Intrusive and Non-Intrusive Techniques for Blood Sugar Measurement: A Practical Review
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Abstract
Measurement of sugar levels in blood is the main means of diagnosing for diabetes and other complications of blood sugar levels. The established principle of the methodology for this is the extraction of blood from the subject and submission of the blood sample to chemical tests that determine the presence of substances, such as glucose, that indicate blood sugar levels. This principle is inherently intrusive; R&D into methods with this principle has the goal of improving convenience and minimizing amount of sampling needed, while maintaining reliable accuracy. There is also R&D into developing non-intrusive methods that estimate blood sugar levels without blood sampling, with the aim of producing results that can be comparable with intrusive methods. The common goal of either approach is making blood sugar measurement more convenient for as many people as possible. At this time of writing, non-intrusive methods have yet to replace the gold standard. A breakthrough in this matter can facilitate the implementation of machine learning in interpreting blood sugar levels.
Manuscript received: 7 Aug 2025 | Revised: 11 Sep 2025 | Accepted: 16 Sep 2025 | Published: 30 Nov 2025
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References
S. Akhtar, J.A. Nasir, A. Ali, M. Asghar, R. Majeed and A. Sarwar, “Prevalence of type-2 diabetes and prediabetes in Malaysia: A systematic review and meta-analysis,” PLoS One, vol. 17, no. 1, pp. e0263139, 2022.
DOI: https://doi.org/10.1371/journal.pone.0263139
L. Guariguata and N. Sobers, “Rising diabetes, lagging treatment, and the need for better systems,” The Lancet, vol. 404, no. 10467, pp. 2026-2028, 2024.
DOI: https://doi.org/10.1016/S0140-6736(24)02422-X
S.A. Antar, N.A. Ashour, M. Sharaky, M. Khattab, N.A. Ashour, R.T. Zaid, E.J. Roh, A. Elkamhawy and A.A. Al-Karmalawy, “Diabetes mellitus: Classification, mediators, and complications; A gate to identify potential targets for the development of new effective treatments,” Biomedicine & Pharmacotherapy, vol. 168, no. 115734, 2023.
DOI: https://doi.org/10.1016/j.biopha.2023.115734
A.S. Bolla and R. Priefer, “Blood glucose monitoring- an overview of current and future non-invasive devices,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, no. 5, pp. 739-751, 2020.
DOI: https://doi.org/10.1016/j.dsx.2020.05.016
A.M. Raoufi, X. Tang, Z. Jing, X. Zhang, Q. Xu and C. Zhou, “Blood Glucose Monitoring and Its Determinants in Diabetic Patients: A Cross-Sectional Study in Shandong, China,” Diabetes Therapy, vol. 9, no. 5, pp. 2055–2066, 2018.
DOI: https://doi.org/10.1007/s13300-018-0499-9
L.D. Woodard, T. Urech, C.R. Landrum, D. Wang and L.A. Petersen, “The Impact of Comorbidity Type on Measures of Quality for Diabetes Care,” Medical Care, vol. 49, no. 6, pp. 605-610, 2011.
DOI: https://doi.org/10.1097/MLR.0b013e31820f0ed0
N.L. Spartano, N. Sultana, H. Lim, H. Cheng, S. Lu, D. Fei, J.M. Murabito, M.E. Walker, H.A. Wolpert and D.W. Steenkamp, “Defining Continuous Glucose Monitor Time in Range in a Large, Community-Based Cohort Without Diabetes,” The Journal of Clinical Endocrinology & Metabolism, no. dgae626, 2024.
DOI: https://doi.org/10.1210/clinem/dgae626
T.P. Minari, L.B. Tacito, L.B.T. Yugar, T.A. Rubio, A.C. Pires, L.N. Cosenso-Martin, J.F. Vilela-Martin, H. Moreno and J.C. Yugar-Toledo, “Nutritional management, skipping breakfast, glycemic control, and cardiovascular risk on type 2 diabetes mellitus,” European Heart Journal, vol. 45, no. 1, pp. ehae666.3399, 2024.
DOI: https://doi.org/10.1093/eurheartj/ehae666.3399
C. Savan, D. Viroja and A. Kyada, “An updated review on diabetes mellitus: Exploring its etiology, pathophysiology, complications and treatment approach,” IP International Journal of Comprehensive and Advanced Pharmacology, vol. 9, no. 1, pp. 31-36, 2024.
DOI: https://doi.org/10.18231/j.ijcaap.2024.005
J.D. Watkins, S. Carter, G. Atkinson, F. Koumanov, J.A. Betts, J.J. Holst and J.T. Gonzalez, “Glucagon-like peptide-1 secretion in people with versus without type 2 diabetes: a systematic review and meta-analysis of cross-sectional studies,” Metabolism - Clinical and Experimental, vol. 140, no. 155375, 2023.
DOI: https://doi.org/10.1016/j.metabol.2022.155375
S. Karayiannides, A. Norhammar, L. Landstedt-Hallin, L. Friberg and P. Lundman, “Prognostic impact of type 1 and type 2 diabetes mellitus in atrial fibrillation and the effect of severe hypoglycaemia: a nationwide cohort study,” European Journal of Preventive Cardiology, vol. 29, no. 13, pp. 1759-1769, 2022.
DOI: https://doi.org/10.1093/eurjpc/zwac093
D.B. Sacks, M. Arnold, G.L. Bakris, D.E. Bruns, A.R. Horvath, A. Lernmark, B.E. Metzger, D.M. Nathan and M.S. Kirkman, “Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus,” Clinical Chemistry, vol. 69, no. 8, pp. 808-868, 2023.
DOI: https://doi.org/10.2337/dci23-0036
L.T. Mujis, C. Racca, M. de Wit, A. Brouwer, T.H. Wieringa, R. de Vries, E.H. Serné, D.H. van Raalte, F. Rutters and F.J. Snoek, “Glucose variability and mood in adults with diabetes: A systematic review,” Endocrinology, Diabetes & Metabolism, vol. 4, no. 1, pp. e00152, 2020.
DOI: https://doi.org/10.1002/edm2.152
A. Schmitt, D. Ehrmann, N. Kuniss¸ N. Müller, B. Kulzer and N. Hermanns, “Assessing fear of complications in people with type 1 and type 2 diabetes with the Fear of Diabetes Complications Questionnaire,” Health Psychology, vol. 42, no. 9, pp. 674-685, 2023.
DOI: https://doi.org/10.1037/hea0001304
L.L. Samuelson and D.A. Gerber, “Recent Developments in Less Intrusive Technology to Monitor Blood Glucose Levels in Patients with Diabetes,” Laboratory Medicine, vol. 40, no. 10, pp. 607-610, 2009.
DOI: https://doi.org/10.1309/LMFGSAPPV3C78REW
M. Karamanou, A. Protogerou, G. Tsoucalas, G. Androutsos and E. Poulakou-Rebelakou, “Milestones in the history of diabetes mellitus: The main contributors,” World Journal of Diabetes, vol. 7, no. 1, pp. 1-7, 2016.
DOI: https://doi.org/10.4239/wjd.v7.i1.1
M.H. Ashraf, M.E. Qureshi, A. Khan, J. Iqbal and M. Ahmed, “DRD-Net: Diabetic Retinopathy Diagnosis Using A Hybrid Convolutional Neural Network,” International Journal on Robotics, Automation and Sciences, vol. 7, no. 2, pp. 96–107, 2025.
DOI: https://doi.org/10.33093/ijoras.2025.7.2.9
V. Vejdanihemmat, H. Azami, L. Tapak, S. Borzouei and K. Oshvandi, “Impact of cooling diabetic patients’ fingertips compared to vibrating stimulation on pain from the glucometer needle: A cross-over trial study,” Complementary Therapies in Medicine, vol. 88, in press (2025, expected).
DOI: https://doi.org/10.1016/j.ctim.2024.103116
B. Todaro, F. Begarani, F. Sartori and S. Luin, “Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?”, Frontiers in Chemistry, vol. 10, 2022.
DOI: https://doi.org/10.3389/fchem.2022.994272
J. Wu, Y. Liu and M. Guo, “A new generation of sensors for non-invasive blood glucose monitoring,” American Journal of Translational Research, vol. 15, no. 6, pp. 3825-3837, 2023.
URL: https://pubmed.ncbi.nlm.nih.gov/37434817/
Y. Li, C. Sun, L. Li, L. Wang, Y. Guo, A. You, Y. Xi and C. Wang, “Resting heart rate as a marker for identifying the risk of undiagnosed type 2 diabetes mellitus: a cross-sectional survey,” BMC Public Health, vol. 14, no. 1052, 2014.
DOI: https://doi.org/10.1186/1471-2458-14-1052
S.I. Raza, S.A. Raza, M. Kazmi, M. Saad and I. Hussain, “100 Years of Glucose Monitoring in Diabetes Management,” Journal of Diabetes Mellitus, vol. 11, no. 5, pp. 221-233, 2021.
DOI: https://doi.org/10.4236/jdm.2021.115019
G. Kaur, P.V.M. Lakshmi, A. Rastogi, A. Bhansali, S. Jain, Y. Teerawattananon, H. Bano and S. Prinja, “Diagnostic accuracy of tests for type 2 diabetes and prediabetes: A systematic review and meta-analysis,” PLoS One, vol. 15, no. 11, pp. e0242415, 2020.
DOI: https://doi.org/10.1371/journal.pone.0242415
M.S.F. Hoffman, J.W. McKeage, J. Xu, B.P. Ruddy, P.M.F. Nielsen and A.J. Taberner, “Minimally invasive capillary blood sampling methods,” Expert Review of Medical Devices, vol. 20, no. 1, pp. 5-16, 2023.
DOI: https://doi.org/10.1080/17434440.2023.2170783
X. Hongxiang, L. Shiyu, Z. Yanying, X. Wanju and W. Sumei, “Consistency analysis of two fingertip capillary blood sampling methods for complete blood count,” Scientific Reports, vol. 14, no. 15011, 2024.
DOI: https://doi.org/10.1038/s41598-024-64448-z
S.M. Johari, N.H. Razalli, K.J. Chua and S. Shahar, “The efficacy of self-monitoring of blood glucose (SMBG) intervention package through a subscription model among type-2 diabetes mellitus in Malaysia: a preliminary trial,” Diabetology & Metabolic Syndrome, vol. 16, no. 135, 2024.
DOI: https://doi.org/10.1186/s13098-024-01379-9
D. Veríssimo, J. Vinhais, C. Ivo, A.C. Martins, J.N. e Silva, D. Passos, L. Lopes, J.J. de Castro and M. Marcelino, “Continuous Glucose Monitoring vs. Capillary Blood Glucose in Hospitalized Type 2 Diabetes Patients,” Cureus, vol. 15, no. 8, pp. e43832, 2023.
DOI: https://doi.org/10.7759/cureus.43832
M-S. Kalogeropoulou, I. Iglesias-Platas and K. Beardsall, “Should continuous glucose monitoring be used to manage neonates at risk of hypoglycaemia?”, Frontiers in Pediatrics, vol. 11, 2023.
DOI: https://doi.org/10.3389/fped.2023.1115228
S. El-Abd and R. Poole, “The accuracy of capillary blood glucose testing versus real time and intermittently scanned continuous glucose monitoring,” Practical Diabetes, vol. 40, no. 5, pp. 40-43a, 2023.
DOI: https://doi.org/10.1002/pdi.2479
K. Fiedorova, M. Augustynek, J. Kubicek, P. Kudrna and D. Bibbo, “Review of present method of glucose from human blood and body fluids assessment,” Biosensors and Bioelectronics, vol. 211, no. 114348, 2022.
DOI: https://doi.org/10.1016/j.bios.2022.114348
A.L. Galant, R.C. Kaufman and J.D. Wilson, “Glucose: Detection and analysis,” Food Chemistry, vol. 188, pp. 149-160, 2015.
DOI: https://doi.org/10.1016/j.foodchem.2015.04.071
D. Romeres, Y. Yadav, F.N.U. Ruchi, R. Carter, C. Cobelli, R. Basu and A. Basu, “Hyperglycemia Suppresses Lactate Clearance During Exercise in Type 1 Diabetes,” Journal of Clinical Endocrinology & Metabolism (JCEM), vol. 109, no. 9, pp. e1720-e1731, 2024.
DOI: https://doi.org/10.1210/clinem/dgae005
M. Darshi, L. Kugathasan, S. Maity, V.S. Sridhar, R. Fernandez, C.P. Limonte, B.I. Grajeda, A. Saliba, G. Zhang, V.R. Drel, J.J. Kim, R. Montellano, J. Tumova, D. Montemayor, Z. Wang, J-J. Liu, J. Wang, B.A. Perkins, Y. Lytvyn, L. Natarajan, S.C. Lim, H. Feldman, R. Toto, J.R. Sedor, J. Patel, S.S. Waikar, J. Brown, Y. Osman, J. He, J. Chen, W.B. Reeves, I.H. de Boer, S. Roy, V. Vallon, S. Hallan, J.A.L. Gelfond, D.Z.I. Cherney and K. Sharma, “Glycolytic lactate in diabetic kidney disease,” Journal of Clinical Investigation (JCI) Insight, vol. 9, no. 11, pp. e168825, 2024.
DOI: https://doi.org/10.1172/jci.insight.168825
S. Mogekar, S. Jayakar, K.S.S.T. Sampath and V. Badangi, “A Study on Urinary Amylase and Serum Amylase in Diagnosing Acute Pancreatitis,” Cureus, vol. 16, no. 10, pp. e70809, 2024.
DOI: https://doi.org/10.7759/cureus.70809
S.J. Davies, D. Coyle, E.J. Lindley, D. Keane, J. Belcher, F.J. Caskey, I. Dasgupta, A. Davenport, K. Farrington, S. Mitra, P. Ormandy, M. Wilkie, J. MacDonald, M. Zanganeh, L. Andronis, I. Solis-Trapala and J. Sim, “Bio-impedance spectroscopy added to a fluid management protocol does not improve preservation of residual kidney function in incident hemodialysis patients in a randomized controlled trial,” Kidney International, vol. 104, no. 3, pp. 587-598, 2023.
DOI: https://doi.org/10.1016/j.kint.2023.05.016
F.M. van der Sande, E.R. van de Wal-Visscher, S. Stuard, U. Moissi and J.P. Kooman, “Using Bioimpedance Spectroscopy to Assess Volume Status in Dialysis Patients,” Blood Purification, vol. 49, no. 1-2, pp. 178-184, 2020.
DOI: https://doi.org/10.1159/000504079
M. Zeynali, K. Alipour, B. Tarvirdizadeh and M. Ghamari, “Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML,” Scientific Reports, vol. 15, no. 581, 2025.
DOI: https://doi.org/10.1038/s41598-024-84265-8
G. Hammour and D.P. Mandic, “An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study,” Sensors (Basel), vol. 23, no. 6, pp. 3319, 2023.
DOI: https://doi.org/10.3390/s23063319
Q. Gong, C. Xu, H. Yuan, X. Shi, W. Li, X. Li and C. Fang, “Non-Invasive and Accurate Blood Glucose Detection Based on an Equivalent Bioimpedance Spectrum,” Applied Sciences, vol. 15, no. 3, pp. 1266, 2025.
DOI: https://doi.org/10.3390/app15031266
T. Li, Q. Wang, Y. An, L. Guo, L. Ren, L. Lei and X. Chen, “Infrared absorption spectroscopy-based non-invasive blood glucose monitoring technology: A comprehensive review,” Biomedical Signal Processing and Control, vol. 106, no. 107750, 2025.
DOI: https://doi.org/10.1016/j.bspc.2025.107750
A. Pfannkuche, A. Alhajjar, A. Ming, I. Walter, C. Piehler and P.R. Merterns, “Prevalence and risk factors of diabetic peripheral neuropathy in a diabetics cohort: Register initiative “diabetes and nerves”,” Endocrine and Metabolic Science, vol. 1, no. 1-2, pp. 100053, 2020.
DOI: https://doi.org/10.1016/j.endmts.2020.100053
J. Sidorova, P. Carbonell and M. Cukic, “Blood Glucose Estimation From Voice: First Review of Successes and Challenges,” Journal of Voice, vol. 36, no. 5, pp. 737.e1-737.e10, 2022.
DOI: https://doi.org/10.1016/j.jvoice.2020.08.034
J. Kaufman, J. Jeon, J. Oreskovic and Y. Fossat, “Linear effects of glucose levels on voice fundamental frequency in type 2 diabetes and individuals with normoglycemia,” Scientific Reports, vol. 14, no. 19012, 2024.
DOI: https://doi.org/10.1038/s41598-024-69620-z
R. Cordeiro, N. Karimian and Y. Park, “Hyperglycemia Identification Using ECG in Deep Learning Era,” Sensors (Basel), vol. 21, no. 18, pp. 6263, 2021.
DOI: https://doi.org/10.3390/s21186263
A. Tas, Y. Alan, M. I. Bayhan, Z. Atay, H. E. Citak, F. Sezer, I. Kara, B.A. Ulker, C. Kitapli, M. Sezer and S. Umman, “Assessment of Electrocardiographic Response to Fluctuating Blood Glucose Levels in People Without Diabetes,” Journal of Diabetes Science and Technology, vol. 17, no. 2, pp. 595-597, 2022.
DOI: https://doi.org/10.1177/19322968221141087
G. Eerdekens, S. Rex and D. Mesotten, “Accuracy of Blood Glucose Measurement and Blood Glucose Targets,” Journal of Diabetes Science and Technology, vol. 14, no. 3, pp. 553-559, 2020.
DOI: https://doi.org/10.1177/1932296820905581
M. Ghosh and V.R. Bora, “Evolution in blood glucose monitoring: a comprehensive review of invasive to non-invasive devices and sensors,” Discover Medicine, vol. 2, no. 74, 2025.
DOI: https://doi.org/10.1007/s44337-025-00273-1
Y. Li, X. Li, Y. Zhang, L. Zhang, Q. Wu, Z. Bai, J. Si, X. Zuo, N. Shi, J. Li and X. Chu, “Prognostic significance of the hemoglobin A1c level in non-diabetic patients undergoing percutaneous coronary intervention: a meta-analysis,” Chinese Medical Journal, vol. 133, no. 18, pp. 2229-2235, 2020.
DOI: https://doi.org/10.1097/CM9.0000000000001029
M. Eichenlaub, P. Stephan, D. Waldenmaier, S. Pleus, M. Rothenbühler, C. Haug, R. Hinzmann, A. Thomas, J. Jendle, P. Diem and G. Freckmann, “Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems,” Journal of Diabetes Science and Technology, vol. 18, no. 4, pp. 857-865, 2022.
DOI: https://doi.org/10.1177/19322968221134639
S.R. Jangam, G. Hayter and T.C. Dunn, “Impact of Glucose Measurement Processing Delays on Clinical Accuracy and Relevance,” Journal of Diabetes Science and Technology, vol. 7, no. 3, pp. 660-668, 2013.
DOI: https://doi.org/10.1177/193229681300700311
S. Sofidazeh, A. Pehrsson, A.F. Ólafsdóttir and M. Lind, “Evaluation of Reference Metrics for Continuous Glucose Monitoring in Persons Without Diabetes and Prediabetes,” Journal of Diabetes Science and Technology, vol. 16, no. 2, pp. 373-382, 2020.
DOI: https://doi.org/10.1177/1932296820965599
L. Czupryniak, L. Barkai, S. Bolgarska, A. Bronisz, J. Broz, K. Cypryk, M. Honka, A. Janez, M. Krnic, N. Lalic, E. Martinka, D. Rahelic, G. Roman, T. Tankova, T. Várkonyi, B. Wolnik and N. Zherdova, “Self-Monitoring of Blood Glucose in Diabetes: From Evidence to Clinical Reality in Central and Eastern Europe—Recommendations from the International Central-Eastern European Expert Group,” Diabetes Technology & Therapeutics, vol. 16, no. 7, pp. 460-475, 2014.
DOI: https://doi.org/10.1089/dia.2013.0302
M.E. Khamseh, M. Ansari, M. Malek, G. Shafiee and H. Baradaran, “Effects of a Structured Self-Monitoring of Blood Glucose Method on Patient Self-Management Behavior and Metabolic Outcomes in Type 2 Diabetes Mellitus,” Journal of Diabetes Science and Technology, vol. 5, no. 2, pp. 388-393, 2011.
DOI: https://doi.org/10.1177/193229681100500228
H. Nemat, H. Khadem, J. Elliott and M. Benaissa, “Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis,” Scientific Reports, vol. 14, no. 21863, 2024.
DOI: https://doi.org/10.1038/s41598-024-70277-x
B. Shi, S.S. Dhaliwal, M. Soo, C. Chan, J. Wong, N.W.C. Lam, E. Zhou, V. Paitimusa, K.Y. Loke, J. Chin, M.T. Chua, K.C.S. Liaw, A.W.H. Lim, F.F. Insyirah, S.C. Yen, A. Tay and S.B. Ang, “Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation,” Journal of Medical Internet Research: Artificial Intelligence, vol. 2, no. e48340, 2023.
DOI: https://doi.org/10.2196/48340
N. Li, H. Zang, H. Sun, X. Jiao, K. Wang, T.C. Liu and Y. Meng, “A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels,” Molecules, vol. 24, no. 8, pp. 1500, 2019.
DOI: https://doi.org/10.3390/molecules24081500
P.P. Harms, A.A. van der Heijden, F. Rutters, H.L. Tan, J.W.J. Beulens, G. Nijpels and P. Elders, “Prevalence of ECG abnormalities in people with type 2 diabetes: The Hoorn Diabetes Care System cohort,” Journal of Diabetes and its Complications, vol. 35, no. 2, pp. 107810, 2021.
DOI: https://doi.org/10.1016/j.jdiacomp.2020.107810
N. Chellamani, S.A. Albelwi, M. Shanmuganathan, P. Amirthalingam and A. Paul, “Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals,” Biosensors (Basel), vol. 15, no. 4, pp. 255, 2025.
DOI: https://doi.org/10.3390/bios15040255
J. Li, J. Ma, O.M. Omisore, Y. Liu, H. Tang and P. Ao, “Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 14491-14505, 2024.
DOI: https://doi.org/10.1109/TNNLS.2023.3279383
I.A. Ebong, A.G. Bertoni, E.Z. Soliman, M. Guo, C.T. Sibley, Y.D.I. Chen, J.I. Rotter, Y.C. Chen and D.C. Goff Jr, “Electrocardiographic Abnormalities Associated with the Metabolic Syndrome and Its Components: The Multi-Ethnic Study of Atherosclerosis,” Metabolic Syndrome and Related Disasters, vol. 10, no. 2, pp. 92-97, 2012.
DOI: https://doi.org/10.1089/met.2011.0090
F.X. Kasujja, M. Daivadanam, R.W. Mayega, F. Nuwaha, R. Kusolo and E. Ekirapa, “Glycated haemoglobin versus fasting plasma glucose for type 2 diabetes point of care screening: a decision model cost-effectiveness analysis,” BMC Health Services Research, vol. 25, no. 664, 2025.
DOI: https://doi.org/10.1186/s12913-025-12840-4
M.J. Distefano, R.B. McQueen, V. Gao, J.K. Snell-Bergeon, C.A. Johnson, M.P. Klein and S. Polsky, “1931-LB: Cost of Continuous Glucose Monitoring vs. Self-Monitoring of Blood Glucose in Type 1 Diabetes Pregnancies,” Diabetes, vol. 73, no. 1931–LB, 2024.
DOI: https://doi.org/10.2337/db24-1931-LB
C.H. Dong and J. Zhong, “PDB53 - Costs of self-monitoring of blood glucose and self-injection of insulin for patients with type 2 diabetes in Beijing and Tianjin: Estimating costs of self-used devices and supplies,” Value in Health, vol. 17, no. 3, pp. 246-247, 2014.
DOI: https://doi.org/10.1016/j.jval.2014.03.1439
R.N. Janapala, J.S. Jayaraj, N. Fathima, T. Kashif, N. Usman, A. Dasari, N. Jahan and I. Sachmechi, “Continuous Glucose Monitoring Versus Self-monitoring of Blood Glucose in Type 2 Diabetes Mellitus: A Systematic Review with Meta-analysis,” Cureus, vol. 11, no. 9, pp. e5634, 2019.
DOI: https://doi.org/10.7759/cureus.5634
D. Castaneda, A. Esparza, M. Ghamari, C. Soltanpur and H. Nazeran, “A review on wearable photoplethysmography sensors and their potential future applications in health care,” International Journal of Biosensors and Bioelectronics, vol. 4, no. 4, pp. 195-202, 2018.
DOI: https://doi.org/10.15406/ijbsbe.2018.04.00125
M.H. Al-Jammas, A.S. Iobaid, M.M.N. Al-Deen and Y.W. Aziz, “A non-invasive blood glucose monitoring system,” Computers in Biology and Medicine, vol 191, pp. 110133, 2025.
DOI: https://doi.org/10.1016/j.compbiomed.2025.110133
R. Bellazi and A. Abu-Hanna, “Data Mining Technologies for Blood Glucose and Diabetes Management,” Journal of Diabetes Science and Technology, vol. 3, no. 3, 2009.
DOI: https://doi.org/10.1177/193229680900300326
A. Ahmed, S. Aziz, U. Qidwai, A. Abd-Alrazaq and J. Sheikh, “Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data,” Biomedicine Update, vol. 3, no. 100094, 2023.
DOI: https://doi.org/10.1016/j.cmpbup.2023.100094
S. Medanki, N. Dommati, H.H. Bodapati, V.N.S.K. Katru, G. Moses, A. Komaraju, N.S. Donepudi, D. Yalamanchili, J. Sateesh and P. Turimerla, “Artificial intelligence powered glucose monitoring and controlling system: Pumping module,” World Journal of Experimental Medicine, vol. 14, no. 1, pp. 87916, 2024.
DOI: https://doi.org/10.5493/wjem.v14.i1.87916
A. Neumann, Y. Zghal, M.A. Cremona, A. Hajji, M. Morin and M. Rekik, “A data-driven personalized approach to predict blood glucose levels in type-1 diabetes patients exercising in free-living conditions,” Computers in Biology and Medicine, vol. 190, no. 110015, 2025.
DOI: https://doi.org/10.1016/j.compbiomed.2025.110015
E. Afsaneh, A. Sharifdini, H. Ghazzaghi and M.Z. Ghobadi, “Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review,” Diabetology & Metabolic Syndrome, vol. 14, no. 196, 2022 .
DOI: https://doi.org/10.1186/s13098-022-00969-9
M.M. Owess, A.Y. Owda, M. Owda and S. Massad, “Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar,” International Journal of Environmental Research and Public Health, vol. 21, no. 7, pp. 840, 2024.
DOI: https://doi.org/10.3390/ijerph21070840
L. Jiang, Z. Xia, R. Zhu, H. Gong, J. Wang, J. Li and L. Wang, “Diabetes risk prediction model based on community follow-up data using machine learning,” Preventive Medicine Reports, vol. 35, no. 102358, 2023.