Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.

Background: The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS).

Objective: We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic.

Methods: First, we extracted features that capture behavior changes due to the stay-at-home order.

Manifestations and impact of the COVID-19 pandemic in neuroinflammatory diseases.

Objective: To report initial results of a planned multicenter year-long prospective study examining the risk and impact of COVID-19 among persons with neuroinflammatory disorders (NID), particularly multiple sclerosis (MS).

Methods: In April 2020, we deployed online questionnaires to individuals in their home environment to assess the prevalence and potential risk factors of suspected COVID-19 in persons with NID (PwNID) and change in their neurological care.

Results: Our cohort included 1115 participants (630 NID, 98% MS; 485 reference) as of 30 April 2020.