A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of...

Area of Science:

• Neurology
• Digital Health
• Rehabilitation

Background:

• Falls are a significant issue for individuals with multiple sclerosis (MS), leading to injuries, fear of falling, and reduced independence.
• Current clinical practice often involves underreporting of falls by patients and undertreatment by clinicians, necessitating improved data integration and intervention strategies.
• Patient-generated data, when combined with clinical information, holds potential for predicting falls and enabling timely interventions, including specialized physical therapy.

Purpose of the Study:

• To describe the design and development process of the Multiple Sclerosis Falls InsightTrack (MS-FIT) application.
• To outline the clinical and technological features of MS-FIT, a closed-loop system aimed at enhancing falls reporting, evaluation, and prevention in MS patients.
• To ensure the app's features align with user needs through human-centered design principles.

Main Methods:

• Employed a double diamond, human-centered design process involving patients and clinicians.
• Utilized patient and clinician interviews, informed by the Capability, Opportunity, and Motivation, and Behavior (COM-B) framework, to guide design.
• Engaged stakeholders from geriatrics, orthopedics, and Parkinson's disease to ensure generalizability and iterated designs based on feedback.

Main Results:

• A user-friendly, biweekly survey (REDCap) for patients to report falls, with optional contextual details.
• A clinician dashboard with visualizations of fall data, an evidence-based action checklist, and local MS resources.
• High scores for usability, likability, usefulness, and perceived effectiveness of the MS-FIT tool, with in-basket messaging for severe falls.

Conclusions:

• MS-FIT is the first falls app utilizing human-centered design to prioritize behavior change, delivering actionable data to clinicians at the point of care.
• The app streamlines data delivery into the electronic health record, minimizing clinician workload and enhancing care quality.
• MS-FIT integrates patient-generated, clinical, and community data to empower self-care and address falls in MS, with potential applicability to other neurological conditions.