title: Development and interval testing of a naturalistic driving methodology to evaluate driving behavior in clinical research.
abstract: Background: The number of older adults in the United States will double by 2056. Additionally, the number of licensed drivers will increase along with extended driving-life expectancy. Motor vehicle crashes are a leading cause of injury and death in older adults. Alzheimer's disease (AD) also negatively impacts driving ability and increases crash risk. Conventional methods to evaluate driving ability are limited in predicting decline among older adults. Innovations in GPS hardware and software can monitor driving behavior in the actual environments people drive in. Commercial off-the-shelf (COTS) devices are affordable, easy to install and capture large volumes of data in real-time. However, adapting these methodologies for research can be challenging. This study sought to adapt a COTS device and determine an interval that produced accurate data on the actual route driven for use in future studies involving older adults with and without AD.Methods: Three subjects drove a single course in different vehicles at different intervals (30, 60 and 120 seconds), at different times of day, morning (9:00-11:59AM), afternoon (2:00-5:00PM) and night (7:00-10pm). The nine datasets were examined to determine the optimal collection interval.Results: Compared to the 120-second and 60-second intervals, the 30-second interval was optimal in capturing the actual route driven along with the lowest number of incorrect paths and affordability weighing considerations for data storage and curation.Discussion: Use of COTS devices offers minimal installation efforts, unobtrusive monitoring and discreet data extraction.  However, these devices require strict protocols and controlled testing for adoption into research paradigms.  After reliability and validity testing, these devices may provide valuable insight into daily driving behaviors and intraindividual change over time for populations of older adults with and without AD.  Data can be aggregated over time to look at changes or adverse events and ascertain if decline in performance is occurring.
authors: Charles F. and Joanne Knight Alzheimer's Disease Research Center, Department of Neurology, Washington University School of Medicine, St. Louis, USA.
authors: Ganesh M
authors: GM
authors: Babulal
authors: University Geographic Information System, Washington University, St. Louis, USA.
authors: Aaron
authors: A
authors: Addison
authors: Charles F. and Joanne Knight Alzheimer's Disease Research Center, Department of Neurology, Washington University School of Medicine, St. Louis, USA.
authors: Nupur
authors: N
authors: Ghoshal
authors: Charles F. and Joanne Knight Alzheimer's Disease Research Center, Department of Neurology, Washington University School of Medicine, St. Louis, USA.
authors: Sarah H
authors: SH
authors: Stout
authors: Charles F. and Joanne Knight Alzheimer's Disease Research Center, Department of Neurology, Washington University School of Medicine, St. Louis, USA.
authors: Elizabeth K
authors: EK
authors: Vernon
authors: University Geographic Information System, Washington University, St. Louis, USA.
authors: Mark
authors: M
authors: Sellan
authors: Charles F. and Joanne Knight Alzheimer's Disease Research Center, Department of Neurology, Washington University School of Medicine, St. Louis, USA.
authors: Catherine M
authors: CM
authors: Roe
keywords: geographic information system; global positioning systems; in-vehicle technology; interval testing; naturalistic driving