Driving habits and behaviors of patients with brain tumors: a self-report, cognitive and driving simulation study

Abstract

The purpose of the study is to determine driving habits and behaviors of patients with brain tumors in order to better inform discussions around driving safety in this population. Eight-four patients with brain tumors participated in a survey on their driving behaviors since their diagnosis. Thirteen of these patients and thirteen sex- and age-matched healthy controls participated in cognitive testing and several driving simulation scenarios in order to objectively assess driving performance. Survey responses demonstrated that patients with brain tumors engage in a variety of driving scenarios with little subjectve difficulty. On the driving simulation tasks, patients and healthy controls performed similarly except that patients had more speed exceedances (U = 41, p < 0.05) and a greater variability in speed (U = 57, p < 0.05). Performance on the selective attention component of the UFOV was significantly associated with greater total errors in the Bus Following task for patients with brain tumors compared to healthy controls (rs = 0.722, p < 0.05, CI [0.080, 0.957]). Better comprehensive driving assessments are needed to identify patients with driving behaviors that put themselves and others at risk on the road.

A systematic review of the impact of brain tumours on risk of motor vehicle crashes

“…Of the three studies identified, only one study directly evaluated MVC incidence, rather than a surrogate marker. Whilst no significantly increased risk of MVC was reported by these studies Mansur et al revealed that almost 40% of patients claimed to have had a MVC following diagnosis, suggesting discordance between real-world data and the driving risk assessment tools used [ 13 ]. Data interpretation is limited by small sample size, lack of a control population, heterogenous tumour types and selection bias surrounding coexistence with seizures.…”

“…Driving is complex, involving several neurocognitive domains and therefore requires a multidisciplinary approach to support clinicians in balancing competing patient advocacy and community safety responsibilities. As demonstrated in Mansur’s study, no single cognitive test can predict driving performance [ 13 ]. A meta-analysis of driving assessments of patients with Alzheimer’s dementia and mild cognitive impairment show that measures of cognitive and sensory domains may be predictive of driving performance with the TMT test (assess attention, processing speed and adaptability) and the Maze Task (measures executive planning and visuospatial awareness) being better predictors of on-road performance than driving simulations [ 25 ].…”

Driving With Distraction: Measuring Brain Activity and Oculomotor Behavior Using fMRI and Eye-Tracking

“…Driving metrics extracted included length of run, longitudinal position, lane position (deviation of lateral lane position of the vehicle referenced in relation to the center of the driver vehicle with respect to the roadway center dividing line), driving speed, vehicle heading angle, and number of collisions. These metrics, which have been used to characterize impaired driving performance in various patient populations with neurological impairment (e.g., Mansur et al, 2018;Hird et al, 2017), were converted into a matrix of size N × M (with N variables and M time points) and read into MATLAB (The MathWorks Inc., Natick, MA, United States). Metrics that were calculated in MATLAB included event onset time (longitudinal position of event onset converted into time), time to turn (time between event onset and when the vehicle heading angle was at 25 • , considered to be start of the turn position), turn speed (mean speed between turn start time and turn end time, after the vehicle heading angle returned to 0 • ), and question response time (time a response button was pressed minus the time the question was administered).…”