title: Predictability of clinical assessments for driving performance.
abstract: OBJECTIVE
  As the number of older drivers grows, it is increasingly important to accurately identify at-risk drivers. This study tested clinical assessments predictive of real-time driving performance.
  
  
  METHODS
  Selected assessment tools considered important in the identification of at-risk older drivers represented the domains of vision, cognition, motor performance, and driving knowledge. Participants were administered the battery of assessments followed by an on-road test. A univariate analysis was conducted to identify significant factors (<.05) to be included in a multivariate regression model.
  
  
  RESULTS
  Assessments identified as independently associated with driving performance in the regression model included: FACTTM Contrast sensitivity slide-B, Rapid Pace Walk, UFOV rating, and MMSE total score.
  
  
  CONCLUSIONS
  The domains of vision, cognitive, and motor performance were represented in the predictive model.
  
  
  CONCLUSIONS
  Due to the dynamic nature of the driving task, it is not likely that a single assessment tool will identify at risk drivers.
  
  
  CONCLUSIONS
  By standardizing the selection of clinical assessments used in driving evaluations, practitioners should be able to provide services more efficiently, more objectively, and more accurately to identify at-risk drivers.
authors.affiliation: Towson University, USA. wstav@towson.edu
authors.forename: Wendy B
authors.initials: WB
authors.lastname: Stav
authors.forename: Michael D
authors.initials: MD
authors.lastname: Justiss
authors.forename: Dennis P
authors.initials: DP
authors.lastname: McCarthy
authors.forename: William C
authors.initials: WC
authors.lastname: Mann
authors.forename: Desiree N
authors.initials: DN
authors.lastname: Lanford
mesh_terms: D000367:Age Factors; D000368:Aged; D001334:Automobile Driving / Q000331:legislation & jurisprudence* / Q000592:standards; D001336:Automobiles / Q000331:legislation & jurisprudence* / Q000592:standards; D003071:Cognition; D005260:Female; D005431:Florida; D006801:Humans; D019359:Knowledge; D008297:Male; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011581:Psychological Tests; D011594:Psychometrics; D011597:Psychomotor Performance; D018570:Risk Assessment; D014786:Vision Disorders / Q000175:diagnosis; D014787:Vision Tests; D014785:Vision, Ocular