Your browser doesn't support javascript.
loading
Dissecting the heterogeneity of "in the wild" stress from multimodal sensor data.
Nagaraj, Sujay; Goodday, Sarah; Hartvigsen, Thomas; Boch, Adrien; Garg, Kopal; Gowda, Sindhu; Foschini, Luca; Ghassemi, Marzyeh; Friend, Stephen; Goldenberg, Anna.
Afiliación
  • Nagaraj S; Department of Computer Science, University of Toronto, Toronto, ON, Canada. s.nagaraj@mail.utoronto.ca.
  • Goodday S; Vector Institute, Toronto, ON, Canada. s.nagaraj@mail.utoronto.ca.
  • Hartvigsen T; The Hospital for Sick Children, Toronto, ON, Canada. s.nagaraj@mail.utoronto.ca.
  • Boch A; 4YouandMe, Seattle, WA, USA.
  • Garg K; Department of Psychiatry, University of Oxford, Oxford, UK.
  • Gowda S; School of Data Science, University of Virginia, Charlottesville, VA, USA.
  • Foschini L; Evidation Health Inc, San Mateo, CA, USA.
  • Ghassemi M; Department of Computer Science, University of Toronto, Toronto, ON, Canada.
  • Friend S; Vector Institute, Toronto, ON, Canada.
  • Goldenberg A; The Hospital for Sick Children, Toronto, ON, Canada.
NPJ Digit Med ; 6(1): 237, 2023 Dec 20.
Article en En | MEDLINE | ID: mdl-38123810
ABSTRACT
Stress is associated with numerous chronic health conditions, both mental and physical. However, the heterogeneity of these associations at the individual level is poorly understood. While data generated from individuals in their day-to-day lives "in the wild" may best represent the heterogeneity of stress, gathering these data and separating signals from noise is challenging. In this work, we report findings from a major data collection effort using Digital Health Technologies (DHTs) and frontline healthcare workers. We provide insights into stress "in the wild", by using robust methods for its identification from multimodal data and quantifying its heterogeneity. Here we analyze data from the Stress and Recovery in Frontline COVID-19 Workers study following 365 frontline healthcare workers for 4-6 months using wearable devices and smartphone app-based measures. Causal discovery is used to learn how the causal structure governing an individual's self-reported symptoms and physiological features from DHTs differs between non-stress and potential stress states. Our methods uncover robust representations of potential stress states across a population of frontline healthcare workers. These representations reveal high levels of inter- and intra-individual heterogeneity in stress. We leverage multiple stress definitions that span different modalities (from subjective to physiological) to obtain a comprehensive view of stress, as these differing definitions rarely align in time. We show that these different stress definitions can be robustly represented as changes in the underlying causal structure on and off stress for individuals. This study is an important step toward better understanding potential underlying processes generating stress in individuals.
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2023 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2023 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido