The reaction times of drivers aged 20 to 80 during a divided attention driving

Objective: Many studies addressing age-related changes in driving performance focus on comparing young vs. older drivers, which might lead to the biased conclusion that driving performance decreases only after the age of 65. The main aim of the study was to show that changes in driving performance are progressive throughout the adult years.

Methods: A sample of 351 drivers aged 20 to 80 was assessed for their reaction times while driving between road cones. The drivers were exposed to 2 conditions varying according to task complexity. In single task conditions, the drivers performed a full stopping maneuver at a given signal; in dual task conditions, the drivers were distracted before the signal for stopping maneuver was triggered. Reaction times were compared across conditions and age groups.

Results: The results showed that both reaction times and variability of driving performance increased progressively between the ages of 20 and 80. The increase in both reaction times and variability was greater in the complex task condition. The high-performing quarter of elderly drivers performed equally well or better than younger drivers did.

Conclusions: The data clearly supported the claim that driving performance changes steadily across age groups: both mean reaction time and interindividual variability progressively increase with age. In addition, a significant group of older drivers was identified who did not show the expected age-related decrease in performance. The findings have important implications, suggesting that in relation to driving, aging is a progressive phenomenon and may lead to variety of driving performance; age-related studies of driving performance should put more emphasis on investigating changes across the whole driver age range rather than only comparing younger and older drivers.     

Exploring the association between working memory and driving performance in Parkinson's disease

Objective: The aim of this study was to explore whether varying levels of operational and tactical driving task demand differentially affect drivers with Parkinson's disease (PD) and control drivers in their sign recall.

Methods: Study participants aged between 50 and 70 years included a group of drivers with PD (n = 10) and a group of age- and sex-matched control drivers (n = 10). Their performance in a sign recall task was measured using a driving simulator.

Results: Drivers in the control group performed better than drivers with PD in a sign recall task, but this trend was not statistically significant (P =.43). In addition, regardless of group membership, subjects' performance differed according to varying levels of task demand. Performance in the sign recall task was more likely to drop with increasing task demand (P =.03). This difference was significant when the variation in task demand was associated with a cognitive task; that is, when drivers were required to apply the instructions from working memory.

Conclusions: Although the conclusions drawn from this study are tentative, the evidence presented here is encouraging with regard to the use of a driving simulator to examine isolated cognitive functions underlying driving performance in PD. With an understanding of its limitations, such driving simulation in combination with functional assessment batteries measuring physical, visual, and cognitive abilities could comprise one component of a multitiered system to evaluate medical fitness to drive.     

The useful field of view assessment predicts simulated commercial motor vehicle driving safety

Objective: The Useful Field of View (UFOV) assessment, a measure of visual speed of processing, has been shown to be a predictive measure of motor vehicle collision (MVC) involvement in an older adult population, but it remains unknown whether UFOV predicts commercial motor vehicle (CMV) driving safety during secondary task engagement. The purpose of this study is to determine whether the UFOV assessment predicts simulated MVCs in long-haul CMV drivers.

Method: Fifty licensed CMV drivers (M_age = 39.80, SD = 8.38, 98% male, 56% Caucasian) were administered the 3-subtest version of the UFOV assessment, where lower scores measured in milliseconds indicated better performance. CMV drivers completed 4 simulated drives, each spanning approximately a 22.50-mile distance. Four secondary tasks were presented to participants in a counterbalanced order during the drives: (a) no secondary task, (b) cell phone conversation, (c) text messaging interaction, and (d) e-mailing interaction with an on-board dispatch device.

Results: The selective attention subtest significantly predicted simulated MVCs regardless of secondary task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC in the simulated drive. The e-mail interaction secondary task significantly predicted simulated MVCs with a 4.14 times greater risk of an MVC compared to the no secondary task condition. Subtest 3, a measure of visual speed of processing, significantly predicted MVCs in the email interaction task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC during the email interaction task.

Conclusions: The UFOV subtest 3 may be a promising measure to identify CMV drivers who may be at risk for MVCs or in need of cognitive training aimed at improving speed of processing. Subtest 3 may also identify CMV drivers who are particularly at risk when engaged in secondary tasks while driving.     

Identifying risky drivers with simulated driving

Objective: The present study aimed to examine whether high-risk drivers differ from low-risk drivers in driving behavior in a simulated environment.

Method: The 2 risk groups including 36 drivers (18 males and 18 females) performed driving tasks in a simulated environment. The simulated driving behaviors are compared between the 2 risk groups.

Results: The high-risk drivers drove much faster and exhibited larger offsets of the steering wheel than did the low-risk drivers in events without incidents. Additionally, the high-risk drivers used turn signals and horns less frequently than the low-risk drivers.

Conclusions: The present study revealed that the high-risk group differed from the low-risk group in driving behavior in a simulated environment. These results also suggest that simulated driving tasks might be useful tools for the evaluation of drivers’ potential risks.     

Self-regulation of driving speed among distracted drivers: An application of driver behavioral adaptation theory

Objective: The adaptive behavior of mobile phone–distracted drivers has been a topic of much discussion in the recent literature. Both simulator and naturalistic studies suggest that distracted drivers generally select lower driving speeds; however, speed adaptation is not observed among all drivers, and the mechanisms of speed selection are not well understood. The aim of this research was to apply a driver behavioral adaptation model to investigate the speed adaptation of mobile phone–distracted drivers.

Methods: The speed selection behavior of drivers was observed in 3 phone conditions including baseline (no conversation) and hands-free and handheld phone conversations in a high-fidelity driving simulator. Speed adaptation in each phone condition was modeled as a function of secondary task demand and self-reported personal/psychological characteristics with a system of seemingly unrelated equations (SURE) accounting for potential correlations due to repeated measures experiment design.

Results: Speed adaptation is similar between hands-free and handheld phone conditions, but the predictors of speed adaptation vary across the phone conditions. Though perceived workload of secondary task demand, self-efficacy, attitude toward safety, and driver demographics were significant predictors of speed adaptation in the handheld condition, drivers' familiarity with the hands-free interface, attitude toward safety, and sensation seeking were significant predictors in the hands-free condition. Drivers who reported more positive safety attitudes selected lower driving speeds while using phones.

Conclusion: This research confirmed that behavioral adaptation models are suitable for explaining speed adaptation of mobile phone distracted drivers, and future research could be focused on further theoretical refinement.     

A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

Objective: This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving.

Method: Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual–manual task) and distracted (poststarting of visual–manual task) driving periods. Average relative spectral power in a low frequency range (0–0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses.

Results: Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual–manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving.

Conclusions: This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses.     

“Are you still driving?” Metasynthesis of patient preferences for communication with health care providers

Aim: The aim of this study was to synthesize published qualitative studies to identify older adults' preferences for communication about driving with health care providers.

Background: Health care providers play a key role in addressing driving safety and driving retirement with older adults, but conversations about driving can be difficult. Guides exist for family members and providers, but to date less is known about the types of communication and messages older drivers want from their health care providers.

Design: A qualitative metasynthesis of studies published on or before October 10, 2014, in databases (PubMed, CINAHL, PsycINFO, and Web of Science) and grey literature was performed.

Review Methods: Twenty-two published studies representing 518 older adult drivers met the following inclusion criteria: the study (1) was about driving; (2) involved older drivers; (3) was qualitative (rather than quantitative or mixed methods); and (4) contained information on older drivers' perspectives about communication with health care providers.

Results: We identified 5 major themes regarding older adults' communication preferences: (1) driving discussions are emotionally charged; (2) context matters; (3) providers are trusted and viewed as authority figures; (4) communication should occur over a period of time rather than suddenly; and (5) older adults desire agency in the decision to stop driving.

Conclusion: Various stakeholders involved in older driver safety should consider older drivers' perspectives regarding discussions about driving. Health care providers can respect and empower older drivers—and support their family members—through tactful communication about driving safety and mobility transitions during the life course.     

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

Objective: Slow eyelid closure is recognized as an indicator of sleepiness in sleep-deprived individuals, although automated ocular devices are not well validated. This study aimed to determine whether changes in eyelid closure are evident following acute sleep deprivation as assessed by an automated device and how ocular parameters relate to performance after sleep deprivation.

Methods: Twelve healthy professional drivers (45.58 ± 10.93 years) completed 2 randomized sessions: After a normal night of sleep and after 24 h of total sleep deprivation. Slow eye closure (PERCLOS) was measured while drivers performed a simulated driving task.

Results: Following sleep deprivation, drivers displayed significantly more eyelid closure (P < .05), greater variation in lane position (P < .01) and more attentional lapses (P < .05) compared to after normal sleep. PERCLOS was moderately associated with variability in both vigilance performance (r = 0.68, P < .05) and variation in lane position on the driving task (r = 0.61, P < .05).

Conclusions: Automated ocular measurement appears to be an effective means of detecting impairment due to sleep loss in the laboratory.     

Covariability in three dimensions of teenage driving risk behavior: impaired driving,risky and unsafe driving behavior,and secondary task engagement

Objective: This research examined the extent to which teenagers who engaged in one form of risky driving also engaged in other forms and whether risky driving measures were reciprocally associated over time.

Methods: The data were from waves 1, 2, and 3 (W1, W2, and W3) of the NEXT Generation study, with longitudinal assessment of a nationally representative sample starting with 10th graders starting in 2009–2010. Three measures of risky driving were assessed in autoregressive and cross-lagged analyses: driving while alcohol/drug impaired (DWI), Checkpoints Risky Driving Scale (risky and unsafe driving), and secondary task engagement while driving.

Results: In adjusted autoregression models, the risk variables demonstrated high levels of stability, with significant associations observed across the 3 waves. However, associations between variables were inconsistent. DWI at W2 was associated with risky and unsafe driving at W3 (β = 0.21, P < .01); risky and unsafe driving at W1 was associated with DWI at W2 (β = 0.20, P < .01); and risky and unsafe driving at W2 is associated with secondary task engagement at W3 (β = 0.19, P < .01). Over time, associations between DWI and secondary task engagement were not significant.

Conclusions: Our findings provide modest evidence for the covariability of risky driving, with prospective associations between the Risky Driving Scale and the other measures and reciprocal associations between all 3 variables at some time points. Secondary task engagement, however, appears largely to be an independent measure of risky driving. The findings suggest the importance of implementing interventions that addresses each of these driving risks.     

Driving self-regulation and ride service utilization in a multicommunity,multistate sample of U.S. older adults

Objectives: This study examined a multicommunity alternative transportation program available 24 hours a day, 7 days a week, for any purpose, offering door-through-door service in private automobiles to members who either do not drive or are transitioning away from driving. Specific aims were to describe the characteristics of members by driving status and ride service usage of these members.

Methods: Data came from administrative records maintained by a nonprofit ride service program and include 2,661 individuals aged 65+ residing in 14 states who joined the program between April 1, 2010, and November 8, 2013. Latent class analysis was used to group current drivers into 3 classes of driving status of low, medium, and high self-regulation, based on their self-reported avoidance of certain driving situations and weekly driving frequency. Demographics and ride service use rate for rides taken through March 31, 2014, by type of ride (e.g., medical, social, etc.) were calculated for nondrivers and drivers in each driving status class.

Results: The majority of ride service users were female (77%) and aged 65–74 years (82%). The primary method of getting around when enrolling for the transportation service was by riding with a friend or family member (60%). Among the 67,883 rides given, nondrivers took the majority (69%) of rides. Medical rides were the most common, accounting for 40% of all rides.

Conclusions: Reported ride usage suggests that older adults are willing to use such ride services for a variety of trips when these services are not limited to specific types (e.g., medical). Further research can help tailor strategies to encourage both nondrivers and drivers to make better use of alternative transportation that meets the special needs of older people.     

Transport company safety climate—The impact on truck driver behavior and crash involvement

Objective: The present study investigated the relationships between safety climate and driving behavior and crash involvement.

Methods: A total of 339 company-employed truck drivers completed a questionnaire that measured their perceptions of safety climate, crash record, speed choice, and aberrant driving behaviors (errors, lapses, and violations).

Results: Although there was no direct relationship between the drivers' perceptions of safety climate and crash involvement, safety climate was a significant predictor of engagement in risky driving behaviors, which were in turn predictive of crash involvement.

Conclusions: This research shows that safety climate may offer an important starting point for interventions aimed at reducing risky driving behavior and thus fewer vehicle collisions.     

Driving in mild cognitive impairment: The role of depressive symptoms

Objectives: Previous studies indicate a negative association between depression and driving fitness in the general population. Our goal was to cover a gap in the literature and to explore the link between depressive symptoms and driving behavior in individuals with mild cognitive impairment (MCI) through the use of a driving simulator experiment.

Methods: Twenty-four individuals with MCI (mean age = 67.42, SD = 7.13) and 23 cognitively healthy individuals (mean age = 65.13, SD = 7.21) were introduced in the study. A valid driving license and regular car use served as main inclusion criteria. Data collection included a neurological/neuropsychological assessment and a driving simulator evaluation. Depressive symptomatology was assessed with the Patient Health Questionnaire (PHQ-9).

Results: Significant interaction effects indicating a greater negative impact of depressive symptoms in drivers with MCI than in cognitively healthy drivers were observed in the case of various driving indexes, namely, average speed, accident risk, side bar hits, headway distance, headway distance variation, and lateral position variation. The associations between depressive symptoms and driving behavior remained significant after controlling for daytime sleepiness and cognition.

Conclusions: Depressive symptoms could be a factor explaining why certain patients with MCI present altered driving skills. Therefore, interventions for treating the depressive symptoms of individuals with MCI could prove to be beneficial regarding their driving performance.     

Personality of young drivers in Oman: Relationship to risky driving behaviors and crash involvement among Sultan Qaboos University students

Objective: Drivers’ behaviors such as violations and errors have been demonstrated to predict crash involvement among young Omani drivers. However, there is a dearth of studies linking risky driving behaviors to the personality of young drivers. The aim of the present study was to assess such traits within a sample of young Omani drivers (as measured through the behavioral inhibition system [BIS] and the behavioral activation system [BAS]) and determine links with aberrant driving behaviors and self-reported crash involvement.

Methods: A cross-sectional study was conducted at the Sultan Qaboos University that targeted all licensed Omani's undergraduate students. A total of 529 randomly selected students completed the self-reported questionnaire that included an assessment of driving behaviors (e.g., Driver Behaviour Questionnaire, DBQ) as well as the BIS/BAS measures.

Results: A total of 237 participants (44.8%) reported involvement in at least one crash since being licensed. Young drivers with lower BIS–Anxiety scores and higher BAS–Fun Seeking tendencies as well as male drivers were more likely to report driving violations. Statistically significant gender differences were observed on all BIS and BAS subscales (except for BAS–Fun) and the DBQ subscales, because males reported higher trait scores. Though personality traits were related to aberrant driving behaviors at the bivariate level, the constructs were not predictive of engaging in violations or errors. Furthermore, consistent with previous research, a supplementary multivariate logistic regression analysis revealed that only driving experience was predictive of crash involvement.

Conclusions: The findings highlight that though personality traits influence self-reported driving styles (and differ between the genders), the relationship with crash involvement is not as clear. This article further outlines the key findings of the study in regards to understanding core psychological constructs that increase crash risk.     

How do significant others influence our driving? A descriptive study of ego–alter dyads in a college population

Objectives: The purpose of this investigation was to describe the mutual influences between drivers and their significant others in a sample of college students, using a social network perspective.

Methods: A web-based survey was made of 65 dyads of drivers and their significant others, in order to examine areas of mutual influence concerning driving. Measures were taken of their frequency of risky driving, including how often they drive, talk on the cell phone, or text with each other while driving. They were also asked whether they have influenced or been influenced by each other to drive safer or in a more risky manner.

Results: The dyads were very similar in how frequently they drive as well as talk to each other on the phone while driving. However, they were unlikely to feel that their driving was influenced by each other, with only 17% of the drivers and 19% of the significant others saying that they have been overtly influenced by (or have influenced) each other's driving behavior often or most of the time. Yet, most (67.7% and 72.1%) said they have ever encouraged or been encouraged by each other to drive more safely, mainly by being told to reduce their speed. In both sets of drivers in these dyads, talking to and texting their significant other while driving was related to risky driving.

Conclusions: These findings suggest that a similarity exits in the driving patterns of young drivers and their significant others, especially concerning talking to each other on the phone while driving. The largest degree of overt social influence appears to center around avoidance of behaviors perceived to be associated with getting a traffic citation. Implications for safety campaign development and future research are presented.     

Risk factors for the severity of injury incurred in crashes involving on-duty police cars

Objective: This article explores the risk factors associated with police cars on routine patrol and/or on an emergency run and their effects on the severity of injuries in crashes.

Methods: The binary probit model is used to examine the effects of important factors on the risk of injuries sustained in crashes involving on-duty police cars.

Results: Several factors significantly increase the probability of crashes that cause severe injuries. Among those causes are police officers who drive at excessive speeds, traffic violations during emergency responses or pursuits, and driving during the evening (6 to 12 p.m.) or in rainy weather. Findings also indicate some potential issues associated with an increase in the probability of crashes that cause injuries. Younger police drivers were found to be more likely to be involved in crashes causing injuries than middle-aged drivers were. Distracted driving by on-duty police officers as well as civilian drivers who did not pull over to let a police car pass in emergency situations also caused serious crashes.

Conclusions: Police cars are exempted from certain traffic laws under emergency circumstances. However, to reduce the probability of being involved in a crash resulting in severe injuries, officers are still obligated to drive safely and follow safety procedures when responding to emergencies or pursuing a car. Enhancement of training techniques for emergency situations or driving in pursuit of an offender and following the safety procedures are essential for safety in driving during an emergency run by police.     

The use of navigation systems in naturalistic driving

Objective: In this study, we assessed the use of portable navigation systems in everyday driving by applying in-vehicle naturalistic driving.

Method: Experienced users of navigation systems, 7 females and 14 males, were provided with a specially equipped vehicle for approximately 1 month. Their trips were recorded using 4 cameras, Global Positioning System (GPS) data, and other sensor data. The drivers’ navigation system use data were coded from the video recordings, which showed how often and for how long the system was activated and how often and for how long a driver operated the system.

Results: The system was activated for 23% of trips, predominantly on longer and unique trips. Analyses of the percentage of time for which the speed limit was exceeded showed no evidence of differences between trips for which the navigation system was used or not used. On trips for which the navigation system was activated, participants spent about 5% of trip time interacting with the device. About 40% of interacting behavior took place in the first 10% of the trip time, and about 35% took place while the car was standing still or moving at a very low speed; that is, 0–10 km/h.

Conclusion: These results shed light on how and when drivers use navigation systems. They suggest that although drivers regulate their use of such systems to some extent, they often perform risky tasks while driving.     

Difficulties in emotion regulation and risky driving among Lithuanian drivers

Background: Risky driving is a common cause of traffic accidents and injuries. However, there is no clear evidence of how difficulties in emotion regulation contribute to risky driving behavior, particularly in small post-Soviet countries. The present study aimed to investigate the relationship between difficulties in emotion regulation and self-reported risky driving behavior in a sample of Lithuanian drivers.

Methods: A total of 246 nonprofessional Lithuanian drivers participated in a cross-sectional survey. Difficulties in emotion regulation were assessed using the Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer 2004), and risky driving behavior was assessed using the Manchester Driver Behaviour Questionnaire (DBQ; Lajunen et al. 2004).

Results: Males scored higher than females in aggressive violations and ordinary violations. Females scored higher for the nonacceptance of emotional responses, whereas males had more difficulties with emotional awareness than females. More difficulties in emotion regulation were positively correlated with driving errors, lapses, aggressive violations, and ordinary violations for both males and females. Structural equation modeling showed that difficulties in emotion regulation explained aggressive and ordinary violations more clearly than lapses and errors. When controlling for interactions among the distinct regulation difficulties, difficulties with impulse control and difficulties engaging in goal-directed behavior predicted risky driving. Furthermore, nonacceptance of emotional responses and limited access to emotion regulation strategies were related to less violations and more driving errors.

Conclusion: Emotion regulation difficulties were associated with the self-reported risky driving behaviors of Lithuanian drivers. This provides useful hints for improving driver training programs in order to prevent traffic injuries.     

Driving performance comparing older versus younger drivers

Objectives: A cross-sectional study was conducted at the Touro University California campus to compare differences in reaction times and driving performance of younger adult drivers (18–40 years) and older adult drivers (60 years and older). Each test group consisted of 38 participants.

Methods: A Simple Visual Reaction Test (SVRT) tool was used to measure reaction times. The STISIM Drive M100 driving simulator was used to assess driving parameters. Driving performance parameters included mean lane position, standard deviation of mean lane position measured, mean speed, standard deviation of mean speed, car-following delay, car-following modulus, car-following coherence, off-road accidents, collisions, pedestrians hit, and traffic light tickets.

Results: Compared to younger participants, older drivers experienced significantly slower reaction times (510.0 ± 208.8 vs. 372.4 ± 96.1 ms, P =.0004), had more collisions (0.18 ± 0.39 vs. none, P =.0044), drove slower (44.6 ± 6.6 vs. 54.9 ± 11.7 mph, P <.0001), deviated less in speed (12.6 ± 4.3 vs. 16.8 ± 6.3, P =.0011), and were less able to maintain a constant distance behind a pace car (0.42 ± 0.23 vs. 0.59 ± 0.24; P =.0025).

Conclusions: Differences exist in driving patterns of older and younger drivers as measured by reaction times and driving simulator outcomes. These results are the first to compare these 2 specific adult age groups' driving performance as measured by a standardized driving simulator scenario. Identifying these differences is essential in addressing them and preventing future traffic injuries.     

The on-road experiences and awareness of sleepiness in a sample of Australian highway drivers: A roadside driver sleepiness study

Objective: Driver sleepiness contributes substantially to road crash incidents. Simulator and on-road studies clearly reveal an impairing effect from sleepiness on driving ability. However, the degree to which drivers appreciate the dangerousness of driving while sleepy is somewhat unclear. This study sought to determine drivers' on-road experiences of sleepiness, their prior sleep habits, and personal awareness of the signs of sleepiness.

Methods: Participants were a random selection of 92 drivers traveling on a major highway in the state of Queensland, Australia, who were stopped by police as part of routine drink driving operations. Participants completed a brief questionnaire that included demographic information, sleepy driving experiences (signs of sleepiness and on-road experiences of sleepiness), and prior sleep habits. A modified version of the Karolinska Sleepiness Scale (KSS) was used to assess subjective sleepiness in the 15 min prior to being stopped by police.

Results: Participants' ratings of subjective sleepiness were quite low, with 90% reporting being alert to extremely alert on the KSS. Participants were reasonably aware of the signs of sleepiness, with many signs of sleepiness associated with on-road experiences of sleepiness. Additionally, the number of hours spent driving was positively correlated with the drivers' level of sleep debt.

Conclusions: The results suggest that participants had moderate experiences of driving while sleepy and many were aware of the signs of sleepiness. The relationship between driving long distances and increased sleep debt is a concern for road safety. Increased education regarding the dangers of sleepy driving seems warranted.     

Sensitivity evaluation of the visual,tactile, and auditory detection response task method while driving

Objectives: In this article, we evaluate the sensitivity to cognitive load of 3 versions of the Detection Response Task method (DRT), proposed in ISO Draft Standard DIS-17488.

Methods: We present a user study with 30 participants in which we compared the sensitivity to cognitive load of visual, audio, and tactile DRT in a simulated driving environment. The amount of cognitive load was manipulated with secondary n-back tasks at 2 levels of difficulty (0-back and 1-back). We also explored whether the DRT method is least sensitive to cognitive load when the stimuli and secondary task are of the same modality. For this purpose, we used 3 forms to present the n-back task stimuli: visual, audio, and tactile. Responses to the task were always vocal. The experiment was based on a between-subject design (the DRT modalities) with 2 levels of within-subject design study (modalities and difficulty of the secondary n-back tasks). The participants' primary task in the study was to drive safely, and a second priority was to answer to DRT stimuli and perform secondary tasks.

Results: The results indicate that all 3 versions of the DRT tested were sensitive to detecting the difference in cognitive load between the reference driving period and driving and engaging in the secondary tasks. Only the visual DRT discriminated between the 0-back and 1-back conditions on mean response time. Contrary to expectations, no interaction was observed between DRT modality and the stimuli modality used for presentation of the secondary tasks.

Conclusions: None of the 3 methods of presenting DRT stimuli showed a consistent advantage in sensitivity in differentiating multiple levels of cognitive load if all response times, hit rates, and secondary task performance are considered. If only response time is considered, the visual presentation of the DRT stimulus used in this study showed some advantages. In interpreting these data, it should be noted that the methods of DRT stimulus presentation varied somewhat from the currently proposed draft ISO standard and it is possible that the relative salience level of the visual DRT stimulus influenced the findings. It is further suggested that more than 2 levels of difficulty of the n-back task should be considered for further investigation of the relative sensitivity of different DRT stimuli modalities. Parameters that indicate change in cognitive load (response time, hit rate, task performance) should be analyzed together in assessing the overall impact on the driver and not individually, in order to obtain a fuller insight of the assessed cognitive load.