This research brief describes changes to the procedures for collection of objective driving data in the Longitudinal Research on Aging Drivers (LongROAD) Study.
Driving patterns and behaviors have generally been obtained through self-reported measures, such as questionnaires and surveys. However, these methods have limitations, including recall bias, making it difficult to collect objective and consistent driving data. To ensure driver-behavior data is accurate, in-vehicle recording devices using Global Positioning Systems (GPS) can be effective. These devices are useful for objectively measuring naturalistic driving trips and behavior. Smartphone travel applications (apps) have emerged in recent years as another method for collecting driving data. Collecting objective driving data can allow for the exploration of changes that happen over time among older adults with regard to safe driving and mobility. Such data are considered integral to the LongROAD study, a multisite prospective cohort study designed to collect data on the medical, behavioral, environmental, and vehicle technological factors influencing older adults’ driving and safety. When the study initially began, travel apps were not as predominant and dataloggers were chosen as the method for collecting objective driving data. However, significant improvements in travel apps have since emerged. Thus, the role of travel apps was reconsidered as the LongROAD study progressed to a new phase in 2018. The objective of this research brief is to provide details on the transition from the Danlaw datalogger to the LongROAD travel app. The brief describes the differences between the datalogger and the LongROAD travel app, the pilot test of the travel app, the implementation of the travel app, the development and implementation of the driver detection algorithm, and lessons learned through the transition.
Prior to the introduction of the travel app into the LongROAD study, a pilot test was conducted at the University of California–San Diego in January 2019. The pilot study was critical in determining participants’ willingness to transition to the app, assessing the quality of the data collected for future research, and providing participants the opportunity to offer feedback on the app before its implementation in the full LongROAD cohort. The pilot study was carried out simultaneously with the development of the LongROAD study app procedures.
In conjunction with the pilot, a procedural document was created to provide a roadmap for each study site to start onboarding and installing the LongROAD travel app on participants’ phones. The development of the document was guided by the pilot study as well as each entity’s Institutional Review Board (IRB) to ensure data quality and privacy standards were upheld. The procedural document included background information on the app’s development by Tourmaline Labs, the functionality of the app, the installation process, the driver detection algorithm, and the data handling procedures. The manual also provided procedures for tracking changes in participant driving activity and troubleshooting difficulties with the installation of the app.
Upon completion of the pilot and development of procedures, each site sought approval from their IRB. Concerns typically revolved around data security during collection, transmission, and storage. As data communication was encrypted, each site received approval from its respective IRB.
Once each IRB approved the changes to the data collection method, the process to install the LongROAD travel app on participants’ phones began. Each site transitioned to the app between April and August 2019, with the University of California–San Diego having started the onboarding process first due to their involvement in the pilot test.