research-article

Understanding Self-Tracked Data from Bounded Situational Contexts

Authors:

Ashley Marie Walker,

Laurie Wakschlag,

Nabil Alshurafa,

Madhu ReddyAuthors Info & Claims

DIS '22: Proceedings of the 2022 ACM Designing Interactive Systems Conference

Pages 1684 - 1697

https://doi.org/10.1145/3532106.3533498

Published: 13 June 2022 Publication History

Abstract

As smartphone and wearable tracking devices have grown in popularity, more individuals have begun collecting their own health data. While these data are often perceived as a persistent record of health and used to inform future behaviors, it is inevitable that some data are captured during a period of disruption or non-routine circumstances. If not appropriately contextualized, visualizations of these data can lead to missed opportunities in self-reflection, or worse, misinterpretation. To better understand how self-tracked data captured during non-routine circumstances are reflected upon after the disruption has ended, we interviewed women about how they might reflect on data from a recent pregnancy. We propose the concept of bounded situational context (BSC) to encapsulate how individuals define the boundaries of disruption within their data based on external and internal contexts. We discuss how self-tracking tools can be designed to align data visualizations with individuals’ perceived boundaries to aid in data interpretation.

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cover image ACM Conferences

DIS '22: Proceedings of the 2022 ACM Designing Interactive Systems Conference

June 2022

1947 pages

ISBN:9781450393584

DOI:10.1145/3532106

Editors:
Florian `Floyd' Mueller
Monash University, Melbourne, Australia
,
Stefan Greuter
Deakin University, Melbourne, Australia
,
Rohit Ashok Khot
RMIT University, Melbourne, Australia
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Penny Sweetser
The Australian National University, Canberra, Australia
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Marianna Obrist
University College London, UK

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Published: 13 June 2022

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Cited By

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https://dl.acm.org/doi/10.1145/3643834.3661615
Gómez Ortega ABourgeois JKortuem G(2024)Sensitive Data Donation: A Feminist Reframing of Data Practices for Intimate Research ContextsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661524(2420-2434)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661524
Lu XPowell JAgapie EChen YEpstein D(2024)Examining the Social Aspects of Pregnancy Tracking ApplicationsProceedings of the ACM on Human-Computer Interaction10.1145/36373288:CSCW1(1-30)Online publication date: 26-Apr-2024
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Xu TJost EMesser LCook PForlenza GSankaranarayanan SFiesler CVoida S(2024)“Obviously, Nothing's Gonna Happen in Five Minutes”: How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes ManagementProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642612(1-16)Online publication date: 11-May-2024
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