Identification of cognitive decline and dementia: Prediction by everyday driving behaviors and physiological responses

识别认知能力下降和痴呆：通过日常驾驶行为和生理反应进行预测

• 批准号: 10044799
• 负责人: BRUNO GIORDANI
• 金额: $ 150.69万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10044799
• 关键词: Age; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Amyloid; Arousal; Automobile Driving; Behavior; Behavior assessment; Behavioral; Brain; Brain Diseases; Breathing; Categories; Characteristics; Classification; Clinical; Cognitive; Collaborations; Collection; Complex; Consensus; Data; Dementia; Detection; Development; Diagnostic; Disease; Early Diagnosis; Elderly; Enrollment; Evaluation; Expert Systems; Eye; Funding; Galvanic Skin Response; Goals; Health Personnel; Heart Rate; Impaired cognition; Individual; Intervention; Language Development; Longitudinal cohort; Machine Learning; Measurement; Measures; Methods; Michigan; Modeling; Monitor; Motor; Neurologic; Neuropsychology; Participant; Pattern; Performance; Personal Satisfaction; Physiological; Population; Positron-Emission Tomography; Ramp; Research; Research Institute; Safety; Sampling; Short-Term Memory; Skin Temperature; System; Technology; Testing; Time; base; clinical Diagnosis; cognitive performance; cost effective; design; diagnostic accuracy; driving behavior; executive function; follow-up; hazard; learning strategy; machine learning method; meetings; mild cognitive impairment; novel strategies; older driver; programs; psychological aspect of aging; rate of change; recruit; response; smart watch; social; visual tracking; young adult; β-amyloid burden

As the population continues to age and rates of late-life cognitive impairment rise, early detection of cognitive impairment is increasingly important for the timely implementation of interventions and safety initiatives. This may be particularly important in individuals found to have high brain amyloid burden, putting them at particular risk for Alzheimer’s disease and related disorders (ADRD) of the brain. Performance changes in challenging, complex, high-stakes daily activities, such as driving, and accompanying physiological responses may together provide an inexpensive avenue for early detection. This may serve the dual purpose of alerting individuals or health care providers to early cognitive impairment, as well as to potential safety issues. Sophisticated in-car technology that is increasingly becoming standard in new vehicles may provide the means to unobtrusively capture sensitive information about naturalistic driving behaviors and potentially assist with early detection of cognitive impairment. The proposed study will apply a novel approach to unobtrusively monitor older drivers in (a) naturalistic, (b) fixed course, and (c) simulator driving situations. Machine learning approaches will be used to select key features of driving behaviors and physiological measures of arousal in all driving scenarios and eye tracking measures from fixed and simulator drives to predict drivers’ clinical diagnosis: young adult drivers, healthy older drivers with and without high amyloid burden, and drivers with mild cognitive impairment with evident amyloid burden. The participants will be followed longitudinally in the Michigan Alzheimer’s Disease Research Center (MADRC) with annual cognitive and neurological evaluations, as well as repeat driving and physiological testing at two years from baseline. Understanding and identifying changes in driving behaviors and how these predict who will develop clinically identifiable cognitive impairment will lead to the development of a model for early detection of cognitive decline and ADRD.

随着人口持续老龄化和老年认知障碍率的上升，早期发现认知障碍