Research Article  |   March 2014
Predicting Road Test Performance in Drivers With Stroke
Author Affiliations
  • Peggy P. Barco, OTD, OTR/L, SCDCM, is Instructor, Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO
  • Michael J. Wallendorf, PhD, is Research Statistician, Division of Biostatistics, Washington University School of Medicine, St. Louis, MO
  • Carol A. Snellgrove, PhD, is Chief Psychologist, South Australia Police Department, Adelaide, South Australia, Australia
  • Brian R. Ott, MD, is Professor, Department of Neurology, Warren Alpert Medical School, Brown University, and Director, Alzheimer’s Disease and Memory Disorders Center, Rhode Island Hospital, Providence, RI
  • David B. Carr, MD, is Professor of Medicine and Neurology and Clinical Director, Division of Geriatrics and Nutritional Science, Washington University, 4488 Forest Park Boulevard, St. Louis, MO 63108; dcarr@wustl.edu
Article Information
Community Mobility and Driving / Neurologic Conditions / Stroke / Productive Aging
Research Article   |   March 2014
Predicting Road Test Performance in Drivers With Stroke
American Journal of Occupational Therapy, March/April 2014, Vol. 68, 221-229. doi:10.5014/ajot.2014.008938
American Journal of Occupational Therapy, March/April 2014, Vol. 68, 221-229. doi:10.5014/ajot.2014.008938
Abstract

OBJECTIVE. The aim of this study was to develop a brief screening battery to predict the on-road performance of drivers who had experienced a stroke.

METHOD. We examined 72 people with stroke referred by community physicians to an academic rehabilitation center. The outcome variable was pass or fail on the modified Washington University Road Test. Predictor measures were tests of visual, motor, and cognitive functioning.

RESULTS. The best predictive model for failure on the road test included Trail Making Test Part A and the Snellgrove Maze Task®.

CONCLUSION. A screening battery that can be performed in less than 5 min was able to assist in the prediction of road test performance in a sample of drivers with stroke. A probability of failure calculator may be useful for clinicians in their decision to refer clients with stroke for a comprehensive driving evaluation.