Predicting and Visualizing Daily Mood of People Using Tracking Data of Consumer Devices and Services

TL;DR

This paper introduces InsightMe, a self-tracking app that analyzes personal device and service data to predict and visualize daily mood, emphasizing explainability and providing insights into factors affecting wellbeing.

Contribution

The paper presents a novel self-tracking app that predicts mood using multiple linear regression and neural networks, with a focus on explainability and user transparency.

Findings

Neural network model explained 50% of mood variance.

Linear regression model explained 55% of mood variance.

Small A-B test demonstrated effective data influence visualization.

Abstract

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.