What Are the Key Themes and Sentiments Captured Through Social Listening in Response to Recent Changes in the Duchenne Muscular Dystrophy Treatment Landscape?

OBJECTIVES: The announcement that fordadistrogene movaparvovec did not show significant improvement in motor function for patients with Duchenne muscular dystrophy (DMD) generated much disappointment amongst the DMD community. However, eight days later, delandistrogene moxeparvovec-rokl, was approved by the US Food and Drug Administration (FDA), for patients with DMD who are at least four years of age.

With such rapid changes in the treatment landscape, this study looks to assess social listening as an approach to capture the evolving patient and caregiver perspective on the DMD treatment landscape. With regulators acknowledging the need for new methods to capture the patient experience and understand what is important to them, social media listening provides an opportunity to augment traditional methods of patient data collection in an efficient way. By looking at social media data in a single therapeutic area over a short period of time, this study looks to understand the impact of treatment landscape changes on patient sentiment.

METHODS: Using a social media listening platform (Brandwatch), we assessed the difference in stakeholder sentiment before and after the fordadistrogene movaparvovec announcement (June 12^th 2024), and after the announcement of the delandistrogene moxeparvovec-rokl approval (June 20^th 2024). Key words, geographic scope and data sources (social media channels and forums) were kept consistent through all time points.

RESULTS: Variability in sentiment amongst patients/caregivers was observed at all time points assessed.

CONCLUSIONS: The variability in sentiment in the DMD population over a relatively short time period confirms the ability of social media listening to capture patient perspectives quickly. However, this variability in sentiment over a 10 day time-period also points to a need for methodological scrutiny to reduce bias in social media listening data collection.