Exploring the Predictive Ability of the Motor-Free Visual Perception Test (MVPT) and Trail Making Test (TMT) for On-Road Driving Performance
Ana Holowaychuk, Yolan Parrott, Ada W. S. Leung
Abstract
IMPORTANCE: Resuming driving after a change in functional ability is challenging for patients with a neurological condition. Although a combination of assessment tools has been suggested for use in driving evaluation, resources and availability of tools have been a problem. OBJECTIVE: To examine the predictive ability of two commonly used tools, the Motor-Free Visual Perception Test (MVPT) and the Trail Making Test, Parts A and B (TMTA and TMTB), on on-road driving performance. DESIGN: Retrospective chart review of 82 patient charts between 2015 and 2016. SETTING: Local rehabilitation hospital. PARTICIPANTS: Eighty-two patients with a primary neurological diagnosis (general neurological condition, n = 13; spinal cord injury, n = 11; stroke, n = 58). OUTCOMES AND MEASURES: MVPT, TMTA, and TMTB. RESULTS: Among the patients, 36 passed and 46 failed the on-road evaluation. The TMTA and TMTB scores were significantly different between those who passed or failed the on-road evaluation. Logistic regression analyses revealed that the TMTB completion time was the only significant predictor of on-road driving performance (for the all-patient model, 66% prediction accuracy, -2 log-likelihood [LL] = 93.47, exp β = 0.98; for the stroke-only model, 76% prediction accuracy, -2LL = 59.61, exp β = 0.97). CONCLUSIONS AND RELEVANCE: Our findings suggest that the TMTB is a better predictor of on-road driving performance for patients with a neurological condition than the MVPT. The findings shed light on the importance of selecting proper tools when assessing driving performance. Future prospective studies with a wider array of predictive variables are recommended to support the present findings. WHAT THIS ARTICLE ADDS: Occupational therapists should revisit the use of the MVPT in driving assessment and consider multiple assessment tools when evaluating and predicting driving performance.