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Predictors of Cyberchondria during the COVID-19 pandemic: A cross-sectional study using supervised machine learning
Alexandre Infanti;
Vladan Starcevic;
Adriano Schimmenti;
Yasser Khazaal;
Laurent Karila;
Alessandro Giardina;
Maèva Flayelle;
Seyedeh Boshra Hedayatzadeh Razavi;
Stéphanie Baggio;
Claus Vögele;
Joël Billieux
ABSTRACT
Background:
Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress.
Objective:
This study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time.
Methods:
Self-report data were collected from 725 participants during the first wave of the COVID-19 pandemic.
Results:
The results show that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased, whereas the reassurance facet of cyberchondria decreased. Also, 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.
Citation
Please cite as:
Infanti A, Starcevic V, Schimmenti A, Khazaal Y, Karila L, Giardina A, Flayelle M, Hedayatzadeh Razavi SB, Baggio S, Vögele C, Billieux J
Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning