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

Content Provider	Europe PMC
Author	Hengst, Tessi M Lechner, Lilian van der Laan, Laura Nynke Hommersom, Arjen Dohmen, Daan Hooft, Lotty Metting, Esther Ebbers, Wolfgang Bolman, Catherine A W
Editor	Mavragani, Amaryllis
Copyright Year	2023
Abstract	BackgroundDuring 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.ObjectiveThis 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.MethodsBecause 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.ResultsThe 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).ConclusionsThe 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 RegistrationOSF Registries osf.io/cq742; https://osf.io/cq742
Journal	JMIR Formative Research
Volume Number	7
PubMed Central reference number	PMC10284059
PubMed reference number	37338969
e-ISSN	2561326X
DOI	10.2196/41479
Language	English
Publisher	JMIR Publications
Publisher Date	2023-06-20
Publisher Place	Toronto, Canada
Access Restriction	Open
Rights License	This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. ©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.
Subject Keyword	contact-tracing app CTA CoronaMelder intention adoption cluster analysis application psychosocial data risk societal social norm norm COVID-19 adaptation acceptance mHealth mobile health
Content Type	Text
Resource Type	Article
Subject	Health Informatics Medicine