A machine learning model has been developed that can accurately predict how isolation and loneliness can affect the mental and general health of people living with chronic neurological disorders like multiple sclerosis (MS).
A research project was established to gather data from smartphones and fitness trackers of people with MS, which was initially done before the COVID-19 pandemic but then was extended to cover the lockdown period.
The team passively collected sensor data to build machine learning models to predict depression, fatigue, poor sleep quality and worsening MS symptoms during the unprecedented stay-at-home period.
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 behaviour patterns were significantly altered.
The research team – from Carnegie Mellon University, University of Pittsburgh and University of Washington – realised the data being collected could inform the effect of isolation and being confided to their home on people with MS.
“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,” said Mayank Goel, head of the Smart Sensing for Humans (SMASH) Lab at CMU.
“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.”
People with MS can experience several chronic co-morbidities, 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.
“It presented us with an exciting opportunity,” said Goel. “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?”
The 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 team also collected heart rate, sleep information and step count data from their fitness trackers. The work was based on previous studies, and built on previous CMU research research that presented a machine learning model that could identify depression in college students at the end of the semester using smartphone and fitness tracker data.
Going forward, 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.
The work 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.
