Quantifying vehicle control from physiology in type 1 diabetes

Abstract

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 hours 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.

Vehicle Control as a Measure of Real‐World Driving Performance in Patients With Rheumatoid Arthritis

“…Similar to other recent efforts examining the impact of health status on driving (6,11), our study focused on measures of vehicle control as a surrogate of safety. Clearly, further work is needed to translate these driving behaviors into other quantifiable measures of driving risk.…”

“…Driving data were preprocessed for quality assurance. Driving segments with 1) problematic amounts of missing data (<20 seconds in length; 4.0% of segments), 2) potential GIS inaccuracy indicated by a wide speed limit range of >15 miles per hour (mph) (<0.1%; 3) speed limit data missing (13.2%), or 4) mean vehicle speed <5 mph (8 kilometers per hour [kph]) for the segment were removed, to reduce confounding effects of stopped or heavy traffic, intersections, work zones, and other (9.7%) (6). Roadway type was derived from GIS speed limit data based on Nebraska statutory speed limits to define residential, commercial, and interstate areas (residential: 20–30 mph [32–45 kph]; commercial: 35–45 mph [55–74 kph]; or interstate: ≥55 mph [≥91 kph]).…”

“…All participants. All RA and non-RA participants were actively driving, had at least 5 years of driving experience, and demonstrated visual acuity that met Nebraska licensure standards and had no dementia based on health history or the Mini-Mental Status Exam (score >24) (6). Individuals were excluded from participation if they experienced select comorbidities (e.g., neurocognitive disease, active malignancy, substance abuse) that may interfere with driving capacity, if they used confounding medications such as narcotics or other sedatives, or if they were pregnant.…”

“…Driving data were consolidated into 45‐second segments, as previously reported (6,10), and linked with Nebraska global information system (GIS) databases, patient demographic data, and relevant clinical information. Driving data were preprocessed for quality assurance.…”

“…Roadway type was derived from GIS speed limit data based on Nebraska statutory speed limits to define residential, commercial, and interstate areas (residential: 20 Measures of vehicle control. Consistent with other quantitative driving studies, measures of vehicle control served as the primary outcome of interest for this study and were quantified using acceleration variability (AV), calculated as the SD of measured acceleration (6,11). AV was quantified for both lateral (AV y ) and longitudinal (AV x ) planes, whereby AV y serves as a measure of steering control, and AV x serves as a measure of braking/ accelerating control.…”