JMIR Publications

ABSTRACT

Background:

Falls are common in multiple sclerosis (MS), causing injuries, fear of falling and loss of independence. Although targeted interventions, i.e., physical therapy (PT) can help, patients under-report and clinicians under-treat this issue. Patient-Generated Data (PGD), combined with clinical data, can support prediction of falls and lead to timely intervention (including referral to specialized PT). Yet, to be actionable, such data must be efficiently delivered to clinicians, with care customized to the patient’s specific context.

Objective:

To describe the clinical and technological features of MS-FIT, a closed-loop application designed to support streamlined falls reporting, timely falls evaluation, and comprehensive and sustained fall prevention efforts.

Methods:

Stakeholders were engaged in a “double diamond” process of human-centered design (HCD). Patient and clinician interviews adopted the Capability, Opportunity, and Motivation approach to Behavioral change (COM-B) and Michie’s Behavioral Change Wheel to align technological features with users’ needs. To support generalizability, patients and experts from other disciplines characterized by falls (geriatrics, orthopedics, Parkinson’s Disease) were also engaged. Final mocks were tested during routine clinical visits.

Results:

A diverse sample of 30 patients and 14 clinicians provided at least one round of feedback. To support falls reporting, patients favored a simple biweekly survey built using REDCap to support “bring your own device” accessibility – with optional additional context (e.g., severity and location of each fall). To support evaluation and prevention of falls, clinicians favored a clinical dashboard featuring several key visualization widgets: (1) a longitudinal falls display coded by time of data, severity, and context, (2) a comprehensive, multidisciplinary, and evidence-based checklist of actions intended to evaluate and prevent falls and (3) MS resources local to a patient’s community. Further, in-basket messaging alerts clinicians of severe falls. The tool scored highly for usability, likability, usefulness, and perceived effectiveness (based on the Health IT Usability Model scoring).

Conclusions:

To our knowledge, this is the first falls app designed using HCD to prioritize behavioral change and while accessible at home for patients, to deliver actionable data to clinicians at the point of care. To test feasibility and effectiveness, an NIH-funded clinical trial is ongoing (NCT05837949).