Title: Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation
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. more »« less
Hargittai, Eszter; Redmiles, Elissa M.; Vitak, Jessica; Zimmer, Michael(
, 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.
Dooley, Samuel; Turjeman, Dana; Dickerson, John P; Redmiles, Elissa M(
, 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.
Wileden, Lydia; Anthony, Denise; Campos-Castillo, Celeste; Morenoff, Jeffrey(
, JMIR Public Health and Surveillance)
Background Digital surveillance tools and health informatics show promise in counteracting diseases but have limited uptake. A notable illustration of the limits of such tools is the general failure of digital contact tracing in the United States in response to COVID-19. Objective We investigated the associations between individual characteristics and the willingness to use app-based contact tracing in Detroit, a majority-minority city that experienced multiple waves of COVID-19 outbreaks and deaths since the start of the pandemic. The aim of this study was to examine variations among residents in the willingness to download a contact tracing app on their phones to provide public health officials with information about close COVID-19 contact during summer 2020. Methods To examine residents’ willingness to participate in digital contact tracing, we analyzed data from 2 waves of the Detroit Metro Area Communities Study, a population-based survey of Detroit, Michigan residents. The data captured 1873 responses from 991 Detroit residents collected in June and July 2020. We estimated a series of multilevel logit models to gain insights into differences in the willingness to participate in digital contact tracing across a variety of individual attributes, including race/ethnicity, degree of trust in the government, and level of education, as well as interactions among these variables. Results Our results reflected widespread reluctance to participate in digital contact tracing in response to COVID-19, as less than half (826/1873, 44.1%) of the respondents said they would be willing to participate in app-based contact tracing. Compared to White respondents, Black (odds ratio [OR] 0.45, 95% CI 0.23-0.86) and Latino (OR 0.32, 95% CI 0.11-0.99) respondents were significantly less willing to participate in digital contact tracing. Trust in the government was positively associated with the willingness to participate in digital contact tracing (OR 1.17, 95% CI 1.07-1.27), but this effect was the strongest for White residents (OR 2.14, 95% CI 1.55-2.93). We found similarly divergent patterns of the effects of education by race. While there were no significant differences among noncollege-educated residents, White college-educated residents showed greater willingness to use app-based contact tracing (OR 6.12, 95% CI 1.86-20.15) and Black college-educated residents showed less willingness (OR 0.46, 95% CI 0.26-0.81). Conclusions Trust in the government and education contribute to Detroit residents’ wariness of digital contact tracing, reflecting concerns about surveillance that cut across race but likely arise from different sources. These findings point to the importance of a culturally informed understanding of health hesitancy for future efforts hoping to leverage digital contact tracing. Though contact tracing technologies have the potential to advance public health, unequal uptake may exacerbate disparate impacts of health crises.
Contact tracing is an essential public health tool for controlling epidemic disease outbreaks such as the COVID-19 pandemic. Digital contact tracing using real-time locations or proximity of individuals can be used to significantly speed up and scale up contact tracing. In this article, we present our project, REACT, for REAal-time Contact Tracing and risk monitoring via privacy-enhanced tracking of users' locations and symptoms. With privacy enhancement that allows users to control and refine the precision with which their information will be collected and used, REACT will enable: 1) contact tracing of individuals who are exposed to infected cases and identification of hot-spot locations, 2) individual risk monitoring based on the locations they visit and their contact with others; and 3) community risk monitoring and detection of early signals of community spread. We will briefly describe our ongoing work and the approaches we are taking as well as some challenges we encountered in deploying the app.
Vitak, Jessica; Zimmer, Michael(
, Social Media + Society)
null
(Ed.)
The global coronavirus pandemic has raised important questions regarding how to balance public health concerns with privacy protections for individual citizens. In this essay, we evaluate contact tracing apps, which have been offered as a technological solution to minimize the spread of COVID-19. We argue that apps such as those built on Google and Apple’s “exposure notification system” should be evaluated in terms of the contextual integrity of information flows; in other words, the appropriateness of sharing health and location data will be contextually dependent on factors such as who will have access to data, as well as the transmission principles underlying data transfer. We also consider the role of prevailing social and political values in this assessment, including the large-scale social benefits that can be obtained through such information sharing. However, caution should be taken in violating contextual integrity, even in the case of a pandemic, because it risks a long-term loss of autonomy and growing function creep for surveillance and monitoring technologies.
Hogan, Katie, Macedo, Briana, Macha, Venkata, Barman, Arko, and Jiang, Xiaoqian. Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation. Retrieved from https://par.nsf.gov/biblio/10323548. JMIR Medical Informatics 9.7 Web. doi:10.2196/27449.
Hogan, Katie, Macedo, Briana, Macha, Venkata, Barman, Arko, & Jiang, Xiaoqian. Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation. JMIR Medical Informatics, 9 (7). Retrieved from https://par.nsf.gov/biblio/10323548. https://doi.org/10.2196/27449
Hogan, Katie, Macedo, Briana, Macha, Venkata, Barman, Arko, and Jiang, Xiaoqian.
"Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation". JMIR Medical Informatics 9 (7). Country unknown/Code not available. https://doi.org/10.2196/27449.https://par.nsf.gov/biblio/10323548.
@article{osti_10323548,
place = {Country unknown/Code not available},
title = {Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation},
url = {https://par.nsf.gov/biblio/10323548},
DOI = {10.2196/27449},
abstractNote = {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.},
journal = {JMIR Medical Informatics},
volume = {9},
number = {7},
author = {Hogan, Katie and Macedo, Briana and Macha, Venkata and Barman, Arko and Jiang, Xiaoqian},
}
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