%0 Journal Article
%@ 2368-7959
%I JMIR Publications
%V 9
%N 8
%P e38495
%T Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping
%A Chikersal,Prerna
%A Venkatesh,Shruthi
%A Masown,Karman
%A Walker,Elizabeth
%A Quraishi,Danyal
%A Dey,Anind
%A Goel,Mayank
%A Xia,Zongqi
%+ Department of Neurology, University of Pittsburgh, 3501 Fifth Avenue,, BST3, Suite 7014, Pittsburgh, PA, 15260, United States, 1 412 383 5377, zxia1@pitt.edu
%K mobile sensing
%K sensor
%K sensing
%K mobile health
%K mHealth
%K algorithm
%K multiple sclerosis
%K disability
%K mental health
%K depression
%K sleep
%K fatigue
%K tiredness
%K predict
%K machine learning
%K feature selection
%K neurological disorder
%K COVID-19
%K isolation
%K behavior change
%K health outcome
%K fitness
%K movement
%K physical activity
%K exercise
%K tracker
%K digital phenotyping
%D 2022
%7 24.8.2022
%9 Original Paper
%J JMIR Ment Health
%G English
%X 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. Then, we adapted and applied an existing algorithm to these behavior-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: Using data collected between November 2019 and May 2020, the algorithm detected 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, which would cause drastic behavior changes. 
%M 35849686
%R 10.2196/38495
%U https://mental.jmir.org/2022/8/e38495
%U https://doi.org/10.2196/38495
%U http://www.ncbi.nlm.nih.gov/pubmed/35849686