JMIR Publications

ABSTRACT

Background:

The use of proximity (contact) tracing mobile phone applications during the COVID-19 pandemic to support manual contact tracing was novel. Uptake of the app was lower than expected.

Objective:

We sought to identify distinct subgroups of individuals based on their level of support for the NHS COVID-19 app in the first 15 months of the app’s implementation, and to identify the attitudes and characteristics associated with membership of more and less supportive groups.

Methods:

Eight waves of a longitudinal survey data of smartphone users conducted between 14/10/2020 and 13/12/2021. We used latent class analysis (LCA) to identify groups of individuals according to their stated level of support for the app. We estimated population-weighted multinomial logistic regression models utilising sociodemographic characteristics as independent variables.

Results:

We identified four subgroups in survey waves 1 to 4 (labelled ‘not supportive’, ‘ambivalent’, ‘somewhat supportive’ and ‘completely supportive’). At wave 5, the ‘somewhat supportive’ and ‘completely supportive’ merged giving three subgroups (‘not supportive’, ‘ambivalent’ and ‘supportive’). From wave 6 onwards, the analysis suggested four subgroups (‘least supportive’, ‘less supportive’, ‘ambivalent’ and ‘supportive’) as the ‘not supportive’ class split into two distinct subgroups. Key characteristics of more supportive individuals included having higher levels of trust in the Government to control the spread of COVID-19, having the app installed, while those less concerned about the risk COVID-19 posed to the country were more likely to be unsupportive.

Conclusions:

When the app was launched just over half respondents were supportive, but this declined over the following 15 months. The attrition in support poses important challenges for governments to the use of apps in future pandemics. A potential reason was mistrust in government’s handling of the pandemic.