Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning

Area of Science:

• Psychology
• Public Health
• Behavioral Science

Background:

• Cyberchondria involves excessive online health searches, increasing anxiety and distress.
• It's a transdiagnostic compulsive behavior linked to health anxiety and internet overuse.
• Cyberchondria lacks formal diagnostic classification, with ongoing debate on its features and causes.

Purpose of the Study:

• To examine changes in cyberchondria severity during the COVID-19 pandemic.
• To identify predictors of cyberchondria during the pandemic.

Main Methods:

• A survey was conducted from May to June 2020 (first COVID-19 wave in Europe) among 725 French-speaking adults.
• Participants completed questionnaires on demographics, socioeconomic factors during lockdown, and psychological constructs.
• Inclusion criteria: age ≥18, proficient in French.

Main Results:

• Cyberchondria-related distress and compulsion significantly increased (P<.001).
• The reassurance-seeking aspect of cyberchondria decreased (P<.001).
• COVID-19 fears and health anxiety were the strongest predictors of cyberchondria distress and functional interference.

Conclusions:

• The COVID-19 pandemic significantly impacted cyberchondria, increasing distress and compulsive behaviors.
• COVID-19 fears and health anxiety are key factors in managing cyberchondria during health crises.
• Findings support a theoretical model of cyberchondria during the pandemic and inform future assessment strategies.