Model predicts MS symptom outcomes during stay-at-home periods

October 06, 2022
A recent study suggests a new model can accurately predict how stay-at-home orders such as those put in place during the COVID-19 pandemic affect the mental health of people with chronic neurological disorders such as multiple sclerosis.

Researchers from Carnegie Mellon University, the University of Pittsburgh, and the University of Washington gathered data from the smartphones and fitness trackers of people with MS both before and during the early wave of the pandemic. 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.

They used the data collected between November 2019 and May 2020 to build machine learning models to predict adverse health outcomes such as worsening of MS symptoms, poor sleep quality, depression, and severe fatigue during the stay-at-home period. The model detected high global MS symptom burden with an accuracy of 90 percent, poor sleep quality with an accuracy of 84 percent, depression with an accuracy of 82.5 percent, and severe fatigue with an accuracy of 75.5 percent.

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.

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 effects 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.

The study was published in the Journal of Medical Internet Research Mental Health.

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