Abstract
Self-tracking can improve people's awareness of their unhealthy behaviors to provide insights towards behavior change. Prior work has explored how self-trackers reflect on their logged data, but it remains unclear how much they learn from the tracking feedback, and which information is more useful. Indeed, the feedback can still be overwhelming, and making it concise can improve learning by increasing focus and reducing interpretation burden. We conducted a field study of mobile food logging with two feedback modes (manual journaling and automatic annotation of food images) and identified learning differences regarding nutrition, assessment, behavioral, and contextual information. We propose a Self-Tracking Feedback Saliency Framework to define when to provide feedback, on which specific information, why those details, and how to present them (as manual inquiry or automatic feedback). We propose SalienTrack to implement these requirements. Using the data collected from the user study, we trained a machine learning model to predict whether a user would learn from each tracked event. Using explainable AI (XAI) techniques, we identified the most salient features per instance and why they lead to positive learning outcomes. We discuss implications for learnability in self-tracking, and how adding model explainability expands opportunities for improving feedback experience.
Abstract (translated)
URL
https://arxiv.org/abs/2109.10231
