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

by | Apr 1, 2014 | Research Publications

Author(s):

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

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.

Documentation:

https://doi.org/10.1145/2556288.2557372

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