Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study.

Zhang, Peng; Fonnesbeck, Christopher; Schmidt, Douglas C; White, Jules; Kleinberg, Samantha; Mulvaney, Shelagh A

Zhang, Peng; Fonnesbeck, Christopher; Schmidt, Douglas C; White, Jules; Kleinberg, Samantha; Mulvaney, Shelagh A.

Afiliación

Zhang P; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States.
Fonnesbeck C; Data Science Institute, Vanderbilt University, Nashville, TN, United States.
Schmidt DC; Department of Biostatistics, Vanderbilt University, Nashville, TN, United States.
White J; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States.
Kleinberg S; Data Science Institute, Vanderbilt University, Nashville, TN, United States.
Mulvaney SA; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States.

JMIR Mhealth Uhealth ; 10(3): e21959, 2022 03 03.

Article en En | MEDLINE | ID: mdl-35238791

Asunto(s)

Diabetes Mellitus Tipo 1; Automanejo; Adolescente; Automonitorización de la Glucosa Sanguínea; Diabetes Mellitus Tipo 1/psicología; Diabetes Mellitus Tipo 1/terapia; Humanos; Aprendizaje Automático; Estudios Retrospectivos; Adulto Joven

Palabras clave

adolescents; behavioral medicine; ecological momentary assessment; informatics; machine learning; mobile phone; psychosocial; self-management; type 1 diabetes

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Diabetes Mellitus Tipo 1 / Automanejo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Humans Idioma: En Revista: JMIR Mhealth Uhealth Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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