From Paper to Precision: A Systematic Review on Digital Technologies for Improving the Quality of Childhood Growth Monitoring
DOI:
https://doi.org/10.58427/apghn.5.2.2026.74-85Keywords:
anthropometry, child growth, health information systems, mobile health, pediatric growth assessmentAbstract
Background: Childhood growth monitoring is essential for detecting malnutrition and growth disorders. However, traditional paper-based methods are prone to errors, incompleteness, and fragmentation. Digital technologies have emerged as potential tools to enhance efficiency of growth monitoring. This systematic review aims to synthesize evidence on the benefits and challenges of implementing digital technologies for growth monitoring in children.
Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted across multiple databases. Eligible studies evaluated digital interventions such as mobile health applications with automated anthropometric calculations, computer-based growth monitoring integrated into the electronic health record, and chatbot-based reporting systems. Data were extracted on study design, population, technology features, and outcomes related to data and implementation.
Result: Seven studies were analyzed, representing over 50,000 child growth assessments and involving approximately 400 frontline health workers. Digital technologies improved the completeness, accuracy, and timeliness of data collection. Automation reduced human error, supporting more consistent interpretation of nutritional status, earlier detection and reporting of inadequate growth, along with improved nutritional outcomes. These technologies were highly accepted by frontline health workers for their ability to simplify complex tasks. However, most challenges arose from constraints in digital infrastructure and uneven technology implementation across healthcare facilities.
Conclusion: Digital technologies can transform growth monitoring from a manual, error-prone process into a precise and scalable system for early detection of malnutrition. Addressing challenges is essential for successful implementation and scale-up in health systems.
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