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• W4285609904 abstract "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).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.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.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).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." @default.
• W4285609904 created "2022-07-16" @default.
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• W4285609904 date "2022-08-24" @default.
• W4285609904 modified "2023-10-18" @default.
• W4285609904 title "Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping" @default.
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• W4285609904 doi "https://doi.org/10.2196/38495" @default.
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