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Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study
BACKGROUND: The field of infodemiology uses health care trends found in public networks, such as social media, to track and quantify the spread of disease. Type 2 diabetes is on the rise worldwide, and social media may be useful in identifying prediabetes through behavior exhibited through social me...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315248/ https://www.ncbi.nlm.nih.gov/pubmed/30552084 http://dx.doi.org/10.2196/10720 |
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author | Xu, Xiaomeng Litchman, Michelle L Gee, Perry M Whatcott, Webb Chacon, Loni Holmes, John Srinivasan, Sankara Subramanian |
author_facet | Xu, Xiaomeng Litchman, Michelle L Gee, Perry M Whatcott, Webb Chacon, Loni Holmes, John Srinivasan, Sankara Subramanian |
author_sort | Xu, Xiaomeng |
collection | PubMed |
description | BACKGROUND: The field of infodemiology uses health care trends found in public networks, such as social media, to track and quantify the spread of disease. Type 2 diabetes is on the rise worldwide, and social media may be useful in identifying prediabetes through behavior exhibited through social media platforms such as Facebook and thus in designing and administering early interventions and containing further progression of the disease. OBJECTIVE: This pilot study is designed to investigate the social media behavior of individuals with prediabetes, before and after diagnosis. Pre- and postdiagnosis Facebook content (posts) of such individuals will be used to create a taxonomy of prediabetes indicators and to identify themes and factors associated with an actual diagnosis of prediabetes. METHODS: This is a single-center exploratory retrospective study that examines 20 adults with prediabetes. The investigators will code Facebook posts 3 months before through 3 months after prediabetes diagnosis. Data will be analyzed using both qualitative content analysis methodology as well as quantitative methodology to characterize participants and compare their posts pre- and postdiagnosis. RESULTS: The project was funded for 2015-2018, and enrollment will be completed by the end of 2018. Data coding is currently under way and the first results are expected to be submitted for publication in 2019. Results will include both quantitative and qualitative data about participants and the similarities and differences between coded social media posts. CONCLUSIONS: This pilot study is the first step in creating a taxonomy of social media indicators for prediabetes. Such a taxonomy would provide a tool for researchers and health care professionals to use social media postings for identifying those at greater risk of having prediabetes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10720 |
format | Online Article Text |
id | pubmed-6315248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-63152482019-01-28 Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study Xu, Xiaomeng Litchman, Michelle L Gee, Perry M Whatcott, Webb Chacon, Loni Holmes, John Srinivasan, Sankara Subramanian JMIR Res Protoc Protocol BACKGROUND: The field of infodemiology uses health care trends found in public networks, such as social media, to track and quantify the spread of disease. Type 2 diabetes is on the rise worldwide, and social media may be useful in identifying prediabetes through behavior exhibited through social media platforms such as Facebook and thus in designing and administering early interventions and containing further progression of the disease. OBJECTIVE: This pilot study is designed to investigate the social media behavior of individuals with prediabetes, before and after diagnosis. Pre- and postdiagnosis Facebook content (posts) of such individuals will be used to create a taxonomy of prediabetes indicators and to identify themes and factors associated with an actual diagnosis of prediabetes. METHODS: This is a single-center exploratory retrospective study that examines 20 adults with prediabetes. The investigators will code Facebook posts 3 months before through 3 months after prediabetes diagnosis. Data will be analyzed using both qualitative content analysis methodology as well as quantitative methodology to characterize participants and compare their posts pre- and postdiagnosis. RESULTS: The project was funded for 2015-2018, and enrollment will be completed by the end of 2018. Data coding is currently under way and the first results are expected to be submitted for publication in 2019. Results will include both quantitative and qualitative data about participants and the similarities and differences between coded social media posts. CONCLUSIONS: This pilot study is the first step in creating a taxonomy of social media indicators for prediabetes. Such a taxonomy would provide a tool for researchers and health care professionals to use social media postings for identifying those at greater risk of having prediabetes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10720 JMIR Publications 2018-12-14 /pmc/articles/PMC6315248/ /pubmed/30552084 http://dx.doi.org/10.2196/10720 Text en ©Xiaomeng Xu, Michelle L Litchman, Perry M Gee, Webb Whatcott, Loni Chacon, John Holmes, Sankara Subramanian Srinivasan. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 14.12.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included. |
spellingShingle | Protocol Xu, Xiaomeng Litchman, Michelle L Gee, Perry M Whatcott, Webb Chacon, Loni Holmes, John Srinivasan, Sankara Subramanian Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study |
title | Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study |
title_full | Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study |
title_fullStr | Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study |
title_full_unstemmed | Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study |
title_short | Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study |
title_sort | predicting prediabetes through facebook postings: protocol for a mixed-methods study |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315248/ https://www.ncbi.nlm.nih.gov/pubmed/30552084 http://dx.doi.org/10.2196/10720 |
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