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Detección de recompensa y afecto momentáneos con datos de sensores digitales pasivos en tiempo real.

Samir Akre-Bhide1, Zachary D Cohen2, Amelia Welborn3

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Los sensores digitales pasivos de teléfonos inteligentes y relojes inteligentes pueden predecir estados mentales como el afecto y la motivación. Esta tecnología ofrece un enfoque escalable y no invasivo para el monitoreo de la salud mental.

Palabras clave:
La depresión depresión depresión depresión depresión depresión.El fenótipo digital es el fenótipo digital.evaluación momentánea ecológica (EMA).Aprendizaje automático de aprendizaje automático.Dispositivos electrónicos portátiles dispositivos electrónicos portátiles.

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Área de la Ciencia:

  • El fenotipo digital es el fenótipo digital.
  • La psiquiatría computacional es la psiquiatría computacional.
  • Ciencia de datos conductuales Ciencia de datos conductuales.

Sus antecedentes:

  • El monitoreo de la salud mental a menudo se basa en autoinformes subjetivos, que pueden ser onerosos.
  • Las evaluaciones momentáneas ecológicas (EMA) proporcionan datos subjetivos en tiempo real, pero requieren la participación activa de los participantes.
  • La detección pasiva ofrece un potencial para la recopilación de datos continua y discreta.

Objetivo del estudio:

  • Para investigar el poder predictivo de los datos del sensor digital pasivo para los estados mentales subjetivos.
  • Evaluar la viabilidad del uso de datos de teléfonos inteligentes y relojes inteligentes para evaluaciones momentáneas ecológicas (EMA).
  • Establecer una línea de base para el monitoreo escalable y no invasivo de la salud mental.

Principales métodos:

  • Se recopilaron datos de 245 participantes con depresión y diferentes niveles de anhedonia.
  • Utilizó modelos de aprendizaje automático para predecir estados subjetivos a partir de datos de sensores.
  • Características conductuales y fisiológicas agregadas en ventanas de tiempo (de 15 minutos a 3 horas).

Principales resultados:

  • Los modelos de aprendizaje automático predijeron con éxito 12 de las 15 preguntas de la EMA por encima del azar aleatorio.
  • La predicción óptima requería períodos de agregación de al menos dos horas de datos de los sensores.
  • El rendimiento del modelo varió según el tipo de sensor, la demografía, la depresión y la gravedad de la anhedonia.

Conclusiones:

  • La detección digital pasiva es factible para detectar estados subjetivos momentáneos.
  • Este enfoque proporciona una base para el monitoreo de salud mental escalable y no invasivo.
  • La investigación adicional puede refinar los modelos y explorar diversas poblaciones.