Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Human-Computer Interaction (HCI) researchers have been increasingly interested in investigating self-trackers’ experience with self-tracking tools (STT) to get meaningful insights from their data. However, the literature lacks a coherent, integrated and dedicated source on designing that support sensemaking practices. To address this, we carried out systematic review by synthesizing findings of 91 articles published before 2021 HCI literature. We identified four data modes self-trackers go through (i.e., self-calibration, augmentation, handling, realization). also design implications for practices customized tracking experience, guided sensemaking, collaborative learning self-experimentation). provide research agenda nine directions advancing studies With these contributions, created an analytical information could guide designers understanding, studying,