Driving errors that predict simulated rear-end collisions in drivers with multiple sclerosis

Sarah Krasniuk; Sherrilene Classen; Sarah A Morrow

doi:10.1080/15389588.2021.1883008

Driving errors that predict simulated rear-end collisions in drivers with multiple sclerosis

Traffic Inj Prev. 2021;22(3):212-217. doi: 10.1080/15389588.2021.1883008. Epub 2021 Mar 10.

Authors

Sarah Krasniuk¹, Sherrilene Classen², Sarah A Morrow³

Affiliations

¹ Health and Rehabilitation Sciences, University of Western Ontario, London, Ontario, Canada.
² Department of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA.
³ Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, University of Western Ontario, London Health Sciences Centre, London, Ontario, Canada.

PMID: 33688770
DOI: 10.1080/15389588.2021.1883008

Abstract

Objective: Drivers with Multiple Sclerosis (MS) may have an increased crash risk. However, the driving performance deficits that contribute to crashes are not fully understood. Based on the extant literature, adjustment to stimuli errors indicate failing an on-road assessment. This study examines whether adjustment to stimuli errors can detect the occurrence of collisions in a driving simulator in drivers with MS.

Methods: As part of a quasi-experiment, 38 participants with MS and 21 participants without MS completed visual-cognitive and driving simulator assessments, which also recorded their adjustment to stimuli maneuvers. We quantified participants' adjustment to stimuli maneuvers via initial pedal reaction time (seconds), time to collision (seconds), mean speed (meters per second), and the occurrence of rear-end collisions (collide vs. did not collide) when a simulated vehicle cut across the lane in front of them.

Results: Logistic regression analyses indicated that, compared to drivers without MS, those with MS had a shorter time to collision (OR= .04, p= .001, 95% CI= [.006, .27]) and a faster mean speed (OR= 1.32, p= .04, 95% CI= [1.01, 1.74]) which increased the odds of experiencing a rear-end collision. Receiver operating characteristic curve analyses indicated that, for MS and control groups, time to collision (MS group = AUC= .94, p<.0001, Control group = AUC= .86, p<.0001) and mean speed (MS group = AUC= .76, p=.005, Control group = AUC = .78, p= .005) differentiated between participants who collided vs. did not collide. For drivers with MS, a time to collision of $\leq$ 1.81 seconds (85% sensitivity, 100% specificity, 15% error rate), and a mean speed of $\geq$ 7.83 meters per second (77% sensitivity, 76% specificity, 47% error rate) predicted the occurrence of collisions with the lowest error rate.

Conclusions: During a driving simulator assessment, adjustment to stimuli errors predicted the occurrence of rear-end collisions in drivers with MS (vs. without MS). Driving assessors may target scenarios that measure participants' adjustment to stimuli, via time to collision and mean speed, to make decisions about their visual-cognitive deficits and driving performance.

Keywords: Multiple sclerosis; computer simulation; crash; driver behavior; screening.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Accidents, Traffic / prevention & control
Accidents, Traffic / psychology*
Adult
Attention / physiology*
Automobile Driving / psychology*
Case-Control Studies
Cognition Disorders / complications
Computer Simulation
Humans
Male
Multiple Sclerosis / complications*
Reaction Time / physiology*
Task Performance and Analysis