MS Falls InsightTrack (MS FIT): A personal health library to reduce falls in patients with MS

PROJECT SUMMARY
Background. Falls occur in >50% patients with multiple sclerosis (MS), worsen participation in daily life and
increase healthcare costs. To date there are no established, accessible, tools to evaluate and reduce fall risk.
MS Falls InsightTrack is a live personal health library that combines a patient's falls-relevant clinical indicators
(from the electronic health record, EHR) with patient-generated data (PGD) from commercial wearables and
patient-reported outcomes (PROs) and community-level data (sociodemographics from UCSF Health Atlas
combined with MS-specific resources from the National MS Society). The tool will track falls/near-falls in real-
time and report changes in status that require intervention. It will offer customized action prompts to support fall
reduction through a behaviorally informed approach. It will be accessed in the clinic and in the patient's home.
Technological features. The tool will accessible, extensible and scalable. We will use modern technologies and
industry standards (e.g back-end: Python, flask framework, PostgreSQL; front-end: HTML, CSS, JavaScript and
d3.js). The tool will launch from Epic via SMART on FHIR, and will communicate with patients using MyChart.
Qualifications of team and setting. The UCSF MS Center is a leading clinical research center in the digital
space. Our sub-leads are experts in all aspects of the study (digital technology, human-centered design,
implementation science, health literacy) with a varied and experienced Stakeholder Advisory Group.
Scientific plan. In Aim 1 (design), we will use a Human-Centered Design approach, engaging 20 patients with
MS, clinicians and stakeholders in a series of focus groups, to identify the critical data, devices, visualizations,
resources, workflows and accessibility/digital divide considerations for the tool, and the key interventions likely
to promote the COM-B model of behavioral change to reduce fall risk. Our key outcomes will be perceived
effectiveness, ease of use and likeability. In Aim 2 (evaluate feasibility), we will deploy MS Falls InsightTrack
in 100 diverse adults with MS who are at risk for falls. Participants will wear a Fitbit Ultra. The tool will be used
by patients in their homes and by clinicians during clinical encounters. We will use an implementation science
approach. Our key outcomes will be study retention, tool uptake and sustained use. We will explore impact on
fall risk. In Aim 3 (test generalizability) we will conduct focus groups with patients with other conditions where
falls are common (Orthopedics, Parkinson's Disease, Geriatrics) to understand additional data and design
features required to promote generalizability. Our key outcomes will parallel those in Aim 1.
Innovation and Broader Significance. MS Falls InsightTrack is a unique, comprehensive, accessible personal
health library that can be deployed in larger efficacy trials for falls reduction. Beyond this clinical use case, the
closed-loop approach of delivering PGD to the care system and back to the patient, interpreted and actionable,
using scalable technology, represents a significant innovation that can sequentially expand the number of
wearables, conditions and clinics in which patients and clinical investigators can ask their own questions of PGD.

Grant Number: 5R01LM013396-04
NIH Institute/Center: NIH
Principal Investigator: Riley Bove