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

doi:10.3390/jcm9103350

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

Printed Edition

A printed edition of this Special Issue is available at MDPI Books....

...

Open AccessEditor’s ChoiceArticle

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

^1,†

^2,*,†

⁴,

⁵

and

Paolo Roma

Associazione Novilunio Onlus, 35020 Ponte San Nicolò (PD), Italy

Department of General Psychology, University of Padua, 35131 Padua, Italy

Department of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy

⁴

Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

⁵

Department of Political Science, Law, and International Studies, University of Padua, 35123 Padua, Italy

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

J. Clin. Med. 2020, 9(10), 3350; https://doi.org/10.3390/jcm9103350

Received: 22 September 2020 / Revised: 11 October 2020 / Accepted: 14 October 2020 / Published: 19 October 2020

(This article belongs to the Special Issue The Impact of the COVID-19 Emergency on the Quality of Life of the General Population)

View Full-Text Download PDF Citation Export

Abstract

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. View Full-Text

Keywords: COVID-19; stress; personality; public health; mental health; coping

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Materials

Supplementary File 1:
PDF-Document (PDF, 198 KiB)
Externally hosted supplementary file 1
Doi: 10.5281/zenodo.3753552
Description: Supplementary materials and open-access dataset

Share and Cite

MDPI and ACS Style

Flesia, L.; Monaro, M.; Mazza, C.; Fietta, V.; Colicino, E.; Segatto, B.; Roma, P. Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models. J. Clin. Med. 2020, 9, 3350. https://doi.org/10.3390/jcm9103350

AMA Style

Flesia L, Monaro M, Mazza C, Fietta V, Colicino E, Segatto B, Roma P. Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models. Journal of Clinical Medicine. 2020; 9(10):3350. https://doi.org/10.3390/jcm9103350

Chicago/Turabian Style

Flesia, Luca, Merylin Monaro, Cristina Mazza, Valentina Fietta, Elena Colicino, Barbara Segatto, and Paolo Roma. 2020. "Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models" Journal of Clinical Medicine 9, no. 10: 3350. https://doi.org/10.3390/jcm9103350

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Printed Edition

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

Abstract

Supplementary Materials

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI