  Background: Diabetes is associated with obesity, poor glucose control and
sleep dysfunction which impair cognitive and psychomotor functions, and, in
turn, increase driver risk. How this risk plays out in the real-world driving
settings is terra incognita. Addressing this knowledge gap requires
comprehensive observations of diabetes driver behavior and physiology in
challenging settings where crashes are more likely to occur, such as
stop-controlled traffic intersections, as in the current study of drivers with
Type 1 Diabetes (T1DM). Methods: 32 active drivers from around Omaha, NE
participated in 4-week, real-world study. Each participant's own vehicle was
instrumented with an advanced telematics and camera system collecting driving
sensor data and video. Videos were analyzed using computer vision models
detecting traffic elements to identify stop signs. Stop sign detections and
driver stopping trajectories were clustered to geolocate and extract
driver-visited stop intersections. Driver videos were then annotated to record
stopping behavior and key traffic characteristics. Stops were categorized as
safe or unsafe based on traffic law. Results: Mixed effects logistic regression
models examined how stopping behavior (safe vs. unsafe) in T1DM drivers was
affected by 1) abnormal sleep, 2) obesity, and 3) poor glucose control. Model
results indicate that one standard deviation increase in BMI (~7 points) in
T1DM drivers associated with a 14.96 increase in unsafe stopping odds compared
to similar controls. Abnormal sleep and glucose control were not associated
with increased unsafe stopping. Conclusion: This study links chronic patterns
of abnormal T1DM driver physiology, sleep, and health to driver safety risk at
intersections, advancing models to identify real-world safety risk in diabetes
drivers for clinical intervention and development of in-vehicle safety
assistance technology.

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