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From Symptom Tracking to Contact Tracing: A Framework to Explore and Assess COVID-19 Apps

Author

Listed:
  • Abinaya Megan Ramakrishnan

    (Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
    Medical Center, Vanderbilt University, Nashville, TN 37232, USA)

  • Aparna Nicole Ramakrishnan

    (Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA)

  • Sarah Lagan

    (Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA)

  • John Torous

    (Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA)

Abstract

Smartphone applications related to coronavirus disease 2019 (COVID-19) continue to emerge and evolve, but despite a wide variety of different app functions, there has yet to be a comprehensive study of what the most prevalent publicly available apps provide, and there exists no standardized evaluation system for end users to determine the safety and efficacy of an app before they download it. Furthermore, limited oversight means that the rapidly growing space creates challenges for end users trying to find a relevant app. We adapted the M-Health Index and Navigation Database (MIND) from apps.digitalpsych.org that previously has been used to evaluate mental health applications to guide the assessment of COVID apps. Using this framework, we conducted a thorough analysis of the top-100 returned coronavirus apps on two separate dates a month apart to understand the clinical utility and features of COVID-19 apps and how these change in a short period of time. We ultimately identified a significant turnover rate, as well as privacy concerns around lack of privacy policies and disclosure of personal information. Our research offers insight into the current status of COVID-19 apps and provides a comprehensive and adaptable framework to help individuals assess the growing number of such digital tools in the wake of the pandemic.

Suggested Citation

  • Abinaya Megan Ramakrishnan & Aparna Nicole Ramakrishnan & Sarah Lagan & John Torous, 2020. "From Symptom Tracking to Contact Tracing: A Framework to Explore and Assess COVID-19 Apps," Future Internet, MDPI, vol. 12(9), pages 1-9, September.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:9:p:153-:d:410265
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    Citations

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    Cited by:

    1. Soukaina Bouarourou & Abderrahim Zannou & El Habib Nfaoui & Abdelhak Boulaalam, 2023. "An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs," Future Internet, MDPI, vol. 15(2), pages 1-27, February.
    2. Mahmood Alshami & Rawad Abdulghafor & Abdulaziz Aborujilah, 2022. "Extending the Unified Theory of Acceptance and Use of Technology for COVID-19 Contact Tracing Application by Malaysian Users," Sustainability, MDPI, vol. 14(11), pages 1-30, June.
    3. Theodoros Oikonomidis & Konstantinos Fouskas & Maro Vlachopoulou, 2021. "A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications," Future Internet, MDPI, vol. 13(11), pages 1-20, October.
    4. Petrović, Nenad & Meško, Maja & Dimovski, Vlado & Peterlin, Judita & Roblek, Vasja, 2022. "Stay Healthy: Slovenian users' Opinions about the Covid-19 Contact-Tracing Mobile Application," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2022), Hybrid Conference, Opatija, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Opatija, Croatia, 17-18 June 2022, pages 108-126, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    5. Abderrahim Zannou & Abdelhak Boulaalam & El Habib Nfaoui, 2020. "SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process," Future Internet, MDPI, vol. 13(1), pages 1-23, December.

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