Multiple Sclerosis Falls Insight Track

Multiple Sclerosis Clinical Trial

— MS FIT  

Official title:

Multiple Sclerosis Falls Insight Track: A Personal Health Library to Reduce Falls in Patients With Multiple Sclerosis

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05837949
• Other study ID #: 22-36680
• Secondary ID: 5R01LM013396-02
• Status: Recruiting
• Phase: N/A
• Start date: April 12, 2023
• Est. completion date: April 12, 2026

Study information

• Verified date: June 2024
• Source: University of California, San Francisco
• Contact: Riley Bove, MD
• Phone: 415.595.2795
• Email: riley.bove[@]ucsf.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The purpose of this study is to develop an application: MS Falls Insight Track (MS FIT) which allows patients to log their falls and near falls, view their MS relevant data and responses to the clinic intake survey as well as communicate with their care team about falls and receive educational material on falls prevention.

Description:

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 wearable tools and patient-reported outcomes (PROs) and community-level data (sociodemographic data from University of California, San Francisco (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 be accessible, extensible and scalable. The investigators 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), the investigators 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 likability. In Aim 2 (evaluate feasibility), investigators will deploy MS Falls Insight Track in 100 diverse adults with MS who are at risk for falls. Participants will wear a Fitbit. The tool will be used by patients in their homes and by clinicians during clinical encounters. The investigators will use an implementation science approach. Our key outcomes will be study retention, tool uptake and sustained use. The investigators will explore impact on fall risk. In Aim 3 (test generalizability) investigators 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 Insight Track 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: April 12, 2026
• Est. primary completion date: April 12, 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Diagnosis of MS (relapsing or progressive) by 2017 McDonald Criteria18
• Ages 18 and above
• Any MS therapy, or no treatment
• California resident to enable clinical telemedicine visits if warranted during the study visit
• EDSS 2.0-8.0 (moderate to severe impairment, 7= wheelchair but independent transfers)
• Fall risk, based on MSWS-12 score and previous report of a fall (Hopkins grade =1)
• Technological criteria: availability of Wi-Fi in the home or workspace for connectivity.

Exclusion Criteria:

• Cognitive dexterity or visual impairment that, in the opinion of the study neurologist (RB), would put the participant at risk or limit their ability to comply with the study protocol
• Inability to provide informed consent

Related Conditions & MeSH terms

• Multiple Sclerosis
• Sclerosis

Intervention

Behavioral:
• MS FIT: Falls Insight Track
Participants will respond to a set of surveys every two weeks to increase communication on falls with their clinician.

Locations

Country	Name	City	State
United States	University of California, San Francisco	San Francisco	California

Sponsors (3)

Lead Sponsor	Collaborator
University of California, San Francisco	National Institutes of Health (NIH), National Library of Medicine (NLM)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Percentage of patients initially use the tool (Adoption)	This will be measured by calculating the percentage of patients who use the tool during the initial month of the study, and by the percentage of patient-clinical dyads who use the tool during the clinical visit	6 months
Primary	Percentage of patient-clinician encounters initially use the tool (Adoption)	This will be measured by calculating the percentage of patient-clinical dyads who use the tool during the clinical visit.	6 months
Primary	Percentage of patients who continue to use the tool (Engagement)	This will calculate the percentage of patients who continued to use the patient-facing tool at least quarterly	12 months
Primary	Percentage of patient-clinician encounters use the tool during the 12-month visit (Engagement)	This will be calculated by the percentage of the clinician-patient dyads in Arm 1 who use the in-visit dashboard at the 12-month clinical visit.	12 months
Primary	Percentage of patients who respond to fall prompts (Adherence)	Adherence will be measured by the percentage of falls reporting prompts adhered to per participant, as well as percentage of participants adhering to >75% falls prompts	12 months