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Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform
Data collection on adverse reactions in recipients after vaccination is vital to evaluate potential health issues, but health observation diaries are onerous for participants. Here, we present a protocol to collect time series information using a smartphone or web-based platform, thus eliminating th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168702/ https://www.ncbi.nlm.nih.gov/pubmed/37148245 http://dx.doi.org/10.1016/j.xpro.2023.102284 |
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author | Yamao, Yasuo Oami, Takehiko Kawakami, Eiryo Nakada, Taka-aki |
author_facet | Yamao, Yasuo Oami, Takehiko Kawakami, Eiryo Nakada, Taka-aki |
author_sort | Yamao, Yasuo |
collection | PubMed |
description | Data collection on adverse reactions in recipients after vaccination is vital to evaluate potential health issues, but health observation diaries are onerous for participants. Here, we present a protocol to collect time series information using a smartphone or web-based platform, thus eliminating the need for paperwork and data submission. We describe steps for setting up the platform using the Model-View-Controller web framework, uploading lists of recipients, sending notifications, and managing respondent data. For complete details on the use and execution of this protocol, please refer to Ikeda et al. (2022).(1) |
format | Online Article Text |
id | pubmed-10168702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-101687022023-05-10 Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform Yamao, Yasuo Oami, Takehiko Kawakami, Eiryo Nakada, Taka-aki STAR Protoc Protocol Data collection on adverse reactions in recipients after vaccination is vital to evaluate potential health issues, but health observation diaries are onerous for participants. Here, we present a protocol to collect time series information using a smartphone or web-based platform, thus eliminating the need for paperwork and data submission. We describe steps for setting up the platform using the Model-View-Controller web framework, uploading lists of recipients, sending notifications, and managing respondent data. For complete details on the use and execution of this protocol, please refer to Ikeda et al. (2022).(1) Elsevier 2023-05-05 /pmc/articles/PMC10168702/ /pubmed/37148245 http://dx.doi.org/10.1016/j.xpro.2023.102284 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Yamao, Yasuo Oami, Takehiko Kawakami, Eiryo Nakada, Taka-aki Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform |
title | Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform |
title_full | Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform |
title_fullStr | Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform |
title_full_unstemmed | Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform |
title_short | Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform |
title_sort | protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168702/ https://www.ncbi.nlm.nih.gov/pubmed/37148245 http://dx.doi.org/10.1016/j.xpro.2023.102284 |
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