JMIR Publications

ABSTRACT

Background:

During the COVID-19 pandemic, there was a limited adoption rate of Contact Tracing Apps (CTAs). Adoption was particularly low among vulnerable people (e.g., people with a low socioeconomic position or older age), whilst 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:

It is important to understand the cause of this lagged adoption of CTAs to facilitate adoption and find indications to make public health applications 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) was analyzed by 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 CM in order to examine how these clusters differ from each other, and what factors are predictive of the intention to use the CTA and the adoption of the CTA. 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 were 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. Clinical Trial: This study was registered in OSF (https://doi.org/10.17605/OSF.IO/CQ742).