Linking driver physiology and stopping responses at stop-controlled intersections in drivers with type 1 diabetes
Date
2023-05
Authors
Barnwal, Ashirwad
Major Professor
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Sharma, Anuj
Wood, Jonathan
Hallmark, Shauna
Sarkar, Soumik
Vardeman, Stephen
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Abstract
Background: Diabetes is a major public health challenge that affects millions of people worldwide and is associated with poor glucose control, sleep dysfunction, and obesity which impair cognitive and psychomotor functions that increase driver risk. Previous literature has linked acute and chronic glucose, sleep, obesity dysfunction to driver impairment, but prior research determining how real-world patterns of diabetes driver health and physiology increase driver risk is limited, particularly in high-risk areas like stop-controlled intersections. This study addresses this need by taking advantage of real-world contemporaneous driving, glucose, and sleep data, in context of clinically measured health, to assess how these factors associate with unsafe stopping in drivers with type 1 diabetes mellitus (T1DM).
Methods: 18 T1DM drivers (21–52 years, µ = 31.2 years) and 14 controls (21–55 years, µ = 33.4 years) from around Omaha, NE participated in a 4-week naturalistic driving study. At induction, each participant’s personal vehicle was instrumented with an advanced telematics and camera system to collect driving data (e.g., speed, acceleration, GPS, forward road and cabin video). Videos were processed with computer vision models capable of detecting traffic elements to identify stop signs. Stop sign detections and driver stopping trajectories were processed via DBSCAN clustering algorithm to geolocate and extract driver-visited stop intersections. Driver videos showing stop intersection approaches were then annotated to record stopping behavior (full, rolling, and no stop) and key intersection traffic characteristics (e.g., presence/absence of lead/crossing vehicles). Stops were categorized as safe or unsafe based on traffic law.
Results: Mixed-effects logistic regression models determined how stopping behavior (safe vs. unsafe) in T1DM drivers was affected by 1) disease, 2) at-risk, acute physiology (hypo- and hyperglycemia), 3) abnormal sleep (above or below 7–9 hours), 4) obesity, and 5) poor glucose control (greater standard deviation [SD], coefficient of variation [CV], low blood glucose index [LBGI], and high blood glucose index [HBGI]). Model results indicated that compared to drivers with normal physiology, diabetes drivers who were acutely hyperglycemic (≥300 mg/dL) had 2.37 increased odds of unsafe stopping (95% CI: 1.26–4.47, p = 0.008). Acute hypoglycemia did not associate with unsafe stopping (p = 0.537), however the lower frequency of hypoglycemia (vs. hyperglycemia) warrants a larger sample of drivers to investigate this effect. Additional results indicate that one standard deviation increase in BMI (~7 points) in T1DM drivers associated with a 11.43 increase in unsafe stopping odds relative to a similar increase in controls. Abnormal sleep and glucose control were not associated with increased unsafe stopping.
Conclusion: This study links acute and chronic patterns of abnormal driver physiology, sleep, and health in drivers with type 1 diabetes to driver safety risk at stop-controlled intersections, providing recommendations for clinicians aimed at improving patient safety, fair licensing guidelines, and targets for developing in-vehicle safety assistance technology.
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