A Novel Wearable Digital Biomarker for Detecting Changes in Multiple Sclerosis (MS) Condition

A Novel Wearable Digital Biomarker for Detecting Changes in Multiple Sclerosis (MS) Disease Condition Through Home Monitoring of MS Patients

To measure the effectiveness of a Remote Patient Monitoring solution based on the use of a smart insole wearable device (and associated smart phone app), for monitoring MS patients' condition on a day-to-day basis. The main focus is the objective measurement of gait, given that 75% of people with MS display clinically significant gait impairments. Initial gait lab "gold standard" data indicate that the Artificial Intelligence (AI)-based digital biomarker will prove to be highly effective at detecting changes in the MS patient's condition.

Study Overview

• Status: Recruiting
• Conditions: Multiple Sclerosis

Detailed Description

Multiple sclerosis (MS) is lifelong autoimmune disease that is typically first diagnosed in young adults; MS affects the central nervous system and can result in various impairments, including walking, cognition, dexterity, sleep, vision and bladder control. Notably, impairments to gait are the most common and are identified as the most impactful to a person with MS's (PwMS's) quality of life. Furthermore, ambulation is a key metric used to assess the severity of MS and is the basis for the Expanded Disability Status Scale (EDSS) that represents the global standard for assessing a patient's MS condition. For these reasons, clinicians employ a variety of gait tests to assess the severity and progression of the disease, which require frequent clinical visits and lack objective measurements as compared to what can be measured in a laboratory setting. Current scales do not detect subtle progression that could be indicative of early transformation into Secondary Progressive MS (SPMS) from Relapsing Remitting MS (RRMS) or significant progression in progressive forms of MS.

With advancements in wearable technologies and Artificial Intelligence (AI)-based algorithm development, clinicians can be provided with meaningful laboratory grade gait metrics collected in the patient's home environment to assist their practice. Objective walking information can be provided to clinicians to track the personalized progression of the disease to enable a more targeted treatment plan. A subset of this data is also shared with the patients via their smart phone app to keep them informed and motivated.

Several times per week, smart insoles in the patient's shoes will collect data from the embedded sensors (pressure sensors, accelerometer, gyroscope). The wearable smart insoles are fitted into a pair of the patient's "everyday use" shoes, and are very similar to the type of "comfort" insoles available from a local pharmacy. The smart insole data will be used to create AI-based personalized models that compute each individual's walking signature; this includes tracking of subtle changes over time (improvement, deterioration) as well as identifying specific gait phenotypes.

• Study Type: Observational
• Enrollment (Estimated): 90

Contacts and Locations

• Study Contact: Name: Bruce Ford; Phone Number: 6132940620; Email: bruce.ford@celestrahealth.com

Study Locations

• Canada
  • Ontario
    • Ottawa, Ontario, Canada, K1H8L6
      • Recruiting
      • The Ottawa Hospital
      • Contact: Gauruv Bose, MD; Email: gabose@toh.ca
• United Kingdom
  • London, United Kingdom, E1 IFR
    • Not yet recruiting
    • The Royal London Hospital
    • Contact: Mays Jawad, Dr.; Phone Number: 6574 020 7882 7275; Email: research.governance@qmul.ac.uk
    • Principal Investigator: Sharmilee Gnanapavan, Dr.
• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02115
      • Recruiting
      • Brigham and Women's Hospital
      • Contact: Tanuja Chitnis, MD; Phone Number: 617-525-6573; Email: tchitnis@bwh.harvard.edu
      • Principal Investigator: Tanuja Chitnis, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 60 years (Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

The study population consists of 90 PwMS equally spread across 3 sites / geographies, with approximately 10% of patients in the EDSS 0 to 2.5 range, 45% of patients in EDSS range 3-4.5 and 45% of patients in EDSS range 5 to 6.5 (some patients within this latter group will use walking supports in the form of AFOs, electronic foot braces, etc.).

Description

Inclusion Criteria:

• Participants must have a diagnosis of Multiple Sclerosis (MS) based on the McDonald criteria, within an age range of 18 to 60.
• The participant must have an Extended Disability Status Scale (EDSS) score at screening less than or equal to 6.5, inclusive.
• The participant cohort will include at least 3 participants at each site exhibiting one of the following gait phenotypes: ataxic, hemiplegic and spastic. (Some participants may exhibit more than one phenotype).
• The participant cohort will include at least 3 participants at each site with a progressive form of MS.

Exclusion Criteria:

• Participants that are currently suffering from a musculoskeletal injury (e.g., sprain, fracture, strain, etc.) that limits their ability to use their full range of motion of any joint at the time of recruitment.
• Inability to provide informed consent.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Participant Adherence	To measure participant adherence, with respect to the wearable smart insoles and the associated smart phone app, for the purpose of MS disease monitoring. Adherence is defined as the collection by the participant of 15-minute walking samples 3x per week using the smart insoles and the associated smart phone app. Adherence will be assessed by calculating the number of tasks completed divided by the number of tasks prompted. > 80% is deemed high adherence.	6 months
Clinician Acceptance	To measure clinician acceptance of the solution, by confirming that the results are readily interpretable and useful to the clinician. Specifically, we will measure the satisfaction of clinicians using a 5-point Likert scale, as follows: Very satisfied, Satisfied, Neither satisfied nor dissatisfied, Dissatisfied, and Very dissatisfied.	6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Gait Quality Measurement	To measure the MS participant's gait quality over a 6-month period under free-living conditions. This includes the detection and measurement of gait stability, gait improvements and gait deterioration. Gait quality is a composite score comprised of a weighted set of standard gait metrics, based on a scale of 0 to 100, with 100 representing perfect gait representative of a healthy individual, and 0 representing the worst score. For each walking sample, a composite gait quality score will be calculated. Standard gait metrics include: (1) temporal metrics such as Step Duration and Single Support Time, (2) spatial metrics such as Stride Length and Step Height and (3) spatiotemporal metrics such as Stride Velocity and Swing Velocity.	6 months
Correlation between AI Gait Algorithms and Patience Perceptions	To correlate gait changes perceived by the Artificial Intelligence (AI)-based gait algorithms with participant perception of gait stability, improvement or worsening.	6 months
AI-based Identification of Gait Phenotypes	To assess the accuracy of the AI-based algorithms for identifying specific gait phenotypes that are common within the MS patient population, including ataxic, hemiplegic and spastic gait patterns.	6 months

Collaborators and Investigators

• Sponsor: Celestra Health Systems
• Collaborators: Brigham and Women's Hospital; The Ottawa Hospital; Barts & The London NHS Trust; University of Ottawa
• Investigators: Principal Investigator: Gauruv Bose, Dr., The Ottawa Hospital

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2023
• Primary Completion (Estimated): September 1, 2024
• Study Completion (Estimated): March 1, 2025

Study Registration Dates

• First Submitted: January 27, 2023
• First Submitted That Met QC Criteria: March 10, 2023
• First Posted (Actual): March 23, 2023

Study Record Updates

• Last Update Posted (Actual): January 17, 2024
• Last Update Submitted That Met QC Criteria: January 12, 2024
• Last Verified: January 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Nervous System Diseases; Immune System Diseases; Demyelinating Autoimmune Diseases, CNS; Autoimmune Diseases of the Nervous System; Demyelinating Diseases; Autoimmune Diseases; Multiple Sclerosis; Sclerosis
• Other Study ID Numbers: CelestraHealthmct1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: There is no plan to share Individual Participant Data (IPD) with other researchers.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No