Advancements in wearable computing provide a wide range of tools for continuous self-monitoring of emotions and behavior. These advances include easier manual recording and exciting new opportunities for passive data collection (e.g., galvanic skin response, step count, sleep). However, the general trend for many of these publically available tools are advances driven by technical improvements to support new hardware capabilities and to generate marketable designs. My research is focused on applying and testing psychological theories to advance well-being system designs.

target-icon The Impact of Emotional Reflection on Behavior Change: For example, one of my early projects showed that simply changing the prompt of a behavior change app to prioritize emotional reflection rather than rich context reporting led to significant improvements in behavior change success for the emotional reflection group.
graph-2-icon Generating Algorithm-Based Insights for Personal Health Information: Another project is looking at how to apply data analytics toward personal mood/behavior information to help users have more actionable insights about how various behaviors affect their mood. A field trial with this app has shown beneficial effects for daily mood reports, compared to a monitoring-only control. Current research is exploring the sensemaking process for analytics on highly personal aspects of one’s mood and behaviors.
stress-2-icon The Promises & Pitfalls of Emotion Analytics: Another section of my research looks at how feedback from technology can influence the emotional appraisal of events. While hardware to passively detect stress is advancing considerably, few psychologists have evaluated how this new source of context influences the way we appraise stress. Results from our lab already highlight issues with presenting real-time feedback on emotional states and how such feedback can influence the emotion construal process.

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