Duke researchers create new app to monitor symptoms of MS

Researchers at Duke have released a new iPhone app that they hope will change the way people understand and manage multiple sclerosis (MS). 

Created by the Duke Center for Research in Autoimmunity and Multiple Sclerosis (DREAMS), MS Mosaic uses surveys and phone sensors to track users' health and MS symptoms. Researchers want the app to be a new way to track patients' symptoms and how MS develops. 

MS is a debilitating disorder that affects an estimated 400,000 people in the US and 2.3 million people worldwide. The disease is characterized by immune-mediated damage to myelin—the insulation-like coating that surrounds the nerve fibers of the brain and spinal cord. Sufficiently damaged myelin distorts the impulses traveling through nerves, resulting in a broad range of cognitive and motor symptoms.  

MS symptoms can vary widely from person to person and from day to day, which makes it an especially difficult disease to manage and study.

“Every person’s experience with the disease is unique to them and is contingent on lesion locations, overall health and environmental conditions,” said Lee Hartsell, assistant professor of neurology. 

Hartsell noted that since regular doctor visits for MS only occur every three to six months, fluctuations in patients' symptoms may be unreliably reported. Additionally, MS studies often rely on pooled data, which makes it difficult to identify unique patterns among patients' symptoms.

MS Mosaic attempts to capture some of the variability with short daily surveys and tasks that use the phone’s sensors—or sensors in wearable technology, such as the Apple Watch—to continuously collect data about the user's health and symptoms. 

“Up until now we haven’t had an ability to monitor all of these things in real time,” Hartsell said. “The best way to do that is through ubiquitous technology, like a smart phone. We can collect this data through the app, and later supplement it with traditional clinic data like MRIs, and begin to make more sense of the disease.”

To sift through the data collected by the app, the team will employ machine learning algorithms which can be trained to search for patterns in the data—a process that can potentially provide novel insights on indicators and causes of different symptoms.

“I think collecting mobile app data paired with MRI data, or genetics data, is really the wave of the future for treating people with chronic diseases where you don’t get a good sense of what’s going on with them in a clinical setting,” said Katherine Heller, assistant professor of statistical science, and the lead statistician on the study.  

The app was created with Apple's ResearchKit, a framework that allows developers to easily collect user health data for the purpose of medical research. The team sought to build an app that provides value for its users, as it functions as a daily health journal of sorts. At the end of each week, it provides a report outlining each user's activities and any changes in their symptoms. 

The app can also generate a report specific to healthcare providers, to summarize what has happened since a patient's last visit. The team hopes this will foster more productive communication between doctors and their patients.

“We want this to be a collaborative process, not just among the two of us, and other MS researchers, but among the MS patients themselves,” Hartsell said. “There are tons of questions that need to be answered, and we want to make sure that the data we’re collecting—the answers that we’re obtaining—are ones that are relevant to the people who have to contend with this condition every day.” 


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