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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Multiple Sclerosis

Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 60 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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What is the study measuring?
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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 |