Common and Specific Information From Neuroimaging and Smartphone

Multiple Sclerosis Clinical Trial

— MS-CSI  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05482906
• Other study ID #: RC20_0203
• Status: Recruiting
• Start date: April 3, 2023
• Est. completion date: April 1, 2025

Study information

• Verified date: March 2023
• Source: Nantes University Hospital
• Contact: David LAPLAUD, PHD
• Phone: 33 2 40 16 52 00
• Email: david.laplaud[@]chu-nantes.fr
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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).

Description:

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: April 1, 2025
• Est. primary completion date: April 1, 2025
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Multiple Sclerosis

Intervention

Other:
• eGait
IMU sensor (as part of eGait device) worn at the hip during T25FW

Locations

Country	Name	City	State
France	Nantes University Hospital	Nantes	Loire-Atlantique

Sponsors (2)

Lead Sponsor	Collaborator
Nantes University Hospital	Rennes University Hospital

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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
Primary	Clustering analyze based on MRI lesion load	MRI characteristics are spinal and extraspinal lesion volumes.	At the inclusion
Secondary	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
Secondary	Correlation with MRI lesion load	Add lesion load (Spinal and extraspinal lesion volume) from MRI to previous correlation.	At the inclusion
Secondary	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
Secondary	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