ArticleOxford open digital health2026
An artificial intelligence-based chatbot as a tool for promoting nutrition in a community setting.
Article in Oxford open digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Malnutrition remains a major public health problem in Tanzania, worsened by limited access to personalized, culturally relevant nutrition information. Existing gaps in community nutrition education across sub-Saharan Africa, combined with conventional approaches ignoring individual dietary needs, contribute to undernutrition and chronic disease. However, while AI-powered chatbots have shown global effectiveness in scalable health education, evidence from low-resource, Swahili-speaking settings is scarce. This study, aligned with Sustainable Development Goals (SDGs) 2, 3, and 4, developed Lishe Yetu, a culturally localized digital nutrition tool aimed at bridging these gaps. A pre-post quasi-experimental mixed-methods design evaluated Lishe Yetu among 400 Tanzanian residents between January and April 2025. Built on retrieval-augmented generation using GPT-3.5-turbo, the platform drew from over 2500 expert-reviewed sources including World Health Organization (WHO), Food and Agriculture Organization of the United Nations (FAO), Tanzania Food and Nutrition Centre, and validated Swahili materials. Nutritional knowledge was measured using a validated 20-item questionnaire, with engagement tracked via server logs and satisfaction assessed through Likert-scale surveys and thematic analysis of qualitative feedback. Results showed strong engagement, with average sessions lasting 12 minutes and 15.2 messages exchanged; 87% completed at least one full session. Satisfaction averaged 4.3/5.0, and 88% found the tool easy to use. Knowledge scores improved significantly from 52% to 73%, a 21 percentage-point gain with a large effect size (Cohen's d = 1.57,
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