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Passive sensing data predicts stress in university students: a supervised machine learning method for digital

Artur Shvetcov¹, Joost Funke Kupper², Wu-Yi Zheng¹

¹Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.

Frontiers in Psychiatry

|September 10, 2024

Summary

University students can now have their stress levels digitally phenotyped using passive sensing data from mental health apps. This study introduces a machine learning pipeline to analyze this data, paving the way for new stress-management tools.

Keywords:

digital phenotype machine learning passive sensing stress university student

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Area of Science:

Digital Phenotyping
Machine Learning in Mental Health
Mobile Health (mHealth)

Background:

University students experience high stress levels, often exceeding coping abilities.
Mental health smartphone apps are increasingly used by students for stress management.
Passive sensing data (e.g., GPS, step detection) from these apps offers a rich, yet underutilized, data source.

Purpose of the Study:

To establish a machine learning (ML) pipeline for processing passive sensing data for mental health applications.
To investigate the relationship between passive sensing data and stress levels in university students.
To provide proof-of-principle for digitally phenotyping student stress using passive sensing.

Main Methods:

Development of an ML-based methodological pipeline for passive sensing data processing.
Feature extraction from passive sensing data relevant to mental health.
Application of the pipeline to analyze passive sensing data and stress in university students.

Main Results:

Successful establishment of a methodological pipeline for passive sensing data analysis.
Demonstration of a relationship between passive sensing data and stress in university students.
Proof-of-concept data showing passive sensing can digitally phenotype student stress.

Conclusions:

Passive sensing data holds significant potential for understanding and managing stress in university students.
The developed ML pipeline offers a novel approach to digital phenotyping of mental health indicators.
Further research can leverage this methodology to create targeted interventions for student mental well-being.