Supporting Clinical Uses of Patient Self-Tracking Data in the Context of Rheumatic Diseases

  • William Hue

Student thesis: Doctoral ThesisPhD


Continued growth in the use of consumer self-tracking technology, such as health apps on smartphones and wearable activity trackers has attracted attention from healthcare and computer science researchers over recent years. Many have explored its use in medi- cal research and patients’ self-management of chronic conditions while others attempted to leverage its value for clinical decision-making and to improve patient-provider col- laboration. Yet challenges such as workflow constraints, sensemaking difficulties and data quality concerns have hindered the effective inclusion and use of self-tracking data during clinical encounters.

Our research addresses this by investigating the many opportunities and challenges associated with the clinical use of self-tracking data in the context of axial spondy- loarthritis - a typical rheumatic chronic condition. We carry out online and field stud- ies using a combination of qualitative research methods to investigate how self-tracking data could fit into existing clinical workflow and benefit routine check-up appointments. Our approach also covers both patients’ and healthcare providers’ perspectives on a variety of subjects including determinants of users’ self-tracking adherence as well as the expectations and concerns regarding collaborative use of data. We provide struc- tural understanding of the healthcare context in which self-tracking data is shared and discussed which helps us identify practical applications of said data in real-life clinical scenarios.

Our findings show that the use and discussion of self-tracking data may improve exist- ing activities associated with the clinical encounters between patients and providers by supporting the process of evidence gathering and decision-making regarding treatment and action plans, despite some challenges relating to tracking adherence, data-sharing and agenda setting as identified in this research. We provide practical design recom- mendations rooted in the field of Human-Computer Interaction, such as graphical user interfaces and data visualisation tools to overcome these issues, thus setting the course for future research and the design of self-tracking technologies.
Date of Award29 Mar 2023
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorChristof Lutteroth (Supervisor) & Stephen Payne (Supervisor)


  • Self-Tracking
  • Human-Computer Interaction
  • Personal Informatics
  • Rheumatic Diseases
  • Axial Spondyloarthritis

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