Assessment of Drivers with Alzheimer's Disease in High Demand Driving Situations: Coping with Intersections in a Driving Simulator

Driving Assessment for Persons with Dementia: How and when?

Dementia is a progressive neurodegenerative disease leading to deterioration in cognitive and physical skills. Driving is an important instrumental activity of daily living, essential for independence. However, this is a complex skill. A moving vehicle can be a dangerous tool in the hand of someone who cannot maneuver it properly. As a result, the assessment of driving capacity should be part of the management of dementia. Moreover, dementia comprises of different etiologies and stages consisting of different presentations. As a result, this study aims to identify driving behaviors common in dementia and compare different assessment methods. A literature search was conducted using the PRISMA checklist as a framework. A total of forty-four observational studies and four meta-analyses were identified. Study characteristics varied greatly with regards to methodology, population, assessments, and outcome measures used. Drivers with dementia performed generally worse than cognitively normal drivers. Poor speed maintenance, lane maintenance, difficulty managing intersections and poor response to traffic stimuli were the most common behaviors in drivers with dementia. Naturalistic driving, standardized road assessments, neuropsychological tests, participant self-rating and caregiver rating were the most common driving assessment methods used. Naturalistic driving and on-road assessments had the highest predictive accuracy. Results on other forms of assessments varied greatly. Both driving behaviors and assessments were influenced by different stages and etiologies of dementia at varying degrees. Methodology and results in available research are varied and inconsistent. As a result, better quality research is required in this field.

Driving Ability Evaluation and Rehabilitation for People With Alzheimer's Disease and Related Dementias

Worldwide, it is estimated that around 50 million older adults have Alzheimer's disease and related dementias (ADRD). Cognitive deficits associated with ADRD may affect a driver's perception and decision-making and potentially cause safety concerns. Despite much research, there lacks a comprehensive cognitive evaluation to determine the driving capability of a person with ADRD and it is unclear what are the most effective training and interventions that help to enhance driving performance for these individuals. The purpose of this article is to conduct a comprehensive literature survey to review and summarize studies of driving performance evaluation and intervention for people with ADRD and discuss perspectives for future studies. Although many studies have investigated the correlations between driving behaviors and cognitive performances for people with ADRD, it remains unclear how driving behaviors and cognitive performances are associated with psychophysiological measures. We discussed the need to develop regular driving evaluation and rehabilitation protocol for people with ADRD. We also highlighted the potential benefit to combine driving tests with psychophysiological measures to assist in characterizing personalized cognitive evaluation in the behavioral evaluation process.

Evaluating the Fitness-to-Drive Using Evoked Visual Responses in Alzheimer's Disease

Alzheimer's Disease (AD) is the sixth leading cause of death in the US. AD causes significant disability due to the devastating impact on the patients' day-to-day living activities and their loss of independence. One such day-to-day activity is driving, a complex task that requires attention, concentration, the ability to follow particular steps, react to stimuli promptly, and the ability to perceive and interpret visual-spatial information, all of which can be impaired in AD. Therefore, to ensure the safety of AD patients and other drivers, it is important to develop accurate and low-cost diagnostic tools to assess patients' fitness-to-drive. In this study, we develop machine learning (ML) models to predict fitness-to-drive using the electroencephalogram (EEG) technique of event-related potential (ERP). Specifically, we develop random forest (RF) models using EEG signals in early-stage AD patients and age-matched controls and conduct numerical experiments to predict fitness-to-drive and other driving performance metrics, collected from driving simulator data. Our results show that RF models predict patients' fitness-to-drive with AUC=0.83 and provide accurate measures of other driving performance metrics. Therefore, ML and ERP offer a valuable approach to assess driving safety for patients with early AD symptoms in the laboratory setting.

Using Video Replay of Simulated Driving to Estimate Driving Safety and Cognitive Status

Cognitive decline resulting from Dementia of Alzheimer’s Type (DAT) can lead to reduced ability to perform complex daily tasks required for independent living, including driving an automobile. This study explores the ability of untrained observers to classify driving safety using short video clips of simulated driving through intersections; it also examined whether untrained observers could predict whether the driver was cognitively healthy or cognitively impaired. Participants (n = 54) were shown a series of 30 video clips arranged in an online survey and asked to answer questions following each clip regarding the safety of the maneuver and the cognitive status of the driver. Results showed that participants’ subjectively rated DAT drivers as significantly less safe in comparison to control drivers, F (1, 52) = 228.44, p < 0.001. Participant’s classification of DAT drivers and controls was also significantly higher than chance (i.e., >50% correct). Findings provide preliminary support for the development of a clinical decision-making aid using video replay of driving simulator performance in fitness-to-drive assessments for individuals with cognitive impairment.

On-Road Behavior in Older Drivers With Mild Cognitive Impairment

OBJECTIVES

Dementia increases the risk of unsafe driving, but this is less apparent in preclinical stages such as mild cognitive impairment (MCI). There is, however, limited detailed data on the patterns of driving errors associated with MCI. Here, we examined whether drivers with MCI exhibited different on-road error profiles compared with cognitively normal (CN) older drivers.

DESIGN

Observational.

SETTING AND PARTICIPANTS

A total of 296 licensed older drivers [mean age 75.5 (SD = 6.2) years, 120 (40.5%) women] recruited from the community.

METHOD

Participants completed a health and driving history survey, a neuropsychological test battery, and an on-road driving assessment including driver-instructed and self-navigation components. Driving assessors were blind to participant cognitive status. Participants were categorized as safe or unsafe based on a validated on-road safety scale, and as having MCI based on International Working Group diagnostic criteria. Proportion of errors incurred as a function of error type and traffic context were compared across safe and unsafe MCI and CN drivers.

RESULTS

Compared with safe CN drivers (n = 225), safe MCI drivers (n = 45) showed a similar pattern of errors in different traffic contexts. Compared with safe CN drivers, unsafe CN drivers (n = 17) were more likely to make errors in observation, speed control, lane position, and approach, and at stop/give-way signs, lane changes, and curved driving. Unsafe MCI drivers (n = 9) had additional difficulties at intersections, roundabouts, parking, straight driving, and under self-navigation conditions. A higher proportion of unsafe MCI drivers had multidomain subtype [n = 6 (67%)] than safe MCI drivers [n = 11 (25%)], odds ratio 6.2 (95% confidence interval, 1.4-29.6).

CONCLUSION AND IMPLICATIONS

Among safe drivers, MCI and CN drivers exhibit similar on-road error profiles, suggesting driver restrictions based on MCI status alone are unwarranted. However, formal evaluation is recommended in such cases, as there is evidence drivers with multiple domains of cognitive impairment may require additional interventions to support safe driving.

Predicting Alzheimer's Disease Using Driving Simulator Data

Early detection of Alzheimer's Disease (AD) is critical in creating better outcomes for patients. Performance in complex tasks such as vehicular driving may be a sensitive tool for early detection of AD and serve as a good indicator of functional status. In this study, we investigate the classification of AD patients and controls using driving simulator data. Our results show that machine learning algorithms, especially random forest classifier, can accurately discriminate AD patients and controls (AUC = 0.96, Sensitivity = 87%, and Specificity = 93%). The model-identified most important features include Pothole Avoidance, Road Signs Recalled, Inattention Measurements, Reaction Time, and Detection Times, among others, all of which closely align with previous studies about cognitive functions that are affected by AD.