About this trial
The goal of this observational study is to validate a novel, cost-effective method for real-world assessment using patient-acquired "selfie" videos in people with multiple sclerosis. The investigators aim to prove the feasibility and validity of monitoring walking changes remotely through a truly patient-centered, low-burden, low-cost approach. The main question this study aims to answer is: do remotely collected walking and speech videos from a mobile phone match the information investigators can gather from an in person visit?
Participants will collect 5 "selfie" videos at baseline, 3 months, 6 months and 12 months (about 15 minutes every 3 months). They will also come in person at baseline, 6 months, and 12 months for in person data collection (about 1 hour per in person visit).
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Any patient with MS who is above the age of 18 yrs. Participants who are unable to consent for themselves must have a surrogate decision maker or LAR.
Age > 18 yrs, EDSS - <6.5, own a video recording device (smartphone, tablet, camera etc).
Disqualifiers
Patient unwilling to participate in the study. Participants unable to consent for themselves who do not have a surrogate decision maker or LAR.
Trial population
A diverse population of adults with MS
Trial design
Cohort
Prospective
Treatments tested in this trial
Not listed
Trial groups
Trial outcomes
Primary outcomes
Validate key metrics derived from video "selfies" against the MS Functional Composite (MSFC)
The investigators will optimize key metrics until they show at least 0.75 correlation coefficient with MSFC z scores, as well as individual domains (walking speed/Timed 25 foot walk, processing speed/Symbol digit modalities test, dexterity/9 hole peg test).
Secondary outcomes
Video acquisition protocol usability
The investigators will explore barriers and facilitators to adoption, and iteratively optimize outreach, instructions, video prompts, camera angles and uploading parameters so diverse PwMS can participate in research and care.
Refine single-domain and composite video metrics
Aim 2. Refine single-domain and composite video metrics. Machine learning techniques will generate comprehensive single-domain and holistic metrics of patient functioning that: can be observed asynchronously by clinicians, recapitulate MSFC and EDSS, but are more relevant to daily life than standard metrics.
Validate video metrics' sensitivity to subtle changes over time.
Aim 3. Validate video metrics' sensitivity to subtle changes over time. Over 12 months, the investigators will detect subtle worsening in substantially more patients than by existing single domain / global metrics (MSFC, EDSS), informing the tool's utility in measuring PIRA and response to neurorehabilitation, remyelination, and symptom management.
Sponsors and contacts
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University of California, San Francisco
Lead sponsor
Genentech, Inc.
Collaborator