RESEARCH ARTICLE
Effects of acute wearable resistance loading
on overground running lower body
kinematics
Karl M. TrounsonID1,2☯*, Aglaja Busch3‡, Neil French Collier1‡, Sam RobertsonID1‡
1 Institute for Health and Sport, Victoria University, Footscray, Victoria, Australia, 2 Western Bulldogs
Football Club, Footscray, Victoria, Australia, 3 University Outpatient Clinic, Sports Medicine & Sports
Orthopedics, University of Potsdam, Potsdam, Germany
☯ These authors contributed equally to this work.
‡ These authors also contributed equally to this work.
* karl.trounson@live.vu.edu.au
Abstract
Field-based sports require athletes to run sub-maximally over significant distances, often
while contending with dynamic perturbations to preferred coordination patterns. The ability
to adapt movement to maintain performance under such perturbations appears to be train-
able through exposure to task variability, which encourages movement variability. The aim
of the present study was to investigate the extent to which various wearable resistance load-
ing magnitudes alter coordination and induce movement variability during running. To inves-
tigate this, 14 participants (three female and 11 male) performed 10 sub-maximal velocity
shuttle runs with either no weight, 1%, 3%, or 5% of body weight attached to the lower limbs.
Sagittal plane lower limb joint kinematics from one complete stride cycle in each run were
assessed using functional data analysis techniques, both across the participant group and
within-individuals. At the group-level, decreases in ankle plantarflexion following toe-off
were evident in the 3% and 5% conditions, while increased knee flexion occurred during
weight acceptance in the 5% condition compared with unloaded running. At the individual-
level, between-run joint angle profiles varied, with six participants exhibiting increased joint
angle variability in one or more loading conditions compared with unloaded running. Loading
of 5% decreased between-run ankle joint variability among two individuals, likely in accor-
dance with the need to manage increased system load or the novelty of the task. In terms of
joint coordination, the most considerable alterations to coordination occurred in the 5% load-
ing condition at the hip-knee joint pair, however, only a minority of participants exhibited this
tendency. Coaches should prescribe wearable resistance individually to perturb preferred
coordination patterns and encourage movement variability without loading to the extent that
movement options become limited.
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OPEN ACCESS
Citation: Trounson KM, Busch A, French Collier N,
Robertson S (2020) Effects of acute wearable
resistance loading on overground running lower
body kinematics. PLoS ONE 15(12): e0244361.
https://doi.org/10.1371/journal.pone.0244361
Editor: Elena Bergamini, University of Rome, ITALY
Received: July 19, 2020
Accepted: December 8, 2020
Published: December 28, 2020
Copyright: © 2020 Trounson 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: The data underlying
this study are available on Dryad (doi:10.5061/
dryad.1c59zw3sk).
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
Introduction
Across many field-based sports, athletes must be capable of running long distances throughout
a match [1–3]. Depending on the sport, total running distance can range from an average of 6
km in rugby league to 12 km in Australian Rules football [1]. In Australian Rules football, soc-
cer, rugby league, and rugby sevens, most of the distance covered during match play can be
classified as “low-intensity activity”, i.e., occurring at velocities <5.4 m.s-1 [1, 3]. While high-
intensity efforts are often associated with significant match events, adequate sub-maximal run-
ning capabilities are also important for effective opponent tracking and retention of team for-
mations during different phases of play throughout a match [4]. As such, training aimed at
developing sub-maximal overground running performance is evidently worthwhile.
Development of sub-maximal running performance for field-based athletes is a multifacto-
rial proposition and requires training of aerobic capacity, biomechanical factors for superior
economy, and muscular strength [5–8]. Coaches should address these factors in training pre-
scription and, in addition, athletes’ ability to adapt their running coordination patterns in
accordance with the dynamic constraints of the sport [9, 10]. The capacity to exhibit “adapt-
ability” in this sense allows for greater maintenance of performance in varied contexts and is a
hallmark of higher performing athletes in many sports [10–13]. In field-based sport, organis-
mic constraints in the form of local metabolite accumulation from intermittent anaerobic
efforts [14, 15], muscle damage arising from high force eccentric contractions during decelera-
tions [16], and muscular contusion from compressive force impacts [17], all present scenarios
in which there is a challenge to an athlete’s preferred running coordinative structure, which
must be adapted to.
Critically, the implementation of a training intervention aimed at encouraging movement
variability in diving [10] suggests that the capacity for athletes to harness movement system
degeneracy to maintain a performance outcome is trainable. This notion is further supported
by nonlinear pedagogical training interventions in youth tennis [18]. Individuals exposed to
greater task variability during training displayed a greater number of unique movement clus-
ters, indicating the presence of degeneracy, during performance tasks. Exposure to task vari-
ability drives exploration of alternate movement strategies, or movement variability, as
movement is adjusted to satisfy novel task demands [19]. Training in this way affords individ-
uals the ability to adapt movement to maintain task performance under the varied constraints
occurring in the dynamic sporting environment [10, 18, 20].
In the context of sub-maximal running kinematics, the effects of deliberately induced task
variability through perturbation have been explored in research using elastic tubes attached
from the hips to the ankles [21–23]. This intervention increases joint kinematic variability
acutely, after which there is relatively rapid stabilisation around a slightly shifted coordinative
structure [21, 23]. Although no post-training running test under novel conditions was under-
taken, the performance benefits associated with exposure to constraints, which encourage
movement variability in this way, are widely reported [24–28].
It is also worth noting that analyses of kinematic variability induced by constraint imple-
mentation to date have typically focussed on group-level changes [21, 29, 30]. Increasingly,
there is support for individual-level consideration given that intrinsic behavioural dynamics
and baseline kinematic characteristics alter the extent to which a particular constraint is expe-
rienced as a perturbation to the system [31–33]. Kinematic changes may vary markedly
between individuals, which may not be clear when considering generalised responses, yet is
important in a practical setting [34–36].
Lightweight wearable resistance (WR) may be a useful training tool for encouraging explo-
ration of movement system degeneracy through movement variability. WR involves
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attachment of small weights to particular body segments, such as the trunk, arms, thighs, and
shanks [37]. To date, research has considered WR in its capacity as a movement specific over-
load stimulus [38, 39], however, WR also presents a perturbation to coordination, which may
induce movement variability. WR application alters segment inertial properties and as such
can be considered an organismic constraint [40, 41]. Exposure to WR may ultimately be a use-
ful stimulus for developing adaptable movement behaviours among athletes in preparation for
changing organismic constraints faced during match play. This study aimed to describe the
extent to which different acute lower limb WR loadings (1%, 3%, and 5% of body weight) alter
coordination and induce movement variability during sub-maximal overground running. By
considering both group- and individual-level responses, findings will provide context for
coaches seeking to promote movement variability without imposing an excessive perturbation
that limits movement options.
Materials and methods
Participants
Fourteen participants (three female and 11 male; mean ± SD: age 28.3 ± 4.4 years; height:
179.9 ± 7.6 cm; body mass: 76.8 ± 6.1 kg) volunteered to participate in this study. Participants
were included on the basis that they were currently undertaking, or had recent previous expe-
rience (past year), in structured field-based sport competition. Participants in the study had no
prior experience with WR. All participants provided written informed consent and were free
from injury at the time of testing. All procedures used in this study complied with the criteria
of the declaration of Helsinki and the ethical approval granted by the Victoria University
Human Research Ethics Committee.
Procedure
Data collection apparatus.
A 10-camera VICON motion analysis system (T-40 series,
Vicon Nexus v2, Oxford, UK) sampling at 250 Hz was used for collection of kinematic data. A
total of thirty-six reflective markers with 14mm diameter were attached to lower body land-
marks on the pelvis, thighs, shanks, and feet according to the Plug-In-Gait model (Plug-In-
Gait Marker Set, Vicon Peak, Oxford, UK) (Fig 1).
Wearable resistance.
Throughout testing, participants wore LilaTM ExogenTM (Sportbo-
leh Sdh Bhd, Kuala Lumpur, Malaysia) compression shorts and calf sleeves. During WR expo-
sure trials, a combination of 50, 100, and 200g fusiform shaped loads (with Velcro backing)
totalling the required proportion of participants’ body weights were attached to the compres-
sion garments (Fig 2). Loads were distributed in a 2:1 thigh:shank ratio about the centre of
mass of each segment [42]. The required loads were added in an alternating fashion between
the anterior and posterior surfaces, and between a proximal-dominant and distal-dominant
orientation, in order to avoid a large shift in the centre of mass of each segment.
Experimental setup.
Testing was undertaken on a 20 m section of the Biomechanics Lab-
oratory at Victoria University. Motion analysis cameras were arranged around the 10 m mark
of the 20 m section and the approximate capture volume was 6.0 m long, 2.5 m high, and 3.0
m, wide.
Data collection.
Following application of compression garments and attachment of
reflective markers, participants undertook an initial warm-up in which they ran back and
forth along the 20 m section in a “shuttle” fashion for 2 min. Running velocity was dictated
through the use of an audible metronome, which counted each second from 1–9, before
repeating for every subsequent shuttle. Participants underwent a 2 min rest period following
the first warm-up run before performing a second warm-up run for 1 min at an increased
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Fig 1. Lower body Plug-In-Gait model. Blue markers define the required anatomical landmarks, red markers are
used for tracking segments.
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Fig 2. Lila™ Exogen™ compression shorts and calf sleeves with thigh and shank loading.
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velocity defined by 6 s shuttle efforts. Owing to the requirement of 180˚ changes of direction
after each shuttle, running velocities achieved through the capture area were greater than the
theoretical straight-line velocity of 3.3 m.s-1. Analysis of pilot data showed mean ± SD veloci-
ties of 4.16 ± 0.36 m.s-1 through the capture area. Such velocities are commonly described as
“striding” or “running” in field based sports, but fall below the “high-intensity” classification,
often defined as >5.4 m.s-1 [43, 44]. The first trial was performed with body weight only (BW),
and participants completed 2 min worth of 20 m shuttles with the 6 s pacing speed per shuttle.
Captures were taken each time participants passed through the 10 m mark capture area during
runs from the start point to the 20 m mark only. Captures were not performed on the return
shuttles. This process yielded capture of 10 complete strides across the 2 min trial.
Participants performed three subsequent 2 min running trials in which they were allocated
WR loading of 1%, 3%, and 5% of body weight in a randomised order. Each trial was inter-
spersed with a 3 min rest period. The result of this protocol was 10 complete overground run-
ning strides per condition, per participant.
Data processing
Visual 3D software (C-motion, Rockville, MD, USA) was used to construct a four segment
model (pelvis, thigh, shank, and foot) for each participant. Within each participant, the leg on
which most complete strides were successfully captured was used for analysis. This approach
maximised available data given that individual stride characteristics tended to allow one side
to be captured more consistently within the bounds of the 6 m capture area (see S1 Table for
the leg used for each participant). Runs in which several marker trajectories were lost or accu-
rate model construction could not be satisfied were excluded from analysis. Out of a possible
560 runs per-joint, 510 were successfully reconstructed for the hip, 530 for the knee, and 521
for the ankle. For a record of excluded runs and the participants and runs to which these per-
tained, see S1 Table. For successfully reconstructed runs, marker trajectories were smoothed
via a fourth order low-pass Butterworth filter with 10 Hz cut-off frequency, based on mean
residual amplitudes [45]. Each run was trimmed to one complete stride cycle, which was
defined as the period between two consecutive toe-off events on the same limb. Toe-off was
defined by the initial rise in vertical displacement of the toe marker proceeding its lowest point
at the end of the support phase [46, 47]. Time-continuous sagittal plane joint angles for the
hip, knee, and ankle (o) were normalised to 100% of the stride cycle for further analysis. Posi-
tive and negative joint angles were defined relative to the positions of joints in upright stand-
ing. Positive joint angles indicate positions of hip flexion, knee flexion, and ankle dorsiflexion
relative to standing, while negative joint angles indicate positions of hip extension, knee exten-
sion, and ankle plantarflexion relative to standing.
Data analysis
Running velocity was compared across different loading conditions using a one-way repeated
measures ANOVA with Bonferroni correction applied to post-hoc pairwise comparisons. A
significance level of α = 0.05 was used.
Statistical parametric mapping t-test.
Comparisons between continuous joint angle
kinematic data in the BW condition and each loading condition were performed across the
group, including participants of both sexes, to identify global effects of loading. Statistical
parametric mapping (SPM) t-tests were used in each instance with α = 0.05, as previously
described [48, 49]. Kinematic data were estimated as functions using B-splines. A smoothing
parameter of 0.01 was used in the fitting procedure. A t-statistic trajectory was created across
the gait cycle and assessed in relation to a critical t-statistic, which was determined using a
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permutation test by randomly shuffling the labels of the curves and recalculating the maximum
t-statistic using these new labels. The analysis was done using R (version 3.6.0) and code used
can be accessed at https://github.com/ktrounson/WR-running/blob/master/FDA%20t-test.
Generalised additive model. Generalised additive models (GAMs) were fit to continuous
joint angle data, with separate GAMs for each joint. In each case, data was modelled as a func-
tion of the percentage of the stride cycle. Cyclic cubic regression splines were used to generate
basis functions for each condition and smoothing was achieved using the restricted maximum
likelihood method. Cubic regression splines are more appropriate for functional data that rep-
resent repeated cycles of the same event [50]. The number of knots was increased until the
maximum deviance explained by the model was reached.
For visualisation of joint kinematic trends and between-run variability on an individual
basis, runs from each participant were treated as random effects. The random effects estimates
were plotted as a function of condition within each participant. Female participants are
labelled F1-F3 and male participants are labelled M1-M11. All GAM code is provided at
https://github.com/ktrounson/WR-running/blob/master/GAMs.
Bivariate functional principal component analysis. Bivariate functional principal com-
ponent analysis (bfPCA) applied to angle-angle kinematic data allows for the dominant modes
of variation to be estimated. bfPCA was used to analyse concurrent hip-knee and knee-ankle
kinematics using B-spline basis functions [51–53]. The smoothing parameter was selected
using a generalised cross validation procedure and was set at 0.1 and 0.18 for the hip-knee and
knee-ankle data, respectively. bfPCs were derived from the smoothed curves. Each bfPC was
varimax rotated to assist with interpretation of results. The occurrence and magnitude of
angle-angle variability was graphically represented by the first two bfPCs on individual plots
containing the ensemble mean of curves along with two additional curves representing +/-
2SD of the bfPC scores for each bfPC. bfPCA was performed in R with code available at
https://github.com/ktrounson/WR-running/blob/master/bfPCA.
Individual-based 2D plots were generated in which mean bfPC scores for each condition
were mapped along the first two bfPCs for each joint pairing. Positive scores along a dimension
indicate that, on average, runs within this condition resembled more closely the characteristics
of the ‘+’ curve, while negative scores indicate a closer resemblance to the ‘-’ curve.
Results
Mean running velocities across participants in each condition are included in Table 1. A signif-
icant main effect of condition was evident (F = 4.77, p = 0.003). Post-hoc analysis showed
slower running velocities in the 5% loading condition compared with all other conditions.
SPM t-test
Continuous ensemble means per-joint and per-condition with associated standard devia-
tions are presented in Fig 3. Sections of significant difference between the BW condition and
Table 1. Mean ± SD running velocities in each condition with post-hoc pairwise comparisons.
Condition
Running velocity (m.s-1)
p-value vs. 1%
p-value vs. 3%
p-value vs. 5%
BW
4.25 ± 0.43
1
1
0.017
1%
4.25 ± 0.47
1
0.005
3%
4.25 ± 0.48
0.022
5%
4.18 ± 0.44
BW, body weight; 1%, 1% of body weight WR loading; 3%, 3% of body weight WR loading; 5%, 5% of body weight WR loading.
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each loading condition according to SPM t-tests are indicated. WR loading of 1% of body
weight led to greater hip extension at 97–99% of the gait cycle (just prior to toe-off) com-
pared with BW (P = 0.045). Loading of 3% of body weight resulted in less ankle plantarflex-
ion from 12–18% of the gait cycle (during heel recovery) compared with BW (P = 0.035).
Loading of 5% of body weight resulted in greater knee flexion from 66–81% of the gait cycle
(during weight acceptance) (P < 0.001) and less ankle plantarflexion from 9–30% of the gait
cycle (during heel recovery) compared with BW (P < 0.001). Pointwise t-statistics and the
maximum critical value for a significance level of 0.05 for each set of curves are provided in
S2 Table.
Fig 3. SPM t-test per-joint and per-condition versus BW. (A) Hip joint BW versus 1%. (B) Hip joint BW versus 3%. (C) Hip joint BW versus 5%. (D) Knee joint BW
versus 1%. (E) Knee joint BW versus 3%. (F) Knee joint BW versus 5%. (G) Ankle joint BW versus 1%. (H) Ankle joint BW versus 3%. (I) Ankle joint BW versus 5%.
Solid lines represent ensemble means and accompanying shaded regions represent ± 1 SD. Grey shaded regions indicate regions of significant difference between
curve sets.
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GAMs
The summary statistics of each joint GAM are shown in Table 2. For BW, the estimate indi-
cates the mean joint angle across the stride cycle. For loading conditions, estimates indicate
the difference in mean joint angle across the stride cycle versus BW. Estimated degrees of free-
dom reflect the number of basis functions used to generate the smooths and therefore a higher
number of estimated degrees of freedom suggests more variable data. For loading conditions,
estimated degrees of freedom are in addition to those listed for BW.
The GAM random effects estimates per-run are shown in Fig 4. Random effects estimates
reflect the prevailing flexion-extension bias throughout the stride cycle relative to the group
mean. The distribution of random effects estimates appeared to be more strongly driven by
the participant in question than within-participant responses to WR loading. However, indi-
vidual-level responses of note include instances in which loading increased between-run vari-
ability, such as at the ankle in the 5% condition for F1, the knee in the 5% condition for M1,
the knee in the 1% condition for M2, the knee in all loading conditions for M3, the hip and
ankle in the 3% condition for F2, and the hip in the 5% condition for M7. Conversely,
decreased between-run variability was evident at the ankle in the 3% condition for M1, the
ankle in the 3% and 5% conditions for M2, the hip in the 5% condition for M5, the ankle in the
5% condition for F2 and M6, and the ankle in the 1% condition for F3. Shifts in the prevailing
random effects estimates on the basis of loading appeared evident at the ankle in the 1% and
3% conditions for M2, the hip in the 3% condition for M3 and F2, the ankle in the 5% condi-
tion for M6, the hip in the 3% and 5% conditions for M7, the hip in the 1% condition for F3,
and the hip in the 1% and 3% conditions for M11.
bfPCA
For hip-knee joint coupling, bfPC1 explained 41.1% of the variability in the group data (Fig 5).
Positive scorers on bfPC1 exhibited less knee flexion during the swing phase, while negative
scorers exhibited greater knee flexion. bfPC2 explained 23.3% of the variability in the group
data. Positive scorers on bfPC2 exhibited greater hip flexion during the swing phase, while neg-
ative scorers exhibited less hip flexion and hip flexion was delayed compared with positive
scorers. For knee-ankle joint coupling, bfPC1 explained 45.6% of the variability in the group
Table 2. Generalised additive model summary statistics per-joint.
Parametric coefficients
Smooth terms
Joint
Condition
Estimate
Standard error
t-value
Pr(>|t|)
EDF
F
p-value
Hip
BW (intercept)
14.16
1.35
10.5
> 0.001
12.96
22666.3
> 0.001
1%
-0.65
0.06
-10.99
> 0.001
3.8
4.94
> 0.001
3%
-0.12
0.06
-2.05
0.04
4.51
3.64
> 0.001
5%
-0.71
0.06
-11.76
> 0.001
7.62
7.12
> 0.001
Knee
BW (intercept)
43.21
1.15
37.65
> 0.001
8
32137.8
> 0.001
1%
0.55
0.1
5.25
> 0.001
4.05
1.47
0.009
3%
0.71
0.1
6.86
> 0.001
3.54
7.42
> 0.001
5%
0.15
0.1
1.48
0.14
7.29
52.18
> 0.001
Ankle
BW (intercept)
-18.5
1.06
-17.48
> 0.001
20.85
6141
> 0.001
1%
0.33
0.07
4.95
> 0.001
5.85
2.6
> 0.001
3%
-0.15
0.07
-2.18
0.03
3.91
1.77
> 0.001
5%
1.31
0.07
19.49
> 0.001
5.55
5.06
> 0.001
EDF, estimated degrees of freedom; BW, body weight; 1%, 1% of body weight WR loading; 3%, 3% of body weight WR loading; 5%, 5% of body weight WR loading.
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data. Positive scorers exhibited less knee flexion during the swing phase and negative scorers
exhibited greater knee flexion. bfPC2 explained 16.8% of the variability in the group data. Posi-
tive scorers on bfPC2 exhibited less ankle plantarflexion, particularly at touchdown, while neg-
ative scorers exhibited greater ankle plantarflexion during late swing and touchdown.
Mean individual bfPC scores along both bfPCs for hip-knee and knee-ankle joint pairs
across runs in each condition are shown in Fig 6. Participants appeared to have mostly distinct
joint coupling profiles and there was some impact of WR loading within-individuals. There
was generally agreement between observations of condition-based shifts in random effects esti-
mates from GAM analysis and differences in mean bfPC scores, including in the knee-ankle
joint couple in the 3% condition for M2, the hip-knee joint couple in the 3% condition for M3
and F2, the knee-ankle joint couple in the 5% condition for M6, the hip-knee joint couple in
the 3% and 5% conditions for M7, and the hip-knee joint couple in the 1% condition for F3
and M11. Additional condition-based shifts apparent from bfPCs included the hip-knee joint
couple in the 1% condition in M2, the hip-knee joint couple in the 5% condition for M3, M6,
M8, and M10, and the knee-ankle joint couple in the 1% condition for M11. Shifts that were
identified from GAM analysis but that appeared to be minimal based on bfPC plots included
the knee-ankle joint couple in the 1% condition for M3 and the hip-knee joint couple in the
3% condition for M11.
Fig 4. GAM random effects estimates per-run, per-individual. Each major panel relates to a given participant, as denoted by labels. Joints are separated by the
three minor panels within each participant plot. Conditions are expressed as categories within each joint and associated colours have been included for clarity.
Positive estimates indicate greater hip flexion, knee flexion, and ankle dorsiflexion relative to the group mean. Negative estimates indicate greater hip extension,
knee extension, and ankle plantarflexion. Thicker regions of coloured portions reflect a greater concentration of runs with similar random effects estimates.
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Fig 5. bfPCA of hip-knee and knee-ankle joint couples throughout stride cycle. First two bfPCs from hip-knee and
knee-ankle bfPCA with the percentage of group variability explained. Solid line represents the mean angle-angle curve.
‘+’ line represents positive scorers +2SD from the mean function. ‘-’ line represents negative scorers -2SD from the
mean function.
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Discussion
This study examined the effects of lower limb WR loading on coordination tendencies during
sub-maximal overground running. Specifically, the study sought to describe the effects of vari-
ous WR magnitudes (1%, 3%, and 5% of body weight) on lower limb sagittal plane joint kine-
matics at a group- and individual-level, both in terms of continuous gait cycle kinematics and
between-run movement variability. The main findings at a group-level were that 3% and 5%
loading decreased ankle plantarflexion during heel recovery, while 5% loading also increased
knee flexion during weight acceptance, compared with BW running. In terms of joint cou-
pling, 5% loading brought about the largest changes in coordination at the hip-knee joint pair.
At an individual-level, six of the fourteen participants clearly exhibited increased between-run
joint angle variability at one or more joints in one or more loading conditions compared with
BW.
Running velocity was slower in the 5% condition compared with all other conditions, how-
ever, the magnitude of this difference was minimal at just 0.07 m.s-1. All participants main-
tained speeds sufficient to successfully complete all shuttles within the allotted time frames in
all conditions.
In terms of kinematics at the group-level, slight decreases in ankle plantarflexion during
heel recovery occurred with 3% loading compared with BW running. The 5% loading condi-
tion led to more substantial decreases in ankle plantarflexion during heel recovery, as well as
increased knee flexion during weight acceptance. The exhibition of greater knee flexion was
likely a mechanism to mitigate increased peak ground reaction force arising from the greater
system load [54, 55]. Explanations for less plantarflexion during heel recovery are more specu-
lative. One possibility is that participants subconsciously attempted to offset the greater
moment of inertia at the thigh by dorsiflexing the ankle to create a mechanical advantage dur-
ing swing leg recovery [56, 57]. Alternatively, or perhaps in addition, heavier loading likely led
to increased co-contraction of muscles around the ankle joint during stance for maintenance
of stiffness and stability [55, 58]. Such alterations in motor unit recruitment and temporal
sequencing of lower leg muscles may constrain the action of this joint during the subsequent
propulsion and swing phase, with the joint returning to a relatively more neutral position
more readily [59, 60]. The impact of coordination dynamics should also be considered. Indi-
viduals performing novel motor tasks often exhibit freezing of distal biomechanical degrees of
freedom to reduce coordinative complexity [61–63]. To the extent that running with an extra
5% of body weight on the lower limbs was perceived as a novel task, there may have been a ten-
dency for participants to return to a more neutral ankle position following toe-off. Given these
factors, 5% loading, and to a lesser extent 3% loading, may be excessive as a means of promot-
ing movement variability for some individuals in the first instance. The group-level changes
suggest a degree of convergence toward a common adaptation strategy and appear consistent
with movement options being limited by task novelty and/or the need to manage high loads.
Coaches should take this into consideration if prescribing WR for multiple athletes without
individualisation [64].
Despite group-level trends, individual responses varied. The practical utility of WR for
inducing movement variability is therefore likely to also be individual-dependent. Coaches
should appreciate the range of individual responses and use the present findings as signposts
to guide individual WR prescription in the field.
Fig 6. Individual mean hip-knee and knee-ankle bfPC1 and bfPC2 scores per-condition. Each panel relates to a given participant, as denoted by labels. Mean hip-
knee bfPC scores across runs within a condition are denoted by circle labels. Mean knee-ankle bfPC scores across runs within a condition are denoted by triangle
labels. Separate conditions are indicated by distinct colours.
https://doi.org/10.1371/journal.pone.0244361.g006
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Participants F1, M1, M2, M3, F2, and M7 all exhibited increased between-run variability in
mean angles at one or more joints in one or more loading conditions compared with BW.
These high variability instances suggest that there was no readily accessible adaptive mode to
satisfy the task goal in the presence of WR and instead a period of search and refinement of
individuals’ preferred coordinative structures was required [65, 66]. WR in this context there-
fore provides an opportunity to explore movement system degeneracy. For these individuals,
exposure to WR over a training period may facilitate development of movement adaptability
and allow running performance to be more readily maintained when perturbations arise
in competition [10, 67]. The propensity for individuals to exhibit greater variability at
one loading condition over another is largely a function of intrinsic behavioural dynamics,
which dictate system tendencies such as attractor state stability and behavioural meta-stability
[68, 69].
A definitive reduction in between-run variability was present in participants F2 and M6 at
the ankle joint in the 5% loading condition. This may align with the proposed group-level
hypothesis of distal joint freezing in this condition. While established literature tends to define
freezing degrees of freedom as restricted movement of a joint within-trials, low between-trial
variability is also indicative of constrained movement [20, 70]. Among these individuals, the
perturbation of 5% loading may have been managed by increasing co-contractions and stiffen-
ing muscles of the lower limbs, as occurs in the early stages of skill acquisition [61]. Interest-
ingly, this can be considered an adaptive strategy in itself, particularly since performance of
shuttle runs was successfully maintained. This therefore raises the need to clarify the benefit of
perturbations that encourage movement variability during training versus those that limit
movement variability. Findings from balance beam walking with different perturbation mag-
nitudes demonstrate that learning under conditions in which sacral movement variability is
maximised leads to superior learning and subsequent task performance post-training [71].
Substantially increasing the level of perturbation through an error augmenting device
decreases movement variability in line with individuals attempting to maintain control of
movement, and has suboptimal outcomes for post-training performance [71]. Separately,
Chmielewski et al. [60] argue that increased co-contractions as a means of adapting to an ACL
rupture reflect a suboptimal compensation pattern wherein the capacity to dynamically stabi-
lise the injured knee without compromising knee motion has not yet been developed. Taken
together, these findings highlight that large magnitude perturbations may be adapted to by
reducing movement variability, however, skill acquisition is not facilitated under these condi-
tions. Reduced movement variability affords fewer opportunities for internal models of limb
dynamics to be updated, which may limit the extent to which adaptability is trained [72].
Among high performing field-based athletes, some individuals are likely to already have
well developed functional movement adaptability [11, 12]. This is typified by an appropriate
mix of movement pattern flexibility and stability, such that coordination can be readily
adjusted in response to a perturbation and movement variability levels remain similar to those
at baseline [10, 73]. Potential exemplars of this in the present study include participant M10 in
all loading conditions and participant M7 in the 3% loading condition.
Participants M4 and M9 exhibited no discernable joint kinematic changes at any loading
magnitude. Between-run variability also appeared consistent across loading. For these individ-
uals, loading even up to 5% of body weight may not have required additional exploitation of
movement system degeneracy to satisfy the task goal [74]. As part of their intrinsic behavioural
dynamics, these individuals likely defer to highly stable movement attractor states in the pres-
ence of manageable perturbations [65, 75]. Practically, WR may not be appropriate to chal-
lenge running coordination among such individuals, as loading beyond 5% of body weight on
the lower limbs presents logistical difficulties due to load placement space limitations.
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Lastly, it is interesting to note that participants M2 and M11 appeared to demonstrate multi-
stability about the ankle joint. There appeared to be two dominant kinematic modes expressed
with apparent condition dependence in M2 but not in M11. Coaches should appreciate that ath-
letes may exhibit multi-stability, wherein two or more patterns of coordination are stable [68].
A limitation of the present study is that only sagittal plane kinematics were considered.
Consequential alterations to kinematics may have also occurred in the transverse and frontal
planes, or in trunk or upper body segments. In terms of the WR, an exactly equal distribution
of load between the anterior and posterior segment surfaces could not always be guaranteed.
In these instances, one surface of each segment experienced 50 g more loading, which although
minimal, may have impacted ensuing running kinematics. In relation to data processing, it is
important to acknowledge that although the initial rise in vertical displacement of the toe
marker has previously been used to define toe-off during running actions [46, 47, 76], valida-
tion of this detection method against force plate measures under the specific running velocities
and floor surface conditions of the present study has not been performed. Lastly, despite the 3
min rest period allowed between running trials, residual after-effects following heavier loading
conditions could have briefly impacted on the kinematics observed during lighter conditions
[77]. When SPM t-tests were repeated with participants separated on the basis of having com-
pleted the 1% condition immediately following the 5% condition as part of their randomisa-
tion, it was evident that the group-level differences in hip extension between the 1% condition
and BW were driven by these participants (S1 Fig). A larger sample size would provide clarity
on this point by enabling direct statistical comparisons between participants who experienced
the 1% condition immediately following the 5% condition, and those that did not. If this type
of loading contrast was an effectual factor, fidelity could be improved by allowing participants
to rest for longer or briefly run without loading in between trials to “re-establish” an unloaded
baseline.
Future research should specifically consider the effects of unloaded running immediately
following a period of loading to clarify the propensity for acute coordinative changes to be
retained following the removal of perturbation. Investigation into the impact of asymmetrical
WR loading on coordination would also be worthwhile given the challenge to the movement
system that such an intervention would pose. As understanding of the effects of WR loading
develops, researchers and/or coaches should consider situating tasks such as loaded running in
a representative, field-based environment. WR coupled with the inherent movement variabil-
ity induced by dynamic constraints and affordances in this environment would present a fur-
ther, more contextual, challenge to coordination [78].
Conclusions
Exposure to WR of 5% of body weight increased knee flexion during weight acceptance and
decreased ankle plantarflexion during heel recovery at the group-level. This appeared to be
due to the high load and novelty of this condition. Among individuals that reflected group-
level trends and exhibited decreased between-run variability at one or more joints, 5% loading
may be an excessive perturbation, as exploration of alternate movement states is limited. Sev-
eral participants exhibited increased between-run joint angle variability in one or more load-
ing conditions compared with BW, suggesting exploration and refinement of coordinative
structures under these conditions. The loading magnitudes at which these increases were elic-
ited, however, varied between individuals. WR therefore appears to show utility for the pur-
pose of perturbing coordination to encourage movement variability among certain
individuals, though the loading magnitudes used should be determined on a case-by-case
basis.
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Supporting information
S1 Table. List of legs analysed and joint angles unable to be reconstructed for analysis.
Joint angle data that could not be reconstructed is highlighted in red.
(DOCX)
S2 Table. Pointwise t-statistics and maximum critical values for SPM t-tests.
(DOCX)
S1 Fig. Hip joint SPM t-test BW versus 1% separated based on condition order. (A) Hip
joint BW versus 1% for participants in which 1% condition did not immediately proceed 5%
condition. (B) Hip joint BW versus 1% for participants in which 1% condition immediately
proceeded 5% condition. Solid lines represent ensemble means and accompanying shaded
regions represent ± 1 SD. Grey shaded regions indicate regions of significant difference
between curve sets.
(TIF)
Acknowledgments
The authors would like to acknowledge the research participants for their involvement in this
study.
Author Contributions
Conceptualization: Karl M. Trounson, Aglaja Busch, Sam Robertson.
Data curation: Karl M. Trounson, Aglaja Busch, Neil French Collier.
Formal analysis: Karl M. Trounson, Neil French Collier.
Investigation: Karl M. Trounson, Aglaja Busch.
Methodology: Karl M. Trounson, Aglaja Busch, Sam Robertson.
Supervision: Sam Robertson.
Visualization: Karl M. Trounson, Neil French Collier.
Writing – original draft: Karl M. Trounson.
Writing – review & editing: Karl M. Trounson, Neil French Collier, Sam Robertson.
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PLOS ONE
Effects of wearable resistance on running kinematics
PLOS ONE | https://doi.org/10.1371/journal.pone.0244361
December 28, 2020
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Effects of acute wearable resistance loading on overground running lower body kinematics.
Trounson, Karl M,Busch, Aglaja,French Collier, Neil,Robertson, Sam