Relevant bibliographies by topics / And continuous glucose monitoring

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Academic literature on the topic 'And continuous glucose monitoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "And continuous glucose monitoring"

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&NA;. "Continuous Glucose Monitoring." Journal of Clinical Engineering 28, no. 3 (July 2003): 148–49. http://dx.doi.org/10.1097/00004669-200307000-00009.

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Bode, B. W., and T. Battelino. "Continuous glucose monitoring." International Journal of Clinical Practice 64 (February 2010): 11–15. http://dx.doi.org/10.1111/j.1742-1241.2009.02272.x.

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Inzucchi, Silvio, Julio Rosenstock, and Guillermo Umpierrez. "Continuous Glucose Monitoring." Journal of Clinical Endocrinology & Metabolism 95, no. 10 (October 2010): 0. http://dx.doi.org/10.1210/jcem.95.10.9998.

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Fritschi, Cynthia, Laurie Quinn, Sue Penckofer, and Patricia M. Surdyk. "Continuous Glucose Monitoring." Diabetes Educator 36, no. 2 (December 16, 2009): 250–57. http://dx.doi.org/10.1177/0145721709355835.

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Wagner, Julie, Howard Tennen, and Howard Wolpert. "Continuous Glucose Monitoring." Psychosomatic Medicine 74, no. 4 (May 2012): 356–65. http://dx.doi.org/10.1097/psy.0b013e31825769ac.

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van Beers, Cornelis A. J., and J. Hans DeVries. "Continuous Glucose Monitoring." Journal of Diabetes Science and Technology 10, no. 6 (July 9, 2016): 1251–58. http://dx.doi.org/10.1177/1932296816653411.

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Yoo, Hye Jin. "Continuous Glucose Monitoring System." Korean Clinical Diabetes 11, no. 1 (2010): 21. http://dx.doi.org/10.4093/kcd.2010.11.1.21.

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Nishimura, Rimei. "1. Continuous Glucose Monitoring." Nihon Naika Gakkai Zasshi 98, no. 4 (2009): 802–8. http://dx.doi.org/10.2169/naika.98.802.

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de Bock, Martin, Matthew Cooper, Adam Retterath, Jennifer Nicholas, Trang Ly, Timothy Jones, and Elizabeth Davis. "Continuous Glucose Monitoring Adherence." Journal of Diabetes Science and Technology 10, no. 3 (February 22, 2016): 627–32. http://dx.doi.org/10.1177/1932296816633484.

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Skyler, Jay S. "Continuous Glucose Monitoring Symposium." Diabetes Technology & Therapeutics 2, supplement 1 (December 2000): 5. http://dx.doi.org/10.1089/15209150050214050.

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Dissertations / Theses on the topic "And continuous glucose monitoring"

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Li, Guang M. Eng Massachusetts Institute of Technology. "Evaluation of continuous glucose monitoring systems." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45357.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2008.
Includes bibliographical references (p. 45-48).
There has been much hype in the research and development of continuous glucose monitoring technologies, driven by the enormous and rapidly expanding glucose monitoring market and the large and growing base of diabetes patients. Continuous glucose monitoring has shown significant benefits over traditional intermittent blood glucose testing in reducing the risks of developing long-term complications associated with diabetes, by maintaining blood glucose concentrations to near-normoglycemic levels and reducing glycemic variability. In this thesis, commercially available continuous glucose monitoring systems as well as those still in development are evaluated. SWOT analysis shows that continuous glucose monitoring has a promising future, but there remain a number of challenges to be overcome, such as accuracy, sensor span, data handling, cost and reimbursement issues. It is concluded that continuous glucose monitoring will be the roadmap for future diabetes management. Ongoing technological advances in continuous glucose monitoring systems will hopefully close the loop for a fully automated artificial pancreas and develop a cure for Type I diabetes.
by Guang Li.
M.Eng.
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Sharma, Shweta Humad. "Continuous glucose monitoring and U.S. market strategy." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90224.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014.
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Includes bibliographical references.
There are about 25M (million) diabetics in the US alone, of which only 5-10% of the type 1 diabetics (1M) market has been penetrated with continuous glucose monitoring (CGM) devices. This thesis will provide an overview of the glucose monitoring, then focus on who the key market players for CGM are. Ensuing sections will explore product offerings, understanding what features patients care for and what critical limitations exist in design. It will also tackle why there hasn't been a more widespread adoption of CGM systems considering the technology has been on the market for a decade now. It will dive into a variety of potential market drivers, such as, first mover's advantage, pricing, product attributes and reimbursement coverage. It will emphasize the two US leaders, Medtronic and Dexcom and analyze the companies by comparing their revenue and underlying strategies. Finally the thesis will cover emerging technologies that could pose a market threat to incumbents.
by Shweta Humad Sharma.
M.B.A.
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Chen, Xuesong. "Impact of Continuous Glucose Monitoring System on Model Based Glucose Control." Thesis, University of Canterbury. Electrical and Computer Engineering, 2007. http://hdl.handle.net/10092/1228.

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Critically ill patients are known to experience stress-induced hyperglycemia. Inhibiting the physiological response to increased glycaemic levels in these patients are factors such as increased insulin resistance, increased dextrose input, absolute or relative insulin deficiency, and drug therapy. Although hyperglycemia can be a marker for severity of illness, it can also worsen outcomes, leading to an increased risk of further complications. Recent studies have shown that tight control can reduce mortality up to 43%. Metabolic modelling has been used to study physiological behaviour and/or to control glycaemia for a long time and many successful approximate system models have been developed. Due to the malfunction of medical equipments, clinical measurements obtained usually come with noise. In addition, the few such systems currently available can have errors in excess of 20-30%. Therefore, to fully simulate the clinical data, the system model also needs to couple with a successful noise model. This research has developed a new noise model that better fits the current available statistical description of the noise profile and therefore can be applied to achieve better simulation results. The research also designed a filter algorithm that is capable of reducing the sensor measurement error down to an acceptable value. Achieving such a goal is a significant step towards fully automated adaptive control of hyperglycaemia in critically ill patients and would therefore reduce mortality.
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Laurell, Thomas. "Microdialysis and continuous glucose monitoring towards wafer integration /." Lund : Lund Institute of Technology, Dept. of Electrical Measurements, 1995. http://catalog.hathitrust.org/api/volumes/oclc/37932770.html.

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Araujo, Cespedes Fabiola. "RF Sensing System for Continuous Blood Glucose Monitoring." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6998.

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The purpose of this research was to design a blood glucose sensing system based on the induced shift in the resonant frequency of an antenna patch operating in the ISM band (5.725 – 5.875 GHz). The underlying concept is the fact that when a person has variations in their blood glucose levels, the permittivity of their blood varies accordingly. This research analyzed the feasibility of using an antenna patch as a blood glucose sensing device in three configurations: 1) as an implantable active sensor, 2) as an implantable passive antenna sensor, and 3) as a non-invasive sensor. In the first arrangement, the antenna is to be implanted inside the body as an active antenna, requiring that its power supply and internal circuitry to be implanted. In the second arrangement, the antenna is also implanted, but would not require a power supply or internal circuity since it would be passive. For the third arrangement, the non-invasive sensing approach, the antenna is placed facing the upper arm while mounted outside the body. In order to evaluate the best approach all the three approaches were simulated using the electromagnetic field tool simulator ANSYS EM15.0 HFSSTM, along with a human tissue model. The tissue model included physiological and electrical characteristics of the human abdomen for simulating the active and passive approaches, and the upper arm for the non-invasive approach. The electromagnetic boundaries were set with perfectly matched layers to eliminate any reflections which would cause a non-physical resonance in the results. Simulation of the active sensing configuration resulted in a resonant frequency shift from 5.76 to 5.78GHz (i.e., a 20 MHz shift) for a simulated blood permittivity variation of 62.0 to 63.6. This corresponds, theoretically, to an approximate glucose shift of 500 mg/dL. The passive configuration simulations did not yield conclusive variations in resonant frequency and this approach was abandoned early on in this research. Thirdly, the non-invasive approach resulted in a simulated shift of resonant frequency from 5.797 to 5.807 (i.e., a 10MHz shift) for simulated blood permittivity variation of 51.397 to 52.642 (an approximate variation of 2000 mg/dL in glucose). In the literature planar, continuous blood-rich layers are used to simulate RF sensing of glucose, which is not applicable when measuring glucose in actual human veins, which are tubular in geometry and of finite extent. Therefore the model employed assumed a 1.8 mm diameter blood vessel, buried under a fatty layer that was capped with skin. The above results, both simulated and verified experimentally, used this more realistic model which is further proof that a practical non-invasive blood glucose measurement system should be possible. The non-invasive approach was tested experimentally by using oil in gel phantoms to mimic the electrical properties of skin, fat, blood and muscle. A fat phantom was placed over a muscle phantom, with a strip of blood phantom within and a skin phantom was placed on top. The blood phantom had a 2000mg/dL variation of D-glucose in the phantom mixture which decreased the relative permittivity from 52.635 to 51.482 and resulted in a shift of resonant frequency from 5.855 to 5.842 (i.e., a 13MHz shift). This is consistent with the non-invasive simulated results thus validating our model of the non-invasive sensing approach. While this variation in blood glucose is non-physical (typical human glucose range can range in the extremes from 30 to 400 mg/dL, where healthy glucose levels vary from 70mg/dL to 180mg/dL) it was necessary to provide a high confidence fit between the simulated and experimental data. This is because the level of precision with which the physical phantoms could be fabricated with was insufficient to match the highly precise simulated data. Analysis on the effect of lateral displacement of the antenna from the blood vessel, its elevation above the skin and variations caused by different skin thickness, and blood vessel depth were evaluated. A calibration technique to correct physical misalignment by the user is proposed in which two additional antennas, located diagonally with respect to the sensing antenna, serve as reference point for placement over the upper arm in line of sight with the blood vessel. Once the non-invasive sensor approach was shown to be viable for continuous glucose monitoring, a sensor platform was designed whereby an RF generator was used to drive the antenna with a frequency sweep between 5.725 to 5.875GHz. A fraction of its output power was coupled to both the antenna and the system analysis circuitry through a directional coupler. The transmitted and received power were then processed with demodulating logarithmic amplifiers which convert the RF signal to a corresponding voltage for downstream processing. Both inputs were then fed into a microcontroller and the measured shift in resonant frequency, fO, converted to glucose concentration which was displayed on glucose meter display.
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Cooley, Daniel Warren. "Data acquisition unit for low-noise, continuous glucose monitoring." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2844.

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As the number of people with diabetes continues to increase, research efforts improving glucose testing methods and devices are under way to improve outcomes and quality of life for diabetic patients. This dissertation describes the design and testing of a Data Acquisition Unit (DAU) providing low noise photocurrent spectra for use in a continuous glucose monitoring system. The goal of this research is to improve the signal to noise ratio (SNR) of photocurrent measurements to increase glucose concentration measurement accuracy. The glucose monitoring system consists of a portable monitoring device and base station. The monitoring device measures near infrared (IR) absorption spectra from interstitial fluid obtained by microdialysis or ultrafiltration probe and transmits the spectra to a base station via USB or a ZigBee radio link. The base station utilizes chemometric calibration methods to calculate glucose concentration from the photocurrent spectra. Future efforts envisage credit card-sized monitoring devices. The glucose monitor system measures the optical absorbance spectrum of an interstitial fluid (ISF) sample pumped through a fluid chamber inside a glucose sensor. Infrared LEDs in the glucose sensor illuminate the ISF sample with IR light covering the 2.2 to 2.4 micron wavelength region where glucose has unique features in its absorption spectrum. Light that passes through the sample propagates through a linearly variable bandpass filter and impinges on a photodiode array. The center frequency of the variable filter is graded along its length such that the filter and photodiode array form a spectrometer. The data acquisition unit (DAU) conditions and samples photocurrent from each photodiode channel and sends the resulting photocurrent spectra to the Main Controller Unit (MCU). The MCU filters photocurrent samples providing low noise photocurrent spectra to a base station via USB or Zigbee radio link. The glucose monitoring system limit of detection (LOD) from a single glucose sensor wavelength is 5.8 mM with a system bandwidth of 0.00108 Hz. Further analysis utilizing multivariate calibration methods such as the net analyte signal method promise to reduce the glucose monitoring system LOD approaching a clinically useful level of approximately 2 mM.
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Olsson, Sara, and Sabina Forsberg. "Exploring the User Experience in Continuous Glucose Monitoring Systems." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20575.

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Typ 1-diabetes kräver ordentlig uppsyn dag och natt för att upprätthålla ett fungerandeliv. Idag använder människor allt oftare kontinuerlig glukosövervakning (CGM) för atthantera sin diabetessjukdom. Detta system mäter blodsockernivån genom en sensor somplaceras på användarens hud. Användaren skannar sedan sensorn med en handenhet ellermobilapplikation för att läsa av den nuvarande blodsockernivån och i vilken riktningblodsockret är på väg. Forskare föreslår att för att kunna skapa den bästaanvändarupplevelsen för diabetespatienter måste designers verkligen förstå användarna ochhur de interagerar med sina CGM-system, vilket är målet med denna studie.Problemen med nuvarande CGM-system är att i många fall upplevs navigeringsstrukturensom otydlig och att det finns brister i användarupplevelsen. På grund av sjukdomenskomplexitet är kategorisering i navigeringen avgörande för att användarna ska kunna förstågränssnittet. Det här är ett område som de flesta studier tar upp, men en väl utformadlösning har ännu inte presenterats. Den centrala delen för att användarna ska kunna förståinformationen är genom att involvera slutanvändare i designprocessen. Medicinskinformation kan vara svår att förstå och när denna information presenteras kan användarenfå en känsla av ”information overload”. Patienterna vill ha ett väl utformat verktyg för atthantera sin sjukdom. Tidigare studier visar även att patienter vill ha ett system för allasina behov, ett så kallat ”system of systems”, snarare än flera separata system.Denna studie syftar till att undersöka tre av de tillgängliga produkterna på den svenskamarknaden för att förstå användarens behov och användarupplevelsen av dessa produkter.Genom en intervju och enkätundersökning med slutanvändare samlas data in för attutvärdera de produkter som används idag. Resultatet från den första fasen analyseras ochfynden lägger sedan grund för nästa fas, där en prototyp utvecklas. Prototypen ärutformad för att validera resultatet av den nya navigeringsstrukturen ochanvändarupplevelsen utifrån de problemområden som uppgetts i första fasen av studien.Valideringen görs genom ytterligare en enkätundersökning där deltagarna får jämföra sinnuvarande produkt med den utvecklade prototypen, i samband med de förutbestämdafrågorna i System Usability Scale (SUS).Denna studie visar på att det är möjligt att skapa en bättre navigationsstruktur ochinformationspresentation med gestaltlagarna i åtanke. Dock påpekas även behovet av attutföra ytterligare forskning av den tekniska lösning som krävs för att möjliggöra ett“system of system” för CGM-systemen.Sökord: Användarbehov, Användarupplevelse, Design, Diabetes, Kontinuerlig Glukosövervakning
Type 1 diabetes requires proper supervision day and night to maintain a healthy living. Tomanage diabetes research shows that people today more often use Continuous GlucoseMonitoring (CGM). This system measures the blood glucose levels through a sensorplaced on the users' skin. The user then scans the sensor with a hand device or mobileapplication to get a reading of current blood glucose level and in which direction the levelsare heading. Researchers suggest that to be able to create the best user experiencesolution for diabetes patients, the designers truly need to understand the users and theway that they interact with their monitoring systems, which is the goal of this study.The problems with current diabetes monitoring systems are, in most cases, the unclearstructure of the navigation and lack of thoughtful and meaningful user experience. Due tothe complexity of the disease, labeling is vital to make users understand the interface.This is an area that most studies acknowledge, but a well thought out solution has not yetbeen presented. The central part of making users understand the information is to involveend users in the design process. Medical information can be hard to grasp and when a lotof information is presented it can lead to information overload. Patients want a well-designed tool to help manage their disease. Previous studies show that patients want tohave one system for all their functions, a so-called system of systems, rather than havingmultiple ones.This study aims to examine three of the available products on the Swedish market tounderstand the user needs and the user experience of these products. Throughinterviews and surveys with end users, data is collected to evaluate currently used products.The data from the first phase is analyzed and findings then lay the foundation for the nextphase, where a prototype is made. The prototype is designed to validate the findings ofuser-needs in terms of navigation structure and user experience from the first phase. Thevalidation is conducted through a second survey where the end users are asked to comparecurrently used product versus the prototype, alongside with the predetermined questions inSystem Usability Scale (SUS).The results show that user experience in CGM systems needs further development tomake the patients satisfied with the way that they can manage their disease. This studysuggests that by designing with the gestalt laws in mind, a better navigation structure andinformation presentation is possible. But also suggests that future research within thetechnical solution of making the CGM systems to a system of system, is required.Keywords: Continuous Glucose Monitoring, Design, Diabetes, User Experience, User-Needs
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Allen, Nancy A. "Changing Physical Activity Behavior with Continuous Glucose Monitoring: A Dissertation." eScholarship@UMMS, 2006. https://escholarship.umassmed.edu/gsn_diss/2.

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Up to 60% of individuals with type 2 diabetes (T2DM) do not participate in regular physical activity (PA) despite the known benefits. To encourage these individuals to increase PA behavior, this study tested the feasibility and implementation of a nurse-directed counseling intervention using continuous glucose monitoring system (CGMS). The study used a framework derived from self-efficacy theory to 1) compare changes in self-efficacy, BP and activity counts between participants receiving CGMS counseling and standard T2DM counseling, 2) examine relationships between PA self-efficacy and BP and activity counts, 3) evaluate recruitment, retention, and screening strategies, and 4) assess instrument reliability and utility. Adults (N=52) with T2DM (non-insulin requiring, inactive) were randomized to intervention (n=27) or control groups (n=25). Both groups received 90 minutes of diabetes education with a follow-up phone call at 4 weeks. The intervention group also received feedback on their own CGMS graphs and a role model's graph depicting PA related reductions in glucose levels. PA benefits/barriers were discussed and goals were set. Outcomes were recorded at 1 and 8 weeks. Participants were older (57±14 years), predominantly (90%) white, about half (52%) female, and had diabetes for 8±7 years. Relative to the control group, participants receiving the intervention had higher self-efficacy scores at 8 weeks, indicating more confidence in sticking to a PA program. Their light/sedentary activity minutes decreased significantly and moderate activity minutes increased significantly; systolic BP, A1c and BMI decreased significantly. Only self-efficacy for "Sticking to it" was positively associated with moderate activity. The most successful recruitment media was multiple newspaper press releases. Most referrals came from endocrinology physicians. Of 231 study volunteers, 106 did not meet the criterion of A1c≥7.5%. These data suggest that CGMS feedback is feasible for counseling individuals with T2DM to improve PA and may improve risk factors for diabetes-related complications. Newspaper press releases are effective for recruiting participants with T2DM. Less restrictive inclusion criteria in a larger study may allow more participation by sedentary individuals with T2DM but may reduce effect size. CGMS was well tolerated and its data aided diabetes-related teaching.
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Barceló, Rico Fátima. "Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms." Doctoral thesis, Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/17173.

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ABSTRACT Diabetes Mellitus (DM) embraces a group of metabolic diseases which main characteristic is the presence of high glucose levels in blood. It is one of the diseases with major social and health impact, both for its prevalence and also the consequences of the chronic complications that it implies. One of the research lines to improve the quality of life of people with diabetes is of technical focus. It involves several lines of research, including the development and improvement of devices to estimate "online" plasma glucose: continuous glucose monitoring systems (CGMS), both invasive and non-invasive. These devices estimate plasma glucose from sensor measurements from compartments alternative to blood. Current commercially available CGMS are minimally invasive and offer an estimation of plasma glucose from measurements in the interstitial fluid CGMS is a key component of the technical approach to build the artificial pancreas, aiming at closing the loop in combination with an insulin pump. Yet, the accuracy of current CGMS is still poor and it may partly depend on low performance of the implemented Calibration Algorithm (CA). In addition, the sensor-to-patient sensitivity is different between patients and also for the same patient in time. It is clear, then, that the development of new efficient calibration algorithms for CGMS is an interesting and challenging problem. The indirect measurement of plasma glucose through interstitial glucose is a main confounder of CGMS accuracy. Many components take part in the glucose transport dynamics. Indeed, physiology might suggest the existence of different local behaviors in the glucose transport process. For this reason, local modeling techniques may be the best option for the structure of the desired CA. Thus, similar input samples are represented by the same local model. The integration of all of them considering the input regions where they are valid is the final model of the whole data set. Clustering is t
Barceló Rico, F. (2012). Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17173
Palancia
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Signal, Matthew Kent. "Continuous Glucose Monitoring and Tight Glycaemic Control in Critically Ill Patients." Thesis, University of Canterbury. Department of Mechanical Engineering, 2013. http://hdl.handle.net/10092/8458.

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Critically ill patients often exhibit abnormal glycaemia that can lead to severe complications and potentially death. In critically ill adults, hyperglycaemia is a common problem that has been associated with increased morbidity and mortality. In contrast, critically ill infants often suffer from hypoglycaemia, which may cause seizures and permanent brain injury. Further complicating the matter, both of these conditions are diagnosed by blood glucose (BG) measurements, often taken several hours apart, and, as a result, these conditions can remain poorly managed or go completely undetected. Emerging ‘continuous’ glucose monitoring (CGM) devices with 1-5 minute measurement intervals have the potential to resolve many issues associated with conventional intermittent BG monitoring. The objective of this research was to investigate and develop methods and models to optimise the clinical use of CGM devices in critically ill patients. For critically ill adults, an in-silico study was conducted to quantify the potential benefits of introducing CGM devices into the intensive care unit (ICU). Mathematical models of CGM error characteristics were implemented with existing, clinically validated, models of the insulin-glucose regulatory system, to simulate the behaviour of CGM devices in critically ill patients. An alarm algorithm was also incorporated to provide a warning at the onset of predicted hypoglycaemia, allowing a virtual dextrose intervention to be administered as a preventative measure. The results of the in-silico study showed a potential reduction in nurse workload of approximately 75% and a significant reduction in hypoglycaemia, while also providing insight into the optimal rescue dose size and resulting dynamics of glucose recovery. During 2012, ten patients were recruited into a pilot clinical trial of CGM devices in critical care with a primary goal of assessing the reliability of CGM devices in this environment, with a specific interest in the effects of CGM device type and sensor site on sensor glucose (SG) data. Results showed the mean absolute relative difference of SG data across the cohort was between 12-24% and CGM devices were capable of monitoring some patients with a high degree of accuracy. However, certain illnesses, drugs and therapies can potentially affect sensor performance, and one particular set of results suggested severe oedema may have affected sensor performance. A novel and first of its kind metric, the Trend Compass was developed and used to assesses trend accuracy of SG in a mathematically precise fashion without approximation, and, importantly, does so independent of glucose level or sensor bias, unlike any other such metrics. In this analysis, the trend accuracy between CGM devices was typically good. A recent hypothesis suggesting that glucose complexity is associated with mortality was also investigated using the clinical CGM data. The results showed that complexity results from detrended fluctuation analysis (DFA) were influenced far more by CGM device type than patient outcome. In addition, the location of CGM sensors had no significant effect on complexity results in this data set. Thus, while this emerging analytical method has shown positive results in the literature, this analysis indicates that those results may be misleading given the impact of technology outweighing that of physiology. This particular result helps to further delineate the range of potential applications and insight that CGM devices might offer in this clinical scenario. In critically ill infants, CGM devices were used to investigate hypoglycaemia during the first 48 hours after birth. More than 50 CGM data sets were obtained from several studies of CGM in infants at risk of hypoglycaemia at the Waikato hospital neonatal ICU (NICU). In light of concerns regarding CGM accuracy, particularly during the first few hours of monitoring and/or at low BG levels, an alternative, novel calibration scheme was developed to increase the reliability of SG data. The recalibration algorithm maximised the value of very accurate calibration BG measurements from a blood gas analyser (BGA), by forcing SG data to pass through these calibration BG measurements. Recalibration increased all metrics of hypoglycaemia (number, duration, severity and hypoglycaemic index) as the factory CGM calibration was found to be reporting higher values at low BG levels due to its least squares calibration approach based on the assumption of a less accurate calibration glucose meter. Thus, this research defined new calibration methods to directly optimise the use of CGM devices in this clinical environment, where accurate reference BG measurements are available. Furthermore, this work showed that metrics such as duration or area under curve were far more robust to error than the typically used counted-incidence metrics, indicating how clinical assessment may have to change when using these devices. The impact of errors in calibration measurements on metrics used to classify hypoglycaemia was also assessed. Across the cohort, measurement error, particularly measurement bias, had a larger effect on hypoglycaemia metrics than delays in entering calibration measurements. However, for patients with highly variable glycaemia, timing error can have a significantly larger impact on output SG data than measurement error. Unusual episodes of hypoglycaemia could be successfully identified using a stochastic model, based on kernel density estimation, providing another level of information to aid decision making when assessing hypoglycaemia. Using the developed algorithms/tools, with CGM data from 161 infants, the incidence of hypoglycaemia was assessed and compared to results determined using BG measurements alone. Results from BG measurements showed that ~17% of BG measurements identified hypoglycaemia and over 80% of episodes occurred in the first day after birth. However, with concurrent BG and SG data available, the SG data consistently identified hypoglycaemia at a higher rate suggesting the BG measurements were not capturing some episodes. Duration of hypoglycaemia in SG data varied from 0-10+%, but was typically in the range 4-6%. Hypoglycaemia occurred most frequently on the first day after birth and an optimal measurement protocol for at risk infants would likely involve CGM for the first week after birth with frequent intermittent BG measurements for the first day. Overall, CGM devices have the potential to increase the understanding of certain glycaemic abnormalities and aid in the diagnosis/treatment of other conditions in critically ill patients. This research has used a range of prospective and retrospective clinical studies to develop methods to further optimise the use of CGM devices within the critically ill clinical environment, as well as delineating where they are less useful or less robust. These latter results clearly define areas where clinical practice needs to adapt when using these devices, as well as areas where device makers could target technological improvements for best effect. Although further investigations are required before these devices are regularly implemented in day-to-day clinical practice, as an observational tool they are capable of providing useful information that is not currently available with conventional intermittent BG monitoring.
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Books on the topic "And continuous glucose monitoring"

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Jia, Weiping, ed. Continuous Glucose Monitoring. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7.

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Kaufman, Francine Ratner. Insulin pumps and continuous glucose monitoring. Alexandria, Va: American Diabetes Assocaiation, 2012.

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Pickup, John C. Insulin pump therapy and continuous glucose monitoring. Oxford: Oxford University Press, 2009.

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Insulin pump therapy and continuous glucose monitoring. Oxford: Oxford University Press, 2009.

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Huch, Albert. Continuous Transcutaneous Monitoring. Boston, MA: Springer US, 1987.

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Jahnke, J. A. Continuous emission monitoring. New York: Van Nostrand Reinhold, 1993.

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Huch, Albert, Renate Huch, and Gösta Rooth, eds. Continuous Transcutaneous Monitoring. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1927-6.

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Husain, Aatif M., and Saurabh R. Sinha, eds. Continuous EEG Monitoring. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-31230-9.

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Quiñones-Grueiro, Marcos, Orestes Llanes-Santiago, and Antônio José Silva Neto. Monitoring Multimode Continuous Processes. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54738-7.

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Hedley-Whyte, J., and PW Thompson, eds. Continuous Anesthesia Gas Monitoring. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 1990. http://dx.doi.org/10.1520/stp1090-eb.

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Book chapters on the topic "And continuous glucose monitoring"

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Wang, Y. F., and W. Jia. "Determination of Glucose and Continuous Glucose Monitoring." In Continuous Glucose Monitoring, 1–12. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_1.

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Jia, W. "Interpretation of the Chinese Clinical Guideline for Continuous Glucose Monitoring." In Continuous Glucose Monitoring, 93–100. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_10.

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Zhou, J., and W. Jia. "Continuous Glucose Monitoring and Glycemic Variability." In Continuous Glucose Monitoring, 101–10. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_11.

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Zhou, J., and W. Jia. "Continuous Glucose Monitoring and Antidiabetic Therapies." In Continuous Glucose Monitoring, 111–19. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_12.

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Ma, X. J., and J. Zhou. "Using Continuous Glucose Monitoring for Patients with Hypoglycemia." In Continuous Glucose Monitoring, 121–28. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_13.

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Zhou, J. "Using Continuous Glucose Monitoring for Patients with Fasting Hyperglycemia." In Continuous Glucose Monitoring, 129–41. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_14.

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Zhou, J. "Using Continuous Glucose Monitoring for Patients with Fulminant Type 1 Diabetes." In Continuous Glucose Monitoring, 143–58. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_15.

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Ma, X. J., and J. Zhou. "Using Continuous Glucose Monitoring for Diabetes Mellitus in Pregnancy." In Continuous Glucose Monitoring, 159–70. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_16.

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Lu, J. Y., and W. Jia. "Using Continuous Glucose Monitoring for Steroid-Induced Diabetes." In Continuous Glucose Monitoring, 171–82. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_17.

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Han, J. F., and Y. Bao. "Using Continuous Glucose Monitoring for Patients with Insulinoma." In Continuous Glucose Monitoring, 183–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7074-7_18.

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Conference papers on the topic "And continuous glucose monitoring"

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Aponte-Becerra, Laura, Rodrigo Quispe, Laura Mendez-Pino, Vera Novak, Magdy Selim, and Vasileios-Arsenios Lioutas. "Continuous glucose monitoring in acute stroke." In the 8th International Workshop on Innovative Simulation for Healthcare. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.iwish.016.

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Abstract:
"Hyperglycaemia upon admission is a pathophysiological response to acute brain ischemia that has been independently associated with high mortality rate and poor prognosis. Glycaemic variability (GV) has also shown association with poor clinical outcomes among stroke patients. GV is best assessed by continuous glucose monitoring (CGM), which enables consecutives glucose measurements every 5 minutes. This pilot study aimed: 1) To describe safety, feasibility and tolerability of CGM in the acute stroke setting; and 2) To compare CGM and conventional FS glucose-based monitoring regimen in terms of their relationship with GUA and the accuracy of hypoglycaemic episodes detection. Safety, feasibility and tolerability of CGM was excellent in our cohort of 23 patients with acute stroke (61% ischemic and 39% intracerebral haemorrhage) and there were no adverse events. CGM recorded ten hypoglycaemic episodes that were not detected by conventional FS monitoring. GUA was associated with coefficient of variation (CV) of CGM (p=0.03), CV of FS (p=0.01), standard deviation (SD) of CGM (p-value=0.01) and mean amplitude of glucose excursions (MAGE) (pvalue= 0.001)."
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Kruse, Theresa, and Knut Graichen. "Moving horizon estimation for continuous glucose monitoring." In The 6th World Congress on Electrical Engineering and Computer Systems and Science. Avestia Publishing, 2020. http://dx.doi.org/10.11159/icbes20.119.

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Mamoun, Ragda, Mohammed El Hadi, Emtithal Ahmed, and Omer Adam. "Design of Noninvasive Continuous Glucose Monitoring System." In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2018. http://dx.doi.org/10.1109/iccceee.2018.8515781.

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Claremont, D. J., G. W. Shaw, and J. C. Pickup. "Biosensors for continuous in vivo glucose monitoring." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94988.

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Bequette, B. W. "Optimal estimation applications to continuous glucose monitoring." In Proceedings of the 2004 American Control Conference. IEEE, 2004. http://dx.doi.org/10.23919/acc.2004.1383731.

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Garrett, Jared R., Xinxin Wu, and Kaiming Ye. "Development of a pH-Insensitive Glucose Indicator for Continuous Glucose Monitoring." In 2007 IEEE Region 5 Technical Conference. IEEE, 2007. http://dx.doi.org/10.1109/tpsd.2007.4380375.

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Mohebbi, Ali, Alexander R. Johansen, Nicklas Hansen, Peter E. Christensen, Jens M. Tarp, Morten L. Jensen, Henrik Bengtsson, and Morten Morup. "Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176695.

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Facchinetti, A., G. Sparacino, F. Zanderigo, and C. Cobelli. "Reconstructing by Deconvolution Plasma Glucose from Continuous Glucose Monitoring Sensor Data." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259966.

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Vaddiraju, Santhisagar, Michail Kastellorizios, Allen Legassey, Diane Burgess, Faquir Jain, and Fotios Papadimitrakopoulos. "Needle-implantable, wireless biosensor for continuous glucose monitoring." In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2015. http://dx.doi.org/10.1109/bsn.2015.7299421.

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Vrancic, Christian, Norbert Gretz, Niels Kröger, Sabine Neudecker, Annemarie Pucci, and Wolfgang Petrich. "Toward minimally invasive, continuous glucose monitoring in vivo." In SPIE BiOS, edited by Anita Mahadevan-Jansen and Wolfgang Petrich. SPIE, 2012. http://dx.doi.org/10.1117/12.908771.

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Reports on the topic "And continuous glucose monitoring"

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Lane, S. M., and J. J. Mastrotaro. Development Of A Prototype Sensor For Continuous Blood Glucose Monitoring Final Report CRADA No. TC-1271-96. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1408980.

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Lane, Stephen M., and John J. Mastrototaro. Development of Chemically Amplified Optical Sensors for Continuous Blood Glucose Monitoring Final Report CRADA No. TSB-1162-95. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1418925.

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Wright, T., P. S. Hu, and J. Young. Variability in continuous traffic monitoring data. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/403972.

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Cam-Winget, N., and L. Lorenzin. Security Automation and Continuous Monitoring (SACM) Requirements. RFC Editor, September 2017. http://dx.doi.org/10.17487/rfc8248.

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Greenstone, Michael, Rohini Pande, Nicholas Ryan, and Anant Sudarshan. Continuous Emissions Monitoring Systems (CEMS) in India. International Initiative for Impact Evaluation (3ie), March 2020. http://dx.doi.org/10.23846/dpw1ie111.

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Donahue, Katrina, Laura Young, John Buse, Mark Weaver, Maihan Vu, C. Madeline Mitchell, Tamara Blakeney, Kimberlea Grimm, Jennifer Rees, and Franklin Niblock. Effect of Glucose Monitoring on Patient and Provider Outcomes in Non-Insulin Treated Diabetes. Patient-Centered Outcomes Research Institute (PCORI), March 2018. http://dx.doi.org/10.25302/3.2018.ce.12114980.

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Garcia-Lomeli, H. D., A. D. Bertsch, and D. M. Fox. Continuous Security and Configuration Monitoring of HPC Clusters. Office of Scientific and Technical Information (OSTI), May 2015. http://dx.doi.org/10.2172/1184182.

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Dempsey, Kelley, Victoria Pillitteri, Chad Baer, Ron Rudman, Robert Niemeyer, and Susan Urban. ISCMA: An Information Security Continuous Monitoring Program Assessment. National Institute of Standards and Technology, March 2021. http://dx.doi.org/10.6028/nist.ir.8212.

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Gomes, Tara, David Juurlink, Baiju Shah, Michael Paterson, and Muhammad Mamdani. Self-monitoring of blood glucose: Patterns, Costs and Potential Cost Reduction Associated with Reduced Testing. ODPRN, December 2009. http://dx.doi.org/10.31027/odprn.2009.01.

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Klosterbuer, S. F., J. K. Halbig, W. C. Harker, H. O. Menlove, J. A. Painter, and J. E. Stewart. Continuous remote/unattended monitoring for safeguards data collection systems. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10102602.

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