The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

Tessi M Hengst; Lilian Lechner; Laura Nynke van der Laan; Arjen Hommersom; Daan Dohmen; Lotty Hooft; Esther Metting; Wolfgang Ebbers; Catherine A W Bolman

doi:10.2196/41479

The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

JMIR Form Res. 2023 Jun 20:7:e41479. doi: 10.2196/41479.

Authors

Tessi M Hengst¹, Lilian Lechner¹, Laura Nynke van der Laan², Arjen Hommersom³, Daan Dohmen⁴, Lotty Hooft⁵, Esther Metting^{6

7}, Wolfgang Ebbers⁸, Catherine A W Bolman¹

Affiliations

¹ Department of Psychology, Open University, Heerlen, Netherlands.
² Department of Communication and Cognition, Tilburg University, Tilburg, Netherlands.
³ Department of Science, Open University, Heerlen, Netherlands.
⁴ Department of Management Science, Open University, Heerlen, Netherlands.
⁵ Department of Epidemiology, University Medical Center Utrecht, Utrecht, Netherlands.
⁶ Data Science Center in Health, University Medical Center, University of Groningen, Groningen, Netherlands.
⁷ Department of General Practice and Elderly Medicine, University Medical Center, University of Groningen, Groningen, Netherlands.
⁸ Department of Social and Behavioral Sciences, Erasmus University, Rotterdam, Netherlands.

PMID: 37338969
PMCID: PMC10284059
DOI: 10.2196/41479

Abstract

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.

Keywords: COVID-19; CTA; CoronaMelder; acceptance; adaptation; adoption; application; cluster analysis; contact-tracing app; data; intention; mHealth; mobile health; norm; psychosocial; risk; social norm; societal.

©Tessi M Hengst, Lilian Lechner, Laura Nynke van der Laan, Arjen Hommersom, Daan Dohmen, Lotty Hooft, Esther Metting, Wolfgang Ebbers, Catherine A W Bolman. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.06.2023.