ArticleThe American journal of tropical medicine and hygiene2025
Feasibility of a Mobile Application for Self and Assisted Reporting of Coronavirus Disease 2019 Self-Testing Results in Tanzania: A Pilot Study.
Article in The American journal of tropical medicine and hygiene, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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28 authors.
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Abstract
A critical impediment to efficient outbreak response is the availability of timely and complete data on cases and their linkage to care. To inform solutions to this issue, this study investigated the utility of self-testers reporting their coronavirus disease 2019 results using a mobile health platform. Our study has demonstrated that the mobile health platform is feasible; it achieved a 74.5% reporting rate, indicating a strong likelihood of data entry into the unstructured supplementary service data platform. Support from community health workers (CHWs) and healthcare professionals, particularly nurses, contributed to this success, especially among users with limited digital literacy. Specifically, 44.9% of self-test results were submitted by study participants themselves, 24.7% were submitted with the assistance of healthcare professionals, and 30.4% were submitted with the assistance of CHWs. The platform broadens the population base by increasing access and equity, allowing participation even among users without smartphones. Additionally, it integrates rapid antigen diagnostic tests with digital reporting, simplifying data processing and enabling standardized screening, real-time data capture, and effective patient follow-up. This technology also lays a foundation for pandemic preparedness in low- and middle-income countries by demonstrating the feasibility of fully integrating response loops for disease management and interventions. Future response loops could leverage artificial intelligence, machine learning, and integration with existing health surveillance systems, directly benefiting users through enhanced support.
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