@Article{info:doi/10.2196/38495,
author="Chikersal, Prerna
and Venkatesh, Shruthi
and Masown, Karman
and Walker, Elizabeth
and Quraishi, Danyal
and Dey, Anind
and Goel, Mayank
and Xia, Zongqi",
title="Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping",
journal="JMIR Ment Health",
year="2022",
month="Aug",
day="24",
volume="9",
number="8",
pages="e38495",
keywords="mobile sensing; sensor; sensing; mobile health; mHealth; algorithm; multiple sclerosis; disability; mental health; depression; sleep; fatigue; tiredness; predict; machine learning; feature selection; neurological disorder; COVID-19; isolation; behavior change; health outcome; fitness; movement; physical activity; exercise; tracker; digital phenotyping",
abstract="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. ",
issn="2368-7959",
doi="10.2196/38495",
url="https://mental.jmir.org/2022/8/e38495",
url="https://doi.org/10.2196/38495",
url="http://www.ncbi.nlm.nih.gov/pubmed/35849686"
}