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1. Privacy Attitudes and COVID Symptom Tracking Apps: Understanding Active Boundary Management by Users

   https://doi.org/10.1007/978-3-030-96960-8_22  

   Park, Jinkyung ; Ahmed, Eiman ; Asif, Hafiz ; Vaidya, Jaideep ; Singh Vivek ( February 2022 , Lecture notes in computer science)

   Multiple symptom tracking applications (apps) were created during the early phase of the COVID-19 pandemic. While they provided crowdsourced information about the state of the pandemic in a scalable manner, they also posed significant privacy risks for individuals. The present study investigates the interplay between individual privacy attitudes and the adoption of symptom tracking apps. Using the communication privacy theory as a framework, it studies how users’ privacy attitudes changed during the public health emergency compared to the pre-COVID times. Based on focus-group interviews (N = 21), this paper reports significant changes in users’ privacy attitudes toward such apps. Research participants shared various reasons for both increased acceptability (e.g., disease uncertainty, public good) and decreased acceptability (e.g., reduced utility due to changed lifestyle) during COVID. The results of this study can assist health informatics researchers and policy designers in creating more socially acceptable health apps in the future. 

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2. Americans’ willingness to adopt a COVID-19 tracking app

   https://doi.org/10.5210/fm.v25i11.11095  

   Hargittai, Eszter ; Redmiles, Elissa M. ; Vitak, Jessica ; Zimmer, Michael ( October 2020 , First Monday)

   null (Ed.)

   The COVID-19 global pandemic led governments, health agencies, and technology companies to work on solutions to minimize the spread of the disease. One such solution concerns contact-tracing apps whose utility is tied to widespread adoption. Using survey data collected a few weeks into lockdown measures in the United States, we explore Americans’ willingness to install a COVID-19 tracking app. Specifically, we evaluate how the distributor of such an app (e.g., government, health-protection agency, technology company) affects people’s willingness to adopt the tool. While we find that 67 percent of respondents are willing to install an app from at least one of the eight providers included, the factors that predict one’s willingness to adopt differ. Using Nissenbaum’s theory of privacy as contextual integrity, we explore differences in responses across distributors and discuss why some distributors may be viewed as less appropriate than others in the context of providing health-related apps during a global pandemic. We conclude the paper by providing policy recommendations for wide-scale data collection that minimizes the likelihood that such tools violate the norms of appropriate information flows. 

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3. Field Evidence of the Effects of Privacy, Data Transparency, and Pro-social Appeals on COVID-19 App Attractiveness

   https://doi.org/10.1145/3491102.3501869  

   Dooley, Samuel ; Turjeman, Dana ; Dickerson, John P ; Redmiles, Elissa M ( April 2022 , CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems)

   COVID-19 exposure-notification apps have struggled to gain adoption. Existing literature posits as potential causes of this low adoption: privacy concerns, insufficient data transparency, and the type of appeal – collective- vs. individual-good – used to frame the app. As policy guidance suggests using tailored advertising to evaluate the effects of these factors, we present the first field study of COVID-19 contact tracing apps with a randomized, control trial of 14 different advertisements for CovidDefense, Louisiana’s COVID-19 exposure-notification app. We find that all three hypothesized factors – privacy, data transparency, and appeals framing – relate to app adoption, even when controlling for age, gender, and community density. Our results offer (1) the first field evidence supporting the use of collective-good appeals, (2) nuanced findings regarding the efficacy of data and privacy transparency, the effects of which are moderated by appeal framing and potential users’ demographics, and (3) field-evidence-based guidance for future efforts to encourage pro-social health technology adoption. 

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4. Predictors of mental health during the Covid-19 pandemic in the US: Role of economic concerns, health worries and social distancing

   https://doi.org/10.1371/journal.pone.0241895  

   Kämpfen, Fabrice ; Kohler, Iliana V. ; Ciancio, Alberto ; Bruine de Bruin, Wändi ; Maurer, Jürgen ; Kohler, Hans-Peter ( November 2020 , PLOS ONE)

   Kwon, Young Dae (Ed.)

   Despite the profound health and economic implications of Covid-19, there is only limited knowledge to date about the role of economic concerns, health worries and social distancing for mental health outcomes during the pandemic. We analyze online survey data from the nationally representative “Understanding America Study” (UAS) covering the period of March 10-31st 2020 (sample size: 6,585). Mental health is assessed by the validated PHQ-4 instrument for measuring symptoms of depression and anxiety. About 29% (CI:27.4-.30.4%) of the US adult population reported some depression/anxiety symptoms over the study period, with symptoms deteriorating over the month of March. Worsening mental health was most strongly associated with concerns about the economic consequences of the pandemic, while concerns about the potential implications of the virus for respondents’ own health and social distancing also predicted increases in symptoms of depression and anxiety during the early stages of the pandemic in the US, albeit less strongly. Our findings point towards the possibility of a major mental health crisis unfolding simultaneously with the pandemic, with economic concerns being a key driving force of this crisis. These results highlight the likely importance of economic countermeasures and social policy for mitigating the impact of Covid-19 on adult mental health in the US over and above an effective public health response. 

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5. Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation

   https://doi.org/10.2196/27449  

   Hogan, Katie ; Macedo, Briana ; Macha, Venkata ; Barman, Arko ; Jiang, Xiaoqian ( January 2021 , JMIR Medical Informatics)

   The global and national response to the COVID-19 pandemic has been inadequate due to a collective lack of preparation and a shortage of available tools for responding to a large-scale pandemic. By applying lessons learned to create better preventative methods and speedier interventions, the harm of a future pandemic may be dramatically reduced. One potential measure is the widespread use of contact tracing apps. While such apps were designed to combat the COVID-19 pandemic, the time scale in which these apps were deployed proved a significant barrier to efficacy. Many companies and governments sprinted to deploy contact tracing apps that were not properly vetted for performance, privacy, or security issues. The hasty development of incomplete contact tracing apps undermined public trust and negatively influenced perceptions of app efficacy. As a result, many of these apps had poor voluntary public uptake, which greatly decreased the apps’ efficacy. Now, with lessons learned from this pandemic, groups can better design and test apps in preparation for the future. In this viewpoint, we outline common strategies employed for contact tracing apps, detail the successes and shortcomings of several prominent apps, and describe lessons learned that may be used to shape effective contact tracing apps for the present and future. Future app designers can keep these lessons in mind to create a version that is suitable for their local culture, especially with regard to local attitudes toward privacy-utility tradeoffs during public health crises. 

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