User experience analysis of AbC-19 Rapid Test via lateral flow immunoassays for self-administrated SARS-CoV-2 antibody testing

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Abstract

Lateral flow immunoassays (LFIA) are low cost, rapid and highly efficacious point-of-care devices, which have been used for SARS-CoV-2 antibody testing by professionals. However, there is a lack of understanding about how self-administered tests are used by the general public for mass testing in different environmental settings. The purpose of this study was to assess the user experience (UX) (including usability) of a self-testing kit to identify COVID-19 antibodies used by a representative sample of the public in their cars, which included 1544 participants in Northern Ireland (NI). The results based on 5-point Likert ratings from a post-test questionnaire achieved an average UX score of 96.03% [95% confidence interval (CI): 95.05-97.01%], suggesting a good degree of user experience. The results of the Wilcoxon rank sum tests suggest that UX scores were independent of the user’s age and education level although the confidence in this conclusion could be strengthened by including more participants aged younger than 18 and those with only primary or secondary education. The agreement between the test result as interpreted by the participant and the researcher was 95.85% [95% CI: 94.85-96.85%], Kappa score 0.75 [95% CI: 0.69-0.81] (indicating substantial agreement). Text analysis via the latent Dirichlet allocation (LDA) model for the free text responses in the survey suggest that the user experience could be improved for blood-sample collection, by modifying the method of sample transfer to the test device and giving clearer instructions on how to interpret the test results. The overall findings provide an insight into the opportunities for improving the design of SARS-CoV-2 antibody testing kits to be used by the general public and therefore inform protocols for future user experience studies of point-of-care tests.
Original languageEnglish
Article number14026
Pages (from-to)1-13
Number of pages13
JournalScientific Reports
Volume11
Early online date7 Jul 2021
DOIs
Publication statusE-pub ahead of print - 7 Jul 2021

Keywords

  • UX analytics
  • text analysis
  • COVID
  • Antibody testing
  • topic modelling

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