Background During the COVID-19 pandemic, there was limited adoption of contact-tracing apps (CTAs). Adoption was particularly low among vulnerable people (eg, people with a low socioeconomic position or of older age), while this part of the population tends to have lesser access to information and communication technology and is more vulnerable to the COVID-19 virus. Objective This study aims to understand the cause of this lagged adoption of CTAs in order to facilitate adoption and find indications to make public health apps more accessible and reduce health disparities. Methods Because several psychosocial variables were found to be predictive of CTA adoption, data from the Dutch CTA CoronaMelder (CM) were analyzed using cluster analysis. We examined whether subgroups could be formed based on 6 psychosocial perceptions (ie, trust in the government, beliefs about personal data, social norms, perceived personal and societal benefits, risk perceptions, and self-efficacy) of (non)users concerning CM in order to examine how these clusters differ from each other and what factors are predictive of the intention to use a CTA and the adoption of a CTA. The intention to use and the adoption of CM were examined based on longitudinal data consisting of 2 time frames in October/November 2020 (N=1900) and December 2020 (N=1594). The clusters were described by demographics, intention, and adoption accordingly. Moreover, we examined whether the clusters and the variables that were found to influence the adoption of CTAs, such as health literacy, were predictive of the intention to use and the adoption of the CM app. Results The final 5-cluster solution based on the data of wave 1 contained significantly different clusters. In wave 1, respondents in the clusters with positive perceptions (ie, beneficial psychosocial variables for adoption of a CTA) about the CM app were older (P<.001), had a higher education level (P<.001), and had higher intention (P<.001) and adoption (P<.001) rates than those in the clusters with negative perceptions. In wave 2, the intention to use and adoption were predicted by the clusters. The intention to use CM in wave 2 was also predicted using the adoption measured in wave 1 (P<.001, β=–2.904). Adoption in wave 2 was predicted by age (P=.022, exp(B)=1.171), the intention to use in wave 1 (P<.001, exp(B)=1.770), and adoption in wave 1 (P<.001, exp(B)=0.043). Conclusions The 5 clusters, as well as age and previous behavior, were predictive of the intention to use and the adoption of the CM app. Through the distinguishable clusters, insight was gained into the profiles of CM (non)intenders and (non)adopters. Trial Registration OSF Registries osf.io/cq742; https://osf.io/cq742

BACKGROUND During the COVID-19 pandemic, there was a limited adoption rate of Contact Tracing Apps (CTAs), with particularly low adoption among vulnerable people (e.g., low-educated, elderly). To facilitate adoption, it is important to examine the cause of this lagged adoption of CTAs. OBJECTIVE By a cluster analysis it was examined whether subgroups could be formed based on six psychosocial perceptions (i.e., trust in the government, beliefs about personal data, social norms, perceived personal and societal benefits, risk perceptions, and self-efficacy) of (non) users concerning the Dutch CTA CoronaMelder (CM) in order to examine how these clusters relate to each other, and what factors are predictive of the intention to use the CTA and the adoption of the CTA. METHODS Intention and adoption of the CM were examined based on longitudinal data consisting of two timeframes in October/November 2020 (N 1900) and December 2020 (N 1594) respectively. The clusters were described by demographics, intention, and adoption accordingly. Moreover, it was examined whether the clusters and other variables shown to influence the adoption of CTAs, such as health literacy, were predictive of the intention to use the CM and the adoption of the CM app. RESULTS The final five-cluster solution based on the data of wave 1 contained significantly different clusters. In wave 1, respondents in the clusters with positive perceptions (i.e., beneficial psychosocial variables for adoption of a CTA) about the CM app were older (P < .001), higher educated (P < .001), and had higher intention (P < .001) and adoption rates (P < .001) than those in the clusters with negative perceptions. In wave 2, the intention and adoption were predicted by the clusters. Intention at wave 2 was also predicted by the adoption measured at wave 1 (P < .001, β -2.904). Adoption at wave 2 was predicted by age (P .022, Exp(B) 1.171), the intention at wave 1 (P < .001, Exp(B) 1.770), and the adoption at wave 1 (P < .001, Exp(B) .043). CONCLUSIONS The five clusters, as well as age, and previous behavior, were predictive of intention and adoption of the CTA CM. Through the distinguishable clusters insight was gained into the profiles of CM (non) intenders and (non) adopters. CLINICALTRIAL This study was registered in OSF (https://doi.org/10.17605/OSF.IO/CQ742).