Back to available technologies

mS: A multiple sclerosis platform leveraging mobile computing and machine learning

Value Proposition

Currently, the ability of Multiple sclerosis (MS) patients to track and monitor the progression of their MS is limited. Traditionally, MS patients discuss their disease development with their physician a few times a year, approximately every 4 months. This creates low resolution and limited quantified tracking of the disease progression. In addition, population-based data have not been sophisticatedly analyzed to identify subpopulations and invention efficacies. The proposed invention is a platform app that collects data about a patient's disease on a near continuous basis, allowing much finer grained information to be gathered on an individualized level as well as improved population analysis. This mobile health software improves MS progression monitoring methods and has relevance to patients, physicians, researchers/students, and pharmaceutical companies.


Duke’s mS app is a ResearchKit­-based iPhone app that collects survey responses, physiological data, and phone-­based task data. The survey responses gather data on the patient's medical history, demographics, symptoms, and current medications. The physiologic data is provided form the phone or paired with a HealthKit-enabled wearable and collects information about a patient's activity level and sleep patterns. In addition, the app collects task data, such as finger taping and spatial memory. Documentation of a patient’s daily disease experiences, combined with logs of their activities, medications, and biorhythms will improve clinical care and disease treatment. In addition, the raw data from all users is uploaded to Duke’s servers and machine-learning algorithms are used to discover disease subpopulations. In the long term, this app may be used to develop predictive models for analysis and the effectiveness of interventions, thereby improving MS treatment methods through precision care and increased disease understanding.


  • Near continuous monitoring of an individual patient’s MS disease progression
  • Tracking of multiple endpoints/symptoms in MS disease development
  • Increased MS disease progression population data
  • Long term potential to improve precision care for MS patients through machine learning

Duke File (IDF) Number



  • Hartsell, F. Lee
  • Heller, Katherine


  • Hartsell, F., & Heller, K. (2016). Multiple Sclerosis App Development: A Study on App Features Using Patient Feedback (P2. 123). Neurology, 86(16 Supplement), P2-123.

For more information please contact


School of Medicine (SOM)

Interested in this Technology?

Submit your interest below.