Driving Behaviour of Drivers with Mild Cognitive Impairment and Alzheimer’s Disease: a Driving Simulator Study

The objective of this research is the analysis of the driving performance of drivers with Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI), on the basis of a driving simulator experiment, in which healthy “control” drivers and impaired drivers drive in different driving scenarios, following a thorough neurological and neuropsychological assessment of all participants. The driving scenarios include driving in rural and urban areas in low and high traffic volumes. The driving performance of drivers impaired by the examined pathologies (AD and MCI) is compared to that of healthy controls by means of Repeated Measures General Linear Modeling techniques. In this paper a sample of 75 participants is analyzed. Various driving performance measures are examined, including speed, lateral position, steering angle, headway, reaction time at unexpected events etc., some in terms of their mean values and some in both their mean values and their variability. The results suggest that the two examined cerebral diseases do affect driving performance, and there are common driving patterns for both cerebral diseases, as well as particular characteristics of specific pathologies. More specifically, drivers with these cerebral diseases drive at lower speeds and with larger headway compared to healthy drivers. Moreover, they appear to have difficulties in positioning the vehicle on the lane. Cerebral diseases also appear to significantly affect reaction times at incidents. Key-words: driving performance; driving simulator; Mild Cognitive Impairment; Alzheimer’s disease Pavlou D., Papadimitriou E., Antoniou C., Papantoniou P., Yannis G., Golias J., Papageorgiou S.G. BACKGROUND 1 2 The task of driving requires the ability to receive sensory information, process the information, 3 and to make proper, timely judgments and responses (1, 2). Various motor, visual, cognitive 4 and perceptual deficits can affect the ability to drive. These deficits are either age-related or 5 caused by neurologic disorders and lead to reduced driver fitness and increased crash risk. 6 More specifically, diseases affecting a person's brain functioning (e.g. presence of specific 7 brain pathology due to neurological diseases as Alzheimer’s disease) may significantly impair 8 the person's driving ability (3, 4, 5, 6). These conditions have obvious impacts on driving 9 performance, but in mild cases and importantly in the early stages, they may be imperceptible 10 in one’s daily routine yet still impact one’s driving ability. Furthermore, neuropsychological 11 parameters associated with driving performance are reaction time, visual attention, speed of 12 perception and processing, and general cognitive and executive functions. These parameters 13 show considerable decline with age or at the presence of cognitive impairments and are 14 associated with the probability of accident involvement (7). 15 Relatively little is known about the competence of drivers with Mild Cognitive 16 Impairment (MCI). This constitutes a considerable gap, given that MCI is a pathological 17 condition with high prevalence in the general population as ~15% of people >65 years old are 18 affected. In addition, MCI eventually develops into dementia with a high annual rate (8). The 19 concept of MCI has been described as a cognitive state that lies between normal aging and 20 dementia (9). Persons with MCI exhibit cognitive decline beyond what is expected to be normal 21 for age, but are otherwise functioning well and do not meet criteria for dementia. Research 22 results are not conclusive on the extent to which MCI is affecting driving behaviour and safety. 23 MCI drivers seem to have statistically significant driving behaviour deviation (maintaining 24 speed, wheel stability, and lateral control) from the control driving population (10). Another 25 study tried to ascertain which cognitive features contribute to the safe driving behaviour of 26 MCI drivers. Participants drove using a driving simulator and seemed to have considerable 27 difficulties in maintaining lateral control on a road and in following the vehicle ahead (11). 28 Moreover, Alzheimer’s disease (AD) is the most frequent form of dementia worldwide 29 (12). In the early stages of the disease, a variety of symptoms can be observed with gradually 30 progressive memory impairment being the most prominent symptom. Additional deficits may 31 be present, including, visuospatial deficits, impaired attention, executive dysfunction and 32 judgment, verbal fluency and confrontation naming (13). Another research showed that AD 33 drivers (especially the elderly) made many more safety errors (the most common errors were 34 lane violations) (14). Longitudinal evidence was provided for a decline in driving performance 35 over time, primarily in early-stage dementia of the Alzheimer type (15). Mild AD significantly 36 impaired simulated driving fitness, while MCI limitedly affected driving performance (16). 37 What is more, an accurate judgment of someone’s own ability to drive and the resultant 38 compensatory behaviour are prerequisites of safe driving, an ability that is often impaired in 39 dementia (17). 40 Given that the percentage of the elderly in society is increasing (18), and that the level 41 of motorization also increases (19), the investigation of the impact of these conditions on driver 42 performance becomes quite critical. It is also highlighted that relatively few studies exist 43 analyzing the effect of a specific pathology on driving performance, and even fewer studies 44 comparing different pathologies. 45 46 47 Pavlou D., Papadimitriou E., Antoniou C., Papantoniou P., Yannis G., Golias J., Papageorgiou S.G. 5 OBJECTIVES 48 49 The objective of this research is to analyze the driving performance of drivers with Alzheimer’s 50 disease (AD) and Mild Cognitive Impairment (MCI), by means of a driving simulator 51 experiment. Various driving performance measures are examined in both rural and urban 52 environment, e.g. mean speed, lateral position, steering angle, headway, reaction time at 53 unexpected events etc. The driving performance of drivers impaired by the above pathologies 54 is compared to that of healthy controls by means of Repeated Measures General Linear 55 Modeling techniques. 56 The research questions that are examined in this paper are: how MCI and AD affect 57 various measures of driving performance and how these diseases interact with road and traffic 58 parameters. 59 The paper starts a presentation of a large driving simulator experiment, in which the 60 driving performance of the impaired and healthy drivers was examined in different driving 61 scenarios, following a thorough neurological and neuropsychological assessment of all 62 participants. The existing sample size and characteristics are presented next, followed by a 63 short description of the analysis methods, dependent and independent variables. The results are 64 presented and discussed, and some concluding remarks are provided. 65 66 DRIVING SIMULATOR EXPERIMENT 67 68 Overview 69 70 This research is based on a methodological framework for the combined assessment of traffic, 71 behavioural, medical, neurological and neuropsychological parameters on driving 72 performance. In this framework, the aspects of driver behaviour and safety research addressed 73 are inherently interdisciplinary, and an experiment was designed by an interdisciplinary 74 research team including: 75  Transportation Engineers Department of Transportation Planning and Engineering, 76 of the National Technical University of Athens (NTUA) 77  Neurologists 2nd Department of Neurology, University of Athens Medical School, at 78 ATTIKON University General Hospital, Haidari, Athens 79  Neuropsychologists Department of Psychology, University of Athens, the 2nd 80 Department of Neurology of ATTIKON University General Hospital, Haidari, Athens 81 and the Aristotle University of Thessaloniki. 82 According to the objectives of the analysis, the experiment includes three types of 83 assessment: 84  Medical / neurological assessment: The first assessment concerns the administration 85 of a full clinical medical, ophthalmological and neurological evaluation, in order to well 86 document the characteristics of each of these disorders (e.g. MCI, AD, PD, 87 Cerebrovascular disease (stroke) as well as other related parameters of potential impact 88 on driving (e.g. use of medication affecting the Central Nervous System). 89  Neuropsychological assessment: The second assessment concerns the administration 90 of a series of neuropsychological tests and psychological-behavioural questionnaires to 91 the participants. The tests carried out cover a large spectrum of Cognitive Functions: 92 visuospatial and verbal episodic and working memory, general selective and divided 93 attention, reaction time, processing speed, psychomotor speed etc. 94  Driving at the simulator: The third assessment concerns the driving behaviour by 95 means of programming of a set of driving tasks into a driving simulator for different 96 driving scenarios. 97 Pavlou D., Papadimitriou E., Antoniou C., Papantoniou P., Yannis G., Golias J., Papageorgiou S.G. 6 The first and second assessments are carried out at the ATTIKON University General 98 Hospital, and their description is beyond the scope of this paper; for details the reader is referred 99 to Papadimitriou et al. (2014) (20). The third assessment, (driving simulator experiment) takes 100 place in the NTUA Road Safety Observatory and is presented in detail in the following section. 101 102 Driving at the simulator 103 104 The NTUA driving simulator is a motion base quarter-cab manufactured by the FOERST 105 Company. The simulator consists of 3 LCD wide screens 40’’ (full HD: 1920x1080pixels), 106 driving position and support motion base. The dimensions at a full development are 107 230x180cm, while the base width is 78cm and the total field of view is 170 degrees. It’s worth 108 mentioning that the simulator is validated against a real world environment (21). 109 The design of the driving scenarios includes driving in different road and traffic 110 conditions, such as in a rural, urban area with high and low traffic volume, with or without 111 external distraction. More specifically, the driving simulator experiment begins with one 112 practice drive (usually 10-15 minutes), until the participant fully familiarizes with the 113 simulation environment. Afterwards, the participant drives two sessions (approximately 20 114 minutes each). Each session corresponds to a different road environment: a rural route that is 115 2.1 km long, single carriageway and the lane width is 3m, with zero gradient and mild 116 horizontal curves and an urban route that is 1.7km long, at its bigger part dual carriageway, 117 separated by guardrails and the lane width is 3.5m. Two traffic controlled junctions, one stop118 controlled junction and one roundabout are placed along the route. 119 Within each road / area type, two traffic scenarios and three distraction conditions are 120 examined in a full factorial within-subject design. The traffic conditions examined include: 121  Low traffic conditions ambient vehicles’ arrivals are drawn from a Gamma 122 distribution with mean m=12sec, and variance σ=6 sec, corresponding to an average 123 traffic volume Q=300 vehicles/hour. 124  High traffic conditions ambient vehicles’ arrivals are drawn from a Gamma 125 distribution with mean m=6sec, and variance σ=3 sec, corresponding to an average 126 traffic volume of Q=600 vehicles/hour. 127 The distraction conditions examined concern undistracted driving, driving while 128 conversing with a passenger and driving while conversing with a mobile phone. 129 Consequently, in total, each session (urban or rural) includes six trials of the simulated 130 route. During each trial, 2 unexpected incidents are scheduled to occur at fixed points along 131 the drive. More specifically, incidents in rural area concern the sudden appearance of an animal 132 (deer or donkey) on the roadway, and incidents in urban areas concern the sudden appearance 133 of an adult pedestrian or of a child chasing a ball on the roadway or of a car suddenly getting 134 out of a parking position and getting in the road. The hazard does appear at the same location 135 for the same trial (i.e. rural area, high traffic) but not at the same location between the trials, in 136 order not to have learning effects. Regarding the time that the hazard appears, it depends on 137 the speed and the time to collision in order to have identical conditions for the participant to 138 react, either they drive fast or slowly. Thus, there is no possibility for the incident to appear 139 closely or more suddenly to a participant than to another. 140 The experiment is counterbalanced concerning the number and the order of the trials. 141 However, rural drives were always first and urban drives were always second. This was decided 142 for the following reasons: It was observed that urban area causes more often simulation 143 sickness to the participants and thus it was decided to have the urban scenario second and 144 secondly, counterbalancing in driving area means that we would have twice as much driving 145 combinations which leads to much larger sample size requirements. 146 Pavlou D., Papadimitriou E., Antoniou C., Papantoniou P., Yannis G., Golias J., Papageorgiou S.G. 7 Finally, impaired participants are to carry out the simulator experiment while under 147 their usual medication, so that their driving performance corresponds to their everyday 148 condition, as treated by their neurologist. 149 150 ANALYSIS METHODS AND DATA 151 152 The aim of this research is to analyze and compare the driving performance of MCI, AD and 153 healthy drivers in rural and urban road environment. For that purpose, four trials of the 154 simulator experiment are selected: the undistracted driving trials in rural area and the 155 undistracted driving trials in urban area in both low and high traffic volumes. 156 The analysis method selected is the Repeated Measures General Linear Model (GLM). 157 The repeated measures GLM is the equivalent of the one-way ANOVA, but for related, not 158 independent groups. A repeated measures GLM may be based on a within-subjects or a mixed 159 design (22). 160 At the present time more than 140 participants have participated in the driving simulator 161 experiment in approximately 15 months time. However, about 30 participants had simulator 162 sickness issues (a usual phenomenon in driving simulators) and didn’t complete the driving 163 trials of the experiment. For that reason they are eliminated from the study. Moreover there are 164 35 participants of younger age (<55 years old) who are eliminated too for age representativity 165 reasons. The analysis is thus based on the existing related sample of the (ongoing) simulator 166 experiment of healthy and impaired participants of over than 55 years of age who completed 167 all of the examined four trials were selected, which consists of 75 participants (49 males). 168 More specifically, the sample of the present study consists of: 169  38 healthy “controls” (66.4 years old on average), 170  14 AD patients (74.6 years old on average) and 171  23 MCI patients (68.3 years old on average). 172 It is noted that the gender distribution of healthy and impaired drivers is currently not 173 fully similar, i.e. the proportion of females is lower in the impaired drivers group (no female 174 AD participant), which is in any case representative of the general population. On the other 175 hand, the age distributions of impaired and healthy drivers are comparable to a satisfactory 176 degree, taking into account that it is expected that impaired drivers are on average older than 177 healthy ones (see Figure 1). 178 179 FIGURE 1 Age distribution of the sample, health condition and gender distribution 180 181 The variables examined in the present research include a between-subject variable, 182 namely the presence of a disease (AD or MCI). They also include one within-subject variables, 183 namely the traffic scenario (low or high traffic volume). It is noted that area type (rural, urban) 184 is not examined as a within-subject variable, because all participants drove first in rural area 185 and then in urban area; this was done for practical reasons but obviously results in order effects, 186 and consequently the two area types are examined separately and not comparatively. The 187 50 55 60 65 70 75 80 85 A g e Sample characteristics Male