IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p3823-d778329.html
   My bibliography  Save this article

Adherence to the Mediterranean Diet and COVID-19: A Segmentation Analysis of Italian and US Consumers

Author

Listed:
  • Francesca Gerini

    (Department of Agriculture, Food, Environment and Forestry, University of Florence, P.le delle Cascine 18, 50144 Firenze, Italy)

  • Tommaso Fantechi

    (Department of Agriculture, Food, Environment and Forestry, University of Florence, P.le delle Cascine 18, 50144 Firenze, Italy)

  • Caterina Contini

    (Department of Agriculture, Food, Environment and Forestry, University of Florence, P.le delle Cascine 18, 50144 Firenze, Italy)

  • Leonardo Casini

    (Department of Agriculture, Food, Environment and Forestry, University of Florence, P.le delle Cascine 18, 50144 Firenze, Italy)

  • Gabriele Scozzafava

    (Department of Agriculture, Food, Environment and Forestry, University of Florence, P.le delle Cascine 18, 50144 Firenze, Italy)

Abstract

The COVID-19 pandemic has led many countries to implement restrictions on individual freedom to stop the contagion. The imposition of lockdowns has affected many socio-economic aspects and, in particular, eating habits, highlighting the need to analyse the healthiness of new consumption patterns. The aim of our study was to investigate the changes in adherence to the Mediterranean diet, a dietary model universally recognized as healthy, that have occurred both during and since the lockdown. The subsequent profiling of consumers allowed us to understand which sociodemographic and psychographic factors favoured the development of more or less adherence to Mediterranean diet consumption patterns. The study was conducted by administering a questionnaire to a representative sample of Italians and New Yorkers. Both groups, defined by deep socio-economic differences and by their own eating habits compared to the Mediterranean diet model, were affected by similar lockdown measures. The data collected were processed by cluster analysis that allowed to identify four homogeneous groups with respect to the adherence to the Mediterranean diet model. The results highlight a worrying situation with respect to the impacts of the pandemic on maintaining a proper dietary style according to the principles of the Mediterranean diet. In fact, there has been a general worsening trend due to an increase in consumption, in part linked to emotional eating, which is a cause for concern about the potential future impacts on the health of consumers. The study highlights the need, therefore, to implement actions by public decision-makers aimed at raising the awareness of citizens on the issue of correct eating habits and at developing adequate food policies to stem the trend towards unhealthy diets.

Suggested Citation

  • Francesca Gerini & Tommaso Fantechi & Caterina Contini & Leonardo Casini & Gabriele Scozzafava, 2022. "Adherence to the Mediterranean Diet and COVID-19: A Segmentation Analysis of Italian and US Consumers," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3823-:d:778329
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/3823/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/3823/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard Tiffin & Matthieu Arnoult, 2010. "The demand for a healthy diet: estimating the almost ideal demand system with infrequency of purchase," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(4), pages 501-521, December.
    2. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gabriele Scozzafava & Caterina Contini & Francesca Gerini & Leonardo Casini, 2022. "Post-lockdown changes in diet in Italy and the USA: Return to old habits or structural changes?," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-20, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    2. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    3. Ghosh, Atish R. & Qureshi, Mahvash S. & Kim, Jun Il & Zalduendo, Juan, 2014. "Surges," Journal of International Economics, Elsevier, vol. 92(2), pages 266-285.
      • Mahvash S Qureshi & Mr. Atish R. Ghosh & Mr. Juan Zalduendo & Mr. Jun I Kim, 2012. "Surges," IMF Working Papers 2012/022, International Monetary Fund.
    4. Tomàs Aluja-Banet & Eduard Nafria, 2003. "Stability and scalability in decision trees," Computational Statistics, Springer, vol. 18(3), pages 505-520, September.
    5. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    6. Schwartz, Ira M. & York, Peter & Nowakowski-Sims, Eva & Ramos-Hernandez, Ana, 2017. "Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience," Children and Youth Services Review, Elsevier, vol. 81(C), pages 309-320.
    7. Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
    8. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2003. "Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)," Interfaces, INFORMS, vol. 33(1), pages 50-66, February.
    9. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    10. Osman Taylan & Abdulaziz S. Alkabaa & Mustafa Tahsin Yılmaz, 2022. "Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    11. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    12. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    13. Vicente-Cera, Isaías & Acevedo-Merino, Asunción & Nebot, Enrique & López-Ramírez, Juan Antonio, 2020. "Analyzing cruise ship itineraries patterns and vessels diversity in ports of the European maritime region: A hierarchical clustering approach," Journal of Transport Geography, Elsevier, vol. 85(C).
    14. Edward Kozłowski & Anna Borucka & Andrzej Świderski & Przemysław Skoczyński, 2021. "Classification Trees in the Assessment of the Road–Railway Accidents Mortality," Energies, MDPI, vol. 14(12), pages 1-15, June.
    15. Javad Hassannataj Joloudari & Edris Hassannataj Joloudari & Hamid Saadatfar & Mohammad Ghasemigol & Seyyed Mohammad Razavi & Amir Mosavi & Narjes Nabipour & Shahaboddin Shamshirband & Laszlo Nadai, 2020. "Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model," IJERPH, MDPI, vol. 17(3), pages 1-24, January.
    16. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
    17. van Riessen, B. & Negenborn, R.R. & Dekker, R., 2016. "Real-time Container Transport Planning with Decision Trees based on Offline Obtained Optimal Solutions," Econometric Institute Research Papers EI2016-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Jonathan Zufferey, 2016. "Investigating the migrant mortality advantage at the intersections of social stratification in Switzerland," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(32), pages 899-926.
    19. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
    20. H Seol & H Lee & S Kim & Y Park, 2008. "The impact of information technology on organizational efficiency in public services: a DEA-based DT approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 231-238, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3823-:d:778329. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.