Permatasari Tria Astika Endah, Chadirin Yudi, Ernirita Ernirita, Syafitri Anisa Nurul, Fadhilah Devina Alifia
Department of Nutrition, Faculty of Medicine and Health, Universitas Muhammadiyah Jakarta, Central Jakarta, Indonesia.
Department of Civil and Environmental Engineering, IPB University, Bogor, Indonesia.
BMC Nutr. 2025 May 13;11(1):93. doi: 10.1186/s40795-025-01074-6.
Adaptive and innovative technologies to prevent stunting are being developed continuously in various countries. This study aimed to develop and evaluate the accuracy of a stunting risk detection application based on nutrition and sanitation indicators in children aged under five years.
This cross-sectional study was conducted between June and September 2023 and involved 316 mother-child pairs selected by simple random sampling from urban (n = 244) and rural (n = 72) areas in Bogor, West Java Province, Indonesia. An application was developed to detect stunting risk based on 25 indicators: eight indicators of maternal and child characteristics, eight nutrition indicators, and nine indicators of personal hygiene and sanitation. The nutrition and sanitation indicators were determined according to the World Health Organization conceptual framework for stunting. The accuracy of the stunting prediction model was analyzed using the Area Under Curve (AUC) and the Receiver Operating Characteristics (ROC) Curve method.
Of the 316 included children, 29.5% were stunting. The developed stunting risk detection application exhibited good sensitivity (88.3%) and specificity (83.3%). It accurately detected children at risk of stunting with an AUC of 89.6%. In urban areas, eight indicators were significantly predictive of stunting: mother's height, child's age, exclusive breastfeeding, frequency of protein consumption, balanced diet, washing hands with soap, availability of complete room functions in the house, and good household waste management. In rural areas, eight indicators were significantly predictive of stunting: mother's height, history of infectious disease, early initiation of breastfeeding, frequency of protein consumption, complementary feeding, washing hands with soap, availability of safe food storage, and availability of clean water sources for drinking. Mother's height was the dominant factor in predicting stunting in urban (adjusted odds ratio [aOR] = 3.321, 95% confidence interval [CI] = 1.202-3.051, p = 0.006) and rural (aOR = 3.927, 95% CI = 1.132-4.281, p = 0.001).
The developed application exhibited good accuracy and quickly assessed the risk of stunting in children, enabling it to provide appropriate recommendations to prevent stunting. However, it must be improved by simplifying the number of included indicators and re-testing on a broader scale.
各国不断研发预防发育迟缓的适应性和创新性技术。本研究旨在开发并评估一款基于营养和卫生指标的5岁以下儿童发育迟缓风险检测应用程序的准确性。
本横断面研究于2023年6月至9月进行，通过简单随机抽样从印度尼西亚西爪哇省茂物市的城市地区（n = 244）和农村地区（n = 72）选取了316对母婴。开发了一款基于25项指标检测发育迟缓风险的应用程序：8项母婴特征指标、8项营养指标以及9项个人卫生和环境卫生指标。营养和卫生指标根据世界卫生组织发育迟缓概念框架确定。使用曲线下面积（AUC）和受试者工作特征（ROC）曲线方法分析发育迟缓预测模型的准确性。
在纳入的316名儿童中，29.5%发育迟缓。所开发的发育迟缓风险检测应用程序表现出良好的敏感性（88.3%）和特异性（83.3%）。其准确检测出发育迟缓风险儿童的AUC为89.6%。在城市地区，8项指标对发育迟缓有显著预测作用：母亲身高、儿童年龄、纯母乳喂养、蛋白质摄入频率、均衡饮食、用肥皂洗手、房屋功能齐全、良好的家庭垃圾管理。在农村地区，8项指标对发育迟缓有显著预测作用：母亲身高、传染病史、早期母乳喂养、蛋白质摄入频率、辅食添加、用肥皂洗手、安全的食物储存、清洁饮用水源。母亲身高是城市（调整优势比[aOR] = 3.321，95%置信区间[CI] = 1.202 - 3.051，p = 0.006）和农村（aOR = 3.927，95% CI = 1.132 - 4.281，p = 0.001）发育迟缓预测的主导因素。
所开发的应用程序表现出良好的准确性，能够快速评估儿童发育迟缓风险，从而能够提供预防发育迟缓的适当建议。然而，必须通过简化纳入指标数量并在更广泛范围内重新测试来加以改进。
