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Fluctuations in behavior and affect in college students measured using deep phenotyping
College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students’ affective and behavioral experiences can help us better characterize the correlates of psychological distress...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8816914/ https://www.ncbi.nlm.nih.gov/pubmed/35121741 http://dx.doi.org/10.1038/s41598-022-05331-7 |
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author | Vidal Bustamante, Constanza M. Coombs, Garth Rahimi-Eichi, Habiballah Mair, Patrick Onnela, Jukka-Pekka Baker, Justin T. Buckner, Randy L. |
author_facet | Vidal Bustamante, Constanza M. Coombs, Garth Rahimi-Eichi, Habiballah Mair, Patrick Onnela, Jukka-Pekka Baker, Justin T. Buckner, Randy L. |
author_sort | Vidal Bustamante, Constanza M. |
collection | PubMed |
description | College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students’ affective and behavioral experiences can help us better characterize the correlates of psychological distress. Here, we leveraged wearables and smartphones to study 49 first-year college students continuously throughout the academic year. Affect and sleep, academic, and social behavior showed substantial changes from school semesters to school breaks and from weekdays to weekends. Three student clusters were identified with behavioral and affective dissociations and varying levels of distress throughout the year. While academics were a common stressor for all, the cluster with highest distress stood out by frequent report of social stress. Moreover, the frequency of reporting social, but not academic, stress predicted subsequent clinical symptoms. Two years later, during the COVID-19 pandemic, the first-year cluster with highest distress again stood out by frequent social stress and elevated clinical symptoms. Focus on sustained interpersonal stress, relative to academic stress, might be especially helpful to identify students at heightened risk for psychopathology. |
format | Online Article Text |
id | pubmed-8816914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88169142022-02-07 Fluctuations in behavior and affect in college students measured using deep phenotyping Vidal Bustamante, Constanza M. Coombs, Garth Rahimi-Eichi, Habiballah Mair, Patrick Onnela, Jukka-Pekka Baker, Justin T. Buckner, Randy L. Sci Rep Article College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students’ affective and behavioral experiences can help us better characterize the correlates of psychological distress. Here, we leveraged wearables and smartphones to study 49 first-year college students continuously throughout the academic year. Affect and sleep, academic, and social behavior showed substantial changes from school semesters to school breaks and from weekdays to weekends. Three student clusters were identified with behavioral and affective dissociations and varying levels of distress throughout the year. While academics were a common stressor for all, the cluster with highest distress stood out by frequent report of social stress. Moreover, the frequency of reporting social, but not academic, stress predicted subsequent clinical symptoms. Two years later, during the COVID-19 pandemic, the first-year cluster with highest distress again stood out by frequent social stress and elevated clinical symptoms. Focus on sustained interpersonal stress, relative to academic stress, might be especially helpful to identify students at heightened risk for psychopathology. Nature Publishing Group UK 2022-02-04 /pmc/articles/PMC8816914/ /pubmed/35121741 http://dx.doi.org/10.1038/s41598-022-05331-7 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Vidal Bustamante, Constanza M. Coombs, Garth Rahimi-Eichi, Habiballah Mair, Patrick Onnela, Jukka-Pekka Baker, Justin T. Buckner, Randy L. Fluctuations in behavior and affect in college students measured using deep phenotyping |
title | Fluctuations in behavior and affect in college students measured using deep phenotyping |
title_full | Fluctuations in behavior and affect in college students measured using deep phenotyping |
title_fullStr | Fluctuations in behavior and affect in college students measured using deep phenotyping |
title_full_unstemmed | Fluctuations in behavior and affect in college students measured using deep phenotyping |
title_short | Fluctuations in behavior and affect in college students measured using deep phenotyping |
title_sort | fluctuations in behavior and affect in college students measured using deep phenotyping |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8816914/ https://www.ncbi.nlm.nih.gov/pubmed/35121741 http://dx.doi.org/10.1038/s41598-022-05331-7 |
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