APPRISE: A personalised, AI-driven dynamic appointment prioritisation system using data from wearables for patients with inflammatory arthritis: impact on disease activity and other outcomes


Patients with rheumatoid arthritis (RA), axial spondylarthritis (axSpA) or psoriatic arthritis (PsA) need to be seen rapidly in case of flare. Physical activity data (steps collected through a wearable) can be analysed using artificial intelligence (AI) to detect disease flares.

Facts and Figures

Project Lead
Prof. Laure Gossec
Sorbonne Université
FOREUM research grant: € 590.000

Meet the Team

Project Lead

Prof. Laure Gossec
Sorbonne Université
MD Uta Kiltz
Prof. Astrid van Tubergen
Maastricht University
MD, PhD Paul Studenic
Medical University of Vienna
Herve Servy
Sanoïa e-health Services
Marieke Voshaar
Patient Research Partner
Dieter Wiek
Patient Research Partner
Sonia Trope
Patient Research Partner


To assess the impact of a personalised prioritisation system driven by physical activity data collected passively by wearables from patients with RA, axSpA or PsA on timeliness of appointments, patient outcomes (disease activity scores, health-related quality of life and treatment changes) and use of healthcare resources over a 1-year period.


Full study completion for APPRISE within a timeframe of 36 months:

  • Study set-up 3 months;
  • Ethical approvals 3-6 months;
  • Test-phase 3-6 months,
  • Patient inclusion and follow-up, 18 months,
  • Analyses and dissemination of results 3-6 months.

Patient Voice

As part of the steering committee, three PRPs participate in all phases of this study from the beginning: in the development of the protocol, the assessment of feasibility and acceptability, the wording of the patient CRF, the co-writing of the patient’ information documents, the planning and interpretation of the analyses, the writing of and co-authorship on publications and the dissemination in lay language and public channels.

Project Map