Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning Article Swipe
Alexandre Infanti
,
Vladan Starčević
,
Adriano Schimmenti
,
Yasser Khazaal
,
Laurent Karila
,
Alessandro Giardina
,
Maèva Flayelle
,
Seyedeh Boshra Hedayatzadeh Razavi
,
Stéphanie Baggio
,
Claus Vögele
,
Joël Billieux
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/42206
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/42206
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.
Related Topics
Concepts
Anxiety
Pandemic
Residence
Psychology
Distress
Coronavirus disease 2019 (COVID-19)
Socioeconomic status
Inclusion (mineral)
Clinical psychology
Cross-sectional study
Mental health
Medicine
Psychiatry
Demography
Environmental health
Social psychology
Disease
Sociology
Pathology
Population
Infectious disease (medical specialty)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/42206
- https://formative.jmir.org/2023/1/e42206/PDF
- OA Status
- gold
- Cited By
- 11
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4353015353
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4353015353Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/42206Digital Object Identifier
- Title
-
Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-03-22Full publication date if available
- Authors
-
Alexandre Infanti, Vladan Starčević, Adriano Schimmenti, Yasser Khazaal, Laurent Karila, Alessandro Giardina, Maèva Flayelle, Seyedeh Boshra Hedayatzadeh Razavi, Stéphanie Baggio, Claus Vögele, Joël BillieuxList of authors in order
- Landing page
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https://doi.org/10.2196/42206Publisher landing page
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https://formative.jmir.org/2023/1/e42206/PDFDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://formative.jmir.org/2023/1/e42206/PDFDirect OA link when available
- Concepts
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Anxiety, Pandemic, Residence, Psychology, Distress, Coronavirus disease 2019 (COVID-19), Socioeconomic status, Inclusion (mineral), Clinical psychology, Cross-sectional study, Mental health, Medicine, Psychiatry, Demography, Environmental health, Social psychology, Disease, Sociology, Pathology, Population, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
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2025: 5, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
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68Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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