Multiple Sclerosis Prediction and Monitoring of Progression Study

Background: Multiple sclerosis (MS) is the most common cause of non-traumatic neurological disability in young adults leading to an important personal and socio-economic burden. From a pathophysiological point of view MS is considered to be an autoimmune disease in which the immune system mistakenly attacks the central nervous system (CNS). MS is usually devided into three clinical phases. Most people with MS experience sudden relapses followed by a remitting periode (RRMS). Fot this type of MS, the therapeutic landscape has evolved extensively over the last decade. Unfortunately, a significant part of the patients still experience progressive decline in function despite not experiencing discrete clinical relapses. The progressive MS phenotype can be divided in two subtypes known as SPMS and primary progressive MS (PPMS) dependent on preceding RRMS or not. A variety of clinical measures has enabled us to compose a valid follow-up of the disease course, yet they do not evaluate outpatient or long-term monitoring and they also lack sensitivity for early detection of disability progression. Up-to-date, there is no clear consensus on how to diagnose SPMS and it remains difficult to define when a patient enters the progressive phase as the diagnosis is usually made retrospectively. Implementing digital biomarkers would potentially provide us with a more realistic and more sensitive view of the progressive evolution in different spheres of functioning. This also counts for autonomic dysfunction and sleeping disorders, where no standardized monitoring is available for MS. Using wearables to capture the digital biomarkers could fill the gap of knowledge in evaluating, monitoring and predicting disability progression in MS. to this day there is no precise biomarker or composite tool that can differentiate the MS phenotypes or help us initiate/adjust therapy earlier on in progression. Introducing wearable's that could collect basic clinical parameters on a day-to-day basis would potentially give researchers a more realistic and more sensitive insight of the general course of the disease. Rationale: Evolution in machine learning enables unbiased detection of biomarkers encoded in different biosignal modalities. The ability to track MS disease-related physiological and behavioral signals over longer periods of time on an outpatient basis serves the unmet need of early diagnosis and adequate monitoring of (relapse independent) disease progression. This has major clinical implications since biomonitoring could be a critical tool for MS care practitioners in patient-centered multidisciplinary care. Study design: This is an open-label, monocentric diagnostic study where the investigators will test the feasibility and validity (as compared to golden standard measures) of wearables, provided by Byteflies, in adequate extended outpatient evaluation and monitoring of PwMS. The investigators will further evaluate how these biosignals correlate with conventional outcome measures at their primary visit to evaluate the prognostic potential of wearable monitoring