Ayobi, A;
(2018)
Informing the Design of Personal Informatics Technologies for Unpredictable Chronic Conditions.
In:
Proceedings of the 2018 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '18).
ACM
(In press).
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Abstract
Personal informatics technologies, such as consumer fitness tracking devices, have an enormous potential to transform the self-management of chronic conditions. However, it is unclear how people living with relapsing and progressive illnesses experience personal informatics tools in everyday life: what values and challenges are associated with their use? This research informs the design of future health tracking technologies through an ethnographic design study of the use and experience of personal informatics tools in multiple sclerosis (MS) self-management. Initial findings suggest that future health tracking technologies should acknowledge people’s emotional wellbeing and foster flexible and mindful self-tracking, rather than focusing only on tracking primary d
| Type: | Proceedings paper |
|---|---|
| Title: | Informing the Design of Personal Informatics Technologies for Unpredictable Chronic Conditions |
| Event: | Proceedings of the 2018 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '18) |
| Location: | Montréal, Canada |
| Dates: | 21 April 2018 - 26 April 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3170427.3173025 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Personal informatics; self-tracking; self-monitoring; chronic conditions; multiple sclerosis. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10044080 |
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