Predictors of cyberchondria during the COVID-19 pandemic: A supervised machine learning approach

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.