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Naturalistic driving data can identify elevated driving risk in adults with Type 1 diabetes
Aparna Joshi1, Matthew Rizzo2, Anuj Sharma1
1Department of Civil, Construction and Environmental Engineering, Iowa State University of Science and Technology, Ames, Iowa.
Traffic Injury Prevention
|July 1, 2026
Summary
Driving behavior patterns can help identify high-risk days for individuals with Type 1 Diabetes Mellitus (T1DM) experiencing glucose events. This research highlights how driving data can complement glucose monitoring for enhanced transportation safety.
Area of Science:
- Endocrinology and Metabolism
- Transportation Safety
- Behavioral Science
Background:
- Individuals with Type 1 Diabetes Mellitus (T1DM) face risks of impaired cognitive function during acute glucose events like hypoglycemia and hyperglycemia.
- These glucose fluctuations can impact daily activities, including driving, posing potential safety concerns.
- While continuous glucose monitoring (CGM) detects glucose events, understanding how driving behavior changes on affected days is less explored.
Purpose of the Study:
- To evaluate if daily driving behavior differs on days when acute glucose-related impairment occurs during driving in individuals with T1DM.
- To determine the effectiveness of driving behavior features in identifying high-risk driving days.
Main Methods:
- 18 adults with T1DM were monitored for 4 weeks, collecting continuous real-world driving data and sleep patterns via actigraphy.
- Continuous Glucose Monitoring (CGM) was used to identify hypoglycemia or severe hyperglycemia during active driving.
- Gradient-boosted decision tree models (XGBoost) were trained using driving-only, sleep-only, and combined feature sets to distinguish high-risk driving days.
Main Results:
- Models using driving features alone were most effective in identifying high-risk driving days (AUROC = 0.67; F1 = 0.65), outperforming sleep-only or combined models.
- Key driving features included afternoon peak trips, proximity of trips to home, and total driving time.
- Sleep features alone had limited ability to distinguish high-risk driving days (AUROC = 0.48).
Conclusions:
- Driving exposure features, such as trip frequency, timing, and duration, are valuable for identifying days with glucose-related impairment during driving in T1DM patients.
- Behavior-based approaches show promise for flagging elevated driving risk associated with glucose fluctuations.
- These findings suggest that analyzing driving behavior can complement physiological monitoring for improved transportation safety and injury prevention.
Keywords:
Naturalistic drivingType 1 diabetes mellitusdriver safetydriver state detectionleave-one-subject-out cross-validationmachine learningMore Related Videos
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