Using the Functional Object Detection—Advanced Driving Simulator Scenario to Examine Task Combinations and Age-Based Performance Differences: A Case Study - Johnell O. Brooks; Rakesh Gangadharaiah; Elenah B. Rosopa; Rebecca Pool; Casey Jenkins; Patrick J. Rosopa; Lauren Mims; Breno Schwambach; Ken Melnrick (Applied Sciences)
Occupational therapists need objective tools to evaluate and provide interventions that promote the recovery and rehabilitation of clients. Driving, a common goal for clients after an injury or illness, is a complex task that relies on visual, cognitive, and motor skills. The Functional Object Detection and Functional Object Detection (FOD)—Advanced driving simulator scenarios were developed to provide objective and repeatable driving experiences allowing clinicians to assess their clients’ forward (focal) and peripheral vision, lane keeping, and speed maintenance, as well as provide interventions. Using FOD—Advanced, clinicians can adjust variables to create various task scenarios or combinations to meet the client’s needs and facilitate recovery by providing an appropriate challenge. This study examined four driving simulator scenario combinations and age-related differences for one combination. Study 1 explored older adults’ performance using four possible combinations of FOD—Advanced. Five out of eleven variables (average target reaction time, percentage of targets detected, average brake reaction time, number of target extra presses, and average speed) were effective in distinguishing among the four combinations of FOD with a cross-validated classification rate of 72%. In Study 2, one combination was selected from Study 1 and a group of teens completed the same tasks to evaluate age-related differences. Four out of thirteen simulator variables (standard deviation of brake reaction time, number of target extra presses, average target reaction time, and standard deviation of target reaction time) maximally distinguished the older adults from the younger participants with a cross-validated classification accuracy of 78%. Implications and recommendations for clinical practice and future research are provided.