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Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data

BACKGROUND: Stress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all-time high. Traditionally, the dominant way to assess stress levels has been through pen-and-paper surveys. OBJECTIVE: The aim of t...

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Autores principales: DaSilva, Alex W, Huckins, Jeremy F, Wang, Rui, Wang, Weichen, Wagner, Dylan D, Campbell, Andrew T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444214/
https://www.ncbi.nlm.nih.gov/pubmed/30888327
http://dx.doi.org/10.2196/12084
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author DaSilva, Alex W
Huckins, Jeremy F
Wang, Rui
Wang, Weichen
Wagner, Dylan D
Campbell, Andrew T
author_facet DaSilva, Alex W
Huckins, Jeremy F
Wang, Rui
Wang, Weichen
Wagner, Dylan D
Campbell, Andrew T
author_sort DaSilva, Alex W
collection PubMed
description BACKGROUND: Stress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all-time high. Traditionally, the dominant way to assess stress levels has been through pen-and-paper surveys. OBJECTIVE: The aim of this study is to use passive sensing data collected via mobile phones to obtain a rich and potentially less-biased source of data that can be used to help better understand stressors in the college experience. METHODS: We used a mobile sensing app, StudentLife, in tandem with a pictorial mobile phone–based measure of stress, the Mobile Photographic Stress Meter, to investigate the situations and contexts that are more likely to precipitate stress. RESULTS: Using recently developed methods for handling high-dimensional longitudinal data, penalized generalized estimating equations, we identified a set of mobile sensing features (absolute values of beta >0.001 and robust z>1.96) across the domains of social activity, movement, location, and ambient noise that were predictive of student stress levels. CONCLUSIONS: By combining recent statistical methods and mobile phone sensing, we have been able to study stressors in the college experience in a way that is more objective, detailed, and less intrusive than past research. Future work can leverage information gained from passive sensing and use that to develop real-time, targeted interventions for students experiencing a stressful time.
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spelling pubmed-64442142019-04-17 Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data DaSilva, Alex W Huckins, Jeremy F Wang, Rui Wang, Weichen Wagner, Dylan D Campbell, Andrew T JMIR Mhealth Uhealth Original Paper BACKGROUND: Stress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all-time high. Traditionally, the dominant way to assess stress levels has been through pen-and-paper surveys. OBJECTIVE: The aim of this study is to use passive sensing data collected via mobile phones to obtain a rich and potentially less-biased source of data that can be used to help better understand stressors in the college experience. METHODS: We used a mobile sensing app, StudentLife, in tandem with a pictorial mobile phone–based measure of stress, the Mobile Photographic Stress Meter, to investigate the situations and contexts that are more likely to precipitate stress. RESULTS: Using recently developed methods for handling high-dimensional longitudinal data, penalized generalized estimating equations, we identified a set of mobile sensing features (absolute values of beta >0.001 and robust z>1.96) across the domains of social activity, movement, location, and ambient noise that were predictive of student stress levels. CONCLUSIONS: By combining recent statistical methods and mobile phone sensing, we have been able to study stressors in the college experience in a way that is more objective, detailed, and less intrusive than past research. Future work can leverage information gained from passive sensing and use that to develop real-time, targeted interventions for students experiencing a stressful time. JMIR Publications 2019-03-19 /pmc/articles/PMC6444214/ /pubmed/30888327 http://dx.doi.org/10.2196/12084 Text en ©Alex W DaSilva, Jeremy F Huckins, Rui Wang, Weichen Wang, Dylan D Wagner, Andrew T Campbell. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 19.03.2019. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
DaSilva, Alex W
Huckins, Jeremy F
Wang, Rui
Wang, Weichen
Wagner, Dylan D
Campbell, Andrew T
Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
title Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
title_full Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
title_fullStr Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
title_full_unstemmed Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
title_short Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
title_sort correlates of stress in the college environment uncovered by the application of penalized generalized estimating equations to mobile sensing data
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444214/
https://www.ncbi.nlm.nih.gov/pubmed/30888327
http://dx.doi.org/10.2196/12084
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