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
em En
| MEDLINE
| ID: mdl-35849686
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
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
JMIR Ment Health
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos