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Driver Monitoring and Feedback Systems for Older Drivers: �Evidence from a Systematic Review, and �Preliminary Findings from a Mixed Methods Study

Nicole Booker, DrPH Student

Johnathon Ehsani, Associate Professor, Health Policy and Management

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Acknowledgements

Joanne Osborne, Librarian Insurance Corporation of British Columbia
Neale Kinnear, Affective Mobility
Aarushi Dedhiya, Students Against Destructive Decisions, Pennsylvania Chapter
Michelle Duren, Johns Hopkins Bloomberg School of Public Health
Sara Seifert, Kevin Kramer and Louis Barrett, Minnesota Health Solutions
Jeff Keller, Pennington Biomedical Research Center

Part of this research was supported by the National Institute on Aging of the National Institutes of Health under Award Number R43AG084374.

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An unexpected email

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How do we extend safe independent mobility for older adults?

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Driving remains the primary mode of mobility as adults age

Fastest growing segment of licensed drivers in the U.S.
Estimated 35million drivers 70 and over in 2023 (FHWA, 2023)
53 million projected by 2023 (US Census Bureau, 2017)

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Prevalence of Chronic Health Conditions among U.S. older adults

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A promising solution?

Driver Monitoring and Feedback

Real–time driver behavior monitoring via

Vehicle devices
Smartphone applications
Direct vehicle integration

Driving behaviors: distraction monitoring, speed compliance, harsh maneuver detection.

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Research Questions

What is the evidence of effectiveness of driver monitoring and feedback interventions for older drivers? (Study 1: Systematic Review)
What do older adults think about driving monitoring and feedback? (Study 2: Mixed Methods - Intervention followed by Interviews)

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Study 1: Systematic Review

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Methods: Systematic Literature Review Inclusion Criteria

Search Criteria:

English-language literature (2000-2024)
Studies with on-road driving data
Telematics- based interventions providing monitoring and feedback to drivers

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Methods: Systematic Literature Review Search Terms

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PRISMA Diagram – Identification, screening, assessment, and inclusion

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Results: Systematic Literature Review - Studies on Older Drivers

1,178 screened by title and abstract
1,090 excluded due to ineligibility
130 records in full
56 excluded due to ineligibility
74 records included in the review

4 studies focused on older drivers

- 2 Randomized Control Trial

- 2 Quasi-experimental designs

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Results: Systematic Literature Review - Findings by Intervention Type

In- vehicle Feedback only

Significant decrease in hard breaking and stop sign violations; marginal for speeding

Post Drive Feedback

Decrease in speeding frequency per week; little to no variability in harsh braking

Monitoring + Feedback and Coaching

Significant decrease in driving errors 25% (P<.05)
Video coaching saw 52% increase in global safety rating, and significant decrease behind the wheel errors

Limitation: treatment effects diminish when feedback is removed.

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Results: Systematic Literature Review - Findings by Design

Randomized Control Trials
Author, year	Feedback Type	N	Key Findings
Porter, 2013	Video coaching + Feedback	54	25% reduction in driving errors; 52% improved safety rating
Sangrar, 2022	Video coaching + feedback	80	Significant reduction in errors at 4-6 week follow up

Quasi - Experimental
Author, year	Feedback Type	N	Key findings
Libby, 2019	Immediate alerts (smartphone app)	28	Reduced hard braking and stop violations; marginal speeding reduction; rebound when app off
Payyanadan, 2017	Post-drive feedback (web portal)	33	0.9% per week reduction in speeding frequency

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Study 2: Mixed Methods – Intervention and Interviews

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Methods – Intervention : StreetCoach

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Location-specific feedback on speeding, rapid acceleration, hard braking, and distraction

Methods – Intervention : StreetCoach

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Methods – Sample and Data Collection Period

Sample:

N =51 older adults (ages 50-85)
Duration: 50 days (Feb-May 2025)
Intervention: StreetCoach smartphone- based app

Data collected:

11,017 trips recorded – avg. 24 trips per week
Metrics: acceleration, breaking, cornering, speeding, phone use
Automated feedback provided post trip

Qualitative data collected:

43 in-depth participant interviews
Experiences, reactions, attitudes on automated feedback

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Results – Average Duration, Number and Distance of Drives by Week

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Results – Usability of the Application

62 percent of older adults rated a smartphone app that coached them on their driving as highly usable
Almost all (95 percent) found the app easy to install and were able to set up the app without assistance.
Participants were periodically prompted to review trips and to label each as a trip where they were the driver or passenger.
90 percent of trips were classified by the participant using this feature.

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Results – Theme 1: Increased awareness of driving behaviors

The app "keeps me honest. I would…see I'm not paying attention, or doing things wrong, you know, it gives me the opportunity to adjust my behavior".
The app made me "aware of these things that I was either not aware of or totally ignoring".
One user noted that after reviewing the app, they focused on speeding: "I stayed at the speed limit on the interstates".
"I'd go to check my score. To see how I did".

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Results – Theme 2: Perceptions of scores was mixed

The score "also made me feel like somebody was looking over my shoulder evaluating everything I did. Which was somewhat annoying". "I wanted to be a a good driver and I know I'm not a terrible driver, but I know I'm not perfect...but I didn’t want to have, like, all these speeding problems". "I did show it to my husband. I said, look. My driving's not that bad. See?” “My score of 59 as pretty crappy compared to how I think I drive".

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Results – Theme 3: Need for transparency for the scoring criteria

“Knowing the parameters of the different criterion that are being used, and that would be helpful". “Is my speeding one mile an hour over or 10 miles an hour over?” "it gives me too many instances where I'm doing something that I don't feel like I'm doing".

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Results – Theme 4: Practical guidance on how to improve scores

“I think it would be good for somebody to understand what they can do to increase their score" ”There should be a clearer linkage between your score and what you could do better". “I would like to know after the drive I've just taken, to get a little text that said, you did too much acceleration or you stopped too fast or you cornered too fast"

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What Older Drivers need from Telematics? �A Hybrid Approach

Sustained Feedback

Transparency

Motivation Alignment

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Discussion – Behavioral Science Perspective

Key behavioral change mechanisms are missing for older adults. For example:

COM-B Model (Capability – Opportunity- Motivation- Behavioral)

Capability: age– appropriate guidance
Opportunity: social benchmarking, family involvement
Motivation: alignment with self-improvement values

Explicit theoretical frameworks are missing from current interventions.

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Discussion – Critical Questions Moving Forward

How do we design telematics that older adults trust and understand?
What interventions sustain behavior change after feedback is removed?
How can academia, government and industry work together to extend the safe driving careers of older adults?

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Conclusion

1. What is the evidence of effectiveness of telematics interventions for older drivers?

There’s a major research gap.

2. How do older adults engage with and interpret driver monitoring and feedback?

Older adults are willing to engage but need:

Transparency in scoring
Practical guidance on how to improve

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Please get in touch:

Nicole Booker, MPH�Nbooker4@jhu.edu

Dr. Johnathon Ehsani�Johnathon.ehsani@jhu.edu

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