Cargando…
Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study
During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influe...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951840/ https://www.ncbi.nlm.nih.gov/pubmed/33705439 http://dx.doi.org/10.1371/journal.pone.0247997 |
_version_ | 1783663613013131264 |
---|---|
author | Eder, Stephanie Josephine Steyrl, David Stefanczyk, Michal Mikolaj Pieniak, Michał Martínez Molina, Judit Pešout, Ondra Binter, Jakub Smela, Patrick Scharnowski, Frank Nicholson, Andrew A. |
author_facet | Eder, Stephanie Josephine Steyrl, David Stefanczyk, Michal Mikolaj Pieniak, Michał Martínez Molina, Judit Pešout, Ondra Binter, Jakub Smela, Patrick Scharnowski, Frank Nicholson, Andrew A. |
author_sort | Eder, Stephanie Josephine |
collection | PubMed |
description | During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influence of ‘macro-level’ environmental factors and ‘micro-level’ psychological variables on both fear and perceived health. Here, we investigate predictors of fear and perceived health using machine learning as lockdown restrictions in response to the COVID-19 pandemic were introduced in Austria, Spain, Poland and Czech Republic. Over a seven-week period, 533 participants completed weekly self-report surveys which measured the target variables subjective fear of the virus and perceived health, in addition to potential predictive variables related to psychological factors, social factors, perceived vulnerability to disease (PVD), and economic circumstances. Viral spread, mortality and governmental responses were further included in the analysis as potential environmental predictors. Results revealed that our models could accurately predict fear of the virus (accounting for approximately 23% of the variance) using predictive factors such as worrying about shortages in food supplies and perceived vulnerability to disease (PVD), where interestingly, environmental factors such as spread of the virus and governmental restrictions did not contribute to this prediction. Furthermore, our results revealed that perceived health could be predicted using PVD, physical exercise, attachment anxiety and age as input features, albeit with smaller effect sizes. Taken together, our results emphasize the importance of ‘micro-level’ psychological factors, as opposed to ‘macro-level’ environmental factors, when predicting fear and perceived health, and offer a starting point for more extensive research on the influences of pathogen threat and governmental restrictions on the psychology of fear and health. |
format | Online Article Text |
id | pubmed-7951840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79518402021-03-22 Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study Eder, Stephanie Josephine Steyrl, David Stefanczyk, Michal Mikolaj Pieniak, Michał Martínez Molina, Judit Pešout, Ondra Binter, Jakub Smela, Patrick Scharnowski, Frank Nicholson, Andrew A. PLoS One Research Article During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influence of ‘macro-level’ environmental factors and ‘micro-level’ psychological variables on both fear and perceived health. Here, we investigate predictors of fear and perceived health using machine learning as lockdown restrictions in response to the COVID-19 pandemic were introduced in Austria, Spain, Poland and Czech Republic. Over a seven-week period, 533 participants completed weekly self-report surveys which measured the target variables subjective fear of the virus and perceived health, in addition to potential predictive variables related to psychological factors, social factors, perceived vulnerability to disease (PVD), and economic circumstances. Viral spread, mortality and governmental responses were further included in the analysis as potential environmental predictors. Results revealed that our models could accurately predict fear of the virus (accounting for approximately 23% of the variance) using predictive factors such as worrying about shortages in food supplies and perceived vulnerability to disease (PVD), where interestingly, environmental factors such as spread of the virus and governmental restrictions did not contribute to this prediction. Furthermore, our results revealed that perceived health could be predicted using PVD, physical exercise, attachment anxiety and age as input features, albeit with smaller effect sizes. Taken together, our results emphasize the importance of ‘micro-level’ psychological factors, as opposed to ‘macro-level’ environmental factors, when predicting fear and perceived health, and offer a starting point for more extensive research on the influences of pathogen threat and governmental restrictions on the psychology of fear and health. Public Library of Science 2021-03-11 /pmc/articles/PMC7951840/ /pubmed/33705439 http://dx.doi.org/10.1371/journal.pone.0247997 Text en © 2021 Eder et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Eder, Stephanie Josephine Steyrl, David Stefanczyk, Michal Mikolaj Pieniak, Michał Martínez Molina, Judit Pešout, Ondra Binter, Jakub Smela, Patrick Scharnowski, Frank Nicholson, Andrew A. Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study |
title | Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study |
title_full | Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study |
title_fullStr | Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study |
title_full_unstemmed | Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study |
title_short | Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study |
title_sort | predicting fear and perceived health during the covid-19 pandemic using machine learning: a cross-national longitudinal study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951840/ https://www.ncbi.nlm.nih.gov/pubmed/33705439 http://dx.doi.org/10.1371/journal.pone.0247997 |
work_keys_str_mv | AT ederstephaniejosephine predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT steyrldavid predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT stefanczykmichalmikolaj predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT pieniakmichał predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT martinezmolinajudit predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT pesoutondra predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT binterjakub predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT smelapatrick predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT scharnowskifrank predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy AT nicholsonandrewa predictingfearandperceivedhealthduringthecovid19pandemicusingmachinelearningacrossnationallongitudinalstudy |