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A life of data: Characteristics and challenges of very long term self-tracking for health and wellness

Meyer, Jochen, Kay, Judy, Epstein, Daniel A., Eslambolchilar, Parisa ORCID: and Tang, Lie Ming 2020. A life of data: Characteristics and challenges of very long term self-tracking for health and wellness. ACM Transactions on Computing for Healthcare 1 (2) , -. 10.1145/3373719

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
ISSN: 2637-8051
Date of First Compliant Deposit: 18 March 2020
Date of Acceptance: 1 November 2019
Last Modified: 07 Nov 2023 06:03

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