Anatomically Asymmetrical Runners Move More
Asymmetrically at the Same Metabolic Cost
Elena Seminati1*, Francesca Nardello2, Paola Zamparo2, Luca P. Ardigo` 2, Niccolo` Faccioli3,
Alberto E. Minetti1
1 Department of Pathophysiology and Transplantation, Faculty of Medicine, University of Milan, Milan, Italy, 2 Department of Neurological and Movement Sciences,
School of Exercise and Sport Sciences, University of Verona, Verona, Italy, 3 Department of Pathology and Diagnostics, Section of Radiology, University of Verona, Verona,
Italy
Abstract
We hypothesized that, as occurring in cars, body structural asymmetries could generate asymmetry in the kinematics/
dynamics of locomotion, ending up in a higher metabolic cost of transport, i.e. more ‘fuel’ needed to travel a given distance.
Previous studies found the asymmetries in horses’ body negatively correlated with galloping performance. In this
investigation, we analyzed anatomical differences between the left and right lower limbs as a whole by performing 3D
cross-correlation of Magnetic Resonance Images of 19 male runners, clustered as Untrained Runners, Occasional Runners
and Skilled Runners. Running kinematics of their body centre of mass were obtained from the body segments coordinates
measured by a 3D motion capture system at incremental running velocities on a treadmill. A recent mathematical
procedure quantified the asymmetry of the body centre of mass trajectory between the left and right steps. During the
same sessions, runners’ metabolic consumption was measured and the cost of transport was calculated. No correlations
were found between anatomical/kinematic variables and the metabolic cost of transport, regardless of the training
experience. However, anatomical symmetry significant correlated to the kinematic symmetry, and the most trained subjects
showed the highest level of kinematic symmetry during running. Results suggest that despite the significant effects of
anatomical asymmetry on kinematics, either those changes are too small to affect economy or some plastic compensation
in the locomotor system mitigates the hypothesized change in energy expenditure of running.
Citation: Seminati E, Nardello F, Zamparo P, Ardigo` LP, Faccioli N, et al. (2013) Anatomically Asymmetrical Runners Move More Asymmetrically at the Same
Metabolic Cost. PLoS ONE 8(9): e74134. doi:10.1371/journal.pone.0074134
Editor: David Carrier, University of Utah, United States of America
Received March 20, 2013; Accepted July 27, 2013; Published September 24, 2013
Copyright:  2013 Seminati 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.
Funding: The project was supported by the Department of Pathophysiology and Transplantation -Human Physiology section- University of Milan. The funders
had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: elena.seminati@unimi.it
Introduction
The symmetry between the left and right sides of the body plays
an important role in legged locomotion. The symmetrical
behaviour of lower limbs during gait has often been taken for
granted, mainly for simplicity in data collection and analysis, while
the lack of it was frequently considered as an indicator of gait
pathology [1]. Differently from what expected, healthy human gait
is rather asymmetrical [2,3]. This seems to reflect a functional
difference inherently associated to the laterality of the dominant
side characterising each individual [4,5]. This topic was intro-
duced more than 80 years ago by Lund [6] who showed the effects
of structural/anatomical asymmetry on lateral drift in human
locomotion. The same experiments were recently repeated and
supported the hypothesis of a relationship between leg length
inequality and asymmetry in locomotion [7–9].
Body symmetry can be further modulated in sports: depending
on
the
discipline,
relevant
muscles
become
asymmetrically
different (tennis, fencing, throwing, etc.), or they are required to
reach similar hypertrophy (ice-skating, downhill skiing, front
crawl, etc.) on the two sides of the sagittal plane. Thus, body
changes towards or from symmetry are not just the consequence of
genetics and laterality, being also caused by specific training
protocols.
As the concept of symmetry has an important influence in
human locomotion, it plays a key role in the design and
maintenance of vehicles, which are periodically inspected and
serviced to guarantee wheel balance and homogeneous tyre
wearing, in order to reduce fuel consumption and ensure a safe
drive. Would it be the same for human running? Can an
anatomical/structural asymmetry of the human body cause
kinematic/dynamic asymmetry of locomotion? Also, can structural
or functional asymmetries be related to some increase of the
metabolic cost of transport?
Several authors studied symmetry in locomotion in humans [1–
4,10,11] and also in animals [12], but only few of them
investigated the possible interaction between symmetry and energy
saving. Manning and collaborators found negative correlations
between anatomical symmetry and race time during competitions,
both in human running and in galloping horses [13,14]. These
preliminary findings encouraged us to study the possible interac-
tions between different kinds of symmetry (anatomical and
dynamical) and the human running performance, not only in
term of race time, but also of energy saving. In the present study,
we investigate the relationship between the cost of transport (C)
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while running at different increasing velocities and individual
anatomical and dynamical symmetries in three differently trained
groups of subjects, with the idea that ‘race cars’ should more
strongly rely on symmetry than ordinary ‘automobiles’.
Materials and Methods
Subjects
Nineteen healthy male subjects volunteered to participate this
investigation. Exclusion criteria included neurological or muscu-
loskeletal pathologies affecting running ability. The institutional
ethics committee of the University of Milano had approved all
methods and procedures, and subjects gave their written informed
consent (approved by the same committee) prior to the start of
testing. We clustered participants into three different groups, based
on their specific running ability:
N group 1, (n = 7): Untrained Runners (UR), who practiced sport
(not specifically running) 3 times per week (less than 2 hours per
week)
N group 2, (n = 7): Occasional Runners (OR), fit athletes, who
trained more than 3 times per week, (between 2 and 6 hours per
week). Each of them had previously participated in a national
competition (half marathon or 10 km competition)
N group 3, (n = 5): Skilled Runners (SR), master athletes who
trained more than 3 times per week (at least 6 hours per week);
they were marathon runners, with a mean performance time of
2 h 44 min 24 s 610 min 12 s standard deviation (SD).
Anthropometric characteristic of the different subject groups are
shown in Table 1.
MR Dataset and 3D Images Processing
In order to evaluate the anatomical symmetries, each partici-
pant underwent Magnetic Resonance (MR) imaging. Subjects
were adjusted in a supine position as to preserve the maximal body
symmetry in the sagittal plane.
MR scans were performed with a 1.5-T superconductive
magnet (Siemens, Erlangen, Germany). In all subjects multiplanar
T1-weighted Spin-echo sequences were obtained (TE 11, TR 565,
flip angle 90u), on a coronal plane for three different anatomical
districts: Pelvis district (PD), Upper-Leg district (UD), including
thigh and knee, Lower-Leg district (LD), including calf and ankle,
with slice thickness of 4 mm. The matrix was 3206320 and the
field of view (FOV) was 4606460. Total examination time was less
than 7 minutes (36 coronal slices for each district).
All the recorded images (saved in DICOM format) were
subsequently analyzed with a custom, ad hoc program written in
LabVIEW 8.6 (National Instrument, Austin, Texas, USA). The
procedure we implemented exports, for each districts, 36 MR
images (slices) as two-dimensional matrix of 3206320 pixels, each
of which 1.4461.44 mm, and includes several post-processing
steps, as shown in Figure 1.
The 36 coronal slices, for every district, assembled together, re-
create a three-dimensional (3D) volume, whose elements (voxel)
are values corresponding to a grey level intensity (8 bit scale),
reflecting proton density, (Figure 1a). In order to compare the
subject’s left lower limb with the right one, firstly, the initial 3D
volume has to be split in two separated volumes, right volume (Rv)
and left volume (Lv), (Figure 1b). Successively the Lv is specularly
reflected, with respect to the sagittal plane (Figure 1c), whilst the
Rv is bordered by zero intensity voxel (Figure 1d), through a zero-
padding operation, so that the left reflected volume (Lrv) can be
virtually superimposed on the Rv (Figure 1e), and moved along the
three axes in order to find the best matching overlap and to
evaluate the ‘overall’ similarity (i.e. symmetry) between the two
limbs. To achieve this aim the algorithm performs a 3D
correlation between the contents of the two respective anatomical
volumes.
Table 1. Subject characteristics.
UR
OR
SR
Participants (n)
7
7
5
Age (years)
33.1613.2
31.9611.8
42.667.4
Body Mass (kg)
70.663.4
67.366.1
68.264.9
Height (cm)
175.964.7
177.364.0
177.864.4
Right leg length (cm)
83.163.6
84.064.1
85.866.3
Left leg length (cm)
82.863.7
83.063.7
84.867.2
Leg length discrepancy (cm)
1.160.7
1.060.8
1.361.0
Number of participants, mean 6 SD for age (yrs), body mass (kg), height (cm),
right and left leg length (cm), and leg length discrepancy (LLD) (cm) for the 3
different groups of subjects: Untrained runners (UR), Occasional runners (OR)
and Skilled runners (SR).
doi:10.1371/journal.pone.0074134.t001
Figure 1. Principal steps involved in the 3D cross-correlation
algorithm. a) The 36 slices of the MR sequence, create a 3D volume
whose sizes are laterally indicated, b) right volume (Rv) and left volumes
(Lv) separated, c) left reflected volume (Lrv) on the sagittal plane (in the
mirror), d) zero-padding operation around right volume, e) Lrv
superimposed to Rv in order to find the position that maximize the
cross-correlation value.
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The correlation between two signals (cross-correlation) is a
standard approach for signal processing and it has been recently
designed in 3D in order to consider simultaneously the full
anatomical volume information, to assist radiologists in providing
correct diagnosis of metastases within the lungs [15,16] or brain
[17], for instance.
Following Lewis’ approach [18], a normalised cross-correlation
coefficient (ri,j,k), was adopted to identify the symmetry degree
between the 3D split volumes:
ri,j,k~
P
x,y,z Rv(x,y,z){Rvi,j,k

: Lrv(x{i,y{j,z{k){Lrv


ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
P
x,y,z Rv(x,y,z){Rvi,j,k

2: P
x,y,z Lrv(x{i,y{j,z{k){Lrv

2
q
where Lrv and Rvi,j,k are the voxel mean value of the left reflected
volume and the right volume, respectively. The two volumes are
virtually superimposed at coordinates i, j and k, and calculations
are performed for all pairs of corresponding voxels along x, y and z
axes.
For every subject and each anatomical district we evaluated the
maximal cross correlation value (rmax) (i.e. the value corresponding
to the best overlap between right and left reflected volumes). This
coefficient can assume a range of values between 21 and 1,
depending upon the similarity of the 3D analyzed volumes, where
a value of 1 indicates an exact matching of the Lrv with the Rv, a
value of 21 indicates opposite grey values for voxels in Lrv with
respect to Rv, and a value of 0 indicates no correlation between the
two volumes.
Figure 2. Examples of obtained cross-correlation values plotted versus iterations. Cross correlation values (r) of all iterations (134,400
overlap positions = 28 (i)660 (j)680 (k), between right volume and left reflected volume); a) comparison between two bottles, filled with the same
volume of water (rmax =0.99); b) comparison between the right upper leg of a subject and the left upper leg of a different subject (rmax =0.51); c)
comparison between right and left upper legs of the same subject. Inset: enlargement of cross correlation pattern showing the inner processing loop
(i coordinates).
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Software reliability and accuracy were validated by comparing
two identical bottles filled up with water (see Figure 2a), resulting
in a maximal cross correlation value of rmax = 0.99. The algorithm
provided a value of rmax = 1 only when the right volume of a
specific subject was compared with itself, while the lowest value of
rmax was obtained when the right volume of a specific subject was
compared with the left reflected volume of an other different
subject (rmax = 0.51, as shown in Figure 2b).
We evaluated for every subject a single maximal cross
correlation value (rmax) for each district, (rmax(PD) for Pelvis
district, rmax(UD) for Upper-Leg district and rmax (LD) for Lower-
Leg district) and secondly a ‘global’ anatomical cross correlation
value (rmax) as the mean of the three districts:
rmax~ rmax(PD)zrmax(UD)zrmax(LD)
3
Kinematics
In order to capture kinematic functional symmetries on many
steps, all the subjects performed level shod running on a treadmill
(h/p/Cosmos Saturn 4.0, Germany).
Human body has been modelled as a series of linked, rigid
segments: 18 reflective markers were placed bilaterally on
anatomical landmark points (immediately anterior to ear tragus,
shoulder, elbow, wrist, greater trochanter, lateral epicondyle of
femur, lateral malleolus, calcaneus, and 5th metatarsal head) and
their 3D position was captured at 100 Hz, using an eight-camera
Vicon MX optoelectronic system (Vicon, Oxford, UK). In this
way, 12 body segments were defined [19].
After a brief period of familiarization on the treadmill, each
subject ran at six different incremental speeds: from 2.22 m/s to
5 m/s, step 0.56 m/s. Each speed was maintained for at least
5 min, with a rest period of at least 5 min between successive
trials.
The 3D recorded coordinates of the 12 segments, together with
the anthropometric tables [20,21], were used to compute the
experimental trajectory of the Body Centre of Mass (BCOM).
Successively, we adopted a recent mathematical method [22,23]
simultaneously capturing the spatial and dynamical features of that
3D BCoM trajectory, which allows to quantify dynamical
symmetry indices of locomotion in the 3 spatial axes; by having
sampled the body motion on a treadmill, the trajectory of the
BCOM can be represented by a closed 3D loops (Lissajous contours),
representing its displacement with respect to the average position.
The 3D trajectory is mathematically defined by a 6-harmonic
Fourier series, whose coefficients are used to calculate the
Dynamical Symmetry Indices SIx (for progression axis), SIy (for
vertical axis) and SIz (for lateral axis). The motion of the BCOM is
expected to exhibit perfect right–left symmetry if it contained just
even harmonics in the progression and y vertical directions, and
just odd harmonics in the lateral direction, as within a stride it
oscillates twice in the sagittal (y–x) plane and only once in the
horizontal (x–z) plane. Dynamical Symmetry indices are then
averaged among the strides number (n) as to obtain for each
velocity and each subject:
SIx~
Pn
j~1 SIx
j
n
SIy~
Pn
j~1 SIy
j
n
SIz~
Pn
j~1 SIz
j
n
Table 2. Statistical correlation matrix results between variable pairs.
Anatomical Symmetry
Dynamical Symmetry
Economy
rmax (PD)
rmax (UD)
rmax (LD)
rmax
SIx
SIy
SIz
GI
C
Anatomical
Sym.
rmax (PD)
1
0.501*;
0.040
0.427;
0.087
0.871**;
0.000
0.651**;
0.005
0.110;
0.675
20.094;
0.719
0.606**;
0.010
0.157;
0.547
rmax (UD)
1
0.507*;
0.038
0.782**;
0.000
0.322;
0.208
0.003;
0.990
0.020;
0.940
0.357;
0.160
20.114;
0.662
rmax (LD)
1
0.748**;
0.001
0.045;
0.863
0.103;
0.694
0.304;
0.236
0.046;
0.860
0.059;
0.822
rmax
1
0.487*;
0.048
0.095;
0.716
0.055;
0.834
0.473;
0.055
0.072;
0.785
Dynamical
Sym.
SIx
1
0.186;
0.447
0.009;
0.972
0.992**;
0.000
0.005;
0.983
SIy
1
0.617**;
0.005
0.186;
0.445
0.105;
0.668
SIz
1
0.012;
0.959
0.211;
0.385
GI
1
20.001;
0.995
Economy
C
1
Pearson Correlation coefficient is presented together with the relative p-value for the following parameters: maximal cross correlation values for each anatomical district
(rmax(PD), rmax(UD) and rmax(LD)), global anatomical cross correlation value (rmax), dynamical symmetry indices for each direction (SIx, SIy and SIz), Global Symmetry
Index (GI) averaged among the different running speeds for each subject and metabolic Cost of transport (C). Values in bold indicate significant correlations (* = p,0.05,
** = p,0.01).
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(SI, 0: no symmetry between right and left steps, 1: complete
symmetry).
Successively, the three mean dynamic indices (SIx, SIy, SIz)
are weighted according to the ‘real’ maximum displacement range
of the BCOM, i.e. dx ( = running speedXstride frequency), dy and dz,
respectively, and a Global symmetry Index (GI) is calculated as
GI~ dx:SIxzdy:SIyzdz:SIz
dxzdyzdz
(GI, 0: no symmetry between right and left steps, 1: complete
symmetry).
Energy Cost Measurement
Oxygen consumption ( _VO2) of running was measured with a
breath-by-breath gas analyzer (Cosmed K4b2, Rome, Italy). Data,
including heart rate (HR), were recorded at each progression
speed, after the metabolic steady state had been achieved (3 min),
for further 2 minutes. 5 minutes of testing was performed at each
speed. Resting _VO2 was measured while standing. Respiratory
Exchange Ratio (RER) was monitored in order to check for
aerobic conditions (RER,1). We expressed the metabolic Cost of
Transport (C), i.e. the oxygen consumed to move 1 kg of body
mass 1 m distance, in J (kg m)21 by dividing the net
_VO2
[measured - resting, [ml O2 (Kg min)21] by the progression speed
(m min21), and by assuming an energy equivalent of 20.9 J ml
O2
21.
Statistical Analysis
Relationships between variable pairs were investigated using
Pearson’s correlation coefficient. To compare speed dependent
variables (C, HR, SIx, SIy, SIz and GI), differences were analyzed
using
a
two-ways
ANOVA
(groupxspeed)
(with
a
post-hoc
Bonferroni
correction).
For
speed
independent
variables
(rmax(PD), rmax(UD) and rmax(LD)), we performed a one-way
ANOVA for repeated measures in order to detect difference
among districts. Furthermore, Principal Component Analysis
(PCA) was performed on the three anatomical indices, in order
to estimate their relative contribution to the total variance.
Statistical significance was accepted when p,0.05.
Results
Since only five OR and five SR subjects were able to complete
all the running protocols up to 5.0 m/s, and UR subjects stopped
at the speed of 4.44 m/s, we did not consider in the statistical
analysis the highest speed level.
Anatomical Symmetries
Anatomical symmetries are described by the maximal cross-
correlation
value
for
each
district
(rmax(PD),
rmax(UD)
and
rmax(LD)), and by the global anatomical cross correlation value
(rmax). These values are limited to only 17 subjects, because two
MR tests (one for the UR and one for the SR) had to be discarded
due to technical problems.
One-way ANOVA between the three groups of subject didn’t
show any difference between UR, OR and SR for the cross-
correlation values, while we found significantly lower values of
anatomical symmetry for pelvis district, compared to the upper
(p,0.05) and lower leg district (p,0.01) (rmax(PD) = 0.7760.09,
rmax(UD) = 0.8260.05 and rmax(LD) = 0.8360.05). PCA showed
that 65.8% of the total variance was explained by the first
principal component, where the three considered parameters
(rmax(LD), rmax(UD) and rmax(PD)) had almost the same weight.
However UD seems to give the greatest contribution to the first
principal component, with respect to the other two districts.
Results regarding pairwise correlations between variables are
summarized in Table 2: rmax(UD) is significantly correlated with
rmax(PD) and rmax(LD) (p,0.05), also rmax(PD) and rmax(LD) seem
to be positively correlated even if not significantly (p = 0.087).
Significant results were found also between anatomical symme-
tries and kinematics (mean values for the Global Symmetry
Index (GI) were evaluated starting from the single values of
SIx, SIy and SIz and averaged within each group of speeds for
each subject); in particular rmax(PD) is positively correlated with
Figure 3. Regression of the mean dynamic Global Symmetry
Index (GI) versus the global anatomical cross correlation value
(rmax). Each point represents the mean Global Symmetry Index
averaged among the different running speeds for each subject;
Untrained Runners (UR), Occasional Runners (OR) and Skilled Runners
(SR) (r = 0.473; p = 0.055).
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Figure 4. Mean values for the dynamic Global Symmetry Index
(GI) plotted against running speed. Mean values for the dynamic
Global Symmetry Index (GI) are evaluated starting from the single
values of SIx, SIy and SIz and averaged within each group of subjects,
6 SD, in untrained runners (UR), occasional runners (OR) and skilled
runners (SR). Two-way ANOVA (group6running speed) show that the
group of UR had a mean GI always lower compared to the OR and SR,
(* = p,0.01), independently from the running speed.
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SIx (p,0.01) and GI (p,0.05) and also rmax is positively and
significantly correlated with SIx (p,0.05), while we observed a
positive trend between rmax and GI, even if not significantly
(p = 0.055) (see Figure 3).
Kinematics
Mean values for the Global Symmetry Index (GI), evaluated
starting from the single values of SIx, SIy and SIz and averaged
within each group of subjects, 6 SD, are shown in Figure 4. We
performed a two-ways ANOVA, where independent variables
were running speed and subject group and the dependent variable
was GI. Results show that UR have a GI significantly lower than
both OR and SR at each velocity (p,0.01). Also, GI for UR seems
to
decrease
with
increasing
running
velocity, even
if
not
significantly. Statistical analysis did not show any difference
between UR, OR and SR for the single kinematic symmetry
indices, while one-way ANOVA for repeated measure shown
significantly lower values for SIx, (0.7260.06) compared to SIy
(0.8860.04) and SIz (0.8560.05), (p,0.01).
Cost of Transport
Results for the metabolic cost C and HR are presented in
Figure 5. C is confirmed to be independent of speed, with no
differences among running groups. At the same speed, HR
decreased as runners’ ability increased, with values for SR
significantly lower than for OR and UR. No significant correlation
was found between the C and the previously analysed parameters,
both for kinematics and for anatomical values (Table 2).
Discussion
The main aim of this project was to investigate the relationship
among the anatomical/structural symmetry of the lower limbs, the
dynamical symmetry of the 3D BCOM displacement and the
metabolic cost of human running. C has been considered as an
indirect index of running performance: at the same sustainable
fraction of maximal
_VO2, the lower the cost the higher the
average speed [24]. While being aware of the speed and training
level independency of C, as debated and reported in the literature
[25229], our hypothesis was that more asymmetrical limbs, in
subjects committed to run with symmetrical steps, would have
involved a higher C. In other words, part of the inter-subject C
variance
could
have
been
explained
by
different
level
of
anatomical asymmetry.
Differently from previous studies dealing with gross morpho-
logical features (bones length [5,8], human face [13] and horse
muzzle [14] landmarks) and isolated gait parameters (stride length
and frequency [30,31], joint angles [3] and ground reaction forces
[10]), we analysed the symmetry of the ‘whole’ (left and right)
lower limb anatomy and of the global running kinematics (3D
trajectory of BCOM), in three groups of differently trained
athletes. Our hypothesis, inspired by the engineering of motor
vehicles, was not completely verified. C was not significantly
correlated either with anatomical symmetries or with dynamical
symmetries in running, while we found significant correlations
between the anatomical and dynamical symmetries indices
(Table 2). This indicates that the more anatomically symmetrical
are the subjects, the more symmetrical is their running gait
(especially in the forward (x) direction).
Figure 5. Mean values ± SD for the cost of transport (C) (lower curves) and for the heart rate (HR) (upper curves). C and HR are plotted
against running speed for untrained runners (UR), occasional runners (OR) and skilled runners (SR). Results obtained with the two-way ANOVA
(group6running speed) show no significant difference among groups of subjects across velocity for C, which results to be independent of the
running speed. HR increased significantly with the running speed for all the three group of subjects. Furthermore we obtained significantly higher HR
values for UR compared to OR and SR (* = p,0.01).
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This finding is in accordance with the recent literature, where
high level of leg length discrepancy (LLD) is correlated with low
symmetrical gait coefficients [7] in walking. In our work,
individual LLD was always lower than 2 cm (Table 1), and had
no effect on C, according to the studies of Gurney [32].
It is possible that some physiological adaptations of the human
machinery compensate for small asymmetries typical of the
mechanics of our legged system [1,2], with no influence on C.
Rather, larger anatomical discrepancies, like a LLD higher than
2 cm [32] or a body mass not uniformly distributed [33,11], could
influence economy. Similar adaptations behaviours might have
occurred in runners wearing new and worn shoes [34], or on
surfaces of different stiffness [35]. Despite of the changed
properties of materials, runners modified their motion pattern as
to retain their original dynamics of running.
This could occur also in the subjects of this study, who seem to
compensate their anatomical body asymmetries and minimize C, a
strategy frequently adopted by animals [36]. With the main
propulsive muscles operating close to isometric in running [37],
tendons can store (stretching) and release (shortening) variable
amounts of elastic energy during each step, in the attempt to adapt
to different anatomical asymmetries. In this way the metabolic cost
can be potentially kept unchanged.
In addition, although HR results (Figure 5) witness the
appropriateness of clustering subjects according to the different
training status (most skilled runners reported the lowest HR, at the
same speed, p,0.01), the almost speed-independent C values seem
not to be influenced by the different fitness level, as also found by
other investigators [27,29].
As also indicated in previous studies, training and experience
seem to be important elements in the lower limb joint angle
symmetry and in the stride variability of running, even at no
apparent metabolic benefit [29231]. The most experienced and
high performance athletes can maintain, even at high velocities,
higher dynamical symmetry than untrained runners (Figure 4). As
step frequency and muscles effort increase, the higher physical
demand and peripheral fatigue could impair the maintenance of a
symmetrical gait and a consistent locomotion pattern, as seen for
the UR group.
Furthermore, MRI measurements showed that the anatomical
symmetry does not depend on the investigated district. PCA and
correlation among lower limb districts could have been caused by
misalignments of the two limbs during MRI test. However, due to
the use of alignment tools during the tests, we feel confident that
the intra-subject symmetry correlation among districts is not a
measurement artefact. Similar eigenvalues from PCA suggest that
the total variance of symmetry is equally explained by the three
districts.
This work brings developments in the study of locomotion
symmetry, also by means of newly introduced methodologies
(BCOM 3D trajectory analysis and 3D cross-correlation between
‘whole’ limb MRI voxels). Differently from the original hypothesis,
asymmetrical limbs generate asymmetrical body running at no
apparent additional metabolic cost. This suggests some plasticity of
the human body in coping with structural changes, with the final
result of preserving locomotion economy. Deeper insights have
been obtained regarding the relationship between the symmetries
correlation residuals and the cost of transport, with the idea that
subjects would be less economic when their anatomical and
dynamical symmetry values do not match. Supplemental analysis
and discussion regarding this hypothesis have been reported in the
Appendix S1. Even if statistical results in this perspective are weak,
possibly due to the relatively small sample size and low asymmetry
level, there are some hints suggesting that only the runners who fail
to match their anatomy and dynamics features have an increased
cost of locomotion. Therefore, the initial hypothesis embedded in
the title ‘‘anatomically asymmetrical runners move more asym-
metrically at the same metabolic cost’’ is still valid (i.e. the cost
would increase when an anatomically asymmetrical runner
attempts to move in a symmetrical way). Further studies focusing
on adaptations of the muscle-tendon interplay could reveal how
human machine compensate the small structural asymmetries that
characterize our legged system. The anatomical asymmetry
threshold, above which the now expected asymmetrical gait will
also involve an increase in running cost, is the challenge for future
investigations.
Acknowledgments
The authors would like to thank all the subjects for their
participation in the study, and the Technician Lauro Dalla Chiara
for his help during the MR scans performed at the University
Hospital Polyclinic ‘‘Borgo Roma’’ in Verona (Italy). Statistical
support from Carlo M. Biancardi is also greatly appreciated.
Supporting Information
Figure S1
Examples of univariate and bivariate regres-
sions. Four different types of linear regressions are presented as
examples of correlation between Dynamical Symmetry index in
forward direction (SIx) and maximal cross-correlation value for
Pelvis District (rmax(PD)): a) Univariate regression, b) Univariate
regression with intercept forced to be equal to 0, c) Bivariate
regression, d) Bivariate regression with intercept forced to be equal
to 0. Untrained Runners (UR), Occasional Runners (OR) and
Skilled Runners (SR) symbols as in Figure 3. N.B. The
determination coefficient in regressions lines forced through the
origin, differently from the general model, does not reflect the
fraction of the variability in the dependent variable explained by
the independent variable. This makes R2 values unrealistically
high and not comparable with the ones obtained in the general
models.
(TIF)
Appendix S1
(DOCX)
Author Contributions
Conceived and designed the experiments: AEM LPA PZ. Performed the
experiments: ES FN NF LPA PZ. Analyzed the data: ES FN AEM.
Contributed reagents/materials/analysis tools: NF. Wrote the paper: ES
AEM. Designed the software used in analysis: AEM ES. Final approval of
the paper version to be published: ES FN PZ LPA NF AEM.
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Anatomically asymmetrical runners move more asymmetrically at the same metabolic cost.
Seminati, Elena,Nardello, Francesca,Zamparo, Paola,Ardigò, Luca P,Faccioli, Niccolò,Minetti, Alberto E