حواس پرتی در هنگام رانندگی: مورد رانندگان مسن تر

Abstract As the impairment of older drivers is especially found in perception and attention, one could assume that they are especially prone to distraction effects of secondary tasks performed while driving. The aim of the study was to examine the effect of age on driving performance as well as the compensation strategies of older drivers under distraction. 10 middle-aged and 10 older drivers drove in a simulator with and without a secondary task. To assess driving performance the Lane Change Task (Mattes, 2003) was used. This method aims at estimating driver demand while a secondary task is being performed, by measuring performance degradation on a primary driving-like task in a standardized manner. The secondary task – a self-developed computer-based version of “d2 Test of Attention” was presented both with and without time pressure. The results show that older participants’ overall driving performance (mean deviation from an ideal path) was worse in all conditions as compared to the younger ones. With regard to lane change reaction time both age groups were influenced by distraction in a comparable manner. However, when the lane keeping performance (standard deviation of the lateral position) was examined, the older participants were more affected than the younger ones. This pattern could be explained by compensation strategies of the older drivers. They focused on the most relevant part of the driving task, the lane change manoeuvres and were able to maintain their performance level in a similar way as did younger drivers. The driving performance of the older participants was not additionally impaired when the secondary task imposed time pressure. Overall, subjective rating of driving performance, perceived workload and perceived distraction was found to be similar for both age groups. The observed trends and patterns associated with distraction while driving should contribute to the further research or practical work regarding in-vehicle technologies and older drivers.

. Introduction The number of older drivers in the industrialized world is rising steadily. This leads to increasing concern about traffic safety as ageing is commonly associated with psychophysiological changes which can decrease driving ability. Literature reviews indicate that three factors are most relevant for the accidents of older drivers: an impaired visual perception, problems with attention allocation and a general slowing in decision making, planning and execution of actions (Ball et al., 1993, Owsley et al., 2001, Oxley et al., 2006 and Rubin et al., 2007). Many older drivers are aware of their limitations in functional capacities and adapt their driving patterns to match these changes by self-regulating when, where, and how to drive (Baldock et al., 2006 and Charlton et al., 2006). Several studies indicate that older drivers are able to compensate for their impairments by not driving in situations that make them uneasy and by simplifying the driving task, e.g., driving slower, driving less at night, on freeways or during bad weather (Bauer et al., 2003, Charlton et al., 2003 and Owsley et al., 1999). Besides, older drivers may profit from their life-long driving experience and maturity, as well as the flexibility to drive at times and places that they perceive as being safer. The traffic insight they have acquired may give them the ability to anticipate possible problematic situations. Hakamies-Blomqvist, Raitanen, and O’Neill (2002) found that compared to middle-aged drivers, older drivers had no increased crash risk per distance driven, when driving distances were controlled for. Driver distraction is a significant contributor to road traffic accidents (Horberry et al., 2006 and McEvoy et al., 2007). Naturalistic driving studies demonstrate that drivers tend to spend a huge amount of driving time with secondary tasks (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). Laboratory experiments and driving studies show that these secondary tasks can substantially deteriorate driving safety. Up to 23% of all crashes and near-crashes are caused by the secondary task distraction (Klauer et al., 2006). The potential for a non-driving task to distract the driver is determined by the complex interaction of a number of factors including task complexity, current driving demands, driver experience and skill, as well as driver’s willingness to engage in the task. A great deal of research has been conducted on the effect of age on dual task performance, sometimes giving conflicting results (Lindenberger et al., 2000, McDowd and Shaw, 2000, Riby et al., 2004, Salthouse and Miles, 2002 and Verhaeghen et al., 2003). The reasons for the variable research findings in the literature could lie in the diversity of methods and the numerous task combinations used. The meta-analysis of Riby et al. (2004) conducted on the results of 34 studies found a strong overall effect size (d = .68), which indicated a clear age-related dual-tasking impairment. However, this effect size was not representative of all the individual studies reported. Subsequent analysis of study characteristics indicated that task domain was critical in moderating age differences in dual task performance. Notably, tasks with a substantial controlled processing (e.g., episodic memory) or motor component (e.g., tracking) showed greater dual task impairment than tasks that were relatively simple or relied on automatic processing (e.g., perceptual tasks). Lindenberger et al. (2000) suggest that a decline in sensorimotor processing in ageing results in more controlled processing mechanisms and therefore when faced with competing demands older adults are particularly impaired due to a deficit in executive control. Disproportional impairments in the performance of elderly people are especially obtained with increasing complexity of the tasks (Kliegl et al., 2003 and Li et al., 2004). In the present experiment we increased the demands in the secondary task inducing time pressure by means of pacing. Eisdorfer (1968) suggested that pacing induces disproportional anxiety (arousal) in old persons, resulting in a performance decline. Plude and Hoyer (1986) found that the ability to discriminate relevant from irrelevant information is most impaired in elderly people if they have to perform the task under time pressure. Logie, Della Sala, MacPherson, and Cooper (2007) examined dual task demands on encoding and retrieval processes in younger and older adults and found that older people were more sensitive to time pressure in responding under dual task conditions. Based on the ageing and dual-task literature, we expect that as the dual-task demands increase, the driving performance of older drivers will deteriorate more than that of younger drivers. Most of the research in the area of older drivers and distraction has focused on the use of mobile phones while driving. Several studies have demonstrated that the distracting effect of concurrent mobile phone use on driving performance is greater for older drivers compared with other age groups (Cooper et al., 2003, Hancock et al., 2003, McPhee et al., 2004 and Reed and Green, 1999). In contrast, in a study by Strayer and Drews (2004) no such age differences have been found. They reported that the effects of hands-free phone conversation tasks on reaction time, following distance, and speed recovery after braking did not differ between younger and older drivers. One explanation for this inconsistent finding is that the performance of older drivers (aged 65–74) was compared to that of young, inexperienced drivers aged 18–25 years, rather than older, more experienced drivers, and these younger drivers may also be particularly susceptible to the effects of distraction. Several studies have reported that older drivers demonstrate difficulty with the dual-task of following a route guidance system while driving (Dingus et al., 1997 and Green, 2001). Dingus et al. reported that older drivers (65 years and older) drove more slowly and cautiously, while making more safety-related errors (e.g., increased lane departures) compared with younger drivers (16–18 years) when using an advanced traveller information system. Despite the observed age-related decrements in dual task performance in many driver distraction studies, research has also shown that older drivers engage in self-regulatory behaviour while driving, in order to compensate for their greater performance decrements. Horberry et al. (2006), for example, examined the effects of distraction on driving performance of young, mid-age and older drivers. Participants were required to perform one of two secondary tasks while driving: operating an entertainment system and conducting a simulated hands-free mobile phone conversation in both simple and complex simulated driving environments. The performance decrements that occurred as a result of in-vehicle distraction were observed in both simple and complex road environments and for drivers in different age groups. The authors reported that older drivers had more difficulty performing the driving task while distracted and compensated by slowing their speed in complex highway environments. Although it appears that older drivers regulate their driving behaviour, they performed as well as younger drivers on the mobile phone task, indicating that they did not trade off mobile phone performance to enable them to drive safely. They slowed down to give themselves an increased margin for error, possibly because they knew they could not respond to hazards as quickly. Whether these compensatory behaviours of older drivers are sufficient to offset the degradation in their driving performance and reduce their crash risk, however, should be the focus of future research. As we see, the number of studies about distraction in older drivers and their ability to cope with that is limited and their results are contradictory. Thus, a driving simulator experiment was conducted to analyze the following questions: • Are older drivers particularly vulnerable to distraction while driving? • How do older drivers react when the secondary task imposes time pressure? • Are older drivers able to compensate for the possible negative effects when driving under distraction? Additionally, we were interested in age differences with regard to the subjective rating of the driving performance, the perceived workload as well as the perceived distraction.

Results 3.1. Driving performance Table 1 presents the descriptive statistics for three driving performance measures described earlier. The MANOVA indicated significant main effects of age (F(3,7) = 5.4, p = .031, View the MathML sourceηp2 = .698), task (F(6,34) = 5.6, p = .001, View the MathML sourceηp2 = .499) and an interaction (F(6,34) = 3.7, p = .006, View the MathML sourceηp2 = .396). The effect sizes show that the main effect of age is the strongest. Thus, driver’s age significantly influences driving performance. Table 1. Descriptive statistics for driving performance measures as a function of age and three conditions. LCT-baseline LCT + d2 no time pressure LCT + d2 time pressure Older Mid-age Older Mid-age Older Mid-age Mean deviation (m) 1.14 (0.25) 0.90 (0.11) 1.43 (0.25) 1.11 (0.19) 1.55 (0.32) 1.12 (0.17) SDLP (m) 0.11 (0.08) 0.04 (0.02) 0.29 (0.19) 0.05 (0.04) 0.25 (0.15) 0.06 (0.04) Reaction time (s) 0.21 (0.18) 0.08 (0.04) 0.33 (0.13) 0.19 (0.11) 0.38 (0.23) 0.20 (0.10) Note. Standard deviations are presented in parentheses. SDLP – standard deviation of the lateral position. Table options The univariate analyses (Table 2) indicated significant main effect of age and secondary task for all LCT performance parameters. Post-hoc tests for the pair-wise comparisons (Bonferonni corrected) indicated that there was no significant difference between the two dual-task conditions for the three driving performance parameters. However, both dual-task conditions differed from the baseline (p = .001). Table 2. ANOVA results for dependent measures related to driving performance. Source Mean deviation (m) SDLP (m) Reaction time (s) F df p View the MathML sourceηp2 F df p View the MathML sourceηp2 F df p View the MathML sourceηp2 Between-participants Age (A) 15.8 1,9 .003 .638 13.5 1,9 .005 .601 7.8 1,9 .021 .465 Within-participants Task (T) 43.9 2,18 .001 .830 11.8 2,18 .001 .568 12.4 2,18 .001 .581 T × A 2.7 2,18 .091 .234 11.2 2,18 .001 .556 0.5 2,18 .602 .055 Table options The older drivers’ mean deviation (see Fig. 1) was larger than that of the middle-aged participants in all three conditions (F(1,9) = 15.8, p = .003, View the MathML sourceηp2 = .638). Additionally, in both age groups an increase was found due to the secondary task. There was also a tendency for a stronger decrement in older participants under time pressure (Task × Age: F(2,18) = 2.7, p = .091, View the MathML sourceηp2 = .234). Mean deviation from an optimal trajectory as a function of age and of a ... Fig. 1. Mean deviation from an optimal trajectory as a function of age and of a secondary task condition (whiskers represent SD). Figure options With regard to lane change reaction time (see Fig. 2) the older drivers reacted slower in all conditions than the mid-age participants did (F(1,9) = 7.8, p = .021, View the MathML sourceηp2 = .465). Both age groups were influenced by distraction in a comparable manner. Lane change reaction time as a function of age and of a secondary task condition ... Fig. 2. Lane change reaction time as a function of age and of a secondary task condition (whiskers represent SD). Figure options The significant Task × Age interaction for the standard deviation of the lateral position (F(2,18) = 11.2, p = .001, View the MathML sourceηp2 = .556) shows that the older drivers were more affected in the lane-keeping in the dual-task conditions in comparison to the younger participants (see also Fig. 3). Standard deviation of lateral position (SDLP) as a function of age and of a ... Fig. 3. Standard deviation of lateral position (SDLP) as a function of age and of a secondary task condition (whiskers represent SD). Figure options 3.2. Secondary task performance Table 3 presents the descriptive statistics for three d2-performance measures described earlier. To analyze the performance in the d2-secondary task a 2 (age group: middle-aged, older) × 2 (conditions: baseline, test) × 2 (time pressure: with, without) repeated measures ANOVA for every dependent variable was computed. Table 3. Descriptive statistics for d2 performance measures as a function of age and task conditions. Mean response time (ms) % Correct responses Number completed (min) Older Mid-age Older Mid-age Older Mid-age d2-Baseline no time pressure 1173.9 (720.7) 841.4 (389.7) 88.6 (11.7) 93.5 (7.3) 41.5 (9.9) 48.3 (8.1) d2-Baseline time pressure 657.3 (57.0) 592.5 (51.2) 54.1 (18.9) 63.5 (28.0) 38.8 (2.9) 39.7 (1.3) LCT + d2 no time pressure 2594.2 (1281.7) 1544.3 (715.9) 90.3 (17.4) 96.9 (1.6) 22.6 (6.6) 31.6 (7.9) LCT + d2 time pressure 569.3 (49.2) 598.9 (37.9) 21.2 (9.1) 38.1 (7.7) 21.2 (7.7) 29.0 (6.3) Note. Standard deviations are presented in parentheses. Table options Table 4 presents ANOVA results for dependent measures related to the secondary task performance. In regard with the mean response time there was a significant interaction Task × Time pressure (F(1,9) = 18.0, p = .002, View the MathML sourceηp2 = .667), indicating that both age groups were slower in the dual-task condition without time pressure in comparison to d2-baseline. The significant Time pressure × Age interaction (F(1,9) = 5.1, p = .049, View the MathML sourceηp2 = .365) indicates that the older drivers took significantly more time to respond to items in the conditions without time pressure than the mid-aged participants. There was almost no difference in the mean response time between the two age groups in the conditions with time pressure (see also Fig. 4). Table 4. ANOVA results for dependent measures related to the secondary task performance. Source Mean response time % Corrected responses Number completed (min) F df p View the MathML sourceηp2 F df p View the MathML sourceηp2 F df p View the MathML sourceηp2 Between-subjects Age (A) 6.4 1,9 .031 .419 5.1 1,9 .049 .365 8.2 1,9 .018 .480 Within-subjects Task (T) 15.4 1,9 .003 .632 31.4 1,9 .001 .777 110.5 1,9 .001 .925 Time pressure (TP) 50.4 1,9 .001 .849 389.2 1,9 .001 .977 4.5 1,9 .062 .336 T × A 1.5 1,9 .239 .150 0.4 1,9 .505 .051 1.5 1,9 .248 .145 T × TP 18.0 1,9 .002 .667 43.1 1,9 .001 .827 3.9 1,9 .078 .305 TP × A 5.1 1,9 .049 .365 0.6 1,9 .449 .065 1.8 1,9 .209 .169 T × TP × A 2.6 1,9 .138 .228 0.3 1,9 .591 .033 2.0 1,9 .188 .184 Table options Mean response time as a function of age, time pressure and a secondary task ... Fig. 4. Mean response time as a function of age, time pressure and a secondary task condition (whiskers represent SD). Figure options Fig. 5 shows the percentage of correct responses as a function of age, time pressure and a secondary task condition. Under time pressure both age groups made more mistakes in the dual-task condition than in d2-baseline (Task × Time pressure: F(1,9) = 43.1, p = .001, View the MathML sourceηp2 = .827). Percentage of correct responses as a function of age, time pressure and a ... Fig. 5. Percentage of correct responses as a function of age, time pressure and a secondary task condition (whiskers represent SD). Figure options With regard to the number of items completed in a minute ( Fig. 6) the strongest effect was that of a dual task (F(1,9) = 110.5, p = .001, View the MathML sourceηp2 = .925). Participants in both groups completed fewer items when concurrently driving. Additionally, older participants completed significantly fewer items per minute than younger ones in all conditions except for d2-Baseline with time pressure (F(1,9) = 8.2, p = .018, View the MathML sourceηp2 = .480). Also, there was a trend towards a significant Task × Time pressure interaction (F(1,9) = 3.9, p = .078, View the MathML sourceηp2 = .305). Number of items completed in a minute as a function of age, time pressure and a ... Fig. 6. Number of items completed in a minute as a function of age, time pressure and a secondary task condition (whiskers represent SD). Figure options 3.3. Subjective measures Table 5 presents the means and standard deviations for subjective measures. A 2 (age group: middle-aged, older) × 3 (secondary task: none, secondary task without time pressure, secondary task with time pressure) repeated measures ANOVA was used to analyze subjective rating of driving performance and perceived workload. To analyze the perceived distraction a 2 (age group: middle-aged, older) × 2 (secondary task: secondary task without time pressure, secondary task with time pressure) repeated measures ANOVA was used. Because perceived distraction was not rated after driving without a secondary task (LCT-Baseline), ANOVA included only two dual-task conditions. Accordingly, post-hoc comparisons were not necessary. Table 5. Descriptive statistics for subjective ratings as a function of age and three conditions. LCT-baseline LCT + d2 no time pressure LCT + d2 time pressure Older Mid-age Older Mid-age Older Mid-age Driving performance 10.8 (1.8) 9.3 (1.9) 7.9 (2.8) 8.3 (1.7) 7.2 (1.6) 7.9 (1.4) Perceived workload 5.5 (2.4) 5.4 (2.1) 7.1 (1.4) 9.1 (1.7) 7.9 (1.7) 9.0 (1.8) Perceived distraction – – 8.4 (1.7) 9.4 (1.7) 11.0 (1.6) 10.0 (1.4) Note. Standard deviations are presented in parentheses. Table options Overall, subjective rating of driving performance, perceived workload and perceived distraction was found to be similar for mid-age and older drivers. The univariate analyses indicated that both groups judged their driving performance (Fig. 7) to be worse in the dual-task conditions (F(2,18) = 18.6, p = .001, View the MathML sourceηp2 = .675). However the post-hoc comparisons indicated no difference between the two dual-task conditions (with and without time pressure) for both age-groups. The significant “age × secondary task” interaction (F(2,18) = 3.9, p = .039, View the MathML sourceηp2 = .303) indicates that older drivers rated their driving performance to be better in the baseline condition compared to driving with secondary task. Subjective rating of the driving performance (whiskers represent SD). Fig. 7. Subjective rating of the driving performance (whiskers represent SD). Figure options Both groups experienced increased workload in the dual-task condition (F(2,18) = 24.4, p = .000, View the MathML sourceηp2 = .731) in comparison to the baseline-drive. However the post-hoc comparisons indicated no difference between the dual-task conditions for both age-groups. There was also a trend towards a significant “age × secondary task condition” interaction (F(2,18) = 3.4, p = .057, View the MathML sourceηp2 = .273) indicating that middle-aged drivers rated their perceived workload to be higher in the secondary task conditions than in the baseline condition. With regard to perceived distraction (Fig. 8) there was a significant difference between the dual-task conditions with and without time pressure: F(1,9) = 15.4, p = .003, View the MathML sourceηp2 = .632. The significant “age × secondary task” interaction (F(1,9) = 6.6, p = .030, View the MathML sourceηp2 = .426) indicates that older drivers stated to be more distracted in comparison to the mid-age participants in the condition with time pressure. Subjective rating of the perceived distraction as a function of age and a ... Fig. 8. Subjective rating of the perceived distraction as a function of age and a secondary task condition (whiskers represent SD).