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
DOI:
https://doi.org/10.59784/glosains.v7i2.670Keywords:
stunting detection, offline Mhealth, edge AI, community health cadres, posyanduAbstract
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.
References
Anam, C., Plaček, M., Valentinov, V., & Del Campo, C. (2023). Village funds and poverty reduction in Indonesia: new policy insight. Discover Global Society, 1(1), 14.
Bäcker-Peral, V., Meursault, V., & Severen, C. (2025). Can LLMs Credibly Transform the Creation of Panel Data from Diverse Historical Tables? ArXiv Preprint ArXiv:2505.11599. https://doi.org/10.48550/arXiv.2505.11599
Choudhary, S., Vijitha, S., Bhavani, D. D., Bhuvaneswari, N., Tiwari, M., & Subburam, S. (2025). Edge AI deploying artificial intelligence models on edge devices for real-time analytics. ITM Web of Conferences, 76, 1009. https://doi.org/10.1051/itmconf/20257601009
Coleman, T., Till, S., Farao, J., Shandu, L., Khuzwayo, N., Muthelo, L., Mbombi, M., Bopape, M., Van Heerden, A., & Mothiba, T. (2023). Reconsidering priorities for digital maternal and child health: community-centered perspectives from South Africa. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW2), 1–31. https://doi.org/10.1145/3610081
Dange, N. S., Khadilkar, V., Kore, V., Mondkar, S., Yewale, S., Gondhalekar, K., & Khadilkar, A. V. (2024). Comparison of WHO 2006 growth standards and synthetic Indian references in assessing growth in normal children and children with growth-related disorders. Indian Journal of Endocrinology and Metabolism, 28(2), 220–226.
Demirelli, H., Isler, Y., & Yuce, Y. K. (2023). Development and Perceived Usability Evaluation of a Mobile application for Notetaking. EAI Endorsed Transactions on E-Learning, 9. https://doi.org/10.4108/eetel.4538
Deng, X., & Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15(4), 2940. https://doi.org/10.3390/su15042940
Fadhilah, A., Handayani, A. S., Ziad, I., Husni, N. L., Chodijah, S., Huda, M. H., Agustini, N., Despitasari, M., & Siregar, R. H. (2023). Design of Android and IoS Applications for Mobile Health Monitoring Devices. Advance Sustainable Science, Engineering and Technology, 5(2), 230206. https://doi.org/10.26877/asset.v5i2.16508
Fadillah, M. A., Usmeldi, U., Lufri, L., Mawardi, M., & Festiyed, F. (2024). Exploring user perceptions: The impact of ChatGPT on high school students’ physics understanding and learning. Advances in Mobile Learning Educational Research, 4(2), 1197–1207. https://doi.org/10.25082/amler.2024.02.013
Gilano, G., Hailegebreal, S., & Seboka, B. T. (2021). Geographical variation and associated factors of vitamin A supplementation among 6–59-month children in Ethiopia. Plos One, 16(12), e0261959. https://doi.org/10.1371/journal.pone.0261959
Haroun, Y., Sambaiga, R., Sarkar, N., Kapologwe, N. A., Kengia, J., Liana, J., Kimatta, S., James, J., Simon, V., & Hassan, F. (2022). A human centred approach to digital technologies in health care delivery among mothers, children and adolescents. BMC Health Services Research, 22(1), 1393. https://doi.org/10.1186/s12913-022-08744-2
Harth, N., Anagnostopoulos, C., Voegel, H.-J., & Kolomvatsos, K. (2022). Local & federated learning at the network edge for efficient predictive analytics. Future Generation Computer Systems, 134, 107–122. https://doi.org/10.1016/j.future.2022.03.030
Huang, K.-Y., Nakigudde, J., Christine, T., Cheng, S., Muyomba, D., Mugisa, E. T., Kisakye, E. N., Sentongo, H., Schoenthaler, A., & El-Shahawy, O. (2024). Implementing a Digital Child Behavioral Health Prevention Program in Faith-Based Settings in Uganda: A Feasibility Study. Medical Research Archives, 12(10), 10–18103. https://doi.org/10.18103/mra.v12i10.5926
Hulliyah, K., Rayyan, F., & Bakar, N. S. A. A. (2022). Development of a chatbot for the online application telegram chat with an approach to the emotion classification text using the indobert-lite method. 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), 1–4.
Hyzy, M., Bond, R., Mulvenna, M., Bai, L., Dix, A., Leigh, S., & Hunt, S. (2022). System usability scale benchmarking for digital health apps: meta-analysis. JMIR MHealth and UHealth, 10(8), e37290. https://doi.org/10.2196/37290
Jeon, E.-T., Jung, S. J., Yeo, T. Y., Seo, W.-K., & Jung, J.-M. (2023). Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning. Frontiers in Neurology, 14, 1243700. https://doi.org/10.3389/fneur.2023.1243700
Keshavjee, K., Johnston-Jewell, D., Lee, B., & Kyba, R. (2022). Designing disease-specific mHealth apps for clinical value. Smart Pervasive Healthcare, 61–83. https://doi.org/10.5772/intechopen.99945
Krisna, J. I. T., Luthfiarta, A., Cahya, L. D., Winarno, S., & Nugraha, A. (2024). Comparing Optimizer Strategies For Enhancing Emotion Classification In IndoBERT Models. Advance Sustainable Science, Engineering and Technology, 6(2), 240203. https://doi.org/10.26877/asset.v6i2.18228
Linder, L., Utendorfer, H., Oliveros, B., Gilliland, S., Tiase, V. L., & Altizer, R. (2024). Usability Evaluation of the Revised Color Me Healthy Symptom Assessment App: Perspectives of Children and Parents. Children, 11(10), 1215. https://doi.org/10.3390/children11101215
Liu, C., Chen, B., Shao, W., Zhang, C., Wong, K. K. L., & Zhang, Y. (2024). Unraveling attacks to machine-learning-based IoT systems: A survey and the open libraries behind them. IEEE Internet of Things Journal, 11(11), 19232–19255. https://doi.org/10.48550/arXiv.2401.11723
Miranda, A. V., Sirmareza, T., Nugraha, R. R., Rastuti, M., Syahidi, H., Asmara, R., & Petersen, Z. (2023). Towards stunting eradication in Indonesia: Time to invest in community health workers. Public Health Challenges, 2(3), e108. https://doi.org/10.1002/puh2.108
Mugauri, H. D., Chimsimbe, M., Shambira, G., Shamhu, S., Nyamasve, J., Munyanyi, M., Gongora, R., Zizhou, S., Gabida, M., & Makurumidze, R. (2025). A decade of designing and implementing electronic health records in Sub-Saharan Africa: a scoping review. Global Health Action, 18(1), 2492913. https://doi.org/10.1080/16549716.2025.2492913
Organization, W. H. (2023). UNICEF/WHO Low Birthweight Estimates: levels and trends 2000-2020. World Health Organization.
Palumbo, R., Nicola, C., & Adinolfi, P. (2022). Addressing health literacy in the digital domain: insights from a literature review. Kybernetes, 51(13), 82–97. https://doi.org/10.1108/k-07-2021-0547
Park, J., Kim, M., El Mistiri, M., Kha, R., Banerjee, S., Gotzian, L., Chevance, G., Rivera, D. E., Klasnja, P., & Hekler, E. (2023). Advancing understanding of just-in-time states for supporting physical activity (Project JustWalk JITAI): protocol for a system ID study of just-in-time adaptive interventions. JMIR Research Protocols, 12(1), e52161. https://doi.org/10.2196/52161
Putri, E. P., & Martha, A. E. (2021). The Importance of Enacting Indonesian Data Protection Law as a Legal Responsibility for Data Leakage. Varia Justicia, 17(3), 287–303. https://doi.org/10.31603/variajusticia.v17i3.6231
Rammohan, A., & Tohari, A. (2023). Rural poverty and labour force participation: Evidence from Indonesia’s Village fund program. Plos One, 18(6), e0283041.
Rinawan, F. R., Susanti, A. I., Amelia, I., Ardisasmita, M. N., Widarti, Dewi, R. K., Ferdian, D., Purnama, W. G., & Purbasari, A. (2021). Understanding mobile application development and implementation for monitoring Posyandu data in Indonesia: A 3-year hybrid action study to build “a bridge” from the community to the national scale. BMC Public Health, 21(1), 1024. https://doi.org/10.1186/s12889-021-11035-w
Sarwar, M. N., Javed, Z., Farooq, M. S., Nazar, M. F., Wasti, S. H., Butt, I. H., Ansari, G. J., Basri, R., Kulsoom, S., & Ullah, Z. (2024). Impact of a Digital Growth Mindset on Enhancing the Motivation and Performance of Chemistry Students: A Non-Cognitive Approach. Societies, 14(8), 133. https://doi.org/10.3390/soc14080133
Saylam, B., & İncel, Ö. D. (2023). Federated learning on edge sensing devices: a review. ArXiv Preprint ArXiv:2311.01201. https://doi.org/10.48550/arXiv.2311.01201
Sedotto, R. N. M., Edwards, A. E., Dulin, P. L., & King, D. K. (2024). Engagement with mHealth alcohol interventions: user perspectives on an app or chatbot-delivered program to reduce drinking. Healthcare, 12(1), 101. https://doi.org/10.3390/healthcare12010101
Takano, E., Maruyama, H., Takahashi, T., Mori, K., Nishiyori, K., Morita, Y., Fukuda, T., Kondo, I., & Ishibashi, Y. (2023). User experience of older people while using digital health technologies: A systematic review. Applied Sciences, 13(23), 12815. https://doi.org/10.3390/app132312815
Thomas, V., Kalidindi, B., Waghmare, A., Bhatia, A., Raj, T., & Balsari, S. (2023). The vinyasa tool for mHealth solutions: supporting human-centered design in nascent digital health ecosystems. JMIR Formative Research, 7, e45250. https://doi.org/10.2196/45250
Thunberg, A., Zadutsa, B., Phiri, E., King, C., Langton, J., Banda, L., Makwenda, C., & Hildenwall, H. (2022). Hypoxemia, hypoglycemia and IMCI danger signs in pediatric outpatients in Malawi. PLOS Global Public Health, 2(4), e0000284. https://doi.org/10.1371/journal.pgph.0000284
Tiruneh, S. A., Fentie, D. T., Yigizaw, S. T., Abebe, A. A., & Gelaye, K. A. (2021). Spatial distribution and geographical heterogeneity factors associated with poor consumption of foods rich in vitamin A among children age 6–23 months in Ethiopia: Geographical weighted regression analysis. PloS One, 16(6), e0252639. https://doi.org/10.1371/journal.pone.0252639
van Stam, G. (2022). Conceptualization and practices in digital health: voices from Africa. African Health Sciences, 22(1), 664–672. https://doi.org/10.4314/ahs.v22i1.77
Wada, A., Nakamura, Y., Kawajiri, M., Takeishi, Y., Yoshida, M., & Yoshizawa, T. (2023). Feasibility and usability of the job adjustment mobile app for pregnant women: longitudinal observational study. JMIR Formative Research, 7(1), e48637. https://doi.org/10.2196/48637
Wandschneider, L., Batram-Zantvoort, S., Alaze, A., Niehues, V., Spallek, J., Razum, O., & Miani, C. (2022). Self-reported mental well-being of mothers with young children during the first wave of the COVID-19 pandemic in Germany: A mixed-methods study. Women’s Health, 18, 17455057221114274. https://doi.org/10.1177/17455057221114274
Weerasinghe, M., Biener, V., Grubert, J., Quigley, A., Toniolo, A., Pucihar, K. Č., & Kljun, M. (2022). Vocabulary: Learning vocabulary in ar supported by keyword visualisations. IEEE Transactions on Visualization and Computer Graphics, 28(11), 3748–3758. https://doi.org/10.1109/TVCG.2022.3203116
Yun, H., Choi, J., & Park, J. H. (2021). Prediction of critical care outcome for adult patients presenting to emergency department using initial triage information: an XGBoost algorithm analysis. JMIR Medical Informatics, 9(9), e30770. https://doi.org/10.2196/30770
Zobair, K. M., Sanzogni, L., Houghton, L., Sandhu, K., & Islam, M. J. (2021). Health seekers’ acceptance and adoption determinants of telemedicine in emerging economies. Australasian Journal of Information Systems. https://doi.org/10.3127/ajis.v25i0.3071
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