Integrating Gait Analysis and AI Into Knee Osteoarthritis Care: Protocol for a 3-Phase Participatory Qualitative...

Area of Science:

• Biomedical Engineering
• Health Informatics
• Orthopedics

Background:

• Knee osteoarthritis (OA) significantly impacts quality of life, with knee arthroplasty as a standard treatment for advanced stages.
• Current surgical decisions for knee OA often lack objective outcome predictors like biomechanical data.
• Despite advances in AI and sensor technology for gait analysis, clinical adoption is limited by usability and system-level barriers.

Purpose of the Study:

• To inform the development of a knee OA clinical decision support (CDS) tool integrating gait analysis and AI.
• To establish a framework for participatory digital health CDS tool implementation.
• To identify and address workflow, stakeholder, and organizational challenges in digital health tool adoption.

Main Methods:

• A 3-phase participatory design process involving in-depth interviews.
• Phase 1: Investigated implementation barriers, facilitators, and current knee OA management workflows.
• Phase 2: Defined user requirements for digital decision support solutions.
• Phase 3: Assessed user acceptance of prototyped solutions, supplemented by field observations and usability testing.

Main Results:

• Study initiation in November 2024 with ethics approval in July 2025.
• As of February 2026, 6 clinicians were interviewed, with 4 transcripts coded for phase 1 analysis.
• Study completion is anticipated by August 2026, with findings expected by January 2027.

Conclusions:

• A stakeholder-driven approach prioritizes technological rigor and practical usability for successful implementation.
• The research offers a scalable framework for integrating digital decision support tools into diverse clinical settings.
• This work advances innovation in knee OA care by addressing human factors in digital health implementation.