Logo image
Back
The Effects of Driver Distraction for Individuals With Traumatic Brain Injuries
Journal article   Peer reviewed

The Effects of Driver Distraction for Individuals With Traumatic Brain Injuries

David M Neyens, Linda Ng Boyle and Maria T Schultheis
Human factors, v 57(8), pp 1472-1488
Dec 2015
DOI: https://doi.org/10.1177/0018720815594057
PMID: 26186925
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Adult Attention - physiology Automobile Driving Bayes Theorem Brain Injuries - physiopathology Female Humans Male Task Performance and Analysis Young Adult
The aim of this study was to evaluate the effects of secondary tasks on the driving performance of individuals with mild traumatic brain injuries (TBIs). Studies suggest detrimental impacts of driving with TBI or while distracted but the impact of driver distraction on TBI drivers is not well documented. Bayesian regression models were used to estimate the effect of relatively simple secondary tasks on driving performance of TBI and healthy control (HC) drivers. A driving simulator was used to develop prior distribution of task effects on driving performance for HCs. An on-road study was conducted with TBI and HC drivers to generate effect estimates for the posterior distributions. The Bayesian models were also compared to frequentist models. During a coin-sorting task, all drivers exhibited larger maximum lateral acceleration and larger standard deviation of speed than in a baseline driving segment. There were no significant driving performance differences between the TBI and the HC drivers during the tasks. Across all tasks, TBI drivers spent more time looking at the tasks and made more frequent glances toward the tasks. The findings show that even drivers with mild TBI have significantly longer and more glances toward the tasks compared to the HCs. This study demonstrates a Bayesian approach and how the results differ from frequentist statistics. Using prior distributions in a Bayesian model helps account for the probabilities associated with otherwise unknown parameters. This method strengthens the Bayesian parameter estimates compared to that of a frequentist model.

Metrics

8 Record Views
10 citations in Web of Science
11 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#11 Sustainable Cities and Communities

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Behavioral Sciences
Engineering, Industrial
Ergonomics
Psychology
Psychology, Applied
Logo image