Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications - Choe, Eun Kyoung; Abdullah, Saeed; Rabbi, Mashfiqui; Thomaz, Edison; Epstein, Daniel A.; Kay, Matthew; Cordeiro, Felicia; Abowd, Gregory D.; Choudhury, Tanzeem; Fogarty, James; Lee, Bongshin; Matthews, Mark; et al. (IEEE Pervasive Computing, 2017)
We present an approach for designing self-monitoring technology called semi-automated tracking, which combines both manual and automated data collection methods. Through this approach, we aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve the goals of self-monitoring. We first specify three design considerations for semi-automated tracking—(1) data capture feasibility; (2) purpose of self-monitoring; and (3) motivation level. We then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies we have developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations