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The walking speed-dependency
of gait variability in bilateral
vestibulopathy and its association
with clinical tests of vestibular
function
Christopher McCrum
1,2*, Florence Lucieer3, Raymond van de Berg3,4, Paul Willems1,
Angélica Pérez Fornos
5, Nils Guinand5, Kiros Karamanidis6, Herman Kingma
3,4 &
Kenneth Meijer
1
Understanding balance and gait decits in vestibulopathy may help improve clinical care and our
knowledge of the vestibular contributions to balance. Here, we examined walking speed eects on
gait variability in healthy adults and in adults with bilateral vestibulopathy (BVP). Forty-four people
with BVP, 12 healthy young adults and 12 healthy older adults walked at 0.4 m/s to 1.6 m/s in 0.2 m/s
increments on a dual belt, instrumented treadmill. Using motion capture and kinematic data, the means
and coecients of variation for step length, time, width and double support time were calculated. The
BVP group also completed a video head impulse test and examinations of ocular and cervical vestibular
evoked myogenic potentials and dynamic visual acuity. Walking speed signicantly aected all gait
parameters. Step length variability at slower speeds and step width variability at faster speeds were
the most distinguishing parameters between the healthy participants and people with BVP, and among
people with BVP with dierent locomotor capacities. Step width variability, specically, indicated an
apparent persistent importance of vestibular function at increasing speeds. Gait variability was not
associated with the clinical vestibular tests. Our results indicate that gait variability at multiple walking
speeds has potential as an assessment tool for vestibular interventions.
Since the chance observation of a dog with acute unilateral vestibulopathy who demonstrated less imbalance
during running than during walking1, the interactions of gait velocity, imbalance and vestibular symptoms in
people with vestibulopathy have become a topic of great interest. Inspired by the observation in the dog, Brandt,
et al.1 demonstrated with a simple setup that humans with acute unilateral vestibulopathy could run with less
deviation to the aected side than while walking. Since then, reductions in temporal gait variability and reduc-
tions in stride length variability in bilateral vestibulopathy (BVP) during faster, compared to slower walking have
been observed2–4. BVP, a severe bilateral reduction of vestibular function that results in severe balance decits
and an increased fall risk5–10, was recently dened by the Bárány Society11. Interestingly, the same studies revealed
that patients with BVP do not self-select walking speeds that minimize temporal or spatial gait variability2–4,
suggesting that these are not the only source of instability or ineciency with which people with BVP must cope.
However, further research into the relationships between vestibulopathy, walking speed and gait variability is
needed to conrm and expand on these previous ndings, as these three previous studies had some potential
1Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in
Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands. 2Institute of Movement and Sport
Gerontology, German Sport University Cologne, Cologne, Germany. 3Division of Balance Disorders, Department of
Otolaryngology, Head and Neck Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.
4Faculty of Physics, Tomsk State University, Tomsk, Russian Federation. 5Service of Otorhinolaryngology and Head
and Neck Surgery, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland. 6Sport
and Exercise Science Research Centre, School of Applied Sciences, London South Bank University, London, UK.
*email: chris.mccrum@maastrichtuniversity.nl
OPEN
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drawbacks, namely a limited number of gait parameters being analysed2, too few strides12–15 for a robust analysis
of gait variability2,3, the use of only preferred walking speeds or percentages of preferred walking speeds (ecolog-
ically valid, but less control over inuencing factors)2–4, small sample size4, lack of a healthy control group3,4 and
the presence of sham vestibular stimulation in the control condition4. e study of balance and gait decits in
BVP is both important for improving clinical care and for objective quantication of the eects of novel inter-
ventions, such as vestibular implants16,17. Furthermore, it is fundamental to our understanding of the vestibular
contributions to gait and balance control.
e sensory contributions to gait appear to depend on walking speed, which may partly explain the above
described ndings and will aect walking speed selection in people with vestibulopathy. Visual perturbations
such as distorting prisms or closed eyes have reducing impact on most gait variability parameters as one walks
faster18,19 with the exception of step width variability, which appears to increase with visual perturbation at faster
walking speeds19. Similarly, vestibular perturbations via galvanic vestibular stimulation have less impact on gait
direction and variability at higher speeds20,21. Additionally, the vestibular inuence on lower limb muscles (deter-
mined by examining vestibulo-muscular coupling via lower limb muscle electromyography during vestibular
stimulation) is selectively suppressed with increased cadence and speed during walking22,23, purported to be
related to a shi in the control mechanisms of mediolateral stability with increasing walking speeds from active
stabilization at the lower limb joints during the stance phase to foot placement22,24. Despite selective suppression
of the vestibular inuence on some lower limb muscles at faster walking speeds, signicant increases in frontal
spatial variability with increasing walking speeds have been reported in BVP4, suggesting that vestibular informa-
tion remains important for mediolateral stability during gait at faster speeds.
To further investigate the walking speed dependency of gait variability in vestibulopathy, we analyzed the gait
of people with BVP and of healthy control participants. We aimed to determine the eects of systematic increases
in walking speed on spatiotemporal gait parameters and their variability in these participant groups. Secondly,
we aimed to assess if these parameters would dierentiate between healthy participants, and participants with
BVP who could and could not complete all of the planned walking speed trials. We hypothesized that, for all par-
ticipants, step and double support time and step length variability would systematically reduce with increases in
walking speed, whereas step width variability would systematically increase, in agreement with previous work2–4.
We further postulated that, based on earlier studies and despite their limitations described above2,3, step and dou-
ble support time and step length variability at slower walking speeds would be most distinguishing between the
healthy control participants and patients with BVP, and also between the patients with BVP that could completely
and only partly complete the measurement protocol, whereas step width variability would be most distinguishing
at faster walking speeds, based on one study showing an increase in BVP4. Additionally, we conducted an explor-
ative analysis in the patient groups examining correlations between the outcomes of the most distinguishing gait
parameters identied and clinical vestibular tests conducted as part of a larger clinical study (video head impulse
test [vHIT], ocular and cervical vestibular evoked myogenic potentials [oVEMP and cVEMP] and dynamic visual
acuity [DVA]) that are indicative of vestibular functional integrity and commonly used in clinical settings, with
the aim to explore if these tests could give an indication about gait-related function in BVP.
Methods
Participants. Forty-four people with BVP participated in this study (22 males, 22 females; age: 57.6 ± 11.5
years, age range: 21 to 74; height: 174.5 ± 9.7 cm; weight: 80.4 ± 17 kg). Inclusion criteria were a prior diagnosis
of bilateral vestibular hypofunction at the Maastricht University Medical Centre+ (imbalance and/or oscillopsia
during locomotion and summated slow phase mean peak velocity of the nystagmus of less than 20°/s during
bithermal caloric tests) and the self-reported ability to walk independently without assistance. Please note that
this study began prior to the publication of the Bárány Society guidelines11, which are slightly dierent. Potential
participants were not included if they were unable or unwilling to stop taking anxiety or depression medication
for the week before the measurements. In addition, two healthy control groups comprised of 12 healthy younger
adults (Young; 5 males, 7 females; 25.1 ± 2.8 years; 174.9 ± 7.3 cm; 72.6 ± 13.5 kg) and 12 healthy older adults
(Older; 8 males, 4 females; 71.5 ± 4.8 years; 171.5 ± 9.1 cm; 79.5 ± 11.8 kg) with no history of balance or gait dif-
culties and no history of dizziness participated in this study. ese specic groups were included to account for
the age range in the BVP group and to provide an estimation of the eect of ageing alone on the outcome param-
eters. e study was explained before obtaining written informed consent, was conducted in accordance with the
Declaration of Helsinki and was approved by the Maastricht University Medical Centre medical ethics committee
(gait measurements: NL58205.068.16; vestibular tests: NL52768.068.15).
Gait analysis setup, data processing and procedure. e gait measurements were conducted using
the Computer Assisted Rehabilitation Environment Extended (CAREN; Motekforce Link, Amsterdam, e
Netherlands), which includes a dual-belt force plate-instrumented treadmill (Motekforce Link, Amsterdam, e
Netherlands; 1000 Hz), a 12 camera motion capture system (100 Hz; Vicon Motion Systems, Oxford, UK) and a
virtual environment (city-style street with passing objects and structures) projected onto a 180 degrees curved
screen (note that our intention was to provide optic ow for all participants, but aer the rst few measurements
with the BVP group, it became clear that optic ow should be turned o for this group to prevent dizziness and
nausea. e implications for this on the results are discussed in the limitations section). For all measurement
sessions, a safety harness connected to an overhead frame was used. At the request of some of the participants
with BVP, a handrail was also positioned on the treadmill, the use of which was monitored and recorded. Six
retroreective markers were attached to anatomical landmarks (C7, sacrum, le and right trochanter and le
and right hallux) and were tracked by the motion capture system. Marker tracks were ltered using a low pass
second order Butterworth lter (zero-phase) with a 12 Hz cut-o frequency. Foot touchdown was determined
using combined force plate (50 N threshold) and foot marker data25. is combined method was used to be able to
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accurately account for foot touchdowns and toe-os occurring in the center of the treadmill triggering both force
plates simultaneously. For these steps, the foot marker method was used and then corrected based on the average
discrepancy between the force plate method and the marker method timing for all steps that contacted only one
force plate. e spatiotemporal gait parameters of interest were step length (anteroposterior distance between the
hallux markers at foot touchdown), step time (time from touchdown of one foot to touchdown of the next foot),
step width (mediolateral distance between the hallux markers at foot touchdown) and double support time (time
spent with both feet on the ground). Means, standard deviations and coecients of variation (CV) were deter-
mined for each speed for each participant.
Each session began with walking familiarization trials at 0.4 m/s up to 1.6 m/s in 0.2 m/s increments. At least
60 s were used for each speed, and further time was provided to familiarize to each speed if deemed necessary by
either the participant, the CAREN operator or the research clinician. At the end of each speed trial, the decision
to continue to the next (faster) speed was made in a similar manner. If the participant was not comfortable pro-
gressing to the next speed or if the CAREN operator or research clinician did not think it was safe or feasible to
progress, then the participant continued at the current speed instead. Participants were then given sucient rest
before continuing with the measurements. Single two-to-three-minute-long measurements (to ensure a mini-
mum of 60 strides per speed) were then conducted at each prescribed speed that was completed during familiar-
ization. Multiple set walking speeds were used as opposed to the majority of previous studies which have used
either percentages of preferred walking speeds or self-perceived slow, normal and fast walking speeds, in order to
have more control over the walking speed condition.
Clinical vestibular function tests setup and procedures. Following a sucient rest period that was deter-
mined on an individual basis, the BVP group proceeded with the clinical vestibular testing battery. Between each
test, sucient rest was provided based on feedback from the patient and the judgement of the clinical researcher.
The vHIT was performed with the EyeSeeCam system (EyeSeeCam VOG; Munich, Germany) and the ICS
Impulse system (GN Otometrics A/S, Denmark) to test semicircular canal function and determine the gain of the
vestibulo-ocular reex (VOR). Both systems measured the movement of the right eye. e distance of the back of the
static chair was 2 meters to the point of xation. e point of xation consisted of a green dot on the wall, produced
by a laser on a tripod. If necessary, adhesive plasters were used to li the upper eyelid a little to secure the visibility of
the pupil for the camera in all directions. Goggle movement was minimized by adjusting the strap of the goggles to
every subject. e vHIT system was calibrated according to the protocol of the system. Aer calibration, the subject
was instructed to not touch their head including the goggles. e examiner stood behind the participant with two
hands rmly on top of the participant’s head without touching the strap of the goggles. e examiner then applied
head impulses in six dierent movements to test each canal26. e horizontal head impulses comprised a peak veloc-
ity of >150°/s and the vertical head impulses a peak head velocity of >100°/s. e amplitude of the movements was
10–20°. Only outward impulses were used27. e vHIT was dened as abnormal if the VOR gain was below 0,7 and/
or if covert saccades were observed in 50% or more of the traces26,28.
DVA, which is used to assess gaze stabilization via the VOR during gait-related head movements, was assessed on
a regular treadmill (1210 model, SportsArt, Inc., Tainan, Taiwan, China.) with the participant positioned 2.8 meters
from a computer screen. Firstly, the static visual acuity was determined during stance, followed by the assessment of
the DVA during walking at 2, 4 and 6 km/h. One letter at a time was randomly displayed on the screen from a chart
of Sloan letters (CDHKNORSVZ)29. Starting at a logMAR (log of the Minimum Angle of Resolution30); of 1.0, ve
random letters were shown at each logMAR (decreasing in steps of 0.1 logMAR). When four out of ve letters were
correctly identied, the corresponding logMAR was considered achieved. e outcome of the DVA was the dier-
ence between the static logMAR and the logMAR for each of the three walking speeds. e result was omitted if the
subject needed a handrail to walk at that speed or if it wasn’t possible to walk at that speed at all31.
cVEMP and oVEMP were assessed with the Neuro-Audio system (v2010, Neuroso, Ivanovo, Russia) in order
to determine the function of the otolith organs (saccule and utricle, respectively) and their corresponding nerves. A
monaural stimulation with in-ear earphones was used with air conduction tone bursts at 500 Hz and a stimulation
rate of 13 Hz using a blackman window function with a two-cycle rise/fall and no plateau phase. Tone bursts of max-
imum 130 dB sound pressure level (SPL) were used. A stepwise approach was used to determine the threshold with
a precision of 5 dB SPL32. Positive (P1) and negative (N1) peaks in the recorded biphasic waveform were marked for
both cVEMPs and oVEMPs. e thresholds were determined as the lowest stimulus intensities to elicit recognizable
peaks. If it wasn’t possible to nd a VEMP response, it was dened as a threshold of >130 dB SPL. For the cVEMP,
the participant was positioned lying down with the back positioned at a 30° angle above the horizontal plane and was
asked to turn their head towards the non-measured side and li their head during the measurement. e cVEMP
was recorded at the ipsilateral sternocleidomastoid muscle. Two electrodes were placed on the sternocleidomastoid
muscles, the reference electrode on the sternum, and the earth electrode on the forehead. Electrode impedances
of 5 kΩ or lower were accepted and otherwise the electrode was replaced. To ensure correct muscle contraction, a
feedback system using a screen was provided. An average of 200 EMG traces with a minimum mean rectied voltage
(MRV) of 65µV and a maximum MRV of 205µV was accepted33,34. e oVEMP was recorded at the contralateral
inferior oblique muscle. Five electrodes were used: the recording electrodes beneath the eyelid, just lateral of the
pupil when gazing forward and centrally, the reference electrodes beneath the recording electrode and the earth
electrode on the forehead. e participant was asked to keep their gaze at a focus point placed at a 30 degrees angle
behind the head. An average of at least 300 EMG traces was accepted35–37.
Statistics. From the 44 participants with BVP that started the study, 38 participants were able to complete
at least the three slowest walking speeds without assistance (group hereaer referred to as BVP) and these par-
ticipants’ data were taken for the comparison with the healthy groups. For the within BVP comparisons, three
groups were formed. One group was able to complete all of the gait measurements without assistance (BVP All
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Gait; n = 26), the second was only able to complete some of the speeds without assistance (BVP Part Gait; n = 12;
all of this group were able to complete the measurements at least up to 0.8 m/s) and the nal group (BVP No Gait;
n = 6) did not start the recorded gait trials (see “Results” for details on this group).
To investigate the walking speed eects on gait and this eect’s potential interaction with vestibular function,
mixed-eects models using the restricted maximum likelihood method with the xed eects walking speed,
participant group, and speed by group interaction were conducted for the means and CVs of step time and length,
step width and double support time. To further investigate the potential of gait variability to distinguish between
BVP groups, mixed-eects models as described above were applied with groups BVP All Gait and BVP Part Gait
to the CV of all four gait parameters across all speeds that included data points from each group. Bonferroni post
hoc comparisons were performed to assess the group dierences within speeds for each of the gait parameters.
e vHIT testing revealed abnormal canal function in all or most directions for almost all of the participants
with BVP (i.e. exceptions were two participants with BVP who had only one abnormal result out of six). As almost
all outcomes were abnormal and there was no possibility to distinguish between groups, analysis of the vHIT
results in relation to gait was not taken further. For all completed DVA trials with a logMAR change value during
the three walking speeds compared to standing and when oVEMP or cVEMP thresholds were detected, these val-
ues were grouped and Pearson correlations with the gait parameters that showed highest variability and/or distin-
guished between BVP groups were conducted (see Results). Age, height, weight and body mass index (BMI) were
compared across the participant groups BVP, Young and Older, and within the three BVP groups (BVP All Gait,
BVP Part Gait, BVP No Gait) using one-way ANOVAs with Bonferroni corrections for multiple comparisons.
Results
Twenty-six participants with BVP were able to complete all of the gait measurements without assistance (BVP All
Gait). Twelve participants with BVP were only able to complete some of the speeds (BVP Part Gait), of which one
participant stopped aer 0.8 m/s, one aer 1.0 m/s, four aer 1.2 m/s and six aer 1.4 m/s. Six participants with
BVP were assigned to the BVP No Gait group for the following reasons: one participant became dizzy and nause-
ated during familiarization and could not continue; three participants were not able to walk during familiarization
without handrail support; two participants found treadmill walking too challenging and could not continue. e
demographic data of these three groups, as well as the healthy control group can be found in Table1. e one-way
ANOVAs revealed a signicant group eect (BVP, Young, Older) for age (F (2,59) = 88), P < 0.0001), with age signif-
icantly diering between each of the groups (P < 0.0001). Height, weight and BMI did not signicantly dier across
these groups. No signicant dierences in demographics were found with the three BVP groups.
e mixed-eects models with walking speed (0.4 to 1.6 m/s) and group (BVP, Young, Older) as factors
revealed signicant walking speed eects for the means and CV of step time and length, step width and double
support time (P ≤ 0.0003), signicant group eects for all parameters except step width means (P ≤ 0.0151) and
signicant walking speed by group interactions for the means of step time, double support time and step width
(P ≤ 0.0053) and the CV of step width (P < 0.0001). e mixed-eects model results and summary of the between
group Bonferroni comparisons are displayed in Fig.1 (means) and Fig.2 (CVs), and the full Bonferroni compar-
ison results are available in Supplementary Tables1 and 2.
e mixed-eects models with walking speed (0.4 to 1.4 m/s) and group (BVP All Gait and BVP Part Gait)
as factors revealed signicant walking speed eects for the CV of all parameters (P < 0.0001). Signicant group
eects were found for the CV of step time, step length and double support time (P ≤ 0.0162) and a signicant
walking speed by group interaction was found for the CV of double support time (P = 0.0172). e mixed-eects
model results and summary of the between group Bonferroni comparisons are displayed in Fig.3 and the full
Bonferroni comparison results are available in Supplementary Table3.
When cVEMP and oVEMP thresholds were detected, and when a speed of the DVA was completed, these
values were taken and Pearson correlations were conducted with the CVs of step time, step length and double
support time at 0.4 m/s and the CV of step width at 1.6 m/s, being the speeds with the highest variability in those
parameters from the previous analysis. ese results can be seen in Table2. Only two signicant correlation of 28
were found (change in logMAR scores during the DVA with Double Support CV at 6 km/h and oVEMP Le and
Step Length CV at 0.4 m/s; Table2).
Post-hoc analysis of gait data based on VEMP results. In order to further investigate dierences
within the patient group, we conducted an analysis of the gait data of the participants with and without at least
one detected VEMP threshold for the same four parameters as the correlations: the CVs of step time, step length
and double support time at 0.4 m/s and the CV of step width at 1.6 m/s. Given that all of the participants with no
nAge (y) Height (cm) Weight (kg) Body Mass Index
Young 12 (7 female) 25.1 ± 2.8*174.9 ± 7.3 72.6 ± 13.5 23.6 ± 2.8
Older 12 (4 female) 71.5 ± 4.8*171.5 ± 9.1 79.5 ± 11.8 26.9 ± 2.2
BVP 38 (20 female) 56.1 ± 11*174.6 ± 10.1 80.2 ± 17.6 26.1 ± 4.2
BVP All Gait 26 (10 female) 55.1 ± 11.4 176.8 ± 9.9 80.3 ± 17.8 25.4 ± 3.8
BVP Part Gait 12 (10 female) 59.2 ± 9 169.7 ± 9 79.9 ± 18 27.6 ± 4.7
BVP No Gait 6 (2 female) 65.3 ± 13.6 174 ± 6.9 82.4 ± 13.4 27.2 ± 3.8
Table 1. Participant Group Characteristics. Values are means ± SD. *Signicantly dierent from each other
(P < 0.0001).
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VEMP threshold detected also had abnormal outcomes on the vHIT for most or all of the six directions tested,
the purpose of this analysis was to compare the gait of participants with and without detectable canal and otolith
function. Independent samples t-tests with Welch’s corrections did not reveal any signicant dierences between
the participants with and without at least one detectable VEMP threshold (0.0965 < P < 0.746).
0.0
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Step Time [s]
BVP
Young
Older
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
Double SupportTime[s]
0.40.6 0.81.0 1.21.4 1.6
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Walking Speed [m/s]
Step Width[m]
0.0
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Step Length [m]
SpeedP<0.0001F (1.364, 75.46) = 564.4
GroupP=0.0012F (2, 59) = 7.564
Interaction P<0.0001F (12, 332) = 5.744
SpeedP<0.0001F (2.127, 117.7) = 2460
GroupP=0.0008F (2, 59) = 8.039
Interaction P=0.5116F (12, 332) = 0.9353
SpeedP<0.0001F (1.327, 73.43) = 743.4
GroupP=0.0151F (2, 59) = 4.503
Interaction P<0.0001F (12, 332) = 7.047
SpeedP=0.0003F (2.338, 129.4) = 7.909
GroupP=0.3964F (2, 59) = 0.9400
Interaction P=0.0053F (12, 332) = 2.410
Figure 1. Boxplots of the median, interquartile range and 5th and 95th percentile of the means of step time,
step length, double support time and step width across all conducted walking speeds in BVP, Young and Older
participant groups. e black horizontal lines indicate signicant between group dierences for the indicated
speed (P < 0.05, Bonferroni adjusted).
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Discussion
We aimed to determine the eects of systematic increases in walking speed on spatiotemporal gait parameters and
their variability in people with BVP. We investigated if these parameters would distinguish between healthy par-
ticipants and participants with BVP, and between patients with BVP who could and could not complete all of the
planned walking speed trials (a simple proxy of locomotor capacity). Our hypothesis, that step and double sup-
port time and step length variability would systematically reduce with increases in walking speed, whereas step
0
2
4
6
8
10
12
Step Time CV [%]
BVP
Young
Older
0
4
8
12
16
Step Length CV [%]
0
10
20
30
40
Double SupportTimeCV[%]
0.40.6 0.81.0 1.21.4 1.6
0
20
40
60
80
Walking Speed [m/s]
Step WidthCV[%]
Speed P<0.0001 F (2.323, 128.5) = 141.2
GroupP=0.0122 F (2, 59) = 4.755
InteractionP=0.7565F (12, 332) = 0.6949
Speed P<0.0001F (2.531, 140.0) = 162.1
GroupP=0.0007F (2, 59) = 8.316
Interaction P=0.3897 F (12, 332) = 1.064
Speed P<0.0001 F (3.321, 183.7) = 42.59
GroupP=0.0033 F (2, 59) = 6.305
InteractionP=0.8176F (12, 332) = 0.6288
Speed P<0.0001 F (3.039, 168.2) = 22.06
GroupP=0.0011 F (2, 59) = 7.718
Interaction P<0.0001 F (12, 332) = 7.006
Figure 2. Boxplots of the median, interquartile range and 5th and 95th percentile of the coecients of variation
(CV) of step time, step length, double support time and step width across all conducted walking speeds in BVP,
Young and Older participant groups. e black horizontal lines indicate signicant between group dierences
for the indicated speed (P < 0.05, Bonferroni adjusted).
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width variability would systematically increase, was conrmed as signicant walking speed eects were found for
all gait variability parameters. We additionally hypothesized that step and double support time and step length
variability at slower walking speeds would be most distinguishing between the healthy control participants and
patients with BVP, and also between the patients with BVP that could completely and only partially complete
the measurement protocol, whereas step width variability would be most distinguishing between these groups at
faster walking speeds. is hypothesis was partly conrmed; step length CV diered between groups BVP and
0
2
4
6
8
10
12
Step Time CV [%]
BVP All Gait
BVP Part Ga
it
0
5
10
15
20
Step Length CV [%]
0
10
20
30
40
Double SupportTimeCV[%]
0.40.6 0.81.0 1.
21
.4
0
20
40
60
80
Walking Speed [m/s]
Step WidthCV[%]
Speed P<0.0001 F (2.474, 84.61) = 113.0
GroupP=0.0162 F (1, 36) = 6.358
InteractionP=0.0663F (5, 171) = 2.112
Speed P<0.0001 F (2.963, 101.3) = 116.3
GroupP=0.0037 F (1, 36) = 9.625
InteractionP=0.0172 F (5, 171) = 2.840
Speed P<0.0001 F (3.010, 102.9) = 32.73
GroupP=0.0073 F (1, 36) = 8.078
InteractionP=0.3285F (5, 171) = 1.165
SpeedP<0.0001F (1.528, 52.25) = 43.54
Group P=0.5427 F (1, 36) = 0.3778
Interaction P=0.0 523F (5, 171) = 2.242
Figure 3. Boxplots of the median, interquartile range and 5th and 95th percentile of the coecients of variation
(CV) of step time, step length, double support time and step width across all walking speeds with data from
participant groups BVP All Gait and BVP Part Gait. e black horizontal lines indicate signicant between
group dierences for the indicated speed (P < 0.05, Bonferroni adjusted).
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Young and between groups BVP All Gait and BVP Part Gait, double support time CV diered between groups
BVP and Young and step width CV diered between groups BVP and Young and BVP and Older for step width
variability, but other parameters did not signicantly dier at the pairwise comparison level, despite the group
eects found for all parameters except step width CV in the BVP All Gait vs. BVP Part Gait analysis.
Regarding our explorative analysis in the patient groups examining correlations between the outcomes of four
clinical vestibular tests (vHIT, oVEMP, cVEMP, DVA) and the most distinguishing gait parameters identied, only
one signicant correlation between the change in logMAR scores during the DVA and the gait parameters were
found (6 km/h and Double Support CV; Table2). One signicant correlation of 16 was found between the VEMP
thresholds and the gait parameters, but only nine pairs of data were included in this test and if a Bonferroni correc-
tion is made for the p values of these 16 tests, it is no longer signicant (oVEMP Le and Step Length CV at 0.4 m/s;
Table2). Similarly, the one signicant correlation between a DVA parameter and gait variability (DVA 6 km/h and
Double support time CV 0.4 m/s) does not meet the signicance threshold if a Bonferroni correction for the 12 tests
is made. Even though this study clearly demonstrates the signicant contribution of vestibular function to gait, our
exploratory analysis conrms the complex contribution of vestibular information during every-day activities and the
diculty in translating current objective clinical measures to highly relevant patient symptoms.
Determining meaningful and distinguishing gait parameters in BVP is vital for the development of interventions,
as is using tasks that suciently replicate the day-to-day challenges of these patients, to determine candidates for inter-
vention and to assess the eect of those interventions. Two promising interventions currently under development and
investigation include noisy galvanic vestibular stimulation (nGVS) and vestibular implants16,17,38–40. Discussions of
these treatment options can be found elsewhere16,38, but it is important to note that both show early signs of utility for
improving gait in BVP4,41. However, it remains to be seen if improvement due to nGVS or a vestibular implant in steady
state gait would likewise be seen in more dynamic locomotor task performance, where even unilateral vestibulopathy
leads to signicantly poorer stability performance42. It should be noted that while this study examined spatiotemporal
variability, dierences in dynamic gait stability were not directly assessed and the two are not necessarily equivalent43–45.
e parameters presented here represent the amount of variability in the gait parameters, but do not necessarily indicate
the overall stability of the participants. erefore, future work should investigate how dynamic gait stability is altered
in BVP and how this is aected by changes in walking speed. Additionally, we suggest that quantication of vestibu-
lospinal reexes and reex gains associated with gait stability control in BVP should be conducted, in order to better
understand the underlying mechanisms of changes in gait stability in vestibulopathy.
Step Time CV 0.4 m/s Step Length CV 0.4 m/s Double Support Time CV 0.4 m/s Step Width CV 1.6 m/s
cVEMP
Right
r 0.08987 0.3259 0.2576 −0.3501
95% CI −0.3935 to 0.5343 −0.1662 to 0.6881 −0.2379 to 0.6467 −0.7554 to 0.2489
P (two-tailed) 0.7229 0.1868 0.302 0.241
n 18 18 18 13
cVEMP
Le
r−0.2425 0.1195 −0.1732 −0.5043
95% CI −0.659 to 0.2878 −0.3999 to 0.5808 −0.616 to 0.3528 −0.8362 to 0.09795
P (two-tailed) 0.3655 0.6595 0.5212 0.0945
n 16 16 16 12
oVEMP
Right
r 0.4653 0.561 0.286 0.4649
95% CI −0.7074 to 0.9554 −0.6361 to 0.9654 −0.7975 to 0.9329 −0.7076 to 0.9553
P (two-tailed) 0.4297 0.3251 0.6408 0.4301
n 5 5 5 5
oVEMP
Le
r−0.04995 0.7914 0.08001 −0.3605
95% CI −0.6911 to 0.6352 0.2684 to 0.9541 −0.6169 to 0.7066 −0.8494 to 0.4614
P (two-tailed) 0.8985 0.0111 0.8379 0.3803
n 9 9 9 8
DVA
2 km/h
r−0.1244 0.01669 −0.2151 −0.09623
95% CI −0.4271 to 0.2034 −0.3046 to 0.3346 −0.5004 to 0.1123 −0.4662 to 0.3024
P (two-tailed) 0.4568 0.9208 0.1947 0.6401
n 38 38 38 26
DVA
4 km/h
r 0.06088 −0.1711 0.03413 0.2422
95% CI −0.2639 to 0.3733 −0.4654 to 0.1572 −0.2887 to 0.35 −0.1602 to 0.5756
P (two-tailed) 0.7166 0.3043 0.8388 0.2332
n 38 38 38 26
DVA
6 km/h
r−0.3145 −0.3199 −0.4338 −0.06129
95% CI −0.6371 to 0.1018 −0.6406 to 0.09588 −0.7125 to −0.0369 −0.4803 to 0.3805
P (two-tailed) 0.1345 0.1275 0.0342 0.7918
n 24 24 24 21
Table 2. Pearson correlations between the cVEMP and oVEMP thresholds, the change in logMAR scores
during each of the three DVA walking speeds and the gait parameters.
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e current study conrmed previous ndings of reductions in temporal gait variability and reductions in
sagittal plane spatial gait variability in vestibulopathy during faster, compared to slower walking2–4. We extend
these previous ndings as the current study employed xed (not self-selected) speeds that were systematically
increased, with 120 steps analyzed per speed, thereby improving the reliability of the outcomes. Our ndings also
align with the growing body of literature indicating a shi from sensory feedback-driven balance control to an
increasingly feedforward control with increasing locomotor speed1,18,22,23,46, but suggest that this may not apply,
at least not to the same extent, for mediolateral balance control during gait which may continue to require active
control of foot placement in the mediolateral plane. Importantly, the current results further the previous ndings
by additionally showing that these parameters are related to the locomotor capacities of people with BVP.
We conrmed previously reported increases in step width variability with increasing walking speeds in people
with BVP4. Previous studies have shown that vestibular perturbations have less impact on direction and variabil-
ity at higher walking speeds20,21 and that the vestibular inuence on lower limb muscles is selectively suppressed
with increased cadence and speed during walking22,23. However, the current step width variability results, com-
bined with those ofWuehr, et al.4 suggest that vestibular information remains important for mediolateral foot
placement at increased walking speeds. During the swing phase when foot placement is coordinated and deter-
mined, there is reduced proprioceptive input due to only one foot being in contact with the ground. erefore,
we could reason that vestibular input becomes more important in this phase, and disturbed or lacking vestibular
input may decrease foot placement accuracy. ese results also provide some explanation as to why people with
BVP do not self-select walking speeds that minimize temporal or sagittal plane spatial gait variability2–4. Dramatic
increases in step width variability may be undesirable due to reduced stability control or increased energetic costs
of mediolateral stabilization47–49. Based on the current results, either reason is plausible, as some participants in
the BVP Part Gait group did not continue to the faster speeds due to instability, while others could not continue
due to being unable to keep up with the speed of the treadmill (implying an energetic or physiological limitation,
not a stability-related one). e vestibular inuence on gait economy has not yet been thoroughly investigated.
e healthy control groups in this study were not directly age matched with the BVP group, but rather repre-
sent healthy participants at the younger and older end of the age range of the BVP group. Variability in step time,
double support time and step length of the older group tended to fall between that of the younger and BVP group,
showing few statistical dierences to either (probably due to a lack of statistical power at the pairwise comparison
level). e boxplots seem to indicate that the group Older tend towards the results of group Young for double sup-
port time and step length variability. In order to further investigate this issue, we calculated the Cohen’s d eect
sizes for each group comparison (Young vs. Older, Young vs. BVP and Older vs. BVP) and averaged these across
the walking speeds for Step Time CV (0.65, 0.98, 0.39), Step Length CV (0.95, 1.31, 0.60), Double Support Time
CV (0.58, 1.00, 0.50) and Step Width CV (0.21, 1.04, 0.84). ese eect sizes conrm that the largest dierences
were always between the Young and BVP groups, but that the dierences between the Older and BVP groups were
also always moderate to large, even if not statistically signicant, indicating that while age may have been a factor
in the Young-BVP comparisons, it certainly does not explain the dierences found. However, the group dier-
ence in step width variability appear to be more robust, with large signicant dierences between the BVP group
and each healthy group, and no dierence due to healthy ageing alone, in agreement with previous studies50,51.
However, other limitations should be kept in mind. Firstly, we did not perform any tests of somatosensory func-
tion in the older adult group, and while we think our inclusions criteria “no history of balance or gait diculties
and no history of dizziness” probably deemed somatosensory dysfunction unlikely, it cannot be entirely ruled out.
We did however perform the DVA and vHIT tests with nine and eight out of 12 older participants, respectively,
which revealed normal function (due to equipment issues, the remaining older adults were not assessed on these
tests). Regarding the gait results, we caution comparing the CV of step width to studies of overground walking, as
it has been shown in healthy participants that walking on the CAREN results in increased step width variability
compared to overground walking52. Additionally, the use of a safety harness may result in small dierences to
unconstrained overground gait53. Furthermore, treadmill walking appears to be more challenging for people with
BVP, evidenced by the fact that the BVP No Gait group were not able to successfully complete the familiarization
period, despite reporting being able to walk independently without assistance. We would therefore caution a
direct comparison of treadmill-derived gait results with overground gait results in BVP. It should also be noted
that the walking speeds used in the current study were not randomized, but progressed from slow to fast, and this
could have led to an order eect. We hope that this was minimized by our familiarization protocol, but it cannot
be ruled out. is should not have aected our comparisons, however, as all participants followed the same pro-
tocol. Minor fatigue may have occurred during the assessments, but this should have been minimized as the par-
ticipants were monitored and breaks were provided when necessary. Regarding the fact that the healthy groups
walked with optic ow and the BVP group walked with the virtual environment xed (so as to provide the same
lighting), we do not expect that this dierence would have altered our results, as two previous studies found no,
or negligible, dierences in the parameters assessed here between xed speed walking with and without virtual
reality54,55. e only previous study that did nd dierences in gait variability due to virtual reality that we are
aware of is that of Hollman, et al.56. However, Hollman, et al.56 used an insucient number of data points to reli-
ably assess gait variability55 and used a substantially dierent virtual reality setup to the current study. Finally, the
eect sizes of the dierence in step width variability with and without virtual reality and optic ow from Hollman,
et al.56 are much smaller than those found in the current study between Young and BVP All Gait groups at similar
walking speeds (Cohen’s d of 0.238–0.657 in Hollman, et al.56 vs. 1.064–1.382 in the current study).
We also acknowledge that our division of participants into the BVP All Gait and BVP Part Gait groups is based
on a rather simple criterion. Of the 12 participants in the BVP Part Gait group, the range of locomotor capacities
within this group is likely broad. Reasons for lack of completion also varied across the participants, with some
stopping due to lack of stability control (too much lateral deviation with a risk of stepping o the treadmill)
and others unable to keep up with a faster belt speed. Nevertheless, we found signicant group eects on gait
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variability, indicating the potential association between gait variability and overall locomotor capacity in BVP.
Further research into gait parameters that can distinguish between patients with dierent functional limitations
is encouraged to aid the development of accurate diagnostic functional testing protocols.
In conclusion, spatiotemporal gait parameters and their variability show speed-dependency in people with
BVP and in healthy adults. In particular, step length variability at slower speeds and step width variability at faster
speeds were the most distinguishing parameters between the healthy participants and people with BVP, and
within groups with BVP who have dierent locomotor capacities. Gait variability in BVP was generally not corre-
lated with the clinical tests of vestibular function. e current ndings indicate that analysis of gait variability at
multiple speeds has potential as an assessment tool for vestibular interventions.
Data availability
e datasets generated during and analysed during the current study are available from the corresponding author
on reasonable request.
Received: 15 August 2019; Accepted: 13 November 2019;
Published: xx xx xxxx
References
1. Brandt, T., Strupp, M. & Benson, J. You are better o running than waling with acute vestibulopathy. Lancet 354, 746, https://doi.
org/10.1016/S0140-6736(99)03179-7 (1999).
2. Schniepp, . et al. Locomotion speed determines gait variability in cerebellar ataxia and vestibular failure. Mov. Disord. 27, 125–131,
https://doi.org/10.1002/mds.23978 (2012).
3. Schniepp, . et al. Clinical and neurophysiological ris factors for falls in patients with bilateral vestibulopathy. J. Neurol. 264,
277–283, https://doi.org/10.1007/s00415-016-8342-6 (2017).
4. Wuehr, M. et al. Noisy vestibular stimulation improves dynamic waling stability in bilateral vestibulopathy. Neurology 86,
2196–2202, https://doi.org/10.1212/WNL.0000000000002748 (2016).
5. Guinand, N., Boselie, F., Guyot, J. P. & ingma, H. Quality of life of patients with bilateral vestibulopathy. Ann. Otol. Rhinol.
Laryngol. 121, 471–477, https://doi.org/10.1177/000348941212100708 (2012).
6. Schlic, C. et al. Falls and fear of falling in vertigo and balance disorders: A controlled cross-sectional study. J. Vestib. Res. 25,
241–251, https://doi.org/10.3233/VES-150564 (2016).
7. Hora, F. B., luzi, J. & Hlavaca, F. Velocity dependence of vestibular information for postural control on tilting surfaces. J.
Neurophysiol. 116, 1468–1479, https://doi.org/10.1152/jn.00057.2016 (2016).
8. Sprenger, A., Woja, J. F., Jandl, N. M. & Helmchen, C. Postural Control in Bilateral Vestibular Failure: Its elation to Visual,
Proprioceptive, Vestibular, and Cognitive Input. Front. Neurol. 8, 444, https://doi.org/10.3389/fneur.2017.00444 (2017).
9. Lucieer, F. et al. Bilateral Vestibular Hypofunction: Insights in Etiologies, Clinical Subtypes, and Diagnostics. Front. Neurol. 7, 26,
https://doi.org/10.3389/fneur.2016.00026 (2016).
10. van de Berg, ., van Tilburg, M. & ingma, H. Bilateral Vestibular Hypofunction: Challenges in Establishing the Diagnosis in
Adults. ORL J. Otorhinolaryngol. Relat. Spec. 77, 197–218, https://doi.org/10.1159/000433549 (2015).
11. Strupp, M. et al. Bilateral vestibulopathy: Diagnostic criteria Consensus document of the Classication Committee of the Barany
Society. J. Vestib. Res. 27, 177–189, https://doi.org/10.3233/VES-170619 (2017).
12. Hollman, J. H. et al. Number of strides required for reliable measurements of pace, rhythm and variability parameters of gait during
normal and dual tas waling in older individuals. Gait Posture 32, 23–28, https://doi.org/10.1016/j.gaitpost.2010.02.017 (2010).
13. Owings, T. M. & Grabiner, M. D. Measuring step inematic variability on an instrumented treadmill: how many steps are enough?
J. Biomech. 36, 1215–1218, https://doi.org/10.1016/s0021-9290(03)00108-8 (2003).
14. iva, F., Bisi, M. C. & Stagni, . Gait variability and stability measures: minimum number of strides and within-session reliability.
Comput. Biol. Med. 50, 9–13, https://doi.org/10.1016/j.compbiomed.2014.04.001 (2014).
15. onig, N., Singh, N. B., von Becerath, J., Jane, L. & Taylor, W. . Is gait variability reliable? An assessment of spatio-temporal
parameters of gait variability during continuous overground waling. Gait Posture 39, 615–617, https://doi.org/10.1016/j.
gaitpost.2013.06.014 (2014).
16. Guyot, J. P. et al. Vestibular assistance systems: promises and challenges. J. Neurol. 263(Suppl 1), S30–35, https://doi.org/10.1007/
s00415-015-7922-1 (2016).
17. Lewis, . F. Vestibular implants studied in animal models: clinical and scientic implications. J. Neurophysiol. 116, 2777–2788,
https://doi.org/10.1152/jn.00601.2016 (2016).
18. Jahn, ., Strupp, M., Schneider, E., Dieterich, M. & Brandt, T. Visually induced gait deviations during dierent locomotion speeds.
Exp. Brain Res. 141, 370–374, https://doi.org/10.1007/s002210100884 (2001).
19. Wuehr, M. et al. Dierential eects of absent visual feedbac control on gait variability during dierent locomotion speeds. Exp.
Brain Res. 224, 287–294, https://doi.org/10.1007/s00221-012-3310-6 (2013).
20. Jahn, ., Strupp, M., Schneider, E., Dieterich, M. & Brandt, T. Dierential eects of vestibular stimulation on waling and running.
Neuroreport 11, 1745–1748 (2000).
21. Fitzpatric, . C., Wardman, D. L. & Taylor, J. L. Eects of galvanic vestibular stimulation during human waling. J. Physiol. 517(Pt
3), 931–939, https://doi.org/10.1111/j.1469-7793.1999.0931s.x (1999).
22. D ain, C. J., Inglis, J. T., Chua, . & Blouin, J. S. Muscle-specic modulation of vestibular reexes with increased locomotor velocity
and cadence. J. Neurophysiol. 110, 86–94, https://doi.org/10.1152/jn.00843.2012 (2013).
23. Forbes, P. A. et al. apid limb-specic modulation of vestibular contributions to anle muscle activity during locomotion. J. Physiol.
595, 2175–2195, https://doi.org/10.1113/JP272614 (2017).
24. Bauby, C. E. & uo, A. D. Active control of lateral balance in human waling. J. Biomech. 33, 1433–1440 (2000).
25. Zeni, J. A. Jr., ichards, J. G. & Higginson, J. S. Two simple methods for determining gait events during treadmill and overground
waling using inematic data. Gait Posture 27, 710–714, https://doi.org/10.1016/j.gaitpost.2007.07.007 (2008).
26. McGarvie, L. A. et al. e Video Head Impulse Test (vHIT) of Semicircular Canal Function - Age-Dependent Normative Values of
VO Gain in Healthy Subjects. Front. Neurol. 6, 154, https://doi.org/10.3389/fneur.2015.00154 (2015).
27. van Dooren, T. S., Lucieer, F. M. P., Janssen, A. M. L., ingma, H. & van de Berg, . e Video Head Impulse Test and the Inuence
of Daily Use of Spectacles to Correct a efractive Error. Front. Neurol. 9, 125, https://doi.org/10.3389/fneur.2018.00125 (2018).
28. Yip, C. W., Glaser, M., Frenzel, C., Bayer, O. & Strupp, M. Comparison of the Bedside Head-Impulse Test with the Video Head-
Impulse Test in a Clinical Practice Setting: A Prospective Study of 500 Outpatients. Front. Neurol. 7, 58, https://doi.org/10.3389/
fneur.2016.00058 (2016).
29. Sloan, L. L. New test charts for the measurement of visual acuity at far and near distances. Am. J. Ophthalmol. 48, 807–813 (1959).
30. Bailey, I. L. & Lovie, J. E. New design principles for visual acuity letter charts. Am. J. Optom. Physiol. Opt. 53, 740–745 (1976).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
11
SCIENTIFIC REPORTS | (2019) 9:18392 | https://doi.org/10.1038/s41598-019-54605-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
31. Guinand, N., Pijnenburg, M., Janssen, M. & ingma, H. Visual Acuity While Waling and Oscillopsia Severity in Healthy Subjects
and Patients With Unilateral and Bilateral Vestibular Function Loss. Archives of Otolaryngology-Head & Neck Surgery 138, 301–306
(2012).
32. van Tilburg, M. J., Herrmann, B. S., Guinan, J. J. Jr. & auch, S. D. Increasing the Stimulation ate educes cVEMP Testing Time by
More Than Half With No Significant Difference in Threshold. Otol. Neurotol. 37, 933–936, https://doi.org/10.1097/
MAO.0000000000001096 (2016).
33. Fujimoto, C. et al. Novel subtype of idiopathic bilateral vestibulopathy: bilateral absence of vestibular evoed myogenic potentials in
the presence of normal caloric responses. J. Neurol. 256, 1488–1492, https://doi.org/10.1007/s00415-009-5147-x (2009).
34. Brantberg, . & Lofqvist, L. Preserved vestibular evoed myogenic potentials (VEMP) in some patients with waling-induced
oscillopsia due to bilateral vestibulopathy. J. Vestib. Res. 17, 33–38 (2007).
35. Valo, Y. et al. Ocular vestibular evoed myogenic potentials as a test for myasthenia gravis. Neurology 86, 660–668, https://doi.
org/10.1212/WNL.0000000000002383 (2016).
36. Pier, E. G., Jacobson, G. P., Burard, . F., McCaslin, D. L. & Hood, L. J. Eects of age on the tuning of the cVEMP and oVEMP. Ear
Hear. 34, e65–73, https://doi.org/10.1097/AUD.0b013e31828fc9f2 (2013).
37. Govender, S., osengren, S. M. & Colebatch, J. G. Vestibular neuritis has selective eects on air- and bone-conducted cervical and
ocular vestibular evoed myogenic potentials. Clin. Neurophysiol. 122, 1246–1255, https://doi.org/10.1016/j.clinph.2010.12.040
(2011).
38. Wuehr, M., Decer, J. & Schniepp, . Noisy galvanic vestibular stimulation: an emerging treatment option for bilateral
vestibulopathy. J. Neurol. 264, 81–86, https://doi.org/10.1007/s00415-017-8481-4 (2017).
39. Perez Fornos, A. et al. e vestibular implant: A probe in orbit around the human balance system. J. Vestib. Res. 27, 51–61, https://
doi.org/10.3233/VES-170604 (2017).
40. Herssens, N. & McCrum, C. Stimulating balance: recent advances in vestibular stimulation for balance and gait. J. Neurophysiol.,
https://doi.org/10.1152/jn.00851.2018 (2019).
41. McCrum, C. et al. Preliminary observations of the acute eects of vestibular nerve stimulation on stride length and time in two
patients with bilateral vestibular hypofunction. Gait Pos ture 49, 124, https://doi.org/10.1016/j.gaitpost.2016.07.179 (2016).
42. McCrum, C. et al. Decient recovery response and adaptive feedbac potential in dynamic gait stability in unilateral peripheral
vestibular disorder patients. Physiol. Rep. 2, e12222, https://doi.org/10.14814/phy2.12222 (2014).
43. Dingwell, J. B., Cusumano, J. P., Cavanagh, P. . & Sternad, D. Local dynamic stability versus inematic variability of continuous
overground and treadmill waling. J. Biomech. Eng. 123, 27–32, https://doi.org/10.1115/1.1336798 (2001).
44. Perry, J. A. & Srinivasan, M. Waling with wider steps changes foot placement control, increases inematic variability and does not
improve linear stability. R. Soc. Open Science 4, 160627, https://doi.org/10.1098/rsos.160627 (2017).
45. Bruijn, S. M., Meijer, O. G., Bee, P. J. & van Dieen, J. H. Assessing the stability of human locomotion: a review of current measures.
J. R. Soc. Interface 10, 20120999, https://doi.org/10.1098/rsif.2012.0999 (2013).
46. MacNeilage, P. . & Glasauer, S. Quantication of Head Movement Predictability and Implications for Suppression of Vestibular
Input during Locomotion. Front. Comput. Neurosci. 11, 47, https://doi.org/10.3389/fncom.2017.00047 (2017).
47. Dean, J. C., Alexander, N. B. & uo, A. D. e eect of lateral stabilization on waling in young and old adults. IEEE Trans. Biomed.
Eng. 54, 1919–1926, https://doi.org/10.1109/TBME.2007.901031 (2007).
48. Donelan, J. M., Shipman, D. W., ram, . & uo, A. D. Mechanical and metabolic requirements for active lateral stabilization in
human waling. J. Biomech. 37, 827–835, https://doi.org/10.1016/j.jbiomech.2003.06.002 (2004).
49. O’Connor, S. M., Xu, H. Z. & uo, A. D. Energetic cost of waling with increased step variability. Gait Posture 36, 102–107, https://
doi.org/10.1016/j.gaitpost.2012.01.014 (2012).
50. Herssens, N. et al. Do spatiotemporal parameters and gait variability dier across the lifespan of healthy adults? A systematic review.
Gait Post ure 64, 181–190, https://doi.org/10.1016/j.gaitpost.2018.06.012 (2018).
51. Hollman, J. H., McDade, E. M. & Petersen, . C. Normative spatiotemporal gait parameters in older adults. Gait Posture 34, 111–118,
https://doi.org/10.1016/j.gaitpost.2011.03.024 (2011).
52. Gates, D. H., Darter, B. J., Dingwell, J. B. & Wilen, J. M. Comparison of waling overground and in a Computer Assisted
ehabilitation Environment (CAEN) in individuals with and without transtibial amputation. J. Neuroeng. Rehabil. 9, 81, https://
doi.org/10.1186/1743-0003-9-81 (2012).
53. Decer, L. M., Cignetti, F. & Stergiou, N. Wearing a safety harness during treadmill waling inuences lower extremity inematics
mainly through changes in anle regularity and local stability. J. Neuroeng. Rehabil. 9, 8, https://doi.org/10.1186/1743-0003-9-8
(2012).
54. Sloot, L. H., van der rogt, M. M. & Harlaar, J. Eects of adding a virtual reality environment to dierent modes of treadmill
waling. Gait Posture 39, 939–945, https://doi.org/10.1016/j.gaitpost.2013.12.005 (2014).
55. atsavelis, D., Muherjee, M., Decer, L. & Stergiou, N. e eect of virtual reality on gait variability. Nonlinear Dynamics Psychol.
Life Sci. 14, 239–256 (2010).
56. Hollman, J. H., Brey, . H., obb, . A., Bang, T. J. & aufman, . . Spatiotemporal gait deviations in a virtual reality environment.
Gait Post ure 23, 441–444, https://doi.org/10.1016/j.gaitpost.2005.05.005 (2006).
57. McCrum, C. et al. Is faster always better? e waling speed-dependency of gait variability in bilateral vestibulopathy. bioRxiv,
https://doi.org/10.1101/413955 (2018).
Acknowledgements
e authors thank Wouter Bijnens, Rachel Senden and Rik Marcellis for their support with the measurements.
CM was funded by the Kootstra Talent Fellowship awarded by the Centre for Research Innovation, Support and
Policy (CRISP) and by the NUTRIM Graduate Programme, both of Maastricht University Medical Center+. FL
was nancially supported by MED-EL. RvdB and HK were supported in part by the Russian Science Foundation
(Project No. 17-15-01249).A pre-print version of this work is available at bioRxiv57.
Author contributions
Conception of the study: C.M., R.v.d.B., K.K., H.K. and K.M. Data Collection: C.M. and F.L. Data Analysis: C.M.,
P.W. and F.L. Interpreted results: All authors. Prepared Figures: C.M. Draed the article: C.M. Reviewed and
revised the article: All authors. Approved nal version: All authors.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41598-019-54605-0.
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