Article

Glucose Monitoring in Individuals With Diabetes Using a Long-Term Implanted Sensor/Telemetry System and Model

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Abstract

Objective: The use of a fully implanted, first-generation prototype sensor/telemetry system is described for long-term monitoring of subcutaneous tissue glucose in a small cohort of people with diabetes. Methods: Sensors are based on a membrane containing immobilized glucose oxidase and catalase coupled to electrochemical oxygen detection and telemetry systems, integrated as an implant. The devices remained implanted for up to 180 days, with signals transmitted every 2 minutes to external receivers. Results: The data include signal recordings from blood glucose clamps and spontaneous glucose excursions, matched to reference blood glucose values. The sensor signals indicate dynamic tissue glucose, for which there is no independent standard, and a model describing the relationship between blood glucose and the signal is therefore included. The values of all model parameters have been estimated, including the permeability of adjacent tissues to glucose, and equated to conventional mass transfer parameters. As a group, the sensor calibration varied randomly at an average rate of -2.6%/week. Statistical correlation indicated strong association between sensor signals and reference glucose values. Conclusions: Continuous, long-term glucose monitoring in individuals with diabetes is feasible with this system. Significance: All therapies for diabetes are based on glucose control and require glucose monitoring. This fully implanted, long-term sensor system may facilitate new approaches for improved management of the disease.

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... Connections are made to healthcare providers through gateways and wireless networks with data transmitted (Movassaghi and Abolhasan, 2014). The implanted sensors, such as electrochemical glucose sensors, help monitor and control diabetes (Lucisano et al., 2016). Patients also self-manage and personalize their diabetes using AI devices. ...
... According to one estimation, the number of people affected by dementia in 2017 was a staggering 47 million. This number is projected to increase to 132 million by 2050 (Lucisano et al., 2016). Advanced remote sensing systems with assistance from the IoT and AI would ease the monitoring and control needed to effectively treat these diseases. ...
... Novel research has been undertaken to develop a framework for healthcare systems that provides a wide variety of analytical data applications for managing data sources ranging from EHRs to medical photographs (Palanisamy and Thirunavukarasu, 2019). While it is inevitable that sick persons and other users use applications, it is obvious that application development is based on the services required (Lucisano et al., 2016). Thus, it could be said that services are based on what the developer has to offer, while applications are developed to suit the users. ...
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The Internet of Things (IoT) and artificial intelligence (AI) are two of the fastest-growing technologies in the world. With more people moving to cities, the concept of a smart city is not foreign. The idea of a smart city is based on transforming the healthcare sector by increasing its efficiency, lowering costs, and putting the focus back on a better patient care system. Implementing IoT and AI for remote healthcare monitoring (RHM) systems requires a deep understanding of different frameworks in smart cities. These frameworks occur in the form of underlying technologies, devices, systems, models, designs, use cases, and applications. The IoT-based RHM system mainly employs both AI and machine learning (ML) by gathering different records and datasets. On the other hand, ML methods are broadly used to create analytic representations and are incorporated into clinical decision support systems and diverse healthcare service forms. After carefully examining each factor in clinical decision support systems, a unique treatment, lifestyle advice, and care strategy are proposed to patients. The technology used helps to support healthcare applications and analyze activities, body temperature, heart rate, blood glucose, etcetera. Keeping this in mind, this paper provides a survey that focuses on the identification of the most relevant health Internet of things (H-IoT) applications supported by smart city infrastructure. This study also evaluates related technologies and systems for RHM services by understanding the most pertinent monitoring applications based on several models with different corresponding IoT-based sensors. Finally, this research contributes to scientific knowledge by highlighting the main limitations of the topic and recommending possible opportunities in this research area.
... The transmission is provided by a wireless system embedded on the device, which provides to send data to a remote receiving unit. In order for communication to occur, the system must be paired, which works approximately 2 m (Lucisano et al., 2016). Continuous systems are designed for long-term monitoring of glucose concentration and these can maintain glucose sensitivity for several days (Joseph et al., 2015) (Levitt et al., 2017). ...
... Its size in diameter is around 0.2-0.7 mm with a length of about 10 mm. Sensors must be inserted by the user into the subcutaneous tissues and replaced every 3 to 7 days (Lucisano et al., 2016). The length of the sensor implantation, ie its durability, is determined by the properties of the enzyme used, its immobilization and the protective membrane used. ...
Article
the work has been aimed to create an overview of available and used methods and ways to determine the concentration of glucose in body fluids, especially from a technical point of view. It also provides an overview of the clinical features of these methods. The survey found that today's market offers a large number of options and approaches to the issue. There are accurate reference laboratory methods, self-monitoring methods for measuring glucose levels using glucometers, or continuous methods for daily monitoring of blood glucose trends and for insulin pump control. However, it must not be forgotten that the development of full closure of feedback is still not complete today. Individual methods cannot always be compared with each other, precisely because of the focus and the use of these methods. Choosing the right method of blood glucose levels in the body measuring can help patients to manage their diabetes mellitus. The methods listed in the overview are divided in terms of measurement continuity and further according to the invasiveness of the method. Finally, the issues of accuracy in the detection of glycaemia variability and the possibility of further development of these methods are discussed, as it is clear from the survey that the development is focused mainly on continuous methods improving that get to the forefront and also on developing a biosensor that is purely non-invasive and continuous.
... Implantable Medical Devices are implanted into the human body as shown in Figure 4. The most common devices belonging to this category are the Pacemaker [42], Neurostimulators [43], Insulin Pumps, Glucose Monitoring Systems [44], Gastric Stimulators [45], Foot Drop Implants [46], Cochlear Implants [47], and Drug Pumps [48], etc. Here, we will discuss each layer with its components. ...
... Implantable Medical Devices are implanted into the human body as shown in Figure 4. The most common devices belonging to this category are the Pacemaker [42], Neurostimulators [43], Insulin Pumps, Glucose Monitoring Systems [44], Gastric Stimulators [45], Foot Drop Implants [46], Cochlear Implants [47], and Drug Pumps [48], etc. ...
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The Internet of Things (IoT) is an emerging field consisting of Internet-based globally connected network architecture. A subset of IoT is the Internet of Healthcare Things (IoHT) that consists of smart healthcare devices having significant importance in monitoring, processing, storing, and transmitting sensitive information. It is experiencing novel challenges regarding data privacy protection. This article discusses different components of IoHT and categorizes various healthcare devices based on their functionality and deployment. This article highlights the possible points and reasons for data leakage, such as conflicts in laws, the use of sub-standard devices, lack of awareness, and the non-availability of dedicated local law enforcement agencies. This article draws attention to the escalating demand for a suitable regulatory framework and analyzes compliance problems of IoHT devices concerning healthcare data privacy and protection regulations. Furthermore, the article provides some recommendations to improve the security and privacy of IoHT implementation.
... When the system is perturbed the concentration of glucose, G(t) and insulin I(t) can be each considered in terms of a known input and noisy output and modelled independently. Differential equations (3,4,5) describe the simplest of these glucose model, where Q is the blood glucose mass with Qb being the basal value, I is the insulin concentration with Ib (basal value). I ′ (t) is the above basal remote insulin, D is the glucose dose and V is the glucose distribution volume k2 and k3 are rate parameters and NHGB is the net hepatic glucose balance that depends on the blood glucose and the remote insulin, see equations (6,7), Rd is the rate of glucose disappearance from the peripheral tissues. ...
... The diffusion delay was identified from the signal in the lag region with gains set to unity, and finally the tissue uptake delay and gain, Ku, were calculated from the model terms. [3] One possible control that can be used is PD controller in which the proportional is determined by the current glucose level, while the derivative term is determined by its variation in the previous half hour or so. However, these control algorithms are not very effective slow nocturnal response as well as a more aggressive response after eating the addition a feed forward component to accommodate regulation after meals. ...
Conference Paper
Merging of human and machine has influenced our imagination and overcome many health problems. In this paper I focused on significant device that give hope to more than 442 million people suffering from diabetes [1], which called artificial pancreas. It includes continuous glucose monitoring in conjunction with miniature automate insulin dispensing pump. This paper presents a study in modeling artificial pancreas, as a closed loop system, where two mechanistic physiologically based models have been developed, minimal (coarse) models and maximal (fine grain) models. In addition to system modeling, we will study the control unit of the system using PD control (instead of PID control), advanced algorithms and control unit with learning ability feature. The study is done by taking benefits of closed loop system (feedforward and feedback) for continuous glucose level monitoring with the help of advanced control algorithms with learning ability feature. Finally, we will study some solutions to overcome hypoglycemia, which may occur during managing diabetes, using some more accurate CGM with filtering, denoising, reduction in biofouling and enzyme degradation as well as numerous proprietary techniques and more sophisticated control unit.
... For glucose monitoring, chemical sensors are used (e.g., a sensor based on two enzymatic reactions catalyzed by oxidase and catalase [64]). This smart sensor is implanted in abdominal tissue sites into subcutaneous adipose tissue. ...
... The device remained implanted in the subject for 180 days, transmitting data every two minutes, and requiring a monthly calibration to reduce errors, which could vary 2.6% per week. Individual sensor clamps have a correlation coefficient of 0.84 and 0.98 for spontaneous glucose excursions [64]. ...
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The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.
... However, PU provides a diffusion barrier to glucose and limits the outer diffusion rate of H 2 O 2 , which is correlated to sensor sensitivity [29]. Due to the strong turnover rate of Cat to H 2 O 2 , using Cat on ampere electrode can eliminate the accumulation of H 2 O 2 in GOD and replenish oxygen, thereby ameliorating oxygen deficiency and ensuring the sufficient sensitivity to glucose [30][31][32]. However, immobilization of Cat on the same sensing plane restricts glucose reacting dose, and decreases GOD quantity [33]. ...
... Moreover, the successive accumulation of H 2 O 2 in the reaction would result in electrode poisoning and the leakage would stimulate surrounding tissue [5,29,33]. An oxygen regeneration system prepared by immobilizing both GOD and Cat on sensing surface was proposed by Lucisano and Takashi et al. [30,31]. H 2 O 2 can be rapidly decomposed, which generates oxygen when Cat is sufficient; the influence of oxygen deficiency can be partly relieved because the oxygen demand is halved. ...
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Article
This study aims to develop an oxygen regeneration layer sandwiched between multiple porous polyurethanes (PU) to improve the performance of implantable glucose sensors. Sensors were prepared by coating electrodes with platinum nanoparticles, Nafion, glucose oxidase and sandwich hierarchically porous membrane with an oxygen supplement function (SHPM-OS). The SHPM-OS consisted of a hierarchically porous structure synthesized by polyethylene glycol and PU and a catalase (Cat) layer that was coated between hierarchical membranes and used to balance the sensitivity and linearity of glucose sensors, as well as reduce the influence of oxygen deficiency during monitoring. Compared with the sensitivity and linearity of traditional non-porous (NO-P) sensors (35.95 nA/mM, 0.9987, respectively) and single porous (SGL-P) sensors (45.3 nA /mM, 0.9610, respectively), the sensitivity and linearity of the SHPM-OS sensor was 98.45 nA/mM and 0.9989, respectively, which was more sensitive with higher linearity. The sensor showed a response speed of five seconds and a relative sensitivity of 90% in the first 10 days and remained 78% on day 20. This sensor coated with SHPM-OS achieved rapid responses to changes of glucose concentration while maintaining high linearity for long monitoring times. Thus, it may reduce the difficulty of back-end hardware module development and assist with effective glucose self-management for people with diabetes.
... According to the estimation of the International Diabetes Federation, the number of people with diabetes will increase from 463 to 700.2 million during the period from 2019 to 2045. 1 Dynamic and accurate monitoring of the patients' blood glucose levels is required to help patients maintain their blood glucose levels at normal levels and avoid complications caused by blood glucose imbalance in patients with diabetes. 2 In general, patients need to extract blood samples from their fingers to obtain glucose levels. 3 However, frequent finger pricking can cause pain and increase the risk of infection, making continuous blood glucose monitoring uncomfortable and not safe. ...
Full-text available
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Daily blood glucose monitoring is important for the disease control of diabetic patients. A 3D earlobe model optimized using the DLA method for modeling the continuity and complexity of blood distribution was designed and tested. Compared with the simple layered structure, the DLA method can better take into account the effects of different locations of blood distribution and blood volume on microwave signal transmission. The method of auxiliary differential equations is also combined into a finite‐difference time‐domain procedure to simulate the dispersion properties of simulated biological tissues, creating a more realistic simulation environment for non‐invasive glucose monitoring. The DLA model is fabricated using 3D printing technology. The detection technique is based on the S11 unwrapped phase and has a maximum detection sensitivity of 0.0198 dB per mg/dL in the ultra‐wideband range of 8.0–10.0 GHz, with a prediction error of 8% or less for blood glucose levels.
... Determining glucose concentrations in human blood is critical for medical and human health monitoring. A high glucose concentration in the human body causes several dangerous conditions, including hypoglycemia and diabetes [96,97]. Diabetes is caused by a higher glucose concentration in the human body, whereas hypoglycemia is caused by a lower concentration in the human blood. ...
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SPR-based technology has emerged as one of the most versatile optical tools for analyzing the binding mechanism of molecular interaction due to its inherent advantages in sensing applications, such as real-time, label-free, and high sensitivity characteristics. SPR is widely used in various fields, including healthcare, environmental management, and food-borne illness analysis. Meanwhile, kidney disease has grown to be one of the world’s most serious public health problems in recent decades, resulting in physical degeneration and even death. As a result, several studies have published their findings regarding developing of reliable sensor technology based on the SPR phenomenon. However, an integrated and comprehensive discussion regarding the application of SPR-based sensors for detecting of kidney disease has not yet been found. Therefore, this review will discuss the recent advancements in the development of SPR-based sensors for monitoring kidney-related diseases. Numerous SPR configurations will be discussed, including Kretschmann, Otto, optical fiber-based SPR, and LSPR, which are all used to detect analytes associated with kidney disease, including urea, creatinine, glucose, uric acid, and dopamine. This review aims to show the broad application of SPR sensors which encouraged the development of SPR sensors for kidney problems monitoring.
... Monitoring daily activity and blood glucose levels that prevent diabetic foot ulcers. Lucisano et al. [18] Spontaneous glucose excursions and glucose clamping Long-term sensor/telemetry system implanted for glucose monitoring, control, and management Edge et al. [19] Glucose monitoring device Free-Style libre flash ...
Chapter
Internet of Things technology (IoT) is a fast-growing area of computing, and it is applicable to almost all human endeavor. The introduction of IoT into medicine brought about the Internet of Medical Things (IoMT) that has really redefined the smart healthcare systems globally, though its apprehension to security threats and risk especially in the field of medicine is second to none. Though it is very challenging to provide a secured expansion using the sensor in the medical domain but the impart of the IoMT-based system can never be denied and was greatly deployed in various countries accordant with available facilities to curb the spread of Covid-19 pandemic. But because of the sensitivity of data and critical information in the IoMT-based systems, it continues posing several perilous challenges and these keep growing. Therefore, this chapter discussed inherent opportunities and challenges facing data-driven solutions for a secured IoMT. This will broaden the research and reassure the users of IoMT for data-driven solutions in healthcare delivery.
... The EM power transfer can be categorized as either IPT for near-field coupling or far-field EM power transfer if the coupling is in the midfield (i.e., transition region) or far-field. a) Inductive power transfer (IPT): The IPT scheme is the oldest and the most established power transfer method, and many works have demonstrated system-level implementations of this scheme in a wide range of implant applications, such as retinal [18], [25], [26], [56], cortical (or deep brain stimulation) [31], [57], cochlear [13], [58], cardiac [7], [21], [22], neural stimulator [19], [59], and blood-glucose monitoring implants [60]. IPT uses an inductive link for transferring power wirelessly, where the transmitting coil (T X ) and receiving coil (R X ) act as the primary element and the secondary element, respectively. ...
Article
For implantable medical devices, it is of paramount importance to ensure uninterrupted energy supply to different circuits and subcircuits. Instead of relying on battery stored energy, harvesting energy from the human body and any external environmental sources surrounding the human body ensures prolonged life of the implantable devices and comfort of the patients. In this article, we present existing issues and challenges related to the state-of-the-art solutions used for harvesting energy to power implantable devices. In addition, the details on existing energy storage technologies and various wireless power transfer techniques incorporating external or internal energy sources and sensors have been discussed. The authors have outlined the performance and power constraints of existing biomedical devices and provided a brief overview of various power architectures found in the literature. This survey has been conducted on existing implantable solutions in terms of output voltage, current, device dimension, application, generated power, energy density, and so on. Finally, the advantages and drawbacks of different solutions have been discussed and compared. Therefore, this article can be considered as an expedient reference for researchers conducting research in the field of energy scavenging, internal energy storage, wireless power transfer techniques, and power management of implantable medical devices.
... To have a controlled level of glucose in the bloodstream and to manage diabetes very well, we have to determine blood glucose levels precisely and regularly [11]. At present, there are various types of machines or devices that we may purchase from the retail market to determine the glucose level continuously. ...
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Diabetes disease that arises from the higher glucose level due to insulin shortage or insulin opposition in the human body has become a common disease in the world. No medicine can cure it completely. However, by taking medicine, maintaining diets, and having exercises regularly, a diabetes patient can keep his glucose level within the specified limits and in this way, he/she can lead a normal life like a healthy person. But to control glucose levels, a patient needs to monitor them regularly. Various techniques are being used over the last four decades. This modern review article aims to provide a comparative study report on various blood glucose monitoring techniques in a very concise and organized manner. The review mainly emphasizes working principles, cost, technology, sensors, measurement types, measurement accuracy, advantages, and disadvantages, etc. of various techniques and then compares among each other. Besides, the use of algorithms and simulators for the growth of this technology is also presented. Finally, current research trends of this measurement technology have also been discussed.
... There are special sensors to monitor rheumatoid arthritis, heart arrhythmias, sleep apnea, cranial pressure, etc. [33]. For example, the electrochemical glucose sensor is popular in diagnosing diabetes [34]. ...
Chapter
The perpetual evolution of IoT continues to make cities smart beyond measure with the abundance of data transactions through expansive networks. Healthcare has been a foremost pillar of settlements and has gained particular focus in recent times owing to the pandemic and the deficiencies it has brought to light. There is an exigency to developing smart healthcare systems that make smart cities more intelligent and sustainable. Therefore, this paper aims to present a study of smart healthcare in the context of a smart city, along with recent and relevant research areas and applications. Several applications have been discussed for early disease diagnosis and emergency services with advanced health technologies. It also focuses on security and privacy issues and the challenges posed by technologies such as wearable devices and big healthcare data. This paper briefly reviews some enhanced schemes and recently proposed security mechanisms as countermeasures to various cyber-attacks. Recent references are primarily used to present smart healthcare privacy and security issues. The issues are laid out briefly based on the different architecture layers, various security attacks, and their corresponding proposed solutions along with other facets of smart health such as Wireless Body Area Network (WBAN) and healthcare data.
... Consider, as energy factor in which energy sourced nodes have greater nodes than remaining nodes in network. The value can be provided as in Equation given below [14]: ...
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Internet-of-things (IoT) based health monitoring systems have turned out to be an interesting topic to enhance quality of health care services. Moreover, there is no advanced IoT based continuous monitoring of glucose systems in real time and some prevailing techniques have numerous limitations. Here, a continuous and invasive glucose monitoring system for transmitting the condition of individuals simultaneously utilizing IoT is modelled and a general system architectural design for processing back end systems to provide body temperature, real time glucose and contextual data in human readable and graphical forms to the physicians or patients is anticipated. As well, a protocol designs for monitoring the continuous data from IoT devices in order to overcome the short comings of existing methods is provided. The design of an energy efficient routing algorithm is a hot topic in the research of IoT based Data mining. Cluster Heads (CH) form backbone of inter-cluster communication. The selection of reliable and efficient cluster head is another important issue. In most of the clustering process, failure of CH occurs due to energy depletion and if the distance between sink and CH is more, it ultimately leads to failure in transmission. During transmission, nodes may fail that means sudden energy loss or node gets out of coverage. Due to relaying high data traffic, some of nodes quickly exhaust their energy and increase the risk of node failure. As a baseline to node failure, data packet loss also occurs in a CH due to congestion and poor link quality. Hence, one of the most crucial feature in designing a protocol is to minimize energy consumption for betterment of network functioning. Here, a clustering routing protocol based on data mining techniques is applied for sensor nodes in medical field called Adaptive Positioning of Cluster Head based Map reducing (APCH-MR) is proposed. Routing table based code book is generated for privacy concern, in which the process of mapping and reducing the data for dissemination is performed. The simulated outcomes depicts that the total number of packets transmitted in round 500 is 11200, total number of dead nodes during round 500 is 58, and time consumed by nodes at 500 rounds is 0.3751s respectively. The proposed method shows better trade off in contrast to conventional techniques.
... In [71], an optical sensor measures blood glucose levels through the fingertip, while in [72], an ultra-wideband microwave technique is used for blood glucose level detection in an earlobeattached sensor. A system is developed in [73], which implants the sensor inside the body for a longer duration (approx. 180 days) ...
Article
The revolution in the Internet of Things (IoT) is redesigning and reshaping the healthcare system technologically, economically and socially. The emerging and rapidly growing IoT-based Smart Healthcare System (SHCS) is seen as a sustainable solution to reduce the burden on the existing healthcare system due to increasing diseases and limited medical infrastructure. IoT-based SHCS plays a vital role in delivery of healthcare services in rural and remote areas where the essential medical amenities, necessary infrastructures and qualified medical practitioners are not available. Therefore, in this paper, a comprehensive investigation of futuristic IoT-based SHCS and its constituents is presented. This paper provides exhaustive review on different techniques and technologies dealing with smart healthcare framework, physiological sensing, signal processing, data communication, cloud computing and data analytics used in IoT-based SHCS. A comparative analysis of existing literature has been carried out to identify the recent trends and advancements in this very dynamic field of global importance. In addition to this, it highlights different issues and challenges, along with the recommendation for further research in the field. The prime objective of this paper is to deliver the state-of-the-art understanding and update about IoT-based SHCS and its constituents by providing a good source of information to the researchers, service providers, technologists, medical practitioners and the general population.
... Telemetry (see below) is one of the techniques that show potential and neuroscience must benefit of telemetry and any other technological advance that is deemed necessary. The diabetes research field is gaining momentum due to tools that allow real-time measurement of glucose levels by telemetry [27][28][29][30] thus avoiding changes due to stress in animal handling, circadian rhythms, etc. Who would not want to know whether CSF glucose levels, that in a healthy individual are 60-70% of those in blood, are altered in neurodegeneration and are restored by a given neuroprotective therapy? ...
... The minimally invasive method using prototype sensor was developed to have frequent monitoring of glucose tissue [39]. The sensor is wearable and is implanted on membrane which contains the immobilized glucose oxidase. ...
Full-text available
Preprint
Diabetes is a chronicle disease where the body of a human is irregular to dissolve the blood glucose properly. The diabetes is due to lack of insulin in human body. The continuous monitoring of blood glucose is main important aspect for health care. Most of the successful glucose monitoring devices is based on methodology of pricking of blood. However, such kind of approach may not be advisable for frequent measurement. The paper presents the extensive review of glucose measurement techniques. The paper covers various non-invasive glucose methods and its control with smart healthcare technology. To fulfill the imperatives for non-invasive blood glucose monitoring system, there is a need to configure an accurate measurement device. Noninvasive glucose-level monitoring device for clinical test overcomes the problem of frequent pricking for blood samples. There is requirement to develop the Internet-Medical-Things (IoMT) integrated Healthcare Cyber-Physical System (H-CPS) based Smart Healthcare framework for glucose measurement with purpose of continuous health monitoring. The paper also covers selective consumer products along with selected state of art glucose measurement approaches. The paper has also listed several challenges and open problems for glucose measurement.
... Prevention of diabetic foot ulcer [99] Glucose monitoring in individuals with diabetes Long-term implanted sensor/telemetry system and model are used [100] Sensor-based method for glucose monitoring Accuracy, safety and acceptability of the glucose monitoring system in the paediatric population is proposed [101] Continuous glucose monitoring sensors Past and present algorithmic challenges of CGM sensors are introduced [102] Continuous glucose monitoring Use of CGM for adjustment of insulin dosing, and automated interpretation [103] Continuous glucose monitoring Reduce the risk of hypoglycemia [104] T2D management Big data technologies [105] T2D management Predictive models using big data analytics [106] The relationship between diet, physical activity, and T2D Regular PA is a primary component in management of T2D [107] The relationship between physical activity and T2D level of physical activity in people with T2D is analyzed [108] Physical activity and incident type 2 diabetes mellitus Dose-response meta-analysis of prospective cohort studies [109] Physical activity and risk of diabetes PA is associated with reduction in the risk of diabeties [110] Sensor based monitoring of PA to improve glucose management Non-invasive sensors using physiological parameters related to PA to improve glucose monitoring [111] Continuous glucose monitoring Minimally non-invasive Continuous glucose monitoring biosensors are proposed [115,116]. ...
Full-text available
Article
Globally, the aging and the lifestyle lead to rabidly increment of the number of type two diabetes (T2D) patients. Critically, T2D considers as one of the most challenging healthcare issue. Importantly, physical activity (PA) plays a vital role of improving glycemic control T2D. However, daily monitoring of T2D using wearable devices/ sensors have a crucial role to monitor glucose levels in the blood. Nowadays, daily physical activity (PA) and exercises have been used to manage T2D. The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors. Finally, challenges and future trends are also highlighted. © 2021 Institute of Advanced Engineering and Science. All rights reserved.
... These implanted sensors are Li-ion battery powered system which are small in size, light in weight and limited capacity of charges [38]. In some scenarios where long-term and continuous monitoring of the human body is important and there are some other scenarios where sensor nodes are implanted replacement of battery is impractical [39,40]. ...
Thesis
The internet of things (IoT) is one of the physical networks that merges different technologies along Wi-Fi, Bluetooth and Cellular on one platform. The IoT for medical health care, is known as internet of medical things (IoMT), needs high data, high speed, and a long battery life along with reliable connectivity. The IoMT plays an important role in improving the healthcare of patients by increasing the accuracy and efficiency. There are many challenges in IoMT due to resource-constrained nature of the sensor driven wearable devices. The main challenge for IoMT is the energy drain and battery charge consumption in tiny size sensor based devices. During charging the charges that are stored in battery and these charges are not fully utilized due to non-linearity of discharging process. The unused charges can be utilized if some idle time is introduced to utilize them and extend battery lifetime. The idle time required to recover these unusable charges is known as recovery effect. The thesis contribution is two-fold. First, four layered architecture of IoMT is proposed. Second, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recover those which are unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Besides, the transition of states are modelled by deterministic mealy finite state machine. Extensive simulation setup is built by considering convex optimization tool in MALTAB. The proposed ABA is also compared with other state of the art existing method named, BRLE. Finally, the proposed ABA resolve the issue of unplanned outages, energy hole and increase the lifetime of battery powered IoMT devices in pervasive healthcare.
... Further it is optimized based on central composite model which further improves the quality of services in ECG telemetry. A fully implanted telemetry system is reported in Joseph et.al [4] research model for long term monitoring. Proposed model considers he subcutaneous glucose level to measure the diabetes range using membrane sensors and oxygen electrodes. ...
Article
The concept of biotelemetry evolved to assess the physiological data of a person under normal circumstances without obstruction to the patient. It allows evaluation of risk factors influence the person health on their daily activities. Recent technology development enhances the features of biotelemetry as wireless applications which allows the physician to monitor the patient health remotely. Biotelemetry obtains great values in hospitals by continues monitoring process as it reduces the burden to physician regular checkups. ECG telemetry is one of the predominant biotelemetry application employed to monitor the heart rate and arrhythmias. Proposed research work focusses the key features of ECG telemetry and provides an internet of things (IoT) based application to monitor the patient health in an indoor and outdoor environment. Along with medical terms, data management parameters are analyzed in the experimental section to emphasize the proposed work performance.
... Continuous movement monitoring sensor  Manage diabetes  Prevention of diabetic foot ulcer by monitoring daily activities and blood glucose [70] Glucose monitoring in individuals with diabetes Glucose clamps and spontaneous glucose excursions  Long-term implanted for sensor/telemety system for glucose monitoring  glucose control and management [71] Sensor-based method for glucose monitoring FreeStyle Libre Flash glucose monitoring system  Accuracy, safety and acceptability of the glucose monitoring system in the paediatric population is proposed  Accuracy, safety and user acceptability are discussed [72] Continuous glucose monitoring sensors [81] IoT-based blood glucose monitoring system Blood glucose sensor  Prototype of an IoT-based glucose testing meter  low-cost and energy-efficient [82] Glycemic control using IoT Photo-acoustic signal  Non-invasive intelligent blood glucose level monitoring system  Alert signals using IoT is provided [83] Continuous glucose monitoring system Blood glucose sensor  Fog computing, blockchain and iot-based monitoring system  Rapid, flexible, scalable, and low-cost mHealth system [84] IoT based detection of hypoglycemia Blood glucose, activities, and dietary  IoT and big data analytics platform [85] IoT based intelligent diabetes management system Activity trackers, continuous glucose monitoring, and implantable defibrillators. ...
Full-text available
Article
span>The latest advances and trends in information technology and communication have a vital role in healthcare industries. Theses advancements led to the Internet of Medical Things (IoMT) which provides a continuous, remote and real-time monitoring of patients. The IoMT architectures still face many challenges related to the bandwidth, communication protocols, big data and data volume, flexibility, reliability, data management, data acquisition, data processing and analytics availability, cost effectiveness, data security and privacy, and energy efficiency. The goal of this paper is to find feasible solutions to enhance the healthcare living facilities using remote health monitoring (RHM) and IoMT. In addition, the enhancement of the prevention, prognosis, diagnosis and treatment abilities using IoMT and RHM is also discussed. A case study of monitoring the vital signs of diabetic patients using real-time data processing and IoMT is also presented . </span
... Whilst such a protracted stabilisation delay is an option, it would only seem so if long term implantation is contemplated, and that demands a high level of confidence in a sensor that needs surgical implantation. A subsequent six month human study with this sensor demonstrated stable oxygen compensated glucose tracking [97]. The collagen capsule imposed response delay was of the order of 10 min., so workable for clinical purposes. ...
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The disruptive action of an acute or critical illness is frequently manifest through rapid biochemical changes that may require continuous monitoring. Within these changes, resides trend information of predictive value, including responsiveness to therapy. In contrast to physical variables, biochemical parameters monitored on a continuous basis are a largely untapped resource because of the lack of clinically usable monitoring systems. This is despite the huge testing repertoire opening up in recent years in relation to discrete biochemical measurements. Electrochemical sensors offer one of the few routes to obtaining continuous readout and, moreover, as implantable devices information referable to specific tissue locations. This review focuses on new biological insights that have been secured through in vivo electrochemical sensors. In addition, the challenges of operating in a reactive, biological, sample matrix are highlighted. Specific attention is given to the choreographed host rejection response, as evidenced in blood and tissue, and how this limits both sensor life time and reliability of operation. Examples will be based around ion, O2, glucose, and lactate sensors, because of the fundamental importance of this group to acute health care.
... The monitoring application in the smartphone is able to give the visualization of the measured values. A long-term monitoring system with an implanted sensor for glucose monitoring for diabetic patients is proposed in [19]. The glucose level of the individuals is updated to the remote server every 2 minutes. ...
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Conference Paper
Internet of Things (IoT) plays a vital role in the field of healthcare. The development of smart sensors, smart devices, advanced lightweight communication protocols made the possibility of interconnecting medical things to monitor biomedical signals and diagnose the diseases of patients without human intervention and termed as Internet of Medical Things (IoMT). This paper portrays an overview of Internet of Medical Things based remote healthcare, tracking ingestible sensors, mobile health, smart hospitals, enhanced chronic disease treatment.
... An important consideration in the light of the advanced nanoscale architect-led building-blocks-in technology provides essential solutions for a significant number of the daily problems of the environment, energy, healthcare, and medicine. These nanomaterials showed evidence to be used as effective platforms for biomolecule immobilization with the desired orientation, high biological activity, and improved biosensor properties [44][45][46][47][48][49][50][51][52][53][54][55]. The developments in nano-/bio-technology, and bioengineering have been controlled and enabled the application of nanosensors/biosensors in numerous areas including industry, agriculture, environmental science, and pharmaceuticals [20, ...
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Early detection and easy continuous monitoring of diseases are of particular interest for controlling healthcare advances and developing effective medical treatments to reduce the high global burden and unawareness regarding advanced diseases. Under an ever increasing demand on biosensor design reliability for early-stage recognition of infectious agents or contagious diseases and potential proteins, nanoscale manufacturing designs were developed effective nanodynamic sensing assays and compact wearable devices. Dynamic developments of biosensor technology are also vital to detect and monitor advanced diseases, such as human immunodeficiency virus, hepatitis B virus, hepatitis C virus, diabetes, cancer, liver diseases, cardiovascular diseases, tuberculosis, and central nervous system disorders. In particular, nanoscale biosensor designs have indispensable contribution to improvement of health concerns by early detection of disease, monitoring ecological and therapeutic agents, and maintaining high safety level in food and cosmetics. This review reports an overview of biosensor designs and their feasibility to early investigation, detection, and quantitative determination of many advanced diseases. Biosensor strategies are highlighted to demonstrate the influence of nano-compact and lightweight designs on accurate analyses and inexpensive sensing assays. To date, the effective and foremost developments in various nanodynamic designs associated with simple analytical facilities and procedures remain challenging. Given the wide evolution of biosensor market requirements and the growing demand in the creation of early-stage and real-time monitoring assays, precise output signals, and easy-to-wear and self-regulating analyses of diseases, innovations in biosensor designs based on novel fabrication of nanostructured platforms with active surface functionalities would produce beneficial merits to remarkable biosensor devices. This review offers evidence for researchers and inventors to focus on biosensor challenge and improve fabrication of nanobiosensors to revolutionize consumer and healthcare markets.
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Present Scenario is the era of smart technologies every person depends on them directly or indirectly. To increase the productivity of technology Wireless Sensor Technologies (WSN) are used. With this changing situation of these new beginnings in the field of technology, the world gets novel technology in the form of the Internet of Things (IoT). IoT is the latest development that allows everything is to be connected to the internet. This technology is transforming different sectors with the latest openings and potentials. The Healthcare industry is one of them that has a direct relation with the life of people globally. Internet of Things the modern technology assists to deal with health-related problems, chronic diseases, personal care for old people who are not able to help themselves. In today’s lifestyle diabetes is the main issue in healthcare. In India, nearly 30 million of the population have been suffering from this chronic disease. This becomes the global epidemic and a long-term metabolic disorder in which the blood glucose level fluctuates and due to this, the production of insulin is inadequate in the body of patients. This chapter presents the role of the Internet of Things in healthcare for monitoring the situation of diabetes patients. This chapter also proposed the edge-based architecture for IoT related to healthcare. Benefits and challenges with its solutions of this technology for diabetic patients were also discussed. At last, the latest security trends with their solutions for managing healthcare data in the Internet of Things were discussed.KeywordsChallengesDiabetic PatientsHealthcareInternet of Things (IoT)SecurityTechnology
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To improve healthcare services, health monitoring-based Internet of things (IoT) is used recently. Diabetes mellitus is an acute metabolic syndrome causing elevated blood sugar levels eventually leading to frequent urination. Failure in the control of diabetes may lead to short as well as long-term complications. So to care daily conditions, frequent observations are necessary. In this work, a continuous glucose monitoring (CGM) system-based IoT is utilized for feasibility study in diabetes patients. This system surely helps to manage health in diabetes patients.
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This work proposes a health care monitoring method of femoral fractured bone healing as a novel application, based on the oblique incidence case and transmission of electromagnetic waves. The difference between the transmitted average power densities of normal and fractured bone cases is used to monitor the bone healing status. The study demonstrates the in-to-out body channel characteristics regarding the electromagnetic wave propagation and angle of incidence on a multi-layered human tissue in the range of frequencies from 100 MHz to 10 GHz. The monitoring method is presented by the analysis of electromagnetic wave propagation through a multilayer model and simulated by the CST Microwave studio. Fully insulated planar and meander half-wave dipole antennas are designed and used to simulate both types of bone fracture healing. Results show good monitoring of common types of fractures. Verification is carried out by experimental measurements on non-living animal tissues.
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Background: The Benin health system has challenges including: (i) the need to provide quality health care at low cost to a growing population, (ii) the reduction of patients' hospitalization time, (iii) and the optimization presence time of the nursing staff. Such challenges can be solved by remote monitoring of patients. Methodology: To achieve this, five steps were followed. 1) The identification of the different characteristics of the WBAN systems and the physiological parameters monitored on a patient. 2) The modeling of the national RIMP architecture in a cloud of Technocenters. 3) Cross analysis between characteristics and functional requirements identified. 4) The simulation of the functionality of each Technocenter through: a) the choice of design approach inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the study of the choice of communication technology and different architectures of sensor networks. 5) An estimate of the material resources of the national RIMP according to physiological parameters. Findings: The main result is that it has designed a National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, for the Benin health system. Conclusion: The implementation of the RNIMP will contribute to improve the care of patients in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance.
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Stem cell derived insulin producing cells or islets have shown promise in reversing Type 1 Diabetes (T1D), yet successful transplantation currently necessitates long-term modulation with immunosuppressant drugs. An alternative approach to avoiding this immune response is to utilize an islet macroencapsulation device, where islets are incorporated into a selectively permeable membrane that can protect the transplanted cells from acute host response, whilst enabling delivery of insulin. These macroencapsulation systems have to meet a number of stringent and challenging design criteria in order to achieve the ultimate goal of reversing T1D. In this progress report, the design considerations and functional requirements of macroencapsulation systems are reviewed, specifically for stem-cell derived islets (SC-islets), highlighting distinct design parameters. Additionally, a perspective on the future for macroencapsulation systems is given, and how incorporating continuous sensing and closed-loop feedback can be transformative in advancing toward an autonomous biohybrid artificial pancreas.
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Background Conventional home blood glucose measurements require a sample of blood that is obtained by puncturing the skin at the fingertip. To avoid the pain associated with this procedure, there is high demand for medical products that allow glucose monitoring without blood sampling. In this review article, all such products are presented. Methods In order to identify such products, four different sources were used: (1) PubMed, (2) Google Patents, (3) Diabetes Technology Meeting Startup Showcase participants, and (4) experts in the field of glucose monitoring. The information obtained were filtered by using two inclusion criteria: (1) regulatory clearance, and/or (2) significant coverage in Google News starting in the year 2016, unless the article indicated that the product had been discontinued. The identified bloodless monitoring products were classified into three categories: (1) noninvasive optical, (2) noninvasive fluid sampling, and (3) minimally invasive devices. Results In total, 28 noninvasive optical, 6 noninvasive fluid sampling, and 31 minimally invasive glucose monitoring products were identified. Subsequently, these products were characterized according to their regulatory, technological, and consumer features. Products with regulatory clearance are described in greater detail according to their advantages and disadvantages, and with design images. Conclusions Based on favorable technological features, consumer features, and other advantages, several bloodless products are commercially available and promise to enhance diabetes management. Paths for future products are discussed with an emphasis on understanding existing barriers related to both technical and non-technical issues.
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The promise of real-time detection and response to life-crippling diseases brought by the Implantable Internet of Medical Things (IIoMT) has recently spurred substantial advances in implantable technologies. Yet, existing medical devices do not provide at once the miniaturized end-to-end body monitoring, wireless communication and remote powering capabilities to implement IIoMT applications. This paper fills the existing research gap by presenting U-Verse, the first FDA-compliant rechargeable IIoMT platform packing sensing, computation, communication, and recharging circuits into a penny-scale platform. Extensive experimental evaluation indicates that U-Verse (i) can be wirelessly recharged and can store energy several orders of magnitude more than state-of-theart capacity in tens of minutes; (ii) with one single charge, it can operate from few hours to several days. Finally, U-Verse is demonstrated through (i) a closed-loop application that sends data via ultrasounds through real porcine meat; and (ii) a real-time reconfigurable pacemaker.
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Diabetes Mellitus is a group of diseases characterized by high blood glucose levels due to patients' inability to produce sufficient insulin. Current interventions often require implants that can detect and correct high blood glucose levels with minimal patient intervention. However, these implantable technologies have not reached their full potential in vivo due to the foreign body response and subsequent development of fibrosis. Therefore, for long-term function of implants, modulating the initial immune response is crucial in preventing the activation and progression of the immune cascade. This review discusses the different molecular mechanisms and cellular interactions involved in the activation and progression of foreign body response (FBR) and fibrosis, specifically for implants used in diabetes. We also highlight the various strategies and techniques that have been used for immunomodulation and prevention of fibrosis. We investigate how these general strategies have been applied to implants used for the treatment of diabetes, offering insights on how these devices can be further modified to circumvent FBR and fibrosis.
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Background Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. Objectives This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. Methods We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. Results The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. Conclusion We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.
Conference Paper
In this paper, the scope of harmonic RFID for using in body area network based batteryless implants is evaluated. The scope of conventional RFID becomes limited under very low tag modulation frequency and/or in strong cluttered environment. Both the conditions are applicable for biological implants. However, use of harmonic RFID can solve both the problems by operating at two different frequencies. Non Linear Transmission Line is the key element in generating the other frequency / second harmonic of the interrogation signal. A detailed design procedure is described to construct a lumped element based non linear transmission line. A single antenna based harmonic RFID tag design is possible with minimum number of components added to the conventional RFID tag circuit. The harmonic tag holds significant potential for very low data rate tag modulation and in highly cluttered environments.
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While over half a century has passed since the introduction of enzyme glucose biosensors by Clark and Lyons, this important field has continued to be the focus of immense research activity. Extensive efforts during the past decade have led to major scientific and technological innovations towards tight monitoring of diabetes. Such continued progress toward advanced continuous glucose monitoring platforms, either minimal- or non-invasive, holds considerable promise for addressing the limitations of finger-prick blood testing toward tracking glucose trends over time, optimal therapeutic interventions, and improving the life of diabetes patients. However, despite these major developments, the field of glucose biosensors is still facing major challenges. The scope of this review is to present the key scientific and technological advances in electrochemical glucose biosensing over the past decade (2010–present), along with current obstacles and prospects towards the ultimate goal of highly stable and reliable real-time minimally-invasive or non-invasive glucose monitoring. After an introduction to electrochemical glucose biosensors, we highlight recent progress based on using advanced nanomaterials at the electrode–enzyme interface of three generations of glucose sensors. Subsequently, we cover recent activity and challenges towards next-generation wearable non-invasive glucose monitoring devices based on innovative sensing principles, alternative body fluids, advanced flexible materials, and novel platforms. This is followed by highlighting the latest progress in the field of minimally-invasive continuous glucose monitoring (CGM) which offers real-time information about interstitial glucose levels, by focusing on the challenges toward developing biocompatible membrane coatings to protect electrochemical glucose sensors against surface biofouling. Subsequent sections cover new analytical concepts of self-powered glucose sensors, paper-based glucose sensing and multiplexed detection of diabetes-related biomarkers. Finally, we will cover the latest advances in commercially available devices along with the upcoming future technologies.
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This article’s objective is to discuss the anatomical and technical challenges associated with designing subcutaneously implanted antennas for continuous wireless medical telemetry. One challenge in this design process is the connection between simulation setup and the anatomical layering of tissues in biological systems. This article presents an overview of the layers of tissues associated with subcutaneous implant (epidermis, dermis, and hypodermis) as well as the measured dielectric properties of these tissues. Additionally, this study presents the design and measurement of an implantable dual-band antenna operating on the Wireless Medical Telemetry Service (WMTS) (1.395 GHz to 1.432 GHz) and Industrial, Scientific, and Medical (ISM) (2.4 GHz to 2.5 GHz) communication bands. This antenna was validated ex vivo and in vivo using porcine animal models. The antenna has a simulated bandwidth of 2.3% for WMTS and 5.7% for ISM bands. Additionally, the antenna saw a 300 MHz shift, ex vivo, to the right for WMTS while not having a shift for the ISM band. For in vivo validation, two antennas were made and tested in a porcine animal model and both show an adequate transmission range of 10 meters.
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Implementation of an IoT system for the detection of Covid-infected 19. A sample of 300 people participated in this experiment. They are assigned wearables that they must wear for a period of one week throughout the day. The data is retrieved in real time at an interval of 60 minutes. These wearables are equipped with temperature, heart rate and GPS sensors to determine people inside or outside virus outbreaks. The data is then retrieved and sent to Oracle Cloud. Here they are processed according to Machine Learning algorithms and sent predictions to the subjects' family doctors, but also to the national health system. If patients are suspected of being infected with the virus, then they should be contacted as soon as possible for testing.
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Electronic Health Records (EHRs) enabled to store and process data recorded by sensors would mean standard-based personalization of medical services and would be a step further to guaranteeing a seamless care access. However, sensor data is subject to several sources of faults and errors which may further lead to imprecise or even incorrect and misleading answers. Thus, it is pivotal to ensure the quality of data collected from e.g. wearable sensors in wireless sensor networks for it to be used in a formal EHR. This article gives comparison of different data-driven models in cleaning eHealth sensor data from wireless sensor networks in order to make sure the data collected is precise and relevant and as such, may be included into a formal EHR. Furthermore, it then suggests optimization of the selected models with the goal of improving their results.
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A reflective optical sensor based on surface plasmon resonance (SPR) was proposed to measure the glucose concentration. The gold film was sputtered on the surface of plastic cladding optical fiber to excite SPR and reflect light at the same time, making the SPR sensor highly sensitive to the variation of surrounding refractive index. Then, glucose oxidase (GOD) was adopted as the sensitive film of glucose and covalently bonded on the gold film. The enzymatic reaction between the GOD and the glucose led to the change of refractive index around the optical fiber sensor, and then cause a shift of the SPR spectrum. Therefore, the glucose concentration could be obtained by monitoring the shift of the resonance wavelength. Experimental results showed that the measurement sensitivity of the SPR sensor could reach 85.4 nm/(mg/mL) (for glucose concentration) with good selectivity and stability, which has great potential application in biomedicine and human health monitoring.
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The article published by Kevin Cowart in this issue of the Journal of Diabetes Science and Technology (JDST) is a detailed overview of the clinical trial data and analysis used to demonstrate the safety and effectiveness of the Eversense continuous glucose monitoring (CGM) System for regulatory approval and clinical acceptance. The article describes the published study results for safety, accuracy, reliability, ease of insertion/removal, adverse events, and ease of diabetes patient-use for controlling their glucose levels short and long term. The author nicely compares Eversense CGM System safety and performance with the short-term subcutaneous tissue CGM systems being commercialized by Dexcom, Medtronic Diabetes, and Abbott Diabetes. This comparison may help the clinician define which type of patient with diabetes might benefit the most from the long-term implantable CGM system. The majority of studied patients describe a positive experience managing their diabetes with the Eversense CGM System and request implantation of a new sensor 90 or 180 days later.
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Benin health system is facing many challenges as: (i) affordable high-quality health care to a growing population providing need, (ii) patients’ hospitalization time reduction, (iii) and presence time of the nursing staff optimization. Such challenges can be solved by remote monitoring of patients. To achieve this, five steps were followed. 1) Identification of the Wireless Body Area Network (WBAN) systems’ characteristics and the patient physiological parameters’ monitoring. 2) The national Integrated Patient Monitoring Network (RIMP) architecture modeling in a cloud of Technocenters. 3) Cross-analysis between the characteristics and the functional requirements identified. 4) Each Technocenter’s functionality simulation through: a) the design approach choice inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the communication technology and different architectures of sensor networks choice studying. 5) An estimate of the material resources of the national RIMP according to physiological parameters. A National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, was designed for Beninese health system. The implementation of the RNIMP will contribute to improve patients’ care in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance.
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Monitoring people's health is useful for enhancing the care provided to them by others or self-management of health. This article is a survey of the latest research on monitoring parameters indicating a person's current health or having potential to affect the person's health in future, using various physical sensors. These sensors include accelerometers, gyroscopes, electromyography sensors, fiber optic sensors, textile electrodes, thermistors, infrared sensors, force sensors, and photo diodes. The health parameters monitored include heart rate, respiration rate, weight, body mass index, calories burnt, pressure distribution, diet, blood pressure, blood glucose, oxygen saturation, posture, duration of sleep, quality of sleep, hand movement, body temperature, skin conductance, exposure to ultraviolet light, adherence to medication-intake schedule, gait characteristics, and steps taken. The population monitored includes elderly people, miners, stroke survivors, osteoarthritis patients, people suffering from anorexia nervosa, obese people, people with Parkinson's disease, people having panic attacks, and wheelchair users.
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The skin, as the largest and most accessible organ in the human body, contains biofluids rich in biomarkers useful not only in diagnosis and monitoring of diseases, but also in profiling an individual’s wellbeing. Advancements in micro- and nanotechnology research have underpinned the development of multifunctional wearable sensing devices. Those sensors may allow monitoring of physiological parameters from different skin sections such as epidermis, dermis and hypodermis by sampling various bodily fluids. Our review summarizes current advances in wearable biosensors for on-skin analysis of sweat, transdermal monitoring of interstitial fluid and analysis of subcutaneous fluids via implanted devices. The review is divided into three main parts describing biosensors acting on the different skin sections. Each part focuses on recent scientific and technological advancements in the wearable biosensing field by highlighting critical challenges as well as providing information on how these barriers are being addressed by the research community.
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Aims/hypothesis: We explored the epidemiology of hypoglycaemia in individuals with insulin-treated diabetes by testing the hypothesis that diabetes type and duration of insulin treatment influence the risk of hypoglycaemia. Materials and methods: This was an observational study over 9-12 months in six UK secondary care diabetes centres. Altogether 383 patients were involved. Patients were divided into the following three treatment groups for type 2 diabetes: (1) sulfonylureas, (2) insulin for <2 years and (3) insulin for >5 years, and into two treatment groups for type 1 diabetes, namely <5 years disease duration and >15 years disease duration. Self-reported (mild and severe) and biochemical episodes (interstitial glucose <2.2 mmol/l using continuous glucose monitoring) were recorded. Results: Mild hypoglycaemia in type 2 diabetic patients on insulin for <2 years was less frequent than in type 1 patients with <5 years disease duration (mean rate: 4 vs 36 episodes per subject-year, p < 0.001). In type 2 diabetic patients treated with sulfonylureas or insulin for <2 years, no differences were observed in the proportion experiencing severe hypoglycaemia (7 vs 7%, difference 0 [95% CI: -7 to 9%]), mild symptomatic (39 vs 51%, difference 12 [-3 to 25%]) or interstitial glucose <2.2 mol/l (22 vs 20%, difference 2 [-13 to 10%]). Severe hypoglycaemia rates were comparable in patients with type 2 diabetes on sulfonylureas or insulin < 2 years (0.1 and 0.2 episodes per subject-year) and far less frequent than in type 1 diabetes (<5 years group, 1.1; >15 years group, 3.2.episodes per subject-year). Conclusions/interpretation: During early insulin use in type 2 diabetes, the frequency of hypoglycaemia is generally equivalent to that observed in patients treated with sulfonylureas and considerably lower than during the first 5 years of treatment in type 1 diabetes.
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The feasibility of continuous long-term glucose monitoring in humans has not yet been demonstrated. Enzyme-based electrochemical glucose sensors with telemetric output were subcutaneously implanted and evaluated in five human subjects with type I diabetes. Subject-worn radio-receiver data-loggers stored sensor outputs. Every 1-4 weeks the subject's glucose levels were manipulated through the full clinical range of interest using standard protocols. Reference blood glucose samples were obtained every 5-10 min and analyzed in our hospital clinical laboratory and/or on glucose meters. The sensor data were evaluated versus the reference data by linear least squares regression and by the Clarke Error Grid method. After surgical explantation and device inspection, the tissue-sensor interface was evaluated histologically. The remaining sensor-membranes were also recalibrated for comparison with preimplant performance. Four of the five glucose sensors tracked glucose in vivo. One sensor responded to manipulated glucose changes for 6.2 months with clinically useful performance (>/=90% of sensor glucose values within the A and B regions of the Clarke Error Grid). For this sensor, recalibration was required every 1-4 weeks. The other three transiently responding sensors had electronic problems associated with packaging failure. The remaining sensor never tracked glucose because of failure to form any sustained connection to adjacent subcutaneous tissue. Thus, stable, clinically useful sensor performance was demonstrated in one of five subjects with diabetes for a sustained interval of greater than 6 months. While this glucose sensor implant technology shows promise in humans, it needs to be made more reliable and robust with respect to device packaging and sensor-tissue connection.
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The objective of this study was to use a subcutaneous continuous glucose sensor to determine time differences in the dynamics of blood glucose and interstitial glucose. A total of 14 patients with type 1 diabetes each had two sensors (Medtronic/MiniMed CGMS) placed subcutaneously in the abdomen, acquiring data every 5 min. Blood glucose was sampled every 5 min for 8 h, and two liquid meals were given. A smoothing algorithm was applied to the blood glucose and interstitial glucose curves. The first derivatives of the glucose traces defined and quantified the timing of rises, peaks, falls, and nadirs. Altogether, 24 datasets were used for the analysis of time differences between interstitial and blood glucose and between sensors in each patient. Time differences between blood and interstitial glucose ranged from 4 to 10 min, with the interstitial glucose lagging behind blood glucose in 81% of cases (95% CIs 72.5 and 89.5%). The mean (+/-SD) difference between the two sensors in each patient was 6.7 +/- 5.1 min, representing random variation in sensor response. In conclusion, there is a time lag of interstitial glucose behind blood glucose, regardless of whether glycemia is rising or falling, but intersensor variability is considerable in this sensor system. Comparisons of interstitial and blood glucose kinetics must take statistical account of variability between sensors.
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Background The value of continuous glucose monitoring in the management of type 1 diabetes mellitus has not been determined. Methods In a multicenter clinical trial, we randomly assigned 322 adults and children who were already receiving intensive therapy for type 1 diabetes to a group with continuous glucose monitoring or to a control group performing home monitoring with a blood glucose meter. All the patients were stratified into three groups according to age and had a glycated hemoglobin level of 7.0 to 10.0%. The primary outcome was the change in the glycated hemoglobin level at 26 weeks. Results The changes in glycated hemoglobin levels in the two study groups varied markedly according to age group (P=0.003), with a significant difference among patients 25 years of age or older that favored the continuous-monitoring group (mean difference in change, −0.53%; 95% confidence interval [CI], −0.71 to −0.35; P<0.001). The between-group difference was not significant among those who were 15 to 24 years of age (mean difference, 0.08; 95% CI, −0.17 to 0.33; P=0.52) or among those who were 8 to 14 years of age (mean difference, −0.13; 95% CI, −0.38 to 0.11; P=0.29). Secondary glycated hemoglobin outcomes were better in the continuous-monitoring group than in the control group among the oldest and youngest patients but not among those who were 15 to 24 years of age. The use of continuous glucose monitoring averaged 6.0 or more days per week for 83% of patients 25 years of age or older, 30% of those 15 to 24 years of age, and 50% of those 8 to 14 years of age. The rate of severe hypoglycemia was low and did not differ between the two study groups; however, the trial was not powered to detect such a difference. Conclusions Continuous glucose monitoring can be associated with improved glycemic control in adults with type 1 diabetes. Further work is needed to identify barriers to effectiveness of continuous monitoring in children and adolescents. (ClinicalTrials.gov number, NCT00406133.)
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Background: Estimates for delays in the interstitial fluid (ISF) glucose response to changes in blood glucose (BG) differ substantially among research groups. We review these findings along with arguments that continuous glucose monitoring (CGM) devices used to measure ISF delay contribute to the variability. We consider the impact of the ISF delay and review approaches to correct for it, including strategies pursued by the manufacturers of these devices. The focus on how the manufacturers have approached the problem is motivated by the observation that clinicians and researchers are often unaware of how the existing CGM devices process the ISF glucose signal. Methods: Numerous models and simulations were used to illustrate problems related to measurement and correction of ISF glucose delay. Results: We find that (1) there is no evidence that the true physiologic ISF glucose delay is longer than 5-10 min and that the values longer than this can be explained by delays in CGM filtering routines; (2) the primary impact of the true ISF delay is on sensor calibration algorithms, making it difficult to estimate calibration factors and offset (OS) currents; (3) inaccurate estimates of the sensor OS current result in overestimation of sensor glucose at low values, making it difficult to detect hypoglycemia; (4) many device companies introduce nonlinear components into their filters, which can be expected to confound attempts by investigators to reconstruct BG using linear deconvolution; and (5) algorithms advocated by academic groups are seldom compared to algorithms pursued by industry, making it difficult to ascertain their value. Conclusions: The absence of any direct comparisons between existing and new algorithms for correcting ISF delay and sensor OS current is, in part, due to the difficulty in extracting relevant details from industry patents and/or extracting unfiltered sensor signals from industry products. The model simulation environment, where all aspects of the signal can be derived, may be more appropriate for developing new filtering and calibration strategies. Nevertheless, clinicians, academic researchers, and the industry would benefit from collaborating when evaluating those strategies.
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Since its introduction three decades ago, self-monitoring of blood glucose (SMBG) using finger-stick blood samples, test strips, and portable meters has aided diabetes management, principally by enabling patients—particularly those treated with insulin—to become full partners along with health professionals in striving for excellent glycemic control. Over time the use of glucose meters has become easier and faster with smaller and smaller blood samples yielding results in a matter of seconds. For this reason, glucose meters are now increasingly used in hospital wards, intensive care units, and other facilities such as dialysis units and infusion centers to provide point-of-care results that would take much longer through routine laboratory channels. This technology has largely taken the guess work out of diabetes management. Without such technology, intensive glucose control such as that achieved in the Diabetes Control and Complications Trial may not have been demonstrated to prevent or decrease microvascular complications; insulin pump therapy would not really be practical; and hypoglycemia would remain an even greater source of anxiety for patients and their families than it already is. We have come to rely so much on finger-stick glucose that it is easy to forget its limitations. In considering this we will discuss accuracy, specificity, and, in light of those, inappropriate usage. ### Accuracy Although there is no universally binding standard, guidelines issued by the International Organization for Standardization (ISO) are widely acknowledged. ISO guideline 15197 suggests that for glucose levels <75 mg/dl, a meter should read within 15 mg/dl of the reference sample, and for levels ≥75 mg/dl, the reading should be within 20%. A meter also should be able to meet these targets in at least 95% of the samples tested (1). Several examples serve to illustrate the implications of this degree of imprecision. Assuming a meter does indeed meet the ISO guideline, …
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Background: There has been considerable debate on what constitutes a good hypoglycemia (Hypo) detector and what is the accuracy required from the continuous monitoring sensor to meet the requirements of such a detector. The performance of most continuous monitoring sensors today is characterized by the mean absolute relative difference (MARD), whereas Hypo detectors are characterized by the number of false positive and false negative alarms, which are more relevant to the performance of a Hypo detector. This article shows that the overall accuracy of the system and not just the sensor plays a key role. Methods: A mathematical model has been developed to investigate the relationship between the accuracy of the continuous monitoring system as described by the MARD, and the number of false negatives and false positives as a function of blood glucose rate change is established. A simulation method with N = 10,000 patients is used in developing the model and generating the results. Results: Based on simulation for different scenarios for rate of change (0.5, 1.0, and 5.0 mg/dl per minute), sampling rate (from 1, 2.5, 5, and 10 minutes), and MARD (5, 7.5, 10, 12.5, and 15%), the false positive and false negative ratios are computed. The following key results are from these computations. 1. For a given glucose rate of change, there is an optimum sampling time. 2. The optimum sampling time as defined in the critical sampling rate section gives the best combination of low false positives and low false negatives. 3. There is a strong correlation between MARD and false positives and false negatives. 4. For false positives of <10% and false negatives of <5%, a MARD of <7.5% is needed. Conclusions: Based on the model, assumptions in the model, and the simulation on N = 10,000 patients for different scenarios for rate of glucose change, sampling rate, and MARD, it is concluded that the false negative and false positive ratio will vary depending on the alarm Hypo threshold set by the patient and the MARD value. Also, to achieve a false negative ratio <5% and a false positive ratio <10% would require continuous glucose monitoring to have an MARD < or =7.5%.
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This study introduces a new method for graphical and numerical evaluation of time lags typically associated with subcutaneous glucose sensing, based on Poincaré-type plot and a maximum statistical agreement criterion. The proposed method is illustrated by retrospective analysis of 56 continuous glucose monitor (CGM) time series collected by the FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA) from 28 patients with type 1 diabetes mellitus, each wearing simultaneously two sensors (on arm and abdomen) and parallel reference blood glucose (BG) collected with a reference YSI (Yellow Springs, OH) analyzer every 15 min. The average duration of a time series was 111 h; there were approximately 10,000 sensor-reference data pairs. When sliding in time CGM readings versus BG, the point of minimal spread of a Poincaré-type plot marks visually the time of CGM delay. The same point is numerically estimated by minimizing the distance between BG and CGM readings. The average observed time lag between reference BG and CGM was 12.5 min. Stratified by BG rate of change, the time lag was longer (16.8 min) when BG was falling, compared to steady or rising BG (11.7 min and 9.9 min, respectively) (P < 0.005). The time lags at the two sensor locations were not significantly different: 12.4 min on the arm, 12.6 min on the abdomen. In this data set, substantial blood-to-sensor time delays were observed, possibly because of both blood-to-interstitial glucose transport and instrumental delay. Analysis of BG-CGM co-dynamics that is free from mathematical approximation of glucose fluctuations resulted in convenient visualization and numerical estimation of these delays.
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An important question about blood glucose control in diabetes is, Can present and future blood glucose values be predicted from recent blood glucose history? If this is possible, new continuous blood glucose monitoring technologies under development may lead to qualitatively better therapeutic capabilities. Not only could continuous monitoring technologies alert a user when a hypoglycemic episode or other blood glucose excursion is underway, but measurements may also provide sufficient information to predict near-future blood glucose values. A predictive capability based only on recent blood glucose history would be advantageous because there would be no need to involve models of glucose and insulin distribution, with their inherent requirement for detailed accounting of vascular glucose loads and insulin availability. Published data analyzed here indicate that blood glucose dynamics are not random, and that blood glucose values can be predicted, at least for the near future, from frequently sampled previous values. Data useful in further exploring this concept are limited, however, and an appeal is made for collection of more.
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The changes in plasma glucose concentration and in interstitial glucose concentration, determined with a miniaturized subcutaneous glucose sensor, were investigated in anesthetized nondiabetic rats. Interstitial glucose was estimated through two different calibration procedures. First, after a glucose load, the magnitude of the increase in interstitial glucose, estimated through a one-point calibration procedure, was 70% of that in plasma glucose. We propose that this is due to the effect of endogenous insulin on peripheral glucose uptake. Second, during the spontaneous secondary decrease in plasma glucose after the glucose load, interstitial glucose decreased faster than plasma glucose, which may also be due to the effect of insulin on peripheral glucose uptake. Third, during insulin-induced hypoglycemia, the decrease in interstitial glucose was less marked than that of plasma glucose, suggesting that hypoglycemia suppressed transfer of glucose into the interstitial tissue; subsequently, interstitial glucose remained lower than plasma glucose during its return to basal value, suggesting that the stimulatory effect of insulin on peripheral glucose uptake was protracted. If these observations obtained in rats are relevant to human physiology, such discrepancies between plasma and interstitial glucose concentration may have major implications for the use of a subcutaneous glucose sensor in continuous blood glucose monitoring in diabetic patients.
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The objectives of this study were 1) to construct new error grids (EGs) for blood glucose (BG) self-monitoring by using the expertise of a large panel of clinicians and 2) to use the new EGs to evaluate the accuracy of BG measurements made by patients. To construct new EGs for type 1 and type 2 diabetic patients, a total of 100 experts of diabetes were asked to assign any error in BG measurement to 1 of 5 risk categories. We used these EGs to evaluate the accuracy of self-monitoring of blood glucose (SMBG) levels in 152 diabetic patients. The SMBG data were used to compare the new type 1 diabetes EG with a traditional EG. Both the type 1 and type 2 diabetes EGs divide the risk plane into 8 concentric zones with no discontinuities. The new EGs are similar to each other, but they differ from the traditional EG in several significant ways. When used to evaluate a data set of measurements made by a sample of patients experienced in SMBG, the new type 1 diabetes EG rated 98.6% of their measurements as clinically acceptable, compared with 95% for the traditional EG. The consensus EGs furnish a new tool for evaluating errors in the measurement of BG for patients with type 1 and type 2 diabetes.
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The objective of this study was to introduce continuous glucose-error grid analysis (CG-EGA) as a method of evaluating the accuracy of continuous glucose-monitoring sensors in terms of both accurate blood glucose (BG) values and accurate direction and rate of BG fluctuations and to illustrate the application of CG-EGA with data from the TheraSense Freestyle Navigator. We approach the design of CG-EGA from the understanding that continuous glucose sensors (CGSs) allow the observation of BG fluctuations as a process in time. We account for specifics of process characterization (location, speed, and direction) and for biological limitations of the observed processes (time lags associated with interstitial sensors). CG-EGA includes two interacting components: 1) point-error grid analysis (P-EGA) evaluates the sensor's accuracy in terms of correct presentation of BG values and 2) rate-error grid analysis (R-EGA) assesses the sensor's ability to capture the direction and rate of BG fluctuations. CG-EGA revealed that the accuracy of the Navigator, measured as a percentage of accurate readings plus benign errors, was significantly different at hypoglycemia (73.5%), euglycemia (99%), and hyperglycemia (95.4%). Failure to detect hypoglycemia was the most common error. The point accuracy of the Navigator was relatively stable over a wide range of BG rates of change, and its rate accuracy decreased significantly at high BG levels. Traditional self-monitoring of BG device evaluation methods fail to capture the important temporal characteristics of the continuous glucose-monitoring process. CG-EGA addresses this problem, thus providing a comprehensive assessment of sensor accuracy that appears to be a useful adjunct to other CGS performance measures.
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Quantitative assessment of the dynamic relationship between plasma and interstitial fluid (ISF) glucose and the estimation of the plasma-to-ISF delay are of major importance to determine the accuracy of subcutaneous glucose sensors, an essential component of open- and closed-loop therapeutic systems for type 1 diabetes mellitus (T1DM). The goal of this work is to develop a model of plasma-to-ISF glucose kinetics from multitracer plasma and interstitium data, obtained by microdialysis, in healthy and T1DM subjects, under fasting conditions. A specific experimental design, combining administration of multiple tracers with the microdialysis technique, was used to simultaneously frequently collect plasma and ISF data. Linear time-invariant compartmental modeling was used to describe glucose kinetics from the tracer data because the system is in steady state. A two-compartment model was shown accurate and was identified from both plasma and ISF data. An "equilibration time" between plasma and ISF of 9.1 and 11.0 min (median) in healthy and T1DM subjects, respectively, was calculated. We have demonstrated that, in steady-state condition, the glucose plasma-to-ISF kinetics can be modeled with a linear two-compartment model and that the "equilibration time" between the two compartments can be estimated with precision. Future studies will assess plasma-to-interstitium glucose kinetics during glucose and insulin perturbations in both healthy and T1DM subjects.
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BACKGROUND Long-term microvascular and neurologic complications cause major morbidity and mortality in patients with insulin-dependent diabetes mellitus (IDDM). We examined whether intensive treatment with the goal of maintaining blood glucose concentrations close to the normal range could decrease the frequency and severity of these complications. METHODS A total of 1441 patients with IDDM -- 726 with no retinopathy at base line (the primary-prevention cohort) and 715 with mild retinopathy (the secondary-intervention cohort) were randomly assigned to intensive therapy administered either with an external insulin pump or by three or more daily insulin injections and guided by frequent blood glucose monitoring or to conventional therapy with one or two daily insulin injections. The patients were followed for a mean of 6.5 years, and the appearance and progression of retinopathy and other complications were assessed regularly. RESULTS In the primary-prevention cohort, intensive therapy reduced the adjusted mean risk for the development of retinopathy by 76 percent (95 percent confidence interval, 62 to 85 percent), as compared with conventional therapy. In the secondary-intervention cohort, intensive therapy slowed the progression of retinopathy by 54 percent (95 percent confidence interval, 39 to 66 percent) and reduced the development of proliferative or severe nonproliferative retinopathy by 47 percent (95 percent confidence interval, 14 to 67 percent). In the two cohorts combined, intensive therapy reduced the occurrence of microalbuminuria (urinary albumin excretion of ≥ 40 mg per 24 hours) by 39 percent (95 percent confidence interval, 21 to 52 percent), that of albuminuria (urinary albumin excretion of ≥ 300 mg per 24 hours) by 54 percent (95 percent confidence interval, 19 to 74 percent), and that of clinical neuropathy by 60 percent (95 percent confidence interval, 38 to 74 percent). The chief adverse event associated with intensive therapy was a two-to-threefold increase in severe hypoglycemia. CONCLUSIONS Intensive therapy effectively delays the onset and slows the progression of diabetic retinopathy, nephropathy, and neuropathy in patients with IDDM.
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Certain types of implanted medical devices depend on oxygen supplied from surrounding tissues for their function. However, there is a concern that the tissue associated with the foreign body response to implants may become impermeable to oxygen over the long term and render the implant nonfunctional. We report oxygen flux recordings from electrochemical oxygen sensor devices with wireless telemetry implanted in subcutaneous porcine tissues. The devices remained implanted for up to 13 weeks and were removed with adjacent tissues at specified times for histologic examination. There are four main observations: (1) In the first few weeks after implantation, the oxygen flux to the sensors, or current density, declined to a sustained mean value, having unsynchronized cyclic variations around the mean; (2) The oxygen mass transfer resistance of the sensor membrane was negligible compared to that of the tissue, allowing for a sensitive estimate of the tissue permeability; (3) The effective diffusion coefficient of oxygen in tissues was found to be approximately one order of magnitude lower than in water; and (4) Quantitative histologic analysis of the tissues showed a mild foreign body response to the PDMS sensor membrane material, with capillaries positioned close to the implant surface. Continuous recordings of oxygen flux indicate that the tissue permeability changes predictably with time, and suggest that oxygen delivery can be sustained over the long term.
Chapter
The sections in this article are1The Problem2Background and Literature3Outline4Displaying the Basic Ideas: Arx Models and the Linear Least Squares Method5Model Structures I: Linear Models6Model Structures Ii: Nonlinear Black-Box Models7General Parameter Estimation Techniques8Special Estimation Techniques for Linear Black-Box Models9Data Quality10Model Validation and Model Selection11Back to Data: The Practical Side of Identification
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An implantable sensor capable of long-term monitoring of tissue glucose concentrations by wireless telemetry has been developed for eventual application in people with diabetes. The sensor telemetry system functioned continuously while implanted in subcutaneous tissues of two pigs for a total of 222 and 520 days, respectively, with each animal in both nondiabetic and diabetic states. The sensor detects glucose via an enzyme electrode that is based on differential electrochemical oxygen detection, which reduces the sensitivity of the sensor to encapsulation by the body, variations in local microvascular perfusion, limited availability of tissue oxygen, and inactivation of the enzymes. After an initial 2-week stabilization period, the implanted sensors maintained stability of calibration for extended periods. The lag between blood and tissue glucose concentrations was 11.8 +/- 5.7 and 6.5 +/- 13.3 minutes (mean +/- standard deviation), respectively, for rising and falling blood glucose challenges. The lag resulted mainly from glucose mass transfer in the tissues, rather than the intrinsic response of the sensor, and showed no systematic change over implant test periods. These results represent a milestone in the translation of the sensor system to human applications.
Article
We studied the increased levels of hemoglobins AIa+Ib and AIc in five hospitalized diabetic patients to determine whether changes in diabetic control would cause parallel changes in the levels of these hemoglobins. Before control of diabetes the mean fasting blood sugar for all patients was 343 mg per deciliter (range, 280 to 450), and hemoglobin AIc concentration 9.8 per cent (range, 6.8 to 12.1). During optimal diabetic control the blood sugar concentration was 84 mg per deciliter (range, 70 to 100), and hemoglobin AIc concentration 5.8 per cent (range, 4.2 to 7.6). Hemoglobin AIc concentration appears to reflect the mean blood sugar concentration best over previous weeks to months. The periodic monitoring of hemoglobin AIc levels provides a useful way of documenting the degree of control of glucose metabolism in diabetic patients and provides a means whereby the relation of carbohydrate control to the development of sequelae can be assessed.
Article
An intravenous glucose sensor was implanted in six dogs for 1-15 wk. The glucose sensor is a flexible cylinder, approximately 0.2 cm diam and 30 cm long, with a tip containing immobilized glucose oxidase and catalase coupled to a potentiostatic O2 sensor. The sensor and a similar O2 reference sensor were implanted in the superior vena cava near the entrance of the right atrium. The sensor response was conveyed externally either by a telemetry system implanted nearby, surgically accessed leads, or chronically maintained percutaneous leads. Summing over the six implants, there was a total implantation period of 333 days during which glucose sensors were functional on demand. The sensor response showed agreement with conventionally assayed blood samples after accounting for a response lag. Sensor response to glucose showed little change over the implant period. Biocompatibility, enzyme lifetime, O2 availability, O2 sensor stability, and biochemical interference were not limitations. Results demonstrated that this sensor can function effectively as an implant in dogs for a period of months and has the potential for long-term operation.
Article
An implantable potentiostat-telemetry system for in vivo operation of glucose and oxygen sensors is described. The device con- veys signals from implanted chemical-specific sensors to a remote re- ceiver via radio telemetry. Reference signals encoded in the analog FM transmission allow the receiver to automatically compensate for vari- ability between simultaneously operated transmitters. The implant has several programmable operating modes that provide different signal gain and power consumption. All CMOS circuitry is^ employed, allow- ing operation for up to three months on a single lithium cell. The data collection capabilities of this implantable unit are comparable to those of bench-top instrumentation. Design, fabrication, and operation of the device are described.
Article
Fabrication and operation of a three-electrode oxygen sensor that utilizes potentiostatic instrumentation are described. The long-term stability of the sensor is demonstrated by continuous operation under quasi-physiologic conditions. With further development, this sensor may find application as an implant in the body or in other long-term monitoring situations.
Article
The enzyme electrode-type sensor holds promise as a tool for continuous monitoring of glucose concentration in physiologic systems. Previous designs based on parallel diffusion of glucose and oxygen into the enzyme-containing membrane may, however, have certain disadvantages for in vivo application. A novel sensor configuration is described in which oxygen diffuses into the membrane from two directions while glucose diffuses from only one. This results in sensitivity to glucose concentration over a wide range, even at very low oxygen concentrations. κ 1985 American Chemical Society.
Article
When biosensors are operated continuously, a dynamic delay and a dynamic error relate the sensor signal to the changing analyte concentration. The dynamic delay is the temporal displacement of the signal, or the lag, and is specified solely by properties of the biosensor and external mass transfer. The dynamic error is the difference between the actual concentration and the simultaneous reported concentration and is the product of the dynamic delay and the instantaneous rate of concentration change. In real-time operation of sensors, a maximal dynamic error based on the maximal expected rate of concentration change must be employed to estimate the worst-case error because the actual instantaneous rate is not independently known. Values of dynamic delay and maximal dynamic error that are acceptable in particular monitoring situations can be used in the design of acceptable continuous biosensors. This analysis suggests experimental alternatives to the standard response time approach for sensor characterization that are particularly advantageous for continuously operated biosensors. The concepts are applied here to in vitro operation of a continuous glucose sensor.
Article
Measurement of blood glucose concentration is central to the diagnosis and treatment of diabetes. Although there are large numbers of historic glucose measurements in individuals with diabetes, until recently there have been very few data sets that were recorded continuously or sampled frequently enough to reveal intrinsic blood glucose dynamics, or the change in blood glucose with time. There have even fewer such recordings from individuals not having diabetes to serve as a therapeutic target. As a result, blood glucose dynamics have generally not been used in the diagnosis or treatment of the disease. Although present blood glucose monitoring is based largely on discrete measurements, future monitoring will likely focus on analysis of blood glucose excursions. New measurements are now being obtained, and there is a need for new methods of analysis to extract the maximal information from the data. Several approaches are demonstrated here for characterization of blood glucose dynamics, and a patient profiling system is proposed. An example of new insights is the observation that there are four time scales of blood glucose variations in individuals without diabetes, and these time scales are modified or lost in diabetes.
Article
Homogeneous membranes containing immobilized glucose oxidase and catalase were stored in buffered solutions at 37 degrees C to determine the mechanisms and rates of catalyst inactivation. The experiments were designed so that inactivation occurred homogeneously throughout the membrane, thereby simplifying the analysis. The mechanism of inactivation is consistent with the reaction of hydrogen peroxide and certain catalytic intermediates of both enzymes. Based on this information, numerical simulations were developed that incorporate spatially heterogeneous catalytic and inactivation processes.
Feasibility of continuous long-term glucose monitoring from a subcutaneous glucose sensor in humans Function of an implanted tissue glucose sensor for more than 1 year in animals
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and served as President and CEO until 2015. Since 2015 he has been GlySens Incorporated's Chief Technology Officer. His research interests include medical devices, implantable and ex-vivo biosensors, biomaterials, tissue-device interfaces, and signal processing algorithms
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Joseph Y. Lucisano (M'95) received the B.S. degree in bioengineering from the University of California San Diego (UCSD) in 1981, the M.S. degree in aeronautics/ astronautics from Stanford University in 1982, and the Ph.D. degree in bioengineering from UCSD in 1987. From 1988 to 1994 he was Research Scientist and Research Program Manager with the Alfred E. Mann Foundation, Sylmar, CA. From 1994 to 1998 he was Principal Sensor Engineer and Program Director, Glucose Systems with Via Medical Corporation, San Diego, CA. In 1998 he co-founded GlySens Incorporated in San Diego, CA and served as President and CEO until 2015. Since 2015 he has been GlySens Incorporated's Chief Technology Officer. His research interests include medical devices, implantable and ex-vivo biosensors, biomaterials, tissue-device interfaces, and signal processing algorithms. Dr. Lucisano is a member of the American Chemical Society and the American Diabetes Association, and is a Founding Member of the Board of Trustees of the Jacobs School of Engineering Department of Bioengineering at the University of California San Diego.
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