一款新冠病毒接触者追踪应用程序的采用情况：聚类分析

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

作者信息

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

机构信息

Department of Psychology, Open University, Heerlen, Netherlands.

Department of Communication and Cognition, Tilburg University, Tilburg, Netherlands.

出版信息

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

DOI:10.2196/41479

PMID:37338969

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10284059/

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.

摘要

背景

在新冠疫情期间，接触者追踪应用程序（CTA）的采用率有限。弱势群体（如社会经济地位较低或年龄较大的人群）的采用率尤其低，而这部分人群往往较少接触信息通信技术，且更容易感染新冠病毒。

目的

本研究旨在了解CTA采用滞后的原因，以促进其采用，并找出使公共卫生应用程序更易获取和减少健康差距的迹象。

方法

由于发现几个社会心理变量可预测CTA的采用情况，因此使用聚类分析对荷兰CTA“新冠追踪者”（CM）的数据进行了分析。我们研究了是否可以根据（非）用户对CM的6种社会心理认知（即对政府的信任、对个人数据的看法、社会规范、感知到的个人和社会利益、风险认知以及自我效能感）形成亚组，以研究这些聚类之间的差异以及哪些因素可预测使用CTA的意愿和采用CTA的情况。基于2020年10月/11月（N = 1900）和2020年12月（N = 1594）两个时间框架的纵向数据，研究了使用CM的意愿和采用情况。相应地，通过人口统计学、意愿和采用情况对聚类进行了描述。此外，我们研究了这些聚类以及发现会影响CTA采用的变量（如健康素养）是否可预测使用CM应用程序的意愿和采用情况。

结果

基于第1波数据的最终5聚类解决方案包含显著不同的聚类。在第1波中，对CM应用程序有积极认知（即对采用CTA有益的社会心理变量）的聚类中的受访者年龄较大（P <.001）、教育水平较高（P <.001），且意愿（P <.001）和采用率（P <.001）均高于有消极认知的聚类中的受访者。在第2波中，聚类可预测使用意愿和采用情况。第2波中使用CM的意愿也可通过第1波中的采用情况进行预测（P <.001，β = -2.904）。第2波中的采用情况可通过年龄（P =.022，exp(B) = 1.171）、第1波中的使用意愿（P <.001，exp(B) = 1.770）和第1波中的采用情况（P <.001，exp(B) = 0.043）进行预测。