of relative load on
countermovement depth, which was not signicant (p = 0.684).
To ensure adequate power to detect eects typically considered
“small” or greater (Cohen, 1988) a within factors repeated- mea-
sures analysis of variance (ANOVA) an a priori power analysis was
performed, albeit based on the eect of load on gross measures,
with statistical power of 0.80 and an alpha level of 0.05, a mini-
mum sample size of 28 participants was determined GPower 3.1
software (Faul et al., 2009).
Participants
Twenty-nine male participants (age 27.9 ± 3.5 years, height
1.79 ± 0.09 m, body mass 85.3 ± 16.8 kg, resistance training
experience 5.6 ± 2.1 years, relative 1RM PC 1.02 BW) from
various national-level sports such as rugby, swimming, martial
arts, athletics (long jump and javelin), and fencing, who parti-
cipated in regular resistance training including experience with
weightlifting derivatives, volunteered to participate in this
study. Due to competition, injury, COVID-19 lockdowns, and
training camps restricted the recruitment of a homogenous
group. Participants were free from injury and provided written
informed consent prior to the commencement of testing. They
were requested to perform no strenuous activity during the
48 hours before testing, maintain their normal dietary intake
before each session, and to attend testing sessions in a
hydrated state.
Procedures
1RM power clean testing
Participants performed a dynamic warm-up consisting of body
weight squats, lunges, and dynamic stretching and 5 minutes
of low-intensity cycling. The 1RM testing protocol followed
procedures previously described (Meechan, Suchomel et al.,
2020). Three sub-maximal PC eorts were performed with
decreasing volume (6–2 repetitions) and increasing loads of
approximate 50–90% 1RM before commencing their rst 1RM
attempt. The 1RM for each participant was then determined
within ve attempts (interspersed by 3–4 minutes of rest) by
gradually increasing the load (2.5–5.0 kg increments) until a
failed attempt occurred. All PC attempts began with the barbell
on the lifting platform and ended with the barbell caught on
the anterior deltoids in a semi-squat position above parallel
(visually monitored and any attempt caught below this was
disallowed). Testing was performed using a lifting platform
(Hammer Strength, Ohio, USA); International Weightlifting
Federation approved weightlifting barbell, and bumper plates
(Eleiko, Halmstad, Sweden). The greatest load achieved across
the two 1RM testing sessions was used to calculate the loads
subsequently used during the CMS. An accredited strength and
conditioning coach supervised all sessions.
Countermovement shrug testing
Participants completed the same standardized warm-up as
during the PC testing session, followed by one set of three
repetitions of the CMS at 40% 1RM PC. Participants then com-
pleted one CMS set at intensities of 40%, 60%, 80%, 100%,
120%, and 140% of their pre-determined 1RM PC in a progres-
sive order (40–140%) to replicate the progression of loads that
occur during training sessions. Three repetitions were per-
formed at each load with 30–60 seconds of rest between
repetitions and 3–4 minutes rest between loads to minimize
fatigue (Comfort et al., 2015, 2012; Meechan, Suchomel et al.,
2020). The barbell was placed on the safety bars of the power
cage between all repetitions to prevent fatigue. Once the body
was stabilized (veried by observing the participant and live
force–time data), the lift was initiated with the countdown “3, 2,
1, go”, and all participants were instructed to exert maximal
intent during each repetition. All lifts were performed in a
power cage on the Fitness Technology ballistic measurement
Figure 1. Sequence of countermovement shrug.
JOURNAL OF SPORTS SCIENCES 3
system with integrated force platform (400 Series, Fitness
Technology, Adelaide, Australia) sampling at 600 Hz.
Standardized verbal encouragement was provided throughout
testing. During all repetitions, participants were required to use
lifting straps for standardization and to reduce technique
breakdown due to loss of grip, especially at higher loads (Hori
et al., 2010).
Prior to the CMS (Figure 1), participants stood completely
vertical with knees and hips extended for 2 s and then transi-
tioned to the mid-thigh position by exing at the knees before
immediately performing a rapid triple extension of the hips,
knees and ankles and a shrug that moved the barbell in a
vertical plane while maintaining elbow extension (i.e., second
pull) in one continuous movement (Meechan, Suchomel et al.,
2020).
Data analysis
Prior to the onset of the pull, participants were instructed to
remain stationary on the force platform for 1 s to allow for
subsequent determination of the system weight (body weight
+ barbell weight; average of this second; Owen et al., 2014). The
onset of movement was deemed to have occurred 30 ms
before the system weight VGRF was exceeded or reduced by
5 multiples of the rst second VGRF standard deviation (Owen
et al., 2014). Vertical velocity and displacement of the system
(barbell + body) centre of mass were calculated from VGRF
force–time data using integration via the trapezoid rule (Kipp
et al., 2021; Owen et al., 2014). The propulsion phase was
deemed to have started when velocity exceeded 0.01 m·s
−1
and ended at peak positive velocity (McMahon, Suchomel et
al., 2018). Time-series data were time-normalized to 101 data
points in line with previous research (Kipp et al., 2021) repre-
senting 0–100% of the movement from initial countermove-
ment to peak velocity. The average of the two trials which
were the closest in propulsive peak velocity at each relative
load was used for statistical analysis. Raw vertical force–time
data for each trial were exported as text les and analysed in
Microsoft Excel (version 2016; Microsoft Corp., Redmond,
WA, USA).
Statistical analyses
Reliability of the 1RM power clean was determined via a two-
way mixed eects intraclass correlation coecients (ICC) and
coecient of variation (CV), as well as their 95% condence
interval (CI). The ICC were interpreted as poor < 0.50; 0.50
moderate < 0.75; 0.75 good < 0.9, and excellent 0.90
(Koo & Li, 2016), and the %CV considered acceptable if < 10%
(Cormack et al., 2008).
The primary analyses performed were SPM-repeated
measures analysis of variance (ANOVA), to assess the eect
of load on force- and velocity-, waveforms during the CMS,
using open-source Matlab 2021b (MathWorks, Natick, MA)
Figure 2. Comparison of the average force–time (a), velocity–time (b), and displacement–time (c) curves during the countermovement shrug with loads of 40%, 60%,
80%, 100%, 120% and 140% 1RM power clean. The differences between loads are described in results section.
4D. MEECHAN ET AL.
Table 1. Comparison of absolute phase durations and expressed as a percentage of movement duration during the CMS.
Intensity
Unweighting phase % of total
movement
Braking phase % of total
movement
Propulsion phase % of total
movement
Unweighting phase
duration (s)
Braking phase
duration (s)
Propulsion phase
duration (s)
Total movement
duration (s)
40% 39 ± 4 15 ± 3 46 ± 3 0.263 ± 0.05 0.104 ± 0.03 0.305 ± 0.02 0.672 ± 0.07
60% 38 ± 5 16 ± 4 46 ± 3 0.263 ± 0.06 0.107 ± 0.03 0.312 ± 0.03 0.685 ± 0.07
80% 37 ± 5 17 ± 4 47 ± 3 0.258 ± 0.04 0.118 ± 0.03 0.325 ± 0.03 0.702 ± 0.06
100% 36 ± 4 16 ± 2 48 ± 3 0.253 ± 0.04 0.117 ± 0.02 0.338 ± 0.03 0.709 ± 0.06
120% 36 ± 4 17 ± 2 47 ± 4 0.268 ± 0.06 0.127 ± 0.02 0.350 ± 0.04 0.748 ± 0.09
140% 35 ± 4 18 ± 2 47 ± 3 0.269 ± 0.04 0.138 ± 0.03 0.365 ± 0.03 0.773 ± 0.07
p (g)
40 vs. 60 >0.05 (0.22) >0.05 (0.28) >0.05 (0.00) >0.05 (0.00) >0.05 (0.10) >0.05 (0.27) >0.05 (0.18)
40 vs. 80 *0.045 (0.44) >0.05 (0.56) >0.05 (0.33) >0.05 (0.11) *0.015 (0.46) *<0.001 (0.77) >0.05 (0.45)
40 vs. 100 *0.003 (0.74) >0.05 (0.39) *0.003 (0.66) >0.05 (0.22) *0.045 (0.50) *<0.001 (1.23) *0.011 (0.56)
40 vs. 120 *0.010 (0.74) >0.05 (0.77) >0.05 (0.28) >0.05 (0.09) *0.001 (0.89) *<0.001 (1.40) *0.003 (0.93)
40 vs. 140 *<0.001(0.99) *0.015 (1.16) *0.045 (0.33) >0.05 (0.13) *<0.001 (1.12) *<0.001 (2.32) *<0.001 (1.42)
60 vs. 80 >0.05 (0.20) >0.05 (0.25) >0.05 (0.33) >0.05 (0.10) *0.03 (0.36) *<0.001 (0.43) >0.05 (0.26)
60 vs. 100 >0.05 (0.44) >0.05 (0.00) >0.05 (0.66) >0.05 (0.19) >0.05 (0.39) *<0.001 (0.86) *0.045 (0.36)
60 vs. 120 *0.03 (0.64) >0.05 (0.31) >0.05 (0.28) >0.05 (0.08) *<0.001 (0.77) *<0.001 (1.06) *0.004 (0.77)
60 vs. 140 *0.002 (0.65) *0.015 (0.62) >0.05 (0.33) >0.05 (0.12) *<0.001 (1.02) *<0.001 (1.74) *<0.001 (1.24)
80 vs. 100 >0.05 (0.22) >0.05 (0.31) >0.05 (0.33) >0.05 (0.12) >0.05 (0.03) *0.014 (0.43) >0.05 (0.12)
80 vs. 120 >0.05 (0.22) >0.05 (0.00) >0.05 (0.00) >0.05 (0.19) >0.05 (0.35) *<0.001 (0.70) *0.003 (0.59)
80 vs. 140 >0.05 (0.44) >0.05 (0.31) >0.05 (0.00) >0.05 (0.27) *0.007 (0.66) *<0.001 (1.32) *<0.001 (1.07)
100 vs. 120 >0.05 (0.00) >0.05 (0.49) >0.05 (0.28) >0.05 (0.12) *0.015 (0.49) >0.05 (0.33) *0.02 (0.50)
100 vs. 140 >0.05 (0.25) *0.007 (0.96) >0.05 (0.33) >0.05 (0.39) <0.001 (0.81) *<0.001 (0.89) *<0.001 (0.97)
120 vs. 140 >0.05 (0.15) >0.05 (0.49) >0.05 (0.00) >0.05 (0.12) *0.03 (0.43) *0.003 (0.42) >0.05 (0.31)
*Denotes significant difference between loads.
JOURNAL OF SPORTS SCIENCES 5
code (http://www.spm1d.org). Where signicant eects
(α = 0.05) were reported, the SPM paired sample t-test
was used to compare between loads. A Bonferroni correc-
tion resulted in a critical threshold for a signicance of p
0.003. For each test, the critical test statistic, and supra-
threshold cluster were reported where the test statistic
eld exceeded the critical test statistic threshold. The sec-
ondary exploratory analysis of the eects of load on phase
durations, both absolute and as a percentage of movement
time, was determined via repeated measures ANOVA with
Bonferroni post hoc analysis. Distribution of data was ana-
lysed via Shapiro–Wilks’ test of normality, with dierences
between loads determined using Wilcoxon’s tests.
Statistical analyses for phase durations were performed
using Statistical Package for the Social Sciences software
version 27 (SPSS, Chicago, Ill, USA). Standardized dier-
ences were calculated using Hedges’ g eect sizes as pre-
viously described (Hedges & Olkin, 1985) and interpreted
as trivial (≤0.19), small (0.20–0.59), moderate (0.60–1.19),
large (1.20–1.99), and very large (2.0–4.0; Hopkins et al.,
2009). An a priori alpha level was set at p 0.05.
Results
The 1RM power clean performances were highly reliable
(ICC = 0.99, [95% CI: 0.97–0.99], %CV = 2.0% [0.9–2.3%])
between sessions 1 (87.84 ± 18.82 kg) and 2
(88.10 ± 18.40 kg). Increased barbell load resulted in an
increased force production throughout the time-
normalized movement durations and a change in the
shape of the velocity–time curve due to decreases in velo-
city and changes in the phases of the movement (Figure 2,
Table 1). For clarity and brevity, any non-signicant dier-
ences between loads or signicant dierences across the
entire waveform (i.e., 0–100%) are not described in detail
but simply highlighted (all gures and results are shown in
the supplementary digital content). The results for the eect
of load on absolute phase durations and percentage of
movement time are shown in Table 1.
Force–time
The SPM repeated-measures ANOVA indicated a signicant
eect of load on force (p < 0.001, F* = 3.559, Figure 3a)
throughout the entire time-normalized movement. Force was
generally greater at greater relative loads (Figure 2a). For exam-
ple, force at 40% 1RM PC was less than at 60%, 80%, 100%,
120%, and 140% 1RM PC during 67%, 91%, 94%, 100% and
100% of the movement, respectively (Figure 4a). All pairwise
comparisons revealed signicantly greater force during higher
loads for early (0–14%), mid (36–54%), and late (90–100%) time-
normalized movement. Peak force was 24.9% greater at 140%
compared to 40% 1RM and occurred between 79% and 82% of
time-normalized movement in all loads (Figure 2a). All dier-
ences between loads are illustrated in Figure 4a.
Velocity–time
Load had a signicant eect on velocity between 12–
38%, 47–79% and 84–100% of time-normalized move-
ment. (p 0.001, F* = 3.713, Figure 3b). The eect of
load on velocity followed these three distinct phases
(Figures 2(b), 4 (b) and Figure 5(b)). Higher, compared
to lower, loads resulted in more negative velocities in
the rst phase, less negative/more positive velocities in
the middle phase, and less positive velocities during the
last phase. There were no signicant dierences in velo-
city between the smallest increments in load of 40 vs.
60%, 60 vs. 80%, 80 vs. 100% and 100 vs. 120 1RM
(Figure 4b). All other comparisons are displayed in
Figure 4b. An example of the SPM output and 95% CI
is shown in Figure 5.
Figure 3. SPM repeated measures ANOVA (SPM{F} statistic) during the countermovement shrug at 40–140% 1RM comparing a) force–time and b) velocity–time series.
The dashed horizontal line designates the critical threshold for the SPM{F}statistics. The grey shaded area represents supra-threshold clusters, indicating statistically
significant differences at those timepoints.
6D. MEECHAN ET AL.
Absolute phase durations
There were no signicant or meaningful dierences (p
> 0.05, g = 0.00–0.39) in the total duration of the unweight-
ing phase between loads (Table 1). The duration of the
braking phase increased with load and was greatest at
140% 1RM, which was signicantly greater (p 0.03, g
= 0.43–1.12) than all other loads, with small to moderate
eect sizes (Table 1). The duration of the propulsion phase
increased with an increase in load and was greatest at 140%
1RM, which was signicantly greater than all other loads (p
0.003, g = 0.43–2.32), with a small to very large eect size.
The total movement duration progressively increased with
Figure 5. Top – mean and 95% confidence intervals for 40 vs.140% 1RM a) time-normalized force, and b) time-normalized velocity. Bottom – Statistical parametric
mapping (SPM) paired t-test for 40 vs.140% 1RM – inference curve as a function of time, with suprathreshold clusters (shaded) and critical threshold for SPM{t} statistics
(dashed line) that indicates the random field theory critical thresholds for significance (α = 0.003). The grey shaded area represents a significant difference at those time
points. Vertical black dashed line = onset of braking 140% 1RM; red dashed line = onset of braking 40% 1RM; black dotted line = onset of propulsion 140% 1RM; red
dotted line = onset of propulsion 40% 1RM.
a)
0 10 20 30 40 50 60 70 80 90 100
40 vs. 60
40 vs. 80
40 vs. 100
40 vs. 120
40 vs. 140
60 vs. 80
60 vs. 100
60 vs. 120
60 vs. 140
80 vs. 100
80 vs. 120
80 vs. 140
100 vs. 120
100 vs. 140
120 vs. 140
0 10 20 30 40 50 60 70 80 90 100
Force(% 1RM PC)
0-22
31-100
0-21
27-100
22-100
0-15
24-58
89-100
0-17
22-65
82-100
0-14
26-57
87-100
Normalized Movement Time %
0-100
0-100
37-54
29-100
83-100
0-17
0-100
0-100
0-15
36-69
90-100
24-100
39-64
75-100
b)
0 10 20 30 40 50 60 70 80 90 100
40 vs. 60
40 vs. 80
40 vs. 100
40 vs. 120
40 vs. 140
60 vs. 80
60 vs. 100
60 vs. 120
60 vs. 140
80 vs. 100
80 vs. 120
80 vs. 140
100 vs. 120
100 vs. 140
120 vs. 140
0 10 20 30 40 50 60 70 80 90 100
14-30
50-71
85-100
52-69
91-100
87-93
19-28
56-69
28-32
55-71
89-100
13-31
52-71
86-100
Velocity ( % 1RM PC)
25-31
20-34
53-79
55-77
25-36
51-78
89-100
16-35
50-77
87-100
Normalized Movement Time %
19-23
50-65
86-100
Figure 4. Summary of differences between countermovement shrug intensity of loads of 40%, 60%, 80%, 100%, 120% and 140% 1RM power clean (1RM PC) from SPM
analysis for a) normalized force–time series, b) normalized velocity–time series. Shaded area illustrates significant differences between time points and intensity of load.
Higher load greater Lower load greater No differences
JOURNAL OF SPORTS SCIENCES 7
load. The greatest duration occurred at 140% 1RM, which
demonstrated a signicantly greater duration (p < 0.001, g
= 0.97–1.42, moderate to large) than 40–100% 1RM, but not
signicantly dierent to 120% 1RM (p > 0.05, g = 0.31). All
other total movement results are shown in Table 1). All
other results are shown in Table 1.
Percentage of absolute movement time
The greatest relative (as a percentage of movement time)
duration of the unweighting phase occurred at 40% 1RM.
which demonstrated a signicantly greater percentage dura-
tion compared to 80–140% 1RM (p ≤ 0.045, g = 0.44–0.99, small
to moderate), but not signicantly dierent to 60% 1RM (p
> 0.05, g = 0.22; Table 1). The greatest relative duration of the
braking phase occurred at 140% 1RM, which was signicantly
greater than at 40–60 (p = 0.015, g = 0.62–1.16, moderate) and
100% (p = 0.007, g = 0.96, moderate) 1RM. All other braking
phase results are shown in Table 1. The greatest relative dura-
tion of the propulsion phase occurred at 100% 1RM, which was
signicantly and moderately greater (p = 0.003, g = 0.66) than
40% 1RM only, with 140% 1RM also showing a signicantly
greater duration than 40% 1RM (p = 0.045, g = 0.33, small).
Discussion
The purpose of this study was to investigate the eect of
load on CMS force–time and velocity–time curves. The nd-
ings may have implications for researchers analysing time-
series data, and strength and conditioning practitioners who
prescribe weightlifting pulling derivatives. As expected, a
greater force was produced as load increased from 40% to
140% 1RM. The greatest force was observed at 140% 1RM,
in line with previous research (Meechan, Suchomel et al.,
2020). There was an initial greater negative velocity
(unweighting earlier) at higher loads, followed by positive
velocity being greater during early propulsion and lower
during late propulsion, with velocity being maximized at
40% 1RM (Figures 2–4b). Force increased with an increase
in load, with 140% 1RM resulting in 24.9% greater peak
force than 40% 1RM (Figures 2–4a) As load increased,
there were dierences throughout the time-normalized
movement. This provides a greater mechanistic understand-
ing to strength and conditioning practitioners about where
dierences may exist outside of peak values, as a previous
investigation during the CMS only reported peak and mean
kinetic and kinematic variables (Meechan, Suchomel et al.,
2020). This is the rst study to include SPM analysis of the
CMS across a spectrum of loads, with other studies compar-
ing weightlifting exercises at the same loads (Kipp et al.,
2021; Suchomel & Sole, 2017a, 2017b), loaded jumps
(Cormie et al., 2008) and unloaded jumps (McMahon,
Jones et al., 2017, 2018). However, these ndings need to
be interpreted with caution in relation to other pulling
derivatives, as the specic task constraints dier compared
to the CMS.
A unique aspect of the current study was the comparison
of time-normalized velocity curves between loads. The
increase of load also alters the shape of the average velo-
city–time curves, with peak negative velocity in 140% 1RM
occurring 9% earlier in the time-normalized total movement
than 40% 1RM, thus aecting the phases of the time-
normalized movement (Figure 2b). The greater the load,
the greater the duration of signicant dierences in velocity
compared to 40% 1RM. Indeed, 140% showed signicant
dierences across 59% of total movement time when com-
pared to 40% 1RM, highlighting key dierences that occur
outside peak variables (Figure 4b). The results of this study
demonstrate that supramaximal loads may not be appropri-
ate to train propulsion velocity. This is particularly true in
late-stage propulsion due to the signicant reduction in
velocity at relative loads >100% 1RM compared to all rela-
tive loads of <100% 1RM (Figure 4b), illustrative of the
load–velocity relationship. Participants likely managed to
accelerate through the full triple extension more at loads
of >100% 1RM. It is important to note that performance
outcomes will be partly inuenced by intent during the
propulsion phase, which may be submaximal at lighter
loads. During the CMS, and particularly at lower loads,
there is likely a deceleration during the late propulsive
phase of the lift as the participants were encouraged not
to jump o the platform as in a jump shrug (Suchomel et
al., 2013, 2015); therefore, the CMS is likely an inferior
exercise to develop propulsive velocity compared to the
jump shrug at comparable loads.
Understanding where dierences occur within the move-
ment (i.e., early, or late phase) may allow for a more precise
exercise prescription to target specic components of the sec-
ond pull. Practically, this is of paramount importance as the
increased phase durations results in increased time under ten-
sion, and the increased force production will likely determine
the adaptive responses, especially within a task where maximal
intent is essential. Visual inspection of the average time-
normalized velocity curves in the present study shows that
load aects when the braking and propulsion phase com-
mences (Figures 2b, 5b). At 140% compared to 40% 1RM, the
braking phase occurs earlier (43–67% compared to 52–73%).
This results in a shorter unweighting phase (43% of movement,
compared to 52%) and longer braking (24% vs 21%) and pro-
pulsive (33% vs 27%) phases. Therefore, caution is warranted
when interpreting dierences between loads due to the mis-
alignment of phases.
Practitioners also should note that the training mesocycle
focus, sets and repetitions in which the loads >100% 1RM
are prescribed may impact performance. Excessive duration
of repetitions may be detrimental to performance in certain
mesocycles, such as speed-strength blocks. As an increase in
load will result in an increased repetition duration, perform-
ing the same set and repetitions for high vs lower loads
may also impact performance due to the increased volume
load and duration. To improve an athlete’s force–velocity
prole with weightlifting derivatives, a combination of
heavy/lighter loads is recommended (Suchomel, Suchomel,
8D. MEECHAN ET AL.
Comfort et al., 2017). Therefore, practitioners need to care-
fully consider excessive volumes in certain training meso-
cycles (e.g., competition) where the avoidance of fatigue
accumulation is important. It is clear that force and velocity
are interdependent and that maximal power occurs at com-
promised levels of maximal force and velocity (Ha &
Nimphius, 2012). Therefore, low-load, high-velocity move-
ments can address the high-velocity component of the
force–velocity relationship, while heavier loads develop the
high-force component (Ha & Nimphius, 2012). This allows
for power output during the CMS to be maximized at loads
of 80–140% 1RM PC, as previously shown (Meechan,
Suchomel et al., 2020).
The present results provide an understanding of the eect
of load on force–, and velocity–time characteristics during the
CMS; however, to fully understand the potential benets of
training at dierent loads during the CMS a longitudinal train-
ing intervention needs to be conducted. As loads of true max-
imal eort during pulling variations such as the CMS have not
yet been investigated, the load percentages may not be a true
reection of true weightlifting pulling ability, and may in fact
result in a greater 1RM, and therefore greater loads during
testing sessions. The authors acknowledge that it may be
impractical to perform 1RM tests for certain weightlifting deri-
vatives due to the absence of criteria for a successful repetition.
This study is not without its limitations. Firstly, although only
male participants were recruited, these results are also gener-
alisable to athletes of comparable strength levels and training
status, with no signicant dierences in the magnitude or ratio
of muscle activity during a maximal isometric squat (Nimphius
et al., 2019), and no dierences in the eect of load between
the sexes on kinetics or kinematics during the mid-thigh pull
(Comfort et al., 2015; Nimphius et al., 2019). It is acknowledged
that a greater sample size may be required for 1D data analysis
(Robinson et al., 2021). It is therefore possible that the present
study was only adequately powered to detect eects of a
slightly larger magnitude than that used in the discrete para-
meter power analysis. Additionally, the onset of movement was
calculated based on thresholds from jump and isometric mid-
thigh pull research. Future research should assess whether this
method is still appropriate for loaded exercises in which large
dynamic system masses are prevalent.
Conclusion
The results indicate that there is greater negative velocity at
heavier compared to lower loads early in the unweighting
phase (12–38% of the movement), and greater positive velocity
at lower loads during the last 16%. These results demonstrate
that load impacts dierently throughout dierent portions of
the time-normalized movement, and practitioners may be able
to prescribe specic loads to target specic phases of the
movement, with relative loads of 40% power clean 1RM most
appropriate to maximize velocity during the CMS, and relative
loads of 140% to maximize force. Practitioners are encouraged
to use a combination of heavy and light loads when prescribing
weightlifting pulling derivatives, to emphasize force and velo-
city or to maximize power. It may be more appropriate to
prescribe the CMS during a strength-speed and maximal
strength phase given the ability to use loads greater than the
athlete’s 1RM. The results also show that the braking and
propulsion phases commence at an earlier percentage of time-
normalized movement at higher loads, whilst absolute dura-
tions are also greatest at higher loads. Future research should
assess the eect of load on individual time-normalized phases
to determine if dierences between loads exist within each
time-normalized phase.
Disclosure statement
No potential conict of interest was reported by the author(s).
Funding
The author(s) reported that there is no funding associated with the work
featured in this article.
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https://doi.org/10.1519/JSC.0000000000002927
10 D. MEECHAN ET AL.
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This study examined the lower-extremity joint level load absorption characteristics of the hang power clean (HPC) and jump shrug (JS). Eleven Division I male lacrosse players were fitted with 3-dimensional reflective markers and performed 3 repetitions each of the HPC and JS at 30, 50, and 70% of their 1 repetition maximum (1RM) HPC while standing on force plates. Load absorption joint work and duration at the hip, knee, and ankle joints were compared using 3-way repeated-measures mixed analyses of variance. Cohen’s d effect sizes were used to provide a measure of practical significance. The JS was characterized by greater load absorption joint work compared with the HPC performed at the hip (p < 0.001, d = 0.84), knee (p < 0.001, d = 1.85), and ankle joints (p < 0.001, d = 1.49). In addition, greater joint work was performed during the JS compared with the HPC performed at 30% (p < 0.001, d = 0.89), 50% (p < 0.001, d = 0.74), and 70% 1RM HPC (p < 0.001, d = 0.66). The JS had a longer loading duration compared with the HPC at the hip (p < 0.001, d = 0.94), knee (p = 0.001, d = 0.89), and ankle joints (p < 0.001, d = 0.99). In addition, the JS had a longer loading duration compared with the HPC performed at 30% (p < 0.001, d = 0.83), 50% (p < 0.001, d = 0.79), and 70% 1RM HPC (p < 0.001, d = 0.85). The JS required greater hip, knee, and ankle joint work on landing compared with the load absorption phase of the HPC, regardless of load. The HPC and JS possess unique load absorption characteristics; however, both exercises should be implemented based on the goals of each training phase.