Cargando…
Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis
BACKGROUND: Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826307/ https://www.ncbi.nlm.nih.gov/pubmed/35076404 http://dx.doi.org/10.2196/26781 |
_version_ | 1784647402891771904 |
---|---|
author | Gonzalez, Gabriela Vaculik, Kristina Khalil, Carine Zektser, Yuliya Arnold, Corey Almario, Christopher V Spiegel, Brennan Anger, Jennifer |
author_facet | Gonzalez, Gabriela Vaculik, Kristina Khalil, Carine Zektser, Yuliya Arnold, Corey Almario, Christopher V Spiegel, Brennan Anger, Jennifer |
author_sort | Gonzalez, Gabriela |
collection | PubMed |
description | BACKGROUND: Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community. OBJECTIVE: This study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography. METHODS: A data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results. RESULTS: A total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7% (27/105) were related to online community support, and 22% (23/105) focused on UTI risk factors and prevention strategies. CONCLUSIONS: Our large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women’s UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way. |
format | Online Article Text |
id | pubmed-8826307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-88263072022-02-11 Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis Gonzalez, Gabriela Vaculik, Kristina Khalil, Carine Zektser, Yuliya Arnold, Corey Almario, Christopher V Spiegel, Brennan Anger, Jennifer J Med Internet Res Original Paper BACKGROUND: Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community. OBJECTIVE: This study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography. METHODS: A data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results. RESULTS: A total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7% (27/105) were related to online community support, and 22% (23/105) focused on UTI risk factors and prevention strategies. CONCLUSIONS: Our large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women’s UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way. JMIR Publications 2022-01-25 /pmc/articles/PMC8826307/ /pubmed/35076404 http://dx.doi.org/10.2196/26781 Text en ©Gabriela Gonzalez, Kristina Vaculik, Carine Khalil, Yuliya Zektser, Corey Arnold, Christopher V Almario, Brennan Spiegel, Jennifer Anger. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.01.2022. 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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Gonzalez, Gabriela Vaculik, Kristina Khalil, Carine Zektser, Yuliya Arnold, Corey Almario, Christopher V Spiegel, Brennan Anger, Jennifer Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis |
title | Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis |
title_full | Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis |
title_fullStr | Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis |
title_full_unstemmed | Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis |
title_short | Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis |
title_sort | using large-scale social media analytics to understand patient perspectives about urinary tract infections: thematic analysis |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826307/ https://www.ncbi.nlm.nih.gov/pubmed/35076404 http://dx.doi.org/10.2196/26781 |
work_keys_str_mv | AT gonzalezgabriela usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis AT vaculikkristina usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis AT khalilcarine usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis AT zektseryuliya usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis AT arnoldcorey usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis AT almariochristopherv usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis AT spiegelbrennan usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis AT angerjennifer usinglargescalesocialmediaanalyticstounderstandpatientperspectivesabouturinarytractinfectionsthematicanalysis |