Predicting practical fitness to drive in drivers with visual field defects caused by ocular pathology.
Information relevant to driving is predominantly visual (e.g., traffic signs, distance to the car ahead, communication with other road participants), and this has led many governments to set firm visual standards for driving licensing. European countries, as well as many states in the United States, require a binocular visual acuity of at least 0.5 (decimal notation, equivalent to 0.3 logMAR) and a horizontal field extent of at least 120[degrees]. If visual criteria are used to determine fitness to drive, sensitivity and specificity of the vision tests should be high. Drivers who meet the vision criteria should demonstrate safe and smooth driving performance, whereas drivers who do not meet the criteria should demonstrate unsafe or otherwise inadequate driving performance. In other words, the number of false alarms (restricting safe drivers from driving) and the number of misses (permitting unsafe drivers to drive) should be as low as possible.
Previous studies have demonstrated that although the relationships between vision requirements and driving safety are significant, they are not conclusive with regard to the identification of individual at-risk drivers (Ball, Owsley, Sloane, Roenker, & Bruni, 1993). Visual acuity is often reported to be a weak predictor of mobility (Black et al., 1997; Kuyk & Elliott, 1999: Long, Rieser, & Hill, 1990; Marron & Bailey, 1982; Turano, Geruschat, Stahl, & Massof, 1999) and driving (Ball & Owsley, 1991; Szlyk, Fishman, Severing, Alexander, & Viana, 1993). (Mobility refers to the ability to walk safely, comfortably, independently, and at a normal or near-normal speed. It is usually assessed by means of an obstacle course in an indoor or outdoor setting. Typical parameters are number of errors, such as contacts with obstacles and loss of balance, and a speed estimator, such as percentage of preferred walking speed and travel speed).
Measures of the visual field (e.g., horizontal extent, mean sensitivity,, percentage remaining visual field) have been reported to be better predictors for mobility (Black et al., 1997; Geruschat, Turano, & Stahl, 1998; Haymes, Guest, Heyes, & Johnston, 1996; Kuyk, Elliott, & Fuhr, 1998; Long et al., 1990; Marron & Bailey, 1982). With regard to driving, it has often been reported that severely restricted visual fields impair driving performance and increase crash risk (Johnson & Keltner, 1983; Szlyk, Alexander, Severing, & Fishman, 1992; Wood & Troutbeck, 1992). However, for less severe visual field defects, the effect on driving performance is much smaller. Wood and Troutbeck (1994), for example, reported only minor changes in driving performance in participants with a simulated binocular visual field extent of 90[degrees] or 105[degrees] and concluded that they compensated for simulated visual field restrictions by driving more slowly rather than by increasing error scores.
The effect of behavioral modifications, such as speed reduction and compensatory viewing strategies, has often been suggested as an intermediate variable between vision and driving safety (Ball et al., 1998; Kuyk et al., 1998; Szlyk et al., 1995; Wood & Troutbeck, 1994). Particularly in the case of visual field defects, compensatory viewing strategies such as an efficient scanning technique or eccentric viewing may be useful in reducing the negative effect of the visual impairment.
In the present study, sensitivity and specificity of the European vision requirements for driving were investigated in a group of drivers with visual field defects caused by ocular pathology, such as age-related macular degeneration, glaucoma, or retinitis pigmentosa. It was investigated whether the predictive power of a model based on the European vision requirements for driving could be improved by taking compensatory viewing efficiency into account. This first model was then compared with a model based on variables that have been reported to be good predictors of driving safety and mobility, such as contrast sensitivity, age, and visual attention.
Ball and Owsley (1991) and Ball et al. (1993) have demonstrated a strong relationship between visual attention and driving. They argued that the low predictive power of visual field measures could be attributable to the difference in complexity between a visual field assessment by means of perimetry and the use of the visual field while driving. They therefore suggested assessing the useful field of view (UFOV). Rather than assessing detection of stimuli in isolation, the UFOV test assesses detection and discrimination of suprathreshold stimuli in a cluttered scene under varying attentional demands. Contrast sensitivity has also been reported to be a better predictor than visual acuity in mobility studies (Black et al., 1997; Cornelissen, Bootsma, & Kooijman, 1995; Geruschat et al., 1998; Haymes et al., 1996; Kuyk & Elliott, 1999; Long et al., 1990; Matron & Bailey, 1982), and therefore its relationship to driving was examined in this study.
The second research question is related to the type of visual field defect. It is often assumed that people with peripheral field loss demonstrate much greater difficulty with mobility than do individuals with central field loss (Marron & Bailey, 1982). Previous studies on driving concur with this hypothesis. Macular degeneration, for example, is usually not associated with increased accident involvement (McCloskey, Koepsell, Wolf, & Buchner, 1994; Szlyk et al., 1993). It should, however, be noted that Szlyk et al. (1995) showed that although participants with age-related macular degeneration did not have increased accident rates, they performed worse on driving simulator indices such as number of accidents, lane boundary crossings, braking response times to stop signs, and driving speed than did a control group of comparable age. In accordance with the hypothesis that people with peripheral field defects are more impaired with regard to driving, Szlyk et al. (1992) reported that a greater proportion of participants with retinitis pigmentosa had accidents as compared with a control group. Owsley, McGwin, and Ball (1998) reported that participants involved in injurious crashes were 3.6 times more likely to report a diagnosis of glaucoma than were controls.
These findings concur with the assumption that ocular pathology causing peripheral field defects impairs driving safety more than does ocular pathology causing central field defects. Attributing the differences in driving safety between different pathology groups solely to the effect of the visual fields, however, is questionable. Owsley, McGwin, et al. (1998), for example, emphasized that glaucoma per se, not visual field sensitivity, was a predictor of crash involvement, suggesting that other characteristics of this pathology elevated crash risk.
When evaluating driving performance, it is important to select valid and reliable criteria. A discussion of various techniques and their methodological weaknesses is provided by Brouwer and colleagues (Brouwer & Ponds, 1994; Brouwer, Withaar, Tant, & van Zomeren, 2002; Withaar, Brouwer, & van Zomeren, 2000). Because of the hierarchical task structure and overlearned nature of driving, a driver has many opportunities to compensate for mild to moderate functional impairments. Assessment of driving performance should take these compensatory abilities into account. In our experience, the Dutch assessment of practical fitness to drive of drivers who do not quite meet the vision requirements for driving complies with this requirement. Drivers are evaluated in their own car and their own neighborhood by an official driving examiner of the Dutch driving license authority, who determines whether they drive smoothly and safely and whether they are able to compensate for the negative effects of their impairment.
The participants were 63 men and 37 women with visual field defects caused by ocular pathology, such as age-related macular degeneration, glaucoma, and retinitis pigmentosa. They were recruited by short reports in newspapers and by pamphlets at ophthalmologists' offices, rehabilitation centers, and patients' associations. All participants were regular drivers, although most of them had been told they did not meet the vision requirements for driving anymore. Most of them (94%) had a valid driver's license. Participation in the study had no impact on their driver's license. Their mean age was 64 years, ranging from 37 to 86 years. When they volunteered to participate, a letter fully explaining the nature of the experiment was sent to them, and they were asked to return a form indicating whether they wished to participate or not. They were also sent a questionnaire related to the inclusion and exclusion criteria.
To be included in the study, visual field defects had to be present, visual acuity had to be greater than 0.1 (decimal notation, equivalent to 1.0 logMAR), and participants had to have sufficient and recent driving experience, defined as a minimum of 2000 km during the last 2 years. Exclusion criteria were severe cognitive impairments, including hemispatial neglect. All participants scored above a predefined cutoff point (22) on a cognitive screening test (the Mini Mental State Examination; Folstein, Folstein, & McHugh, 1975). Hemispatial neglect was screened by means of the Bells test (Vanier et al., 1990). The mean number of errors was one, ranging from zero to eight. Two participants made more than four errors and were further tested by means of a line bisection task (Schenkenberg, Bradford, & Ajax, 1980). They scored within normal limits, and it was therefore assumed that the impaired score on the Bells test was attributable to the visual impairment rather than to hemispatial neglect. Three major types of diagnosis could be distinguished in our sample: impairments related to degeneration of the macular area (Category 1, n = 42), impairments related to glaucoma (Category 2, n = 31), and impairments related to retinitis pigmentosa or choroideremia (Category 3, n = 13). Diagnoses not belonging to the first three categories were grouped in a nonspecified category (Category 4, n = 14).
To gain insight into the effect of vision parameters on driving performance, we also classified participants into four groups based on the European requirements for driving. According to these requirements, binocular visual acuity should be at least 0.5 (decimal notation, equivalent to 0.3 logMAR) and the horizontal field should extend for at least 120[degrees] ("Council Directive 91/439/EEC," 1991). Participants in the central field defect group (Group 1) did not fulfill the visual acuity requirements, but the visual fields outside the central 10[degrees] area were intact and extended for at least 120[degrees]. Participants in the peripheral field defect group (Group 2) met the visual acuity requirement but not the visual field requirement. Participants in the central and peripheral field defect group (Group 3) met neither of the requirements. Participants in the mild visual field defect group (Group 4) had scotomas in the paracentral or midperipheral area that did not restrict the horizontal field extent and did not affect visual acuity. As this group met the standard vision requirements for driving, this group was considered as a reference group for further analyses. Vision parameters for the four groups are presented in Table 1.
The research study was approved by the ethical review committee of the University of Groningen.
Results presented here are part of an extensive research study, consisting of five major phases: a first preassessment, a second preassessment, a training phase, a first postassessment, and a second postassessment. In this report, only the preassessment phases are discussed. Results of the first preassessment are based on the measurements from three sessions. Frequency of testing was one session per week. Each session lasted for 2 to 3 hr. At the first session an extensive vision examination was carried out, including refraction (if necessary) and assessment of visual acuity, near visual acuity, visual field, contrast sensitivity, dark adaptation, and eye motility. The second session consisted of an assessment of visual attention and compensatory viewing strategies. During this session, a cognitive screening was carried out and questionnaires were given to the participant. The third session consisted of a driving test on the road. After 3 weeks the second preassessment started. It included a retest of the driving test on the road as well as an assessment of visual attention and compensatory viewing strategies. In the course of the two preassessment phases, an ophthalmologic screening was performed in order to obtain a clear and recent diagnosis.
Vision. Visual acuity was assessed by means of a Bailey-Lovie chart (Bailey & Lovie, 1976). Visual acuity was assessed for each eye separately as well as binocularly. Binocular visual acuity is presented here. Visual acuity is expressed as logMAR. LogMAR, the logarithm of the minimum angle of resolution, refers to the angular size of the optotype that can just be discerned. A logMAR value of 0.0 indicates standard acuity. Larger logMAR values indicate worse vision. Visual acuity was assessed when participants were wearing their own refractive correction and when looking through a pinhole. If required, full refraction was carried out and participants were referred to an optician. Contrast sensitivity was assessed by means of the Pelli-Robson letter chart (Pelli, Robson, & Wilkins, 1988) and expressed as log contrast sensitivity. It was assessed for each eye separately as well as binocularly. Only binocular contrast sensitivity is presented.
The visual field was assessed by means of Goldmann perimetry with the III4 and V4 isopters. The central area was examined by means of the Humphrey Field Analyzer (central 10[degrees], Swedish interactive test algorithm standard method; Bengtsson, Olsson, Heijl, & Rootzen, 1997) because this test allows a more detailed examination of the central area than does Goldmann perimetry. The horizontal field extent was assessed by superimposing the Goldmann III4 isopters for both eyes separately. A functional field score (FFS) was obtained using an overlay grid with 110 points, of which 50 points are situated in the central 10[degrees] (Colenbrander, 1994); 66 points are located in the lower half field and 44 in the upper half field. Grid points enclosed by the III4 isopter (10dB) were counted. Grid points within scotomas were not counted. This procedure was administered for each eye separately. For the binocular field, the visual fields for the left and right eye were superimposed. The FFS equals (2 x binocular score + right score + left score)/4 and should be viewed as an ability scale on which 100 = normal performance and 0 = absence of any ability to perform. Normal ability scores range from 110 to 92.5. The FFS was transformed to obtain a normal distribution: Deviation from the maximum was calculated and divided by 100,
FFS' = 100 - FFS/100.
The score was then log transformed.
log FFS'/1 - FFS',
as described by Stevens (1996).
Visual attention. Visual attention was assessed by a test based on Condition 6 of the useful field of view test (UFOV[R]) as described by Ball. Beard. Roenker, Miller, and Griggs (1988). We extended this condition with an adaptive phase to control for general slowness and adjusted stimulus size to ensure that participants with a visual acuity of 0.1 (decimal notation, equivalent to 1.0 logMAR) were able to identify the stimuli. Stimulus presentation and response recording were controlled by an 80486 PC and presented on a 20-inch (51-cm) computer screen. The program was written with MEL 2.0 software. The stimuli were white (luminance = 50 cd/[m.sup.2]) on a dim background (luminance = 8 cd/[m.sup.2]). Line width of the stimuli was 0.4[degrees]. The central stimulus was a sad or happy face with a diameter of 6[degrees] (mouth width = 3[degrees], mouth height = 1[degrees]). The peripheral target consisted of a circle (diameter = 4[degrees]), which could appear at 1 of 24 positions. The positions were arranged into eight evenly spaced radial spokes. The target could appear at three eccentricities (at 7[degrees], 14[degrees], or 21[degrees]). The distraction consisted of 47 squares subtending 4[degrees] x 4[degrees], evenly spaced on 16 spokes. The testing room was illuminated (500 lx). Participants viewed the screen from a distance of 30 cm.
The test was performed binocularly. Every trial started with a central fixation marker (a square subtending 4[degrees] x 4[degrees]), followed by the stimulus display, a mask, and the response screen. The response screen for the central task was a dark screen with "sad or happy?" written on the bottom of it and a pattern of eight numbered spokes for the peripheral task. The test consisted of four blocks. In Block 1, the participant viewed a screen with a face in the center and one target on one of 24 positions in the periphery for 25, 50, 75, 100, or 125 ms (Figure 1a), after which the image was masked. The participant was then instructed to indicate the position of the circle by naming the number of the spoke on which the target was positioned (Figure 1c). The shortest presentation time at which a participant responded correctly in at least 90% of the trials was used as the presentation time for the subsequent conditions, with a minimum of 50 ms. Only targets presented in the intact visual field were used to determine the presentation time for the subsequent blocks.
[FIGURE 1 OMITTED]
In Block 2, the participant was instructed to locate the circle and to indicate whether the central face was sad or happy. Presentation times depended on the scores of the first block and could vary from 50 to 125 ms. The short presentation times prohibited observers from making eye movements during the target display. Blocks 3 and 4 were similar to Blocks 1 and 2, respectively, except for the presence of the distraction (Figure 1b). The proportion correct responses was recorded. The visual attention score is the mean of scores on Blocks 2, 3, and 4. The visual attention score was obviously related to the quality of the visual field, as was the case in studies on the UFOV (Ball & Owsley, 1991; Owsley, Ball, et al., 1998). If a participant could not perform the test because the severity of his or her visual field defect prohibited detection of any peripheral target, empty cells were replaced by scores reflecting chance performance. Data were arcsine transformed for statistical purposes (Ball et al., 1988; Sekuler, Bennett, & Mamelak, 2000).
Compensatory viewing efficiency. Compensatory viewing efficiency was assessed by means of the attended field of view (AFOV) test (Coeckelbergh, Cornelissen, Brouwer, & Kooijman, 2004). Unlike the visual attention test, this peripheral localization test allows participants to move their eyes and head. The AFOV test is based on a visual search paradigm. In each trial, 30 closed circles and 1 open circle were presented on a 20-inch (51-cm) screen (stimulus luminance = 40 cd/[m.sup.2], background luminance = 16 cd/[m.sup.2]). The 31 stimuli were arranged in three elliptical rings around a central stimulus (Figure 2a). The visual angle of this stimulus array was 60[degrees] horizontally and 24[degrees] vertically. No stimuli were presented on the vertical axis. The size of the stimulus elements was determined by eccentricity and could be adjusted in relation to visual acuity (Table 2). The testing room was illuminated (500 lx). The participant sat in front of the screen at a viewing distance of 30 cm and was instructed to locate an open circle (e.g., C shaped) among 30 closed circles (O shaped) and subsequently indicate the direction of the gap (left, right, top, or bottom of the circle). Eye and head movements were allowed after the central fixation marker had disappeared (a diamond consisting of four red dots, luminance = 14 cd/[m.sup.2]).
[FIGURE 2 OMITTED]
The test was performed binocularly. The stimuli were presented with varying presentation times (range: 8-10 s). By means of a staircase procedure, the threshold presentation time at which the participant could correctly identify the target in 67% of the trials was determined for 19 positions. Some stimulus elements were pooled (Figure 2b) such that 6 positions were tested per ellipse. In this way 19 positions were analyzed (6 positions per ring and the central position), although the target could appear on any of the 31 positions. Linear threshold presentation times were log transformed and corrected for different stimulus sizes by a linear transformation.
Two measurements were derived from AFOV performance: mean threshold presentation time and the percentage deviation from the median (PDM). The mean threshold presentation time is the mean of the threshold presentation times for 19 positions and is an estimate of speed of visual search. The PDM refers to the distribution of the visual search strategy. It is expressed as a percentage scale, with 0 = flat distribution (threshold presentation times are equal for all 19 positions) and 100 = distribution with maximum variation (i.e., half of the positions at the minimum threshold presentation time and half of the positions at maximum threshold presentation times). An efficient scanning strategy is here defined as scanning strategy resulting in low threshold presentation times and/or a low PDM.
Practical fitness to drive. Practical fitness to drive is the ability of the driver to drive safely and smoothly despite a physical impairment, such as a visual field defect. It was assessed by means of a driving test on the road. Participants were evaluated in their own car and their own neighborhood by an experienced driving examiner of the Dutch Central Bureau of Driving Licenses. This way of assessing practical fitness to drive is the official standard in the Netherlands to examine drivers who do not quite meet the vision requirements. The driving examiner determined whether the individual had adapted his or her behavior to minimize the negative effects of his impairment. The driving examiner made use of a checklist (test ride for investigating practical fitness to drive, or TRIP; De Raedt & Ponjaert-Kristoffersen, 2000; Withaar, 2000). Items of the TRIP checklist included lateral position, steering control, choice of lane, car following, speed, viewing behavior, detection of traffic signals, mechanical operations, overtaking, anticipatory behavior, communication with other traffic participants, turning left, and merging into another driving lane. The items were scored on a 4-point scale (0-3).
After the driving test, the examiner accredited a final score, which varied from 0 (insufficient) to 3 (good). This final score was recoded to a pass/fail score and indicated whether the participant had failed (scores of 0 or 1) or passed (scores of 2 or 3) the driving test. Practical fitness to drive was assessed twice. The first driving test was regarded as a session to accustom the participants to the assessment procedure and to determine whether it was safe for them to drive on the road. During the second session, the actual practical fitness to drive was assessed. Only the final score of the second assessment is reported here. Although we do not report the data of the first driving test, we nevertheless collected the data to compute the correlation coefficient between the first and second driving tests. The correlation coefficient of the mean TRIP scores equaled .88, and this was interpreted as an indicator of good test-retest reliability.
Using the Kolmogorov-Smirnov test, we observed that the functional field score (corrected), visual attention (arcsine transformation), AFOV mean threshold presentation time, AFOV PDM, age, and years of driving experience were normally distributed. Small departures from normality were observed for visual acuity (logMAR, p = .05) and log contrast sensitivity (p = .04). Sensitivity is the proportion of unfit drivers among the participants who actually failed the driving test. Specificity is the proportion of fit drivers among the participants who passed the driving test. Spearman's rho was calculated to analyze correlations because some variables were ordinal measures (e.g., final score) and because the assumptions of normality were not met for every variable (e.g., logMAR). Logistic regression was used to predict the pass/fail score. Two models were compared: a model based on the current vision requirements for driving and compensatory viewing efficiency (Model 1) and a model based on variables that have been reported to be good predictors of driving performance or mobility (Model 2). Variables were entered in the model on a theoretical basis.
In Model 1, visual acuity was entered first because this variable is the most imperative vision requirement for driving. Next, visual field was included in the model. Finally, AFOV mean threshold presentation time was included in the model, as we wanted to assess the improvement of the predictive value of the model when this variable was added. In the Model 2, the visual attention score was entered first because the UFOV has often been reported as the most important predictor of driving performance. Next, contrast sensitivity was entered on the basis of the results on mobility in low vision. Finally, age was entered into the model. The -2 log likelihood for the model is presented, as is the Nagelkerke [R.sup.2]. This "pseudo" [R.sup.2] is presented for comparison reasons and should be viewed as a rough guide without attributing great importance to the precise figure (Pampel, 2000). The Nagelkerke [R.sup.2] was preferred over the Cox and Snell [R.sup.2] as it ensures a maximum of 1.
Current Vision Requirements
Applying the current European guidelines, people with a binocular visual acuity less than 0.5 (decimal notation, equivalent to 0.3 logMAR) and/or a horizontal field extent of less than 120[degrees] are considered unfit to drive. In our sample, 67 participants were classified as unfit to drive. Of these, 44 failed the driving test whereas 23 passed. Of the 33 participants who were considered to be fit to drive on the basis of the current guidelines, 12 failed and 21 passed the test on the road. These figures resulted in a sensitivity of 79% and a specificity of 48%.
Correlation coefficients (Spearman's rho, Table 3) were calculated among age, years of driving experience, visual acuity (logMAR), log contrast sensitivity, AFOV (presentation time), AFOV PDM, visual field (functional field score), visual attention, and the final score (Grade 0 to 3). Visual acuity, log contrast sensitivity, AFOV (presentation time), and visual attention correlated significantly with the final score (p < .05).
Compensatory Viewing Efficiency
In the Introduction, it was hypothesized that efficient compensatory viewing strategies, such as increased scanning and eccentric viewing, might reduce the negative impact of a visual field defect. An efficient compensatory viewing strategy was defined as a strategy resulting in low threshold presentation times and/or a low PDM on the AFOV test. The mean threshold presentation time of the AFOV test was moderately correlated with the final score (r = -.524, p < .01) of the driving test. To detect the target, participants who failed the driving test needed presentation times that were twice as long as those for participants who passed the driving test. Mean log threshold presentation time was .394 (2.477 s) for participants who failed the test and .086 (1.218 s) for participants who passed the test. This difference was statistically significant, t(98) = -4.84, p < 01. The AFOV PDM score did not correlate significantly with either of the driving test scores.
Predicting the Pass/Fail Score
Logistic regression was used to predict the pass/fail score on the basis of the vision requirements for driving (Model 1). Model fit and change statistics are presented in Table 4. Visual acuity (logMAR) and visual field (functional field score) accounted for approximately 24% of the variation (Nagelkerke [R.sup.2]). Sensitivity (to detect an unfit driver) was 80%, and specificity was 43% (criterion = .42). Adding AFOV (mean threshold presentation times) to the model significantly improved prediction (Nagelkerke [R.sup.2] = .32). Sensitivity (to detect an unfit driver) was 80%, and specificity increased to 64% (criterion = .44). This model was compared with a model that is based on variables that have been reported to be good predictors: visual attention, contrast sensitivity, and age (Model 2). The percentage variation that was accounted for by visual attention was approximately 23% (Nagelkerke [R.sup.2]). Adding contrast sensitivity to the model improved the model (Nagelkerke [R.sup.2] = .34). Age did not further improve the model (Table 4). Sensitivity and specificity of the model based on visual attention and contrast sensitivity were 80% and 64%, respectively (criterion = .46).
The predictor variables of the two models that were evaluated in this study were selected on a theoretical basis--that is, on the basis of the current vision requirements for driving (Model 1) and on the basis of previous studies on this issue (Model 2). An atheoretical analysis based on the (partial) correlation matrix might have revealed a different selection of variables. To study this, we performed a post hoc regression analysis to predict the pass/fail driving test score on the basis of the variables that correlated significantly with the final score (Table 3). A forward (Wald) regression analysis resulted in the same predictor selection as did Model 2 (visual attention and contrast sensitivity).
Visual Field Defect and Driving Performance
Of the participants with mild visual field defects, 64% passed the driving test, as compared with 42% of participants with peripheral field defects and 25% of participants with central field defects. The relationship between type of visual field defect and the pass/fail score was statistically significant, [chi square](2) = 8.7, p < .05. Results of the participants with central and peripheral visual field defects (Group 3) were not included in the analysis, as the sample size was too small. Of this group, 2 participants (out of 7) passed the driving test. Of the participants in Category 1 (diagnoses related to degeneration of macular area), 38% passed the driving test, as compared with 45% in Category 2 (diagnoses related to glaucoma), 38.5% in Category 3 (retinitis pigmentosa and choroideremia), and 64% in Category 4 (nonspecified). The percentage of participants passing the driving test was not related to type of diagnosis, [chi square](3) = 3.1, ns.
A significant relationship between defect group and the pass/fail score of the TRIP subtasks (Table 5) was observed for the detection of traffic signals, [chi square](2) = 12.6, p = .002. Only 58% of participants with central field defects detected traffic signals in time, as compared with 86% in the peripheral field defect group and 94% in the mild visual field defect group. No significant relationship between defect group and the pass/fail score was observed for any of the other driving subtasks (e.g., speed, lateral position, choice of lane). Data from the participants with central and peripheral visual field defects (Group 3) were not included in the analyses because of their low number, but the results in Table 5 indicate that only a small percentage passed the TRIP subtasks.
Two research questions were investigated in this paper: First, will the inclusion of a variable that accounts for compensatory viewing efficiency improve sensitivity and specificity of the current European vision requirements for driving? Second, are people with peripheral field defects more impaired to drive, with regard to practical fitness, than are people with a central field defect?
Vision Requirements for Driving
As in many states in the United States, the European vision requirements for driving are a binocular visual acuity of at least 0.5 (decimal notation, equivalent to 0.3 logMAR) and a horizontal field extent of at least 120[degrees]. These vision requirements do not take into account compensatory viewing strategies, which people with visual field defects might use to reduce the negative effects of their impairment. We hypothesized that drivers with visual field defects who compensated would have better driving performance than would drivers who do not use compensatory viewing strategies. The results of the present study corroborated this hypothesis. To detect the target on the AFOV test, participants who failed the driving test needed presentation times that were twice as long as those needed by participants who passed the driving test. We then hypothesized that sensitivity and specificity of the vision requirements for driving would improve when compensatory viewing efficiency was taken into account. Sensitivity and specificity of the current vision requirements for driving were low. Adding the mean threshold presentation time of the AFOV improved the model significantly. Sensitivity and specificity, however, still remained quite low. Sensitivity (to detect an unfit driver) was 80%, and specificity was 64% in our sample.
The results of this model were compared with those of a model that is based on variables that have been reported to be strong predictors of driving performance and mobility: visual attention, contrasts sensitivity, and age. A model based on visual attention and contrast sensitivity resulted in a sensitivity (to identify an unfit driver) of 80% and a specificity of 64%. These results were identical to the results of the first model. Age did not further improve the model.
Interestingly, an atheoretical stepwise model based on variables that correlated with the final score of the driving test yielded the same set of predictors as the second model to predict the pass/fail score. Contrast sensitivity and visual attention thus seemed to be the most important predictors. It was hypothesized that the AFOV might be a stronger predictor than the visual attention score, as it encompassed compensatory viewing efficiency. The data did not confirm this hypothesis. Once the visual attention score was included in the model, the AFOV scores did not further improve the model. The AFOV and visual attention score were highly correlated, causing a multicollinearity problem. The high correlation between the AFOV and the visual attention score implies that both tests assess a common function, such as speed of visual information processing. This finding needs more detailed investigation.
Although 34% of the variance of practical fitness to drive could be explained by visual attention and contrast sensitivity, sensitivity and specificity of the tests remained low, limiting the use of these tests as a tool to distinguish between fit and unfit drivers. The low sensitivity and specificity might be caused by the selection of predictor variables or by the nature of the criterion variable. The final score of the driving test is a subjective and overall score and incorporates other aspects of driving, such as routine and a personal driving style. The on-road driving test is the official standard in the Netherlands to evaluate drivers who do not quite meet the vision requirements, and the examiners are very skilled and experienced. From a theoretical point of view, however, there is a need for reliability and validity research in this area.
Visual Field Defect and Driving Performance
A smaller percentage of participants with central visual field defects or with both central and peripheral field defects passed the on-road driving test. Only 25% to 29% passed the driving test, as compared with 42% in the peripheral field defect group and 64% in the mild visual field defect group. In contrast to the suggestion in the literature, we observed that participants with a central visual field defect were at a disadvantage with regard to driving, as compared with participants with either peripheral or mild visual field defects.
We observed no effect of diagnosis on the percentage of participants passing the on-road driving test. Vision parameters varied widely within a category and showed a large overlap between categories. Making use of a diagnosis to assess driving performance is useful only when dealing with homogenous categories and when the vision parameters are constant within a category. The categories in the present study were not homogenous, and even within one category vision was not constant. In case of glaucoma, for example, visual acuity, contrast sensitivity, and visual field extent differed significantly among individuals. If the goal is to assess the effect of vision on driving performance, we therefore think that groups should be selected on the basis of vision parameters, rather than on the basis of diagnosis.
Predictive power of the current vision requirements for driving was low, but it could be improved by taking compensatory viewing efficiency into account. Contrast sensitivity and visual attention could equally well predict practical fitness to drive. Despite this improvement, however, the percentage of explained variance remained low, limiting the use of these tests to identify individual unfit drivers. The percentage of participants who passed the on-road driving test was smaller among those with central visual field defects than among those with peripheral or mild visual field defects.
Implications for Practice
According to this study, the European vision requirements for driving are not sufficient to determine fitness to drive in a group of drivers with visual field defects. Sensitivity and specificity were low. Too many drivers who were legally unlit to drive proved to be safe drivers, and vice versa. We would therefore recommend the inclusion of more complex measurements, such as visual attention and compensatory viewing efficiency. However, as sensitivity and specificity remained low even after inclusion of these variables, we further argue that those drivers who do not meet the standards should be evaluated on a case-by-case basis by means of an on-road driving test.
TABLE 1: Group Characteristics as a Function of Visual Field Defect (Means and Standard Deviations) Group 1: Central 2: Peripheral Field Defect Field Defect (n = 24) (n = 36) Vision parameters Visual acuity (logMAR) .64 (.03) .14 (.02) Horizontal field extent (a) ([degrees]) 142 (13) 84 (35) Sample characteristics Male:female 16:8 29:7 Age 65 (13) 60 (12) Driving license (years) 38 (11) 37 (10) Group 3: Central and 4: Mild Visual Peripheral Field Defect (n = 7) (n = 33) Vision parameters Visual acuity (logMAR) .72 (.08) .11 (.02) Horizontal field extent (a) ([degrees]) 91 (35) 141 (13) Sample characteristics Male:female 4:3 14:19 Age 63 (15) 67 (9) Driving license (years) 39 (17) 38 (8) (a) Goldmann III4 isopter. TABLE 2: Size of Stimuli in the AFOV Test Target Size Ring Width Gap Width Position ([degrees]) (Relative) (Relative) AFOV 1: logMAR [less than or equal to] 0.0 Center + Ellipse 1 1.4 .2 .2 Ellipse 2 1.9 .2 .2 Ellipse 3 2.4 .2 .2 AFOV 2: 0.0 < logMAR [less than or equal to] 0.3 Center + Ellipse 1 1.4 .2 .3 Ellipse 2 1.9 .2 .3 Ellipse 3 2.4 .2 .3 AFOV 3: 0.3 < log MAR [less than or equal to] 0.7 Centre + Ellipse 1 1.9 .2 .5 Ellipse 2 1.9 .2 .5 Ellipse 3 2.4 .2 .5 AFOV 4: 0.7 < logMAR [less than or equal to] 1.0 Center + Ellipse 1 3.4 .2 .6 Ellipse 2 3.4 .2 .6 Ellipse 3 3.4 .2 .6 Note. The use of AFOV 1, 2, 3, or 4 depends on the visual acuity (logMAR) of the participants. TABLE 3: Correlation Matrix (Spearman's Rho) Driving Visual Age Exp. (a) Acuity (b) Age -- .680 ** .104 Driving experience -- -.033 Visual acuity -- Log CS AFOV pres. Time AFOV PDM Functional field score Visual attention Final score Log AFOV AFOV CS (c) PT (d) PDM Age -.273 ** .318 ** -.199 * Driving experience -.076 .168 -.225 * Visual acuity -.816 ** .437 ** .241 * Log CS -- -.527 ** -.001 AFOV pres. Time -- -.442 ** AFOV PDM -- Functional field score Visual attention Final score Functional Field Visual Final Score Attention Score (e) Age -.332 ** -.058 -.131 Driving experience -.186 -.024 -.008 Visual acuity -.219 * -.302 ** -.520 ** Log CS .175 .336 ** .475 ** AFOV pres. Time .261 ** -.615 ** -.524 ** AFOV PDM -.332 ** .292 ** .046 Functional field score -- -.283 ** -.150 Visual attention -- .518 ** Final score -- (a) Number of years' driving experience. (b) logMAR visual acuity. (c) Pelli-Robson log contrast sensitivity. (d) AFOV mean threshold presentation time. (e) final score of the driving test (0-3). ** p < .01. * p < .05. TABLE 4: Results of Regression Analysis Pass/Fail Score Predictor -2 LL (a) (est. [R.sup.2]) (b) Model 1 Visual acuity (logMAR) 121.2 (.20) Visual field (FFS) 117.4 (.24) AFOV, threshold presentation time 109.8 (.32) Model 2 Visual attention 118.4 (.23) Contrast sensitivity (log CS) 108.0 (.34) Age 108.0 (.34) Pass/Fail Score Predictor Model Model 1 Visual acuity (logMAR) [chi square](1) = 15.9, p < 0.01 Visual field (FFS) [chi square](2) = 19.8, p < 0.01 AFOV, threshold presentation time [chi square](3) = 27.4, p < 0.01 Model 2 Visual attention [chi square](1) = 18.8, p < 0.01 Contrast sensitivity (log CS) [chi square](2) = 29.2, p < 0.01 Age [chi square](3) = 29.2, p < 0.01 Pass/Fail Score Predictor Change Statistic Model 1 Visual acuity (logMAR) [chi square](1) = 15.9, p < .05 Visual field (FFS) [chi square](1) = 3.9, p = .05 AFOV, threshold presentation time [chi square](1) = 7.6, p < .05 Model 2 Visual attention [chi square](1) = 18.8, p < .05 Contrast sensitivity (log CS) [chi square](1) = 10.4, p < .05 Age [chi square](1) = 0.02, ns (a) -2 Log likelihood is a measure of how well the model fits the data. The smaller the value, the better the fit. (b) Estimated Nagelkerke [R.sup.2]. TABLE 5: Percentage of Participants Passing Subtasks of the TRIP Group 1: Central 2: Peripheral Field Defect Field Defect Lateral position 66.7 83.3 Lane choice 70.8 69.4 Car following 95.8 88.9 Speed 66.7 69.4 Viewing behavior 33.3 33.3 Detection of traffic signs 58.3 86.1 Overtaking 45.8 61.1 Mechanical operations 75.0 97.2 Anticipatory behavior 37.5 52.8 Communication 45.8 63.9 Turning left 37.5 50.0 Merging into another lane 37.5 58.3 Group 3: Central and 4: Mild Visual Peripheral (a) Field Defect Lateral position 42.9 84.8 Lane choice 42.9 66.7 Car following 85.7 97.0 Speed 42.9 84.8 Viewing behavior 57.1 39.4 Detection of traffic signs 42.9 93.9 Overtaking 28.6 72.7 Mechanical operations 57.1 84.8 Anticipatory behavior 28.6 63.6 Communication 28.6 78.8 Turning left 28.6 51.5 Merging into another lane 28.6 57.6 Group Statistic [chi square](2) = Lateral position 3.3, ns Lane choice 0.1, ns Car following 2.1, ns Speed 3.1, ns Viewing behavior 0.3, ns Detection of traffic signs 12.6, p = .002 Overtaking 4.2, ns Mechanical operations 6.6, ns Anticipatory behavior 3.8, ns Communication 6.6, ns Turning left 1.3, ns Merging into another lane 3.3, ns (a) The group with central and peripheral field defects was not included in the analysis.
Tanja R. M. Coeckelbergh was supported by Grant 904-65-062 from the Dutch Research Council (NWO). Frans W. Cornelissen was supported by Visio, the Dutch National Foundation for the Visually Impaired and Blind. The authors thank the Central Bureau for Driving Licenses (CBR) for assessing practical fitness to drive; Visio, Dutch National Foundation for the Visually Impaired and Blind for carrying out the vision examination; and Prof. dr. W. A. Houtman for performing the ophthalmologic assessment.
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Tanja R. M. Coeckelbergh is a researcher in the Department of Ophthalmology at the University Hospital of Antwerp. She received her Ph.D. in medicine at the University of Groningen in 2002.
Wiebo H. Brouwer is a professor of traffic medicine and neuropsychology in the Departments of Neurology and Psychology at the University of Groningen, where he received his Ph.D. in psychology in 1985.
Frans W. Cornelissen is an assistant professor in the Laboratory of Experimental Ophthalmology at the University of Groningen, where he received his Ph.D. in medicine in 1994.
Aart C. Kooijman is a professor in the Laboratory of Experimental Ophthalmology at the University of Groningen, where he received his Ph.D. in medicine in 1986.
Address correspondence to Tanja R. M. Coeckelbergh, University Hospital of Antwerp, Department of Opthalmology, Wilrijkstraat 10, 2650 Edegem, Belgium; firstname.lastname@example.org.
Date received: June 11, 2002
Date accepted: April 22, 2004
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|Title Annotation:||Surface Transportation Systems|
|Author:||Coeckelbergh, Tanja R.M.; Brouwer, Wiebo H.; Cornelissen, Frans W.; Kooijman, Aart C.|
|Date:||Dec 22, 2004|
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