skip to main content
research-article

A Life of Data: Characteristics and Challenges of Very Long Term Self-Tracking for Health and Wellness

Published:11 March 2020Publication History
Skip Abstract Section

Abstract

As self-tracking has evolved from a niche movement to a mass-market phenomenon, it has become possible for people to track a broad range of activities and vital parameters over years, even decades. The associated opportunities, as well as the challenges, have had very little research attention so far. With the phenomenon of long-term tracking becoming widespread and important, we have identified its key characteristics by drawing on work from UbiComp, HCI, and health informatics. We identify important differences between long- and short-term tracking, and discuss consequences for the tracking process. Going beyond previous models for short-term tracking, we now present a model for long-term tracking, integrating its distinctive characteristics in purposeful and incidental tracking. Finally, we present major topics for future research.

References

  1. Young-Ho Kim, Jae Ho Jeon, Bongshin Lee, Eun Kyoung Choe, and Jinwook Seo. 2017. OmniTrack: A flexible self-tracking approach leveraging semi-automated tracking. Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies 1, 3 (Sept. 2017), Article 67, 28 pages. DOI:https://doi.org/10.1145/3130930Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ian Li, Anind Dey, and Jodi Forlizzi. 2010. A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10). ACM, New York, NY, 557. DOI:https://doi.org/10.1145/1753326.1753409Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Jochen Meyer, Elke Beck, Merlin Wasmann, and Susanne Boll. 2017. Making sense in the long run: Long-term health monitoring in real lives. In Proceedings of the 2017 IEEE International Conference on Healthcare Informatics (ICHI’17). 285--294. DOI:https://doi.org/10.1109/ICHI.2017.11Google ScholarGoogle Scholar
  4. Jochen Meyer, Daniel Epstein, Parisa Eslambolchilar, Judy Kay, and Lie Ming Tang. 2018. A short workshop on next steps towards long term self tracking. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA’18). ACM, New York, NY, Article W05, 8 pages. DOI:https://doi.org/10.1145/3170427.3170605Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Tamar Sharon. 2017. Self-tracking for health and the quantified self: Re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philosophy 8 Technology 30, 1 (2017), 93--121. DOI:https://doi.org/10.1007/s13347-016-0215-5Google ScholarGoogle Scholar
  6. Grace Shin, Yuanyuan Feng, Mohammad Hossein Jarrahi, and Nicci Gafinowitz. 2018. Beyond novelty effect: A mixed-methods exploration into the motivation for long-term activity tracker use. JAMIA Open 2, 1 (2018), 62--72. DOI:https://doi.org/10.1093/jamiaopen/ooy048Google ScholarGoogle ScholarCross RefCross Ref
  7. Statista. 2019. Wearables Unit Shipments Worldwide by Vendor from 2014 to 2018. Retrieved February 15, 2020 from https://www.statista.com/statistics/515634/wearables-shipments-worldwide-by-vendor/.Google ScholarGoogle Scholar
  8. Lie Ming Tang, Jochen Meyer, Daniel A. Epstein, Kevin Bragg, Lina Engelen, Adrian Bauman, and Judy Kay. 2018. Defining adherence: Making sense of physical activity tracker data. Proceedings of the ACM on Interactive, Mobile, and Ubiquitous Technologies 2, 1 (March 2018), Article 37, 22 pages. DOI:https://doi.org/10.1145/3191769Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A Life of Data: Characteristics and Challenges of Very Long Term Self-Tracking for Health and Wellness

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Computing for Healthcare
          ACM Transactions on Computing for Healthcare  Volume 1, Issue 2
          April 2020
          90 pages
          EISSN:2637-8051
          DOI:10.1145/3387924
          Issue’s Table of Contents

          Copyright © 2020 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 11 March 2020
          • Revised: 1 November 2019
          • Accepted: 1 November 2019
          • Received: 1 May 2019
          Published in health Volume 1, Issue 2

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format