This research project tackles the NIH/NIA grand challenge of using a person's own vehicle as a passive-detection system for flagging potential age- and disease-related aberrant driving that may signal early warning signs of functional decline or incipient Alzheimer's disease (AD). Early identification and treatment are essential steps to mitigating the growing costs and burden of AD. Our foundational advancements in quantifying driver behavior from in-vehicle systems (Black Boxes) and wearable sensors, and strategic analytic methods and pipelines using statistical and machine learning approaches, are directly relevant to meeting this NIH/NIA challenge. The proposal builds strategically on current project discoveries and successes that comprehensively characterized patterns of real-world driving exposure and risk in 136 older drivers across 500,000 miles driven. Under the proposal's conceptual framework, functional abilities determine specific driver behavior patterns and errors. Behaviors, in tum, index driver functional abilities and clinical features of NIA-Alzheimer's Association (AA) core clinical criteria of mild cognitive impairment (MCI) and AD (operationalized by Alzheimer's clinical syndrome [ACS]). Sleep and mobility play roles as key mediators of relationships between driver behavior and functional impairment. Accordingly, our Specific Aims (SA) are: SA1) Extract key real-world driver behavior features over a continuous, 3-month, baseline period that classify normally aging, MCI, and ACS drivers by NIA-AA core clinical criteria. SA2) Determine the extent to which real-world driver sleep and mobility factors, collected over a continuous, 3-month baseline period, mediate the relationship between extracted driver behavior and clinical features (SA1). SA3) Develop models (statistical and supervised machine learning) that combine features of driver behavior (SA 1) and real-world sleep and mobility (SA2) to detect clinical feature severity of MCI and AD and predict disease progression. To address these aims, our team of experts-in medicine, AD, driving in aging and disease, cognitive neuroscience, transportation engineering, machine learning, computer vision, and longitudinal biostatistics--will apply our approach to drivers with a broader range of impairments across the aging to AD spectrum. A total of 180 drivers, ages 65- 90 years, who have ACS (N=40), MCI (N=80), or are normally aging (N=60) based on NIA-AA clinical criteria will be studied across a 3-month baseline period of real-world naturalistic driver behavior, sleep, and mobility monitoring. Two longitudinal assessments, each 1 year apart, will comprehensively assess each driver's risk for and severity of functional decline. By extracting digital fingerprints of aberrant driver behavior in drivers al risk for AD, this project complements seismic advances in biologic diagnosis of preclinical AD and advances NIH priorities lo improve older driver safely, mobility, quality of life, with unprecedented access lo diagnostic care. Passive monitoring of real-world behavior to predict clinical status in individuals at risk for AD directly promotes interventions aimed at early treatment of and preventing progression of AD in its preclinical stages.
The goal of this research project is to address the NIH and NIA's grand challenge of using a person's own vehicle as a passive-detection system for flagging potential age- and/or disease-related aberrant driving behaviors that may signal early warning signs of functional decline of Alzheimer's disease (AD), even before standard clinical tests do so. Our Specific Aims will assess and combine key factors affecting driver behavior and clinical status in aging, mild cognitive impairment (MCI), and Alzheimer's clinical syndrome (ACS) as markers and moderators of risk: SA 1) Extract key real-world driver behavior features over a 3-month continuous, baseline period that classify NIA-AA core clinical criteria of MCI and ACS in drivers who are normally aging (N = 60), MCI (N = 80), or ACS (N = 40); SA2) Determine the extent to which real-world driver sleep and mobility mediate the relationship between extracted driver behavior and clinical features; SA3) Develop models (statistical and supervised machine learning) that combine features of driver behavior (from SA 1) and driver sleep and mobility (from SA2) to detect early signs of MCI and AD and predict disease progression. By building on our extensive successes in the current phase, the project directly advances NIH and NIA goals lo detect early warnings signs of decline and incipient AD-with the goal of screening, identifying, and tracking individuals at risk for AD from passive-monitoring of real-world behavior to predict clinical status and progression-promoting early treatment of AD and the next stage of interventions to prevent the progression of AD even in its preclinical stages.
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