COVID-19; cyberchondria; fear of COVID-19; health anxiety; machine learning; online health information; Health Informatics
Résumé :
[en] [en] BACKGROUND: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multidimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome," which is associated with health anxiety, problematic internet use, and obsessive-compulsive symptoms. Cyberchondria is not included in the International Classification of Diseases 11th Revision or the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and its defining features, etiological mechanisms, and assessment continue to be debated.
OBJECTIVE: This study aims to investigate changes in the severity of cyberchondria during the COVID-19 pandemic and identify the predictors of cyberchondria at this time.
METHODS: Data collection started on May 4, 2020, and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the study took place, French-speaking countries in Europe (France, Switzerland, Belgium, and Luxembourg) all implemented lockdown or semilockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level, and country of residence) and information about socioeconomic circumstances during the first lockdown (eg, economic situation, housing, and employment status) and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18-77 (mean 33.29, SD 12.88) years, with females constituting the majority (416/725, 57.4%).
RESULTS: The results showed that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and compulsion increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). In addition, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic.
CONCLUSIONS: These findings provide evidence of the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. In addition, they are consistent with a theoretical model of cyberchondria during the COVID-19 pandemic proposed in 2020. These findings have implications for the conceptualization and future assessment of cyberchondria.
Précision sur le type de document :
Compte rendu
Disciplines :
Sciences sociales & comportementales, psychologie: Multidisciplinaire, généralités & autres
Starcevic, Vladan ; Department of Psychiatry, Nepean Hospital, Penrith, Australia ; Discipline of Psychiatry, Faculty of Medicine and Health, Sydney Medical School Nepean Clinical School, University of Sydney, Sydney, Australia
Schimmenti, Adriano ; Faculty of Human and Social Sciences, Kore University of Enna, Enna, Italy
Khazaal, Yasser ; Addiction Medicine, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland ; Department of Psychiatry and Addictology, University of Montreal, Montreal, QC, Canada ; Department of Psychiatry, Lausanne University, Lausanne, Switzerland
Karila, Laurent ; Centre d'Enseignement, de Recherche et de Traitement des Addictions, Hôpital Universitaire Paul Brousse, Université Paris-Saclay, Villejuif, France
GIARDINA, Alessandro
Flayelle, Maèva ; Institute of Psychology, University of Lausanne, Lausanne, Switzerland
Hedayatzadeh Razavi, Seyedeh Boshra ; Institute of Psychology, University of Lausanne, Lausanne, Switzerland
Baggio, Stéphanie ; Division of Prison Health, Geneva University Hospitals and University of Geneva, Thônex, Switzerland ; Institute of Primary Health Care, University of Bern, Bern, Switzerland
Vögele, Claus ; Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg
Billieux, Joël ; Addiction Medicine, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland ; Institute of Psychology, University of Lausanne, Lausanne, Switzerland
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning.
Date de publication/diffusion :
25 avril 2023
Titre du périodique :
JMIR Formative Research
eISSN :
2561-326X
Maison d'édition :
JMIR Publications Inc., Canada
Volume/Tome :
7
Pagination :
e42206
Peer reviewed :
Peer reviewed vérifié par ORBi
Subventionnement (détails) :
This work is part of the DRIVEN project funded by the Luxembourg National Research Fund under the PRIDE program (PRIDE17/12252781).
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