  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.
