Predictors of fitness to drive in people with Parkinson disease

H. Devos, W. Vandenberghe, A. Nieuwboer, M. Tant, G. Baten, W. De Weerdt

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


OBJECTIVE: To develop an efficient clinical screening battery to accurately predict the fitness to drive in people with Parkinson disease (PD). METHODS: This prospective study included 80 participants: 40 patients with PD and 40 healthy age- and sex-matched control subjects. All participants were assessed using a driving simulator, a driving history survey, and the Clinical Dementia Rating. The patients with PD also underwent a clinical test battery and an evaluation of fitness to drive performed by an official center, which included visual, cognitive, and on-road tests. A two-class decision from this driving assessment center was the main outcome measure. RESULTS: A screening battery assessing four clinical variables (disease duration, contrast sensitivity, Clinical Dementia Rating, and motor part of the Unified Parkinson's Disease Rating Scale) provided the best model (R = 0.52) to predict the fitness to drive and correctly classified 36 (90%) of the patients with PD as pass or fail (sensitivity = 91%, specificity = 90%). The Test Ride for Investigating Practical fitness to drive (TRIP) driving simulator score discriminated significantly between drivers with PD and their healthy peers (p = 0.0008). When the TRIP driving simulator score was added to the clinical model, the total explained variance increased (R = 0.60) and correctly classified 39 (97.5%) of drivers with PD into the pass/fail category (sensitivity = 91%, specificity = 100%). CONCLUSIONS: A short clinical screening battery that measures disease duration, contrast sensitivity, cognitive and motor functions can predict fitness to drive in people with Parkinson disease with a high degree of accuracy.

Original languageEnglish (US)
Pages (from-to)1434-1441
Number of pages8
Issue number14
StatePublished - Oct 1 2007

ASJC Scopus subject areas

  • Clinical Neurology


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