Telemedicine and AI in Remote Prediabetes Monitoring Among Adolescents
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The escalating prevalence of prediabetes in Indonesia, particularly among children and adolescents, necessitates the development of lightweight, adaptable, and cost-effective telemedicine solutions for the noninvasive monitoring of blood glucose levels. Existing systems predominantly employ machine learning and deep learning approaches that require substantial computational resources and stable internet connectivity, limiting their applicability in regions with constrained digital infrastructure. The objective of this study is to develop an artificial intelligence (AI)–driven telemedicine system that employs an expert system to determine prediabetes status by utilizing commercially available smartwatches as noninvasive optical sensors. The methodological approach includes an examination of smartwatch capabilities to identify Bluetooth Low Energy (BLE) sensors, service architectures, and the Generic Attribute Profile (GATT); the development of a Rule-Based Reasoning (RBR) expert system to determine prediabetes status using Fasting Plasma Glucose (FPG) and Postprandial Plasma Glucose (PP2) measurements; and the application of Rapid Application Development (RAD) methods in the development of Flutter-based mobile applications and Laravel Inertia Vue–based web applications. The results of this study demonstrate that the telemedicine system operates in both offline and online modes and incorporates AI functionality on mobile devices and servers without requiring extensive computational resources. All system functionalities successfully passed testing, and the expert system achieved 100% accuracy in determining prediabetes status. In conclusion, the integration of telemedicine and AI-based expert systems provides an effective, economical, and flexible solution that can be widely implemented in Indonesia to reduce the increasing incidence of prediabetes through continuous digital health monitoring.
[1] S. A. Setianingrum and D. Y. Handayani, “Analisis Tingkat Aktivitas Fisik Pola Konsumsi Fast Food Dan IMT Terhadap Kejadian Prediabetes Remaja Di SMA Negeri 1 Banyumas,” Jurnal Ilmiah Wahana Pendidikan, vol. 10, no. 3, pp. 788–800, 2024.
[2] E. Asoka, F. Fathoni, A. Primanita, and I. G. T. Isa, “Machine Learning Models for Metabolic Syndrome Identification with Explainable AI,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 3, pp. 1159–1172, 2025.
[3] M. Z. Malik, A. A. Putri, M. Y. Tahir, I. K. Wijaya, and others, “Analisis Faktor Risiko Prediabetes Pada Remaja di STIKES Panakkukang Makassar,” Caring: Jurnal Keperawatan, vol. 14, no. 1, pp. 37–46, 2025.
[4] Z. Golsanamlou, M. Mahmoudpour, J. Soleymani, and A. Jouyban, “Applications of advanced materials for non-enzymatic glucose monitoring: from invasive to the wearable device,” Crit Rev Anal Chem, vol. 53, no. 5, pp. 1116–1131, 2023.
[5] S. Laha, A. Rajput, S. S. Laha, and R. Jadhav, “A concise and systematic review on non-invasive glucose monitoring for potential diabetes management,” Biosensors (Basel), vol. 12, no. 11, p. 965, 2022.
[6] D. Rahmadaniar, I. Salamah, and M. M. Rose, “Application of Polynomial Regression Method in Non-invasive Measurement of Blood Sugar, Cholesterol, and Non-invasive Uric Acid Based on IoT,” INVOTEK: Jurnal Inovasi Vokasional dan Teknologi, vol. 24, no. 3, pp. 187–196, 2024.
[7] S. N. S. Anis and R. Alias, “A portable non-invasive blood glucose monitoring device with IoT,” Evolution in Electrical and Electronic Engineering, vol. 2, no. 1, pp. 36–44, 2021.
[8] N. Chinnamadha, R. Z. Ahmed, and K. Kalegowda, “Development of health monitoring system using smart intelligent device,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 28, no. 3, pp. 1381–1387, 2022.
[9] S. Pikulin, I. Yehezkel, and R. Moskovitch, “Enhanced blood glucose levels prediction with a smartwatch,” PLoS One, vol. 19, no. 7, p. e0307136, 2024.
[10] Ö. Pektaş, M. Köseoğlu, M. Muzny, G. Hartvigsen, and others, “Design of an android wear smartwatch application as a wearable interface to the diabetes diary application,” Academic Platform-Journal of Engineering and Science, vol. 9, no. 1, pp. 126–133, 2021.
[11] S. Pikulin, I. Yehezkel, and R. Moskovitch, “Enhanced blood glucose levels prediction with a smartwatch,” PLoS One, vol. 19, no. 7, p. e0307136, 2024.
[12] E. Årsand, M. Muzny, M. Bradway, J. Muzik, and G. Hartvigsen, “Performance of the first combined smartwatch and smartphone diabetes diary application study,” J Diabetes Sci Technol, vol. 9, no. 3, pp. 556–563, 2015.
[13] R. Marzouk, A. S. Alluhaidan, and S. A. El_Rahman, “An analytical predictive models and secure web-based personalized diabetes monitoring system,” IEEE Access, vol. 10, pp. 105657–105673, 2022.
[14] S. Badriah, Y. Bahtiar, P. Cahyati, A. Andang, F. Fathurrohman, and others, “Identification of blood sugar based on non-invasive measurements UsingPhotoplethysmography method signal decoding in diabetics in Tasikmalaya,” Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 3, 2024.
[15] M. A. Darwich, A. Shahen, A. Daoud, A. Lahia, J. Diab, and E. Ismaiel, “Non-invasive IR-based measurement of human blood glucose,” Engineering Proceedings, vol. 35, no. 1, p. 27, 2023.
[16] N. Oktariadi, M. Alaydrus, and others, “Monitoring of Blood Cholesterol Measurement System Using ICT-Based Non-Invasive Methods,” Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 5, no. 4, pp. 224–231, 2023.
[17] M. A. Al-Shareeda, M. A. Saare, S. Manickam, and S. Karuppayah, “Bluetooth low energy for internet of things: review, challenges, and open issues,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 31, no. 2, pp. 1182–1189, 2023.
[18] A. Lacava, V. Zottola, A. Bonaldo, F. Cuomo, and S. Basagni, “Securing Bluetooth Low Energy networking: An overview of security procedures and threats,” Computer Networks, vol. 211, p. 108953, 2022.
[19] C. Hirsch, L. Davoli, R. Grosu, and G. Ferrari, “DynGATT: A dynamic GATT-based data synchronization protocol for BLE networks,” Computer Networks, p. 109560, 2023.
[20] N. Azizah et al., “A Vital Sign Monitoring System Exploiting BT/BLE on Low-cost Commercial Smartwatch for Home Care Patients,” in 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2023, pp. 405–410. doi: 10.1109/ISITIA59021.2023.10221157.
[21] R. K. V. Penmatsa, K. K. V. Penmatsa, L. S. P. Kethinedi, D. Sakkuri, and R. K. S. S. Ramineedi, “A Novel Approach for Bluetooth Mesh Network Detection using Machine Learning and Correlation Analysis,” Procedia Comput Sci, vol. 233, pp. 580–589, 2024.
[22] L. Bock, Learn Wireshark: A definitive guide to expertly analyzing protocols and troubleshooting networks using Wireshark. Packt Publishing Ltd, 2022.
[23] A. Eliasz, “Developing Zephyr BLE Applications,” in Zephyr RTOS Embedded C Programming: Using Embedded RTOS POSIX API, Springer, 2024, pp. 305–373.
[24] P. Nagaraj and P. Deepalakshmi, “An intelligent fuzzy inference rule-based expert recommendation system for predictive diabetes diagnosis,” Int J Imaging Syst Technol, vol. 32, no. 4, pp. 1373–1396, 2022.
[25] S. M. Azizah, B. Hendrasuryawan, and others, “Expert System for Pregnancy Risk Diagnosis Using Decision Tree and Dempster-Shafer Method,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 4, pp. 382–396, 2024.
[26] B. P. Bhuyan, A. Ramdane-Cherif, T. P. Singh, and R. Tomar, “Rule-Based Systems and Expert Systems,” in Neuro-Symbolic Artificial Intelligence: Bridging Logic and Learning, Springer, 2024, pp. 63–85.
[27] P. Nagaraj and P. Deepalakshmi, “An intelligent fuzzy inference rule-based expert recommendation system for predictive diabetes diagnosis,” Int J Imaging Syst Technol, vol. 32, no. 4, pp. 1373–1396, 2022.
[28] H. Kim, D. A. Chuvikov, D. V Aladin, O. O. Varlamov, L. E. Adamova, and V. G. Osipov, “Creating a knowledge base for a mivar expert system for the diagnosis of diabetes mellitus,” Biomed Eng (NY), vol. 54, pp. 421–424, 2021.
[29] B. Reddy and R. Fields, “From past to present: a comprehensive technical review of rule-based expert systems from 1980–2021,” in Proceedings of the 2022 ACM Southeast Conference, 2022, pp. 167–172.
[30] H. Kere et al., “An Intelligent Knowledge Based System for Diagnosis and Treatment of Diabetes,” Indian J Sci Technol, vol. 17, no. 48, pp. 5033–5045, 2024.
[31] M. R. G. Qowindra and J. Wiratama, “The Development of Enterprise Resource Planning (ERP) using the Rapid Application Development (RAD) Method for the Garment Industry in Indonesia,” G-Tech: Jurnal Teknologi Terapan, vol. 7, no. 2, pp. 504–513, 2023.
[32] T. Wahyuningrum, G. F. Fitriana, A. C. Wardhana, M. A. Sidiq, and D. Wahyuningsih, “Developing suicide risk idea identification for teenager (SERIINA) mobile apps prototype using extended rapid application development,” in 2021 9th International Conference on Information and Communication Technology (ICoICT), 2021, pp. 92–97.
[33] D. J. C. Sihombing, “Development of construction project cost budget application using rapid application development method,” Jurnal Mantik, vol. 7, no. 3, pp. 1685–1696, 2023.
[34] T. Oswari, T. Yusnitasari, R. D. Kusumawati, and S. Mittal, “Music Recommendation System Design for Online Platform with the Concept of Unified Modeling Languange (UML),” 2018 International Conference on Sustainable Energy, Electronics and coMputing System, SEEMS 2018, no. 978, pp. 1–7, 2019, doi: 10.1109/SEEMS.2018.8687346.
[35] A. Firdhayanti, T. Taufik, B. Bachry, and others, “User Acceptance Testing through Blackbox Evaluation for Corn Distribution Information System,” bit-Tech, vol. 6, no. 2, pp. 208–215, 2023.
[36] K. Shen et al., “Association between serum uric acid and prediabetes in a normal Chinese population: A cross-sectional study,” Medicine, vol. 103, no. 48, p. e40544, 2024.
[37] M. Shimodaira, Y. Minemura, and T. Nakayama, “Elevated serum uric acid is a risk factor for progression to prediabetes in Japanese women: A 5-year retrospective chort study,” J Diabetes Investig, vol. 14, no. 11, pp. 1237–1245, 2023.
[38] Y. Ji and W.-C. Xu, “Association between blood lipid, lipid ratios and glucose metabolism disorders; [血脂参数及脂质比值与糖代谢异常的关联研究],” Modern Preventive Medicine, vol. 51, no. 14, pp. 2628 – 2633, 2024, doi: 10.20043/j.cnki.MPM.202401307.
Copyright (c) 2025 Siti Aisyah Solechah, Setyo Wahyu Saputro, Muhammad Hifdzi Adini, Mohammad Reza Faisal, Erick Kurniawan, Umiatin Umiatin (Author)
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