IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i17p9279-d627960.html
   My bibliography  Save this article

The Influence of Media Exposure on Anxiety and Working Memory during Lockdown Period in Italy

Author

Listed:
  • Rosa Angela Fabio

    (Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy)

  • Rossella Suriano

    (Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy)

Abstract

The rapid spread of the coronavirus pandemic has caused anxiety around the world. During lockdown, the media became a point of reference for people seeking information. However, little is known on the relationships between anxiety resulting from persistent media exposure to coronavirus-related programs and the effects produced on working memory. In this work, a total of 101 Italian citizens (53.7% female) aged between 18 and 45 years old, who were from 14 provinces in Italy, participated in an online survey. Participants were presented with media exposure and anxiety questionnaires and they were instructed to carry out working memory tasks (visual and auditory n-back). The results showed that media exposure is related to anxiety. It was also found that high levels of anxiety have a negative influence on the performance of both visual and auditory working memory tasks in terms of increased reaction times of responses and decreased accuracy. The results were critically discussed in the light of the Social Compensation Hypothesis.

Suggested Citation

  • Rosa Angela Fabio & Rossella Suriano, 2021. "The Influence of Media Exposure on Anxiety and Working Memory during Lockdown Period in Italy," IJERPH, MDPI, vol. 18(17), pages 1-11, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9279-:d:627960
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/17/9279/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/17/9279/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    2. Anne C Krendl & Brea L Perry & Derek M Isaacowitz, 2021. "The Impact of Sheltering in Place During the COVID-19 Pandemic on Older Adults’ Social and Mental Well-Being," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 76(2), pages 53-58.
    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. László Árpád Kostyál & Zsuzsa Széman & Virág Erzsébet Almási & Paolo Fabbietti & Sabrina Quattrini & Marco Socci & Cristina Gagliardi, 2022. "The Impact of COVID-19 on the Health and Experience of the Carers of Older Family Members Living with Dementia: An Italian–Hungarian Comparative Study," IJERPH, MDPI, vol. 19(9), pages 1-29, April.
    2. Rosa Angela Fabio & Massimo Ingrassia & Marco Massa, 2021. "Transient and Long-Term Improvements in Cognitive Processes following Video Games: An Italian Cross-Sectional Study," IJERPH, MDPI, vol. 19(1), pages 1-12, December.
    3. Rosa Angela Fabio & Alessia Stracuzzi & Riccardo Lo Faro, 2022. "Problematic Smartphone Use Leads to Behavioral and Cognitive Self-Control Deficits," IJERPH, MDPI, vol. 19(12), pages 1-13, June.
    4. Himanshu Grover, 2023. "Public risk perception of covid-19 transmission and support for compact development," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, 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. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    2. Małgorzata Pańkowska & Mariusz Żytniewski, 2019. "Modele architektury biznesowej administracji publicznej w warunkach przetwarzania danych masowych," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 56, pages 171-183.
    3. Hans-Joachim Schramm & Carolin Nicole Czaja & Michael Dittrich & Matthias Mentschel, 2019. "Current Advancements of and Future Developments for Fourth Party Logistics in a Digital Future," Logistics, MDPI, vol. 3(1), pages 1-17, February.
    4. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    5. van den Broek, Tijs & van Veenstra, Anne Fleur, 2018. "Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 330-338.
    6. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    7. Mohammad Ali Yamin, 2021. "Investigating the Drivers of Supply Chain Resilience in the Wake of the COVID-19 Pandemic: Empirical Evidence from an Emerging Economy," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    8. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    9. Amankwah-Amoah, Joseph, 2016. "Emerging economies, emerging challenges: Mobilising and capturing value from big data," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 167-174.
    10. Maniyassouwe Amana & Pingfeng Liu & Mona Alariqi, 2022. "Value Creation and Capture with Big Data in Smart Phones Companies," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    11. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    12. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    13. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    14. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    15. Olumide Emmanuel Oluyisola & Fabio Sgarbossa & Jan Ola Strandhagen, 2020. "Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications," Sustainability, MDPI, vol. 12(9), pages 1-29, May.
    16. Aleš Popovič & Ray Hackney & Rana Tassabehji & Mauro Castelli, 2018. "The impact of big data analytics on firms’ high value business performance," Information Systems Frontiers, Springer, vol. 20(2), pages 209-222, April.
    17. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    18. Tiago Carneiro & Winnie Ng Picoto & Inês Pinto, 2023. "Big Data Analytics and Firm Performance in the Hotel Sector," Tourism and Hospitality, MDPI, vol. 4(2), pages 1-13, April.
    19. v. Alberti-Alhtaybat, Larissa & Al-Htaybat, Khaldoon & Hutaibat, Khalid, 2019. "A knowledge management and sharing business model for dealing with disruption: The case of Aramex," Journal of Business Research, Elsevier, vol. 94(C), pages 400-407.
    20. Mohamed Zaghloul & Mofreh Salem & Amr Ali-Eldin, 2021. "A new framework based on features modeling and ensemble learning to predict query performance," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-18, October.

    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:jijerp:v:18:y:2021:i:17:p:9279-:d:627960. 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.