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Patient and Clinician Requirements for AI-Supported Fatigue Tracking in Multiple Sclerosis: Protocol for a Multistakeholder Qualitative User-Centered Design Study
ABSTRACT
Background:
Fatigue in multiple sclerosis (MS) fluctuates in daily life but is poorly captured during periodic clinic visits. Wearable data, short check-ins, and artificial intelligence (AI) summaries may help patients and clinicians understand these patterns, provided the tools meet both groups' needs.
Objective:
To identify and compare patient and clinician requirements for an AI-supported MS fatigue tracking app and translate them into design recommendations.
Methods:
This qualitative, user-centered study has two separately approved strands: a target of 6 to 10 adults living with MS (up to 10 permitted) and 6 to 10 clinicians with at least one year of MS care experience. Participants will complete one 45- to 60-minute session involving a questionnaire, semistructured interview, and review of a common mid-fidelity Figma prototype of a planned iOS fatigue tracking app. Patients will perform brief tasks; clinicians will discuss the concepts and their clinical relevance. Interviews and notes will be analyzed within strands using framework-based thematic analysis, followed by a comparison of de-identified themes.
Results:
The University of Manitoba Research Ethics Board approved Strand A (HE2026-0136) on July 27, 2026, and Strand B (HE2026-0145) on August 27, 2026. The mid-fidelity prototype version 1.0 has been completed. Recruitment is planned to begin in November 2026, and data collection is expected to finish in January 2027, with analysis targeted for December 2027 and results submitted in 2028.
Conclusions:
Comparing patient and clinician reactions to a common prototype will identify where requirements concerning tracking burden, AI summary wording, and information sharing align or conflict.
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Copyright
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