A Theory and Evidence-Based Artificial Intelligence-Driven Motivational Digital Assistant to Decrease Vaccine Hesitancy: Intervention Development and Validation Article Swipe
Yan Li
,
Kit-Ching Lee
,
Daniel Bressington
,
Qiuyan Liao
,
Mengting He
,
Ka-Kit Law
,
Angela Yee Man Leung
,
Alex Molassiotis
,
Mengqi Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/vaccines12070708
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/vaccines12070708
Vaccine hesitancy is one of the top ten threats to global health. Artificial intelligence-driven chatbots and motivational interviewing skills show promise in addressing vaccine hesitancy. This study aimed to develop and validate an artificial intelligence-driven motivational digital assistant in decreasing COVID-19 vaccine hesitancy among Hong Kong adults. The intervention development and validation were guided by the Medical Research Council’s framework with four major steps: logic model development based on theory and qualitative interviews (n = 15), digital assistant development, expert evaluation (n = 5), and a pilot test (n = 12). The Vaccine Hesitancy Matrix model and qualitative findings guided the development of the intervention logic model and content with five web-based modules. An artificial intelligence-driven chatbot tailored to each module was embedded in the website to motivate vaccination intention using motivational interviewing skills. The content validity index from expert evaluation was 0.85. The pilot test showed significant improvements in vaccine-related health literacy (p = 0.021) and vaccine confidence (p = 0.027). This digital assistant is effective in improving COVID-19 vaccine literacy and confidence through valid educational content and motivational conversations. The intervention is ready for testing in a randomized controlled trial and has high potential to be a useful toolkit for addressing ambivalence and facilitating informed decision making regarding vaccination.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/vaccines12070708
- https://www.mdpi.com/2076-393X/12/7/708/pdf?version=1720601254
- OA Status
- gold
- Cited By
- 9
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400089482
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400089482Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/vaccines12070708Digital Object Identifier
- Title
-
A Theory and Evidence-Based Artificial Intelligence-Driven Motivational Digital Assistant to Decrease Vaccine Hesitancy: Intervention Development and ValidationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-25Full publication date if available
- Authors
-
Yan Li, Kit-Ching Lee, Daniel Bressington, Qiuyan Liao, Mengting He, Ka-Kit Law, Angela Yee Man Leung, Alex Molassiotis, Mengqi LiList of authors in order
- Landing page
-
https://doi.org/10.3390/vaccines12070708Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-393X/12/7/708/pdf?version=1720601254Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-393X/12/7/708/pdf?version=1720601254Direct OA link when available
- Concepts
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Motivational interviewing, Chatbot, Digital health, Medical education, Psychology, Intervention (counseling), Test (biology), Applied psychology, Computer science, Medicine, Nursing, Artificial intelligence, Health care, Paleontology, Biology, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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