Quantifying vehicle control from physiology in type 1 diabetes

Date
2019-10-16
Authors
Chakraborty, Pranamesh
Merickel, Jennifer
Shah, Viraj
Sharma, Anuj
Hegde, Chinmay
Desouza, Cyrus
Drincic, Andjela
Gunaratne, Pujitha
Rizzo, Matthew
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Abstract

Objective: Our goal is to measure real-world effects of at-risk driver physiology on safety-critical tasks like driving by monitoring driver behavior and physiology in real-time. Drivers with type 1 diabetes (T1D) have an elevated crash risk that is linked to abnormal blood glucose, particularly hypoglycemia. We tested the hypotheses that (1) T1D drivers would have overall impaired vehicle control behavior relative to control drivers without diabetes, (2) At-risk patterns of vehicle control in T1D drivers would be linked to at-risk, in-vehicle physiology, and (3) T1D drivers would show impaired vehicle control with more recent hypoglycemia prior to driving.

Methods: Drivers (18 T1D, 14 control) were monitored continuously (4 weeks) using in-vehicle sensors (e.g., video, accelerometer, speed) and wearable continuous glucose monitors (CGMs) that measured each T1D driver’s real-time blood glucose. Driver vehicle control was measured by vehicle acceleration variability (AV) across lateral (AVY, steering) and longitudinal (AVX, braking/accelerating) axes in 45-second segments (N = 61,635). Average vehicle speed for each segment was modeled as a covariate of AV and mixed-effects linear regression models were used.

Results: We analyzed 3,687 drives (21,231 miles). T1D drivers had significantly higher overall AVX, Y compared to control drivers (BX = 2.5 × 10−2 BY = 1.6 × 10−2, p < 0.01)—which is linked to erratic steering or swerving and harsh braking/accelerating. At-risk vehicle control patterns were particularly associated with at-risk physiology, namely hypo- and hyperglycemia (higher overall AVX,Y). Impairments from hypoglycemia persisted for hours after hypoglycemia resolved, with drivers who had hypoglycemia within 2–3 h of driving showing higher AVX and AVY. State Department of Motor Vehicle records for the 3 years preceding the study showed that at-risk T1D drivers accounted for all crashes (N = 3) and 85% of citations (N = 13) observed.

Conclusions: Our results show that T1D driver risk can be linked to real-time patterns of at-risk driver physiology, particularly hypoglycemia, and driver risk can be detected during and prior to driving. Such naturalistic studies monitoring driver vehicle controls can inform methods for early detection of hypoglycemia-related driving risks, fitness to drive assessments, thereby helping to preserve safety in at-risk drivers with diabetes.

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This is an Accepted Manuscript of an article published by Taylor & Francis as Chakraborty, Pranamesh, Jennifer Merickel, Viraj Shah, Anuj Sharma, Chinmay Hegde, Cyrus Desouza, Andjela Drincic, Pujitha Gunaratne, and Matthew Rizzo. "Quantifying vehicle control from physiology in type 1 diabetes." Traffic Injury Prevention (2019): 1-6. Available online at DOI: 10.1080/15389588.2019.1665176. Posted with permission.

Keywords
Diabetes mellitus, driving risk, hypoglycemia, vehicle control
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