SiKurang: Development and Field Evaluation of an Offline-First mHealth System for Community-Based Stunting Risk Detection and Counselling in Low-Connectivity Primary Care Settings

Background: Despite growing interest in mHealth solutions for community nutrition, no prior study has evaluated an integrated offline system combining on-device machine learning, NLP-based counseling, and geospatial visualization for non-specialist cadres within Indonesia's Posyandu network the gap this study addresses.

Objective: We aimed to evaluate the feasibility, accuracy, usability, and early effects of an offline-first Android application (SiKurang) for stunting risk assessment, counseling, and geospatial visualization.

Methods: We conducted a convergent mixed-methods, single-arm pre–post pilot at two Posyandu over 12 weeks, involving mothers/caregivers, community health cadres, and nutritionists. Outcomes included AUROC for on-device risk scoring, System Usability Scale (SUS), User Experience Questionnaire (UEQ-S), caregiver knowledge, administrative burden, and targeted home visits; interviews were thematically analyzed.

Results: On-device risk scoring achieved AUROC 0.87; usability was high (SUS 84.2; UEQ-S 1.86). Caregiver knowledge improved markedly (Cohen's d = 1.28). Risk maps supported a 22% increase in targeted home visits. The app operated reliably offline and synchronized upon connectivity, reducing administrative workload, with no major cultural or usability barriers reported.

Conclusion: The application was feasible and acceptable in primary care, enabling timely, data-informed counseling and referral in low-connectivity environments. This study provided field evidence for an offline-first, low-cost mHealth model delivering on-device analytics and geovisualization for non-specialist cadres, offering a scalable template for strengthening maternal–child health at the last mile. Scientifically, this study contributes the first field-validated, multi-component offline mHealth framework for community-level stunting surveillance in a low-resource LMIC setting.