Accepted for/Published in: JMIR Mental Health
Date Submitted: Apr 6, 2022
Date Accepted: Jul 16, 2022
Date Submitted to PubMed: Jul 18, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Predicting Multiple Sclerosis Outcomes during the COVID-19 Stay-at-Home Period using Passively Sensed Behaviors
ABSTRACT
Background:
The coronavirus disease 2019 (COVID-19) pandemic has broad negative impact on physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS).
Objective:
We present 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 extract features that capture behavioral changes due to the "stay-at-home" order. Then, we adapt and apply an existing algorithm to these behavioral change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the "stay-at-home" period.
Results:
The algorithm detects depression with an accuracy of 82.5% (65% improvement over baseline; f1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; f1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; f1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; f1-score: 0.84).
Conclusions:
Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics that would cause drastic behavioral changes.
Citation
