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How COVID-19 Pandemic Has Influenced Public Interest in Foods: A Google Trends Analysis of Italian Data

Author

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  • Andrea Maugeri

    (Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, University of Catania, 95123 Catania, Italy)

  • Martina Barchitta

    (Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, University of Catania, 95123 Catania, Italy)

  • Vanessa Perticone

    (Department of Economics and Business, University of Catania, 95129 Catania, Italy)

  • Antonella Agodi

    (Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, University of Catania, 95123 Catania, Italy)

Abstract

Controversy exists about the impact of the COVID-19 pandemic on dietary habits, with studies demonstrating both benefits and drawbacks of this period. We analyzed Google Trends data on specific terms and arguments related to different foods (i.e., fruits, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, and sugar-sweetened beverages) in order to evaluate the interest of Italian people before and during the COVID-19 pandemic. Joinpoint regression models were applied to identify the possible time points at which public interest in foods changed (i.e., joinpoints). Interestingly, public interest in specific food categories underwent substantial changes during the period under examination. While some changes did not seem to be related to the COVID-19 pandemic (i.e., legumes and red meat), public interest in fruit, vegetables, milk, and whole grains increased significantly, especially during the first lockdown. It should be noted, however, that the interest in food-related issues returned to prepandemic levels after the first lockdown period. Thus, more efforts and ad hoc designed studies should be encouraged to evaluate the duration and direction of the COVID-19 pandemic’s influence.

Suggested Citation

  • Andrea Maugeri & Martina Barchitta & Vanessa Perticone & Antonella Agodi, 2023. "How COVID-19 Pandemic Has Influenced Public Interest in Foods: A Google Trends Analysis of Italian Data," IJERPH, MDPI, vol. 20(3), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1976-:d:1043043
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    References listed on IDEAS

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    1. P. M. Lerman, 1980. "Fitting Segmented Regression Models by Grid Search," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 77-84, March.
    2. Valentina Gomes Haensel Schmitt & Mirza Marvel Cequea & Jessika Milagros Vásquez Neyra & Marcos Ferasso, 2021. "Consumption Behavior and Residential Food Waste during the COVID-19 Pandemic Outbreak in Brazil," Sustainability, MDPI, vol. 13(7), pages 1-21, March.
    3. Berta Vidal-Mones & Héctor Barco & Raquel Diaz-Ruiz & Maria-Angeles Fernandez-Zamudio, 2021. "Citizens’ Food Habit Behavior and Food Waste Consequences during the First COVID-19 Lockdown in Spain," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
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