Driving Errors Predicting Pass/Fail On-Road Assessment Outcomes Among Cognitively Impaired Older Drivers

Abstract

Older drivers with cognitive impairment (CI)/dementia make significantly more driving errors than healthy controls; however, whether driving errors are predictive of pass/fail outcomes in older drivers with CI/dementia are unclear. This study determined the driving errors that predicted failing an on-road assessment in drivers with CI. We retrospectively collected comprehensive driving evaluation data of 80 participants (76.1 ± 9.3 years) from an Ontario driving assessment center. Adjustment to stimuli (area under the curve [AUC] = 0.88), lane maintenance (AUC = 0.84), and speed regulation errors (AUC = 0.85) strongly predicted pass/fail outcomes. Worse performance on the Trails B (time) and Useful Field of View® (Subtest 2, Subtest 3, and risk index) were significantly correlated with adjustment to stimuli ( p < .05), lane maintenance ( p < .05), and speed regulation errors ( p < .05). Adjustment to stimuli, lane maintenance, and speed regulation errors may be critical indicators of failing an on-road assessment in older drivers with CI. Prioritizing these errors may help identify at-risk drivers.

A 10-Year Scoping Review of Screening and Assessment Tools Used for Driving Rehabilitation in Older Adults

“…UFOV subtest 2 (divided attention), in particular, confirmed its utility among older adults by demonstrating the best single predictor of at-risk drivers with an area under the curve (AUC) of .84 (33). Moreover, a recent study reported that UFOV subtest 3 (selective attention) could optimally predict pass/fail outcomes with a sensitivity of 78.9% and a specificity of 73.5% (14). Besides, this review also identified pencil-and-paper-based tools such as the SDSA, which could also use to predict on-road driving performance specifically.…”

“…From the result, it was notable that Mini-Mental State Examination (MMSE) (n = 10) (13,(16)(17)(18)20,23,25,26,33,34) (27). Trail-Making Tests (TMT) (14)(15)(16)(17)20,22,23,27,28,30,32,33,(35)(36)(37)(38)(39)(40)(41)Australia. Questionnaires were administered to assess driving habits and functional assessments to assess driving-related function.…”

Different deployments of attentional breadth selectively predict UFOV task performance in older adults

“…Indeed, three separate meta-analyses have concluded that UFOV robustly and consistently predicts driving performance in a range of settings (Clay et al, 2005 ; Mathias & Lucas, 2009 ; Stefanidis et al, 2023 ). Another advantage of UFOV is that it also predicts when older drivers will have difficult with specific real-world driving settings, such as entering into traffic (Pietras et al, 2006 ), turning across an intersection (Rusch et al, 2016 ), dealing with distractions while driving (Wood et al, 2012 ), and dealing with unexpected stimuli (Krasniuk et al, 2022 ). Finally, a unique aspect of UFOV’s utility is its potential as an intervention—training on variants of the task improves driving performance in older adults (Ball et al, 2002a ; Edwards et al, 2005a , 2005b ; Vance et al, 2007 ), and this improvement is reportedly maintained for as long as ten years after initial training (Edwards et al, 2018 ; Rebok et al, 2014 ).…”