Blake Matthew, Brown David C, Chen Chen, Al-Hammadi Noor, Casanova Ramon, Zhu Yiqi, Babulal Ganesh M
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Department of Biostatistics and Data Science, Wake Forest University, Winston-Salem, NC, USA.
Sci Data. 2025 Jul 12;12(1):1209. doi: 10.1038/s41597-025-05554-z.
Alzheimer's disease and related dementia (ADRD) are becoming increasingly prevalent and are predicted to affect up to 153 million globally by 2050. Outside of biomarkers that measure pathology, there is a lack of methods to quantify and study complex behaviors such as driving and managing finances that precede cognitive decline among older adults. The DRIVES Project at Washington University School of Medicine has developed a pipeline to measure naturalistic driving behavior of older adult drivers enrolled in longitudinal studies of aging and ADRD. This driving behavior is captured in the form of tabular data for each trip a participant takes and is processed in two formats: low-frequency driving data, comprising approximately 2.8 million trips (37 GB), and high-frequency driving data, with approximately 1.4 million trips (2.6 TB). This pipeline also captures common participant sociodemographic characteristics, clinical features, and environmental context across various weather conditions, as well as the Area Deprivation Index and the Social Vulnerability Index, to comprehensively characterize the multidimensional nature of neurodegenerative processes among older adults.
阿尔茨海默病及相关痴呆症（ADRD）正变得越来越普遍，预计到2050年全球将有多达1.53亿人受其影响。除了用于测量病理的生物标志物外，目前缺乏量化和研究老年人认知衰退之前复杂行为（如驾驶和理财）的方法。华盛顿大学医学院的DRIVES项目开发了一套流程，用于测量参与衰老和ADRD纵向研究的老年驾驶员的自然驾驶行为。这种驾驶行为以表格数据的形式记录参与者每次出行的情况，并以两种格式进行处理：低频驾驶数据，包含约280万次出行（37GB），以及高频驾驶数据，约140万次出行（2.6TB）。该流程还记录了常见的参与者社会人口特征、临床特征以及各种天气条件下的环境背景，以及地区贫困指数和社会脆弱性指数，以全面描述老年人神经退行性过程的多维度性质。
