Understanding quantified-selfers' practices in collecting and exploring personal data

Eun Kyoung Choe, Nicole B. Lee, Bongshin Lee, Wanda Pratt, Julie A. Kientz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

320 Scopus citations

Abstract

Researchers have studied how people use self-tracking technologies and discovered a long list of barriers including lack of time and motivation as well as difficulty in data integration and interpretation. Despite the barriers, an increasing number of Quantified-Selfers diligently track many kinds of data about themselves, and some of them share their best practices and mistakes through Meetup talks, blogging, and conferences. In this work, we aim to gain insights from these "extreme users," who have used existing technologies and built their own workarounds to overcome different barriers. We conducted a qualitative and quantitative analysis of 52 video recordings of Quantified Self Meetup talks to understand what they did, how they did it, and what they learned. We highlight several common pitfalls to self-tracking, including tracking too many things, not tracking triggers and context, and insufficient scientific rigor. We identify future research efforts that could help make progress toward addressing these pitfalls. We also discuss how our findings can have broad implications in designing and developing self-tracking technologies.

Original languageEnglish (US)
Title of host publicationCHI 2014
Subtitle of host publicationOne of a CHInd - Conference Proceedings, 32nd Annual ACM Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Pages1143-1152
Number of pages10
ISBN (Print)9781450324731
DOIs
StatePublished - Jan 1 2014
Event32nd Annual ACM Conference on Human Factors in Computing Systems, CHI 2014 - Toronto, ON, Canada
Duration: Apr 26 2014May 1 2014

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Other

Other32nd Annual ACM Conference on Human Factors in Computing Systems, CHI 2014
CountryCanada
CityToronto, ON
Period4/26/145/1/14

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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    Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A. (2014). Understanding quantified-selfers' practices in collecting and exploring personal data. In CHI 2014: One of a CHInd - Conference Proceedings, 32nd Annual ACM Conference on Human Factors in Computing Systems (pp. 1143-1152). (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/2556288.2557372