Digital transformation of community child health services: Strengthening local food and toddler nutrition

Abstract

Background: Stunting remains a major issue in Indonesia, especially among toddlers, driven by limited maternal nutrition knowledge, poor healthcare access, and inadequate sanitation in rural areas. This study introduces PosyGrow, a digital tool integrating IoT and cloud computing to enhance the traditional Posyandu system. The application automates height and weight measurement and uses machine learning to provide personalized dietary recommendations based on local foods. Methods: Data were collected through community service activities involving 12 mothers and one Posyandu cadre. Participants were educated on stunting prevention and local food utilization, including preparing moringa leaf pudding. Nutritional content analysis and participant feedback were gathered via surveys and observations. Findings: PosyGrow streamlined toddler growth monitoring with real-time cloud data synchronization. Participants responded positively to moringa as an affordable, locally available food for stunting prevention. The program also significantly improved maternal awareness of nutrition and stunting prevention practices. AI-based nutritional recommendations were well-received, supporting reduced stunting risks in the monitored group. Conclusion: PosyGrow represents an innovative step in digitalizing Posyandu services, enhancing monitoring and prevention of stunting. By integrating IoT, AI, and community-based education, it addresses gaps in maternal knowledge and nutritional practices, promoting long-term health improvements. Novelty/Originality of this Article: This study presents a novel approach to stunting prevention by combining IoT, cloud technology, and machine learning within Posyandu services, alongside practical use of local food resources like moringa leaves, offering a fresh perspective for rural Indonesian communities.