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Machine Learning Predicts MS Mental Health During Stay-at-Home Orders


A Carnegie Mellon researcher and colleagues used data from smartphones and fitness trackers to build machine learning models to predict depression, fatigue, poor sleep quality and worsening multiple sclerosis symptoms.

Mayank Goel

Mayank Goel

Researchers have developed a machine learning model that can accurately predict how stay-at-home orders, such as those during the COVID-19 pandemic affect the mental health of people with multiple sclerosis (MS).

Mayank Goel, M.S., Ph.D., head of the Smart Sensing for Humans (SMASH) Lab and an associate professor at Carnegie Mellon University in Pittsburgh, developed the model, which was described in a recent described in a recent article in the Journal of Medical Internet Research. Goel worked with colleagues at Carnegie Mellon, University of Pittsburgh and the University of Washington.

Before the pandemic began, the original research question was whether digital data from the smartphones and fitness trackers of people with MS could predict clinical outcomes. By March 2020, as study participants were required to stay at home, their daily behavior patterns were significantly altered, said in a news release.

“The research team realized the data being collected could inform the effect of the stay-at-home orders on people with MS,” said the news release

They used the passively collected sensor data in smartphones and fitness trackers to build machine learning models to predict depression, fatigue, poor sleep quality and worsening MS symptoms during the unprecedented stay-at-home period.

"It presented us with an exciting opportunity. If we look at the data points before and during the stay-at-home period, can we identify factors that signal changes in the health of people with MS?” Goel said in the press release.

The research team gathered data passively over three to six months, collecting information such as the number of calls on the participants' smartphones and the duration of those calls; the number of missed calls; and the participants' location and screen activity data. The also collected heart rate, sleep information and step count data from their fitness trackers.

People with MS can experience several chronic comorbidities, which gave the team a chance to test if their model could predict adverse health outcomes such as severe fatigue, poor sleep quality and worsening of MS symptoms — in addition to depression, said the press resaid.

“Building on this study, the team hopes to advance precision medicine for people with MS by improving early detection of disease progression and implementing targeted interventions based on digital phenotyping,” according to the press release.

The research could also help inform policymakers tasked with issuing future stay-at-home orders or other similar responses during pandemics or natural disasters. “When the original COVID-19 stay-at-home orders were issued, there were early concerns about its economic impacts but only a belated appreciation for the toll on peoples' mental and physical health — particularly among vulnerable populations such as those with chronic neurological conditions,” said the press release

"We were able to capture the change in people's behaviors and accurately predict clinical outcomes when they are forced to stay at home for prolonged periods," Goel said in the news release. "Now that we have a working model, we could evaluate who is at risk for worsening mental health or physical health, inform clinical triage decisions, or shape future public health policies."

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