Eye-tracking Retrospective Think-aloud as a Novel Approach for a Usability Evaluation

Hwayoung Cho

Dakota Powell

Adrienne Pichon

Lisa M. Kuhns

Robert Garofalo

Rebecca Schnall

Abstract:

Objective
To report on the use of an eye-tracking retrospective think-aloud for usability evaluation and to describe its application in assessing the usability of a mobile health app.

Materials and Methods
We used an eye-tracking retrospective think-aloud to evaluate the usability of a HIV prevention mobile app among 20 young men (15-18 years) in New York City, NY; Birmingham, AL; and Chicago, IL. Task performance metrics, critical errors, task completion rate per participant and task completion rate per task, were measured. Eye-tracking metrics including fixation, saccades, time to first fixation, time spent, and revisits were measured and compared among participants with/without a critical error.

Results
Using task performance analysis, we identified 19 critical errors on four activities, and of those, two activities had a task completion rate of less than 78%. To better understand these usability issues, we thoroughly analyzed participants’ corresponding eye movements and verbal comments using an in-depth problem analysis. In areas of interest created for the activity with critical usability problems, there were significant differences in time spent (p = 0.008), revisits (p = 0.004), and total numbers of fixations (p = 0.007) by participants with/without a critical error. The overall mean score of perceived usability rated by the Health IT Usability Evaluation Scale was 4.64 (SD = 0.33), reflecting strong usability of the app.

Discussion and Conclusion
An eye-tracking retrospective think-aloud enabled us to identify critical usability problems as well as gain an in-depth understanding of the usability issues related to interactions between end-users and the app. Findings from this study highlight the utility of an eye-tracking retrospective think-aloud in consumer health usability evaluation research.

This publication uses Eye Tracking and Eye Tracking Screen Based which is fully integrated into iMotions Lab

Learn more

Learn more about the technologies used

Other publications you might be interested in