Tracking and Modeling Subjective Well-Being Using Smartphone-Based Digital Phenotype

Abstract

Subjective well-being (SWB) is a well-studied, widely used construct that refers to how people feel and think about their lives as one of many comprehensive perspectives on well-being. Much research has analyzed the role and utilization of technologies to improve one's SWB; however, especially when it comes to user modeling, multifaceted and variational aspects of SWB are less frequently considered. This paper presents an analysis on identifying factors for smartphone-based data on SWB and modeling SWB changes, based on a four-month user study with 78 college students. Our regression analysis highlights the significance of user attributes (e.g., personality, self-esteem) on SWB and salient factors derived from smartphone data (e.g., time spent on campus, ratio of standing/sitting stationary, expenses) that significantly account for SWB. Our classification analysis shows the potential for detecting SWB changes with reasonable performance, as well as for improving a model to be more tailored to individuals.