Responses to a COVID-19 vaccination intervention: Qualitative analysis of 17K unsolicited SMS replies.

The development of effective interventions for COVID-19 vaccination has proven challenging given the unique and evolving determinants of that behavior. A tailored intervention to drive vaccination uptake through machine learning-enabled personalization of behavior change messages unexpectedly yielded a high volume of real-time short message service (SMS) feedback from recipients. A qualitative analysis of those replies contributes to a better understanding of the barriers to COVID-19 vaccination and demographic variations in determinants, supporting design improvements for vaccination interventions. OBJECTIVE: The purpose of this study was to examine unsolicited replies to a text message intervention for COVID-19 vaccination to understand the types of barriers experienced and any relationships between recipient demographics, intervention content, and reply type. METHOD: We categorized SMS replies into 22 overall themes. Interrater agreement was very good (all κpooled > 0.62). Chi-square analyses were used to understand demographic variations in reply types and which messaging types were most related to reply types. RESULTS: In total, 10,948 people receiving intervention text messages sent 17,090 replies. Most frequent reply types were "already vaccinated" (31.1%), attempts to unsubscribe (25.4%), and "will not get vaccinated" (12.7%). Within "already vaccinated" and "will not get vaccinated" replies, significant differences were observed in the demographics of those replying against expected base rates, all p > .001. Of those stating they would not vaccinate, 34% of the replies involved mis-/disinformation, suggesting that a determinant of vaccination involves nonvalidated COVID-19 beliefs. CONCLUSIONS: Insights from unsolicited replies can enhance our ability to identify appropriate intervention techniques to influence COVID-19 vaccination behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personalized Digital Health Communications to Increase COVID-19 Vaccination in Underserved Populations: A Double Diamond Approach to Behavioral Design.

The COVID-19 pandemic exacerbated pre-existing health disparities. People of historically underserved communities, including racial and ethnic minority groups and people with lower incomes and educational attainments, experienced disproportionate premature mortality, access to healthcare, and vaccination acceptance and adoption. At the same time, the pandemic increased reliance on digital devices, offering a unique opportunity to leverage digital communication channels to address health inequities, particularly related to COVID-19 vaccination. We offer a real-world, systematic approach to designing personalized behavior change email and text messaging interventions that address individual barriers with evidence-based behavioral science inclusive of underserved populations. Integrating design processes such as the Double Diamond model with evidence-based behavioral science intervention development offers a unique opportunity to create equitable interventions. Further, leveraging behavior change artificial intelligence (AI) capabilities allows for both personalizing and automating that personalization to address barriers to COVID-19 vaccination at scale. The result is an intervention whose broad component library meets the needs of a diverse population and whose technology can deliver the right components for each individual.