The fovea is the specialized central area of the macula, and it provides the highest level of visual acuity because it contains the maximum density of cone photoreceptors.1 As a result, normally sighted individuals fixate with their fovea when performing various activities of daily living such as watching television2 and reading.3 There are ocular diseases, however, that result in a progressive degeneration of the macula including the fovea, which results in central vision loss. Age-related macular degeneration is the leading cause of central vision loss in people 50 years and older in Western countries.4 Stargardt disease and degenerative myopia can also cause central vision loss but early in life, typically during the teenage years or as an early adult.5,6 The central vision loss experienced by people with these macular degenerative diseases is characterized by reduction in visual acuity and contrast sensitivity, and by central field loss.5–8
With compromised foveal vision, individuals with central vision loss often use a relatively healthy, nonfoveal retinal location to fixate and perform visual tasks. This adaptive strategy is called eccentric viewing, and the selected nonfoveal retinal location used for fixation is termed the preferred retinal locus.9,10 However, numerous studies have shown that most patients with central field loss are unaware of the presence of their central scotomata,11 which makes it difficult for them to maintain eccentric viewing and thus effectively use their preferred retinal locus.12,13
Despite long-term use of eccentric viewing, individuals with central vision loss still have difficulties when performing various activities of daily living such as recognizing faces, reading, and driving.14–16 Given that driving is the main method used for commuting in Western countries,17 those individuals who cease driving need to rely on alternative transportation modes (such as public transportation, taxis, or lifts from relatives and friends) to maintain their independence. Driving cessation may also result in an increase in walking as a means of personal transportation. Patients with central vision loss report having difficulty with mobility,18 and it has been shown that mobility performance becomes significantly less efficient as the size of the binocular central field loss increases.19 This reduced mobility performance in the presence of central field loss might therefore also significantly affect a person's ability to perform the high risk and complex mobility task of crossing the street.
Crossing the street is a common activity of daily life. Pedestrians are among the most vulnerable users of the road, and an inappropriate crossing decision may result in injury or even loss of life. According to the World Health Organization, more than 275,000 pedestrians are killed globally each year accounting for 22% of all road traffic deaths worldwide.20 Pedestrian crashes affect people from all different age groups. In the United States in 2017, pedestrians 65 years and older accounted for 20% of all traffic fatalities, whereas people aged between 20 and 34 years accounted for 12% of all traffic fatalities.21 In Australia in 2017, pedestrians aged between 17 and 39 years accounted for 22% of pedestrian fatalities,22 whereas 64% of pedestrians involved in road traffic crashes in India in 2017 were aged between 18 and 45 years.23 Although the aforementioned statistics of pedestrian fatalities are not specific to visually impaired pedestrians and do not differentiate between fatalities due to pedestrian or driver error/carelessness, these high percentages of fatalities demonstrate that pedestrian safety is an important global concern that impacts society across the entire age spectrum.
The effect of central vision loss on street-crossing performance has been assessed in only a few studies.24–28 In three of these earlier central vision loss street-crossing studies,24–26 subjects only had reduced visual acuity and contrast sensitivity and no central field loss (i.e., absolute scotoma). In the remaining two studies,27,28 subjects had a central field loss in addition to reduced visual acuity and contrast sensitivity.
Most of these earlier studies have shown that the street-crossing performance of subjects with central vision loss, including those with central field loss, was comparable with that of age-matched normally sighted subjects.24–27 However, Geruschat et al.24 found that age-related macular degeneration subjects with central vision loss but without a central field loss took significantly longer to identify crossable vehicular arrival times and had significantly larger negative (unsafe) safety margins compared with the age-matched normally sighted subjects.
No studies, however, have assessed the street-crossing decision-making performance of young individuals with central field loss in real outdoor environments. Wu et al.28 studied crossing decisions of young normally sighted subjects simulated with a gaze-contingent central field loss within a virtual environment. They found that, with increasing scotoma size, subjects had longer curb delay and selected longer time gaps between traffic when crossing an exit lane of a virtual roundabout. However, this behavior was expected because their subjects were required to fixate centrally. Thus, as the scotoma size became larger, subjects had to wait increasingly longer until the angular subtense of the approaching vehicle was greater than that of the central scotoma. In addition, although their virtual environment was immersive and contained 3D auditory cues, it still may not have resulted in behaviorally equivalent performance as observed in the real world.
Given that young people as a whole are involved in pedestrian fatalities and that they walk significantly more than older people,29 they may encounter the task of street crossing and, hence, its associated risks and dangers, more frequently than older people when they travel to school, work, and/or exercise. The aim of the present study was to determine the effects of simulated central field loss and eccentric viewing on street-crossing decision-making performance in a group of young adult pedestrians along a real-world street. The central field loss was induced using a gaze-contingent experimental model of central field loss.30
We hypothesized that normally sighted young subjects using eccentric viewing under the simulated central field loss condition would adopt a risk-averse street-crossing strategy compared with the habitual vision condition. This is because with the elimination of their central vision for the first time, subjects may become more cautious by classifying only those vehicular gap times that are significantly longer in duration than their actual crossing time as being “enough time to cross.” Adopting such a strategy will result in subjects to become more inaccurate with their decision-making under the central field loss condition compared with when using their normal central (foveal) vision.
In addition, when maintaining eccentric viewing, subjects will use a preferred retinal locus that is most likely located within the peripheral visual field. Given that vehicular gap time judgments are more variable when using the peripheral visual field than when using foveal (central) vision,31 we hypothesize that subjects will be more variable and hence less reliable in their street-crossing decisions when using eccentric viewing (peripheral visual field) compared with when using their normal central vision.
Twenty-four normally sighted subjects aged between 23 and 31 years (average [standard deviation], 26.98 [2.23] years) participated in the study. Inclusion criteria included age between 18 and 40 years, full cognitive and physical function, the ability to walk and stand unaided, corrected right eye visual acuity of 20/30 or better, absence of any ocular diseases resulting in vision loss, and good general health. Subjects were recruited from friends and students at the Indiana University School of Optometry after obtaining informed consent. The present study adhered to the tenets of the Declaration of Helsinki and was approved by the institutional review board of Indiana University.
Visual Function Measures
Habitual Vision Condition
Monocular, right eye visual acuity; contrast sensitivity; and visual field were measured in each subject while the left eye was occluded. The visual acuity and contrast sensitivity were assessed while subjects wore their habitual spectacle prescription. Visual acuity was measured using a Lighthouse Early Treatment Diabetic Retinopathy Study acuity chart that was transilluminated to approximately 85 cd/m2 and reported as the logMAR using the scoring of Bailey and Lovie.32 Contrast sensitivity was measured at 1 m using an Evans letter contrast sensitivity chart transilluminated to approximately 85 cd/m2. Subjects were assigned logCS scores using the scoring of Elliott et al.33,34
Monocular, right eye semiautomated kinetic perimetry was also performed on each subject using the Octopus 900 perimeter (Haag Streit, Mason, OH). To avoid lens rim effects, subjects did not wear their habitual spectacle prescription during the visual field assessment. Subjects were instructed to fixate a center point while a III4e Goldmann stimulus (0.43° stimulus diameter, 0-dB intensity) was presented against a 10-cd/m2 background luminance level along 12 meridians separated by 30° intervals. The light stimulus was moved from the far periphery toward the perimeter bowl center. Subjects were instructed to press a button whenever they first saw the light stimulus moving in their peripheral visual field. This corresponded to their visual field extent. To obtain the borders of the blind spot or any scotoma, subjects were instructed to press the button if the light disappeared as it moved from the visual field extent toward the center of the bowl and press the button again if the light reappeared. These two locations corresponded to the outer and inner edges of the blind spot or scotoma, respectively. The blind spot or any scotoma was then mapped by moving the stimulus at a rate of 5°/s from the blind spot or scotoma center (nonseeing) along the eight principal meridians until the subject reported when they first saw the stimulus (seeing). For each subject, visual field extent (radius) was averaged across the 12 meridians, and the visual field area was computed as the area contained within the visual field extent. The remaining visual field area was calculated by subtracting the combined area of the blind spot and any scotoma from the area contained within the visual field extent. As expected, no central scotomata were detected under the habitual vision condition.
Simulated Central Field Loss Condition
Each subjects' right eye was fitted with a centrally opaque soft contact lens using the methods described by Almutleb et al.30 In summary, each subject's right eye was fitted with a prism ballast, plano, centrally opaque soft contact lens with a central-opacity diameter of 2.8, 3.0, or 3.2 mm. Only one central-opacity contact lens was fitted for each subject, for a total of 24 eye–contact lens pairings.
With the left eye occluded, right eye visual acuity at the street test site was measured using the Early Treatment Diabetic Retinopathy Study acuity chart while subjects maintained eccentric viewing that resulted in the best vision for them (central field loss visual acuity with eccentric viewing). The central field loss visual acuity with eccentric viewing was reported as the logMAR and scored using the methods of Bailey and Lovie.32
With the left eye occluded, the extent and position of the induced central, absolute scotoma in the right eye was assessed at the street test site using a custom-made Tangent (Bjerrum) Screen positioned at 0.90 m and a custom-made 5-mm white Traquair target. The Tangent (Bjerrum) Screen had a reflectance of 28% (gray background), which was similar to the average reflectance of the environment at the test street (i.e., average combined reflectance of grass, trees, aged-asphalt, and black, white, and colored vehicles).35,36 To ensure that the contact lens opacity obscured the fovea, thus resulting in an absolute central field loss, subjects were instructed to centrally fixate on a large central fixation target on the Tangent (Bjerrum) Screen. Being able to fixate the central target meant that the contact lens opacity was decentered to such an extent that the resulting displacement of the scotoma enabled foveal fixation. In these cases, subjects were excluded from the study. In this study, 4 of the 24 subjects were excluded because of their ability to centrally fixate while wearing the centrally opaque contact lens. As a result, data were collected on a total of 20 eye–contact lens pairings.
When subjects were unable to see the fixation target on the Tangent (Bjerrum) Screen, they were instructed to maintain eccentric viewing by placing one edge of the scotoma inferior, superior, nasal, or temporal to the fixation target on the Tangent (Bjerrum) Screen. Subjects selected which eccentric viewing direction resulted in the best vision for them. Scotoma extent was then mapped from nonseeing to seeing using standard clinical practice.37,38 Scotoma diameter in degrees was obtained for each subject by averaging the scotoma radius across the eight principal meridians and multiplying this result by two. The centrally opaque contact lenses induced central, absolute scotomata that ranged from 6.32° to 28.20° in diameter, with a mean (standard deviation) diameter of 17.12° (5.83°).
Cognitive and Health Assessments
Cognitive functions were assessed in each subject using the Mini-Mental State Examination and the Trail Making Test—Part B. Refer to Table 1 for the average (standard deviation) Mini-Mental State Examination and Trail Making Test—Part B scores for all subjects.
The health status of participating subjects was also assessed using the Short Form-36 Health Questionnaire, which measures physical health and mental health.39 The Short Form-36 Health Questionnaire consists of eight domains (Table 1), and each domain has a score ranging between 0 (poorest health) and 100% (best health).39 Refer to Table 1 for subjects' average (standard deviation) score for each of the eight Short Form-36 domains.
Subjects completed a survey about their street-crossing habits for nonsignalized streets. Subjects answered whether they independently crossed a nonsignalized street by selecting one of the following options: never required assistance, sometimes required assistance, always required assistance, or never crossed a nonsignalized street. Subjects also answered questions rating the frequency of crossing different types of streets (such as a one-way street, two-way street, and roundabout) by selecting one of the following options: never, rarely (defined as a few times per month), often (defined as several times per week), or I do not encounter this type of street on foot. For questions rating the perceived difficulty of crossing each of the different types of streets, a 5-point Likert scale from no difficulty (rating 1) to impossible (rating 5) was used. A 5-point Likert scale from very conservative (rating 1) to very liberal (rating 5) was also used when subjects rated their own crossing behavior.
The Street Test Site
The test site used in the present study was located in Bloomington, Indiana, and has been described previously.27,40 In summary, the street was a nonsignalized, two-way street with one lane of traffic approaching in either direction. A traffic island separated both lanes, and this allowed subjects to make street-crossing decisions about a single lane of traffic (approximately 4.62 m wide) approaching from the lane closest to them. Subjects were seated by the curb, at a designated location called the “crossing point,” where they were required to make street-crossing decisions. This crossing point remained the same throughout the duration of the study.
Vehicular Arrival Time Measurements
Vehicular velocity and arrival times were measured using the methods described in previous studies.25–27 In summary, two laser sensors were positioned on the curb of the test street. Laser sensor 1 was positioned in front of the subject at the crossing point and was defined as 0 m, whereas laser sensor 2 was approximately 2.0 m to the left of the crossing point where the subject was sitting. Two retroreflectors on the other side of the street were aligned with each of the laser sensors. Whenever a vehicle interrupted the laser beam, the event was recorded, timestamped, and sent, via a USB cable, to a laptop computer that contained a custom-written “street-crossing” program. Vehicular speed was computed by dividing the known distance between the two sensors by the time it took the vehicle to travel between the two sensors. The physical vehicular arrival time was defined as the duration between the time of the prompt signal (prompt signal is explained in Experimental Procedure section hereinafter) and the time when the first approaching vehicle interrupted the laser sensor positioned at the crossing point.
The experimental procedure used to obtain street-crossing decisions and crossing times has been previously described by Hassan,25 Hassan and Massof,26 and Hassan and Snyder.27 All street-crossing decisions and crossing times were measured monocularly with the right eye while the subject's left eye was occluded. At the start of the study with their left eye occluded, subjects physically crossed the street at their usual walking pace four times under two viewing conditions (habitual vision and the simulated central field loss conditions). This provided subjects with information on the time and distance required to physically cross the street. The order of measuring subjects' actual crossing times under habitual vision and simulated central field loss conditions was counterbalanced across subjects. Subjects then sat by the side of the road at the crossing point and made street-crossing decisions under two conditions, habitual vision with central fixation and simulated central field loss with eccentric viewing, while monocularly viewing with their right eye the test street for different vehicular arrival times (0.1 to 19 seconds in duration).
Under the habitual vision condition, subjects were instructed to direct their head forward while wearing earbuds that played white noise. This was done to prevent subjects from sampling any visual and auditory information before they had to make their street-crossing decision. After an audible “get-ready” signal was given, the white noise stopped playing and subjects were instructed to view and listen to approaching traffic for 2 seconds. Because previous studies have shown that normally sighted subjects41 and subjects with central field loss24 take on average up to 2 seconds to make a street-crossing decision, subjects were given 2 seconds to observe traffic from which to make their crossing decision. At the end of the 2 seconds, a “prompt” signal was automatically given and the white noise also automatically resumed playing. At the time of the prompt signal, subjects were instructed to immediately look straight ahead again and were required to judge whether they believed that there was enough time to cross before vehicular arrival by clicking a button that was attached to a laptop computer.
Under the simulated central field loss condition, the same procedure was used as with the habitual vision condition except that subjects were instructed to maintain eccentric viewing by placing the edge of the induced scotoma to one side of the street just adjacent to the curb such that they could see approaching vehicles using their peripheral visual field. This is analogous to a patient using a preferred retinal locus at one of the edges of the scotoma. The scotoma/preferred retinal locus location used by subjects when making their street-crossing decisions was determined by the subject when their visual field was measured using the Tangent (Bjerrum) Screen. For example, if they placed the scotoma to the right of the fixation target (i.e., left-field preferred retinal locus), they would place the scotoma to the right side of the street just adjacent to the curb. More than half of the subjects (55%) used a left-field preferred retinal locus, where 35 and 10% of the subjects used a right-field and superior-field preferred retinal locus, respectively.
Trials where the approaching vehicle changed speed during the trial were discarded. The average (standard deviation) speeds for all trials and sessions under habitual vision and simulated central field loss conditions were 49.58 (1.64) and 49.24 (2.58) km/h, respectively. Therefore, approaching vehicles were on average slightly faster than the 48-km/h speed limit of the street test site for all conditions and sessions. All subjects were instructed to make street-crossing decisions based on the assumption that the approaching vehicle would never slow down or yield to them.
Subjects used a 5-point rating scale as validated by Hassan and Massof26 to indicate whether they believed that the vehicular arrival time was longer or shorter in duration than their crossing time (Table 2). Subjects pressed the button the same number of times corresponding to the desired rating number.
Street-crossing decisions were collected for nine different vehicular arrival time categories (Table 3), and a minimum of 10 trials were collected for each vehicular arrival time category. Before collecting street-crossing decision data for both viewing conditions, practice trials were given until the subject demonstrated understanding of the instructions regarding the experimental task, using the full rating scale and for the simulated central field loss condition how to maintain eccentric viewing. The testing order of the two viewing conditions (habitual and central field loss with eccentric viewing) was counterbalanced across subjects.
We analyzed the crossing decision data using the methods developed and validated by Hassan and Massof.26 In summary, we computed the cumulative frequency distributions of subjects' crossing decisions for each vehicular arrival time category by calculating the proportion of times that subjects used a specific rating for a given vehicular arrival time category. Graphing the cumulative frequency distribution of one vehicular arrival time category against the cumulative frequency distribution of another vehicular arrival time category gave us the receiver operating characteristic curve for that vehicular arrival time pair. Receiver operating characteristic curves were graphed for all vehicular arrival time pairs for each subject for both test conditions, resulting in a total of 36 receiver operating characteristic curves for each subject for each condition. Computing the area under each receiver operating characteristic curve and converting each area to a z score and multiplying this result by the square root of 2, we obtained a dissimilarity value (d′) for each vehicular arrival time pair. d prime (d′) represents the distance between the means of the frequency distributions of each vehicular arrival time pair. There were a total of 36 d′ values for each subject and condition. Entering the 36 d′ values into a 1D scaling model, we estimated the means of the decision variable for each vehicular arrival time category for each subject and condition. Plotting these nine means as a function of vehicular arrival time categories for each subject and condition, we obtained the best-fitting nonlinear curve from which we obtained two parameters for each subject and condition: the x-intercept and the slope of the nonlinear function at the x-intercept. The x-intercept represented the time in seconds when a subject classified vehicular arrival time from being “not enough time to cross” to being “enough time to cross.” Thus, this point represented the transition point between perceived unsafe and safe vehicular arrival times.25–27 The slope at the x-intercept represented the rate of change in subjects' criterion for street-crossing decisions.25–27
Calculating the difference between the x-intercept of the nonlinear function and subjects' averaged actual crossing time for the respective viewing condition gave us each subject's bias for each condition. Bias is the amount of time in seconds where subjects either overestimated or underestimated vehicular arrival time relative to their actual crossing time.25–27 A zero bias indicated perfect accuracy in street-crossing decisions. Positive bias values suggest inaccurate but safe street-crossing decision-making performance. This is because subjects would not classify vehicular arrival time as being “enough time to cross” until they were longer in duration than their actual crossing time. The converse is true for negative bias values.
The reliability of subjects' street-crossing decision-making for each condition was determined as the slope of the nonlinear function at the x-intercept.25–27 Steep slopes indicated that subjects were reliable in their street-crossing decisions because the subject transitioned quickly from judging vehicular arrival time as not enough to enough time to cross. The converse is true for subjects with shallow slopes.
Kolmogorov-Smirnov tests were performed to determine the normality of the distributions of bias, slope, vision measurements, cognitive functions, and health status assessments obtained under the habitual vision and/or simulated central field loss with eccentric viewing condition. Parametric analyses were used with normally distributed data, whereas nonparametric analyses were used on nonnormally distributed data if they could not be transformed into a normal distribution.
To assess for accuracy in street-crossing decisions under both conditions (habitual vision and simulated central field loss with eccentric viewing), the two distributions of bias values were each tested if they were significantly different from zero using one-sample sign tests. Wilcoxon signed ranks tests were performed to assess for significant differences in accuracy (bias) and reliability (slope) between habitual vision and simulated central field loss with eccentric viewing conditions. To assess if there was clustering in our data, we tested whether the difference scores (habitual − central field loss with eccentric viewing) for accuracy (bias) and reliability (slope) were normally distributed. Spearman rank order correlations were performed to assess for any relationship between accuracy (bias) and reliability (slope) with measures of vision, cognition, and health status under both viewing conditions (habitual and simulated central field loss with eccentric viewing).
Backward elimination multiple regression analyses were performed in two steps to determine the best predictor(s) of accuracy (bias) and reliability (slope) of street-crossing decision-making under habitual and simulated central field loss with eccentric viewing conditions. Four backward multiple regression analyses were initially performed for the two dependent variables (i.e., accuracy [bias] and reliability [slope]) under the two viewing conditions (i.e., habitual vision and simulated central field loss with eccentric viewing conditions). The independent variables (predictors) in these four multiple regression analyses were the eight domains of the Short Form-36 Health Questionnaire. This was done to reduce the number of independent variables (predictors) in the multiple regression analysis models given the small sample size. Another four multiple regression analyses were then performed for the same dependent variables under the two viewing conditions. The predictor variables under the habitual vision condition were habitual visual acuity, contrast sensitivity, visual field extent, visual field area, remaining visual field area, Mini-Mental State Examination, Trail Making Test—Part B, and any significant Short Form-36 Health Questionnaire domains from the initial round of multiple regression analyses for each dependent variable (i.e., accuracy [bias] and reliability [slope]). For the simulated central field loss with eccentric viewing multiple regression analysis models, the same predictor variables as those used in the habitual vision condition multiple regression analyses were used except that habitual visual acuity and contrast sensitivity were excluded and central field loss visual acuity with eccentric viewing and scotoma diameter were included along with any significant Short Form-36 Health Questionnaire domains from the initial round of multiple regression analyses for each dependent variable. To enter a variable into the multiple regression analysis models, a P ≤ .05 criterion was used and a P > .05 criterion was used to remove a predictor variable from the multiple regression analysis model. Assumptions about normality, homoscedasticity of residuals, and multicollinearity were all examined in all multiple regression analyses.
Under the habitual vision condition, the normally sighted young subjects had average (standard deviation) logMAR and logCS values of −0.14 (0.07) and 2.17 (0.10), respectively. Subjects' unoccluded visual fields were within normal limits and had average (standard deviation) visual field extent radius, area, and remaining area of 63.12° (2.82°), 12,335.35 (1057.45) degrees2, and 12,290.30 (1058.50) degrees2, respectively.
Under the simulated central field loss with eccentric viewing condition, subjects' average (standard deviation) logMAR visual acuity was 0.80 (0.25), which was significantly worse than the visual acuity under the habitual vision condition (paired t test, t19 = −15.10, P < .0001).
The average (standard deviation) results of the cognitive and general health assessments are shown in Table 1.
Accuracy and Reliability of Street-crossing Decisions
Fig. 1 shows the median bias values under habitual vision and simulated central field loss with eccentric viewing. No significant difference in bias was found between the two viewing conditions (Wilcoxon rank test, z = −0.93, P = .35; Fig. 1). Bias values, however, under both conditions were on average significantly different from zero (one-sample sign tests, P < .0001 for both habitual and central field loss with eccentric viewing conditions; Fig. 1) but positive in value, indicating that subjects on average made significantly inaccurate but safe street-crossing decisions under the habitual vision and the simulated central field loss with eccentric viewing conditions.
Median slope values at the x-intercept of subjects' nonlinear function under habitual vision and simulated central field loss with eccentric viewing conditions are shown in Fig. 2. No significant differences in the levels of reliability were found between the habitual vision and simulated central field loss with eccentric viewing conditions (Wilcoxon signed rank test, z = −1.72, P = .09; Fig. 2). Therefore, subjects on average performed similarly irrespective of their viewing condition.
The computed difference scores between the habitual and central field loss with eccentric viewing conditions for bias and slope were normally distributed (Shapiro-Wilk test of normality, W = 0.98 [P = .95] and W = 0. 93 [P = .16] for bias and slope, respectively). These findings are suggestive that there was no data clustering, and thus, the effect of the induced central field loss on bias and slope did not differ systematically from person to person.
Relationship between Street-crossing Performance and Vision, Cognition, and General Health Assessments
Significant correlations were found between bias and slope with various vision, cognitive, and self-reported health measures (Table 4).
Backward multiple regression analyses revealed that habitual visual acuity and the Short Form-36 Health Questionnaire domain of general health were the best predictors of bias under the habitual vision condition, accounting for 45% of the variance in bias (F2,17 = 8.75, P = .002, adjusted R2 = 0.45). Specifically, habitual visual acuity was negatively associated with bias (b = −2.92, P = .02), suggesting that a decline in visual acuity was associated with a decrease in bias and hence less safe street-crossing decision-making. The general health domain (b = −0.02, P = .02) was also significantly associated with bias under the habitual vision condition, indicating that poorer self-rated health was associated with a more positive bias. No significant predictors, however, were found for bias under the simulated central field loss with eccentric viewing condition.
Backward multiple regression analyses revealed that habitual visual acuity and Trail Making Test—Part B were the best predictors of reliability under habitual vision, explaining 22.4% of the variance in slope values (F2,17 = 3.74, P = .04, adjusted R2 = 0.22). Declines in visual acuity were associated with an increase in street-crossing reliability (b = 1.72, P = .02). Better performance on the Trail Making Test—Part B (i.e., better executive function) was associated with higher street-crossing reliability (b = −0.01, P = .04). No significant predictors were found for street-crossing reliability under the simulated central field loss with eccentric viewing condition.
The street-crossing survey revealed that all subjects crossed nonsignalized streets independently without assistance. More than half of the subjects (55%) reported that they crossed a moderately busy nonsignalized one-way street with one lane of traffic (similar to the street assessed in this study) several times per week, whereas 45% of subjects self-reported crossing this type of street only a few times per month. Ninety percent of the subjects reported no difficulty crossing a moderately busy nonsignalized street similar to the type of street assessed in this study. Only 10% of subjects found it slightly difficult to cross a moderately busy nonsignalized one-way street with one lane of traffic.
When self-rating their crossing behavior, 10% of subjects rated themselves as very conservative, 15% as moderately conservative, 15% as slightly conservative, 30% as slightly liberal, 25% as moderately liberal, and 5% as very liberal. For perceived difficulty in judging vehicular arrival time when crossing a nonsignalized street, 80% of subjects reported no difficulty, whereas 15 and 5% of subjects self-reported this task as slightly and moderately difficult, respectively.
This is the first study to systematically assess the effect of an experimentally induced central scotoma on the street-crossing decision-making performance of young pedestrians in real outdoor traffic environments. We found that our young-aged subjects were reliable but significantly inaccurate with their street-crossing decisions irrespective of whether or not an absolute central scotoma was present. However, under both conditions, subjects on average showed positive bias values, suggesting that subjects underestimated vehicular arrival time relative to their actual crossing times, and thus, they adopted a safe crossing strategy.
Finding no significant difference in accuracy or reliability between the conditions of with and without a simulated central field loss is in agreement with the findings of Hassan and Snyder,27 Hassan,25 and Hassan and Massof,26 although these earlier studies were a between-subject design where the control group was a different group of age-matched normally sighted subjects, the vision loss subjects were binocular and had real (i.e., not simulated) central vision loss, and their subjects were significantly older than the subjects in the current study and thus had comorbidities and possible declines in their cognitive and/or motor skills that may have affected their performance. The agreement between the current study and these earlier central vision loss street-crossing studies is suggestive that our central field loss simulation method is an effective tool at characterizing the decision-making behavior of pedestrians with real central field loss.
Although Geruschat et al.24 also found that subjects with central vision loss from age-related macular degeneration were just as accurate as normally sighted subjects in correctly identifying crossable and uncrossable opportunities, they did report that their age-related macular degeneration subjects made unsafe crossing decisions because they took significantly longer to identify crossable vehicular arrival times and had large, negative (unsafe) safety margins. A possible reason for the discrepancy in results between the findings of Geruschat et al.24 and the current study may relate to the complexity of the street used in each study and the ages of the subjects. Unlike the current study, Geruschat et al.24 measured crossing decisions at a large, busy, double-lane roundabout in elderly subjects with age-related macular degeneration. Thus, the findings of Geruschat et al.24 may be explained by the fact that elderly, normally sighted pedestrians have been shown to make significantly more unsafe crossing decisions compared with young, normally sighted pedestrians,41–43 most likely because of comorbidities and declines in perceptual, cognitive, and/or motor skills, and they make significantly more unsafe crossing decisions at complex crossing environments than at simpler environments such as a one-way street.44,45
Our finding that subjects with a simulated central field loss were reliable and safe in their crossing decisions suggests that eccentric viewing can successfully compensate for any deficits in street-crossing decision-making performance as a result of a central field loss. The average visual acuity of our subjects was 20/120, and they had an absolute, monocular scotoma that ranged from 6 to 28° in diameter. Despite the presence of these large absolute scotomata and poor visual acuity, the performance of subjects when they used eccentric viewing was comparable with when they had intact, normal central vision. Numerous studies have shown that, despite the degradation of visual acuity with increasing eccentricity,46–48 the peripheral visual field is sensitive to detecting motion and low spatial frequencies.49–51 Because the stimuli in the present study were approaching vehicles, this meant that subjects made judgments about large, moving objects that contained low spatial frequencies. Therefore, the experimental task required of subjects when using eccentric viewing with their simulated central field loss was well suited to the capabilities of the peripheral visual field. This may explain why our results did not support our hypotheses, which predicted that subjects' accuracy and reliability would worsen when subjects had a simulated central field loss.
The effectiveness of using eccentric viewing when making street-crossing decisions is further demonstrated when the results of the present study are compared with those of Wu et al.,28 who showed that young subjects fixating centrally with a simulated, gaze-contingent central scotoma made increasingly less safe crossing decisions as the size of the central scotoma increased. Based on our findings and those of Wu et al.,28 we therefore recommend that people with central field loss use a preferred retinal locus to improve the safety and accuracy of their street-crossing decisions. However, further research is required to assess the effectiveness of eccentric viewing at more complex streets because the current study assessed performance at only a relatively simple street environment.
Despite the simplistic nature of the street used in the current study, it is still important to assess street-crossing decision-making at simple crossing environments. This is because more than half of the subjects in the current study reported that they crossed the simple street design assessed in this study several times every week. In addition, the young subjects in the current study self-reported adopting a variety of street-crossing strategies ranging from “very conservative” to “very liberal.” It is therefore possible that subjects' varying street-crossing strategies contributed to the variability in street-crossing accuracy and reliability observed in this study (Figs. 1, 2). This variation may also explain why few significant univariate correlations and predictors of performance were found between street-crossing accuracy/reliability, measures of vision, cognition, and general health status. However, as found in the current study, previous street-crossing studies have also found visual acuity44 and various cognitive function measures43,52 to be significant predictors of street-crossing decision-making performance.
It is interesting to note that, with the multiple regression analysis for street-crossing accuracy under habitual vision, the Short Form-36 domain of vitality did not emerge as a significant predictor of performance, although it had the highest univariate correlation with bias for the same viewing condition (Table 4). This is most likely explained by the fact that, after simultaneously adjusting for all variables in the multiple regression analysis model, vitality was no longer significant compared with the Short Form-36 domain of general health and visual acuity, which had the second and third highest univariate correlations with bias under the habitual vision condition, respectively (Table 4).
Although we found that young subjects' crossing decisions made while using their normal vision were safe, they were significantly inaccurate, a finding that is in agreement with Hassan and Snyder.27 When these same subjects made decisions with a simulated scotoma, their decisions remained safe and inaccurate, which is in disagreement with our previous work25–27 that found that central vision loss subjects were on average accurate with their street-crossing decisions. A possible reason for the discrepancy in results most likely relates to the fact that the subjects in the current study were significantly younger than the central vision loss subjects in these earlier street-crossing studies25–27 (who were at least 43 years and older). Certainly, Hassan and Snyder27 reported that decision accuracy declined with increasing age and worsening vision. Therefore, as one ages (and there are accompanying declines in perception, cognition, and motor skills) and/or as vision declines, the bias will transition from a positive value (significantly inaccurate yet safe) to a negative value (significantly inaccurate yet unsafe). Thus, there will be a period, both in age and in level of vision loss, when the bias will be close to zero, hence resulting in it not being significantly different from zero and thus “accurate.” It is possible that the older subjects in our previous studies25–27 were in the age that resulted in their bias being centered close to zero.
In addition, it is very likely that the subjects in our earlier studies25–27 had experienced their central vision loss for a long time. Consequently, these subjects might have been accustomed to their visual status and thus were able to accurately compensate for their visual deficits. This is supported by the fact that the central vision loss subjects in all of our earlier studies25–27 self-reported that they traveled independently and crossed streets regularly without any assistance. Although the subjects in the current study were also active pedestrians, they only experienced central field loss for the very first time. It is therefore possible that, in response to this sudden and absolute loss of their central visual field, subjects became extra cautious with their crossing decisions, which explains why more inaccurate but safer crossing performance was observed in the current study compared with those reported in our previous studies.25–27
Limitations of the current study include the fact that subjects' street-crossing decision-making performance was evaluated at a simple street and that the vision loss was simulated and only monocular. It is likely that subjects' decision-making performance would have improved under binocular viewing conditions, as subjects' gap time judgments may have benefited from the addition of binocular cues, and when subjects used eccentric viewing, their ability to detect approaching vehicles would have also improved because peripheral vision improves binocularly beyond summation levels for detection and resolution tasks.53
The slight increase in bias values found in the current study (1.37 seconds for the habitual vision condition) compared with that obtained by Hassan and Snyder27 (1.14 seconds) further supports the suggestion that testing under monocular conditions resulted in subjects having slightly worse performance (i.e., greater inaccuracy). Hassan and Snyder27 assessed street-crossing decision-making in a different group of similarly aged young, normally sighted subjects at the same street using the same methods and analyses but under binocular viewing conditions. It is therefore possible that the binocular viewing conditions in the study by Hassan and Snyder27 resulted in subjects being slightly more accurate. In addition, the somewhat different manner of viewing traffic monocularly for the usually binocular subjects may have also caused them to have been more conservative with their crossing decisions, thus resulting in more positive-valued bias scores, compared with if they had used binocular vision.
Another limitation of the current study was that no mechanism was in place to monitor vehicular speed in real time during a trial. As a result, the speed of approaching vehicles may have varied during a trial, either speeding up or slowing down. Such speed variations would alter the measured gap time and the accuracy (bias) of subjects' crossing decisions.
To minimize drivers from altering their speed during a trial, a sign was positioned well in advance from where trials began to warn drivers about our study and that they would see people positioned by the side of the road. In addition, experimenters monitored the speeds of all approaching vehicles both before and during a trial. Whenever an experimenter observed that the approaching vehicle's speed changed (either during the 2-second sampling period or after the prompt signal), the trial was canceled and not used in any analyses.
Although the aforementioned procedures minimized the risk of drivers changing speed during a trial, it is still possible that on some trials drivers slowed down, which may have contributed to subjects' positive bias values.
Another weakness of our study was that subjects were able to visualize the simulated scotoma and hence did not experience perceptual “filling-in” as observed in patients with pathology generated central field loss. As a result, the fixation stability of our subjects when simulated with a central field loss is expected to be better than that observed in real central field loss, possibly resulting in them having better performance compared with subjects with real central field loss.
The present study showed that when subjects with an induced, absolute central scotoma adopted eccentric viewing, they were able to be as accurate and reliable in their street-crossing decisions compared with when they used their habitual, normal vision. Furthermore, all subjects, irrespective of whether or not they had an induced central field loss, adopted a safe street-crossing strategy by underestimating vehicular arrival times relative to their crossing time. These results therefore suggest that the near peripheral retina and the technique of eccentric viewing can be used successfully to make appropriate street-crossing decisions.
We found that visual acuity and self-rated general health were significant predictors of street-crossing accuracy, whereas visual acuity and a cognitive measure were predictive of street-crossing reliability. Finding visual acuity and a cognitive measure to be predictive of street-crossing performance is in agreement with earlier street-crossing studies.43,44,52
1. Curcio CA, Sloan KR, Kalina RE, et al. Human Photoreceptor Topography. J Comp Neurol 1990;292:497–523.
2. Costela FM, Kajtezovic S, Woods RL. The Preferred Retinal Locus Used to Watch Videos. Invest Ophthalmol Vis Sci 2017;58:6073–81.
3. Stevens M, Grainger J. Letter Visibility and the Viewing Position Effect in Visual Word Recognition. Percept Psychophys 2003;65:133–51.
4. Congdon N, O'Colmain B, Klaver CC, et al. Causes and Prevalence of Visual Impairment among Adults in the United States. Arch Ophthalmol 2004;122:477–85.
5. Neelam K, Cheung CM, Ohno-Matsui K, et al. Choroidal Neovascularization in Pathological Myopia. Prog Retin Eye Res 2012;31:495–525.
6. Rotenstreich Y, Fishman GA, Anderson RJ. Visual Acuity Loss and Clinical Observations in a Large Series of Patients with Stargardt Disease. Ophthalmology 2003;110:1151–8.
7. Schuchard RA, Naseer S, de Castro K. Characteristics of AMD Patients with Low Vision Receiving Visual Rehabilitation. J Rehabil Res Dev 1999;36:294–302.
8. Sunness JS, Applegate CA, Haselwood D, et al. Fixation Patterns and Reading Rates in Eyes with Central Scotomas from Advanced Atrophic Age-related Macular Degeneration and Stargardt Disease. Ophthalmology 1996;103:1458–66.
9. Fletcher DC, Schuchard RA. Preferred Retinal Loci Relationship to Macular Scotomas in a Low-vision Population. Ophthalmology 1997;104:632–8.
10. Schuchard RA. Preferred Retinal Loci and Macular Scotoma Characteristics in Patients with Age-related Macular Degeneration. Can J Ophthalmol 2005;40:303–12.
11. Fletcher DC, Schuchard RA, Renninger LW. Patient Awareness of Binocular Central Scotoma in Age-related Macular Degeneration. Optom Vis Sci 2012;89:1395–8.
12. Bellmann C, Feely M, Crossland MD, et al. Fixation Stability Using Central and Pericentral Fixation Targets in Patients with Age-related Macular Degeneration. Ophthalmology 2004;111:2265–70.
13. Crossland MD, Culham LE, Rubin GS. Fixation Stability and Reading Speed in Patients with Newly Developed Macular Disease. Ophthalmic Physiol Opt 2004;24:327–33.
14. Bullimore MA, Bailey IL, Wacker RT. Face Recognition in Age-related Maculopathy. Invest Ophthalmol Vis Sci 1991;32:2020–9.
15. DeCarlo DK, Scilley K, Wells J, et al. Driving Habits and Health-related Quality of Life in Patients with Age-related Maculopathy. Optom Vis Sci 2003;80:207–13.
16. Fine EM, Rubin GS. Reading with Simulated Scotomas: Attending to the Right Is Better than Attending to the Left. Vision Res 1999;39:1039–48.
17. Newbold KB, Scott DM, Spinney JEL, et al. Travel Behavior within Canada's Older Population: A Cohort Analysis. J Transp Geogr 2005;13:340–51.
18. Mangione CM, Berry S, Spritzer K, et al. Identifying the Content Area for the 51-item National Eye Institute Visual Function Questionnaire: Results from Focus Groups with Visually Impaired Persons. Arch Ophthalmol 1998;116:227–33.
19. Hassan SE, Lovie-Kitchin JE, Woods RL. Vision and Mobility Performance of Subjects with Age-related Macular Degeneration. Optom Vis Sci 2002;79:697–707.
20. World Health Organization. Global Status Report on Road Safety 2015. Available at: https://www.who.int/violence_injury_prevention/road_safety_status/2015/en/
. Accessed January 17, 2018.
21. National Highway Traffic Safety Administration. Traffic Safety Facts: 2017 Data. Washington, DC: National Center for Statistics and Analysis; 2017.
22. Department of Infrastructure, Regional Development and Cities: Bureau of Infrastructure Transport and Regional Economics (BITRE). Road Trauma Australia 2017 Statistical Summary. Canberra: BITRE; 2018.
23. Government of India: Ministry of Road Transport and Highways. Road Accidents in India—2017. New Delhi, India: Ministry of Road Transport and Highways; 2017. Available at: http://www.indiaenvironmentportal.org.in/files/file/road%20accidents%20in%20India%202017.pdf
. Accessed February 8, 2020.
24. Geruschat DR, Fujiwara K, Wall Emerson RS. Traffic Gap Detection for Pedestrians with Low Vision. Optom Vis Sci 2011;88:208–16.
25. Hassan SE. Are Normally Sighted, Visually Impaired, and Blind Pedestrians Accurate and Reliable at Making Street Crossing Decisions? Invest Ophthalmol Vis Sci 2012;53:2593–600.
26. Hassan SE, Massof RW. Measurements of Street-crossing Decision-making in Pedestrians with Low Vision. Accid Anal Prev 2012;49:410–8.
27. Hassan SE, Snyder BD. Street-crossing Decision-making: A Comparison between Patients with Age-related Macular Degeneration and Normal Vision. Invest Ophthalmol Vis Sci 2012;53:6137–44.
28. Wu H, Ashmead DH, Adams H, et al. Using Virtual Reality to Assess the Street Crossing Behavior of Pedestrians with Simulated Macular Degeneration at a Roundabout. Front ICT 2018;5:27.
29. Notthoff N, Carstensen LL. Positive Messaging Promotes Walking in Older Adults. Psychol Aging 2014;29:329–41.
30. Almutleb ES, Bradley A, Jedlicka J, et al. Simulation of a Central Scotoma Using Contact Lenses with an Opaque Centre. Ophthalmic Physiol Opt 2018;38:76–87.
31. Regan D, Vincent A. Visual Processing of Looming and Time to Contract throughout the Visual Field. Vision Res 1995;35:1845–57.
32. Bailey IL, Lovie JE. New Design Principles for Visual Acuity Letter Charts. Am J Optom Physiol Opt 1976;53:740–5.
33. Elliott DB, Bullimore MA, Bailey IL. Improving the Reliability of the Pelli-Robson Contrast Sensitivity Test. Optom Vis Sci 1991;6:471–5.
34. Elliott DB, Whitaker D, Bonette L. Differences in the Legibility of Letters at Contrast Threshold Using the Pelli-Robson Chart. Ophthalmic Physiol Opt 1990;10:323–6.
35. McEvoy AJ, Markvart T, Castaner L. Practical Handbook of Photovoltaics Fundamentals and Applications. 2nd ed. Waltham, MA: Academic Press; 2012.
36. Wijeyesekera N, Affida R, Binti M, et al. Investigation into the Urban Heat Island Effects from Asphalt Pavements. OIDA Int J Sustain Dev 2012;5:97–118.
37. Dolderer J, Vonthein R, Johnson CA, et al. Scotoma Mapping by Semi-automated Kinetic Perimetry: The Effects of Stimulus Properties and the Speed of Subjects' Responses. Acta Ophthalmol Scand 2006;84:338–44.
38. Levin LA, Adler FH, Kaufman PL, et al. Adler's Physiology of the Eye. 11th ed. Edingburgh, United Kingdom: Saunders/Elsevier; 2011.
39. Ware JE Jr. SF-36 Health Survey Update. Spine (Phila Pa 1976) 2000;25:3130–9.
40. Roper JM, Hassan SE. How Do Vision and Hearing Impact Pedestrian Time-to-arrival Judgments? Optom Vis Sci 2014;91:303–11.
41. Oxley JA, Ihsen E, Fildes BN, et al. Crossing Roads Safely: An Experimental Study of Age Differences in Gap Selection by Pedestrians. Accid Anal Prev 2005;37:962–71.
42. Dommes A, Cavallo V, Dubuisson JB, et al. Crossing a Two-way Street: Comparison of Young and Old Pedestrians. J Safety Res 2014;50:27–34.
43. Dommes A, Cavallo V, Oxley J. Functional Declines as Predictors of Risky Street-crossing Decisions in Older Pedestrians. Accid Anal Prev 2013;59:135–43.
44. Dommes A, Lay TL, Vienne F, et al. Towards an Explanation of Age-related Difficulties in Crossing a Two-way Street. Accid Anal Prev 2015;85:229–38.
45. Oxley J, Fildes B, Ihsen E, et al. Differences in Traffic Judgements between Young and Old Adult Pedestrians. Accid Anal Prev 1997;29:839–47.
46. Banks MS, Sekuler AB, Anderson SJ. Peripheral Spatial Vision: Limits Imposed by Optics, Photoreceptors, and Receptor Pooling. J Opt Soc Am (A) 1991;8:1775–87.
47. Duncan RO, Boynton GM. Cortical Magnification within Human Primary Visual Cortex Correlates with Acuity Thresholds. Neuron 2003;38:659–71.
48. Westheimer G. The Spatial Grain of the Perifoveal Visual Field. Vision Res 1982;22:157–62.
49. Coletta NJ, Williams DR, Tiana CL. Consequences of Spatial Sampling for Human Motion Perception. Vision Res 1990;30:1631–48.
50. Galvin SJ, Williams DR, Coletta NJ. The Spatial Grain of Motion Perception in Human Peripheral Vision. Vision Res 1996;36:2283–95.
51. Lappin JS, Tadin D, Nyquist JB, et al. Spatial and Temporal Limits of Motion Perception across Variations in Speed, Eccentricity, and Low Vision. J Vis 2009;9:30.1–14.
52. Dommes A, Cavallo V. The Role of Perceptual, Cognitive, and Motor Abilities in Street-crossing Decisions of Young and Older Pedestrians. Ophthalmic Physiol Opt 2011;31:292–301.
53. Zlatkova MB, Anderson RS, Ennis FA. Binocular Summation for Grating Detection and Resolution in Foveal and Peripheral Vision. Vision Res 2001;41:3093–100.