Date Presented 03/31/2022

Research demonstrates that decline in visual processing speed is a critical risk factor for drivers with medical conditions. Using visual scanning technology, this research examined the reaction times between medically at-risk adult drivers and healthy controls. In addition to significant differences between the two groups, results among three common types of diagnoses suggest that the Vision Coach™ can discriminate fitness-to-drive outcomes using a simple timed test using the technology.

Primary Author and Speaker: Victoria Penna

Additional Authors and Speakers: Anne E. Dickerson

Visual processing speed is considered a critical factor for poor driving capacity in older adults (Dickerson & Niewoehner, 2012; Elgin et al., 2012). Visual perceptual skills are conceptualized as a hierarchy of skill that work together to help process our visual information (Warren, 1993). Individuals with deficits in this area will likely have difficulty with judgement and reaction to on-road events. There are limited assessments of visual processing speed. Recent studies support the use of the Vision Coach™ as an assessment tool, finding a statistically significant difference (p ≤ 0.001) between healthy young and older adult groups (Register, 2016). This study established normative data and these norms allowed us to examine if significant differences in visual reaction times exist between healthy adults and medically-at-risk adult drivers using the Vision Coach™, an interactive lightboard.

PURPOSE: The specific research questions were: 1) is there a statistically significant difference in performance time between medically at-risk individuals and healthy controls, 2) does the type of medical condition (e.g., neurological, cognition, complex medical conditions) differentiate performance, and 3) can the Vision Coach™ visual processing speed feature differentiate between fit and unfit drivers.

DESIGN: A cross sectional quasi-experimental design was used to compare the visual processing reaction times between at-risk adult drivers and healthy controls.

METHOD: Participants were medically-at-risk drivers referred for a comprehensive driving evaluation to determine their fitness to drive. The Vision Coach™ Full Field 60 task was used to collect reaction times. This task required participants to tap 60 randomly illuminated red dots on the board. The dots appeared one at a time measuring the time (seconds) the participant saw and touched all 60 dots. One practice trial was used, followed by three testing trials that were averaged together. The healthy controls group (n = 242) had an average age of 50 and the medically-at-risk group (n = 35) had an average age of 60.5. Within the at-risk group, participants were divided into three separate diagnostic categories based on their primary diagnosis. The three fitness to drive outcomes were pass, fail, or needing restrictions.

RESULTS: A propensity score method based on age and gender was used to account for the difference in sample sizes by weighting the participants from the two studies for a fair comparison between the two groups. Independent t-tests showed a significant difference t(275) = -6.42, p = <.001 in trial times between healthy controls (M = 53.52 +/-10.82) and medically-at-risk adults (M = 72.54 +/-17.04). No significant difference was found between the diagnostic groups, F(2, 32) = 2.09, p = .141. Lastly, the Vision Coach™ was able to differentiate between those who passed and those who failed a driving evaluation, F(2, 32) = 8.28, p = 0.001.

CONCLUSION: Results demonstrated medically-at-risk drivers have significantly slower or impaired visual processing skills. The diagnosis type did not make a difference, which allows this task to be used as a universal screening tool for determining driving risk. Most importantly, the Vision Coach™ differentiated between the three outcomes of fitness to drive: pass, fail, and needing restrictions.

IMPACT: This study provides research evidence that visual processing speed is a critical skill that helps in determining fitness to drive. The use of this relatively easy task can significantly assist the general practice of OTs in their ability to determine who needs further assessment for a driving evaluation, and it can be used as an outcome tool for improving processing speed.


Dickerson, A, E. & Niewoehner, P. (2012). Analyzing the complex instrumental activities of daily living of driving and community mobility. In M. J, McGuire & E. S. Davis (Eds.), Driving and community mobility: Occupational therapy strategies across the lifespan (pp. 137-171). Bethesda, MD: American Occupational Therapy Association, Inc.

Elgin, J., Owsley, C., & Classen, S. (2012). Vision and driving. In M. J, McGuire & E. S. Davis (Eds.), Driving and community mobility: Occupational therapy strategies across the lifespan (pp. 173-219). Bethesda, MD: American Occupational Therapy Association, Inc.

Warren, M. (1993). A hierarchical model for evaluation and treatment of visual perceptual dysfunction in adult acquired brain injury, part 1. American Journal of Occupational Therapy, 47(1), 42-54.

Register, Joshua. (2016). Effects of gender and age on reaction time during Vision Coach™ task [Unpublished master’s thesis]. East Carolina University.