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https://doi.org/10.1167/tvst.7.4.10
Article
Glaucoma-Related Differences in Gaze Behavior When
Negotiating Obstacles
Kim Lajoie
1
, Andreas B. Miller
1
, Robert A. Strath
1
, David R. Neima
2
, and Daniel S.
Marigold
1
1
Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
2
Ophthalmology Private Practice, New Westminster, British Columbia, Canada
Correspondence: Daniel S. Mari-
gold, Department of Biomedical
Physiology and Kinesiology, Simon
Fraser University, 8888 University Dr,
Burnaby, British Columbia, V5A 1S6,
Canada. E-mail: daniel_marigold@
sfu.ca
Received: 1 December 2017
Accepted: 4 June 2018
Published: 24 July 2018
Keywords: glaucoma; locomotion;
mobility; gaze; obstacle avoidance
Citation: Lajoie K, Miller AB, Strath
RA, Neima DR, Marigold DS. Glau-
coma-related differences in gaze
behavior when negotiating obsta-
cles. Trans Vis Sci Tech. 2018;7(4):10,
https://doi.org/10.1167/tvst.7.4.10
Copyright 2018 The Authors
Purpose: Safe navigation requires avoiding objects. Visual field loss may affect how
one visually samples the environment, and may thus contribute to bumping into
objects and falls. We tested the hypothesis that gaze strategies and the number of
collisions differ between people with glaucoma and normally sighted controls when
navigating around obstacles, particularly under multitasking situations.
Methods: Twenty persons with moderate–severe glaucoma and 20 normally sighted
controls walked around a series of irregularly spaced vertical obstacles under the
following three conditions: walking with obstacles only, walking and counting
backward to simulate a conversation, and walking while performing a concurrent
visual search task to simulate locating a landmark. We quantified gaze patterns and
the number of obstacle contacts.
Results: Compared with controls, people with glaucoma directed gaze closer to their
current position (P,0.05). They also directed a larger proportion of fixations (in terms
of number and duration) to obstacles (P,0.05). Despite this finding, considerably
more people with glaucoma contacted an obstacle (P,0.05). Multitasking led to
changes in gaze behavior in both groups, and this was accompanied by a large
increase in obstacle contacts among those with glaucoma (P,0.05).
Conclusions: Glaucoma alters gaze patterns when negotiating a series of obstacles
and increases the likelihood of collisions. Multitasking in this situation exacerbates
these changes.
Translational Relevance: Understanding glaucoma-related changes in gaze behavior
during walking in cluttered environments may provide critical insight for orientation
and mobility specialists and guide the design of gaze training interventions to
improve mobility.
Introduction
Eye and/or head movements are used to change
the line of sight to visually sample the cluttered
environment in which we must navigate. This gaze
behavior is necessary to acquire both advanced
planning information about the general layout of
our surroundings, such as potential hazards and safe
locations to step, and information about the relative
position of obstacles or other individuals as we
move.
1–4
When walking around stationary obstacles,
individuals tend to look ahead toward the end goal
and at features bordering their path,
5
which allows for
proper steering of the body along a set trajectory. In
an environment with moving obstacles, other pedes-
trians for instance, individuals look more frequently
at those people exhibiting a higher probability of
collision.
6
However, continual fixation of obstacles is
not required to properly avoid them, as both children
and adults can successfully navigate cluttered envi-
ronments using only intermittent fixations on obsta-
cles in their path.
5,7,8
This highlights the significance
of peripheral vision for navigation.
Glaucoma leads to progressive and irreversible loss
of vision, typically beginning in the midperiphery.
9
It
is projected to affect more than 110 million people
1TVST j2018 jVol. 7 jNo. 4 jArticle 10
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
worldwide by 2040.
10
Mobility problems are a major
concern in this population. For example, bumping
into objects is frequently reported, both during
experimental mobility courses
11,12
and with subjective
quality of life questionnaires.
13,14
Perhaps more
importantly, glaucoma is associated with a high rate
of falls.
15–18
Together, these mobility problems may
explain why people with glaucoma are less likely to
leave their house for extended periods of time.
19
Visual field loss may affect how one visually
samples the environment, and may thus contribute
to bumping into objects and falls. Indeed, people with
glaucoma exhibit changes in the frequency, latency,
and size of saccades.
20–22
For instance, when viewing
a virtual driving scene, glaucoma patients make
significantly more saccades overall but often miss
hazards due to their visual field deficit.
23
However,
there is limited evidence in the literature regarding
how the gaze strategies of people with glaucoma relate
to natural motor behavior. Existing work shows that,
in a sandwich-making task, people with glaucoma
fixate longer and make more frequent saccades than
normally sighted controls, albeit saccade amplitude is
similar.
24
Saccade amplitude is also similar when
having to identify safe traffic gaps at intersections,
though in this case the fixation area is decreased.
25
In
addition, people with glaucoma that can search for
and collect a series of items in a supermarket within a
prescribed time relative to control subjects exhibit a
greater frequency of glances toward their visual field
defect.
26
Recently, we also found the timing of gaze
shifts to and from stepping targets with respect to foot
placement is significantly altered in this population,
particularly when performing a concurrent secondary
task.
27
These differences in gaze timing are accompa-
nied by reduced foot-placement accuracy.
Understanding the gaze strategies employed dur-
ing walking in people with glaucoma may provide
critical insight for orientation and mobility specialists,
and facilitate the refinement or development of
interventions aimed at improving mobility in this
population. In this study, subjects walked through an
array of vertical obstacles to an end goal while we
tracked their movement and gaze behavior. We chose
this mobility task because avoiding obstacles is a
necessity when navigating in our cluttered world, and
bumping into objects is a frequent occurrence in the
glaucoma population. Because people often engage in
conversation or attempt to identify landmarks when
walking, subjects performed the obstacle negotiation
task in isolation and while performing a concurrent
task that simulated these everyday situations. Prelim-
inary research in glaucoma suggests that multitasking
affects gaze behavior in other walking tasks
27
and
general walking function.
28
Here, we tested the
hypothesis that gaze strategies and the number of
collisions differ between people with glaucoma and
normally sighted controls when navigating around
obstacles, particularly under multitasking situations.
The spatial-temporal pattern of gaze to gather
necessary environmental information as well as the
frequency and duration of gaze fixations to obstacles
and route-planning features served as measures of
gaze strategies.
Methods
Subjects
We used G*Power (version 3.1.9; http://www.
gpower.hhu.de/) to calculate the required sample size
to achieve over 80%power with an alpha of 0.05 for a
repeated-measures ANOVA. Unfortunately, there are
no relevant studies to help determine an appropriate
effect size. Because of the known importance of visual
information for negotiating obstacles, and visual field
loss associated with glaucoma, we based our sample
size calculation on a large effect size (Cohen’s f ¼0.4).
This yielded 18 subjects per group, which we
increased to 20 in case of potential equipment
problems. Thus, we recruited 20 persons with
glaucoma through a collaborating ophthalmologist
and 20 normally sighted control subjects through eye
clinics, doctor’s offices, and community centers. An
optometrist screened the control subjects’ vision as
needed. Potential subjects that met our inclusion
criteria (see below) were asked if they would like to
participate; we did not recruit based on glaucoma
severity. All subjects provided informed written
consent prior to participating in this study. The
Office of Research Ethics at Simon Fraser University
approved the study procedures, which adhered to the
tenets of the Declaration of Helsinki.
An ophthalmologist (DRN) had previously diag-
nosed all persons with glaucoma based on visual field
loss on repeated testing, including a Glaucoma
Hemifield Test outside of normal limits, and retinal
nerve fiber layer (RNFL) loss. Inclusion criteria
specific to glaucoma patients included: a Humphrey
visual field mean deviation worse than 2 dB in both
eyes, habitual binocular visual acuity better than 0.4
logMAR (20/50 Snellen equivalent), and absence of
another visual disease that could affect the visual field
(e.g., clinically significant cataracts, macular degen-
2TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
eration). Inclusion criteria specific to control subjects
included: Humphrey visual field mean deviation
better than 2 dB in both eyes, and absence of an
eye disease. Additional inclusion criteria for both
groups included: aged 60 years and over, free of
neurologic (e.g., Parkinson’s disease, stroke) or
musculoskeletal (e.g., arthritis) disorders that impair
mobility, ability to walk without assistance (or
mobility aid) for more than 5 minutes, and more
than 26 on the Mini-Mental State Exam.
In addition to the above screening, we quantified
how often subjects had fallen in the past 12 months.
We defined a fall as an unexpected event in which the
person landed or came to a rest on the ground, floor,
or lower level.
29
Visual Assessment
We tested monocular visual fields of the glaucoma
subjects using the SITA Fast central 30-2 threshold
test procedure (size III Goldmann white target and
background luminance of 10.03 cd/m
2
)witha
Humphrey Field Analyzer (model HFA-II 750; Carl
Zeiss Meditec, Inc., Dublin, CA). To determine
binocular mean deviation (MD) for further analyses,
we used the best location model.
30
This involved
taking the highest sensitivity between the right and
left eye at each visual field location, and then
averaging these sensitivities across all points. The
MD served as a proxy for glaucoma severity. We also
determined the location of visual field loss for each
eye; loss in a hemifield (inferior, superior) required
having a cluster of three or more points depressed
below the 5%level on the pattern deviation plot.
For controls, we tested monocular visual fields
using frequency-doubling technology (Humphrey 710
FDT Visual Field Instrument; Carl Zeiss Meditec,
Inc.). We used this different piece of equipment for
these subjects because they were recruited in a
different manner than the glaucoma group. However,
we did not use these visual field scores in any analyses;
they only served to ensure that controls did not have
significant visual field loss that could confound our
results. This required controls to have a MD score
calculated by the device of better than 2 dB in both
eyes.
We measured RNFL thickness to confirm the
diagnosis of glaucoma using spectral-domain optical
coherence tomography (OCT) after pupil dilation
with a CIRRUS HD-OCT (model 4000, software
version 6.0.2.81; Carl Zeiss Meditec, Inc.). We used
the device software and Optic Disc Cube 200 3200
protocol, which acquires a 6 3632-mm data cube in
the peripapillary region, to calculate the average
RNFL thickness taken from a 3.46-mm circle
centered on the optic disc. Controls did not undergo
OCT testing.
All subjects also performed the Useful Field of
View (UFOV) test (17-inch touch monitor with 75-Hz
refresh rate; version 7.0.2; Visual Awareness Research
Group, Inc., Punta Gorda, FL) under binocular
conditions, which included three subtests of increas-
ing difficulty. Subjects sat 50 cm from the screen.
Subtest 1 (central processing) required subjects to
identify a central target (car or truck). Subtest 2
(divided attention) required subjects to identify the
central target plus a peripheral target (car) simulta-
neously at one of eight radial locations. Subtest 3
(selective attention) is similar to the previous one
except the peripheral target is embedded among 47
triangle distractors. The software decreases the
duration of the stimulus presentation until a subject
is able to produce a 75%correct response rate.
Obstacle Task
Subjects performed the obstacle negotiation task
under single- and dual-task conditions. There were 12
walking trials per condition (i.e., 36 trials total), with
the order of conditions randomized. In each condi-
tion, subjects walked along a 4.5-m long and 1.25-m
wide path to an ‘‘end gate’’ consisting of two blue
vertical poles (height ¼25 cm; diameter ¼6 cm) after
navigating four dark gray vertical poles (height ¼165
cm; diameter ¼3.5 cm; Fig. 1). We positioned the
poles 60 cm away from each other in the anterior-
posterior direction (i.e., the plane of progression) and
varied the pole and end gate positions in the medial-
lateral direction on a trial-to-trial basis using one of
four predetermined arrangements. This meant that
subjects experienced each arrangement for a total of
three walking trials. We randomized the order of
arrangements on a trial-to-trial basis. An opaque
wooden board occluded subjects’ vision of the
walkway before each trial. The visual occlusion and
the use of four, randomized arrangements served to
reduce the likelihood of subjects using spatial memory
to navigate; thus, ensuring they had to use visual
information to complete the task. We instructed
subjects to walk at a self-selected speed, to navigate
the course without stopping, to take the simplest path
through the poles without any part of their body
going outside the path’s borders, and to avoid contact
with the poles. An experimenter demonstrated the
task to make sure subjects understood the instruc-
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Lajoie et al.
tions. Subjects started walking immediately once
cued.
In one (count) dual-task condition, subjects
walked while counting backward by threes, a com-
mon secondary task that increases cognitive load
31
and simulates having a detailed conversation with
someone. In the other (visual search) dual-task
condition, subjects had to identify the location of a
shape at the end of each trial after being cued by an
experimenter. In this condition, we positioned four
tiles (20 315 cm) on the ground on both sides of the
path containing the obstacles, each with a different
black shape on a white background (see Fig. 1). We
randomly varied the sequence of shapes (plus sign,
triangle, circle, and square) on a trial-to-trial basis.
This dual task condition purposely forced subjects to
temporarily direct their gaze away from the path and
poles, simulating real-life situations in which we have
to both monitor our walking direction and identify
landmarks, a task identified by people with eye
disease as challenging.
32
We also had subjects count and perform visual
search trials while not walking. These represented
count and visual search baseline (or single-task)
conditions. For the count single task, subjects
counted by threes for a total of 10 seconds in each
of three trials. We calculated the number of correct
responses in this task and during the dual-task
situation, then divided these values by their respective
trial durations. For the visual search single task,
subjects viewed the shapes for 5 seconds before having
their vision blocked in each of 12 trials. We divided
the proportion of correct responses in the single- and
dual-task situations by their respective average trial
durations. To determine how dual tasking affected
counting and visual search performance, we calculat-
ed a dual task cost (DTC) value
33
for each using the
following formula: (dual task – single task) / single
task. A negative value represents worse performance
in the dual-task situation.
A high-speed, head-mounted, mobile eye-tracker
(model H6-HS; Applied Science Laboratories, Bed-
ford, MA) recorded gaze position during the task at
120 Hz. A video camera mounted on the eye tracker
recorded the subjects’ view of the path at 30 Hz. We
calibrated the eye tracker using the system’s standard
9-point calibration method. Subjects wore their
habitual spectacles, if applicable, during testing in
the obstacle task. Two Optotrak Certus (Northern
Digital, Inc., Waterloo, Canada) cameras, synchro-
nized with the eye tracker, recorded (at 120 Hz) the
time-varying positions of infrared-emitting diodes
placed on the obstacles and subject’s body. We fixed
these markers to the subject’s head (using a rigid
block at the back of the head and mounted on the eye
tracker), chest (midway between the sternal notch and
xiphoid process), right shoulder (on the acromion),
and bilaterally on the heels, mid-feet (dorsal surface at
the approximate level of the metatarsal joint), and
above the toes on their shoes.
Gaze and Kinematic Measures
We low-pass filtered kinematic data (with a
Butterworth algorithm) at 6 Hz. We calculated gait
speed between the first and last obstacle using the
marker on the chest, and determined obstacle-
crossing events by calculating the time at which the
chest marker crossed the anterior-posterior position
of each obstacle. Given the reported mobility
Figure 1. Experimental set-up. An illustration (not drawn to scale)
of the obstacle course is shown. The shapes were only present in
the visual search dual task condition.
4TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
problems of people with glaucoma and the nature of
our mobility task, we also quantified the number of
obstacle contacts throughout the experiment. We
defined an obstacle contact as a noticeable sway of
the obstacle or it falling to the ground after being
bumped into with any part of the subject’s body.
After each walking trial, two experimenters came to a
consensus as to whether any contacts occurred and
then one of them recorded the event.
For each walking trial, we filtered the horizontal
and vertical components of the gaze data (4th order,
low-pass, Butterworth algorithm) at 12 Hz and then
calculated the vector gaze position at each point in
time. The time at which the local angular gaze velocity
crossed above or below a threshold of 1008/s for a
minimum of 16 ms defined the onset and offset of
gaze shifts.
27,34
We defined gaze fixations as instances
with stable gaze on a location for a minimum of 66
ms.
27,35
To determine which aspects of the visual
scene subjects fixated, we used the 30-Hz video of the
path with crosshairs of gaze position superimposed.
Fixation locations included route-planning features
(gap between obstacles, ground regions, and end goal
region), obstacles (or end gates) and, in the case of the
visual search dual-task condition, shapes. Subjects
rarely, if ever, fixated outside of these locations.
To determine how subjects allocate gaze to plan
their route through the obstacles, we developed the
following two measures: spatial gaze distance and
spatial-temporal gaze distance. For both measures,
we used the positions of the obstacle and the end gates
to divide the path into eight segments (S1–S8; see Fig.
2A). Each segment is the same length, expect for the
first one (S1) because of the subject’s start position
and the last one (S8) that represents the end region of
the path; these unequal segment lengths are accounted
for in the calculation of each of the two gaze distance
measures. The anterior-posterior position of the
Figure 2. Gaze distance measures. (A) An illustration of how the gaze distance scores were assigned. In this example, the subject is
walking through segment 2 (S2). Gaze is directed to different locations and each fixation is then given a score based on how far ahead
the fixation was with respect to the subject. Scores are averaged for each segment. For the spatial-temporal gaze distance measure,
scores are scaled according to fixation duration and how long the subject walks within that particular segment. See text for additional
details. (B) Spatial gaze distance scores and (C) spatial-temporal gaze distance scores for each condition and group across the five
segments included in the analysis. Data are represented as mean 6SE.
5TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
subject’s chest marker determines which segment they
are located in. For both measures, we determined
which segment(s) subjects fixated relative to their
location for the first five segments they walked
through. We excluded the last three segments because
the subjects have walked past the fourth obstacle by
this point and gaze begins to deviate from the walking
path and becomes erratic as the subjects approach the
end of the lab. Each fixation is then assigned a spatial
gaze distance score based on how far it is from the
subject. This is illustrated in Figure 2A. If a subject
fixated the segment within which they were currently
located, we scored that fixation a 0.5; this rarely
occurred. We scored a fixation to the next segment as
a 1, and so on. For example, if the subject is located
within segment #2 and is fixating segment #4, that
gaze fixation is scored a two. If the subject is located
within segment #3 and is fixating segment #4, that
gaze fixation is scored a one. Note that subjects had a
very strong tendency to direct gaze toward the ground
when looking between obstacles (and even fixated
obstacles near the bottom). Thus, we are able to
assign these fixations to specific ground locations
based on where the point of gaze appears on the video
image coming from the camera mounted on the eye
tracker.
For the spatial gaze distance measure, we then
averaged the score for each segment. Large values
indicate that the subject fixates, on average, a greater
distance ahead when negotiating the obstacles. For
the spatial-temporal gaze distance measure, the
duration of each fixation is divided by the total time
the subject spends walking in the segment they are
currently located. The spatial gaze distance score of
that fixation then multiplies this temporal value. In
this way, the spatial gaze distance is scaled based on
how long a subject fixates at that distance. These
spatial-temporal gaze distance scores are then aver-
aged for each segment. Larger values indicate that the
subject allocates gaze, on average, farther ahead for a
greater amount of time.
We next quantified (1) the proportion of the
number of fixations and (2) the proportion of gaze
fixation time to route-planning features, obstacles,
and shapes (if applicable) until the subject walked
past the fourth obstacle. We excluded data beyond
this point because the stability of gaze decreases, as
described earlier. We also quantified the time interval
(in seconds) between a gaze shift away from an
obstacle and anterior-posterior chest crossing time
(i.e., the gaze obstacle-crossing interval). Because
some subjects fixated a given obstacle more than
once, we used the last gaze shift to the obstacle before
crossing in this calculation. A negative interval
indicates gaze transfer away before crossing the
obstacle.
Statistical Analyses
For all ANOVAs, we included subject as a random
factor and used Tukey post hoc tests with significant
main effects and/or interactions. We also included age
as a covariate in all analyses described in this section
because the difference in age between groups almost
reached significance (see below). To determine differ-
ences in the spatial gaze distance and spatial-temporal
gaze distance scores between groups, conditions, and
segments, we used separate three-way (group 3
condition 3segment) ANOVAs. To determine
differences in the proportion of the number of
fixations to obstacles and to route-planning features
between groups (glaucoma and controls) and across
conditions (single task, count dual task, visual search
dual task), we used separate two-way (group 3
condition) ANOVAs. We used identical analyses for
the proportion of gaze fixation time to obstacles and
to route-planning features. One-way (group) AN-
OVAs determined differences between groups for
both the proportion of the number of fixations to
shapes and the proportion of gaze fixation time to
shapes as well as the count and visual search DTC
measures. Separate two-way (group 3condition)
ANOVAs assessed differences in gait speed and gaze
obstacle-crossing intervals. We used separate gener-
alized estimating equation (GEE) models (negative
binomial with log link or binomial logit link,
respectively) to compare differences in the number
of obstacle contacts and the number of subjects that
contacted obstacles between the different conditions
and groups. To determine the relationship between
obstacle contacts and visual field loss in the glaucoma
group, we performed a Poisson regression. We used a
similar analysis to determine the relationship between
the count (or visual search) dual task cost and
obstacle contacts. For all statistical analyses, we used
an alpha level of 0.05 and JMP 13 software (SAS
Institute, Cary, NC), expect for the GEE model in
whichweusedSPSS24software(IBMSPSS
Statistics, Armonk, NY).
Results
Characteristics of the glaucoma subjects and
normally sighted controls are shown in Tables 1 and
6TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
2. The type of glaucoma ranged from primary open-
angle (N¼17) to normal- (or low-) tension (N¼2) to
pseudoexfoliative (N¼1). Two-sample t-tests showed
that age (t
38
¼1.99, P¼0.054), height (t
38
¼1.22, P¼
0.228), weight (t
38
¼1.04, P¼0.305), and shoulder
width (t
38
¼1.08, P¼0.289) did not differ between
the groups. Although a greater number of glaucoma
subjects fell in the past year compared with controls,
this difference did not reach significance (Fisher’s
Exact test: P¼0.451). All three UFOV subtests and
the UFOV total score differed between groups
(Wilcoxon rank-sum tests, P,0.007).
The Effects of Glaucoma and Dual Tasking
on Gait Speed When Navigating Around
Obstacles
Gait speed differed between groups (group main
effect: F
1,39
¼7.5, P¼0.009), such that control
subjects walked faster in the obstacle only condition
(controls: 0.89 60.14 m/s; glaucoma: 0.74 60.19 m/
s), count dual-task condition (controls: 0.80 60.17
m/s; glaucoma: 0.65 60.2 m/s), and the search dual-
task condition (controls: 0.83 60.14 m/s; glaucoma:
0.68 60.19 m/s). Condition also had an effect on gait
speed (F
2,75
¼35.0, P,0.0001), as both groups
walked faster in the obstacle only condition compared
with the count dual task and the visual search dual-
Table 1. Subject Characteristics
Glaucoma (n¼20) Controls (n¼20)
Age, yr 74.8 (6.5) 70.5 (7.0)
Sex, n
Male 14 14
Female 6 6
Weight, kg 72.1 (17.7) 78.3 (19.8)
Height, cm 167.3 (13.8) 171.9 (9.4)
Shoulder width, cm 42.2 (4.9) 43.8 (4.5)
Race/ethnicity
Asian 5 4
Black 2 0
South Asian 1 0
White 12 16
Self-reported faller, nin past 12 mo 6 3
RNFL thickness, lm
Better eye 71.8 (12.0) N/A
Worse eye 66.6 (12.1) N/A
Visual field, MD in dB
Binocular best location 8.41 (5.30) N/A
Better eye 8.92 (5.56) 1.14 (1.71)
Worse eye 16.65 (7.69) 0.58 (1.87)
UFOV, ms
Subtest 1: central processing 20 (13–57) 13 (13–13.75)
Subtest 2: divided attention 163 (57–500) 30 (14.75–146.75)
Subtest 3: selective attention 370 (203–500) 158 (115.5–232.25)
Total: Sum of subtests 480 (347–1027) 222.5 (157–380)
Data are mean (SD) for age, weight, height, shoulder width, visual field; median (IQR) for UFOV; and counts for sex, race/
ethnicity, and self-reported fallers.
Table 2. Hemifield Location of Visual Field Loss
Better Eye Worse Eye
Location of visual field loss
Inferior hemifield 2 1
Superior hemifield 2 2
Double hemifield 16 17
Data are counts.
7TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
task conditions. Despite these differences, our gaze
measures are largely independent of gait speed
because of the way each measure is calculated (i.e.,
scaled to segment duration, quantified as propor-
tions).
Gaze Strategies When Navigating Around
Obstacles
To determine how subjects allocate gaze to plan
their route through the obstacle course, we first
quantified how far ahead, on average, they fixated.
Figure 2B illustrates these spatial gaze distance
scores. Subjects fixated further ahead in the obstacle
only and count dual-task conditions compared with
thesearchdual-taskcondition(F
2,532
¼26.8, P,
0.0001). Furthermore, gaze distance scores progres-
sively decreased as subjects walked across each
segment (F
4,532
¼252.7, P,0.0001), fixating
approximately four segments ahead early in the path
and approximately two segments ahead near the end.
Spatial gaze distance between groups differed de-
pending on the segment (F
4,532
¼5.2, P¼0.0004).
Specifically, the glaucoma group demonstrated
smaller gaze distance scores versus controls for the
first segment, suggesting that they did not shift gaze
as far to initially plan their route. However, we
found no other group differences in spatial gaze
distance for the other segments.
Although knowing the particular distances of
fixations provides information on how far ahead
subjects may plan their walking trajectory, it is
important to recognize that the amount of time spent
fixating these distances is also relevant. Our spatial-
temporal gaze distance measure captures this aspect
of gaze behavior (Fig. 2C). Controls exhibited greater
spatial-temporal gaze distance scores compared with
the glaucoma group in the obstacle only and count
dual-task conditions but not in the search dual task
condition (F
2,532
¼3.2, P¼0.041). We also found
main effects of group (F
1,37
¼10.8, P¼0.002),
condition (F
2,532
¼45.0, P,0.0001), and segment
(F
4,532
¼9.7, P,0.0001) for this measure.
The majority of fixations were directed to route-
planning features, ranging from approximately 55%
in the search dual-task condition to approximately
80%in the count dual-task condition (Fig. 3A). The
proportion of fixations to route-planning features
differed depending on the group and condition
(F
2,76
¼3.1, P¼0.049). However, post hoc tests
indicated no group differences, which was also
supported by a nonsignificant group main effect
(F
1,40
¼3.7, P¼0.063). Of note, if age is not
included as a covariate, we find that controls make
a greater proportion of fixations to these features
than the glaucoma group in the obstacle only
condition. Regardless of group, the proportion of
fixations was significantly reduced in the search
dual task relative to the other conditions. This
occurred because of the presence of the shapes. In
fact, controls made a greater proportion of fixations
to the shapes compared with the glaucoma group
(F
1,37
¼10.0, P¼0.003) as shown in Fig. 3A (right
panel). Less than 25%of fixations were directed to
the obstacles across all conditions. A greater
proportion of obstacle fixations occurred in the
obstacle only condition compared with the other
conditions (condition main effect: F
2,76
¼10.6, P,
0.0001). Furthermore, the glaucoma group exhibited
a greater proportion of obstacle fixations versus
controls (group main effect: F
1,40
¼9.9, P¼0.003).
We did not find a statistically significant interaction
(F
2,76
¼1.8, P¼0.174).
The proportion of fixation time on route-planning
features, obstacles, and shapes closely matched the
proportion of fixations to these regions of interest (see
left, middle, and right panel, respectively, of Fig. 3B).
Although we did not find a statistically significant
interaction (F
2,76
¼2.8, P¼0.069) or effect of group
(F
1,39
¼3.2, P¼0.079) for route-planning features, we
found an effect of condition (F
2,76
¼80.2, P,
0.0001). Post hoc tests indicated that the proportion
of fixation time decreased in the search dual-task
condition compared with the other conditions. In the
search dual task, controls spent a greater proportion
of fixation time on the shapes compared with the
glaucoma group (27%vs. 18%;F
1,37
¼6.1, P¼0.018).
In contrast, the glaucoma group spent a greater
proportion of time fixating the obstacles compared
with controls independent of the condition (F
1,40
¼
9.5, P¼0.004). However, we did not find a
statistically significant effect of condition (F
2,76
¼
2.5, P¼0.088) or an interaction (F
2,76
¼2.3, P¼
0.111).
Figure 4 illustrates the gaze obstacle-crossing
interval. On average, subjects shifted gaze away from
a given obstacle approximately 2 seconds before
crossing past it. We found no main effect of group
(F
1,37
¼1.8, P¼0.185) or group 3condition
interaction (F
2,76
¼1.7, P¼0.191). However, subjects
shifted gaze away from obstacles closer to when they
crossed past them in the search dual task compared
with the count dual task (F
2,76
¼3.2, P¼0.045).
8TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
Obstacle Contacts
Table 3 summarizes the results of the obstacle
contacts. In total, the glaucoma group contacted
obstacles 146 times compared with only 35 contacts
among the controls. Accordingly, we found an effect
of group in the GEE model (v
2
¼25.6; P,0.0001).
We also found an effect of condition (v
2
¼9.7; P¼
0.008) in which post hoc tests indicated a greater
number of contacts in the count and search dual task
conditions versus the obstacle only condition. The
group 3condition interaction did not reach signifi-
cance (v
2
¼5.0; P¼0.083). A significantly greater
number of glaucoma patients contacted the obstacles
than controls (group main effect: v
2
¼9.3, P¼0.002).
Furthermore, more subjects contacted an obstacle at
least once in the count and search dual-task condi-
tions compared with the obstacle only condition
(condition main effect: v
2
¼14.1, P¼0.001). Finally,
in the glaucoma group, we determined the relation-
ship between integrated visual field loss and obstacle
Figure 4. Gaze obstacle-crossing interval for each condition and
group. Negative intervals indicate gaze transfer away before
crossing the obstacle. Data are represented as mean 6SE.
**Statistically significant post hoc test based on a condition main
effect, P,0.05.
Figure 3. Gaze fixation locations and times. (A) Proportion of route-planning, obstacle, and shape fixations between groups and
conditions. (B) Proportion of route-planning, obstacle, and shape fixation times between groups and conditions. Data are represented as
mean 6SE. *Statistically significant post hoc test based on a group 3condition interaction, P,0.05. **Statistically significant main
effect of condition or group, P,0.05.
9TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
contacts using a Poisson regression, corrected for over
dispersion (over dispersion ¼2.45). The results are
shown in Figure 5A. Greater visual field loss
associated with a greater number of obstacle contacts
(P¼0.016).
Dual-Task Costs
The findings above show that gaze measures and
the likelihood of contacting an obstacle are affected
by having to perform a secondary task. We found a
greater cost associated with counting (i.e., worse
performance) for the glaucoma group compared with
the controls as quantified by our DTC measure
(glaucoma count DTC: 0.33 60.21; control count
DTC: 0.12 60.19; F
1,35
¼9.5, P¼0.004). Note that
one subject in the glaucoma group refused to perform
the counting task due to its difficulty. In addition, due
to technical difficulties we were unable to normalize
count performance to trial duration for one glaucoma
subject. Therefore, these subjects were excluded from
this analysis. Interestingly, greater count dual task
cost associated with a greater number of obstacle
contacts in this condition (Fig. 5B;P¼0.036). We
also found greater cost in terms of visual search
performance for the glaucoma group (glaucoma
search DTC: 0.20 60.32; control search DTC:
0.09 60.28; F
1,37
¼5.5, P¼0.024). However, we did
not find a relationship between the visual search dual-
task cost and obstacle contacts in this condition (P¼
0.315).
Discussion
Safe navigation requires avoiding stationary and
moving objects in the environment. Appropriate gaze
behavior plays a key role in this success. Unfortu-
nately, people with glaucoma may visually sample the
environment differently due to their visual field loss
and are more likely to bump into objects than
normally sighted individuals. Here, we show that,
compared with controls, people with glaucoma direct
gaze closer to their current position, and direct a
greater proportion of fixations (in terms of number
and duration) to obstacles. Despite the latter behav-
ior, considerably more people with glaucoma con-
tacted an obstacle. We also show that multitasking
leads to changes in gaze behavior in both groups, and
this is accompanied by a large increase in obstacle
contacts in those with glaucoma. Greater glaucoma-
Figure 5. Poisson regressions for obstacle contacts in the glaucoma group. (A) Relationship between visual field loss and total obstacle
contacts. (B) Relationship between count dual task cost and obstacle contacts in that condition.
Table 3. Obstacle Contacts
Measure/Condition
Glaucoma
(n¼20)
Controls
(n¼20)
Total number of contacts
Single-task obstacle only 29 8
Dual-task count 44 14
Dual-task search 73 13
Mean number of contacts
Single-task obstacle only 1.5 (0.3) 0.4 (0.2)
Dual-task count 2.2 (0.4) 0.7 (0.2)
Dual-task search 3.7 (0.6) 0.7 (0.2)
Number of subjects making contact
Single-task obstacle only 13 5
Dual-task count 18 11
Dual-task search 19 11
Data are counts or mean (SE).
10 TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
related visual field loss and difficulty counting as a
dual task both associate with a greater number of
obstacle contacts. Taken together, we suggest that
altered (and arguably inappropriate) gaze strategies as
well as deficits in multitasking ability contribute to
the mobility problems seen with glaucoma.
As evident from the spatial and spatial-temporal
gaze distance measures, the glaucoma subjects directed
gaze closer to their current position compared with
controls when negotiating the obstacles. This suggests
that they prioritize more immediate locations rather
than features important for planning a route to the
goal area. Interestingly, this is opposite to the gaze
strategy observed when walking and stepping onto
targets. When precise foot placement is required,
people with glaucoma make saccades away from a
target sooner in relation to stepping on it.
27
This is
accompanied by reduced foot-placement accuracy,
similar to normally sighted older adults at a high risk
of falling.
36
In this precision-walking situation, glau-
coma subjects prioritize planning future accurate steps
at the expense of the current step. Directing gaze closer
to their current position may allow glaucoma subjects
to better locate obstacles but can also lead to poor path
choices that result in the need to make sharper, more
frequent turns and/or greater trunk rotations, thus
increasing the risk of collision. Orientation and
mobility specialists often teach people with low vision
to use a gridline scan, which involves scanning in a grid
pattern, from in front of the person outward to the
destination or farthest point visible, then left-to-right
and up-and-down back toward the person to locate
hazards.
37,38
As ones moves forward, it is important to
periodically re-establish lines of direction or the goal.
Our results suggest that this may represent an
appropriate training strategy for this population.
We found that people with glaucoma allocated a
greater proportion of fixations (number and duration)
to obstacles but still contacted them more frequently.
A reduced visual field may cause subjects to rely more
on central rather than peripheral vision to detect
obstacles, and thus explain, in part, these results.
People with peripheral visual field loss due to retinitis
pigmentosa fixate the edges of a doorframe when
having to pass through it more than controls; the
latter direct fixations more to the door aperture.
39
At
first glance, a strategy to increase fixations to an
obstacle might make sense. Indeed, fixating an
obstacle that one must step over is important for
determining its location and height.
40,41
Recent work
shows that people with retinitis pigmentosa spend an
equal amount of time fixating an obstacle in this
situation, but they spend less time looking past the
obstacle to the path ahead, and a greater amount of
time fixating the ground before the obstacle compared
with normally sighted controls.
42
When stepping over
an obstacle, normally sighted older adults shift gaze
to the obstacle earlier and for longer than young
adults.
43,44
However, spending more time fixating an
obstacle when having to avoid it, rather than when
having to step over it, is not necessarily an ideal
strategy. This is because there is a natural tendency to
veer in the direction you look; that is, you look where
you want to go.
45–47
The number of obstacle collisions as well as the
number of subjects colliding was considerably greater
in the glaucoma group. This occurred despite the fact
that all subjects were specifically instructed to avoid
contact with the obstacles as they traversed the
walking path. This suggests that the altered gaze
patterns in this group, namely that they direct gaze
closer to their current position and fixate obstacles
more frequently and for a greater duration, are
maladaptive. Although these changes are likely a
compensatory strategy due to their eye disease, and
they may ultimately let these individuals navigate in
the environment, there is a clear need for better
training on how to use any remaining vision more
effectively.
Counting or searching for a shape concurrently
with obstacle negotiation led to changes in gaze
patterns and a greater number of obstacle contacts in
the glaucoma group. Interestingly, the cost of having
to count contributed to obstacle contacts in that
condition, as shown in Figure 5B. Difficulty dividing
or selecting attention may also partially explain these
results, given that the glaucoma group scored
considerably worse on subtests one and two of the
UFOV. Dual tasking is known to alter gait in older
adults. For instance, when circumventing an obstacle
and having to remember auditory messages presented
during the task, normally sighted older adults walk
slower,
48
similar to both groups in our experiment.
However, there is less research in those with eye
disease. Greater visual field loss due to glaucoma does
associate with a wider base of support and stride-to-
stride variability of step length, stride length, and
stride velocity when walking and carrying a cup or
tray.
28
Furthermore, in a recent study from our lab in
people with glaucoma, we found that the count and
visual search dual tasks led to a reduction in foot-
placement accuracy when walking and stepping onto
targets.
27
We also found these dual-task conditions
affected gaze behavior, such that transferring gaze to
11 TVST j2018 jVol. 7 jNo. 4 jArticle 10
Lajoie et al.
and from stepping targets differed compared with the
single-task situation. Similarly, Yamada et al.
49
found
that normally sighted older adults with a history of
falls transfer gaze away from an obstacle to step over
sooner during a counting dual task condition
compared with those without a history of falling.
Taken together, it is clear that multitasking can
influence gaze and mobility function. This indicates
that interventions aimed at improving mobility
should consider incorporating dual-task training as
a component.
Our study has at least two limitations. First, given
the characteristics of our sample, we were unable to
stratify glaucoma subjects based on the location and
severity of visual field loss. Future research should
explore whether gaze strategies differ depending on
these factors. Second, although our obstacle negoti-
ation task was designed to mimic real-life scenarios, it
may not generalize to all variations and/or environ-
ments (e.g., outdoor versus indoor lighting, uneven
terrain, moving objects). However, our results provide
necessary insight to design future experiments and to
guide potential gaze training interventions.
In conclusion, people with glaucoma exhibit
altered (and arguably inappropriate) gaze behavior
compared with normally sighted controls when
negotiating an array of stationary obstacles. Further-
more, they experience a greater number of obstacle
contacts. These results suggest that interventions
aimed at refining gaze strategies for those with visual
field loss might be beneficial. For instance, the use of
a gridline scan may serve to increase awareness of
hazards in the environment and facilitate the planning
of safer routes. In addition, teaching people with
glaucoma how to determine safe gaps between
obstacles and then look where you want to go may
improve mobility. Future research should determine
whether these ideas are effective.
Acknowledgments
The authors wish to thank Javier Dom´
ınguez-
Zamora for help with data collection, and Shaila
Gunn for help with data collection and a portion of
the data analysis.
Supported by a Glaucoma Research Society of
Canada grant and a Simon Fraser University VPR
Bridge grant.
Disclosure: K. Lajoie, None; A.B. Miller, None;
R.A. Strath, None; D.R. Neima, None; D.S. Mari-
gold, None
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