Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.

Chikersal, Prerna; Venkatesh, Shruthi; Masown, Karman; Walker, Elizabeth; Quraishi, Danyal; Dey, Anind; Goel, Mayank; Xia, Zongqi

Chikersal, Prerna; Venkatesh, Shruthi; Masown, Karman; Walker, Elizabeth; Quraishi, Danyal; Dey, Anind; Goel, Mayank; Xia, Zongqi.

Afiliação

Chikersal P; School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States.
Venkatesh S; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Masown K; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Walker E; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Quraishi D; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Dey A; Information School, University of Washington, Seattle, Seattle, WA, United States.
Goel M; School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States.
Xia Z; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.

JMIR Ment Health ; 9(8): e38495, 2022 Aug 24.

Article em En | MEDLINE | ID: mdl-35849686

Palavras-chave

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

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Texto completo: 1 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

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