Passive sensing data predicts stress in university students: A supervised machine learning method for digital phenotyping DOI Open Access
Artur Shvetcov,

Joost Funke Kupper,

Wu-Yi Zheng

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 5, 2023

ABSTRACT University students are particularly susceptible to developing high levels of stress, which occur when environmental demands outweigh an individual’s ability cope. The growing advent mental health smartphone apps has led a surge in use by university seeking ways help them cope with stress. Use these afforded researchers the unique collect extensive amounts passive sensing data including GPS and step detection. Despite this, little is known about relationship between Further, there no established methodologies or tools predict stress from this group. In study, we establish clear machine learning-based methodological pipeline for processing extracting features that may be relevant context health. We then methodology determine students. doing so, offer first proof-of-principle utility our highlight can indeed digitally phenotype

Language: Английский

Passive sensing data predicts stress in university students: A supervised machine learning method for digital phenotyping DOI Open Access
Artur Shvetcov,

Joost Funke Kupper,

Wu-Yi Zheng

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 5, 2023

ABSTRACT University students are particularly susceptible to developing high levels of stress, which occur when environmental demands outweigh an individual’s ability cope. The growing advent mental health smartphone apps has led a surge in use by university seeking ways help them cope with stress. Use these afforded researchers the unique collect extensive amounts passive sensing data including GPS and step detection. Despite this, little is known about relationship between Further, there no established methodologies or tools predict stress from this group. In study, we establish clear machine learning-based methodological pipeline for processing extracting features that may be relevant context health. We then methodology determine students. doing so, offer first proof-of-principle utility our highlight can indeed digitally phenotype

Language: Английский

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