Reference : Predictors of cyberchondria during the COVID-19 pandemic: A supervised machine learn...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Social & behavioral sciences, psychology : Neurosciences & behavior
	To cite this reference:	http://hdl.handle.net/10993/54553

Title :	Predictors of cyberchondria during the COVID-19 pandemic: A supervised machine learning approach
Language :	English
Author, co-author :	Infanti, Alexandre [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) >]
	Starcevic, Vladan []
	Schimmenti, Adriano []
	Khazaal, Yasser []
	Karila, Laurent []
	Giardina, Alessandro []
	Flayelle, Maèva []
	Baggio, Stéphanie []
	Vögele, Claus [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) >]
	Billieux, Joël []
Publication date :	3-Aug-2022
Journal title :	Journal of Behavioral Addictions
Publisher :	Akademiai Kiado
Pages :	73
Peer reviewed :	Yes
Audience :	International
ISSN :	2062-5871
e-ISSN :	2063-5303
City :	Budapest
Country :	Hungary
Event name :	7th International Conference on Behavioral Addictions (ICBA 2022) June 20–22, 2022, Nottingham, United Kingdom
Event date :	from 20-06-2022 to 22-06-2022
Event place (city) :	Nottingham
Event country :	United Kingdom
Keywords :	[en] cyberchondria ; COVID-19 ; machine learning
Abstract :	[en] Background and aims: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. This behavior has been considered an emerging public health issue, which may have been exacerbated by the COVID-19 pandemic. The present study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time. Method: Self-reported data on cyberchondria severity (before and during the pandemic), attachment style, impulsivity traits, somatic symptoms, COVID-19-related fears, health anxiety, and intolerance of uncertainty were collected from 725 participants using an online survey distributed in French-speaking European countries during the first wave of the COVID-19 pandemic. Results: COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased, whereas the reassurance facet of cyberchondria decreased. Using supervised machine learning regression analyses, the specific 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 about 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, the findings have implications for the conceptualization and future assessment of cyberchondria.
Permalink :	http://hdl.handle.net/10993/54553
DOI :	10.1556/2006.2022.00700
FnR project :	FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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	Fulltext file(s): File Commentary Version Size Access Open access [Journal of Behavioral Addictions] 7th International Conference on Behavioral Addictions (ICBA2022).pdf Publisher postprint 21.01 MB View/Open Additional material(s): File Commentary Size Access Open access Présentation ICBA 2022.pptx 1.72 MB View/Open

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