Accuracy of growth monitoring using KMS/Buku KIA and the e-kaderqu application among Posyandu cadres in North Sangatta, Indonesia: a cross-sectional study
DOI:
https://doi.org/10.66472/isadd.v1i4.798Keywords:
e-kaderqu, posyandu, growth monitoring, KMS, user acceptance, digital health, logistic regressionAbstract
Accurate growth monitoring is essential for detecting nutritional problems among children under five, yet manual Posyandu recording with KMS/Buku KIA remains vulnerable to errors in age determination, plotting, interpretation, and reporting. This study evaluated cadre characteristics associated with manual and digital growth-monitoring accuracy and user acceptance of the e-kaderqu application in North Sangatta, Indonesia. A cross-sectional study was conducted among 64 Posyandu cadres in the working areas of BLUD Puskesmas Sangatta Utara and BLUD Puskesmas Teluk Lingga. Cadre age, education level, duration of service, training history, and knowledge were assessed using questionnaires. Accuracy was tested using standardized dummy toddler data for KMS/Buku KIA age-column determination, growth-chart plotting, and e-kaderqu input. User acceptance was measured using seven indicators and categorized by the median score. All cadres accurately determined the KMS age column. Age was the only significant predictor of KMS/Buku KIA plotting accuracy (odds ratio = 0.885; p = 0.005) and e-kaderqu input accuracy (odds ratio = 0.808; p < 0.001), indicating lower odds of accurate performance with increasing age. Education, training history, duration of service, and knowledge were not significant predictors. Good acceptance of e-kaderqu was reported by 53.1% of cadres, and no measured cadre characteristic significantly predicted acceptance. The findings indicate that e-kaderqu is acceptable across cadre groups, but digital implementation requires practical training, digital mentoring, input validation, and supervision to improve data accuracy.
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