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Understanding quantified-selfers' practices in collecting and exploring personal data
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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“Following methodological recommendations concerning compliance of Mechanical Turk participants [18], we eliminated 26 respondents based on their responses to open ended questions, leaving us with a sample of 74 users.”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [18]
In the citing paper
“Emotional analytics is perceived as a key application by many users [16,17,18,39,90].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [18]
In the citing paper
“Research grounded in lived experiences of this extreme user group [5, 8] remains notably absent.”
From 'AI-Induced Delusional Spirals': Understanding Lived Experiences During Maladaptive Human-Chatbot Interactions (2026) · cites this paper as [8]
In the citing paper
“Recent studies suggest that individuals are willing to engage in this effortful process when they expect that the recorded information will serve one of the functions of autobiographical memory described above (Stone et al., 2022; see also Broekhuijsen et al., 2017;Choe et al., 2014;Elsden, Durrant, et al., 2016;Finley et al., 2018;Soares & Storm, 2022).”
From Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model (2024) · cites this paper as Choe et al., 2014
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Metadata
- Authors
- Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A.
- Affiliations
- University of Washington (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2014-04-26
- Pages
- 1143-1152
- 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
- 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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