ArticleJMIR formative research2025
Usability and Usefulness of SMS-Based Artificial Intelligence Intervention (Mwana) on Breastfeeding Outcomes in Lagos, Nigeria: Pilot App Development Study.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Effects of Digital Health Interventions on Breastfeeding Rates, Self-Efficacy, and Knowledge: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Nigeria has one of the highest child mortality rates globally, with 111 deaths per 1000 live births. Exclusive breastfeeding (EBF) improves infant survival by providing essential nutrients and antibodies that protect against infections and diseases. Despite its benefits, EBF rates in Nigeria remain low at 29%, largely due to limited health care support and breastfeeding guidance. With the proliferation of mobile phones in Nigeria, mobile health (mHealth) interventions are being explored as scalable solutions. SMS text messaging interventions have demonstrated success in delivering behavioral interventions; yet, few use artificial intelligence (AI) for personalized breastfeeding support. Objective: This study evaluates the effectiveness of Mwana, an AI-powered SMS-based app, in improving breastfeeding outcomes for postpartum mothers in Lagos, Nigeria. Methods: Mwana was developed using TextIt for SMS integration and Meta's Wit.ai for natural language processing (NLP). The chatbot provides breastfeeding support via SMS, offering personalized tips, addressing common challenges, and connecting users to human agents when necessary. The intervention was piloted with 216 postpartum mothers recruited through local health care networks, focusing on usability, usefulness, and engagement. The study used a mixed methods approach, using structured surveys and observation to assess participant experiences at multiple intervals over a 6-month period. Primary outcomes measured were app usability, usefulness, and breastfeeding adherence. Results: The intervention was well-received, with high scores for both usefulness (mean 4.01, SD 1.41) and usability (mean 3.92, SD 1.35) on a 5-point scale. The majority of respondents, 57% (118/206), rated the chatbot's usefulness at the highest score of 5. Qualitative feedback statements identified areas for improvement, including enhancing AI comprehension, response times, and human-like interaction. Conclusions: The study highlights the potential of Mwana to improve breastfeeding outcomes in resource-limited settings, contributing to the growing body of evidence supporting mHealth interventions in maternal and child health. By leveraging personalized messaging, SMS-based delivery, and language localization, Mwana offers a scalable, accessible solution. However, challenges remain regarding AI comprehension, and further research is necessary to evaluate Mwana's effectiveness among populations not actively engaged with health care services. Future iterations will expand AI training datasets, refine NLP capabilities, and scale to broader populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.