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Page 601 – Stanford Medicine X

MS Mosaic: A mobile multiple sclerosis research and management platform utilizing patient-centered machine learning

Katherine Heller kheller@gmail.com
Lee Hartsell lee.hartsell@duke.edu


Multiple sclerosis (MS) is an immune-mediated neurodegenerative disorder whose course is the culmination of a complex and prolonged interplay between genetics, environmental factors, and human behavior. Advances in our understanding and management of MS have historically required costly prospective studies that often become constrained by infrequently collected, or unreliable, data with uncertain immediate benefits to the study participants. Recent innovations, like smartphones, wearable sensors, cloud computing, and machine learning provide more affordable ways to collect, store, and analyze nearly continuous data from an engaged community of participant collaborators, thereby accelerating discovery and facilitating responsive and personalized health care.

MS Mosaic (clinicaltrials.gov ID: NCT02845635) is a longitudinal study designed to gather daily disease experiences and then facilitate collaboration between participants, their care partners, researchers, and clinicians, to better illuminate MS.

Initial study emphasis is placed on characterizing the daily symptoms of MS patients. With support from Duke University and Duke Neurology, the study’s principal investigators developed a mobile MS research platform that takes advantage of wearables and Apple’s ResearchKit framework to continuously collect information from MS patients regarding symptom changes, medication usage, and physiology (sleep, activity levels, etc).

To maintain study engagement, participants have customizable visualizations of their data so they can see how different symptoms and/or physiologic measures relate to one another over time. Provider reports are also available, which allow participants to share their experiences with their MS specialist. In providing these features, the principle investigators seek to engender participant self-efficacy and intellectual engagement, while facilitating more meaningful patient-provider communication.

The principal investigators also introduced the “Mosaic Artisans” Initiative, which invites participants to join the study’s researchers and clinicians as equal partners in every study aspect. One of the initial tasks has been a natural extension of participant’s experimentation within their app visualizations. Along with clinicians, they have advised the study’s data scientists while they 1) use longitudinal models to predict patient symptoms and their fluctuations, and then 2) create new statistical learning methods for subtype discovery within a particular symptom. Insights from this analysis become incorporated into the study’s mobile platform in future versions. We also intend to incorporate additional sensors, link to home automation systems, and jointly model the MS Mosaic mobile platform data with genomics, imaging, and EHR data sources. We aim to use this study design to develop a rich platform that simultaneously facilitates research, empowers patients, and improves clinical care.
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