What Does All This Data Mean for My Future Mood? Actionable Analytics and Targeted Reflection for Emotional Well-Being. (2nd November 2017)

• Record Type: Journal Article
• Authors: Hollis, Victoria; Konrad, Artie; Springer, Aaron; Antoun, Matthew; Antoun, Christopher; Martin, Rob; Whittaker, Steve
• Is Part Of: Human-computer interaction. Volume 32:Isuse 5/6(2017)
• Page Start: 208
• Page End: 267
• Publication Date: 2017-11-02
• Subjects: System design -- Periodicals; Computers -- Psychological aspects -- Periodicals; Human-machine systems -- Periodicals; 004.019
• DOI: 10.1080/07370024.2016.1277724
• Languages: English
• ISSNs: 0737-0024

Abstract

Abstract : We explore the Examined Life, informing the design of reflective systems to promote emotional well-being, a critical health issue. People now have increasingly rich, digital records of highly personal data about what they said, did, and felt in the past. But social science research shows that people have difficulty in tracking and regulating their emotions. New reflective technologies that promote constructive analysis of rich personal data potentially offer transformative ways that individuals might better understand themselves and improve well-being. However, there are important system design challenges in supporting effective reflection about personal data. We explore fidelity in recording and representing past personal mood data, and forecasting future actions, feelings, and thoughts. Much prior personal informatics work has been dedicated to past-centric tools for recording and capture. In contrast, forecasting examines how we might use such past data to inform and motivate our future selves, providing recommendations about remedial actions to improve future well-being. Fidelity addresses both how and what reflective systems should show people about their pasts, in particular whether we should filter negative past experiences. To inform reflective system design, we examine forecasting and fidelity in controlled field trial interventions that explore two novel system designs for presenting and reflecting on mood data. We detail findings from 165 participants, 4, 693 participant logfiles, 65 surveys, and 15 user interviews. Our novel forecasting system, EmotiCal, uses past mood data to model and visualize future user moods with the goal of encouraging participants to adopt remedial new behaviors to regulate negative moods before they occur. Such forecasting both improved mood and subsequent emotional self-awareness compared with controls who simply monitored their past. Consistent with system goals, interview responses also indicated that participants generated important insights into behaviors that affect their moods. Our second intervention examined filtering; it assessed the impact on well-being of recording and revisiting past experiences containing negative emotions. We compared participants who were encouraged to record and reflect on positive versus negative experiences. Long-term measures of happiness and ruminative behaviors improved by recording and reflecting on positive but not negative experiences, although this depended on the intensity of the negative experience. We discuss general design and theory implications for future systems that support monitoring, reflection, and forecasting to facilitate productive examination of our emotional lives.