What do people really think about the RSV vaccine? Study of unsolicited text replies from adults over 60

E. Susanne Blazek, Sarah Deedat, Olivia Kinney, Allison M. Londerée, Stacey Frede and Amy Bucher ()
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E. Susanne Blazek: Stripe
Sarah Deedat: Behavioral Reinforcement Learning Lab (BReLL)
Olivia Kinney: Kroger Health
Allison M. Londerée: Behavioral Reinforcement Learning Lab (BReLL)
Stacey Frede: Kroger Health
Amy Bucher: Behavioral Reinforcement Learning Lab (BReLL)

Palgrave Communications, 2025, vol. 12, issue 1, 1-15

Abstract: Abstract A digital health intervention (DHI) using SMS precision nudging to drive RSV vaccine uptake among adults over 60 was launched with a large community pharmacy chain in 2023, two months after the vaccine’s FDA approval for adult administration in the United States. Tens of thousands of patients texted back. In this exploratory investigation, we employed thematic analysis and structural topic modelling to extract topics (e.g., Kind declines, Moral disgust, etc.), sentiment (negative to positive), and function (emotional to practical) expressed in the text replies in order to understand patient attitudes and behavioural determinants of RSV vaccination. The analyses reveal 10 topics from the thematic analysis and 30 more granular topics from the structural topic modelling. Expressed attitudes shifted over the course of the DHI, with less negativity later in the intervention. People who did not receive the flu vaccine and people with commercial insurance responded more frequently that they would not get vaccinated. Specific behaviour change techniques (BCTs) were associated with overall replies and specific topics. Framing intervention messages with information about emotional consequences elicited the highest proportion of replies; framing with anticipated regret elicited the lowest proportion. We extend previous work by leveraging unsolicited replies to analyse public attitudes toward a new vaccine, with implications for future RSV and other vaccine interventions at pharmacies and elsewhere.

Date: 2025
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DOI: 10.1057/s41599-025-04961-2

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