APPLYING THE POMELO BEST PRACTICES FRAMEWORK TO ENHANCE REPORTING STANDARDS IN PATIENT LISTENING STUDIES FROM SOCIAL MEDIA
Author(s)
Philipp Cimiano, PhD1, Nicole Brazda2, Cornelius Starke-Knäusel, M.Sc.2, Matthias Hartung, Ph.D.3, Ana L. Schmidt, PhD4, Maria Carmela de Vuono, MSc5, Aditya S. Tyagi, MSc6, Juergen Gottowik, PhD7, Raul Rodriguez-Esteban, MSc4, Ben Collins, PhD8, Chris Wieckowski, MSc9, thierry Escudier, MSc6;
1Bielefeld University, Bielefeld, Germany, 2Semalytix GmbH, Bielefeld, Germany, 3Boehringer Ingelheim, Ingelheim am Rhein, Germany, 4Roche, Basel, Switzerland, 5Chiesi, Parma, Italy, 6Pistoia Alliance, Wakefield, MA, USA, 7Hoffman La Roche AG, Basel, Switzerland, 8Boehringer Ingelheim GmbH, Ingelheim, Germany, 9Boerhinger Ingelheim, Ingelheim, Germany
1Bielefeld University, Bielefeld, Germany, 2Semalytix GmbH, Bielefeld, Germany, 3Boehringer Ingelheim, Ingelheim am Rhein, Germany, 4Roche, Basel, Switzerland, 5Chiesi, Parma, Italy, 6Pistoia Alliance, Wakefield, MA, USA, 7Hoffman La Roche AG, Basel, Switzerland, 8Boehringer Ingelheim GmbH, Ingelheim, Germany, 9Boerhinger Ingelheim, Ingelheim, Germany
OBJECTIVES: The POMELO framework provides structured best-practice guidelines for the use of social media listening (SML) in patient-focused drug development (PFDD). It was developed by the Expert Community Group “Exploiting Real-World Data From Social Media in Patient-Focused Drug Development” of the Pistoia Alliance by (1) analysis of methodological standards in peer-reviewed SML studies, (2) refinement based on applied research experience, and (3) evaluation in an illustrative case study in type 2 diabetes mellitus as described in a recent publication.
This study aims to validate the POMELO framework through a posthoc review of recently published SML studies by assessing the alignment of current SML practice with the POMELO methodology. In addition, we propose a reporting guideline for key parameters of SML studies.
METHODS: A structured literature search identified 10 peer-reviewed publications between 2023 and 2025 that applied SML in the context of PFDD, which were manually reviewed and mapped against the methodological domains defined in the POMELO framework. Reporting practices were evaluated with respect to the presence and clarity of key study parameters, including research setting, data sources, algorithmic approaches, and analytical methods.
RESULTS: Comparison with the POMELO framework showed a high overlap of current practical standards. Regarding the reporting standards in SML studies, we found a high inconsistency and uncertainty of which parameters should be included in a publication (e.g. number of fora, quality measures of algorithms) underlining the importance of establishing and disseminating best practices in this field.
CONCLUSIONS: The POMELO framework consolidates the most effective and commonly applied best practices for SML in PFDD. By synthesizing existing methodological standards and addressing gaps in reporting, POMELO provides a structured foundation that supports improved study quality, interpretability, and the credibility and acceptance of SML. Part of the activities were supported by the Pistoia Alliance.
This study aims to validate the POMELO framework through a posthoc review of recently published SML studies by assessing the alignment of current SML practice with the POMELO methodology. In addition, we propose a reporting guideline for key parameters of SML studies.
METHODS: A structured literature search identified 10 peer-reviewed publications between 2023 and 2025 that applied SML in the context of PFDD, which were manually reviewed and mapped against the methodological domains defined in the POMELO framework. Reporting practices were evaluated with respect to the presence and clarity of key study parameters, including research setting, data sources, algorithmic approaches, and analytical methods.
RESULTS: Comparison with the POMELO framework showed a high overlap of current practical standards. Regarding the reporting standards in SML studies, we found a high inconsistency and uncertainty of which parameters should be included in a publication (e.g. number of fora, quality measures of algorithms) underlining the importance of establishing and disseminating best practices in this field.
CONCLUSIONS: The POMELO framework consolidates the most effective and commonly applied best practices for SML in PFDD. By synthesizing existing methodological standards and addressing gaps in reporting, POMELO provides a structured foundation that supports improved study quality, interpretability, and the credibility and acceptance of SML. Part of the activities were supported by the Pistoia Alliance.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
PCR45
Topic
Patient-Centered Research
Topic Subcategory
Instrument Development, Validation, & Translation, Patient-reported Outcomes & Quality of Life Outcomes
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)