Continuous glucose monitoring sensors for diabetes management: A review of technologies and applications
Open accessAbstract
By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.
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CGM sensors allow tracking glucose dynamics and prompt detection of asymptomatic high/low glucose events, which SMBG cannot.
“Indeed, in contrast to SMBG, CGM sensors allow to track glucose dynamics and they ensure prompt detection of even asymptomatic high/low glucose events, which are difficult to capture with SMBG.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [10]
The use of rtCGM sensors has proved to be very beneficial for glycemic control.
“The use of rtCGM sensors has proved to be very beneficial for glycemic control [10,16,17].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [10]
rtCGM devices provide vibratory or auditory alerts when hypo/hyperglycemic events are detected.
“Moreover, rtCGM devices provide vibratory or auditory alerts when hypo/hyperglycemic events are detected, i.e., when the glucose concentration measured exceeds pre-set, customizable, thresholds for hypoglycemia and hyperglycemia [10].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [10]
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Cites
Electrochemical Glucose Biosensors
Wang, J. (2008) · Chemical Reviews · doi:10.1021/cr068123a · Cited by 3,498 · see citing works
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069 · see citing works
Continuous glucose monitoring and intensive treatment of type 1 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559 · see citing works
Effectiveness of Sensor-Augmented Insulin-Pump Therapy in Type 1 Diabetes
Bergenstal, R. M., Tamborlane, W. V., Ahmann, A., Buse, J. B., Dailey, G., Davis, S. N., Joyce, C., Peoples, T., Perkins, B. A., Welsh, J. B., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1002853 · Cited by 894 · see citing works
The Performance and Usability of a Factory-Calibrated Flash Glucose Monitoring System
Bailey, T., Bode, B. W., Christiansen, M. P., Klaff, L. J., & Alva, S. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0378 · Cited by 685 · see citing works
Threshold-based insulin-pump interruption for reduction of hypoglycemia
Bergenstal, R. M., Klonoff, D. C., Garg, S. K., Bode, B. W., Meredith, M., Slover, R. H., Ahmann, A. J., Welsh, J. B., Lee, S. W., & Kaufman, F. R. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1303576 · Cited by 652 · see citing works
Haak, T., Hanaire, H., Ajjan, R., Hermanns, N., Riveline, J.-P., & Rayman, G. (2017) · Diabetes Therapy · doi:10.1007/s13300-016-0223-6 · Cited by 623 · see citing works
WEINZIMER, S. A., Steil, G. M., Swan, K. L., Dziura, J., Kurtz, N., & Tamborlane, W. V. (2008) · Diabetes Care · doi:10.2337/dc07-1967 · Cited by 567 · see citing works
the SWITCH Study Group, Battelino, T., Conget, I., Olsen, B., Schütz-Fuhrmann, I., Hommel, E., Hoogma, R., Schierloh, U., Sulli, N., & Bolinder, J. (2012) · Diabetologia · doi:10.1007/s00125-012-2708-9 · Cited by 494 · see citing works
Effect of Continuous Glucose Monitoring on Hypoglycemia in Type 1 Diabetes
Battelino, T., Phillip, M., Bratina, N., Nimri, R., Oskarsson, P., & Bolinder, J. (2011) · Diabetes Care · doi:10.2337/dc10-1989 · Cited by 490 · see citing works
Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes
Steil, G. M., Rebrin, K., Darwin, C. H., Hariri, F., & Saad, M. F. (2006) · Diabetes · doi:10.2337/db06-0419 · Cited by 473 · see citing works
Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes
Rodbard, D. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0035 · Cited by 458 · see citing works
A history of blood glucose meters and their role in self-monitoring of diabetes mellitus
Clarke, S. F., & Foster, J. R. (2012) · British Journal of Biomedical Science · doi:10.1080/09674845.2012.12002443 · Cited by 456 · see citing works
The T1D exchange clinic registry
Beck, R. W., Tamborlane, W. V., Bergenstal, R. M., Miller, K. M., DuBose, S. N., Hall, C. A., & for the T1D Exchange Clinic Network (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-1561 · Cited by 456 · see citing works
Toward Defining the Threshold Between Low and High Glucose Variability in Diabetes
Monnier, L., Colette, C., Wojtusciszyn, A., Dejager, S., Renard, E., Molinari, N., & Owens, D. R. (2017) · Diabetes Care · doi:10.2337/dc16-1769 · Cited by 390 · see citing works
Aleppo, G., Ruedy, K. J., Riddlesworth, T. D., Kruger, D. F., Peters, A. L., Hirsch, I., Bergenstal, R. M., Toschi, E., Ahmann, A. J., Shah, V. N., et al. (2017) · Diabetes Care · doi:10.2337/dc16-2482 · Cited by 329 · see citing works
Tanenbaum, M. L., Hanes, S. J., Miller, K. M., Naranjo, D., Bensen, R., & Hood, K. K. (2017) · Diabetes Care · doi:10.2337/dc16-1536 · Cited by 296 · see citing works
Current and Emerging Technology for Continuous Glucose Monitoring
Chen, C., Zhao, X.-L., Li, Z.-H., Zhu, Z., Qian, S., & Flewitt, A. J. (2017) · Sensors · doi:10.3390/s17010182 · Cited by 293 · see citing works
Glycemic Variability and Diabetes Complications: Does It Matter? Of Course It Does!
Hirsch, I. B. (2015) · Diabetes Care · doi:10.2337/dc14-2898 · Cited by 286 · see citing works
Technologies for Continuous Glucose Monitoring: Current Problems and Future Promises
Vaddiraju, S., Burgess, D. J., Tomazos, I., Jain, F. C., & Papadimitrakopoulos, F. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400632 · Cited by 285 · see citing works
MD-Logic Artificial Pancreas System
Atlas, E., Nimri, R., Miller, S., Grunberg, E. A., & Phillip, M. (2010) · Diabetes Care · doi:10.2337/dc09-1830 · Cited by 266 · see citing works
Peters, A. L., Ahmann, A. J., Battelino, T., Evert, A., Hirsch, I. B., Murad, M. H., Winter, W. E., & Wolpert, H. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-2534 · Cited by 250 · see citing works
Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment
Cappon, G., Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2017) · Electronics · doi:10.3390/electronics6030065 · Cited by 242 · see citing works
Closed-loop insulin delivery for glycemic control in noncritical care
Bally, L., Thabit, H., Hartnell, S., Andereggen, E., Ruan, Y., Wilinska, M. E., Evans, M. L., Wertli, M. M., Coll, A. P., Stettler, C., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1805233 · Cited by 232 · see citing works
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 · see citing works
DeSalvo, D. J., Miller, K. M., Hermann, J. M., Maahs, D. M., Hofer, S. E., Clements, M. A., Lilienthal, E., Sherr, J. L., Tauschmann, M., Holl, R. W., et al. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12711 · Cited by 212 · see citing works
Coming of age: the artificial pancreas for type 1 diabetes
Thabit, H., & Hovorka, R. (2016) · Diabetologia · doi:10.1007/s00125-016-4022-4 · Cited by 205 · see citing works
Kropff, J., Favero, S. D., Place, J., Toffanin, C., VISENTIN, R., Monaro, M., Messori, M., Palma, F. D., Lanzola, G., Farret, A., et al. (2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00335-6 · Cited by 201 · see citing works
Real-world use of open source artificial Pancreas systems
Lewis, D., Leibrand, S., & Community, #. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816665635 · Cited by 180 · see citing works
7. Diabetes Technology: <i>Standards of Medical Care in Diabetes—2019</i>
American Diabetes Association (2019) · Diabetes Care · doi:10.2337/dc19-s007 · Cited by 179 · see citing works
Continuous glucose monitoring sensors: past, present and future algorithmic challenges
Facchinetti, A. (2016) · Sensors · doi:10.3390/s16122093 · Cited by 176 · see citing works
Self-citationPrevention of Nocturnal Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension
Buckingham, B., Chase, H. P., Dassau, E., Cobry, E., Clinton, P., Gage, V., Caswell, K., Wilkinson, J., Cameron, F., Lee, H., et al. (2010) · Diabetes Care · doi:10.2337/dc09-2303 · Cited by 169 · see citing works
Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm
Bailey, T. S., Chang, A., & Christiansen, M. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814559746 · Cited by 168 · see citing works
Yu, F., Lv, L., Liang, Z., Wang, Y., Wen, J., Lin, X., Zhou, Y., Mai, C., & Niu, J. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-4332 · Cited by 162 · see citing works
Battelino, T., Nimri, R., Dovč, K., Phillip, M., & Bratina, N. (2017) · Diabetes Care · doi:10.2337/dc16-2584 · Cited by 154 · see citing works
Accuracy of a Fourth-Generation Subcutaneous Continuous Glucose Sensor
Christiansen, M. P., Garg, S. K., Brazg, R., Bode, B. W., Bailey, T. S., Slover, R. H., Sullivan, A., Huang, S., Shin, J., Lee, S. W., et al. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0087 · Cited by 151 · see citing works
Abraham, M. B., Nicholas, J. A., Smith, G. J., Fairchild, J. M., King, B. R., Ambler, G. R., Cameron, F. J., Davis, E. A., Jones, T. W., & on behalf of the PLGM Study Group (2017) · Diabetes Care · doi:10.2337/dc17-1604 · Cited by 141 · see citing works
Christiansen, M. P., Bailey, T. S., Watkins, E., Liljenquist, D. R., Price, D. A., Nakamura, K., Boock, R. J., & Peyser, T. A. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0077 · Cited by 136 · see citing works
Bailey, T. S., Grunberger, G., Bode, B. W., Handelsman, Y., Hirsch, I. B., Jovanovič, L., Roberts, V. L., Rodbard, D., Tamborlane, W. V., & Walsh, J. S. A. (2016) · Endocrine Practice · doi:10.4158/ep151124.cs · Cited by 126 · see citing works
Aleppo, G., Laffel, L. M., Ahmann, A., Hirsch, I. B., Kruger, D. F., Peters, A. L., Weinstock, R. S., & Harris, D. R. (2017) · Journal of the Endocrine Society · doi:10.1210/js.2017-00388 · Cited by 124 · see citing works
Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2018) · Biosensors · doi:10.3390/bios8010024 · Cited by 122 · see citing works
Choudhary, P., Olsen, B. S., Conget, I., Welsh, J. B., Vorrink, L., & Shin, J. J. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0324 · Cited by 118 · see citing works
Stone, M. P., Agrawal, P. K., Chen, X., Liu, M., Shin, J., Cordero, T. L., & KAUFMAN, F. R. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0202 · Cited by 114 · see citing works
Hermanns, N., Ehrmann, D., Schipfer, M., Kröger, J., Haak, T., & Kulzer, B. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.03.003 · Cited by 113 · see citing works
Boughton, C. K., Bally, L., Martignoni, F., Hartnell, S., Herzig, D., Vogt, A. P., Wertli, M. M., Wilinska, M. E., Evans, M. L., Coll, A. P., et al. (2019) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(19)30061-0 · Cited by 110 · see citing works
Closed-Loop Control Without Meal Announcement in Type 1 Diabetes
Cameron, F. M., Ly, T. T., Buckingham, B. A., Maahs, D. M., Forlenza, G. P., Levy, C. J., Lam, D. W.-H., Clinton, P. K., Messer, L. H., Westfall, E., et al. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0078 · Cited by 105 · see citing works
Buckingham, B. A., Cameron, F., Calhoun, P. M., Maahs, D. M., Wilson, D. M., Chase, H. P., Bequette, B. W., Lum, J. W., Sibayan, J., Beck, R. W., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0040 · Cited by 103 · see citing works
Tumminia, A., Crimi, S., Sciacca, L., Buscema, M., Frittitta, L., Squatrito, S., Vigneri, R., & Tomaselli, L. (2015) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2557 · Cited by 100 · see citing works
Accuracy and Acceptability of the 6-Day Enlite Continuous Subcutaneous Glucose Sensor
Bailey, T. S., Ahmann, A., Brazg, R., Christiansen, M. P., Garg, S. K., Watkins, E., Welsh, J. B., & Lee, S. W. (2014) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0222 · Cited by 100 · see citing works
Breton, M. D., Patek, S. D., Lv, D., Schertz, E. M., Robic, J., Pinnata, J., Kollar, L. L., Barnett, C. L., WAKEMAN, C. A., Oliveri, M. C., et al. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0079 · Cited by 98 · see citing works
Continuous glucose monitoring: Current use in diabetes management and possible future applications
Vettoretti, M., Cappon, G., Acciaroli, G., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818774078 · Cited by 96 · see citing works
Hughes, C. S., Patek, S. D., Breton, M. D., & Kovatchev, B. P. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400513 · Cited by 89 · see citing works
Agrawal, P. K., Welsh, J. B., Kannard, B., Askari, S., Yang, Q., & KAUFMAN, F. R. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500514 · Cited by 86 · see citing works
Litchman, M. L., Lewis, D. M., Kelly, L. A., & Gee, P. M. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818795705 · Cited by 86 · see citing works
Zisser, H., Bailey, T. S., Schwartz, S. L., Ratner, R. E., & Wise, J. K. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300519 · Cited by 77 · see citing works
Zhong, A., Choudhary, P., McMahon, C., Agrawal, P. K., Welsh, J. B., Cordero, T. L., & KAUFMAN, F. R. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0216 · Cited by 77 · see citing works
Dual‐hormone artificial pancreas: benefits and limitations compared with single‐hormone systems
Peters, T. M., & Haidar, A. (2018) · Diabetic Medicine · doi:10.1111/dme.13581 · Cited by 72 · see citing works
Outpatient diabetes clinical decision support: current status and future directions
O’Connor, P. J., Sperl‐Hillen, J. M., Fazio, C. J., Averbeck, B. M., Rank, B. H., & Margolis, K. L. (2016) · Diabetic Medicine · doi:10.1111/dme.13090 · Cited by 64 · see citing works
DeBoer, M. D., Cherñavvsky, D. R., Topchyan, K. A., Kovatchev, B. P., Francis, G. L., & Breton, M. D. (2016) · Pediatric Diabetes · doi:10.1111/pedi.12454 · Cited by 63 · see citing works
Pettus, J., Price, D. A., & Edelman, S. V. (2015) · Endocrine Practice · doi:10.4158/ep14520.or · Cited by 62 · see citing works
Cappon, G., Vettoretti, M., Marturano, F., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818759558 · Cited by 62 · see citing works
Clinical Implications of Accuracy Measurements of Continuous Glucose Sensors
Bailey, T. S. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0050 · Cited by 60 · see citing works
McGarraugh, G. V., Brazg, R., & Weinstein, R. L. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500114 · Cited by 57 · see citing works
Inpatient Studies of a Kalman-Filter-Based Predictive Pump Shutoff Algorithm
Cameron, F., Wilson, D. M., Buckingham, B. A., Arzumanyan, H., Clinton, P. K., Chase, H. P., Lum, J. W., Maahs, D. M., Calhoun, P. M., & Bequette, B. W. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600519 · Cited by 56 · see citing works
Evaluation of a Predictive Low-Glucose Management System In-Clinic
Buckingham, B. A., Bailey, T. S., Christiansen, M. P., Garg, S. K., WEINZIMER, S. A., Bode, B. W., Anderson, S. M., Brazg, R., Ly, T. T., & KAUFMAN, F. R. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0319 · Cited by 52 · see citing works
Vettoretti, M., & Facchinetti, A. (2019) · BioMedical Engineering OnLine · doi:10.1186/s12938-019-0658-x · Cited by 52 · see citing works
The Impact of Accelerometer and Heart Rate Data on Hypoglycemia Mitigation in Type 1 Diabetes
Stenerson, M. B., Cameron, F., Wilson, D. M., Harris, B., Payne, S. R., Bequette, B. W., & Buckingham, B. A. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296813516208 · Cited by 48 · see citing works
The Role of Continuous Glucose Monitoring in Gestational Diabetes Mellitus
Jovanovic, L. G. (2000) · Diabetes Technology & Therapeutics · doi:10.1089/15209150050214159 · Cited by 46 · see citing works
Automated blood glucose control in type 1 diabetes: A review of progress and challenges
Bertachi, A. H., Ramkissoon, C. M., Bondía, J., & Vehı́, J. (2017) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2017.10.011 · Cited by 39 · see citing works
DEHENNIS, A. D., Mortellaro, M., & Ioacără, S. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815596760 · Cited by 36 · see citing works
Evaluation of clinical decision support systems for diabetes care: An overview of current evidence
Jia, P., Zhao, P., Chen, J., & Zhang, M. (2018) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.12968 · Cited by 36 · see citing works
Abraham, M. B., de Bock, M. I., Paramalingam, N., O'Grady, M. J., Ly, T. T., George, C., Roy, A., Spital, G., Karula, S., Heels, K., et al. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0364 · Cited by 34 · see citing works
Advances in Closed-Loop Insulin Delivery Systems in Patients with Type 1 Diabetes
Dadlani, V., Pinsker, J. E., Dassau, E., & Kudva, Y. C. (2018) · Current Diabetes Reports · doi:10.1007/s11892-018-1051-z · Cited by 30 · see citing works
Jamiołkowska‐Sztabkowska, M., Jamiołkowska, I., Łuczyński, W., Tołwińska, J., Bossowski, A. T., & Głowińska‐Olszewska, B. (2015) · Journal of Diabetes Research · doi:10.1155/2016/4385312 · Cited by 25 · see citing works
Self-measurement of Blood Glucose and Continuous Glucose Monitoring – Is There Only One Future?
Heinemann, L., & Stuhr, A. (2018) · touchREVIEWS in Endocrinology · doi:10.17925/ee.2018.14.2.24 · Cited by 24 · see citing works
Taleb, N., Carpentier, A. C., Messier, V., Ladouceur, M., Haidar, A., & RABASA-LHORET, R. P. R. (2019) · Diabetes Care · doi:10.2337/dc18-2406 · Cited by 24 · see citing works
FOSTER, N. C., MILLER, K., DIMEGLIO, L., MAAHS, D. M., TAMBORLANE, W. V., BERGENSTAL, R. M., CLEMENTS, M. A., RICKELS, M. R., SMITH, E., OLSON, B. A., et al. (2018) · Diabetes · doi:10.2337/db18-1689-p · Cited by 19 · cited 1× · see citing works
“Most recent data from the T1D Exchange Registry reported that about 30% of participants have been using CGM in the period from 2016 to 2018 [40].”
BackgroundLongato, E., Acciaroli, G., Facchinetti, A., Hakaste, L., Tuomilehto, J., Maran, A., & Sparacino, G. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.03.007 · Cited by 19 · see citing works
Cappon, G., Marturano, F., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818777524 · Cited by 18 · see citing works
Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm
Cameron, F., Wilson, D. M., Buckingham, B. A., Arzumanyan, H., Clinton, P. K., Chase, H. P., Lum, J. W., Maahs, D. M., Calhoun, P. M., & Bequette, B. W. (2012) · Diabetes Technology & Therapeutics · Cited by 17 · see citing works
Collecting Patient Data from Sensor-Based Systems: Benefits and Challenges.
Gashgari, H., Attallah, N., Muallem, Y. A., Dogether, M. A., Al Moammary, E., Almeshari, M., & Househ, M. S. (2016) · PubMed · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [39]Centers for Medicare & Medicaid Services CGM Rulings. Ruling No. [CMS-1682-R] cited 2019 Jul 15 Available from: www.cms.gov/Regulations-and-Guidance/Guidance/Rulings/Downloads/CMS1682R.pdf 26110197
- [64]Herrero P Lopez B Martin C PEPPER: Patient empowerment through predictive personalized decision support Proceedings of 1st Workshop on Artificial Intelligence for Diabetes (ECAI) 2016 Aug 30 Hague, NL ECAI 2016 8 9
Cited by
Sorted by citations
Electrochemical glucose sensors in diabetes management: an updated review (2010–2020)
Teymourian, H., Barfidokht, A., & Wang, J. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00304b · Cited by 890 · see citing works
Wearable Electrochemical Glucose Sensors in Diabetes Management: A Comprehensive Review
Saha, T., Del Caño, R., Mahato, K., De la Paz, E., Chen, C., Ding, S., Yin, L., & Wang, J. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00078 · Cited by 442 · see citing works
Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020) · Sensors · doi:10.3390/s20143870 · Cited by 176 · see citing works
Self-citationPrendin, F., Pavan, J., Cappon, G., Favero, S. D., Sparacino, G., & Facchinetti, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-44155-x · Cited by 168 · see citing works
Ben-Yacov, O., Godneva, A., Rein, M., Shilo, S., Kolobkov, D. S., Koren, N., Cohen-Dolev, N., Shmul, T. T., Wolf, B. C., Kosower, N., et al. (2021) · Diabetes Care · doi:10.2337/dc21-0162 · Cited by 156 · see citing works
Glycemic variability and cardiovascular disease in patients with type 2 diabetes
Martinez, M., Santamarina, J., Pavesi, A., Musso, C., & Umpierrez, G. E. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-002032 · Cited by 155 · see citing works
Self-charging wearables for continuous health monitoring
Kim, J., Khan, S. A., Wu, P. F., Park, S., Park, H., Yu, C., & Kim, W. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105419 · Cited by 149 · see citing works
Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices
Das, S. K., Nayak, K. K., Krishnaswamy, P. R., Kumar, V., & Bhat, N. (2022) · ECS Sensors Plus · doi:10.1149/2754-2726/ac7abb · Cited by 142 · see citing works
Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review
Hina, A., & Saadeh, W. (2022) · Sensors · doi:10.3390/s22134855 · Cited by 144 · see citing works
Is HbA1c an ideal biomarker of well-controlled diabetes?
Kaiafa, G. D., Veneti, S., Polychronopoulos, G., Pilalas, D., Daios, S., Kanellos, I., Didangelos, T., Pagoni, S., & Savopoulos, C. G. (2020) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2020-138756 · Cited by 145 · see citing works
Co-design of human-centered, explainable AI for clinical decision support
Panigutti, C., Beretta, A., Fadda, D., Giannotti, F., Pedreschi, D., Perotti, A., & Rinzivillo, S. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3587271 · Cited by 127 · see citing works
Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation
Perkins, B. A., Sherr, J. L., & Mathieu, C. (2021) · Science · doi:10.1126/science.abg4502 · Cited by 114 · see citing works
Artificial intelligence biosensors for continuous glucose monitoring
Jin, X., Cai, A., Xu, T., & Zhang, X. (2023) · Interdisciplinary materials · doi:10.1002/idm2.12069 · Cited by 121 · see citing works
Zou, Y., Chu, Z., Guo, J., Liu, S., Ma, X., & Guo, J. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115103 · Cited by 112 · see citing works
Kap, Ö., Kılıç, V., Hardy, J. G., & Horzum, N. (2021) · The Analyst · doi:10.1039/d0an02031a · Cited by 108 · see citing works
Elbalshy, M., Haszard, J., Smith, H., Kuroko, S., Galland, B., Oliver, N., Shah, V., de Bock, M. I., & Wheeler, B. J. (2022) · Diabetic Medicine · doi:10.1111/dme.14854 · Cited by 104 · see citing works
Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications
Origlia, C., Rodriguez-Duarte, D. O., Vasquez, J. A. T., Bolomey, J., & Vipiana, F. (2024) · Sensors · doi:10.3390/s24144515 · Cited by 111 · see citing works
Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence
Moon, S. J., Jung, I., & Park, C. (2021) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2021.0177 · Cited by 103 · see citing works
Yoo, J. H., Choi, M. S., Ahn, J., Park, S. W., Kim, Y., Hur, K. Y., Jin, S., Kim, G., & Kim, J. H. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0499 · Cited by 102 · see citing works
Technology in the management of type 2 diabetes: Present status and future prospects
DALY, A. B., & Hovorka, R. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14418 · Cited by 93 · see citing works
Applications of Microwaves in Medicine
Chiao, J. ‐., Li, C., Lin, J., Caverly, R. H., Hwang, J. C. M., Rosen, H. D., & Rosen, A. (2022) · IEEE Journal of Microwaves · doi:10.1109/jmw.2022.3223301 · Cited by 88 · see citing works
Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges
Mujahid, O., Contreras, I., & Vehı́, J. (2021) · Sensors · doi:10.3390/s21020546 · Cited by 88 · see citing works
A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management
Laha, S., Rajput, A., Laha, S. S., & Jadhav, R. (2022) · Biosensors · doi:10.3390/bios12110965 · Cited by 91 · see citing works
Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies
Li, J., Liang, J., Laken, S. J., Langer, R., & Traverso, G. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.02.009 · Cited by 73 · see citing works
Prendin, F., Favero, S. D., Vettoretti, M., Sparacino, G., & Facchinetti, A. (2021) · Sensors · doi:10.3390/s21051647 · Cited by 77 · see citing works
Dual‐hormone artificial pancreas: benefits and limitations compared with single‐hormone systems
Peters, T. M., & Haidar, A. (2018) · Diabetic Medicine · doi:10.1111/dme.13581 · Cited by 72 · see citing works
Prigge, R., McKnight, J. A., Wild, S. H., Haynes, A., Jones, T. W., Davis, E. A., Rami-Merhar, B. M., Fritsch, M., Prchla, C., Lavens, A., et al. (2021) · Diabetic Medicine · doi:10.1111/dme.14766 · Cited by 68 · see citing works
Moon, S. J., Kim, K., Lee, W. J., Lee, M. Y., Vigersky, R., & Park, C. (2023) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.14852 · Cited by 69 · see citing works
Reddy, N., Verma, N., & Dungan, K. M. (2020) · MDText.com, Inc. eBooks · Cited by 68 · see citing works
Battelino, T., Bergenstal, R. M., Rodríguez, A., Landó, L. F., Bray, R., Tong, Z., & Brown, K. (2022) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00077-8 · Cited by 65 · see citing works
Lu, H. Y., Ding, X., Hirst, J. E., Yang, Y., Yang, J., Mackillop, L. H., & Clifton, D. A. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3242261 · Cited by 65 · see citing works
Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience
Godfrey, A., Vandendriessche, B., Bakker, J. P., Fitzer‐Attas, C., Gujar, N., Hobbs, M., Liu, Q., Northcott, C. A., Parks, V., Wood, W. A., et al. (2021) · Clinical and Translational Science · doi:10.1111/cts.12865 · Cited by 60 · see citing works
Optical glucose biosensor built-in disposable strips and wearable electronic devices
Reda, A., El‐Safty, S. A., Selim, M. M., & Shenashen, M. A. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113237 · Cited by 59 · see citing works
Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy
Noaro, G., Cappon, G., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3004031 · Cited by 57 · see citing works
Tackling the challenges of developing microneedle-based electrochemical sensors
Abdullah, H., Phairatana, T., & Jeerapan, I. (2022) · Microchimica Acta · doi:10.1007/s00604-022-05510-3 · Cited by 56 · see citing works
Digital health and diabetes: experience from India
Kesavadev, J., Krishnan, G., & Mohan, V. (2021) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/20420188211054676 · Cited by 55 · see citing works
Bimetallic PtAu alloy nanomaterials for nonenzymatic selective glucose sensing at low potential
Lin, L., Weng, S., Zheng, Y., Liu, X., Ying, S., Chen, F., & You, D. (2020) · Journal of Electroanalytical Chemistry · doi:10.1016/j.jelechem.2020.114147 · Cited by 52 · see citing works
Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments
Diouri, O., Cigler, M., Vettoretti, M., Mader, J. K., Choudhary, P., & Renard, É. (2021) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3449 · Cited by 49 · see citing works
Wearable biomolecular sensing nanotechnologies in chronic disease management
Tu, J., Flynn, C. D., Yeom, J., Wu, Z., Kelley, S. O., & Gao, W. F. (2025) · Nature Nanotechnology · doi:10.1038/s41565-025-02010-2 · Cited by 52 · see citing works
A review of biosensor technology and algorithms for glucose monitoring
Zhang, Y., Sun, J., Liu, L., & Qiao, H. (2021) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2021.107929 · Cited by 49 · see citing works
Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System
Khadka, B., Lee, B., & Kim, K.-T. (2023) · Biomolecules · doi:10.3390/biom13060929 · Cited by 48 · see citing works
Li, J., Ma, J., Omisore, O. M., Liu, Y., Tang, H., Ao, P., Yan, Y., Wang, L., & Nie, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3279383 · Cited by 50 · see citing works
Pors, A., Rasmussen, K. G., Inglev, R., Jendrike, N. R. M., Philipps, A., Ranjan, A. G., Vestergaard, V., Henriksen, J. E., Nørgaard, K., Freckmann, G., et al. (2023) · ACS Sensors · doi:10.1021/acssensors.2c02756 · Cited by 46 · see citing works
A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring
Elfouly, T., & Alouani, A. T. (2025) · Electronics · doi:10.3390/electronics14173443 · Cited by 56 · see citing works
Divan, V., Greenfield, M., Morley, C. P., & Weinstock, R. S. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820978769 · Cited by 46 · see citing works
Lin, X., Luo, J., Liao, M., Su, Y., Lv, M., Li, Q. X., Xiao, S., & Xiang, J. (2022) · Biosensors · doi:10.3390/bios12121131 · Cited by 44 · see citing works
GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks
Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3271615 · Cited by 45 · see citing works
Continuous glucose monitoring devices: A brief presentation (Review)
Mihai, D. A., Ștefan, S., Stegaru, D., Bernea, E. G., Văcăroiu, I. A., Papacocea, T. M., Lupușoru, M. O. D., Nica, A. E., Ştiru, O., Dragoș, D., et al. (2021) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2021.11097 · Cited by 41 · see citing works
Schretzlmaier, P., Hecker, A., & Ammenwerth, E. (2022) · JMIR Human Factors · doi:10.2196/34918 · Cited by 62 · see citing works
Pu, Z., Lai, L., Yang, X.-S., Wang, Y., Dong, P., Wang, D., Xie, Y., & Han, Z. (2019) · Endocrine · doi:10.1007/s12020-019-02150-1 · Cited by 41 · see citing works
Tagougui, S., Taleb, N., Legault, L., Suppère, C., Messier, V., Boukabous, I., Shohoudi, A., Ladouceur, M., & Rabasa-Lhoret, R. (2020) · Diabetologia · doi:10.1007/s00125-020-05244-y · Cited by 40 · see citing works
Efficacy of telemedicine for persons with type 1 diabetes during Covid19 lockdown
Boscari, F., Ferretto, S., Uliana, A., Avogaro, A., & Bruttomesso, D. (2021) · Nutrition & Diabetes · doi:10.1038/s41387-020-00147-8 · Cited by 31 · see citing works
Verma, A., Yadav, D. K., Natesan, S., Gupta, M., Yadav, B. C., & Mishra, Y. K. (2024) · Microchemical Journal · doi:10.1016/j.microc.2024.110713 · Cited by 38 · see citing works
Jesky, R. G., Lo, L. H. Y., Siu, R. H. P., & Tanner, J. A. (2025) · Biosensors · doi:10.3390/bios15100637 · Cited by 38 · see citing works
Samojlik, M. M., & Stabler, C. L. (2021) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.05.039 · Cited by 38 · see citing works
Kim, J. Y., Jin, S.-M., Sim, K. H., Kim, B.-Y., Cho, J. H., Moon, J. S., Lim, S., Kang, E. S., Park, C.-Y., Kim, S. G., et al. (2024) · Diabetologia · doi:10.1007/s00125-024-06152-1 · Cited by 36 · see citing works
Valenzano, M., Cibrario Bertolotti, I., Valenzano, A., & Grassi, G. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001045 · Cited by 34 · see citing works
Kalra, S., Shaikh, S. S., Priya, G., Baruah, M., Verma, A., Das, A. K., Shah, M., Das, S., Khandelwal, D., Sanyal, D., et al. (2020) · Diabetes Therapy · doi:10.1007/s13300-020-00973-0 · Cited by 32 · see citing works
Prioleau, T., Bartolome, A., Comi, R. J., & Stanger, C. (2023) · Scientific Data · doi:10.1038/s41597-023-02469-5 · Cited by 32 · see citing works
Davis, T. M. E., Dwyer, P., England, M., Fegan, P. G., & Davis, W. A. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0331 · Cited by 31 · see citing works
Continuous Glucose Monitoring in the Intensive Care Unit
ARROYO, L. I. G., Faulds, E. R., Perez‐Guzman, M. C., Davis, G. M., Dungan, K. M., & Pasquel, F. J. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231169522 · Cited by 31 · see citing works
Vettoretti, M., Battocchio, C., Sparacino, G., & Facchinetti, A. (2019) · Sensors · doi:10.3390/s19235320 · Cited by 31 · see citing works
Rossi, A., Venema, A., Haarsma, P., Feldbrugge, L., Burghard, R., Rodriguez‐Buritica, D., Parenti, G., Oosterveer, M. H., & Derks, T. G. J. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac411 · Cited by 31 · see citing works
Barchiesi, M. A., Calabrese, A., Costa, R. M. D. S., Pillo, F. D., D’Uffizi, A., Tiburzi, L., & Zahid, E. (2025) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzaf046 · Cited by 35 · see citing works
Datye, K. A., Tilden, D. R., Parmar, A. M., Goethals, E. R., & Jaser, S. S. (2021) · Current Diabetes Reports · doi:10.1007/s11892-021-01389-1 · Cited by 32 · see citing works
Stock, A. A., Gonzalez, G. C., Pete, S. I., Toni, T. D., Berman, D. M., Rabassa, A., Díaz, W., Geary, J. C., Willman, M. A., Jackson, J. M., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm3145 · Cited by 30 · see citing works
Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study
Filippo, D. D., Ahmadzai, M., Chang, M. H. Y., Horgan, K., Ong, R. M., Darling, J., Akhtar, M., Henry, A., & Welsh, A. W. (2022) · Journal of Diabetes Research · doi:10.1155/2022/5142918 · Cited by 30 · see citing works
Diabetes Technology Trends: A Review of the Latest Innovations
Lundgrin, E. L., Kelly, C. A., Bellini, N. J., Lewis, C. J., Rafi, E., & Hatipoğlu, B. (2025) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaf034 · Cited by 31 · see citing works
A Non-Invasive Flexible Glucose Monitoring Sensor Using a Broadband Reject Filter
Bteich, M., Hanna, J., Costantine, J., Kanj, R. N., Tawk, Y., Ramadan, A., & Eid, A. A. (2020) · IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · doi:10.1109/jerm.2020.3023053 · Cited by 29 · see citing works
Artificial intelligence perspective in the future of endocrine diseases
Hasanzad, M., Meybodi, H. R. A., Sarhangi, N., & Larijani, B. A. (2022) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-021-00949-2 · Cited by 30 · see citing works
Lameijer, A., Lommerde, N., Dunn, T. C., Fokkert, M. J., Edens, M. A., Kao, K., Xu, Y., Gans, R. O. B., Bilo, H. J. G., & van Dijk, P. R. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108897 · Cited by 27 · see citing works
Zhu, T., Li, K., & Georgiou, P. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3303367 · Cited by 32 · see citing works
A case study on using a large language model to analyze continuous glucose monitoring data
Healey, E., Tan, A. L. M., Flint, K. L., Ruiz, J., & Kohane, I. S. (2025) · Scientific Reports · doi:10.1038/s41598-024-84003-0 · Cited by 28 · see citing works
Jeerapan, I., Sonsa‐ard, T., & Nacapricha, D. (2020) · Chemosensors · doi:10.3390/chemosensors8030071 · Cited by 26 · see citing works
Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review
Farabolini, G., Baldini, N., Pagano, A., Andrenelli, E., Pepa, L., Morone, G., Ceravolo, M. G., & Capecci, M. (2025) · Sensors · doi:10.3390/s25164889 · Cited by 27 · see citing works
Wafa, I. A., Pratama, N. R., Sofia, N. F., Anastasia, E. S., Konstantin, T., Wijaya, M. A., Wiyono, M. R., Djuari, L., & Novida, H. (2022) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2021.0125 · Cited by 24 · see citing works
Oriot, P., Viry, C., Vandelaer, A., Grigioni, S., Roy, M., Philips, J., & Prévost, G. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221092050 · Cited by 26 · see citing works
Biswas, T., Behera, B. K., & Madhu, N. R. (2023) · Springer eBooks · doi:10.1007/978-981-19-0027-3_6 · Cited by 32 · see citing works
Nafion based biosensors: a review of recent advances and applications
Kakhki, R. M. (2023) · International Journal of Polymeric Materials · doi:10.1080/00914037.2023.2297436 · Cited by 25 · see citing works
Bogue-Jimenez, B., Huang, X., Powell, D., & Doblas, A. (2022) · Sensors · doi:10.3390/s22093534 · Cited by 24 · see citing works
The Role of Neuroinflammation in the Comorbidity of Psychiatric Disorders and Internal Diseases
Cătălina, G. R., Gheorman, V., Gheorman, V., & Forțofoiu, M.-C. (2025) · Healthcare · doi:10.3390/healthcare13070837 · Cited by 25 · see citing works
Ahmad, N. N., Ghazali, N. N. N., & Wong, Y. H. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113384 · Cited by 24 · see citing works
Jensen, M. V., Broadley, M. M., Speight, J., Scope, A., Preston, L. R., Heller, S. R., de Galan, B. E., Pouwer, F., & Hendrieckx, C. (2021) · Diabetic Medicine · doi:10.1111/dme.14666 · Cited by 23 · see citing works
Continuous Glucose Monitoring System Based on Percutaneous Microneedle Array
Chien, M.-N., Chen, Y.-J., Bai, C.-H., & Huang, J.-T. (2022) · Micromachines · doi:10.3390/mi13030478 · Cited by 23 · see citing works
Chiu, I.-M., Cheng, C.-Y., Chang, P.-K., Li, C.-J., Cheng, F., & Lin, C. R. (2022) · Biosensors · doi:10.3390/bios13010023 · Cited by 22 · see citing works
Kim, J. H. (2020) · Annals of Pediatric Endocrinology & Metabolism · doi:10.6065/apem.2040038.019 · Cited by 22 · see citing works
Nallicheri, A., Close Concerns, S. F., Mahoney, K. M., Close Concerns, S. F., Gutow, H. A., Close Concerns, S. F., Bellini, N. J., & Isaacs, D. M. (2022) · touchREVIEWS in Endocrinology · doi:10.17925/ee.2022.18.1.27 · Cited by 24 · see citing works
Noaro, G., Zhu, T., Cappon, G., Facchinetti, A., & Georgiou, P. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3249571 · Cited by 22 · see citing works
Park, J. I., Lee, H. Y., Kim, H., Lee, J., Shinn, J., & Kim, H. (2021) · Journal of Korean Medical Science · doi:10.3346/jkms.2021.36.e253 · Cited by 22 · see citing works
The Evolution of Diabetes Technology – Options Toward Personalized Care
Zahid, M., Dowlatshahi, S., Kansara, A., & Sadhu, A. (2023) · Endocrine Practice · doi:10.1016/j.eprac.2023.04.007 · Cited by 22 · see citing works
Kim, G., Lim, S. K., Kwon, H., Park, I. B., Ahn, K. J., Park, C., Kwon, S. K., Kim, H. S., Park, S. W., Kim, S. G., et al. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14061 · Cited by 21 · see citing works
Wu, X., Zhao, X., Chen, W., Chen, Q., Kong, L., & Li, P. (2025) · Sensors and Actuators Reports · doi:10.1016/j.snr.2025.100361 · Cited by 20 · see citing works
POC Sensor Systems and Artificial Intelligence—Where We Are Now and Where We Are Going?
Prashanthi, K., Kovur, K. M., Rayat, D. Y., & Wishart, D. S. (2025) · Biosensors · doi:10.3390/bios15090589 · Cited by 21 · see citing works
Continuous Glucose Monitoring in Dogs and Cats
Baldo, F. D., & Fracassi, F. (2023) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2023.01.008 · Cited by 20 · see citing works
Wang, Q., Li, S., Chen, J., Yang, L., Qiu, Y., Du, Q., Wang, C., Teng, M., Wang, T., & Dong, Y. (2023) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkad289 · Cited by 20 · see citing works
Chang, A. Z., Swain, D. L., & Trotter, M. G. (2020) · Translational Animal Science · doi:10.1093/tas/txaa155 · Cited by 18 · see citing works
Pease, A., Andrikopoulos, S., Abraham, M. B., Craig, M. E., Fenton, B., Overland, J., Price, S., Simmons, D. S., & Ross, G. P. (2021) · The Medical Journal of Australia · doi:10.5694/mja2.51118 · Cited by 18 · see citing works
Kalita, D., Sharma, H., & Mirza, K. B. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3396880 · Cited by 18 · see citing works
Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes
Liu, X., & Zhang, J. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s493128 · Cited by 18 · see citing works
Xiao, Y., Wang, Z., Zhang, L., Xie, N., Chen, F., Song, Z., & Zhao, S. (2025) · Journal of Medical Internet Research · doi:10.2196/66441 · Cited by 21 · see citing works
- Authors
- Cappon, G., Vettoretti, M., Sparacino, G., & Facchinetti, A.
- Affiliations
- University of Padua (IT)
- Corresponding author
- Facchinetti, Andrea
- Venue
- Diabetes & Metabolism Journal
- Publisher
- Korean Diabetes Association
- Published
- 2019
- Volume
- 43
- Issue
- 4
- Pages
- 383
- ISSN
- 2233-6079 (ISSN-L)
- 2233-6087
- PMC
- PMC6712232
- OpenAlex
- W2964434942
- Cited by
- 477
- References
- 81
- Citations by year
- 2026: 70 · 2025: 105 · 2024: 65 · 2023: 81 · 2022: 65 · 2021: 48 · 2020: 38 · 2019: 4 · 2018: 1
- FWCI
- 5.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diabetes & Metabolism Journal (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Keywords
- continuous glucose monitoring, diabetes management, blood glucose monitoring, hypoglycemia, glycemic variability, insulin dose, insulin infusion, decision support, wearable sensors
- MeSH terms
- Wearable Electronic Devices, Adolescent, Adult, Algorithms, Blood Glucose, Child, Child, Preschool, Decision Support Systems, Management, Diabetes Mellitus, Female, Humans, Hyperglycemia, Hypoglycemia, Insulin Infusion Systems, Male, Blood Glucose Self-Monitoring, Young Adult
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 ›
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