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Social media analytics of overactive bladder posts: what do patients know and want to know?

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International Urogynecology Journal Aims and scope Submit manuscript

Abstract

Objective

To assess women’s knowledge, patient experience, and treatment decision making regarding overactive bladder (OAB) using digital ethnography.

Methods

Online posts were identified using a data mining service. Two hundred randomized posts were reviewed and coded using grounded theory. We then applied a latent Dirichlet allocation (LDA) probabilistic topic modeling process to review the entire collection of identified posts.

Results

A total of 2618 posts by 1867 unique users from 203 different websites were identified. Our analysis yielded six themes: the impact of OAB on quality of life, patient-physician interactions, online engagement, symptom management, patient knowledge acquisition, and alternative therapies.

Conclusion

Overall, online communities are a source of support for women to self-manage the OAB symptom complex and help overcome treatment pathway challenges. Digital ethnography provides insight into patient knowledge and barriers to patient-centered care, which are important to improve patient outreach. Additionally, we identify similar findings to prior work, indicating the reliability of studying social media.

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Funding

This study was funded by a pilot grant from the NIDDK Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium (JTA, BS).

Author information

Authors and Affiliations

  1. Department of Urology, Davis School of Medicine, University of California, Sacramento, CA, USA

    Gabriela Gonzalez

  2. Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA, USA

    Kristina Vaculik, Carine Khalil, Christopher V. Almario & Brennan M. R. Spiegel

  3. David Geffen School of Medicine at University of California, Los Angeles, CA, USA

    Yuliya Zektser

  4. Computational Diagnostics, Departments of Radiology and Pathology, UCLA, Los Angeles, CA, USA

    Corey W. Arnold

  5. Department of Surgery, Division of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jennifer T. Anger

Authors
  1. Gabriela Gonzalez
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  2. Kristina Vaculik
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  3. Carine Khalil
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  4. Yuliya Zektser
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  5. Corey W. Arnold
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  6. Christopher V. Almario
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  7. Brennan M. R. Spiegel
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  8. Jennifer T. Anger
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Contributions

G Gonzalez: project development, data analysis/collection, manuscript writing.

K Vaculik: data analysis/collection, manuscript writing.

C Khalil: data analysis/collection, manuscript writing.

Y Zekster: project development, manuscript writing.

C Arnold: project development, data analysis/collection, manuscript writing.

CV Almario: project development, data analysis/collection, manuscript writing.

B Spiegel: project development, data analysis/collection.

JT Anger: project development, data analysis/collection, manuscript writing.

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Conflicts of interest

Dr. Jennifer T. Anger is an expert witness for Boston Scientific and advisory board member for Axonics. All other authors report no disclosures.

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Appendices

Appendix 1

Table 3 Search and exclusion terms used in the Treato® algorithm
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Appendix 2

Table 4 Websites from which posts were identified
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Gonzalez, G., Vaculik, K., Khalil, C. et al. Social media analytics of overactive bladder posts: what do patients know and want to know?. Int Urogynecol J 32, 2729–2736 (2021). https://doi.org/10.1007/s00192-021-04686-1

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  • DOI: https://doi.org/10.1007/s00192-021-04686-1

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