{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:54Z","timestamp":1782846234085,"version":"3.54.5"},"reference-count":62,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The use of proximity (contact) tracing mobile phone apps during the COVID-19 pandemic to support manual contact tracing was novel. Uptake of the app was lower than expected.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We sought to identify distinct subgroups of individuals based on their level of support for the National Health Service (NHS) COVID-19 app in the first 15 months of the app\u2019s implementation, and to identify the attitudes and characteristics associated with membership of more and less supportive groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted 8 waves of a longitudinal survey data of smartphone users, recruited from an online panel (n=2023 at baseline and n=1198 at survey wave 6) between October 14, 2020, and December 13, 2021. We used latent class analysis to identify subgroups of individuals with different inclinations of support for the NHS COVID-19 app. Sankey diagram analysis was used to assess individuals whose subgroup changed over the study period. We estimated population-weighted multinomial logistic regression models using sociodemographic characteristics as independent variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 4 subgroups in survey waves 1 to 4\u2014\u201cnot supportive\u201d (1765\/7210, 25%), \u201cambivalent\u201d (2124\/7210, 30%), \u201csomewhat supportive\u201d (1421\/7219, 20%), and \u201ccompletely supportive\u201d (1900\/7210, 26%). At wave 5, a total of 3 subgroups of support for the app emerged\u2014\u201cnot supportive\u201d (549\/1613, 34%), \u201cambivalent\u201d (497\/1613, 31%), and \u201csupportive\u201d (567\/1613, 35%). From wave 6 onward, the results showed 4 subgroups emerging\u2014\u201cleast supportive\u201d (1568\/6952, 23%), \u201cless supportive\u201d (1179\/6952, 17%), \u201cambivalent\u201d (2105\/6952, 30%), and \u201csupportive\u201d (2100\/6952, 29%). The majority of respondents remained within their identified subgroups between survey waves. Among those who moved into different subgroups, most moved into a less supportive subgroup. Exceptions to this were from waves 2 to 3 and from waves 3 to 4, when higher percentages of respondents moved into more supportive subgroups. The biggest movement to less supportive subgroups occurred after wave 1 (October 2020), when 38% (2740\/7210) of respondents moved into a less supportive subgroup. The biggest movement to more supportive subgroups, on the other hand, occurred after wave 2, when 22% (1586\/7210) of respondents moved into more supportive subgroups. Over the course of the 8 waves, the percentage of respondents in supportive subgroups declined from 56% (3353\/5988) to 29% (1737\/5988). Key characteristics of more supportive individuals included having higher levels of trust in the government to control the spread of COVID-19 and having the app installed, while those less concerned about the risk COVID-19 posed to the country were more likely to be unsupportive (P&lt;.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>When the app was launched, just over half of 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 the government\u2019s handling of the pandemic.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/76863","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:35:06Z","timestamp":1769639706000},"page":"e76863","source":"Crossref","is-referenced-by-count":0,"title":["Characteristics Influencing Support for the National Health Service COVID-19 App in England and Wales: Findings From a Longitudinal 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