Kinematic driving data studies are a novel methodology relevant to health care, but prior studies have considerable variance in their methods, populations, and findings suggesting a need for critical analysis and appraisal for feasibility and methodological guidelines.

1. Driving and Chronic
Conditions
Systematic Review of Applied
Research Using Kinematic Driving
Sensors
Mukherjee, S, McDonald, AD, Kesler, SR, Cuevas, H,
Swank, C, Stevens, A, Ferris, TK, & Danesh, V. (2024).
Driving among individuals with chronic conditions: A
systematic review of applied research using kinematic
driving sensors. Journal of the American Geriatrics
Society, doi: 10.1111/jgs.18738. Advance online
publication.

2. Background
 Driving is an essential task for the independence, mobility and
quality of life of adults.
 Vehicle driving is complex and requires considerable cognitive,
physical and visual resources to main safety.
 Many chronic health conditions are associated with functional
limitations attributed to physical or cognitive impairments that
may contribute to driving safety decrements. For example,
hypoglycemic episodes among diabetes can cause dizziness,
and weak or blurred vision.
Mukherjee 2024 JAGS

3. Purpose
 While crash report databases, driving logs and driving simulation
studies provide insight into driving safety, they are limited in
measuring longitudinal changes and generalizability to individual
drivers.
 Naturalistic driving studies use data-loggers installed in vehicles to
measure real-time kinematic driving data (i.e., vehicle acceleration,
speed, location)
 The goals of this review are to assess kinematic driving studies of
adults with chronic conditions for study feasibility, characteristics, and
key findings, to generate recommendations for future applications of
kinematic driving data.
Mukherjee 2024 JAGS

4. Methods
PubMed, CINAHL, and Compendex were searched for material published
between January 1, 1990 and March 30, 2022. Search queries focused on
studies that assessed driving behavior for those living with chronic health
conditions.
The search strategy for chronic conditions was guided by the Charlson
Comorbidity Index, a widely adopted and validated measure of comorbidity
burden derived from common medical conditions.
Specific conditions included Alzheimer’s Disease (AD), dementia, chronic kidney
disease, acute cerebrovascular accident, transient ischemic attack, rheumatoid
arthritis, asthma, and chronic obstructive pulmonary disease, among others.
Mukherjee 2024 JAGS

5. Results
 Kinematic data sourced from in-vehicle sensors can provide
objective measures of functional outcomes of driving safety.
 Data-based driving safety measures are relevant as preclinical
biomarkers for people with chronic health conditions associated
with long-term physical and cognitive changes.
 Kinematic driving data combined with machine learning algorithms
may offer new diagnostic platforms for predicting functional
disabilities attributed to chronic conditions.
Mukherjee 2024 JAGS

6. Summary
Vehicle sensors can provide driver-specific measures relevant to clinical
assessments and interventions.
 Driving modification, resumption and cessation have significant impacts on
the safety of older adults, and kinematic driving data data can provide
nuanced information to inform and refine these decisions.
 Using kinematic driving data to assess and address driving measures of
individuals with multiple chronic conditions is positioned to amplify a
functional outcome measure that matters to patients.
Mukherjee, S, McDonald, AD, Kesler, SR, Cuevas, H,
Swank, C, Stevens, A, Ferris, TK, & Danesh, V.
(2024). Driving among individuals with chronic
conditions: A systematic review of applied research
using kinematic driving sensors. Journal of the
American Geriatrics Society, doi: 10.1111/jgs.18738.
Advance online publication.

7. References
 Mukherjee, S, McDonald, AD, Kesler, SR, Cuevas, H, Swank, C, Stevens, A, Ferris, TK, &
Danesh, V. (2024). Driving among individuals with chronic conditions: A systematic
review of applied research using kinematic driving sensors. Journal of the American
Geriatrics Society, doi: 10.1111/jgs.18738. Advance online publication.