Predicting and Visualizing Daily Mood of People Using Tracking Data of Consumer Devices and Services
This work addresses the challenge for individuals to understand how their personal data relates to wellbeing, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of users struggling to gain insights from personal tracking data by developing InsightMe, a self-tracking meta app that predicts daily mood using data from devices and services, achieving explained variances of 0.55 with multiple linear regression and 0.50 with a neural network.
Users can easily export personal data from devices (e.g., weather station and fitness tracker) and services (e.g., screentime tracker and commits on GitHub) they use but struggle to gain valuable insights. To tackle this problem, we present the self-tracking meta app called InsightMe, which aims to show users how data relate to their wellbeing, health, and performance. This paper focuses on mood, which is closely associated with wellbeing. With data collected by one person, we show how a person's sleep, exercise, nutrition, weather, air quality, screentime, and work correlate to the average mood the person experiences during the day. Furthermore, the app predicts the mood via multiple linear regression and a neural network, achieving an explained variance of 0.55 and 0.50, respectively. We strive for explainability and transparency by showing the users p-values of the correlations, drawing prediction intervals. In addition, we conducted a small A-B test on illustrating how the original data influence predictions. The source code and app are available online.