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Behavioral and Physiological Measures of Driving Across the Spectrum of Alzheimer’s Disease
Ahmadnezhad, Pedram
Ahmadnezhad, Pedram
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Abstract
Driving is a well-learned, yet complex instrumental activity of daily living that requires simultaneous execution of several cognitive, visual, and motor functions to respond to a constantly changing environment. Cognitive impairment (CI) associated with Alzheimer’s disease (AD) may negatively impact the ability to drive, leading to a higher risk of motor vehicle crashes (MVC). Even in the absence of CI, neuropathological changes related to AD, such as elevated beta-amyloid accumulation in the brain, affect the ability to drive. Drivers in this preclinical AD stage particularly have difficulties attending to two tasks at the same time (dual tasking).
With the advent of automated vehicle (AV) technology, there are new opportunities to potentially prolong mobility and independence in individuals with preclinical AD or CI. While highly AV technology that requires no interaction between the driver and vehicle, is heralded as the future of transportation, conditional AV technology is currently tested for mass production. In conditional automated driving, drivers are allowed to disengage from the driving task, but are required to quickly take over control of the vehicle when needed. Taking over control of the vehicle requires intact driving reaction times (DRT), which may be impaired in older adults with preclinical AD and those with CI. Monitoring the driver state (e.g., alertness, drowsiness, cognitive workload) using physiological tools, may prompt the driver to remain attentive while driving. While electroencephalography (EEG) has been tested as a reliable tool to record driver state during AV, the implementation of EEG in the wild does not seem feasible. Conversely, eye tracking technology is increasingly employed in vehicles to monitor alertness and drowsiness. Pupillary response may accurately reflect driver state but has not been tested in drivers with preclinical AD and CI. The goals of this PhD dissertation were to evaluate driving performance using behavioral and physiological outcomes, in both non-automated and automated driving conditions in older adults with preclinical AD and CI.
We used a desktop driving simulator for the assessment of non-automated and automated driving. Three groups were included: older adults with no CI; older adults with preclinical AD; and older adults with CI. All participants completed four scenarios in a driving simulator while an eye tracker recorded changes in pupillary size. Two scenarios included non-automated driving and two other scenarios included conditional automated driving. At the end of each scenario an emergency event took place that required participants to quickly respond to. Two scenarios also included a cognitive distractor task.
In Chapter 2, we conducted a cross-sectional study evaluating the impact of preclinical AD on the safety and cognitive workload while operating AV. In Chapter 3, we replicated the study described in Chapter 2 in individuals with CI. In Chapter 4, we examined the impact of preclinical AD on driving reaction time and cognitive workload during non-automated driving. In Chapter 5, we replicated the study described in Chapter 4 in individuals with CI. Finally, in Chapter 6, we compared changes in cortical activity recorded by electroencephalography (EEG) during conditional automated driving between older adults with normal cognition and older adults with CI.
Our main findings showed that individuals with preclinical AD safely interact with automated driving technology. Drivers with preclinical AD exhibited no increased reaction times when taking over manual control of the vehicle compared to control drivers. Conversely, they showed increased reaction times when no AV technology was available. We concluded that AV may be potentially safe for older drivers with preclinical AD and prolonged mobility in this group. On the other hand, drivers with CI showed increased response times, both in non-automated and in automated driving conditions. This result may indicate that drivers with CI may not immediately benefit from AV technology that requires interaction between the driver and the vehicle to maintain safety. Finally, while EEG showed to be sensitive to changes in driver state, changes in pupillary size were not able to reliably detect the cognitive workload associated with different driving conditions. The results of this PhD pave the path for clinical guidelines to assist older adults on the spectrum of AD in their quest for mobility and independence.
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2024-01-01
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University of Kansas
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Keywords
Neurosciences, Alzheimer, automation, cognition, driving, EEG, older