This is the author’s version of a work that was submitted/accepted for pub-lication in the following source:
Lee, Sze-Yee Samantha,Wood, Joanne M., &Black, Alexander A. (2015)
Blur, eye movements and performance on a driving visual recognition slide test.
Ophthalmic and Physiological Optics, 35(5), pp. 522-529.
This file was downloaded from: http://eprints.qut.edu.au/87290/
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c 2015 The Authors
This is the peer reviewed version of the following article: Blur, eye move-ments and performance on a driving visual recognition slide test, which has been published in final form at 10.1111/opo.12230. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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Page 1 of 20 TITLE:
Blur, eye movements and performance on a driving visual recognition slide test
RUNNING TITLE:
Blur and age effects on drivers' eye movements
AUTHORS:
Samantha Sze-Yee Lee,1 Joanne M. Wood,1 Alexander A. Black1 1
School of Optometry and Vision Science, and Institute of Health and Biomedical Innovation, Queensland University of Technology
CORRESPONDING AUTHOR: Samantha Sze-Yee Lee
KEYWORDS:
Page 2 of 20 ABSTRACT
Purpose: Optical blur and ageing are known to affect driving performance but their effects on drivers’ eye movements are poorly understood. This study examined the effects of optical blur and age on eye movement patterns and performance on the DriveSafe slide recognition test which is purported to predict fitness to drive.
Methods: Twenty young (27.1 _ 4.6 years) and 20 older (73.3 _ 5.7 years) visually normal drivers performed the DriveSafe under two visual conditions: best-corrected vision and with +2.00 DS blur. The DriveSafe is a Visual Recognition Slide Test that consists of brief
presentations of static, real-world driving scenes containing different road users (pedestrians, bicycles and vehicles). Participants reported the types, relative positions and direction of travel of the road users in each image; the score was the number of correctly reported items (maximum score of 128). Eye movements were recorded while participants performed the DriveSafe test using a Tobii TX300 eye tracking system.
Results: There was a significant main effect of blur on DriveSafe scores (best-corrected: 114.9 vs blur: 93.2; p < 0.001). There was also a significant age and blur interaction on the DriveSafe scores (p < 0.001) such that the young drivers were more negatively affected by blur than the older drivers (reductions of 22% and 13% respectively; p < 0.001): with best-corrected vision, the young drivers performed better than the older drivers (DriveSafe scores: 118.4 vs 111.5; p = 0.001), while with blur, the young drivers performed worse than the older drivers (88.6 vs 95.9; p = 0.009). For the eye movement patterns, blur significantly reduced the number of fixations on road users (best-corrected: 5.1 vs blur: 4.5; p < 0.001), fixation duration on road users (2.0 s vs 1.8 s; p < 0.001) and saccade amplitudes (7.4° vs 6.7°; p < 0.001). A main effect of age on eye movements was also found where older drivers made smaller saccades than the young drivers (6.7° vs 7.4°; p < 0.001).
Conclusions: Blur reduced DriveSafe scores for both age groups and this effect was greater for the young drivers. The decrease in number of fixations and fixation duration on road users, as well as the reduction in saccade amplitudes under the blurred condition, highlight the difficulty experienced in performing the task in the presence of optical blur, which suggests that uncorrected refractive errors may have a detrimental impact on aspects of driving performance.
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INTRODUCTION
Driving is a key mode of transport for many older adults,1 and is crucial to their mobility, sense of independence, social interactions, and quality of life.2 However, older drivers are at higher risk of motor vehicle crashes than their younger counterparts.2 The decline in visual function, visual processing, and cognitive abilities with increasing age, 3, 4 as well as the age-related increase in the prevalence of eye disease,5, 6 may contribute to this increase in crash risk.
Eye tracking technology provides the opportunity to better understand the scanning behaviour and visual attention of drivers and may potentially assist in explaining why some drivers have high crash rates. For example, the differences in scanning behaviour between novice and experienced drivers have been studied through the use of eye tracking techniques. These studies have found that novices tend to concentrate their gaze positions more centrally than experienced drivers, 7, 8 and exhibit longer fixations and smooth pursuits on the lane markings in front of them, suggesting an increased focus on vehicle positioning.7, 8 In addition, novices make fewer fixations on road hazards, suggesting reduced visual attention on these hazards which may be an important contributor to their elevated crash rates.8
Despite the potential for eye tracking techniques to facilitate a better understanding of drivers’ scanning and attentional behaviours, the technique has been under-utilised in other vulnerable road users, such as older drivers. Age-related changes in eye movement patterns have been reported, where compared to young adults, older adults were found to make smaller saccades9 and exhibit greater saccadic averaging;10 the latter describes the increase in saccadic endpoint errors in the presence of visual distractors. Furthermore, in a naturalistic walking experiment, older adults were reported to have decreased saccade amplitude and velocity compared to younger adults.11 Hence there may also be corresponding age variations in driving-related eye movements. Recently, Urwyler et al.12 in a driving simulator study reported that older drivers spent less time fixating non-relevant driving areas, but made fewer side and rear view mirror checks compared to younger drivers. Conversely, Underwood et al.13 found no effect of age on driver’s eye movements when viewing videos clips of driving scenes. The limited and conflicting findings on age-related variations in driving-related eye movement patterns thus requires further exploration.
The eye movement patterns of drivers with visual impairment have also been under-investigated. Among the few studies that have explored this area, most have focussed on
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drivers with glaucomatous14,15 or hemianopic16 visual field defects However, the most common cause of visual impairment, although reversible, is uncorrected refractive error.5,6 Studies have demonstrated the negative impact of refractive error (in the form of optical blur) on driving performance,17–20 including poorer pedestrian and hazard detection ability, road sign recognition, longer time to complete a driving course, and worse overall driving
performance on a closed-road course. However, the effect of optical blur on eye movements related to driving are poorly understood.
In some driving-related studies,13,21 laboratory-based off-road tests have been used as surrogates for specialist on-road testing given their feasibility and cost-effectiveness. In addition, laboratory-based tests allow experimenters to have greater control over testing parameters such as lighting levels, and the avoidance of any unexpected external factors such as changes in weather and traffic conditions, thus allowing consistency between participants. One such off-road test is the DriveSafe, which was developed to identify unsafe drivers, particularly those with cognitive and/or functional impairments (including visual
impairments).22 The DriveSafe is a shortened version of the Visual Recognition Slide Test developed at the University of Sydney (VRST-USyd) for occupational therapists to assess their clients’ fitness to drive.22
The test requires the driver to recall information regarding different road users (pedestrians, bicycles, and vehicles) from a briefly presented image of a real-world driving scene. The nature of the visual and cognitive functions that the DriveSafe assesses was not specified by the developers, with the test being described as an assessment of ‘global awareness of the driving environment’. Nevertheless, the test involves a visual search component, where participants scan the images to obtain information about road users within it, as well as a memory component, where participants need to recall the types and location of road users after the image disappears; both visual search8 and memory23 abilities have been shown to be important contributors to driving safety. The DriveSafe, in
conjunction with a questionnaire that evaluates self-awareness of driving capabilities (the DriveAware), has been reported to have sensitivity and specificity levels of 95% and 96% respectively for discriminating between unsafe and safe drivers as classified by their driving performance in an on-road assessment.22, 24
This study utilised the DriveSafe test to address two aims. First, in view of the limited research on blur and eye movements in driving, this study sought to better understand the impact of uncorrected refractive errors, in the form of optical blur, on the performance and eye movement patterns of normally-sighted drivers on the DriveSafe visual recognition slide
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test. The second aim involved investigation of the effects of age on driving-related eye movements. It was hypothesised that DriveSafe scores would be negatively affected by blur, and that this effect would be less pronounced among older adults, as suggested from previous reports of age-related blur tolerance.25, 26 In addition, it was hypothesised that blur would result in altered eye movement patterns, including reduced fixations on road users and decreased saccade amplitudes, and that older drivers would exhibit decreased saccadic amplitudes compared to that of young drivers.11, 27
METHODS
Twenty young (mean age 27.1 ± 4.6 years, 10 males and 10 females) and 20 older, visually normal drivers (mean age 73.3 ± 5.7 years, 15 males and 5 females) were recruited from the QUT Optometry clinic, and the University’s staff and students. All participants were current drivers, defined as having driven within the past three months, and had at least two years of driving experience. Participants with any ocular diseases, and self-reported medical and cognitive impairments that could affect driving performance were excluded. Informed consent was obtained prior to participation. The study followed the tenets of the Declaration of Helsinki and was approved by the Queensland University of Technology Human Research Ethics Committee. Participants were given a full explanation of the nature of the study and written informed consent was obtained, with the option to withdraw from the study at any time.
Driving questionnaire and visual assessment
Participants completed the Driving Habits Questionnaire (DHQ) which is a well-validated instrument used to assess driving habits, exposure, and frequency.28 Participants’ driving characteristics are shown in Table 1. As expected, the older drivers had significantly longer driving experience than the young drivers, however, driving exposure and crash history did not differ significantly between the two groups.
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Table 1. Driving characteristics of study sample. Continuous variables presented in mean ±
S.D.
Young (n=20) Old (n=20) p-value†
Driving experience (years) 7.0 ± 4.4 53.8 ± 7.6 <0.001* Weekly mileage (km) 236 ± 268 139 ± 131 0.154 Days driven per week 4.8 ± 2.3 5.1 ± 1.9 0.706 No. of drivers with a history of crashes (%)
In previous 12 months 2 (10%) 1 (5%) 0.548
In previous 5 years 7 (35%) 3 (15%) 0.144 †
Independent Samples t-test used for continuous variables, and chi-square test for categorical variables.
*Statistically significant (p<0.05).
An ocular health screening, including slit lamp biomicroscopy and fundus photography, was conducted to ensure the absence of any ocular disease. Optimal refractive correction was determined through non-cycloplegic monocular subjective refraction using a Jackson cross-cylinder, with the sphere determined as the maximum plus for best visual acuity (VA), followed by binocular balancing. Best-corrected VA visual acuity was measured with the Early Treatment for Diabetic Retinopathy Study (ETDRS) chart at 4 m, at a luminance of 100 cd/m2, using the letter-by-letter scoring method.29 Contrast sensitivity (CS) was measured with the Pelli-Robson Contrast Sensitivity chart at 1 m at a luminance of 110 cd/m2, using the letter-by-letter scoring method.30 A working distance lens of +1.00 DS was used for the older participants. Visual fields were assessed monocularly using the SITA-Fast 24-2 threshold strategy on a Humphrey Field Analyser (model 750, Carl Zeiss-Meditec, Dublin, CA, http://www.zeiss.com/meditec/en_de/home.html) to exclude participants with visual field defects.
The DriveSafe test (www.pearsonclinical.com.au/products/view/374) consists of 11 different images of a four-way roundabout viewed through a car windscreen from the perspective of the driver. The images vary in complexity and involve different types and numbers of road users including pedestrians, cyclists, cars and trucks. Images were presented on the computer monitor of the Tobii TX300 eye tracking system (www.tobii.com; Tobii Technology) which subtended a visual angle of 21.4° × 14.3° horizontally and vertically, with the individual road
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users ranging from 0.2° to 3.8° in width and 1.0° to 7.8° in height, at the viewing distance of 64 cm. As per the test instructions, participants viewed each image for 3-s, and then reported specific features of each of the road users present in the image including: (1) the type of road user, (2) their location relative to the roundabout (e.g. at the rear or front of the roundabout), (3) their location relative to the observer (e.g. centre, left or right), and (4) their direction of travel (e.g. moving towards or away from the observer). Participants were scored by the number of correctly identified features, with a maximum possible score of 128 (a total of 32 road users). A score of 95 was used as the pass/fail cut-off, as recommended by the test developers.22 Three practice images were presented prior to testing to ensure that participants understood the task, with feedback provided on their responses.
Participants’ eye movements were tracked with the Tobii TX300 eye tracking system while they completed the DriveSafe test. The system is a desk-mounted remote infrared eye tracker that allows unrestricted head movements, with a sampling rate of 300 Hz and a reported tracking accuracy of 0.4°.31 An in-built 5-point calibration procedure was performed prior to testing. Outcome measures collected from the Tobii Studio eye tracking software included the number of fixations per second, average fixation duration (s), average saccade amplitude (°), number of fixations on road users, and fixation duration on road users (s) for each image. Fixations were defined as static eye movements with gaze positions remaining within a visual angle of 1.6° for at least 0.1s,32-34 and saccades were defined as the eye movements that occur between fixations. To evaluate the gaze behaviours on the road users, areas-of-interest were marked out in each DriveSafe image. Areas-of-interest were defined using an elliptical shape fitted around the road users, with a 2° of visual angle margin from their edges (Figure 1) to account for involuntary eye movements that occur during fixation (e.g. microsaccades) as well as potential imprecision in eye tracking.35 This AOI margin was confirmed to be appropriate, following examination of the eye tracking heat maps.35
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Figure 1. An example of the elliptical areas-of-interest (yellow) defined around the road users
This was a repeated-measures design, where participants completed the DriveSafe twice under two visual conditions: best-corrected vision and with optical blur, with the order of visual conditions counterbalanced. A +2.00 DS level of blur was selected for the optical blur condition as it reduced VA to approximately 6/20, slightly better than the minimum VA of 6/24 for a conditional driving licence in Australia.36 Trial lenses were positioned in a trial frame, which included a working distance correction of +1.50 DS for the 64 cm screen distance.
Prior to performing the DriveSafe test, participants adapted to each visual condition for 15-min21 by watching a movie on the monitor at a distance of 64 cm, after which VA was measured with the ETDRS chart. This procedure was repeated for the second visual condition.
Statistical analyses were performed using SPSS version 21.0 (SPSS,
http://www-01.ibm.com/software/au/analytics/spss/), and the level of significance was set at p = 0.05. As there were missing data for some eye movement variables and the data were not normally-distributed, the data was analysed using generalised estimating equations (GEE) with an exchangeable correlation structure,37 where the between-group effects (young and old), within-subject visual effects (best-corrected vision and with +2.00 DS blur), and the two-way interactions were examined. Where interactions were significant, the simple effects were
2° visual angle
2° visual angle
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examined. Recordings of DriveSafe images that had <50% of eye tracking data were not included in the analyses, consistent with the threshold adopted by other studies using similar eye tracking systems.38-40 Twenty-eight out of 880 slide recordings (3.2%) were excluded due to poor tracking; of those that were analysed, the mean eye tracking data recorded was 91.3% (S.D. 10.0%). A mean of 10.7 slides (±0.9) out of 11 were available for each of the visual conditions.
RESULTS
The visual function characteristics of the participants are presented in Table 2. While the younger group had significantly better VA than the older group, the VA of the older participants was relatively high, being at least 0.04 logMAR (Snellen 6/6−2 or 20/20−2) or better. There was no significant between-group differences in terms of vision with +2.00 DS blur, visual field mean deviation, or CS.
Table 2. Mean values and S.D. for visual function characteristics.
Young (n=20) Old (n=20) p-Value†
Best-corrected visual acuity (logMAR) -0.13 ± 0.08 -0.05 ± 0.06 0.003* Visual acuity with +2.00D blur (logMAR) 0.54 ± 0.18 0.53 ± 0.14 0.755 Difference in visual acuity (blur -
best-corrected; logMAR) 0.67 ± 0.19 0.59 ± 1.19 0.107 Visual field 24-2 mean deviation (dB)
Better eye Worse eye 1.92 ± 0.93 1.24 ± 0.85 1.23 ± 0.91 0.49 ± 1.18 0.992 0.072 Binocular contrast sensitivity (logCS) 1.99 ± 0.22 1.95 ± 0.00 0.384 †
Independent Samples t-test *Statistically significant (p<0.05).
The mean DriveSafe scores for the two groups for each visual condition are presented in Table 3 and Figure 2. There was a significant main effect of visual condition, with scores
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being reduced by 17.5% in the presence of blur (best-corrected: 114.9 vs. blur: 93.2; χ21 = 225.3, p < 0.001). While no significant main effect of age group on the DriveSafe score was found (χ21 = 0.4, p = 0.52), there was a significant group and blur interaction (χ21 = 14.8, p < 0.001); the younger drivers were more affected by blur than the older drivers, with reductions in scores of 22.0% and 13.0% respectively (both p < 0.001; Figure 2). For the best-corrected vision condition, the young drivers performed significantly better than the older drivers (χ21 = 10.2, p = 0.001), but exhibited worse performance than the older drivers in the blurred
condition (χ21 = 6.7, p = 0.009), with the average scores of the young drivers falling below the recommended pass/fail cut-off test score.22
Table 3. Group mean values and SD for DriveSafe performance and eye tracking outcome
measures
Group Young Old
Visual condition No blur With blur No blur With blur Score (max 128) 118.4 ± 4.1 88.6 ± 15.3 111.5 ± 6.5 95.9 ± 7.0 Fixations per second 2.41 ± 0.51 2.43 ± 0.59 2.40 ± 0.58 2.48 ± 0.63 Average fixation duration (s) 0.49 ± 0.20 0.51 ± 0.39 0.52 ± 0.21 0.50 ± 0.22 Average saccade amplitude (°) 7.8± 2.2 7.1 ± 2.1 7.0 ± 2.0 6.3 ± 1.8 Number of fixations on road
users 5.2 ± 1.96 4.6 ± 2.0 5.0 ± 2.57 4.4 ± 2.0
Total fixation duration on road
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Figure 2. The effect of blur on DriveSafe scores for both groups. Dotted line represents the
recommended pass/fail cut-off test score for fitness to drive (score of 95).22 Error bars represent standard errors.
To further investigate whether the between-group differences in DriveSafe scores with best-corrected vision were independent of VA and CS, a supplementary analysis of the effect of age group on DriveSafe scores was repeated which included VA and CS as covariates. The effect of age on DriveSafe scores with best-corrected vision remained significant (χ21 = 10.8, p = 0.001), while the effects of VA and CS were not (χ21 = 2.7, p = 0.10 and χ21 = 0.2, p = 0.66 respectively).
The eye movement parameters are also presented in Table 3. Visual condition had a significant main effect on the gaze behaviours on road users, with the total number of fixations on road users reducing by 10.6% (best-corrected: 5.1 vs. blur: 4.5 fixations per image; χ21 = 31.8, p < 0.001), and total fixation duration on road users reducing by 10.8% (best-corrected: 2.0 s vs. blur: 1.8 s per image; χ21 = 10.0, p < 0.001) with blur as compared to the best-corrected vision condition. Figure 3 shows an example of the fixation patterns of one participant, with fewer fixations on the road users and more on non-relevant locations with optical blur than with best-corrected vision. There was also a significant main effect of visual condition on saccade amplitude, which decreased by an average of 0.7° with blur
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compared to best-corrected vision (best-corrected: 7.4° vs. blur: 6.7°; χ21 = 27.5, p < 0.001). Finally, there was a significant main effect of age group on saccade amplitudes, with older drivers making smaller saccades by an average of 0.8° than the young drivers (older: 6.7° vs. young: 7.4°; χ21 = 27.9, p < 0.001). This effect of age on saccade amplitudes remained significant (χ21 = 9.6, p = 0.002) even after correction for VA and CS, neither of which significantly affected saccade amplitudes (p = 0.90 and p = 0.36 respectively). There was no other significant main or interaction effect of visual condition and age group on the eye movement patterns.
Figure 3. Eye movement patterns of a young participant with best-corrected vision (green)
and with blur (red) while completing the DriveSafe test. Circles represent fixations, with larger circles indicating longer fixation durations, the numbers within the circles indicate the fixation sequence, and lines connecting fixations represent saccades. The three road users present in the scene are indicated with yellow arrows.
DISCUSSION
This study is the first to explore the effects of optical blur and age on performance on the DriveSafe slide recognition test. The findings are in general agreement with the initial hypotheses, demonstrating that blur negatively affected DriveSafe score, with the effects being greater for the young than the older drivers. In addition, blur resulted in changes in eye movement patterns suggestive of reduced visual attention on the road users, while age
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reduced saccade amplitudes but did not have any significant effect on the other eye movement parameters.
The detrimental effect of blur on the DriveSafe scores is likely to reflect the well-known impact of blur on visual function, where VA and CS are decreased.17, 18, 20 In addition, the current findings are in agreement with previous closed-road studies showing that low-contrast hazard detection, pedestrian recognition, sign recognition, and overall driving performance are poorer with optical blur in both young and older drivers. 18, 19 However, while a previous closed-road study20 did not find a significant age and blur interaction effect on driving performance, the current study found that blur had a significantly greater negative effect on the DriveSafe scores of the young compared to the older drivers. A likely reason for this inconsistency is the nature of the performance measures. In the current study, the DriveSafe is a timed test which only captures selected aspects of the driving task compared to real-world driving. In addition, the DriveSafe test consists of static images, where the effects of any age-related increase in blur tolerance may be more evident, as has been observed in non-driving studies, 25, 26 compared to real-world driving where the visual scene is dynamic. It has been suggested that these previous observations of increased blur tolerance with age are partly explained by age-related pupil miosis. 25, 26
While there was no main effect of age group on the DriveSafe scores, there was a significant blur and age interaction effect as described above, where the older drivers performed
significantly worse than the young drivers in the best-corrected vision condition, an effect which was reversed for the blurred condition. The effect of age on DriveSafe scores in the best-corrected vision condition remained significant even after adjustments for VA and CS, suggesting that the poorer scores of older drivers at baseline are not related to their vision but rather, may be related to declines in memory4 and visual search abilities with age.3 In
addition, the presence of other objects in the DriveSafe images such as trees, lampposts, and the varying luminance levels across the scene may have acted as visual distractors, potentially exaggerating the negative effect of age on visual search ability.41 What remains unclear, however, is why the young drivers performed worse than the older drivers under the blurred condition, even though both groups were blurred to the same level and had similar levels of VA. It is possible that for this timed task, the effect of blur tolerance25, 26 plays a greater role on performance than other age-related changes, this is an issue that warrants further
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Blur significantly altered several eye movement parameters, with a 10% reduction in the number of fixations and fixation duration on road users. Given the 3-s image duration, these changes are likely to impact on performance, and are reflected by the reduction in DriveSafe scores seen with blur. These findings, however, are not in complete agreement with previous studies on non-driving visual search tasks, which reported that fixation durations on targets (and distractors alike) were longer in the presence visual impairment, specifically central scotomas from macular degeneration,42 and with simulated central and peripheral visual field scotomas.43 Longer fixation durations on an object are associated with increased awareness and visual attention on the object,44 and one might expect that fixation duration on the targets would increase with blur. However, participants in these previous studies42, 43 were given an unlimited amount of time to complete the task, compared to the 3-s viewing time of the DriveSafe images in the current study which may better reflect the critical temporal demands required for driving, where timely decisions are required. The time limit required participants to process as much visual information from each DriveSafe image as quickly as possible; therefore, with blur, participants did not dwell on road users that were difficult to see, and appeared to have prioritised searching for other road users.
The difficulty experienced by both young and older drivers in the blur condition is further demonstrated by the reduction in their saccade amplitudes with blur. Previous studies have observed that saccade amplitudes reduced with increasing visual difficulty of a search task (e.g. reduced target-background contrast,45 smaller targets,46 or increasing the complexity of the targets and distractors).46 Maltz and Shinar27 likewise reported that saccade amplitude was negatively correlated with the time taken to complete their visual search task
(sequentially locating numbers on a photograph of a driving scene). Pomplun et al.46 suggested that making smaller saccades is part of a systematic search strategy adopted for completing challenging visual tasks. In the current study, the mechanisms underlying the effect of blur on saccade amplitude may also reflect increasing difficulty in completing the task.
Saccade amplitudes were also smaller in the older than the younger drivers. Similar findings were recently reported by Dowiasch et al.,11 who found that the saccades of older adults were significantly smaller than young adults for a walking task. Maltz and Shinar27 also found a trend towards smaller saccade amplitudes in older relative to younger participants in their visual search study that involved locating numbers embedded within photographs of a driving scene; however, these differences did not reach statistical significance, possibly due to their
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small sample size (n = 10). The decrease in saccade amplitudes in older drivers is likely to be associated with the reduced accuracy of their saccades, the end-point of which often fall short of the target.9 These changes in saccade amplitudes and accuracy have been attributed to deterioration in extra-ocular muscle function and visuo-neurological processing that occur with ageing, including deficiency of the motor signal that directs the eye to the desired position.9 Age differences in saccade amplitudes may also be linked to increases in saccadic averaging in the presence of visual distractors in older adults.10 In the present study, visual distractors such as trees and lampposts in the DriveSafe images may have increased the saccadic averaging of the older group, which may explain their smaller saccade amplitudes. The findings of the current study need to be considered in light of its limitations. The
DriveSafe consists of 11 images, hence there is the potential for a learning effect as a result of repeated presentations between the different visual conditions. However, this effect was minimised as much as possible by counterbalancing the visual conditions, randomising the order of slide presentations, and providing a 30 min period between presentations (15 min rest plus 15 min of washout period/blur adaptation). In addition, no changes to the statistical outcome were found for the DriveSafe score after adjusting the analysis for the potential influence of testing order. Another potential limitation of this study is that the use of simulated optical blur may not be fully representative of a longstanding refractive error. However, a 15-min period of blur adaptation was implemented before DriveSafe testing, given that the impact of blur on VA has been reported to plateau following 14-min of adaptation.21
In summary, this study provides preliminary evidence that optical blur negatively affects performance on the off-road DriveSafe test for both young and older drivers. Importantly, blur altered several eye movement parameters, notably resulting in fewer and shorter fixations on road users, and smaller saccade amplitudes. The changes in eye movement patterns are a demonstration of the negative effects of optical blur on aspects of driving performance, in particular, an overall decrease in visual attention on other road users in the driving environment. While the DriveSafe test is considered to reflect on-road driving performance, it is essential to understand how these patterns of eye movements and performance on the DriveSafe translate to real-world driving. On-road studies of the eye movement patterns and performance of individuals with true blurred vision resulting from uncorrected refractive error are therefore warranted; the findings from the current study provide an important basis for such investigations.
Page 16 of 20 DISCLOSURE
The authors report no conflicts of interest and have no proprietary interest in any of the materials mentioned in this article.
ACKNOWLEDGEMENTS
This study was supported by a QUT School of Optometry and Vision Science Scholarship, as well as a QUT HDR Tuition Fees Sponsorship. The authors would also like to thank Trent Carberry and Dr Philippe Lacherez for their assistance with the study, and the study
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