Cited by 198Diabetes Management and ResearchElectrochemical sensors and biosensorsPancreatic function and diabetes

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Delays in Minimally Invasive Continuous Glucose Monitoring Devices: A Review of Current Technology

Open access

Keenan, D. Barry, Mastrototaro, John J., Voskanyan, Gayane, Steil, Garry M.·2009·Journal of Diabetes Science and Technology

DOI 10.1177/193229680900300528PMC PMC2769894

Open this paper in CoChat — free account

Abstract

Through the use of enzymatic sensors—inserted subcutaneously in the abdomen or ex vivo by means of microdialysis fluid extraction—real-time minimally invasive continuous glucose monitoring (CGM) devices estimate blood glucose by measuring a patient's interstitial fluid (ISF) glucose concentration. Signals acquired from the interstitial space are subsequently calibrated with capillary blood glucose samples, a method that has raised certain questions regarding the effects of physiological time lags and of the duration of processing delays built into these devices. The time delay between a blood glucose reading and the value displayed by a continuous glucose monitor consists of the sum of the time lag between ISF and plasma glucose, in addition to the inherent electrochemical sensor delay due to the reaction process and any front-end signal-processing delays required to produce smooth traces. Presented is a review of commercially available, minimally invasive continuous glucose monitors with manufacturer-reported device delays. The data acquisition process for the Medtronic MiniMed (Northridge, CA) continuous glucose monitoring system—CGMS® Gold—and the Guardian® RT monitor is described with associated delays incurred for each processing step. Filter responses for each algorithm are examined using in vitro hypoglycemic and hyperglycemic clamps, as well as with an analysis of fast glucose excursions from a typical meal response. Results demonstrate that the digital filters used by each algorithm do not cause adverse effects to fast physiologic glucose excursions, although nonphysiologic signal characteristics can produce greater delays.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1177/193229680900300528

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
The scientist and engineer's guide to digital signal processing
Smith, S. W. (1997) · Cited by 2,805
Improved Glycemic Control in Poorly Controlled Patients with Type 1 Diabetes Using Real-Time Continuous Glucose Monitoring
Deiss, D., Bolinder, J., Riveline, J.-P., Battelino, T., Bosi, E., Tubiana-Rufi, N., Kerr, D., & Phillip, M. (2006) · Diabetes Care · doi:10.2337/dc06-1134 · Cited by 539
Improvement in Glycemic Excursions With a Transcutaneous, Real-Time Continuous Glucose Sensor
Garg, S. K., Zisser, H., Schwartz, S. L., Bailey, T. S., Kaplan, R. A., Ellis, S. L., & Jovanovič, L. (2006) · Diabetes Care · doi:10.2337/diacare.29.01.06.dc05-1686 · Cited by 429
The MiniMed Continuous Glucose Monitoring System
Mastrototaro, J. J. (2000) · Diabetes Technology & Therapeutics · doi:10.1089/15209150050214078 · Cited by 394
Self-citation
Performance Evaluation of the MiniMed® Continuous Glucose Monitoring System During Patient Home Use
Gross, T. M., Bode, B. W., EINHORN, D. F., Kayne, D. M., Reed, J. H. C., White, N. H., & Mastrototaro, J. J. (2000) · Diabetes Technology & Therapeutics · doi:10.1089/152091500316737 · Cited by 304
Self-citation
Noninvasive Glucose Monitoring
Tamada, J. A., Garg, S. K., Jovanovič, L., Pitzer, K. R., Fermi, S. J., & Potts, R. O. (1999) · JAMA · doi:10.1001/jama.282.19.1839 · Cited by 281
Continuous glucose monitoring used to adjust diabetes therapy improves glycosylated hemoglobin: a pilot study
Bode, B. W., Gross, T. M., Thornton, K. R., & Mastrototaro, J. J. (1999) · Diabetes Research and Clinical Practice · doi:10.1016/s0168-8227(99)00113-8 · Cited by 232
Self-citation
Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System
Weinstein, R. L., Schwartz, S. L., Brazg, R., Bugler, J., Peyser, T. A., & McGarraugh, G. V. (2007) · Diabetes Care · doi:10.2337/dc06-1602 · Cited by 228
Can Interstitial Glucose Assessment Replace Blood Glucose Measurements?
Rebrin, K., & Steil, G. M. (2000) · Diabetes Technology & Therapeutics · doi:10.1089/15209150050194332 · Cited by 227
Self-citation

Show all 27 references

Research works printed but not linked: 2

Mastrototaro JJ, 1999, J Pediatr Endoncrinol Metab., 12, 751
Bode B, 2008, Managed Care., 17, 40

Sorted by citations

Items 101–197 of 197

Diabetes Mellitus Glucose Prediction by Linear and Bayesian Ensemble Modeling
Ståhl, F. L. (2012) · Lund University Publications (Lund University) · Cited by 13
Feasibility and Precision of Subcutaneous Continuous Glucose Monitoring in Patients Undergoing CABG Surgery
Aust, H., Dinges, G., Nardi-Hiebl, S., Koch, T., Lattermann, R., Schricker, T., & Eberhart, L. H. J. (2014) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2014.02.022 · Cited by 13
Accuracy and Time Delay of Glucose Measurements of Continuous Glucose Monitoring and Bedside Artificial Pancreas During Hyperglycemic and Euglycemic Hyperinsulinemic Glucose Clamp Study
Kuroda, A., Taniguchi, S., Akehi, Y., Mori, H., Tamaki, M., Suzuki, R., Otsuka, Y., & Matsuhisa, M. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817735122 · Cited by 13
Continuous glucose monitoring as a screening tool for neonatal hypoglycemia in infants of diabetic mothers
Tabery, K., Černý, M., Urbaniec, K., Vaniš, M., Zoban, P., & Štechová, K. (2018) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2018.1533941 · Cited by 13
Evaluation of liver glycogen catabolism during hypercortisolism induced by the administration of dexamethasone in rats
Tavoni, T. M., Obici, S., Marques, A. D. C. R., Minguetti-Câmara, V. C., Curi, R., & Bazotte, R. B. (2013) · Pharmacological Reports · doi:10.1016/s1734-1140(13)70972-1 · Cited by 12
Perioperative Considerations for Evolving Artificial Pancreas Devices
Long, M. T., Coursin, D. B., & Rice, M. J. (2018) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000003779 · Cited by 12
Design and rationale of the IN CONTROL trial: the effects of real-time continuous glucose monitoring on glycemia and quality of life in patients with type 1 diabetes mellitus and impaired awareness of hypoglycemia
van Beers, C. A. J., Kleijer, S. J., Serné, E. H., Geelhoed-Duijvestijn, P. H. L. M., Snoek, F. J., Kramer, M. H. H., & Diamant, M. (2015) · BMC Endocrine Disorders · doi:10.1186/s12902-015-0040-3 · Cited by 12
A Stepwise Approach toward Closed-Loop Blood Glucose Control for Intensive Care Unit Patients: Results from a Feasibility Study in Type 1 Diabetic Subjects Using Vascular Microdialysis with Infrared Spectrometry and a Model Predictive Control Algorithm
Feichtner, F., Mader, J. K., Schaller, R., Schaupp, L. A., Ellmerer, M., Korsatko, S., Kondepati, V. R., Heise, H. M., Wilinska, M. E., Hovorka, R., et al. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500412 · Cited by 12
The barriers against initiating insulin therapy among patients with diabetes living in Yazd, Iran
Pour, M. S., Sadeghiyeh, T., Hadavi, M., Besharati, M., & Bidaki, R. (2019) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s200867 · Cited by 13
Microneedle-based enzymatic biosensors for continuous glucose monitoring: A review of materials, architectures, and performance
Alkasem, O., & Can, M. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.116030 · Cited by 11
Regulation of peripheral glucose levels during human sleep
Yang, X., Fedumenti, F. T., Niethard, N., Hallschmid, M., Jan Born, A., & Rauss, K. S. (2025) · SLEEP · doi:10.1093/sleep/zsaf042 · Cited by 11
Glucose Diffusion Limiting Membrane Based on Polyethyleneimine (PEI) Hydrogel for the Stabilization of Glucose Sensor
Kim, S.-J., & Shin, W. (2020) · Journal of Electrochemical Science and Technology · doi:10.33961/jecst.2020.01487 · Cited by 11
Graphene Nanoribbon Field Effect Transistor Simulations for the Detection of Sugar Molecules: Semi-Empirical Modeling
Wasfi, A., Hamarna, A. A., Shehhi, O. M. H. A., Ameri, H. F. M. A., & Awwad, F. R. (2023) · Sensors · doi:10.3390/s23063010 · Cited by 11
Evaluation of a Novel Continuous Glucose Measurement Device in Patients with Diabetes Mellitus across the Glycemic Range
Morrow, L., Hompesch, M., Tideman, A. M., Matson, J., Dunne, N., Pardo, S. A., Parkes, J. L., Schachner, H. C., & Simmons, D. A. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500406 · Cited by 11
Are prolonged sitting and sleep restriction a dual curse for the modern workforce? a randomised controlled trial protocol
Vincent, G. E., Gupta, C. C., Sprajcer, M., Vandelanotte, C., Duncan, M. J., Tucker, P., Lastella, M., Tuckwell, G. A., & Ferguson, S. A. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-040613 · Cited by 11
Continuous glucose monitoring in acute coronary syndrome
Rodríguez-Quintanilla, K. A., Lavalle-Gonzalez, F. J., Mancillas‐Adame, L. G., Zapata-Garrido, A. J., Villarreal‐Pérez, J. Z., & Tamez-Pérez, H. E. (2013) · Archivos de cardiología de México · doi:10.1016/j.acmx.2013.08.001 · Cited by 11
Noninvasive Continuous Glucose Monitoring With a Novel Wearable Dial Resonating Sensor: A Clinical Proof-of-Concept Study
Handy, C., Chaudhry, M. S., Qureshi, M. R. A., Love, B., Shillingford, J., Plum‐Mörschel, L., & Zijlstra, E. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231170242 · Cited by 10
Predicting Plasma Glucose From Interstitial Glucose Observations Using Bayesian Methods
Hansen, A. H., Duun‐Henriksen, A. K., Juhl, R., Schmidt, S., Nørgaard, K., Jørgensen, J. B., & Madsen, H. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814523878 · Cited by 10
Modeling and Prediction in Diabetes Physiology
Cescon, M. (2013) · Lund University Publications (Lund University) · Cited by 9
Noninvasive blood glucose monitoring during oral intake of different sugars with optical coherence tomography in human subjects
Zhang, Y., Wei, H., Yang, H., He, Y., Wu, G., Xie, S., Zhu, Z., & He, R. (2012) · Journal of Biophotonics · doi:10.1002/jbio.201200128 · Cited by 8
Identification of CGM Time Delays and Implications for BG Control in T1DM
Reiterer, F., Polterauer, P., Freckmann, G., & del Re, L. (2016) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-32703-7_39 · Cited by 8
Quantitative analysis of time delays of glucose - insulin dynamics using artificial pancreas
Rathee, S., & Nilam (2015) · Discrete and Continuous Dynamical Systems - B · doi:10.3934/dcdsb.2015.20.3115 · Cited by 8
Higher blood glucose and larger fluctuations detected postoperatively using continuous glucose monitoring: a preliminary study following total knee or hip arthroplasty
Maeda, Y., Nakamura, N., Takashi, T., & Sugano, N. (2019) · Journal of Experimental Orthopaedics · doi:10.1186/s40634-019-0181-9 · Cited by 8
Accuracy Performance of the Medtronic NexSensor® for 6 Days in an Inpatient Setting Using Abdomen and Buttocks Insertion Sites
Peoples, T., Bailey, T. S., Brazg, R., Zisser, H., Janowski, B., Huang, S., Talbot, C., & Yang, Q. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500224 · Cited by 8
Non-invasive sensing techniques for glucose detection: a review
Al-Nabulsi, J., Owida, H. A., Ma’touq, J., Matar, S., Al-Aazeh, E., Al-Maaiouf, A., & Bleibel, A. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i4.3584 · Cited by 7
Noninvasive blood glucose monitoring technology based on PSO optimized CNN-BiGRU-attention neural network model and photoplethysmography
Xiong, L., Wan, S., Chen, C.-Y., & Yu, J. (2025) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2025.116914 · Cited by 6
Accuracy of Continuous Glucose Monitoring in Adults with Type 1 Diabetes Admitted to Hospital: A Real-World Multicenter Observational Study
Wang, R., Kyi, M., Krishnamoorthi, B., Tjahyadi, J., Connell, A., Chiang, C. Y., Renouf, D., Barmanray, R., & Fourlanos, S. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0604 · Cited by 7
Time delay of microdialysis in vitro
Rong, J., Wu, Z., Huang, S., Zhang, J., & Li, Y. (2013) · North American Journal of Medical Sciences · doi:10.4103/1947-2714.107540 · Cited by 6
Continuous Monitoring of Glucose in Subcutaneous Tissue Using Microfabricated Differential Affinity Sensors
Huang, X. L., LeDuc, C. A., Ravussin, Y., Li, S., Davis, E. N., Song, B., Wang, Q., Accili, D., Leibel, R. L., & Lin, Q. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600625 · Cited by 6
Optimization of Insulin Dosing in Patients with Type 1 Diabetes Mellitus
Zavitsanou, S., Mantalaris, A., Georgiadis, M. C., & Pistikopoulos, E. N. (2014) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-444-63455-9.50078-7 · Cited by 6
Monitoring Technology
Klonovs, J., Haque, M. A., Krueger, V., Nasrollahi, K., Andersen‐Ranberg, K., Moeslund, T. B., & Spaich, E. G. (2016) · SpringerBriefs in computer science · doi:10.1007/978-3-319-27024-1_4 · Cited by 6
Latest Progresses in Developing Wearable Monitoring and Therapy Systems for Managing Chronic DiseasesPreprint
Gui, H., & Liu, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.01747 · Cited by 11
A low-power approach to optical glucose sensing via polarisation switching
Hassanpour, E., Nasehi, M., Meymandinezhad, A., & Witthauer, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-99367-0 · Cited by 5
Bio-inspired microneedles with hierarchical microchannels for rapid interstitial fluid collection and analysis
Li, G., Yu, Y., Meng, Z., Ai, D., Li, M., & Wang, Y. (2025) · Lab on a Chip · doi:10.1039/d5lc00145e · Cited by 5
Carbon Dot-Based Biosensors for Continuous Glucose Monitoring in Point-of-Care Devices: Advancements, Challenges, and Future Prospects
Elsa, G., Hanan, A., Mustafa, M. N., Walvekar, R., Mathkor, D. M., Haque, S., Numan, A., & Khalid, M. (2025) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/adc8d7 · Cited by 5
Calibration of CGM systems
Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816714-4.00009-0 · Cited by 5
Catalytic effects of magnetic and conductive nanoparticles on immobilized glucose oxidase in skin sensors
Segovia, L. C. A., Bandodkar, A. J., Rogers, J. A., & Rintoul, I. (2021) · Nanotechnology · doi:10.1088/1361-6528/ac0668 · Cited by 5
Optical imaging of microfluidic integrated smart hydrogels for research and sensing applications
Boroomand, S., Binder, S., Leber, M., Torres, J. P. B., Magda, J. J., Solzbacher, F., Reiche, C. F., & Laurentius, L. B. (2025) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2025.137952 · Cited by 4
Pulse-Operated First-Generation Biosensors with Extended Linear Range for Continuous Glycemia Monitoring in Oxygen-Deficient Environments
Komkova, M. A., Kasimovskaya, V. S., Eliseev, A. A., Poyarkov, A. A., Sushkov, A. I., Eliseev, A. A., & Karyakin, A. A. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c01058 · Cited by 5
Feasibility and Safety of Continuous Glucose Monitoring in Infants at Risk of Hypoglycemia in a Rooming-in Setting
Tabery, K., Doležalová, L., Černý, M., Janota, J., Zoban, P., & Štechová, K. (2021) · Fetal and Pediatric Pathology · doi:10.1080/15513815.2021.1945716 · Cited by 4
A dielectric affinity glucose microsensor using hydrogel-functionalized coplanar electrodes
Zhang, Z., Maturavongsadit, P., Shang, J., Yan, J., Li, D., Wang, Q., & Lin, Q. (2017) · Microfluidics and Nanofluidics · doi:10.1007/s10404-017-1926-3 · Cited by 4
Noise spectral analysis and error estimation of continuous glucose monitors under real-life conditions of diabetes patients
Garnica, Ó., Lanchares, J., Velasco, J. M., Hidalgo, J. I., & Botella, M. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101934 · Cited by 4
The Strategies to Reduce Healthcare Costs and Increase the Quality of Healthcare Service Delivery Using Hybrid Technologies
Geetha, G., & Prasad, K. M. (2021) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon51346.2021.9544947 · Cited by 5
CGM sensor technology
DEHENNIS, A. D., & Mortellaro, M. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816714-4.00006-5 · Cited by 4
Continuous Glucose Monitoring and Tight Glycaemic Control in Critically Ill Patients
Signal, M. (2013) · UC Research Repository (University of Canterbury) · doi:10.26021/3559 · Cited by 4
Treatment of Canine Type 1 Diabetes Mellitus: The Long Road from Twice Daily Insulin Injection towards Long-Lasting Cell-Based Therapy
Oliveira, F. C. M., Voorbij, A. M. W. Y., Pereira, E. C., Almeida, L. M. M. A. E., de Moraes, C. C. G., Oliveira, J. T. D., Gouw, B. H. T., Legatti, E., Kooistra, H. S., Spee, B., et al. (2024) · Organoids · doi:10.3390/organoids3020006 · Cited by 3
Pilot Study of a Prototype Minimally Invasive Intradermal Continuous Glucose Monitor
Weinstock, R. S., Bristol, S., Armenia, A., Gesswein, A. C., Bequette, B. W., & Willis, J. P. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600627 · Cited by 3
Glucose Variations During Driving in People With Type 1 Diabetes Using a Continuous Glucose Monitoring System
Schmied, L., & Zulewski, H. (2019) · Diabetes Care · doi:10.2337/dc18-0822 · Cited by 3
Symbolic models approximating possibly unstable time-delay systems with application to the artificial pancreas
Pola, G., Borri, A., Pepe, P., Palumbo, P. J., & Benedetto, M. D. D. (2019) · European Control Conference (ECC) · doi:10.23919/ecc.2019.8795968 · Cited by 3
LMI-based online estimation of a time-varying time-delay in continuous glucose measurement devices
Kirchsteiger, H., Efendic, H., Freckmann, G., & del Re, L. (2014) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2014.7040486 · Cited by 2
Wearble Noninvasive Glucose Estimation based on Multi-wavelength Reflective Photoplethysmography
Long, N. M. H., & Chung, W. (2021) · IEEE Photonics Conference · doi:10.1109/ipc48725.2021.9592997 · Cited by 2
A modelling approach to hepatic glucose production estimation
Panunzi, S., & Gaetano, A. D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278837 · Cited by 2
Phenomenological-based model of glucose transport from liver to abdominal subcutaneous adipose tissue
Herrón-Bedoya, A., Walteros‐León, M., Lema-Pérez, L., & Álvarez, H. (2021) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2021.110883 · Cited by 2
A Continuous Oral-fluid Monitoring of Glucose (O.M.G.) Device with Near-field & Bluetooth Communication Capability
Cheng, G. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i3.4805 · Cited by 1
Breaking through lag: enhancing intradermal electro-osmotic flow and the future of delay-free continuous glucose monitoring
Ciarrocchi, D., den Eeckhoudt, R. V., Rusli, N. I., Zompanti, A., Grasso, S., di Biase, L., Santonico, M., Pennazza, G., & Taurino, I. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1717650 · Cited by 1
Real-Time Continuous Glucose Monitoring in Adult Outpatients
Bonomo, M. A., Grassi, G., Bartolo, P. D., & Maran, A. (2014) · Frontiers in diabetes · doi:10.1159/000363482 · Cited by 1
Bio-Electrochemistry and Chalcogens
Domínguez‐Álvarez, E., Viswanathan, U. M., Burkholz, T., Khairan, K., & Jacob, C. (2013) · Modern aspects of electrochemistry · doi:10.1007/978-1-4614-6148-7_7 · Cited by 1
La monitorización continua de glucosa a tiempo real y los servicios nacionales de salud. ¿Hay suficiente evidencia científica para justificar su financiación pública?
de Adana, M. R., Dominguez-Lopez, M.-E., Machado, A., Colomo, N., Ortiz, M. T. A., & C-Soriguer, F. (2011) · Avances en Diabetología · doi:10.1016/j.avdiab.2011.11.003 · Cited by 2
Development of a fluorescent method for simultaneous measurement of glucose concentrations in interstitial fluid and blood
Shi, T., Li, D., Li, G., Chen, L., Lin, Y. H., Xu, K., & Lu, L. (2013) · Measurement Science and Technology · doi:10.1088/0957-0233/24/12/125701 · Cited by 1
Liquid Metal Enabled Wearable Electronics
Liu, J., & Yi, L. (2018) · Springer series in biomaterials science and engineering · doi:10.1007/978-981-10-5607-9_14 · Cited by 1
Glukosemessverfahren und deren Einfluss auf die Diabetologie
Thomas, A., Siegmund, T., & Kolassa, R. (2019) · Diabetes aktuell · doi:10.1055/a-0900-7099 · Cited by 1
Glucose Transport Model Between the Liver and the Abdominal Subcutaneous Adipose Tissue
Walteros‐León, M., & Herrón-Bedoya, A. (2018) · Argentine Conference on Automatic Control (AADECA) · doi:10.23919/aadeca.2018.8577411 · Cited by 1
Relevancia del monitoreo continuo de glucosa en la práctica clínica: revisión de la evidencia
Gómez, A. M., Carrillo, D. C. H., Tapoada, L., & Toro, M. R. (2018) · Revista Colombiana de Endocrinología Diabetes & Metabolismo · doi:10.53853/encr.5.3.432 · Cited by 1
The Unscented Kalman Filter as a Real-Time Algorithm to Simultaneously Estimate Insulin and Model Parameters
Murata, M., & Palumbo, P. J. (2022) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc51059.2022.9992731 · Cited by 1
Data acquisition unit for low-noise, continuous glucose monitoring
Cooley, D. W. (2012) · doi:10.17077/etd.xcizv7ow · Cited by 1
Glucose(monitoring): from bench to real world experiences
Fokkert, M. J. (2022) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.207217778 · Cited by 1
Continuous monitoring blood glucose level in clinical practice and new methods for its analysis
Dreval', A. V., Dreval, O., & Starostina, E. G. (2013) · Problems of Endocrinology · doi:10.14341/probl201359441-49
Microdialysis Probe Requirements
Córcoles, E. P., & Boutelle, M. G. (2013) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-319-00360-3_8
The Use of Advanced Statistical Concepts and Analysis to Improve Nonlinear Dynamic Glucose Modeling
Beverlin, L. (2011) · doi:10.31274/etd-180810-1238
Propuesta de algoritmos de predicción de glucosa en pacientes diabéticos
Gandía, C. P. (2014) · doi:10.20868/upm.thesis.25604
The Artificial Pancreas
Keenan, D. B., & Steil, G. M. (2014) · Wiley eBooks · doi:10.1002/9781118873434.ch51
Self-citation
Study on Continuous Monitoring Glucose Concentration with Terbium Gallium Garnet Crystal
Li, D. M., Jia, S., & Wang, J. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.317-319.53
Contributions to interoperability, scalability and formalization of personal health systems
de la Torre, A. B. (2016) · doi:10.5821/dissertation-2117-96346
PK/PD modelling of subcutaneous glucose dynamics
Hansen, A. H. (2013)
Glucose control with incomplete information
Borri, A., Panunzi, S., Gaetano, A. D., Manes, C., & Palumbo, P. J. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844496
Bombas de insulina en diabetes tipo 2, del fundamento a la práctica
Bolivariana, C. U., Álvarez, M. F., Barrientos, A. F. P., Bolivariana, U. P., Botero, J. F., Bolivariana, U. P., Rincón, A. R., & Bolivariana, U. P. (2017) · CES Medicina · doi:10.21615/cesmedicina.31.1.7
Part A: Type 1 Diabetes Mellitus
Zavitsanou, S., Mantalaris, A., Georgiadis, M. C., & Pistikopoulos, E. N. (2017) · Wiley eBooks · doi:10.1002/9781118965580.ch5
Adaptive Control with Heuristic Disturbance Rejection in T1DM: A Performance Assessment
Rebro, M., Tárník, M., & Murgaš, J. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.604
Utilização do aparelho FreeStyle Libre para monitoração contínua da glicemia em equinos
Françoso, R. (2018) · doi:10.11606/t.10.2018.tde-28092018-164512
Acute Glycemic Changes in Brain and Subcutaneous Tissue Measured by Continuous Glucose Monitoring System in Hereditary Hypertriglyceridemic Rat
ŽOUREK, M., Kyselová, P., Cechurová, D., & Rušavý, Z. (2018) · Physiological Research · doi:10.33549/physiolres.933620
Basal Insulin Adjustments and Continuous Glucose Monitoring During Exercise in Type 1 Diabetes
Zaharieva, D. P. (2018) · YorkSpace (York University)
A Semi-physical Model for Glucose Kinetics and Dynamics in Subcutaneous Adipose Tissue
Herrón-Bedoya, A., & Walteros‐León, M. (2019) · IEEE Colombian Conference on Automatic Control (CCAC) · doi:10.1109/ccac.2019.8920830
Microneedle mediated optical detection of glucose concentration in aqueous solutions
Nicholas, D. (2019) · Ulster University Research Portal (Ulster University)
CONTROLLING SILICONE-SACCHARIDE INTERFACES THROUGH BORONIC ACID MODIFICATION OF SILICONE POLYMERS
Macphail, B. (2018) · MacSphere (McMaster University) · doi:10.71548/8240
Messwertunterschiede und Messgenauigkeit von Systemen zum kontinuierlichen Glukosemonitoring
Thomas, A., & Kolassa, R. (2020) · Diabetes aktuell · doi:10.1055/a-1296-1433
The Optimal Time for Blood Glucose Measurements in Patients Who Underwent Primary or Revision Total Joint Arthroplasty: A Retrospective ReviewPreprint
Ye, Y., Pan, B., Xian, G., Chen, W., Zhang, Z., & Sheng, P. (2020) · doi:10.21203/rs.3.rs-16693/v1
Discussion
Klonovs, J., Haque, M. A., Krueger, V., Nasrollahi, K., Andersen‐Ranberg, K., Moeslund, T. B., & Spaich, E. G. (2016) · SpringerBriefs in computer science · doi:10.1007/978-3-319-27024-1_6
A Mathematical Modeling Approach to Estimate Blood Glucose Behavior in Individuals with Prediabetes
Alonso‐Bastida, A., Salazar-Piña, D. A., Adam‐Medina, M., Gutiérrez‐Xicoténcatl, L., Rios‐Enriquez, C., Ramos‐García, M. D. L., & Vásquez, D. V. (2025) · Diabetology · doi:10.3390/diabetology6110123
FAD-glucose dehydrogenase anchored with poly(Methylene Green) nanoparticles in bioelectrocatalysis: Towards non-invasive glycemia monitoring
Komkova, M. A., Alexandrovich, A. S., Eliseev, A. A., Poyarkov, A. A., Eliseev, A. A., & Karyakin, A. A. (2026) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2026.139511
Dual-monitoring of β-hydroxybutyrate and glucose via a microneedles-based biosensor for personalized nutrition
Tortolini, C., Caprio, M., Degjoni, A., Lenzi, A., Isidori, A. M., & Antiochia, R. (2026) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2026.100747
Non-invasive Blood Glucose Monitoring Using Infrared Photoacoustic Spectroscopy
He, L., Wu, R., Liu, W., Li, Y., Wang, P., & Duan, H. (2025) · International Conference on Smart Healthcare and Wearable Intelligent Devices (SHWID) · doi:10.1145/3788112.3788121
Blood-glucose Profile Evaluation with a Model-based Approach using Continuous Glucose Monitoring Data
Kunina, H., Chien, J. Y., Garhyan, P., Geiser, J. S., & Kjellsson, M. C. (2026) · The AAPS Journal · doi:10.1208/s12248-026-01245-8
Electrochemical biosensing across microsampled blood and interstitial fluid for metabolic, hormonal, and therapeutic monitoring
Yoon, S. J., Park, J. T., Yun, J. M., & Lee, Y. K. (2026) · Soft Science · doi:10.20517/ss.2025.125
Accuracy of a Continuous Glucose Monitoring System Under Hyperglycemic Challenge in Chinese Hospitalized Patients With Diabetes
Liu, X., Zheng, D., Yang, Y., XiujuanZhang, M. D., PhD, C., Jin, X., Tian, Z., Dai, A., Xu, C., Xu, F.-Y., et al. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261442573
MXene-graphene hybrid multi-resonator terahertz metasurface biosensor for potential non-invasive glucose monitoring: a computational design and proof-of-concept study
Elsayed, H. A., Solouma, E., Wekalao, J., Mehaney, A., Messaoudi, M., Rajakannu, A., & Kabarokole, P. (2026) · Journal of Computational Electronics · doi:10.1007/s10825-026-02566-9
Calibration algorithms for continuous glucose monitoring from user calibration to intelligent adaptive no-user calibration
Yang, R., Yang, Z., Fan, R., Wang, X., & Ya, Z. (2026) · Intelligent Pharmacy · doi:10.1016/j.ipha.2026.08.005
A wearable closed-loop insulin micropump toward a miniaturized artificial pancreas for diabetes
Kuang, Z., Kong, X., Cao, T., Lin, R., Long, Z., Jiang, T., Jin, X., Xue, X., Zhao, Z., & Chen, M. (2026) · Microsystems & Nanoengineering · doi:10.1038/s41378-026-01463-2 · Cited by 1

Authors: Keenan, D. B., Mastrototaro, J. J., Voskanyan, G., & Steil, G. M.
Affiliations: Medtronic (United States) (US)
Boston Children's Hospital (US)
Corresponding author: Keenan, D. Barry
ORCID: Mastrototaro, John J.
Steil, Garry M.
Venue: Journal of Diabetes Science and Technology
Publisher: SAGE Publications
Published: 2009-09
Volume: 3
Issue: 5
Pages: 1207-1214
ISSN: 1932-2968 (ISSN-L)
DOI: 10.1177/193229680900300528
PMC: PMC2769894
OpenAlex: W2127835324
Cited by: 198
References: 27
Citations by year: 2026: 9 · 2025: 12 · 2024: 2 · 2023: 8 · 2022: 11 · 2021: 13 · 2020: 15 · 2019: 11 · 2018: 17 · 2017: 18 · 2016: 10 · 2015: 9 · 2014: 13 · 2013: 22 · 2012: 12
FWCI: 6.80
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Diabetes Science and Technology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Physical Sciences › Chemistry › Analytical Chemistry › Electrochemical sensors and biosensors
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
Keywords: continuous glucose monitoring, interstitial glucose, sensor calibration, digital filters, hypoglycemic clamp, hyperglycemic clamp, meal response, diabetes management
MeSH terms: Algorithms, Blood Glucose, Diagnosis, Computer-Assisted, Dietary Carbohydrates, Equipment Design, Humans, Predictive Value of Tests, Signal Processing, Computer-Assisted, Time Factors, Blood Glucose Self-Monitoring, Reproducibility of Results, Artifacts, Diagnostic Equipment, Extracellular Fluid, Electrochemical Techniques

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Delays in Minimally Invasive Continuous Glucose Monitoring Devices: A Review of Current Technology

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks