Penentuan Lama Waktu Optimal pada Pengukuran Glukosa Darah Noninvasif
DOI:
https://doi.org/10.23887/jstundiksha.v11i1.43185Keywords:
diabetes, darah, glukosa, optimal, regresiAbstract
Pengukuran kadar glukosa darah menggunakan metode invasif, yaitu melukai bagian tubuh, seperti jari, merupakan metode yang kurang disukai oleh sebagian besar masyarakat. Tujuan penelitian ini untuk mengembangkan teknologi berupa alat pengukur kadar glukosa darah noninvasif. Alat ini menggunakan prinsip kerja spektroskopi inframerah. Oleh karena itu, lama waktu pengukuran menjadi hal yang harus dipertimbangkan. Keoptimalan lama waktu pengukuran diperlukan agar proses pemeriksaan kadar glukosa darah efisien dan bisa merekam seluruh informasi. Tujuan penelitian ini adalah menentukan lama waktu optimal pada alat pengukur kadar glukosa darah noninvasif. Data yang digunakan merupakan data primer hasil pengukuran kadar glukosa darah dari tiga responden. Data tersebut dianalisis menggunakan metode eksplorasi dan regresi linier. Hasil pemodelan dengan persamaan , lama waktu optimal tersebut berada pada waktu perlakuan sebesar 1700 ms dengan menggunakan metode gradien pada kurva. Maka, lama waktu tersebut secara umum dikatakan sebagai waktu yang sangat singkat dalam dalam melakukan pengukuran glukosa dalam darah secara noninfasif.
References
Abd Salam, N. A. B., Bin Mohd Saad, W. H., Manap, Z. B., & Salehuddin, F. (2016). The Evolution of Non-Invasive Blood Glucose Monitoring System for Personal Application. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(1), 59–65. https://jtec.utem.edu.my/jtec/article/view/680.
Agbaje, I., Rogers, D. A., McVicar, C. M., McClure, N., Atkinson, A. B., Mallidis, C., & Lewis, S. E. M. (2007). Insulin Dependant Diabetes Mellitus: Implications for Male Reproductive Function. Human Reproduction, 22(7), 1871–1877. https://doi.org/10.1093/humrep/dem077.
Ahmad, K. (2014). Insulin Sources and Types: A Review of Insulin in Terms of Its Mode on Diabetes Mellitus. Journal of Traditional Chinese Medicine, 34(2), 234–237. https://doi.org/10.1016/s0254-6272(14)60084-4.
Ali, I. R., & Irasanti, S. N. (2021). Longer Mastication Cycles Tend to Lower Post-Prandial Blood Sugar Levels. Journal of Health and Dental Sciences, 1(1), 11–18. https://doi.org/10.54052/jhds.v1n1.p11-18.
Azrimaidaliza, A. (2011). Asupan Zat Gizi dan Penyakit Diabetes Mellitus. Jurnal Kesehatan Masyarakat Andalas, 6(1), 36–41. https://doi.org/10.24893/jkma.v6i1.86.
Begum, S. A., Afroz, R., Khanam, Q., Khanom, A., & Choudhury, T. S. (2014). Diabetes Mellitus and Gestational Diabetes Mellitus. Journal of Paediatric Surgeons of Bangladesh, 5(1), 30–35. https://doi.org/10.3329/jpsb.v5i1.23887.
Bharath, C., Saravanan, N., & Venkatalakshmi, S. (2017). Assessment of Knowledge Related to Diabetes Mellitus Smong Patients Attending A Dental College in Salem City-A Cross Sectional Study. Brazilian Dental Science, 20(3), 93–100. https://doi.org/10.14295/bds.2017.v20i3.1437.
Cavender, M. A., Steg, P. G., Smith Jr, S. C., Eagle, K., Ohman, E. M., Goto, S., Kuder, J., Im, K., Wilson, P. W. F., & Bhatt, D. L. (2015). Impact of Diabetes Mellitus on Hospitalization for Heart Failure, Cardiovascular Events, and Death: Outcomes at 4 Years from The Reduction of Atherothrombosis for Continued Health (REACH) Registry. Circulation, 132(10), 923–931. https://doi.org/10.1161/Circulationaha.114.014796.
Chen, X. D., GAO, J., & Ding, H. Q. (2012). Infrared Spectroscopy for Non-Invasive Blood Glucose Monitoring. Chinese Optics, 4(4), 317–326. https://doi.org/10.3788/co.20120504.0317.
Ciudin, A., Hernández, C., & Simó, R. (2012). Non-Invasive Methods of Glucose Measurement: Current Status and Future Perspectives. Current Diabetes Reviews, 8(1), 48–54. https://doi.org/10.2174/157339912798829197.
de Silva, D. D., Rapior, S., Hyde, K. D., & Bahkali, A. H. (2012). Medicinal Mushrooms in Prevention and Control of Diabetes Mellitus. Fungal Diversity, 56(1), 1–29. https://doi.org/10.1007/s13225-012-0187-4.
Dermawan, R., Erfiani, & Rizki, A. (2018). Statistical Process Control pada Pengujian Kestabilan Alat Pengukur Kadar Glukosa Darah Non-Invasif. Xplore: Journal of Statistics, 2(1), 41–48. https://doi.org/10.29244/xplore.v2i1.77.
Dias, C. C., Kamath, S., & Vidyasagar, S. (2020). Blood Glucose Regulation and Control of Insulin and Glucagon Infusion using Single Model Predictive Control for Type 1 Diabetes Mellitus. IET Systems Biology, 14(3), 133–146. https://doi.org/10.1049/iet-syb.2019.0101.
Dinh, T., Tecilazich, F., Kafanas, A., Doupis, J., Gnardellis, C., Leal, E., Tellechea, A., Pradhan, L., Lyons, T. E., & Giurini, J. M. (2012). Mechanisms Involved in The Development and Healing of Diabetic Foot Ulceration. Diabetes, 61(11), 2937–2947. https://doi.org/10.2337%2Fdb12-0227.
Farhood, H. B., Ra’id, M., & Radhi, M. N. (2014). Clinical Studies to Evaluate Pancreatic Functions in The Patients of Type 2 Diabetes Mellitus. International Journal of Innovation and Applied Studies, 7(1), 413. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.687.3964&rep=rep1&type=pdf.
Fitriani, Y., Pristianty, L., & Hermansyah, A. (2019). Pendekatan Health Belief Model (HBM) untuk Menganalisis Kepatuhan Pasien Diabetes Melitus Tipe 2 dalam Menggunakan Insulin. PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia), 16(2), 167–177. https://doi.org/10.30595/pharmacy.v16i2.5427.
Ginter, E., & Simko, V. (2013). Type 2 Diabetes Mellitus, Pandemic in 21st Century. Diabetes, 42–50. https://doi.org/10.1007/978-1-4614-5441-0_6.
Green, M. J., Espie, C. A., Popham, F., Robertson, T., & Benzeval, M. (2017). Insomnia Symptoms as A Cause of Type 2 Diabetes Incidence: a 20 year cohort study. BMC Psychiatry, 17(1), 1–8. https://doi.org/10.1186/s12888-017-1268-4.
Güemes, M., Rahman, S. A., & Hussain, K. (2016). What is A Normal Blood Glucose? Archives of Disease in Childhood, 101(6), 569–574. https://doi.org/10.1136/archdischild-2015-308336.
Gupta, S. sen, Kwon, T.-H., Hossain, S., & Kim, K.-D. (2021). Towards Non-Invasive Blood Glucose Measurement using Machine Learning: An All-Purpose PPG System Design. Biomedical Signal Processing and Control, 68, 102706. https://doi.org/10.1016/j.bspc.2021.102706.
Jain, S. K., Kahlon, G., Morehead, L., Lieblong, B., Stapleton, T., Hoeldtke, R., Farrington Bass III, P., & Levine, S. N. (2012). The Effect of Sleep Apnea and Insomnia on Blood Levels of Leptin, Insulin Resistance, IP-10, and Hydrogen Sulfide in Type 2 Diabetic Patients. Metabolic Syndrome and Related Disorders, 10(5), 331–336. https://doi.org/10.1089/met.2012.0045.
Knutson, K. L., Van Cauter, E., Zee, P., Liu, K., & Lauderdale, D. S. (2011). Cross-Sectional Associations Between Measures of Sleep and Markers of Glucose Metabolism Among Subjects with and Without Diabetes: the Coronary Artery Risk Development in Young Adults (CARDIA) Sleep Study. Diabetes Care, 34(5), 1171–1176. https://doi.org/10.2337/dc10-1962.
Lunze, K., Singh, T., Walter, M., Brendel, M. D., & Leonhardt, S. (2013). Blood Glucose Control Algorithms for Type 1 Diabetic Patients: A Methodological Review. Biomedical Signal Processing and Control, 8(2), 107–119. https://doi.org/10.1016/j.bspc.2012.09.003.
Mandal, N., Grambergs, R., Mondal, K., Basu, S. K., Tahia, F., & Dagogo-Jack, S. (2021). Role of Ceramides in The Pathogenesis of Diabetes Mellitus and Its Complications. Journal of Diabetes and Its Complications, 35(2), 107734. https://doi.org/10.1016/j.jdiacomp.2020.107734.
Narisawa, H., Komada, Y., Miwa, T., Shikuma, J., Sakurai, M., Odawara, M., & Inoue, Y. (2017). Prevalence, Symptomatic Features, and Factors Associated with Sleep Disturbance/Insomnia in Japanese Patients with Type-2 Diabetes. Neuropsychiatric Disease and Treatment, 13, 1873. https://doi.org/10.2147/ndt.s134814.
Nentwich, M. M., & Ulbig, M. W. (2015). Diabetic Retinopathy-Ocular Complications of Diabetes Mellitus. World Journal of Diabetes, 6(3), 489. https://doi.org/10.4239%2Fwjd.v6.i3.489.
Ngo, P. D., Wei, S., Holubová, A., Muzik, J., & Godtliebsen, F. (2018). Control of Blood Glucose forTtype-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2018/4091497.
Piero, M. N., Nzaro, G. M., & Njagi, J. M. (2015). Diabetes Mellitus-A Devastating Metabolic Disorder. Asian Journal of Biomedical and Pharmaceutical Sciences, 5(40), 1. https://doi.org/10.15272/ajbps.v4i40.645.
Pratiwi, P., Amatiria, G., & Yamin, M. (2016). Pengaruh Stress terhadap Kadar Gula Darah Sewaktu pada Pasien Diabetes Melitus yang Menjalani Hemodialisa. Jurnal Kesehatan, 5(1), 11–16. https://doi.org/10.26630/jk.v5i1.59.
Sabu, C., Henna, T. K., Raphey, V. R., Nivitha, K. P., & Pramod, K. (2019). Advanced Biosensors for Glucose and Insulin. Biosensors and Bioelectronics, 141, 111201. https://doi.org/10.1016/j.bios.2019.03.034.
Sutrisno, Laksono, Y. A., & Hidayat, N. (2017). Noninvasive and Painless Urine Glucose Detection by Using Computer-Based Polarimeter. IOP Conference Series: Materials Science and Engineering, 202, 012030. https://doi.org/10.1088/1757-899X/202/1/012030.
Tankasala, D., & Linnes, J. C. (2019). Noninvasive Glucose Detection in Exhaled Breath Condensate. Translational Research, 213, 1–22. https://doi.org/10.1016/j.trsl.2019.05.006.
Uwadaira, Y., Shimotori, A., Ikehata, A., Fujie, K., Nakata, Y., Suzuki, H., Shimano, H., & Hashimoto, K. (2015). Logistic Regression Analysis for Identifying The Factors Affecting Development of Non-Invasive Blood Glucose Calibration Model by Near-Infrared Spectroscopy. Chemometrics and Intelligent Laboratory Systems, 148, 128–133. https://doi.org/10.1016/j.chemolab.2015.09.012.
Villena, J. E. (2015). Diabetes mellitus in Peru. Annals of Global Health, 81(6), 765–775. https://doi.org/10.1016/j.aogh.2015.12.018.
Yadav, J., Rani, A., Singh, V., & Murari, B. M. (2014). Near-infrared LED Based Non-Invasive Blood Glucose Sensor. International Conference on Signal Processing and Integrated Networks (SPIN), 591–594. https://doi.org/10.1109/SPIN.2014.6777023.
Zafar, U., Khaliq, S., Ahmad, H. U., Manzoor, S., & Lone, K. P. (2018). Metabolic Syndrome: An Update on Diagnostic Criteria, Pathogenesis, and Genetic Links. Hormones, 17(3), 99–313. https://doi.org/10.1007/s42000-018-0051.
Zhang, Y., Lin, Y., Zhang, J., Li, L., Liu, X., Wang, T., & Gao, Z. (2019). Association BItween insomnia and Type 2 Diabetes Mellitus in Han Chinese individuals in Shandong Province, China. Sleep and Breathing, 23(1), 349–354. https://doi.org/10.1007/s11325-018-1687-6.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 JST (Jurnal Sains dan Teknologi)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with the Jurnal Sains dan Teknologi (JST) agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. (See The Effect of Open Access)
