Creating a driving profile for older adults using GPS devices and naturalistic driving methodology [version 1; referees: 2 approved, 1 approved with reservations]

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. We adapted an existing, commercial, off-the-shelf, in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking DeviceTM was installed in each participant’s vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven. Setting: The Knight Alzheimer’s Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance. Participants were aged 65+ years and had normal cognition. Measurements: Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations. Temporal components included number of trips taken during different times of the day. Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods: Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS. Results: Primary locations were confirmed to be 100% accurate when compared to known addresses. Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns. Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night. Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: This pilot study indicated that Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.