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Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models

The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on people’s daily lives, with strong implications for stress levels due to the threat of contagion and restrictions to freedom. Given the link between high stress levels and adverse physical and mental consequences, the...

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Autores principales: Flesia, Luca, Monaro, Merylin, Mazza, Cristina, Fietta, Valentina, Colicino, Elena, Segatto, Barbara, Roma, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603217/
https://www.ncbi.nlm.nih.gov/pubmed/33086558
http://dx.doi.org/10.3390/jcm9103350
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author Flesia, Luca
Monaro, Merylin
Mazza, Cristina
Fietta, Valentina
Colicino, Elena
Segatto, Barbara
Roma, Paolo
author_facet Flesia, Luca
Monaro, Merylin
Mazza, Cristina
Fietta, Valentina
Colicino, Elena
Segatto, Barbara
Roma, Paolo
author_sort Flesia, Luca
collection PubMed
description The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on people’s daily lives, with strong implications for stress levels due to the threat of contagion and restrictions to freedom. Given the link between high stress levels and adverse physical and mental consequences, the COVID-19 pandemic is certainly a global public health issue. In the present study, we assessed the effect of the pandemic on stress levels in N = 2053 Italian adults, and characterized more vulnerable individuals on the basis of sociodemographic features and stable psychological traits. A set of 18 psycho-social variables, generalized regressions, and predictive machine learning approaches were leveraged. We identified higher levels of perceived stress in the study sample relative to Italian normative values. Higher levels of distress were found in women, participants with lower income, and participants living with others. Higher rates of emotional stability and self-control, as well as a positive coping style and internal locus of control, emerged as protective factors. Predictive learning models identified participants with high perceived stress, with a sensitivity greater than 76%. The results suggest a characterization of people who are more vulnerable to experiencing high levels of stress during the COVID-19 pandemic. This characterization may contribute to early and targeted intervention strategies.
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spelling pubmed-76032172020-11-01 Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models Flesia, Luca Monaro, Merylin Mazza, Cristina Fietta, Valentina Colicino, Elena Segatto, Barbara Roma, Paolo J Clin Med Article The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on people’s daily lives, with strong implications for stress levels due to the threat of contagion and restrictions to freedom. Given the link between high stress levels and adverse physical and mental consequences, the COVID-19 pandemic is certainly a global public health issue. In the present study, we assessed the effect of the pandemic on stress levels in N = 2053 Italian adults, and characterized more vulnerable individuals on the basis of sociodemographic features and stable psychological traits. A set of 18 psycho-social variables, generalized regressions, and predictive machine learning approaches were leveraged. We identified higher levels of perceived stress in the study sample relative to Italian normative values. Higher levels of distress were found in women, participants with lower income, and participants living with others. Higher rates of emotional stability and self-control, as well as a positive coping style and internal locus of control, emerged as protective factors. Predictive learning models identified participants with high perceived stress, with a sensitivity greater than 76%. The results suggest a characterization of people who are more vulnerable to experiencing high levels of stress during the COVID-19 pandemic. This characterization may contribute to early and targeted intervention strategies. MDPI 2020-10-19 /pmc/articles/PMC7603217/ /pubmed/33086558 http://dx.doi.org/10.3390/jcm9103350 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Flesia, Luca
Monaro, Merylin
Mazza, Cristina
Fietta, Valentina
Colicino, Elena
Segatto, Barbara
Roma, Paolo
Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
title Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
title_full Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
title_fullStr Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
title_full_unstemmed Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
title_short Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
title_sort predicting perceived stress related to the covid-19 outbreak through stable psychological traits and machine learning models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603217/
https://www.ncbi.nlm.nih.gov/pubmed/33086558
http://dx.doi.org/10.3390/jcm9103350
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