Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Xiaomeng, Litchman, Michelle L, Gee, Perry M, Whatcott, Webb, Chacon, Loni, Holmes, John, Srinivasan, Sankara Subramanian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
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
_version_ 1783384249614729216
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
work_keys_str_mv AT xuxiaomeng predictingprediabetesthroughfacebookpostingsprotocolforamixedmethodsstudy
AT litchmanmichellel predictingprediabetesthroughfacebookpostingsprotocolforamixedmethodsstudy
AT geeperrym predictingprediabetesthroughfacebookpostingsprotocolforamixedmethodsstudy
AT whatcottwebb predictingprediabetesthroughfacebookpostingsprotocolforamixedmethodsstudy
AT chaconloni predictingprediabetesthroughfacebookpostingsprotocolforamixedmethodsstudy
AT holmesjohn predictingprediabetesthroughfacebookpostingsprotocolforamixedmethodsstudy
AT srinivasansankarasubramanian predictingprediabetesthroughfacebookpostingsprotocolforamixedmethodsstudy