RESEARCH ARTICLE
Footwear Decreases Gait Asymmetry during
Running
Stefan Hoerzer1*, Peter A. Federolf2,3, Christian Maurer1,4, Jennifer Baltich1, Benno
M. Nigg1
1 Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada,
2 Institute for Sport Science, University of Innsbruck, Innsbruck, Tyrol, Austria, 3 Department of
Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway, 4 Red Bull Diagnostic
and Training Center, Thalgau, Salzburg, Austria
* stefan.hoerzer@ucalgary.ca
Abstract
Previous research on elderly people has suggested that footwear may improve neuromus-
cular control of motion. If footwear does in fact improve neuromuscular control, then such
an influence might already be present in young, healthy adults. A feature that is often used
to assess neuromuscular control of motion is the level of gait asymmetry. The objectives of
the study were (a) to develop a comprehensive asymmetry index (CAI) that is capable of
detecting gait asymmetry changes caused by external boundary conditions such as foot-
wear, and (b) to use the CAI to investigate whether footwear influences gait asymmetry dur-
ing running in a healthy, young cohort. Kinematic and kinetic data were collected for both
legs of 15 subjects performing five barefoot and five shod over-ground running trials. Thirty
continuous gait variables including ground reaction forces and variables of the hip, knee,
and ankle joints were computed for each leg. For each individual, the differences between
the variables for the right and left leg were calculated. Using this data, a principal compo-
nent analysis was conducted to obtain the CAI. This study had two main outcomes. First,
a sensitivity analysis suggested that the CAI had an improved sensitivity for detecting
changes in gait asymmetry caused by external boundary conditions. The CAI may, there-
fore, have important clinical applications such as monitoring the progress of neuromuscular
diseases (e.g. stroke or cerebral palsy). Second, the mean CAI for shod running (131.2 ±
48.5; mean ± standard deviation) was significantly lower (p = 0.041) than the CAI for bare-
foot running (155.7 ± 39.5). This finding suggests that in healthy, young adults gait asymme-
try is reduced when running in shoes compared to running barefoot, which may be a result
of improved neuromuscular control caused by changes in the afferent sensory feedback.
Introduction
Falls are one of the main causes for fatal injury and hospitalization in older adults [1–3]. Identi-
fying factors that contribute to falls has become an important objective in clinical geriatric
research. The absence of footwear was identified as an important risk factor for the occurrence
of falls in elderly adults [4]. The reduced risk of falls reported in the mentioned study concurs
PLOS ONE | DOI:10.1371/journal.pone.0138631
October 21, 2015
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OPEN ACCESS
Citation: Hoerzer S, Federolf PA, Maurer C, Baltich
J, Nigg BM (2015) Footwear Decreases Gait
Asymmetry during Running. PLoS ONE 10(10):
e0138631. doi:10.1371/journal.pone.0138631
Editor: Jose Manuel Garcia Aznar, University of
Zaragoza, SPAIN
Received: May 29, 2015
Accepted: August 31, 2015
Published: October 21, 2015
Copyright: © 2015 Hoerzer et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.
Funding: The authors would like to acknowledge
NSERC Create, Biomechanigg Sport & Health
Research, and the da Vinci Foundation for financial
support. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript. In addition, the Red
Bull Diagnostic and Training Center provided support
in the form of a salary for CM, but did not have any
additional role in the study design, data collection and
analysis, decision to publish, or preparation of the
manuscript. The specific roles of this author are
articulated in the 'author contributions' section.
with other studies that assessed the effect of footwear on the likelihood of falls or balance [5–7].
In addition to mechanical factors potentially causing a reduced risk of falls when wearing foot-
wear [8], it is also possible that footwear may alter the type or amount of afferent sensory feed-
back causing improved neuromuscular control. If footwear does in fact improve
neuromuscular control, then such an influence might already be present in young, healthy
adults, long before it may become clinically relevant in the prevention of falls. A feature that is
often used to assess neuromuscular control of motion is the level of asymmetry between the
contra-lateral limbs during gait. In fact, in many neurophysiological disorders such as stroke
[9, 10], Parkinson’s disease [11], or cerebral palsy [12], gait asymmetry can be seen as one of
the indicators of the severity of the condition.
One challenge when assessing gait asymmetry in healthy, young adults is that the kinematic
and kinetic differences between the left and right lower limbs are rather small compared to the
inherent movement variability. In addition, one could argue that gait asymmetry is a character-
istic that applies to several body segments simultaneously [13–15], especially when investigat-
ing changes caused by external boundary conditions such as footwear. Therefore, a new
asymmetry index, a comprehensive asymmetry index (CAI), is required that is especially sensi-
tive to changes in gait asymmetry caused by external boundary conditions. Three actions can
be taken in order to increase the sensitivity of the CAI: First, all available kinematic and kinetic
data should be incorporated to provide an all-encompassing assessment of an individual’s
lower limb gait asymmetry. This allows considering the moving human body as a whole system
rather than analysing individual variables [16, 17]. Second, the waveforms of all gait variables
should be normalized to their standard deviation waveform to account for asymmetry caused
by the natural variability of the movement. This should be done since previous studies indi-
cated that gait asymmetry may only be relevant when it exceeds the inherent variability of a
gait variable [13, 18]. Third, a principal component analysis (PCA) can be used to filter out the
covariate structure of gait asymmetry [16, 19]. This is based on the assumption that gait asym-
metry observed in one variable can only occur if it is accompanied by asymmetries in other var-
iables [19]. To give a simplified example: contra-lateral asymmetries in the knee joint angle can
only occur within a given motion task, if ankle and/or hip angles change accordingly.
In summary, a CAI with enhanced sensitivity to detect gait asymmetry changes is required
in order to investigate whether footwear influences the level of asymmetry between the contra-
lateral limbs during gait. A reduction in gait asymmetry may support previous research indicat-
ing that footwear improves neuromuscular control. The new CAI should be tested on a highly
automated movement, i.e. running, rather than more complex movements in which higher
cognitive functions are more likely to interfere with the movement pattern and may potentially
affect gait asymmetry.
Therefore, the objectives of the study were (a) to develop a comprehensive asymmetry index
(CAI) that can be used to study changes in gait asymmetry caused by external boundary condi-
tions such as footwear, and (b) to use the CAI to investigate whether footwear influences gait
asymmetry during running in a healthy, young cohort. Based on the aforementioned studies, it
was hypothesized that footwear decreases gait asymmetry as compared to barefoot running.
Methods
Study participants
Fifteen subjects were recruited for this study, seven females and eight males: age: 25.4 (SD 4.4)
years; height: 1.74 (SD 0.07) m; mass: 71.2 (SD 8.4) kg. The subjects were healthy, with no neu-
romuscular or neurological disorders, and had no lower-extremity pain at the time of testing.
All study participants provided written informed consent in accordance with the University of
Footwear Decreases Gait Asymmetry during Running
PLOS ONE | DOI:10.1371/journal.pone.0138631
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Competing Interests: The authors of this manuscript
have the following competing interests: CM has a
commercial affiliation (Red Bull Diagnostic and
Training Center). However, this does not alter the
authors' adherence to PLOS ONE policies on sharing
data and materials.
Abbreviations: CAI, Comprehensive Asymmetry
Index; EV, Eigenvalue; PCA, Principal Component
Analysis; PC-vector, Principal Component Vector.
Calgary’s policy on research using human subjects. The study protocol was approved by the
Conjoint Health Research Ethics Board of the University of Calgary.
Data collection
Kinematic and kinetic data were collected while the subjects performed for each leg five bare-
foot and five shod heel-toe over-ground running trials (running speed: 4.00 ± 0.6 ms−1). A
standard, neutral running shoe, without unique design features that potentially could have
influenced gait asymmetry, was provided for each subject (New Balance 506; New Balance Ath-
letic Shoe Inc., USA). A running trial was considered successful when the subject’s foot that
was being tested landed within the edges of a force platform (Kistler Instrumente AG, Switzer-
land). The force platform was used to record ground reaction forces (GRFs) at a sampling rate
of 2,400 Hz. At the same time, kinematic data were collected by means of a marker-based
motion capture system having eight synchronized, digital, high-speed, infrared cameras
(Motion Analysis Corporation, USA). Twenty-two retro-reflective markers were mounted on
each study participant. Marker locations included the right and left anterior superior iliac
spine, the right and left posterior superior iliac spine, and proximal, lateral, and distal aspects
of the thigh and shank. To describe the foot motion, markers were placed at proximal and dis-
tal, and lateral locations of the test shoe and on corresponding locations on the bare foot. For
the purpose of a neutral standing trial, additional markers were also placed on (and after the
neutral trial removed from) the right and left greater trochanters, the medial and lateral knee
joint, and the medial and lateral malleoli to define joint centres. A sampling rate of 240 Hz was
used to record the trajectories of the markers.
Data pre-processing
Cortex motion analysis software (Motion Analysis Corporation, USA) was used to reconstruct
the trajectories of the 22 markers for each running trial. A fourth-order, low-pass, Butterworth
filter was applied to the kinematic and kinetic data to filter out movement artefacts and mea-
surement noise with cut-off frequencies of 6 Hz for kinematic data and 50 Hz for kinetic data
[20]. Standard motion analysis software (KinTrak 7.0; Human Performance Laboratory, Cal-
gary, Canada) was used to compute 30 time-continuous gait variables. The 30 variables
included joint angles, joint moments, and joint angular velocities of the ankle, knee, and hip, as
well as ground reaction forces in all three planes of motion: frontal, sagittal, and transverse
(Table 1). Joint moments and GRFs were normalized to body weight. All variables were resam-
pled to 101 time points representing 0 to 100% of the stance phase.
Comprehensive asymmetry index
The following data-processing steps were conducted for each subject and shoe condition (i.e.
barefoot and shod). First, the mean waveform for each of the 30 variables was calculated based
Table 1. Gait variables.
Segment
Variables (frontal, sagittal, and transverse planes)
Hip joint
Angles [°]
Moments [BWm]
Angular velocities [°s−1]
Knee joint
Angles [°]
Moments [BWm]
Angular velocities [°s−1]
Ankle joint
Angles [°]
Moments [BWm]
Angular velocities [°s−1]
Centre of pressure
Ground reaction forces [BW]
Time-continuous gait variables that were computed over the stance phase for each subject, leg, and shoe
condition. These variable types were used for the comprehensive asymmetry index.
doi:10.1371/journal.pone.0138631.t001
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on the five collected trials. Second, the mean waveform for each variable was divided by the
average of the corresponding standard deviation waveforms. This was done to normalize the
variables to account for asymmetry caused by the natural variability of the movement [13, 18].
Third, all normalized waveforms were vectorized into a 3,030-dimensional (30 variables x 101
time points) row vector, q, by horizontally appending the waveforms. Hence, qleft_leg and
qright_leg incorporated all available information about an individual’s movement during the
stance phase. Finally, a difference vector, Δq = qright_leg—qleft_leg, between the multi-dimen-
sional row vectors of the right and left legs was calculated for each participant and shoe condi-
tion. The difference vector Δq quantified all measured aspects of asymmetry of the
participants’ gait. Therefore, the vector norm of Δq (i.e. the Euclidean distance from the origin
to Δq) may serve as a single CAI of the study participants’ overall gait asymmetry.
However, Δq is a complex high-dimensional (3,030 dimensions) construct. It is possible
that some components of Δq contain artefacts that appear to indicate asymmetry. These arte-
facts are actually the result of random fluctuations of the data due to the natural variability of
the movement. The expected gait asymmetry changes within an individual were rather small
and the signal-to-noise ratio is unfavourable. Relevant changes in the gait pattern and, there-
fore, in gait asymmetry between shoe conditions in one variable have to be interrelated with
changes in the asymmetry of other variables [19]. It was speculated that the use of a PCA
would allow increasing the sensitivity of the CAI to detect small changes in gait asymmetry.
For the PCA, an input matrix M was created containing the difference vector for each individ-
ual with each shoe condition:
M ¼
Dq1
...
Dq30
2
6664
3
7775
ð1Þ
The input matrix contained 3,030 columns (30 variables x 101 time points) and 30 rows (15
subjects x 2 shoe conditions). The PCA comprised the following steps: (1) calculation of the
covariance matrix of M; and (2) calculation of the eigenvectors and eigenvalues of the covari-
ance matrix [21]. The eigenvectors represent the orthogonal principal component vectors (PC-
vectors), p. The PC-vectors are defined by the direction of the highest correlated variance in
the data. Since in the current study the input matrix for the PCA contained the difference vec-
tors (right-left) for each of the individuals, the variance in the matrix and the definition of the
PC-vectors were due to the asymmetry of the individuals’ gait.
The eigenvalue (EV) spectrum was assessed to determine a suitable number k of PC-vectors
for the definition of the CAI. Within the first 15 EVs a drop is visible between EV8 and EV9
(Fig 1). Therefore, the first eight PC-vectors (k = 8) were expected to provide the best compro-
mise between retaining as much correlated asymmetry as possible and filtering out uncorre-
lated noise [16].
The difference vectors Δq were then represented in a subspace spanned by the eight selected
PC-vectors by projecting each difference vector Δq onto the PC-vectors:
Psi ¼ Dqs  pi
ð2Þ
where s indicates the study participants and i represents the number of the PC-vector. A sub-
ject- and condition-speciﬁc CAI was then calculated as the Euclidean distance from the origin
Footwear Decreases Gait Asymmetry during Running
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using the projections (Psi):
CAIs ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
X
k
i¼1
ðPsiÞ
2
v
u
u
t
ð3Þ
Sensitivity analysis and statistics
To assess the sensitivity of the CAI, it was determined whether the difference vectors by them-
selves would be able to confirm the hypothesized difference in gait asymmetry between shod
and barefoot running and how the CAI depended on the number k of PC-vectors used. There-
fore, different variations of the CAI for each individual and shoe condition were calculated: (1)
CAIs without PCA, using the vector norm (i.e. Euclidean distance) of the raw Δq only; (2)
CAIs with PCA, based on all possible numbers of PC-vectors (k = 1. . .30). A paired samples t-
test (p0.05; IBM SPSS Statistics 20, IBM Corporation, USA) was then used to assess the sig-
nificance of the difference between the different mean CAIs for barefoot and shod running.
Relevant asymmetry variables
The relevant asymmetry variables and their correlations were identified by analysing the load-
ings of the eight PC-vectors. The loading magnitude indicates the amount of variance in a vari-
able that is captured by the corresponding PC-vector [22]. Since this variance was caused by
gait asymmetry, variables with higher loadings contributed more to an asymmetrical gait. The
loadings were multiplied with their corresponding EVs to weight the loadings according to the
amount of variance/asymmetry covered by each PC-vector.
Results
The eight PC-vectors that were used for the calculation of the CAI contained 76.4% of the over-
all asymmetry in all gait variables (Fig 1). The subject-specific CAIs for barefoot running ran-
ged from 103.9 to 210.9, whereas the range for shod running was from 48.4 to 212.1 (Fig 2).
Fig 1. Eigenvalue spectrum. Eigenvalue spectrum of the first 15 principal component vectors that
was used to determine the number of principal component vectors for the definition of the
comprehensive asymmetry index (CAI). After the first eight eigenvalues (black bars) a drop can be seen.
Hence, the first eight principal component vectors (k = 8) were used for the definition of the CAI.
doi:10.1371/journal.pone.0138631.g001
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Averaged over all participants the CAI (k = 8) for running barefoot was 155.7 ± 39.5
(mean ± standard deviation) and for running in the shoe condition was 131.2 ± 48.5 (Table 2).
The difference between the two conditions was significant (p = 0.041). Comparing barefoot
and shod running using the CAI calculated as the direct Euclidean distance of the raw Δq to
the origin (i.e. without filtering out uncorrelated asymmetries by the PCA) revealed no signifi-
cant difference (p = 0.067; Table 2). The evaluation of how the CAI depended on the number k
of PC-vectors used for the definition of the CAI showed that k  3 was not sufficient to detect
significant asymmetry differences between barefoot and shod running (Table 2). For 4  k  8
and 12  k  13 the differences between the mean CAIs for barefoot and shod running were
significant.
The relevant asymmetry variables (i.e. variables with the highest PC-vector loadings) were
mainly located in the ankle and knee joint (Fig 3). The frontal knee angle had the highest PC-
vector loading (1.73) followed by the frontal ankle moment (1.50) and the frontal ankle angle
(1.39). The PC-vector loadings showed correlations particularly between the frontal ankle
angle/moment and the frontal knee angle/moment (PC-vector 1, PC-vector 2).
Discussion
The current study had two main outcomes. First, a novel approach to quantify gait asymmetry
was proposed that combined correlated asymmetries in multiple gait variables into one com-
prehensive asymmetry index, the CAI. The sensitivity analysis suggested that considering corre-
lated asymmetries improves the sensitivity for detecting changes in gait asymmetry caused by
external boundary conditions. This would be particularly useful when assessing the progression
of clinical conditions such as cerebral palsy or the progress of rehabilitation treatments. The
proposed method allowed to examining the structure of gait asymmetry by assessing the indi-
vidual loadings of principal component vectors. Again, this has potential for clinical gait analy-
sis and may contribute to a better understanding of the specific manifestations of a patient’s
underlying condition, for example, in stroke and cerebral palsy patients. Second, the result of
the CAI supported the hypothesis that even in healthy, young adults, gait asymmetry is reduced
when running in shoes compared to running barefoot. This suggests that footwear seems to
Fig 2. Subject-specific comprehensive asymmetry index (CAI) for barefoot and shod running. Study
participants are arranged by increasing CAI for barefoot running. All CAIs calculated using eight
principal component vectors (k = 8).
doi:10.1371/journal.pone.0138631.g002
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affect certain aspects of the neuromuscular control system that are involved in the coordination
of the movements of left and right lower limbs.
Comprehensive asymmetry index
The development of the CAI was motivated by the goal to provide a comprehensive asymmetry
index with enhanced sensitivity for changes in gait asymmetry. Considering this main goal and
the way it was implemented led to advantageous and disadvantageous characteristics of the
proposed method, which will be discussed in the following paragraphs.
Since the CAI is a single value representing the totality of gait asymmetry of an individual
(based on the measured variables), it facilitates direct comparisons between individuals with
Table 2. Mean comprehensive asymmetry indexes (CAI) for barefoot and shod running.
k
Mean CAI Barefoot
Mean CAI Shod
p-Value
Δq
177.7 (SD 33.7)
157.9 (SD 39.1)
0.067
1
61.5 (SD 40.1)
68.6 (SD 48.2)
0.302
2
106.3 (SD 46.4)
84.0 (SD 43.5)
0.084
3
123.2 (SD 43.3)
98.6 (SD 41.2)
0.060
4
136.0 (SD 39.3)
104.0 (SD 45.4)
0.020
5
141.4 (SD 40.8)
113.9 (SD 48.6)
0.045
6
147.0 (SD 42.9)
121.4 (SD 48.4)
0.042
7
152.9 (SD 40.7)
126.3 (SD 47.6)
0.031
8
155.7 (SD 39.5)
131.2 (SD 48.5)
0.041
9
157.8 (SD 39.8)
135.1 (SD 46.8)
0.061
10
161.0 (SD 39.2)
136.9 (SD 47.0)
0.052
11
163.3 (SD 38.1)
139.6 (SD 46.2)
0.059
12
165.9 (SD 37.3)
142.0 (SD 43.5)
0.042
13
167.2 (SD 37.2)
144.2 (SD 42.9)
0.050
14
168.4 (SD 37.2)
146.2 (SD 42.8)
0.064
15
169.8 (SD 36.4)
147.4 (SD 42.8)
0.060
16
171.2 (SD 35.2)
148.7 (SD 42.4)
0.058
17
171.8 (SD 35.3)
150.1 (SD 42.6)
0.069
18
172.6 (SD 35.6)
151.0 (SD 42.8)
0.072
19
173.4 (SD 35.9)
151.8 (SD 42.3)
0.073
20
174.2 (SD 35.1)
152.8 (SD 42.1)
0.072
21
174.5 (SD 35.3)
153.7 (SD 42.0)
0.078
22
175.2 (SD 35.4)
154.3 (SD 41.6)
0.075
23
175.4 (SD 35.4)
155.3 (SD 41.1)
0.083
24
176.1 (SD 34.6)
155.7 (SD 40.9)
0.071
25
176.5 (SD 34.5)
156.2 (SD 40.8)
0.071
26
176.8 (SD 34.5)
156.7 (SD 40.4)
0.072
27
177.1 (SD 34.4)
157.1 (SD 40.0)
0.072
28
177.4 (SD 34.1)
157.4 (SD 39.7)
0.069
29
177.6 (SD 33.8)
157.6 (SD 39.5)
0.067
30
177.7 (SD 33.7)
157.9 (SD 39.1)
0.067
Mean comprehensive asymmetry indexes (CAI) and p-values (paired samples t-test) for comparisons
between barefoot and shod running based on different CAIs calculated with the raw difference vector (Δq)
and different numbers of principal component vectors (k = 1. . .30).
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Fig 3. Weighted loadings of the first eight principal component vectors. These eight principal component vectors (PC-vectors) were used to calculate
the comprehensive asymmetry index (CAI). Y-axes indicate the magnitude of the loading. X-axes represent the analysed biomechanical variables:
V-Vertical; ML-Medial lateral; AP-Anterior posterior; GRF-Ground reaction force; CoP-Centre of pressure; S-Sagittal plane; F-Frontal plane; T-Transverse
plane; A-Angle; M-Moment; V-Velocity.
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respect to overall gait asymmetry. The CAI offers no advantage, however, when it is necessary
to quantify gait asymmetries of isolated variables (e.g. sagittal knee joint angle) at a specific
time-point (e.g. at mid stance). In this case, other methods may provide a faster and more pre-
cise assessment of gait asymmetry [15, 23–26]. It is important to realize that CAIs can only be
compared among individuals when they have been calculated using the same variables.
Another limitation of the current method is that it is possible that unique gait asymmetries
present in only one individual may not contribute sufficiently to be represented in the lower
order PC-vectors. Therefore, if this method is applied as a diagnostic tool to quantify asymme-
try in an individual patient, then both the PCA-filtered and direct Euclidean distance-based
CAI should be assessed to ensure that the patient does not exhibit an unusual asymmetry
pattern.
The results of the sensitivity analysis (Table 2) suggested that the PCA acted as a filter sepa-
rating correlated from uncorrelated gait asymmetry variables [16]. Correlated asymmetries are
more likely to contain actual differences in the movement pattern while uncorrelated asymme-
tries are more likely to contain a high proportion of noise [19]. Another advantage of determin-
ing the correlation structure of gait asymmetry using a PCA is that the resultant PC-vector
loadings show the relevant asymmetry variables and their correlations. In fact, investigating
the relevant asymmetry variables and their correlations suggested that the ankle and knee joint
seemed to have the highest importance for the generation and compensation of gait asymmetry
(Fig 3). Gait variables of the hip seemed to be less involved. Determining the relevant asymme-
try variables and their correlation has potential for clinical gait analysis and may contribute to
a better understanding of the specific manifestations of a patient’s underlying condition.
PCA has been used before when investigating gait asymmetry [14, 15, 24]. However, to the
best knowledge of the authors, it has not yet been applied in the all-encompassing form that
was set up in this study.
The CAI was based on data measured with a 3D motion capture system and a force platform
during over-ground running. This experimental setup limits the amount of strides that can be
measured and may also reduce the applicability of the CAI to monitor gait asymmetry in spe-
cific cases (i.e. a laboratory setting is required). Therefore, future studies should investigate the
sensitivity of the CAI to detect gait asymmetry changes using data acquired with wearable sen-
sors (e.g. accelerometers) to increase the amount of data that can be collected and the applica-
bility of the CAI.
Because of the small sample size (15 study participants) and the recruitment of healthy indi-
viduals only, a systematic discussion of CAI values is not possible, and an actual non-patholog-
ical asymmetry range was not identified. Further studies should determine specific
pathological and non-pathological ranges, as well as investigate how limb dominance, gender,
or other external boundary conditions affect the CAI.
Effect of footwear on gait asymmetry
Gait asymmetry in a healthy population has been documented in several studies [14, 15, 27].
Previous research has also reported an impact of footwear on the running kinematics and
kinetics of healthy adults [28–30]. From a purely mechanical perspective, one would expect
that wearing footwear, which may not be manufactured perfectly symmetrical, would either
not affect or increase gait asymmetry. However, as pointed out in the introduction, previous
studies indicated that footwear may improve neuromuscular control of motion. This might
lead to a decrease in gait asymmetry as suggested by Vagenas and Hoshizaki [31] based on a
limited set of isolated kinematic variables of the foot. The findings of the comprehensive analy-
sis of this study support this hypothesis (Table 2).
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Improved motor control mechanisms associated with wearing footwear might be a result of
altered cutaneous sensory information of the plantar or dorsal surface of the feet [32–34]. Two
recent review studies attested to the significance of plantar sensory feedback for the control of
movement and supported the utilization of textured materials for improving perceptual-motor
performance [35, 36].
The magnitude of the effect of footwear on gait asymmetry was subject-dependent (Fig 2).
In fact, a few study participants (3 out of 15) even demonstrated an increase in gait asymmetry
when running in shoes. De Wit et al. [28] reported a subject-depended impact of footwear on
the kinematics and kinetics during running. However, it remains unknown which mechanisms
cause these subject-dependent responses to footwear. One mechanism might be related to sub-
ject-specific sensitivity thresholds of the plantar or dorsal surface of the feet that may influence
the afferent feedback to the neuromuscular control system [33].
Conclusion
Footwear seems to reduce gait asymmetry during running in healthy, young individuals.
Changes in the afferent sensory feedback to the neuromuscular control system may be a possi-
ble explanation for this observation.
Supporting Information
S1 File. Supplementary Data. Subject demographics, eigenvalue spectrum, subject-specific
comprehensive asymmetry index (CAI) for barefoot and shod running calculated using the
raw Δq and different numbers of principal component vectors (k = 1. . .30), and weighted load-
ings of the first eight principal component vectors.
(XLSX)
Acknowledgments
The authors wish to thank María Fernanda Frías for data acquisition and Beatrix Vereijken for
helpful feedback on the manuscript.
Author Contributions
Conceived and designed the experiments: BMN. Performed the experiments: JB BMN. Ana-
lyzed the data: SH PAF CM. Contributed reagents/materials/analysis tools: SH PAF CM.
Wrote the paper: SH PAF CM JB BMN.
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Footwear Decreases Gait Asymmetry during Running.
Hoerzer, Stefan,Federolf, Peter A,Maurer, Christian,Baltich, Jennifer,Nigg, Benno M