Common and Specific Information From Neuroimaging and Smartphone

Study Overview
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Brief Summary
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Gait alteration is frequent in MS and limitation in walking ability is a major concern in MS patients. Umanit and LMJL (Nantes university) has developed a device call egait to assess walking ability in individuals (eg MS patients).

Detailed Description
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This device consists in a commercialized IMU sensor (MetaMotionR Sensor, Mbientilab) worn at the right hip, a smartphone app and dedicated algorithm/mathematical model to extract raw sensor data and calculate individual gait pattern (IGP). This IGP consists of a curve, based on quaternion and representing the rotation recorded by the IMU during an average gait cycle. Pursue previous works conducted on (IGP to assess) gait alteration in MS by adding (to IGP) new information from MRI.

Official Title
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Individual Gait Pattern and MRI Lesion Load to Quantify Gait Impairment in MS: A Cross Sectional Study.

Conditions
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Multiple Sclerosis

Intervention / Treatment
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* Other: eGait


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria : Diagnosis of MS based on McDonald criteria (including Relapsing-remitting and progressive MS) Over 18 years old /age greater than 18 years Patients followed at Nantes university hospital or Rennes university hospital Last known EDSS before inclusion ranging from 0 to 6 inclusive/EDSS of 0 to 6 inclusive, prior inclusion No relapse within 3 months With a Medullar MRI planed as part as usual care Affiliated person or beneficiary of a social security scheme Exclusion Criteria : Bilateral aid needed to walk Women who are pregnant Patient having expressed their opposition Patient under guardianship or security measure

Ages Eligible for Study
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Minimum Age: 18 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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Arms and Interventions

| Intervention/Treatment |
| --- |
|Other: eGait|IMU sensor (as part of eGait device) worn at the hip during T25FW|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Clustering analyze based on IGP | IGP consists of a curve, based on quaternion and representing the rotation recorded by the IMU during an average gait cycle (0-1). | At the inclusion | 
| Clustering analyze based on EDSS score | EDSS is an ordinal scale measuring disability and ranging from 0 (normal examination) to 10 (death due to MS) in a 0,5-point increments from score 1. | At the inclusion | 
| Clustering analyze based on MRI lesion load | MRI characteristics are spinal and extraspinal lesion volumes. | At the inclusion | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Correlation with disability | Correlation of IGP obtained during a walk of 25 feet with Expanded Disability Status Scale (EDSS). EDSS is an ordinal scale measuring disability and ranging from 0 (normal examination) to 10 (death due to MS) in a 0,5-point increments from score 1. Here EDSS of 0 to 2 inclusive defined as mild, 2,5 to 4 inclusive as moderate and EFDSS of 4,5 to 6 inclusive defined as severe | At the inclusion | 
| Correlation with MRI lesion load | Add lesion load (Spinal and extraspinal lesion volume) from MRI to previous correlation. | At the inclusion | 
| Building a predictive model for lesion load involving in walk ability from IGP | Root mean square error between observed and lesion load predicted by the model, calculated by cross-validation. | At the inclusion | 
| Building a predictive model for group belonging from group established in main outcome based on IGP | Multiclass accuracy between real and predict group, calculated by cross-validation | At the inclusion | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Wearable sensor, Gait, MRI