Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications

Eun Kyoung Choe, Saeed Abdullah, Mashfiqui Rabbi, Edison Thomaz, Daniel A. Epstein, Felicia Cordeiro, Matthew Kay, Gregory D. Abowd, Tanzeem Choudhury, James Fogarty, Bongshin Lee, Mark Matthews, Julie A. Kientz

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


The authors present an approach for designing self-monitoring technology called 'semi-automated tracking,' which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.

Original languageEnglish (US)
Article number7807194
Pages (from-to)74-84
Number of pages11
JournalIEEE Pervasive Computing
Issue number1
StatePublished - Jan 1 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computational Theory and Mathematics


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