Multiple Sclerosis Falls Insight Track

NCT ID: NCT05837949

Last Updated: 2025-10-23

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 100 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-04-12
• Study Completion Date: 2026-01-01

Brief 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.

Detailed 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.

Conditions

Multiple Sclerosis

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

MS FIT: Falls Insight Track

Participants in this arm will receive 12 months use of MS FIT mobile tool intervention

Group Type: EXPERIMENTAL

MS FIT: Falls Insight Track

Intervention Type: BEHAVIORAL

Participants will respond to a set of surveys every two weeks to increase communication on falls with their clinician.

Interventions

MS FIT: Falls Insight Track

Participants will respond to a set of surveys every two weeks to increase communication on falls with their clinician.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

National Library of Medicine (NLM)

NIH

Sponsor Role: collaborator

University of California, San Francisco

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Riley Bove, MD

Role: PRINCIPAL_INVESTIGATOR

University of California, San Francisco

Locations

University of California, San Francisco

San Francisco, California, United States

Countries

United States

References

Block VJ, Koshal K, Wijangco J, Miller N, Sara N, Henderson K, Reihm J, Gopal A, Mohan SD, Gelfand JM, Guo CY, Oommen L, Nylander A, Rowson JA, Brown E, Sanders S, Rankin K, Lyles CR, Sim I, Bove R. A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack. JMIR Hum Factors. 2024 Jan 11;11:e49331. doi: 10.2196/49331.

Reference Type: DERIVED

PMID: 38206662 (View on PubMed)

Other Identifiers

5R01LM013396-02

Identifier Type: NIH

Identifier Source: secondary_id

View Link

22-36680

Identifier Type: -

Identifier Source: org_study_id