A Longitudinal Analysis of a Mood Self-Tracking App: The Patterns Between Mood and Daily Life Activities

Self-tracking apps are becoming popular because they can be useful tools for users to self-manage a variety of health-related issues. However, many of the existing apps are not supported by empirical and scientific evidence which results in poor quality interventions and low adherence. This research is the foundational work for designing and developing an evidence-based AI-driven mental health app. In this paper, we report on the results of an in-the-wild quantitative and qualitative study of users’ interactions and engagement with a mental health app (called Feeling Moodie) for two years. Based on data collected from 434 users, we have evidence suggesting that users’ moods and activities are strongly related. Quantitative results show that Home, Work, Relaxation, and Family-related activities are the most frequent activities that can have both positive and negative influence on a user’s mood. Qualitative results suggest that when users are engaged in Family-related activities, they are usually concerned about a family member and wish they can spend more time with their loved ones, whereas for Work-related activities, users are constantly thinking about work and feeling exhausted because they are overworked. This paper contributes to a better understanding of the relationship between moods and daily activities and sheds light on how the design and quality of mood-tracking apps can be improved.