Cited by 766Innovative Human-Technology InteractionMobile Health and mHealth ApplicationsContext-Aware Activity Recognition Systems

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

Choe, Eun Kyoung, Lee, Nicole B., Lee, Bongshin, Pratt, Wanda, Kientz, Julie A.·2014·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

DOI 10.1145/2556288.2557372

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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.

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Authors: Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A.
Affiliations: University of Washington (US)
Microsoft (United States) (US)
ORCID: Choe, Eun Kyoung
Lee, Bongshin
Pratt, Wanda
Kientz, Julie A.
Venue: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher: ACM
Published: 2014-04-26
Pages: 1143-1152
DOI: 10.1145/2556288.2557372
OpenAlex: W2151586234
Cited by: 766
References: 21
Citations by year: 2026: 43 · 2025: 65 · 2024: 37 · 2023: 49 · 2022: 53 · 2021: 70 · 2020: 59 · 2019: 60 · 2018: 96 · 2017: 79 · 2016: 65 · 2015: 69 · 2014: 20
FWCI: 59.91
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: CHI Conference on Human Factors in Computing Systems (publishedVersion)
Topics: Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords: self-tracking, quantified self, personal data collection, scientific rigor, user practices

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

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