Abstract

Rationale: Patients with rheumatoid arthritis (RA) experience a range of symptoms including joint pain and inflammation, stiffness, fatigue, anxiety, and low mood. Similar to patients with other long-term conditions, they may have periods of time when their disease is under control, and times when their condition is less stable, requiring treatment adjustments. The REMORA2 feasibility study explored the implementation of an integrated symptom tracking system using a smartphone application (app), enabling patients to track day-to-day symptoms. The data was available in the electronic health record to be viewed at subsequent consultations.

Aims and objectives: This paper explores patients’ comments on living with RA, and how patient-reported symptom data supports informed interactions as patient and clinician work together to co-produce meaning from the data.

Method: Individual semi-structured interviews were conducted with 21 patients and 7 clinicians, supplemented by non-participant observations of 5 clinical appointments. Thematic analysis was used to analyse data from the interviews, with an ethnographic approach used to analyse the observational data.

Results: Both clinicians and patients reported the benefits of reviewing the data in clinic together. This helped informed decisions about pain management, and identified patients who might otherwise have dismissed symptoms such as pain, because of their natural inclination to be stoical.

Conclusion: Improved insights on the care of RA were generated as patients and clinicians discuss symptom tracking data. This can assist the patient-clinician dyad in the process of two-way learning and shared decision-making on the management of a long-term condition.

Original languageEnglish
Number of pages8
JournalJournal of Evaluation in Clinical Practice
Early online date13 Oct 2024
DOIs
Publication statusE-pub ahead of print - 13 Oct 2024

Keywords

  • Arthritis
  • Rheumatoid
  • Symptom Assessment
  • Decision Making
  • Shared
  • Mobile Applications
  • Patient Generated Health Data
  • Qualitative Research

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