Personal informatics systems are tools that capture, aggregate, and analyze data from distinct facets of their users’ lives. This article adopts a mixed-methods approach to understand the problem of information overload in personal informatics systems. We report findings from a 3-month study in which 20 participants collected multifaceted personal tracking data and used a system called Exist to reveal statistical correlations within their data. We explore the challenges that participants faced in reviewing the information presented by Exist, and we identify characteristics that exemplify “interesting” correlations. Based on these findings, we develop automated filtering mechanisms that aim to prevent information overload and support users in extracting interesting insights. Our approach deals with information overload by reducing the number of correlations shown to users by about 55% on average and increases the percentage of displayed correlations rated as interesting to about 81%, representing a 34 percentage point improvement over filters that only consider statistical significance at p <.05. We demonstrate how this curation can be achieved using objective data harvested by the system, including the use of Google Trends data as a proxy for subjective user interest.
ASJC Scopus subject areas
- Applied Psychology
- Human-Computer Interaction
- Department of Computer Science - Senior Lecturer
- UKRI CDT in Accountable, Responsible and Transparent AI
- EPSRC Centre for Doctoral Training in Cyber Security
Person: Research & Teaching