Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.
JMIR Ment Health
; 9(8): e38495, 2022 Aug 24.
Article
in English
| MEDLINE | ID: covidwho-1952078
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.
COVID-19; algorithm; behavior change; depression; digital phenotyping; disability; exercise; fatigue; feature selection; fitness; health outcome; isolation; mHealth; machine learning; mental health; mobile health; mobile sensing; movement; multiple sclerosis; neurological disorder; physical activity; predict; sensing; sensor; sleep; tiredness; tracker
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
JMIR Ment Health
Year:
2022
Document Type:
Article
Affiliation country:
38495
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