Sistem Pengingat Pemberian Insulin Berbasis Aplikasi dan Alarm Pintar untuk Meningkatkan Kepatuhan Terapi pada Pasien Diabetes Mellitus

Authors

  • Estu Nugrahaning Widi Universitas Muhammadiyah Semarang
  • Nurul Auliyah Universitas Muhammadiyah Semarang
  • Agus Suseno Universitas Muhammadiyah Semarang
  • Wahyuni Wahyuni Universitas Muhammadiyah Semarang
  • Marcos De Oliveira Soares Universitas Muhammadiyah Semarang
  • Aric Vranada Universitas Muhammadiyah Semarang

DOI:

https://doi.org/10.37287/jppp.v8i3.2214

Keywords:

alarm pintar, aplikasi kesehatan digital, diabetes mellitus, insulin, kepatuhan terapi

Abstract

Diabetes Mellitus (DM) merupakan penyakit kronis yang terus meningkat prevalensinya di seluruh dunia. Salah satu hambatan utama dalam pengelolaan DM adalah rendahnya kepatuhan pasien terhadap jadwal pemberian insulin. Penelitian ini bertujuan untuk mengevaluasi efektivitas sistem pengingat berbasis aplikasi ponsel pintar dan alarm pintar dalam meningkatkan kepatuhan terapi insulin pada pasien diabetes mellitus. Penelitian menggunakan metode kuantitatif deskriptif dengan pendekatan cross-sectional. Pengambilan sampel dilakukan dengan teknik purposive sampling, melibatkan 30 pasien diabetes mellitus yang menggunakan insulin. Hasil penelitian menunjukkan bahwa penggunaan alarm pintar dan aplikasi pengingat secara signifikan meningkatkan kepatuhan pemberian insulin, tercermin dari peningkatan nilai time-in-range (TIR) dan penurunan kadar HbA1c secara konsisten pada mayoritas peserta. Tingkat kepatuhan terhadap jadwal yang direkomendasikan sistem mencapai angka yang tinggi tanpa adanya peningkatan kejadian hipoglikemia. Penelitian ini menyimpulkan bahwa integrasi teknologi digital berupa aplikasi dan alarm pintar terbukti efektif sebagai alat bantu manajemen mandiri diabetes, dan berpotensi menjadi komponen penting dalam rencana perawatan holistik pasien diabetes mellitus.

References

Adolfsson, P., Björnsson, V., Hartvig, N. V., Kaas, A., Møller, J. B., & Lange, E. O. (2021). Improved Glycemic Control Observed in Children with Type 1 Diabetes Following the Introduction of Smart Insulin Pens: A Real-World Study. Diabetes Therapy, 13(1), 43–56. https://doi.org/10.1007/s13300-021-01177-w

Amadou, C., Franc, S., Benhamou, P., Lablanche, S., Huneker, E., Charpentier, G., & Penfornis, A. (2021). Diabeloop DBLG1 Closed-Loop System Enables Patients With Type 1 Diabetes to Significantly Improve Their Glycemic Control in Real-Life Situations Without Serious Adverse Events: 6-Month Follow-up. Diabetes Care, 44(3), 844–846.

Bhoyar, P. K., & Yadav, P. J. (2026). Artificial Intelligence in Personalized Insulin Therapy for Diabetes Mellitus. International Journal of Innovative Science and Research Technology (IJISRT), 2297–2297.

Bisio, A., Anderson, S. M., Norlander, L., O'Malley, G., Robic, J., Ogyaadu, S., et al. (2021). Impact of a Novel Diabetes Support System on a Cohort of Individuals With Type 1 Diabetes Treated With Multiple Daily Injections: A Multicenter Randomized Study. Diabetes Care, 45(1), 186–193.

Castle, J. R., Wilson, L. M., Tyler, N. S., Espinoza, A. Z., Mosquera-Lopez, C., et al. (2022). Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections. Diabetes Technology & Therapeutics, 24(12), 892–897.

Crabtree, T., Griffin, T. P., Yap, Y. W., Narendran, P., Gallen, G., et al. (2023). Hybrid Closed-Loop Therapy in Adults With Type 1 Diabetes and Above-Target HbA1c: A Real-world Observational Study. Diabetes Care, 46(10), 1831–1838.

Cranston, I., Jamdade, V., Liao, B., & Newson, R. S. (2023). Clinical, Economic, and Patient-Reported Benefits of Connected Insulin Pen Systems: A Systematic Literature Review. Advances in Therapy, 40(5), 2015–2037.

Danne, T., Joubert, M., Hartvig, N. V., Kaas, A., Knudsen, N., & Mader, J. K. (2024). Association Between Treatment Adherence and Continuous Glucose Monitoring Outcomes in People With Diabetes Using Smart Insulin Pens in a Real-World Setting. Diabetes Care, 47(6), 995–1003.

Davies, M. J., Aroda, V. R., Collins, B. S., et al. (2022). Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the ADA and EASD. Diabetes Care, 45(11), 2753–2786.

Déniz-García, A., Fabelo, H., Rodríguez-Almeida, A. J., et al. (2023). Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges. Journal of Medical Internet Research, 25.

Dsouza, S. M., Venne, J., Shetty, S., & Brand, H. (2024). Identification of challenges and leveraging mHealth technology, with need-based solutions to empower self-management in type 2 diabetes: a qualitative study. Diabetology & Metabolic Syndrome, 16(1).

ElSayed, N. A., et al. (2022). 7. Diabetes Technology: Standards of Care in Diabetes—2023. Diabetes Care, 46.

ElSayed, N. A., et al. (2023). 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes—2024. Diabetes Care, 47.

ElSayed, N. A., et al. (2024). 7. Diabetes Technology: Standards of Care in Diabetes—2025. Diabetes Care, 48.

Fraser, R., Walker, R. J., Campbell, J. A., Ekwunife, O. I., & Egede, L. E. (2025). Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes. NPJ Digital Medicine, 8(1), 687.

Gómez-Peralta, F., Abreu, C., Fernández-Rubio, E., et al. (2022). Efficacy of a Connected Insulin Pen Cap in People With Noncontrolled Type 1 Diabetes: A Multicenter Randomized Clinical Trial. Diabetes Care, 46(1), 206–208.

Guan, Z., Li, H., Liu, R., et al. (2023). Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Reports Medicine, 4(10), 101213.

Han, C., Zhang, J., Ye, X., et al. (2023). Telemedicine-assisted structured self-monitoring of blood glucose in management of T2DM: results of a randomized clinical trial. BMC Medical Informatics and Decision Making, 23(1).

Huang, J., Chan, S. C., Ko, S., et al. (2023). Associations between adoption of eHealth management module and optimal control of HbA1c in diabetes patients. NPJ Digital Medicine, 6(1).

Huang, Y., & Shiyanbola, O. O. (2021). Investigation of Barriers and Facilitators to Medication Adherence in Patients With Type 2 Diabetes Across Different Health Literacy Levels. Frontiers in Pharmacology, 12.

Islam, S. M. S., Mishra, V., Siddiqui, M. U., et al. (2022). Smartphone Apps for Diabetes Medication Adherence: Systematic Review. JMIR Diabetes, 7(2).

Jakob, R., Harperink, S., Rudolf, A. M., et al. (2022). Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review. Journal of Medical Internet Research, 24(5).

Kerr, D., Ahn, D., Waki, K., et al. (2024). Digital Interventions for Self-Management of Type 2 Diabetes Mellitus: Systematic Literature Review and Meta-Analysis. Journal of Medical Internet Research, 26.

Kopanz, J., Mader, J. K., Donsa, K., et al. (2022). Digital algorithm-guided insulin therapy in home healthcare for elderly persons with type 2 diabetes: A proof-of-concept study. Frontiers in Clinical Diabetes and Healthcare, 3.

Krishnakumar, A., Verma, R., Chawla, R., et al. (2021). Evaluating Glycemic Control in Patients of South Asian Origin With Type 2 Diabetes Using a Digital Therapeutic Platform: Analysis of Real-World Data. Journal of Medical Internet Research, 23(3).

Lee, Y., Kim, G., Jun, J. E., et al. (2023). An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management: 48-Week Results From a Randomized Controlled Trial. Diabetes Care, 46(5), 959–966.

Mackenzie, S. C., Sainsbury, C., & Wake, D. J. (2023). Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges. Diabetologia, 67(2), 223–235.

Mahendiran, N., & Ramya, N. (2024). Implantation of Android for Better Health with Medication Management App. International Journal of Innovative Science and Research Technology (IJISRT), 350–355.

Nayak, A., Vakili, S., Nayak, K., et al. (2023). Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes. JAMA Network Open, 6(12).

Nomura, A., Noguchi, M., Kometani, M., Furukawa, K., & Yoneda, T. (2021). Artificial Intelligence in Current Diabetes Management and Prediction. Current Diabetes Reports, 21(12), 61.

Pérez, C. T., Chico, A., Azriel-Mira, S., et al. (2023). Connected Insulin Pens and Caps: An Expert's Recommendation from the Area of Diabetes of the Spanish Endocrinology and Nutrition Society (SEEN). Diabetes Therapy, 14(7), 1077–1091.

Phillip, M., Nimri, R., Bergenstal, R. M., et al. (2022). Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice. Endocrine Reviews, 44(2), 254–280.

Putri, D. M. P., Suhoyo, Y., Pertiwi, A. A. P., & Effendy, C. (2022). Integrated Diabetes Self-Management (IDSM) mobile application to improve self-management and glycemic control among patients with Type 2 Diabetes Mellitus (T2DM) in Indonesia: A mixed methods study protocol. PLoS ONE, 17(11).

Seng, J. J. B., Gwee, M. F. R., Yong, M. H. A., et al. (2023). Role of Caregivers in Remote Management of Patients With Type 2 Diabetes Mellitus: Systematic Review of Literature. Journal of Medical Internet Research, 25.

Sng, G. G. R., Tung, J. Y. M., Lim, D. Y. Z., & Bee, Y. M. (2023). Potential and Pitfalls of ChatGPT and Natural-Language Artificial Intelligence Models for Diabetes Education. Diabetes Care, 46(5).

Steenkamp, D., Eby, E. L., Gulati, N., & Liao, B. (2021). Adherence and Persistence to Insulin Therapy in People with Diabetes: Impact of Connected Insulin Pen Delivery Ecosystem. Journal of Diabetes Science and Technology, 16(4), 995–1002.

Stephen, D., Nordin, A., Johansson, U., & Nilsson, J. (2024). Psychosocial Self-efficacy and its Association with Selected Potential Factors Among Adults with Type 1 Diabetes: A Cross-Sectional Survey Study. Diabetes Therapy, 15(6), 1361–1373.

Stevens, S., Gallagher, S., Andrews, T., et al. (2022). The effectiveness of digital health technologies for patients with diabetes mellitus: A systematic review. Frontiers in Clinical Diabetes and Healthcare, 3.

Wang, C., Zhou, F. L., Gandhi, A. B., et al. (2024). Real-World Effectiveness of the Gla-300 + Cap + App Program in Adult Users Living with Type 2 Diabetes in Taiwan. Diabetes Therapy, 15(6), 1389–1401.

Yoo, J. H., & Kim, J. H. (2023). Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy. Diabetes & Metabolism Journal, 47(1), 27–41.

Zahedani, A. D., Torbaghan, S. S., Rahili, S., et al. (2021). Improvement in Glucose Regulation Using a Digital Tracker and Continuous Glucose Monitoring in Healthy Adults and Those with Type 2 Diabetes. Diabetes Therapy, 12(7), 1871–1886.

Zakaria, H., Said, Y., Aleabova, S., et al. (2024). Measuring the effectiveness of hybrid diabetes care over 90 days through continuous data monitoring in type 2 diabetic patients. Frontiers in Endocrinology, 15.

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Published

2026-06-26

How to Cite

Widi, E. N., Auliyah, N., Suseno, A., Wahyuni, W., Soares, M. D. O., & Vranada, A. (2026). Sistem Pengingat Pemberian Insulin Berbasis Aplikasi dan Alarm Pintar untuk Meningkatkan Kepatuhan Terapi pada Pasien Diabetes Mellitus. Jurnal Penelitian Perawat Profesional, 8(3), 151–156. https://doi.org/10.37287/jppp.v8i3.2214

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