An Artificial Intelligence-Based, Personalized Smartphone App to Improve Childhood Immunization Coverage and...

Area of Science:

• Public Health
• Digital Health Interventions
• Vaccinology

Background:

• Low immunization uptake rates in Pakistan are a significant public health concern.
• Barriers include lack of awareness, forgetfulness, misinformation, and COVID-19 related disruptions.
• Innovative, cost-effective interventions are needed to improve routine childhood immunization (RCI) coverage.

Purpose of the Study:

• To evaluate a personalized mobile app's effectiveness in improving on-time RCI visits at 10 and 14 weeks.
• To assess the feasibility of integrating an artificial intelligence model into the app.
• To explore caregiver perceptions and influencing factors for mobile-based vaccination interventions.

Main Methods:

• A mixed-methods randomized controlled trial comparing a mobile app intervention to standard care.
• Recruitment of caregivers of newborns/infants at Aga Khan University Hospital.
• Development of app content based on qualitative findings on caregiver perceptions and barriers to RCI.

Main Results:

• Pretrial qualitative interviews completed; randomized controlled trial enrollment is ongoing.
• Study exit interviews will be conducted at 14-week immunization visits or via phone at 18 weeks.
• Data collection is in progress to assess app impact and user experience.

Conclusions:

• The study will provide insights into the feasibility, acceptability, and usability of an Android-based smartphone app for RCI in Pakistan.
• Results will inform the potential of digital health tools to improve childhood vaccination coverage in low- and middle-income countries.
• This research addresses a critical need for scalable solutions to enhance immunization rates.