Integrating Technology into Self-Management Ecosystems: Young Adults with Type 1 Diabetes in the UK using Smartwatches

Sam Gordon James, Miranda Elaine Glynis Armstrong, Zahraa S. Abdallah, Harry Emerson, Aisling Ann O'Kane

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)

Abstract

Self-managing chronic conditions typically involves a diverse network of individuals and devices, forming a self-management ecosystem. For this ecosystem to be effective, components need to work together cohesively. The rapid advancement of technology means new devices need to be repeatedly integrated into existing self-management ecosystems. To examine this process, we used the case study of young adults with type 1 diabetes (T1D) in the UK who were given a smartwatch. Over six months, interviews and focus groups were performed to explore their smartwatch use alongside T1D management. Thematic analysis highlighted that smartwatches have potential as a display, interface and data source in T1D management, which is of particular importance as artificial intelligence plays a growing role in self-management ecosystems. It also emphasised the need for customisation, flexibility and adaptability, and automation in the design of technology to promote integration into existing self-management ecosystems for both T1D and other chronic conditions.

Original languageEnglish
Title of host publicationCHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400713941
DOIs
Publication statusPublished - 26 Apr 2025
Event2025 CHI Conference on Human Factors in Computing Systems, CHI 2025 - Yokohama, Japan
Duration: 26 Apr 20251 May 2025

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2025 CHI Conference on Human Factors in Computing Systems, CHI 2025
Country/TerritoryJapan
CityYokohama
Period26/04/251/05/25

Funding

Huge thanks go to the participants for their time and engagement, Breakthrough T1D for their help in participant recruitment and Will for his valuable insights. This work was supported by the Engineering and Physical Sciences Research Council Digital Health and Care Centre for Doctoral Training at the University of Bristol (UKRI Grant No. EP/S023704/1), seed corn funding from Jean Golding Institute for data science and data-intensive research at the University of Bristol, and funding for the AI for Collective Intelligence Research Hub from the UKRI AI Programme and EPSRC (Grant No. EP/Y028392/1). The National Institute for Health and Care Research (NIHR) Bristol Biomedical Research Centre also funds one of the study's co-authors (MEGA). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

FundersFunder number
UK Research and InnovationEP/S023704/1
Engineering and Physical Sciences Research CouncilEP/Y028392/1

Keywords

  • Empirical study that tells us about how people use a system
  • Health - Wellbeing
  • Individuals with Disabilities & Assistive Technologies
  • Wearable Computers

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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