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Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping.
Shvetcov, Artur; Funke Kupper, Joost; Zheng, Wu-Yi; Slade, Aimy; Han, Jin; Whitton, Alexis; Spoelma, Michael; Hoon, Leonard; Mouzakis, Kon; Vasa, Rajesh; Gupta, Sunil; Venkatesh, Svetha; Newby, Jill; Christensen, Helen.
Afiliación
  • Shvetcov A; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
  • Funke Kupper J; Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC, Australia.
  • Zheng WY; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
  • Slade A; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
  • Han J; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
  • Whitton A; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
  • Spoelma M; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
  • Hoon L; Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC, Australia.
  • Mouzakis K; Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC, Australia.
  • Vasa R; Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC, Australia.
  • Gupta S; Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia.
  • Venkatesh S; Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia.
  • Newby J; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
  • Christensen H; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
Front Psychiatry ; 15: 1422027, 2024.
Article en En | MEDLINE | ID: mdl-39252756
ABSTRACT

Introduction:

University students are particularly susceptible to developing high levels of stress, which occur when environmental demands outweigh an individual's ability to cope. The growing advent of mental health smartphone apps has led to a surge in use by university students seeking ways to help them cope with stress. Use of these apps has afforded researchers the unique ability to collect extensive amounts of passive sensing data including GPS and step detection. Despite this, little is known about the relationship between passive sensing data and stress. Further, there are no established methodologies or tools to predict stress from passive sensing data in this group.

Methods:

In this study, we establish a clear machine learning-based methodological pipeline for processing passive sensing data and extracting features that may be relevant in the context of mental health.

Results:

We then use this methodology to determine the relationship between passive sensing data and stress in university students.

Discussion:

In doing so, we offer the first proof-of-principle data for the utility of our methodological pipeline and highlight that passive sensing data can indeed digitally phenotype stress in university students. Clinical trial registration Australia New Zealand Clinical Trials Registry (ANZCTR), identifier ACTRN12621001223820.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Psychiatry Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Suiza
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: Front Psychiatry Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Suiza