2021
Winner

The Effect of Input Device on a Novel Digital Version of the Trail Making Test

Presenter: Garrit Strenge

Research Category: Health Sciences
College: College of Engineering
Major(s): Computer Engineering, Computer Science
Student Type: Undergraduate
Graduation Date: 2024
Additional Authors: Ethan Wong, Tyler Greffrath, Nathaniel Pinkes, Caitlyn Celestino, Misha Pavel, Holly Jimison, Mathew Yarossi
Award Winner Category: COE Solution Awards

The Trail Making Task (TMT) is a popular neurocognitive tool used to assess an individualÕs executive functioning, visual tracking, and motor capabilities. Traditionally, participants use a pencil to connect a series of 25 encircled numbers in numerical order (TMT-A) and are timed with a stopwatch. A novel digitized browser-based version of the TMT (dTMT) was developed with automated instructions to replicate clinical settings to provide more detailed information about the individualÕs performance (i.e. pathlength, movement velocity, etc.). The dTMTÕs remote administration capabilities are critical to rural health monitoring during the Covid-19 pandemic. This study aimed to test whether the input method (mouse, touchscreen, trackpad) had a significant impact on dTMT performance.  Fifty-one young healthy right-handed participants were recruited in accordance with IRB permissions for classroom research. No protected health information was collected. Participants chose either a mouse (N=20), trackpad (N=25), or finger on touchscreen (N=6) at their preference. Completion times on the dTMT-A were 28.2_10.6s (mouse), 17.9_6.2s (touchscreen), and 20.5_5.7s (trackpad). A one-way ANOVA indicated a significant main effect of input device (F(2,50)=6.48, P=.003). Post-hoc t-test with Bonferonni correction for multiple comparisons indicated a significant difference between mouse and touchscreen (P=.026), and mouse and trackpad (P=.008), but no difference between touchscreen and trackpad. These data indicate that input device may have an effect on TMT completion time. Future research will work to better understand these differences, and create normative databases per input device.

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