Facilitating Individuals' Sensemaking about Sedentary Behavior via Contextualized Data

Abstract

The sedentary lifestyle increases individuals' risks of developing chronic diseases. To support individuals to be more physically active, we propose a mobile system, MotionShift, that presents users with step count data alongside contextual information (e.g., location, weather, calendar events, etc.) and self-reported records. By implementing and deploying this system, we aim to understand how contextual information impacts individuals' sense-making on sensor-captured data and how individuals leverage contextualized data to identify and reduce sedentary activities. The findings will advance the design of context-aware personal informatics systems, empowering users to derive actionable insights from sensor data while minimizing interpretation biases, ultimately promoting opportunities to be more physically active.