Self-Tracking to Do Less: An Autoethnography of Long COVID That Informs the Design of Pacing Technologies

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Self-Tracking to Do Less : An Autoethnography of Long COVID That Informs the Design of Pacing Technologies. / Homewood, Sarah.

CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, Inc., 2023. p. 1-14 656.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Homewood, S 2023, Self-Tracking to Do Less: An Autoethnography of Long COVID That Informs the Design of Pacing Technologies. in CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems., 656, Association for Computing Machinery, Inc., pp. 1-14, 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023, Hamburg, Germany, 23/04/2023. https://doi.org/10.1145/3544548.3581505

APA

Homewood, S. (2023). Self-Tracking to Do Less: An Autoethnography of Long COVID That Informs the Design of Pacing Technologies. In CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-14). [656] Association for Computing Machinery, Inc.. https://doi.org/10.1145/3544548.3581505

Vancouver

Homewood S. Self-Tracking to Do Less: An Autoethnography of Long COVID That Informs the Design of Pacing Technologies. In CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, Inc. 2023. p. 1-14. 656 https://doi.org/10.1145/3544548.3581505

Author

Homewood, Sarah. / Self-Tracking to Do Less : An Autoethnography of Long COVID That Informs the Design of Pacing Technologies. CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, Inc., 2023. pp. 1-14

Bibtex

@inproceedings{651d66d44c2a4c3caaaa3659256a4986,
title = "Self-Tracking to Do Less: An Autoethnography of Long COVID That Informs the Design of Pacing Technologies",
abstract = "Long COVID is a post-viral illness where symptoms are still experienced more than three months after an infection of COVID 19. In line with a recent shift within HCI and research on self-tracking towards first-person methodologies, I present the results of an 18-month long autoethnographic study of using a Fitbit fitness tracker whilst having long COVID. In contrast to its designed intentions, I misused my Fitbit to do less in order to pace and manage my illness. My autoethnography illustrates three modes of using fitness tracking technologies to do less and points to the new design space of technologies for reducing, rather than increasing, activity in order to manage chronic illnesses where over-exertion would lead to a worsening of symptoms. I propose that these {"}pacing technologies{"}should acknowledge the interoceptive and fluctuating nature of the user's body and support user's decision-making when managing long-term illness and maintaining quality of life.",
keywords = "autoethnography, COVID 19, Fitbit, fitness tracking technologies, Heart-rate monitor, Long COVID, pacing technologies, Phenomenology, Post COVID-19 syndrome, Self-Tracking, Step counting",
author = "Sarah Homewood",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 ; Conference date: 23-04-2023 Through 28-04-2023",
year = "2023",
doi = "10.1145/3544548.3581505",
language = "English",
pages = "1--14",
booktitle = "CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems",
publisher = "Association for Computing Machinery, Inc.",

}

RIS

TY - GEN

T1 - Self-Tracking to Do Less

T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023

AU - Homewood, Sarah

N1 - Publisher Copyright: © 2023 Owner/Author.

PY - 2023

Y1 - 2023

N2 - Long COVID is a post-viral illness where symptoms are still experienced more than three months after an infection of COVID 19. In line with a recent shift within HCI and research on self-tracking towards first-person methodologies, I present the results of an 18-month long autoethnographic study of using a Fitbit fitness tracker whilst having long COVID. In contrast to its designed intentions, I misused my Fitbit to do less in order to pace and manage my illness. My autoethnography illustrates three modes of using fitness tracking technologies to do less and points to the new design space of technologies for reducing, rather than increasing, activity in order to manage chronic illnesses where over-exertion would lead to a worsening of symptoms. I propose that these "pacing technologies"should acknowledge the interoceptive and fluctuating nature of the user's body and support user's decision-making when managing long-term illness and maintaining quality of life.

AB - Long COVID is a post-viral illness where symptoms are still experienced more than three months after an infection of COVID 19. In line with a recent shift within HCI and research on self-tracking towards first-person methodologies, I present the results of an 18-month long autoethnographic study of using a Fitbit fitness tracker whilst having long COVID. In contrast to its designed intentions, I misused my Fitbit to do less in order to pace and manage my illness. My autoethnography illustrates three modes of using fitness tracking technologies to do less and points to the new design space of technologies for reducing, rather than increasing, activity in order to manage chronic illnesses where over-exertion would lead to a worsening of symptoms. I propose that these "pacing technologies"should acknowledge the interoceptive and fluctuating nature of the user's body and support user's decision-making when managing long-term illness and maintaining quality of life.

KW - autoethnography

KW - COVID 19

KW - Fitbit

KW - fitness tracking technologies

KW - Heart-rate monitor

KW - Long COVID

KW - pacing technologies

KW - Phenomenology

KW - Post COVID-19 syndrome

KW - Self-Tracking

KW - Step counting

UR - http://www.scopus.com/inward/record.url?scp=85160017930&partnerID=8YFLogxK

U2 - 10.1145/3544548.3581505

DO - 10.1145/3544548.3581505

M3 - Article in proceedings

AN - SCOPUS:85160017930

SP - 1

EP - 14

BT - CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery, Inc.

Y2 - 23 April 2023 through 28 April 2023

ER -

ID: 355094517