The Complex Relationship Between Driving and Alzheimer’s Disease and Related Dementia
Driving serves as a primary method of transportation for older adults, preserving their independence. However, it is often one of the first of many forms of autonomy at threat when individuals receive a diagnosis of Alzheimer’s Disease or Related Dementia (ADRD) (de Almeida et al., 2024; Greenstein, 2017). Alzheimer’s Disease and Related Dementia is a neurodegenerative condition characterized by deficits in attention, visual-spatial processing, judgment, memory, and executive functioning (Malvitz et al., 2023). Almost 13.5 million adults aged sixty-five and older in the United States have a dementia diagnosis. Out of this statistic, 7 million diagnoses are of Alzheimer’s disease (Kramarow, 2022). Despite receiving a diagnosis, approximately 60% of individuals with ADRD continue to drive in an attempt to preserve their independence, even with the potential of further neurocognitive decline (Malvitz et al., 2023).
Aside from simply being a means of transportation, driving is deeply connected with an adult’s ability to maintain participation in meaningful activities and social engagement. This is especially important for older adults, as decreases in social interaction related to driving cessation are tied to enhanced risk for complications such as depression, physical or cognitive decline, institutionalization, and social isolation (Davis & Owens, 2021; Stamatelos et al., 2021; Allison et al., 2018). Existing literature concurs that ADRD symptoms will eventually inhibit the safe driving of an individual, while also acknowledging the valuable role driving plays in promoting independence among older adults (de Almeida et al., 2024; Stamatelos et al., 2021; Greenstein, 2017).
Opinions vary regarding driving cessation or continuation of people with ADRD (de Almeida et al., 2024; Stamatelos et al., 2021; Greenstein, 2017). This is further exacerbated by the lack of federal or state legislation on this topic. Within the United States, neither the federal government nor individual state governments enforce the cessation of driving for individuals who have received an ADRD diagnosis. ADRD has a complex and unpredictable progression; therefore, driving assessments must be frequent, valid, and reliable in determining the appropriate time for an individual to stop driving. However, current assessment methods are limited in that they rely on just a short demonstration of real driving behavior or are time-consuming and expensive; additionally, these assessments provide a source of anxiety for the examinee (Davis & Owens, 2021; Babulal et al., 2019; Greenstein, 2017). Although several methods of assessment exist, none are considered the standardized approach that indisputably determines when an individual should stop driving (Greenstein, 2017; Curl et al., 2013).
With support from the Hamel Center for Undergraduate Research through a 2025 Summer Undergraduate Research Fellowship (SURF), I participated in a study that implemented a Global Positioning System (GPS)–based onboard technology to observe the driving behavior of individuals with ADRD. The use of this technology was not intended to assess driving skills or determine whether an individual is fit to drive, but rather to provide caregivers a method to monitor the real-time driving behavior of their loved ones, alleviating some of their safety concerns while allowing care recipients to maintain independence. Ultimately, our study aimed to reduce caregiver burden and anxiety while mitigating the limitations of current assessment methods. A secondary aim was to support mutually informed decisions between the caregiver and the care recipient regarding driving cessation or continuation based on observations made using this technology. Throughout this research article, I will describe how my mentor, Dr. Sajay Arthanat, and I used GPS technology in the study, discuss the current stage of my research, and share my future plans.
Methods
The study involved a mixed-methods design, collecting data over three months through naturalistic observations, interviews, and by using the Goal Attainment Scaling (GAS) framework, which is a method of developing personalized goals and measuring progress over time (Turner-Stokes, 2009). My mentor and I recruited one participant dyad in the New Hampshire Seacoast region. The dyad was composed of a care recipient who had been given a diagnosis of ADRD and their informal caregiver, who was a relative. The care recipient met all the inclusion criteria, including being the primary driver of the vehicle monitored by the caregiver, driving at least once a week, and having a valid state driver’s license. The caregiver also met the inclusion criteria by being at least eighteen years old.
The author affixing a GPS device to the diagnostic port of a vehicle.
The technology used in this study is called Bouncie, a commercially available onboard-GPS device that can be plugged into the diagnostic port of a vehicle. It provides a wide array of real-time information on the vehicle’s status, including but not limited to speed, geo-location, fuel level, engine status, and vehicle breakdown. Bouncie automatically transmits this information to the associated app on the caregiver’s smartphone, providing real-time data and even personalized notifications. This device is both unobtrusive and reliable because of its compact hardware design. Even in areas with limited or poor internet connection, the device will continue to record data and transmit it to the caregiver’s smartphone app once the connection is restored, preventing lost data caused by geographic areas with poor internet connection. The Bouncie GPS was purchased and provided to the participants free of charge by Dr. Arthanat, and I installed it into the onboard diagnostics port of the care recipient’s vehicle. Both the caregiver and the care recipient consented to this installation. We then assisted the caregiver with app setup, personalizing their account experience by selecting which driving parameters they would like to receive notifications about.
Three sets of research data were collected from the participants. The first set was through the GAS framework. This involved Dr. Arthanat and me conducting an initial interview with the caregiver and care recipient to develop personalized goals that they hoped Bouncie would help them achieve during the three-month duration of the data collection period. Each month after the initial interview, we conducted a follow-up interview to collect data on goal progress. Each interview was recorded to allow for transcription and coding of the data later in the analysis process. The GAS framework considers the importance of each goal and the extent to which each goal is met or not met at each follow-up interview (Turner-Stokes, 2009).
The next method of data collection occurred during the final interview, which also served as the third follow-up meeting with the care partners at the end of the data collection period, to evaluate and understand their overall acceptance of the technology based on the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT). The UTAUT is a theory used to predict an individual’s likelihood of accepting and using technology based on four main constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003). This interview considered whether the technology met the caregiver’s expectations, as well as its ease of use, its reliability, and how these considerations related to goals developed through the GAS framework and the caregiver and care recipient’s progress toward these goals.
The final form of data collection was a three-month driving log, which was provided by the caregiver. It contained data such as instances of harsh braking, total mileage, and speeding, which were recorded by the GPS technology. The caregiver identified and noted any trips that were not driven by the care recipient, to ensure that the data considered reflected only the care recipient’s driving behavior. This log included parameters for each trip, such as miles driven, speed, acceleration, and the presence of adverse events.
Preliminary Results and Future Plans
I have completed data collection for the first participant dyad, transcribed the interviews, and finished the initial coding as part of my analysis. This process involved spending time familiarizing myself with the narrative data collected from each interview, going through each segment of the conversations across all four interviews, and assigning unique codes to the data. Both Dr. Arthanat and I engaged in independent coding to support triangulation and credibility of the results. Rather than having preset codes that I would categorize data into, I used an inductive approach to identify codes as I methodically analyzed the data. Codes were either a word or a phrase that reflected the piece of data. For example, one code that emerged consistently throughout that data from each interview during my coding process was expectations. It often emerged from data related to either caregiver or care recipient expectations for the technology’s impact on their lived experience. Others included caregiver experience and changes in driving habits.
I engaged in several rounds of coding and compiled my final codes into Excel spreadsheets organized by each interview. General feedback from the first participant group has been positive and indicative of the technology successfully supporting the caregiver. For example, the caregiver reported that the device was reliable, easy to use, and “definitely worth it” in terms of the initial ninety-dollar device investment and ten-dollar-per-month subscription cost. Additionally, when asked if the device created any new anxiety or concerns, the caregiver claimed that “it actually helps set my mind at ease.”
The GPS data has been exported from the caregiver’s Bouncie account in the form of a driving log. Dr. Arthanat and I will be analyzing the log and considering the data in relation to the caregiver’s experiences to support our understanding of the reliability of the technology, along with identifying the care recipient’s driving patterns and any unanticipated events. A complete analysis of this data will inform the dependability of this technology and its potential benefit to care partners. Throughout this project, recruitment has been a challenge, as potential participants often expressed concern about being monitored and the implications for their current life. However, Dr. Arthanat and I are optimistic about recruiting a second and ideally third participant group to further enhance the generalizability of the themes identified so far, along with strengthening the reliability and validity of the results overall.
Currently, I am writing my Honors thesis, which will be based on my findings from this research project and support my role in the Occupational Therapy Departmental Honors Program. I intend to present this at the spring 2026 Undergraduate Research Conference, and shortly afterward, I will graduate with a bachelor of science degree in occupational therapy. Because I am a student on the advanced standing bachelor of science degree in occupational therapy/occupational therapy doctorate track, I will immediately continue my education at the University of New Hampshire as a graduate student. If the results of my study demonstrate significant positive impacts on the participants, I am interested in continuing this research through the development of a community program to support my occupational therapy doctoral capstone, which I will submit in spring 2028.
Final Remarks
A diagnosis of a neurodegenerative condition like ADRD does not have to mark the end of engagement in valuable activities such as driving. Rather, it should be a time when individuals are empowered to make informed decisions regarding their care and participation in activities that promote independence and autonomy. I hope this project provides valuable insight into the positive impact that this novel use of GPS-based technology can have on caregivers when used to monitor the driving behavior of their care recipients experiencing ADRD. I am grateful for the opportunity to support drivers with ADRD by giving them a method to make decisions alongside their caregivers based on fact, not fear or anxiety.
I must extend my sincerest gratitude to those who have helped make this experience possible. I’d first like to thank Dr. Sajay Arthanat, my research mentor, for guiding me through out each step of the research process. Thank you for supporting and encouraging me to pursue research that mattered to me personally. To Dr. Sarah Smith, my honors advisor, thank you for fostering a positive learning environment and always showing genuine interest in my academic and research goals. I’d also like to thank the Department of Occupational Therapy and Department Chair Dr. Vidya Sundar for providing students like me with the opportunity to learn experientially through an individualized research project. It has truly been invaluable to my education. To the Hamel Center for Undergraduate Research and the generous donors including Mr. Dana Hamel, Mr. William Doran (Class of 1962 Student Enrichment Fund) and Dr. Elizabeth Blesedell Crepeau and Dr. Vidya Sundar (Occupational Therapy 50th Anniversary Endowment for Undergraduate Research), thank you so much for supporting my project through a Summer Undergraduate Research Fellowship and trusting me with the responsibility this past summer. Finally, I’d like to thank my friends and family. Your support motivates me to work hard and strive for excellence throughout my academic pursuits.
References
Allison, S., Babulal, G., Stout, S. H., Barco, P. P., Carr, D. B., Fagan, A. M., Morris, J. C., Roe, C. M., & Head, D. (2018). Alzheimer disease biomarkers and driving in clinically normal older adults: Role of spatial navigation abilities. Alzheimer Disease & Associated Disorders, 32(2), 101–106. https://doi.org/10.1097/WAD.0000000000000257
Babulal, G. M., Stout, S. H., Benzinger, T. L. S., Ott, B. R., Carr, D. B., Webb, M., Traub, C. M., Addison, A., Morris, J. C., Warren, D. K., & Roe, C. M. (2019). A naturalistic study of driving behavior in older adults and preclinical Alzheimer’s disease: A pilot study. Journal of Applied Gerontology, 38(2), 277–289. https://doi.org/10.1177/0733464817690679
Curl, A. L., Stowe, J. D., Cooney, T. M., & Proulx, C. M. (2013). Giving up the keys: How driving cessation affects engagement in later life. The Gerontologist, 54(3), 423–433. https://doi.org/10.1093/geront/gnt037
Davis, R., & Owens, M. (2021). Self-regulation of driving behaviors in persons with early-stage Alzheimer’s disease. Journal of Gerontological Nursing, 47(1), 21–27. https://doi.org/10.3928/00989134-20201209-01
de Almeida, W. M., Quintas, J. L., Trindade, I. O. A., Pitta, L. S. R., Louzada, L. L., Nóbrega, O. T., & Camargos, E. F. (2024). Diagnosis of Alzheimer’s dementia and vehicle driving restriction: A scoping review. Psychogeriatrics, 24(1), 138–144. https://doi-org.unh.idm.oclc.org/10.1111/psyg.13049
Greenstein, A. R. (2017). Dementia and driving: Ethics and the law. TCNJ Journal of Student Scholarship, 19. https://joss.tcnj.edu/wp-content/uploads/sites/176/2017/04/2017-Greenstein.pdf
Kramarow, E. A. (2022). Diagnosed dementia in adults age 65 and older: United States, 2022. National Health Statistics Report, 203. https://www.cdc.gov/nchs/data/nhsr/nhsr203.pdf
Malvitz, M., Zahuranec, D. B., Chang, W., Heeringa, S. G., Brice?o, E. M., Mehdipanah, R., Gonzales, X. F., Levine, D. A., Langa, K. M., Garcia, N., & Morgenstern, L. B. (2023). Driving predictors in a cohort of cognitively impaired Mexican American and non-Hispanic white individuals. Journal of the American Geriatrics Society, 71(11), 3520–3529. https://doi.org/10.1111/jgs.18493
Stamatelos, P., Economou, A., Stefanis, L., Yannis, G., & Papageorgiou, S. G. (2021). Driving and Alzheimer’s dementia or mild cognitive impairment: A systematic review of the existing guidelines emphasizing on the neurologist’s role. Neurological Sciences, 42(12), 4953–4963. https://doi.org/10.1007/s10072-021-05610-7
Turner-Stokes, L. (2009). Goal attainment scaling (GAS) in rehabilitation: A practical guide. Clinical Rehabilitation, 23(4), 362–370. https://doi.org/10.1177/0269215508101742
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://www.jstor.org/stable/30036540
Author and Mentor Bios
Julia Ferris is an occupational therapy student at the University of New Hampshire, originally from Ledyard, Connecticut. She will graduate with a bachelor’s degree in occupational therapy and a minor in applied human anatomy and physiology in May 2026 before continuing at the University of New Hampshire as a graduate student. In spring 2028, Julia will graduate with a clinical doctorate in occupational therapy. During her time at the University of New Hampshire Julia has been a member of the UNH Red Cross Club, Student Occupational Therapy Association, and worked with the Institute on Disability as a campus ambassador and with the Department of Occupational Therapy as a teaching assistant.
Sajay Arthanat is a professor in the Department of Occupational Therapy at the University of New Hampshire. His clinical expertise is in addressing physical dysfunctions across the lifespan through assistive, rehabilitative, and information-communication technologies. His research broadly focuses on user-centered design and implementation of innovative technologies such as wearables, robotics, and smart-home automation to promote health, community living, and aging in place. His research has been supported by over $5 million in funding from the National Institutes of Health and the National Science Foundation and highlighted in more than fifty peer-reviewed publications in top occupational therapy and interdisciplinary journals. He has served in various roles with professional organizations such as the American Occupational Therapy Foundation and the Rehabilitation Engineering Society and Assistive Technology of North America.
Copyright 2026 ? Julia Ferris
