Comparing Usability and User Acceptance of two Mobile Mood Tracking Applications with Different Input Methods

Abstract

Self-monitoring technologies are new technological tool and have changed the interactions between humans and computers on mobile devices. However, the usability of mobile health apps is critically important to the use or abandonment of such applications. Usability and user acceptance analysis methods can play significant roles during the development life cycle. User acceptance and the usability of mood-tracking applications can be influenced by the mood-entering mode of the application. This pilot study compares two mood-tracking methods in different mobile applications, a self-reporting app and automatic mood detection from facial expression, to determine which input method is easier and more usable and acceptable to users. The applications were presented to participants before completing system usability scale and technology acceptance model questionnaires. The results show that the self-reporting application had a better usability score and innovation features; however, the automatic-mood-detection from facial expression app was rated higher on the pleasantness scale by users