Predictors of driving in individuals with relapsing–remitting multiple sclerosis

The authors reported previously on the performance of the Stroke Driver Screening Assessment (SDSA), a battery of four cognitive tests that takes less than 30 min to administer. The SDSA predicted the driving performance of participants with multiple sclerosis (MS) on a road test with 86% accuracy, 80% sensitivity, and 88% specificity. In this study, the authors investigated if the addition of driving-related physical and visual tests and other previously identified cognitive predictors, including performance on the Useful Field of View test, result in a better accuracy of predicting participants’ on-road driving performance. Forty-four individuals with relapsing–remitting MS (age = 46 ± 11 years, 37 females) and Expanded Disability Status Scale values between 1 and 7 were administered selected physical, visual and cognitive tests including the SDSA. The model that explained the highest variance of participants’ performance on a standardized road test was identified using multiple regression analysis. A discriminant equation containing the tests included in the best model was used to predict pass or fail performance on the test. Performance on 12 cognitive and three visual tests were significantly associated with performance on the road test. Five of the tests together explained 59% of the variance and predicted the pass or fail outcome of the road test with 91% accuracy, 70% sensitivity, and 97% specificity. The authors concluded that participants’ on-road performance was more accurately predicted by the model identified in this study than by using only performance on the SDSA test battery. The five psychometric/off-road tests should be used as a screening battery, after which a follow-up road test should be conducted to finally decide the fitness to drive of individuals with relapsing–remitting MS. Future studies are needed to confirm and validate the findings in this study.


  • English

Media Info

  • Media Type: Digital/other
  • Features: References; Tables;
  • Pagination: pp 344-350
  • Serial:

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Filing Info

  • Accession Number: 01489917
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 31 2013 3:41PM