Predictors of driving in individuals with relapsing-remitting multiple sclerosis

Abiodun Emmanuel Akinwuntan, Hannes Devos, Lara Stepleman, Rhonda Casillas, Rebecca Rahn, Suzanne Smith, Mitzi Joi Williams

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Background: We previously reported that performance on the Stroke Driver Screening Assessment (SDSA), a battery of four cognitive tests that takes less than 30 min to administer, predicted the driving performance of participants with multiple sclerosis (MS) on a road test with 86% accuracy, 80% sensitivity, and 88% specificity. Objectives: In this study, we further 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, will result in a better accuracy of predicting participants' on-road driving performance. Methods: 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. Results: 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. Conclusion: Participants' on-road performance was more accurately predicted by the model identified in this study than 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.

Original languageEnglish (US)
Pages (from-to)344-350
Number of pages7
JournalMultiple Sclerosis Journal
Volume19
Issue number3
DOIs
StatePublished - Mar 2013

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Relapsing-Remitting Multiple Sclerosis
Stroke
Sensitivity and Specificity
Psychometrics
Multiple Sclerosis
Regression Analysis

Keywords

  • Physical function
  • cognition
  • driving
  • multiple sclerosis
  • visual function

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

Cite this

Predictors of driving in individuals with relapsing-remitting multiple sclerosis. / Akinwuntan, Abiodun Emmanuel; Devos, Hannes; Stepleman, Lara; Casillas, Rhonda; Rahn, Rebecca; Smith, Suzanne; Williams, Mitzi Joi.

In: Multiple Sclerosis Journal, Vol. 19, No. 3, 03.2013, p. 344-350.

Research output: Contribution to journalArticle

Akinwuntan, Abiodun Emmanuel ; Devos, Hannes ; Stepleman, Lara ; Casillas, Rhonda ; Rahn, Rebecca ; Smith, Suzanne ; Williams, Mitzi Joi. / Predictors of driving in individuals with relapsing-remitting multiple sclerosis. In: Multiple Sclerosis Journal. 2013 ; Vol. 19, No. 3. pp. 344-350.
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abstract = "Background: We previously reported that performance on the Stroke Driver Screening Assessment (SDSA), a battery of four cognitive tests that takes less than 30 min to administer, predicted the driving performance of participants with multiple sclerosis (MS) on a road test with 86{\%} accuracy, 80{\%} sensitivity, and 88{\%} specificity. Objectives: In this study, we further 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, will result in a better accuracy of predicting participants' on-road driving performance. Methods: 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. Results: 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. Conclusion: Participants' on-road performance was more accurately predicted by the model identified in this study than 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.",
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