@article{ref1,
title="Application of tablet-based cognitive tasks to predict unsafe drivers in older adults",
journal="Transportation research interdisciplinary perspectives",
year="2020",
author="Bakhtiari, Reyhaneh and Tomczak, Michelle V. and Langor, Stephen and Scanlon, Joanna E. M. and Granley, Aaron and Singhal, Anthony",
volume="4",
number="",
pages="e100105-e100105",
abstract="Background Due to aging and medication interferences, a wide range of motor, sensory, and cognitive skills that are imperative for driving are affected in older adults. Though on-road tests are most indicative of driving ability, they are costly, stressful, time-consuming, and risky. Application of tablet-based cognitive tasks is investigated in identifying unsafe drivers in a population of healthy and at-risk for driving older adults.   Method Forty-nine older adult participants aged 54 to 81 (M = 78.08, SD = 9.78) that were screened by their physicians as &quot;at-risk for driving impairment&quot;, and forty-eight control participants aged 54 to 81 years (M = 65.85, SD = 6.93) completed an on-road driving test designed specifically to evaluate cognitive decline related to driving, and a set of tablet-based cognitive tasks (composed of reaction speed, decision making, memory, and bi-manual perceptual-motor tasks) that measured the cognitive skills needed during driving. Accuracy and reliability of predicting unsafe drivers based on the cognitive tasks were investigated using different trichotomous classifiers (class outputs: safe, unsafe, undefined).   Results Trichotomous naive Bayes demonstrated the highest overall accuracy performance of 73%, a sensitivity of 69%, and a specificity of 75%. The rate of misclassified unsafe drivers was 19%, and the rate of misclassified safe drivers was 8%.   Conclusion High accuracy and reliable prediction of unsafe drivers using cognitive-only tasks in a sample of older adults population demonstrate the efficacy of a widely available screening tool that can be applied in other cognitively impaired populations such as drug users.<p /> <p>Language: en</p>",
language="en",
issn="2590-1982",
doi="10.1016/j.trip.2020.100105",
url="http://dx.doi.org/10.1016/j.trip.2020.100105"
}