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Background: During the worldwide COVID-19 pandemic crisis, the role of digital contact tracing (DCT) intensified. However, the uptake of this technology expectedly differed among age cohorts and national cultures. Various conceptual tools were introduced to strengthen DCT research from a theoretical perspective. However, little has been done to compare theory-supported findings across different cultural contexts and age cohorts., Objective: Building on the original study conducted in Belgium in April 2020 and theoretically underpinned by the Health Belief Model (HBM), this study attempted to confirm the predictors of DCT adoption in a cultural environment different from the original setting, that is, the Czech Republic. In addition, by using brief qualitative evidence, it aimed to shed light on the possible limitations of the HBM in the examined context and to propose certain extensions of the HBM., Methods: A Czech version of the original instrument was administered to a convenience sample of young (aged 18-29 y) Czech adults in November 2020. After filtering, 519 valid responses were obtained and included in the quantitative data analysis, which used structural equation modeling and followed the proposed structure of the relationships among the HBM constructs. Furthermore, a qualitative thematic analysis of the free-text answers was conducted to provide additional insights about the model's validity in the given context., Results: The proposed measurement model exhibited less optimal fit (root mean square error of approximation=0.065, 90% CI 0.060-0.070) than in the original study (root mean square error of approximation=0.036, 90% CI 0.033-0.039). Nevertheless, perceived benefits and perceived barriers were confirmed as the main, statistically significant predictors of DCT uptake, consistent with the original study (β=.60, P<.001 and β=-.39; P<.001, respectively). Differently from the original study, self-efficacy was not a significant predictor in the strict statistical sense (β=.12; P=.003). In addition, qualitative analysis demonstrated that in the given cohort, perceived barriers was the most frequent theme (166/354, 46.9% of total codes). Under this category, psychological fears and concerns was a subtheme, notably diverging from the original operationalization of the perceived barriers construct. In a similar sense, a role for social influence in DCT uptake processes was suggested by some respondents (12/354, 1.7% of total codes). In summary, the quantitative and qualitative results indicated that the proposed quantitative model seemed to be of limited value in the examined context., Conclusions: Future studies should focus on reconceptualizing the 2 underperforming constructs (ie, perceived severity and cues to action) by considering the qualitative findings. This study also provided actionable insights for policy makers and app developers to mitigate DCT adoption issues in the event of a future pandemic caused by unknown viral agents., (©Michal Dolezel, Zdenek Smutny. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 16.11.2023.)

Background: In several countries, contact tracing apps (CTAs) have been introduced to warn users if they have had high-risk contacts that could expose them to SARS-CoV-2 and could, therefore, develop COVID-19 or further transmit the virus. For CTAs to be effective, a sufficient critical mass of users is needed. Until now, adoption of these apps in several countries has been limited, resulting in questions on which factors prevent app uptake or stimulate discontinuation of app use., Objective: The aim of this study was to investigate individuals' reasons for not using, or stopping use of, a CTA, in particular, the Coronalert app. Users' and nonusers' attitudes toward the app's potential impact was assessed in Belgium. To further stimulate interest and potential use of a CTA, the study also investigated the population's interest in new functionalities., Methods: An online survey was administered in Belgium to a sample of 1850 respondents aged 18 to 64 years. Data were collected between October 30 and November 2, 2020. Sociodemographic differences were assessed between users and nonusers. We analyzed both groups' attitudes toward the potential impact of CTAs and their acceptance of new app functionalities., Results: Our data showed that 64.9% (1201/1850) of our respondents were nonusers of the CTA under study; this included individuals who did not install the app, those who downloaded but did not activate the app, and those who uninstalled the app. While we did not find any sociodemographic differences between users and nonusers, attitudes toward the app and its functionalities seemed to differ. The main reasons for not downloading and using the app were a perceived lack of advantages (308/991, 31.1%), worries about privacy (290/991, 29.3%), and, to a lesser extent, not having a smartphone (183/991, 18.5%). Users of the CTA agreed more with the potential of such apps to mitigate the consequences of the pandemic. Overall, nonusers found the possibility of extending the CTA with future functionalities to be less acceptable than users. However, among users, acceptability also tended to differ. Among users, functionalities relating to access and control, such as digital certificates or "green cards" for events, were less accepted (358/649, 55.2%) than functionalities focusing on informing citizens about the spread of the virus (453/649, 69.8%) or making an appointment to get tested (525/649, 80.9%)., Conclusions: Our results show that app users were more convinced of the CTA's utility and more inclined to accept new app features than nonusers. Moreover, nonusers had more CTA-related privacy concerns. Therefore, to further stimulate app adoption and use, its potential advantages and privacy-preserving mechanisms need to be stressed. Building further knowledge on the forms of resistance among nonusers is important for responding to these barriers through the app's further development and communication campaigns., (©Michel Walrave, Cato Waeterloos, Koen Ponnet. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 14.01.2022.)

To diminish the risk of spreading COVID-19 as society exits the lockdowns, several apps have been developed for contact tracing. These apps register which users have been in proximity of each other. If a user is diagnosed with COVID-19, app users who have been recently in proximity to this person are notified. The effectiveness of these apps highly depends on public support. Therefore, this study investigated the factors that influence app use intention, based on an extended unified theory of acceptance and use of technology model. A survey was administered in Belgium (Flanders) to 1,500 participants aged 18 to 64 years old. Structural equation modeling was used to test the relationships among the model's constructs. Our results indicated that 48.70 percent of the respondents wanted to use the app. The model explained 39 percent of the variance in app use intention. The most important predictor was performance expectancy, followed by facilitating conditions and social influence. Effort expectancy was not related to intention. Moreover, individuals' innovativeness was positively related with app use intention, whereas app-related privacy concerns negatively influenced intention. Based on the results, suggestions are made for policy makers and developers.

Globally, countries have been developing contact tracing applications to control the spread of the coronavirus (COVID-19) disease. In this work, we present the findings of eight focus groups we conducted with participants living in France and Germany, to explore why they decided to adopt, or not adopt, a contact tracing application as well as understand how they perceived the benefits, drawbacks, and threat model of a contact tracing application. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has created a need for rapid, population-wide digital contact tracing. One solution, Bluetooth-enabled digital proximity tracing using smartphones, promises to preserve individual privacy while helping to contain society-wide viral outbreaks. However, this digital solution works effectively only if adopted by the majority of the population. This poses a collective action problem: everyone would benefit from wide-spread proximity tracing, but the benefits for the individual are indirect and limited. To facilitate such collective action at the societal level, this paper conceptualises the option space of IT governance actions for proximity tracing adoption along two dimensions: decision-making entities (who will govern the roll-out) and accountability enforcement (how strictly will adoption and use be enforced). Examining coherent governance approaches that arise from the framework, we show that there are no globally ideal approaches but only locally contextualised ones that depend on immediate health risk, prior experience with pandemics, societal values and national culture, role of government, trust in government and trust in technology in each society. The paper contributes specific propositions for governing digital contact tracing in the COVID-19 pandemic and general theoretical implications for IT governance for collective action at the societal level. [ABSTRACT FROM AUTHOR]

The continued proliferation of information technology in all aspects of our lives fosters benefits but also generates risks to individuals' privacy. In emerging contexts, such as government surveillance technologies, there is a dearth of research investigating the positive and negative drivers of citizens' acceptance. This is an important gap given the importance of citizen acceptance to the success of these technologies and the need to balance potentially wide-reaching benefits with any dilution of citizen privacy. We conduct a longitudinal examination of the competing influences of positive beliefs and privacy concerns on citizens' acceptance of a COVID-19 national contact tracing mobile application among 405 Irish citizens. Combining privacy calculus theory with social exchange theory, we find that citizens' initial acceptance is shaped by their perceptions of health benefits and social influence, with reciprocity exhibiting a sustained influence on acceptance over time and privacy concerns demonstrating a negative, albeit weak influence on willingness to rely on the application. The study offers important empirical and theoretical implications for the privacy literature in the government surveillance, location-based services, and mobile health application contexts, as well as practical implications for governments and developers introducing applications that rely on mass acceptance and reciprocal information disclosure. • Contact tracing applications offer both benefits and risks to individuals' privacy. • Perceived health benefits influence willingness to download and rely on these apps. • Social influence is an important factor in acceptance of contact tracing apps. • Reciprocity shapes acceptance of contact tracing apps prior to and post launch. [ABSTRACT FROM AUTHOR]

Background: Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation.Objective: We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020.Methods: The smart contact tracing-based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2.Results: As of February 29, a total of 67 contacts who were tested by reverse transcription-polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020.Conclusions: Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing. [ABSTRACT FROM AUTHOR]