Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 27, 2022
Open Peer Review Period: Jul 27, 2022 - Sep 21, 2022
Date Accepted: Mar 27, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Cluster Analysis on the Adoption of a COVID-19 Contact Tracing App
ABSTRACT
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. Clinical Trial: This study was registered in OSF (https://doi.org/10.17605/OSF.IO/CQ742).
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