Monitoring real-world driver behavior for classification and early prediction of Alzheimer’s disease

这个研究项目解决了NIH/NIA使用自己的车辆作为被动检测的巨大挑战 用于标记可能发出预警的潜在年龄和疾病相关异常驾驶的系统 功能衰退或早期阿尔茨海默病（AD）的迹象。早期识别和治疗 采取必要措施，减轻日益增长的成本和AD负担。我们在以下方面的基础性进展 从车载系统（“黑匣子”）和可穿戴传感器量化驾驶员行为， 使用统计和机器学习方法的分析方法和管道，直接关系到 迎接NIH/NIA的挑战。该提案战略性地建立在当前项目发现和成功的基础上 该研究全面描述了136名老年驾驶员在现实世界中的驾驶暴露和风险模式， 开了50万英里。在该提案的概念框架下，职能能力决定了具体的驱动因素 行为模式和错误。NIA-Alzheimer病的行为、行为、指标驱动功能和临床特征 轻度认知功能障碍（MCI）和AD的关联（AA）核心临床标准（操作性） 阿尔茨海默病临床综合征（Alzheimer's Clinical Syndrome，ACS）睡眠和流动性作为关系的关键调解人发挥作用 驾驶行为和功能障碍之间的联系因此，我们的具体目标（SA）是：SA 1）提取密钥 在连续的3个月的基线期内，真实世界的驾驶员行为特征对正常老化进行分类， 根据NIA-AA核心临床标准的MCI和ACS驱动因素。SA 2）确定现实世界的驱动程序 在连续3个月的基线期内收集的睡眠和活动性因素介导了这种关系 提取的驾驶员行为和临床特征之间的关系（SA 1）。SA 3）开发模型（统计和监督 结合驾驶员行为（SA 1）和真实世界睡眠和移动性（SA 2）的联合收割机特征， 检测MCI和AD的临床特征严重程度并预测疾病进展。为了实现这些目标，我们 医学、AD、衰老和疾病中的驾驶、认知神经科学、运输等领域的专家团队 工程，机器学习，计算机视觉和纵向生物统计学-将应用我们的方法， 驾驶员在整个老化到AD谱中具有更广泛的损伤。共有180名司机，年龄65岁- 90岁，患有ACS（N=40）、MCI（N=80）或根据NIA-AA临床标准正常老化（N=60） 将在3个月的基线期内研究真实世界的自然驾驶员行为，睡眠和移动性 监测.两次纵向评估，每次间隔1年，将全面评估每个驾驶员的风险 功能衰退的严重程度。通过提取驾驶员异常行为的“数字指纹”， 该项目补充了临床前AD生物学诊断的巨大进展， NIH优先考虑改善老年驾驶员的安全性，流动性，生活质量，前所未有的诊断访问 在乎被动监测真实世界行为，直接预测AD风险个体的临床状态 促进旨在早期治疗和预防AD在其临床前阶段进展的干预措施。

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.