<![CDATA[ABSTRACT Accurate and consistent anthropometric data are essential for reliable interpretation of toddler nutritional status based on WHO Z-score. The use of Internet of Things (IoT)-based toddler scales enables automated integration between anthropometric measurements and the data management system; however, the reliability and integrity of the generated data require systematic evaluation prior to their application in growth monitoring. This study aimed to assess the measurement performance and data integrity of an IoT-based toddler scale through standard calibration testing, as well as to examine the potential impact of measurement error on Z-score variation and nutritional status classification. An applied experimental approach was employed by adopting medical device calibration principles. The scale was tested under static conditions using reference weights and standardized measurements to evaluate the accuracy and repeatability of body weight and height measurements. Measurement data were automatically recorded, stored in a centralized databased, and analyzed for growth chart visualization and z-score-based nutritional status classification. The evaluation demonstrated a maximum measurement error of 1,25% for body weight and 0% for height measurements. Quantitative simulation indicated that a ±1,25% measurement error at a nominal body weight of 10 kg could result in a ±0,18SD variation in z-score. Such variation may lead to a shift in nutritional status classification when measurements are located near the -2SD threshold. Although the observed measurement errors were relatively small, their impact on nutritional status classification may be significant under specific conditions. Calibration-based evaluation underscores the importance of measurement accuracy and data integrity as essential prerequisites for the technical readiness of IoT-based systems to support toddler growth monitoring. Keywords: Internet of Things, Toddler Growth Monitoring, Measurement Accuracy, Data Integrity, Z-Score.]]>
