TY - JOUR
T1 - Sugar and stops in drivers with insulin-dependent type 1 diabetes
AU - Barnwal, Ashirwad
AU - Chakraborty, Pranamesh
AU - Sharma, Anuj
AU - Riera-Garcia, Luis
AU - Ozcan, Koray
AU - Davami, Sayedomidreza
AU - Sarkar, Soumik
AU - Rizzo, Matthew
AU - Merickel, Jennifer
N1 - Funding Information:
We gratefully acknowledge the Toyota Collaborative Safety Research Center for funding this study and the Mind & Brain Health Labs at UNMC's Department of Neurological Sciences for leading study operations. In particular, we extend our thanks to Drs. Cyrus Desouza and Andjela Drincic for their invaluable clinical guidance on all aspects of this study, the Mind & Brain Health Labs research staff, and Adam Hornig for recruiting and running study participants.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/8
Y1 - 2022/8
N2 - Background: Diabetes is a major public health challenge, affecting millions of people worldwide. Abnormal physiology in diabetes, particularly hypoglycemia, can cause driver impairments that affect safe driving. While diabetes driver safety has been previously researched, few studies link real-time physiologic changes in drivers with diabetes to objective real-world driver safety, particularly at high-risk areas like intersections. To address this, we investigated the role of acute physiologic changes in drivers with type 1 diabetes mellitus (T1DM) on safe stopping at stop intersections. Methods: 18 T1DM drivers (21–52 years, μ = 31.2 years) and 14 controls (21–55 years, μ = 33.4 years) participated in a 4-week naturalistic driving study. At induction, each participant's personal vehicle was instrumented with a camera and sensor system to collect driving data (e.g., GPS, video, speed). Video was processed with computer vision algorithms detecting traffic elements (e.g., traffic signals, stop signs). Stop intersections were geolocated with clustering methods, state intersection databases, and manual review. Videos showing driver stop intersection approaches were extracted and manually reviewed to classify stopping behavior (full, rolling, and no stop) and intersection traffic characteristics. Results: Mixed-effects logistic regression models determined how diabetes driver stopping safety (safe vs. unsafe stop) was affected by 1) disease and 2) at-risk, acute physiology (hypo- and hyperglycemia). 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) compared to those with normal physiology. 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. Critically, presence of diabetes alone did not associate with unsafe stopping, underscoring the need to evaluate driver physiology in licensing guidelines. Conclusion: This study links acute, abnormal physiologic fluctuations in drivers with diabetes to driver safety based on unsafe stopping at stop-controlled intersections, providing recommendations for clinicians aimed at improving patient safety, fair licensing guidelines, and targets for developing advanced driver assistance systems.
AB - Background: Diabetes is a major public health challenge, affecting millions of people worldwide. Abnormal physiology in diabetes, particularly hypoglycemia, can cause driver impairments that affect safe driving. While diabetes driver safety has been previously researched, few studies link real-time physiologic changes in drivers with diabetes to objective real-world driver safety, particularly at high-risk areas like intersections. To address this, we investigated the role of acute physiologic changes in drivers with type 1 diabetes mellitus (T1DM) on safe stopping at stop intersections. Methods: 18 T1DM drivers (21–52 years, μ = 31.2 years) and 14 controls (21–55 years, μ = 33.4 years) participated in a 4-week naturalistic driving study. At induction, each participant's personal vehicle was instrumented with a camera and sensor system to collect driving data (e.g., GPS, video, speed). Video was processed with computer vision algorithms detecting traffic elements (e.g., traffic signals, stop signs). Stop intersections were geolocated with clustering methods, state intersection databases, and manual review. Videos showing driver stop intersection approaches were extracted and manually reviewed to classify stopping behavior (full, rolling, and no stop) and intersection traffic characteristics. Results: Mixed-effects logistic regression models determined how diabetes driver stopping safety (safe vs. unsafe stop) was affected by 1) disease and 2) at-risk, acute physiology (hypo- and hyperglycemia). 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) compared to those with normal physiology. 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. Critically, presence of diabetes alone did not associate with unsafe stopping, underscoring the need to evaluate driver physiology in licensing guidelines. Conclusion: This study links acute, abnormal physiologic fluctuations in drivers with diabetes to driver safety based on unsafe stopping at stop-controlled intersections, providing recommendations for clinicians aimed at improving patient safety, fair licensing guidelines, and targets for developing advanced driver assistance systems.
KW - Driver risk
KW - Hyperglycemia
KW - Hypoglycemia
KW - Naturalistic driving
KW - Type 1 diabetes
KW - Unsafe stopping
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U2 - 10.1016/j.aap.2022.106692
DO - 10.1016/j.aap.2022.106692
M3 - Article
C2 - 35605288
AN - SCOPUS:85130859402
SN - 0001-4575
VL - 173
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
M1 - 106692
ER -