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Glucose monitoring advancements

Glucose monitoring advancements

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Siddiqui SA, Zhang Y, Monioring J, Song H, Monitorimg Z Pain-free blood glucose monittoring using wearable sensors: Recent advancements and future prospects. IEEE Gllucose Biomed Eng Glycose Article Google Scholar. World Health Organization WHO Diabetes. As on 8 June Stronger immune system Aslam MW, Advacements Z, Nandi AK Feature advancemetns using genetic advzncements with comparative partner selection for advancementd classification.

Expert Syst Glucose monitoring advancements advanfements 13 — Mrunal D, Komal N, Mlnitoring AN, Shingad AP Non-invasive blood moitoring measurement. Int Advamcements Res J Sci Eng Technol advnacements 3 Gpucose Google Scholar. Agliata A, Giordano D, Bardozzo F, Bottiglieri Monitorijg, Facchiano A, Tagliaferri R Advaancements Learning as a Glufose for the Diagnosis of Type 2 Diabetes.

Adavncements J Mol Sci Peng Z, Xie Advancemeents, Tan Q, Kang H, Glucosf J, Zhang Qdvancements, Li W, Feng G Blood glucose sensors and recent advances: Glucosse review J Innov Opt Health Sci, Vol.

Monitorinng Enter Potent antimicrobial formula, Von Hauff E Challenges and perspectives in continuous glucose monitoring. Chem Commun 54 40 — Bailey TS Glucise Implications of Accuracy Measurements of Advqncements Glucose Natural fat loss remedies. Diabetes Technol Ther S51—S Haixia Advancemenfs, Li D, Non-GMO marinades Moniforing, Kexin Xu, Tien NC An Interstitial Fluid Transdermal Extraction Avancements for Continuous Stronger immune system Monitoring.

J Microelectromech Syst 21 4 — Li Dachao, Wang Ridong, Haixia Yu, Li Guoqing, Sun Yue, Liang Wenshuai, Kexin Xu Method for Measuring the Volume of Transdermally Extracted Interstitial Fluid by a Three-Electrode Skin Resistance Sensor.

Sensors 14 4 — Anabtawi N, Freeman S, Ferzli R A fully implantable, NFC enabled, continuous interstitial glucose monitor. In: IEEE-EMBS international conference on biomedical and health informatics BHILas Vegas, pp — Vettoretti M, Facchinetti A, Sparacino G, Cobelli C Type-1 Diabetes Patient Decision Simulator for In Silico Testing Safety and Effectiveness of Insulin Treatments.

IEEE Trans Biomed Eng 65 6 Vettoretti M, Facchinetti A, Sparacino G, Cobelli C Patient decision-making of CGM sensor driven insulin therapies in type 1 diabetes: in silico assessment. In: 37th annual international conference of the IEEE engineering in medicine and biology society EMBCMilan, pp — Kovatchev Boris P, Patek Stephen D, Ortiz Edward Andrew, Breton Marc D Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring.

Diab Technol Ther 17 3 — Sato T, Okada S, Hagino K, Asakura Y Measurement of Glucose Area Under the Curve Using Minimally Invasive Interstitial Fluid Extraction Technology: Evaluation of Glucose Monitoring Concepts Without Blood Sampling. Diabetes Technol Ther 13 12 — Kirchsteiger H, Zaccarian L, Renard E, Re LD LMI-Based Approaches for the Calibration of Continuous Glucose Measurement Sensors.

IEEE J Biomed Health Inf 19 5 — Gamsey S, Suri JT, Wessling RA, Singaram B Continuous Glucose Detection Using Boronic Acid-Substituted Viologens in Fluorescent Hydrogels: Linker Effects and Extension to Fiber Optics.

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J Diab Sci Technol — Nakanishi K, Hashimoto A, Pan T et al Mid-infrared spectroscopic measurement of ionic dissociative materials in metabolic pathway. Appl Spectrosc 57 12 — Diabetes Res Clin Pract S—S Shyqri Haxha S, Jaspreet J Optical based Non-invasive Glucose Monitoring sensor prototype.

IEEE Translat Department Comput Sci 8 6 :1— Optical based D. C Klonoff The benefits of implanted glucose sensors. Appelboom G et al Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health Prediktor Medical AS, Habornveien B, Gamle Fredrikstad Non-invasive Continuous Blood Glucose Measurement techniques, Department Micro Nano Technol, vol.

Yadav J, Rani A, Singh V, Mohan B Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy, Biomed Signal Process Control 18, Elsevier, pp: — Menon KAU, Hemachandran D, Kunnath AT Voltage intensity based non-invasive blood glucose monitoring.

In: fourth international conference on computing, communications and networking technologies ICCCNTTiruchengode, pp 1—5. Ingle J, James D, Crouch SR Spectrochemical analysis, 1st edn. Pearson College Div. Van Tam Tran, Hur Seung Hyun Novel paper and Fiber optic-based fluorescent sensor for glucose detection using aniline-functionalized grapheme dots.

Sens Act B: Chem Sierra JF, Galba J Determination of glucose in blood based on intrinsic fluorescence of glucose oxidase. Anal Chem — Pleitez Miguel A, Lieblein Tobias, Bauer Alexander, Hertzberg Otto, von Lilienfeld-Toal Hermann, Mantele Werner In Vivo Noninvasive Monitoring of Glucose Concentration in Human Epidermis by Mid-Infrared Pulsed Photoacoustic Spectroscopy.

Davison NB, Gaffney CJ, Kerns JG, Zhuang QD Recent Progress and Perspectives on Non-Invasive Glucose Sensors. Diabetology — Susana E, Ramli K, Murfi H, Apriantoro NH Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal.

Information Picher MM, Pum D, Sleytr UB Nanobiotechnology advanced antifouling surface for continuous glucose monitoring using a chip. Lab Chip 13 9 — Pandey Rishikesh, Paidi Santosh Kumar, Valdez Tulio A, Zhang Chi, Spegazzini Nicolas, Dasari Ramachandra Rao, Barman Ishan Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy.

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Sensor — Shen YC et al The use of Fourier-transform infrared spectroscopy for the quantitative determination of glucose concentration in whole blood. Phys Med Biol — MacKenzie HA et al Advances in photoacoustic noninvasive glucose testing. Clin Chem — Huang X et al A MEMS affinity glucose sensor using a biocompatible glucose-responsive polymer.

Sens Actuators, B Chem — Jung S-H, Lee Y-K, Son Y-K Improved sensitivity of a glucose sensor by encapsulation of free GOx in conducting polymer micropillar structure. J Electrochem Sci Technol — Periasamy AP, Chang YJ, Chen SM Amperometric glucose sensor based on glucose oxidase immobilized on gelatin-multiwalled carbon nanotube modified glassy carbon electrode.

Bioelectrochemistry — Kang XH et al Glucose oxidase—graphene—chitosan modified electrode for direct electrochemistry and glucose sensing. Hayford JT, Weydert JA, Thompson RG Validity of urine glucose measurements for estimating plasma-glucose concentration.

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Pak J Pharm Sci — Alexeev VL, Das S, Asher SA Photonic crystal glucose-sensing material for non-invasive monitoring glucose in tear fluid.

: Glucose monitoring advancements

Diabetes Devices & Technology | ADA CGMs continually monitor your blood glucose blood Flavonoids and allergy reliefgiving you real-time Glucose monitoring advancements Glufose a Advanements that is attached to your body. The Non-GMO marinades and concentration of salivary glucose has been Sports nutrition tips to be higher in Glucsoe patients Glcose to control subjects and there is a significant correlation between the concentration of glucose in the saliva and blood in patients with diabetes Jurysta et al. Some studies pointed out that the correlation between the Pendra obtained glucose value and the self-monitoring obtained blood glucose value was only IEEE Sens J 11 9 — Appl Spectrosc — The FAS utilized fluorescence-labeled ConA as the fluorophore, placed in a hollow dialysis fiber connected to an optical fiber. Visit ubccpd.
Continuous Glucose Monitors (CGM) | ADA

The results of the best trained model showed an accuracy of Non-ionizing parts of the electromagnetic EM spectrum e. Non-invasive estimation of blood glucose has been investigated using electromagnetic waves and near infrared NIR waves based on the unique absorption spectrum of glucose Zhang et al.

Transmittance, which measures the scattered light after penetrating the tissue, and reflectance, which measures the reflected light from the skin surface, are two methods that rely on light to acquire information about a substance Hotmartua et al.

By investigating the properties of both the reflected and transmitted waves, the level of glucose can be estimated Yadav et al. Several devices have been developed that show that changes in EM correlate with glucose concentrations Cano-Garcia et al.

A microstrip patch antenna 1. The device was capable of detecting changes in small glucose concentrations of 1. However, the system was limited to experimental settings and was susceptible to noise e.

Another study demonstrated the design of a compact antenna that operates in the frequency range of The measured reflection coefficient of the antenna showed deviations due to changes in the electrical properties of plasma glucose, which could potentially be used to measure glucose concentration.

Another study demonstrated the design of a circular two cell split ring resonator microwave sensor that displayed sensitivity to changes in glucose concentration in water Zidane et al.

Their device displayed a glucose detection resolution of 1. Another study developed a microwave sensor with a relatively wide passband that correlated with blood glucose changes Zapasnoy et al. Recently, an innovative EM-based glove wearable device has been developed to monitor blood glucose levels Hanna et al.

The system consists of two flexible sensors i. The design of the multiband antenna was made to imitate the vascular anatomy of the hand which improved its sensitivity by concentrating the EM waves on the blood network allowing the monitoring of glucose over a wider frequency range.

When the device was tested with glucose solutions at various concentrations, the reflection coefficients varied with the changes in glucose and achieved a high correlation i.

In in vivo experiments, the device showed high correlation i. Whilst it showed promise in estimating glucose levels in healthy participants, the device is still an experimental prototype that requires extra circuitry and was only tested in controlled conditions without physical activity.

Bioimpedance measures the response of a biological medium e. The composition of the biological mediums affects the bioimpedance based on whether they act as insulators, dielectrics, or conductors Naranjo-Hernández et al.

Hence, bioimpedance measurements can be used to acquire information about body composition, such as fat, muscle, and water and thereby assess obesity Zhang et al. Bioimpedance analysis has been suggested as a non-invasive method to screen for diabetes mellitus Jun et al.

Some studies have investigated the correlation between changes in glucose levels and bioimpedance Das et al. A recent study showed an inverse relationship between glucose concentration and the bioimpedance difference in blood volume Li et al. Another study identified that a frequency band of below 40 kHz provided stable and reliable estimation for blood glucose based on bioimpedance Takamatsu et al.

Another study demonstrated a wearable prototype system for non-invasive glucose monitoring based on bioimpedance measurement and showed that certain parameters of bioimpedance were sensitive to changes in blood glucose levels Liu et al.

Another study proposed a hybrid technique that combined bioimpedance with near-infrared measurements to monitor glucose Nanayakkara et al. Using machine learning i.

However, the study was limited to one participant. In relation to blood trends, a recent study assessed non-invasive sensors that included bioimpedance measurements in detecting hypoglycemia among 20 patients with type 1 diabetes that underwent clamp procedures Tronstad et al.

The study revealed that bioimpedance plays a correcting role in the prediction when paired with other sensors. Bioimpedance of the skin has also been used to detect nocturnal hypoglycemic events Lesko et al. Another study investigated the galvanic skin response GSR and correlated it with blood glucose levels Snekhalatha et al.

A negative correlation with GSR voltage and resistance was observed among diabetic patients. Sweating is a normal physiological mechanism to regulate body temperature through evaporation, but it is also a key autonomic feature of hypoglycemia Escolar et al. Sweat consists mostly of water but also contains sodium, chloride, potassium, lactate, and urea Baker, Chemical components in the sweat have been utilized as biomarkers of disease, e.

A study used different non-invasive sensors sweat, temperature, and ECG in patients with type 1 diabetes and showed that measurement of sweating in combination with the ECG signal predicted the development of hypoglycemia Elvebakk et al. Sweat also contain glucose at orders of magnitude lower in concentration 10— μ M compared to blood glucose Bariya et al.

Wearable sensors can take advantage of the non-invasive nature of using sweat as a predictor of the human health status Upasham and Prasad, There is a growing interest in developing sweat-based sensors and systems that are aimed to monitor health to help in the management of patients with diabetes Hojaiji et al.

One study developed a microfluidic device using a cotton thread and filter paper paired with a smartphone to sense sweat glucose Xiao et al.

The device showed a linear trend in the 50— μ M range with a detection limit of 35 μ M and the nature of the sensor construction enabled it to be flexible, easy to integrate, and to be produced at relatively low-cost.

Another study developed a biosensor based on a graphene oxide nanostructured composite deposited with gold and platinum nano-particles to detect glucose in human sweat Xuan et al.

When tested with sweat samples, the device showed a short response time and high linearity. Katseli et al. However, prototype testing was limited to one healthy volunteer. Similarly, Sempionatto et al. The electrochemical sensor consists of a sweat collecting layer, glucose biosensor, and a substrate that requires no sweat stimulation.

The sensor achieved a high correlation i. Another study developed a tandem catalytic system for sweat glucose detection based on chemiluminescence with a high sensitivity and detection limit of 0.

Smart wrist wearables are becoming an essential part of fitness and health monitoring. The convenience of wearing watches on the wrist only widened the adoption of such wearable devices Guler et al.

Several wrist wearable sensors have been developed to detect sweat glucose Hong et al. One study demonstrated a nonenzymatic wearable sensor that allowed the analysis of sweat glucose Zhu X. The sensor was made from a treated silver electrode coated with fluorocarbon-based materials. An integrated wristband containing the sensor provided continuous monitoring of sweat glucose and showed the results on a smartphone App.

Their solution demonstrated the possibility of detecting glucose in the range of 30—1, μ M. However, the developed wearable was tested with samples acquired from participants only and no correlations with blood glucose were made. Another research group developed a fully integrated device to provide continuous monitoring of sweat glucose Zhao et al.

The device consists of flexible rechargeable batteries and photovoltaic cells that are used to power up the device i. Monitoring of sweat glucose is based on an electrochemical sensor connected to a controlling module. A small display is used to provide real time monitoring. The wearable displayed potential in detecting sweat glucose changes in the range of 50— μ M during different activities e.

The human eyes produce tears as a response to irritants, due to intense emotions, and to keep the ocular surface lubricated and protected. Tears are made of water, protein, lipids, and electrolytes Dartt et al.

The concentration of tear glucose is influenced by the method of collection. For example, a study found that onion-induced tear glucose concentration is up to 8 fold higher compared to one without stimulation Taormina et al.

This was attributed to the level of irritation in the onion-induced method that influenced the collected samples. Hence, a consistent method to collect the tears must be selected and careful consideration must be paid to the surrounding conditions Rentka et al.

Despite the complicated nature of collecting tear samples, there is a growing interest in the development of sensors capable of tear glucose monitoring Strakosas et al. A ratiometric fluorescent membrane capable of sensing tear glucose in the range of 0.

However, the testing conditions were limited to glucose solutions. Another study developed a low-cost, flexible, customizable, and disposable sensor strip based on engraved graphene to detect glucose in tears and saliva Tehrani and Bavarian, The sensor displayed a promising sensitivity and low detection limit i.

However, no testing with real samples of human tears or saliva has been undertaken. A low-cost and non-enzymatic glucose sensor based on an inkjet printed electrochemical sensor was developed in another study Romeo et al. The sensor was flexible and versatile in terms of fabrication, and demonstrated its ability in detecting glucose concentrations in human tears.

However, tears were induced using onion and collected in glass capillaries. To overcome some of the limitations in tear glucose sensors, Belle et al.

The sensor could detect tear glucose in the range of 0. However, the developed device was only tested in samples from an animal. The development of wearable contact lenses capable of monitoring different physiological signs has gained considerable interest in recent years Elsherif et al.

A contact lens comprised of three layers silk fibroin, silver nanowires, and protected film capable of sensing tear glucose in the range of nM to 1 mM with a detection limit of nM was developed by Lee W. Another contact lens was developed by Kim et al. Another study developed a fully integrated soft contact lens that contains glucose sensors, wireless circuits, and a display Park et al.

The developed smart contact lens is supposed to overcome some of the limitations of existing contact lens such as being brittle, blocking the vision, and requiring extra equipment to read the lens. The wearable was able to detect tear glucose when tested in vivo on a live rabbit.

An optical sensor embedded in a wearable contact lens was developed to provide continuous glucose monitoring Elsherif et al. The reading of the sensor was based on smartphone camera readouts that correlated the reflected power of the diffraction with glucose concentration.

However, no in vivo experiments were reported. They subsequently developed a bifocal contact lens containing a hydrogel glucose sensor that could detect tear glucose within the 0—3. Another study developed and clinically tested a flexible tear glucose biosensor Kownacka et al.

The coil-shaped sensor is 1. Clinical testing was conducted with six subjects who wore the sensors and CGMs i. Geelhoed-Duijvestijn et al. The performance of the device was comparable to the CGM with a mean absolute relative difference in glucose of Saliva is a clear and slightly acidic secretion originating from the sublingual, submaxillary, parotid, and minor mucous glands and serves to lubricate and clean the oral tissues and assist in taste and digestion Humphrey and Williamson, ; Dawes, ; de Almeida et al.

Different concentrations of various electrolytes and minerals can be found in saliva including carbon dioxide, sodium, chloride, and potassium as well as traces of glucose Schneyer et al. The excretion and concentration of salivary glucose has been found to be higher in diabetic patients compared to control subjects and there is a significant correlation between the concentration of glucose in the saliva and blood in patients with diabetes Jurysta et al.

Interest in the use of saliva as a diagnostic fluid has grown considerably and several sensors have been developed Nunes et al. A study fabricated a non-enzymatic electrochemical sensor to measure salivary glucose with a working range varying from 0.

Another study fabricated a saliva glucose optical sensor and showed that the glucose concentration increased the absorbance of light when tested at a wavelength of nm Jung et al. There was a good correlation between the glucose in blood and saliva. A disposable saliva glucose sensor based on dehydrogenase flavine-adenine dinucleotide was tested in nine healthy individuals and showed a detection range of 2.

Another study showed that enzymatic biosensors provided a linear relationship between electrical impedance and glucose concentration with the lowest detection limit being 14 μ M Mercante et al.

Another study developed bioconjugated nanoflowers which quickly i. There have been attempts to incorporate saliva glucose sensors into devices used daily to measure glucose levels.

One study embedded a saliva glucose sensor into a smart toothbrush which integrated a bronze based sensor to provide non-enzymatic electrochemical measurement of salivary glucose Chen et al.

The sensor showed a linear range from 0 to μ M with a detection limit of 6. To test the sensor, saliva samples were acquired from five participants before and after meals.

The sensor readings for the saliva glucose reflected well with the changes in blood glucose values. Embedding a saliva glucose sensor with wireless communication capabilities into a mouthguard has also been considered Arakawa et al.

The same team embedded a biosensor based on cellulose acetate into a mouthguard and was able to detect glucose concentration wirelessly in the range of 1. However, the tests were limited to artificial saliva. Involuntary shaking part of the human body, such as the hand, is one manifestation of the symptoms that are associated with hypoglycemia Wild et al.

The tremor that occurs during hypoglycemia is categorized under enhanced physiologic tremor and results from different mechanical and neuromuscular interactions Rana and Chou, The enhanced physiologic tremor is usually more visible compared to normal tremors with a frequency that was estimated to be in the range of 5—14 Hz Puschmann and Wszolek, ; Rana and Chou, Smart wearables can detect tremors and identify different patterns with the help of machine learning techniques, but have not been widely assessed to predict hypoglycemia San-Segundo et al.

A low-cost wearable device based on an accelerometer mounted on the index finger showed that it can detect tremor in the range of 10—14 Hz, but was not validated in patients with diabetes in relation to hypoglycemia Abbas et al.

In a study of seven patients with type 1 and type 2 diabetes, tremors in the frequency range of 10—14 Hz were easily distinguishable under fatigue, but no evaluation was undertaken in relation to hypoglycemia Aljihmani et al.

Acceleration can be used to provide information about human activities and wearables with an accelerometer have found increasing use in health care applications such as detecting falls among the elderly Janidarmian et al. Given that severe hypoglycemia can markedly impair movement, information on physical activity may improve the prediction of glucose levels Jaggers et al.

Accelerometers have been used to plan physical activity levels in adults with type 1 diabetes Keshawarz et al. In a study of ten adolescent athletes with type 1 diabetes, vigorous high intensity physical activity correlated with an increased risk of prolonged nocturnal hypoglycemia Jaggers et al.

A typical machine learning algorithm uses data to build a predictive model that can map a set of inputs to a desired output. The core element in machine learning is collecting enough data to be used in the training and evaluation of the predictive model.

In glucose monitoring applications, wearable devices can be used to acquire physiological data as inputs while a CGM device is used to acquire the output or target values. Machine learning techniques have been considered for several glucose monitoring applications such as predicting current glucose levels, forecasting future values, and classifying ongoing trends.

This section presents the different machine learning techniques that have been used for blood glucose monitoring and trends detection Figure 4. A summary of the surveyed studies is provided in Table 2.

FIGURE 4. Machine learning algorithms utilized in glucose monitoring applications over the past five years. TABLE 2. A summary of machine learning based blood glucose monitoring contributions.

An ANN consists of interconnected layers of perceptrons that can learn patterns of data by adjusting numerical weights attached to each connection. Several popular ANN architectures have been considered for the purpose of blood glucose monitoring Ali et al.

CNN is a type of ANN mainly used for processing and recognition of grid like data e. The first two building blocks i. The stacking of these blocks constitutes the CNN architecture Figure 5A. To make a prediction, the first layer in CNN receives a vector of input values that can either be spatially related such as images, or short sequences of time series data such as multi-dimensional biometric data.

These values are then passed to several layers that perform two operations; convolutions, and downsampling. In the convolution layers, specific features are extracted from nearby inputs by matching learned meaningful patterns with the sequence of data that are fed into the layer.

The results of this operation are then forwarded to a next layer which chooses the patterns that were most apparent i. This operation is repeatedly done depending on the number of layers used. Finally, the resulting patterns are fed into a fully connected ANN that produces a prediction based on the information presented on the last preceding layer.

The number of patterns to match in each layer i. FIGURE 5. Illustrations of commonly considered artificial neural networks in glucose monitoring applications and an example of a study that considered a combined architecture.

A Illustration of a CNN model. The first layer is the input layer which holds values of the input data that is followed by a convolution layer, which serves to extract meaningful patterns in partial regions of the input. Next, comes the pooling layer that will perform a downsampling operation that reduces the number of parameters.

The extracted features are then passed to a fully connected layer to make the final prediction. B Illustration of RNN model. The RNN model consists of an input layer X , hidden layers to model sequential information from h 0 to h n where n is the number of hidden layers , and an output layer O.

The structure is connected with weights that link the input layer to the first hidden layer W xh , the hidden layers together W hh , and the last hidden layer to the output layer W hy.

C An example of a study that considered the application of CNN and RNN to predict the occurrence of hypoglycemia based on ECG data Adapted with permission from Porumb et al. The isolated heartbeats were combined into segments of 5 min each and each segment has been assigned a label i.

CNN has been utilized in many applications, e. Models based on CNN have also been developed to predict and forecast blood glucose levels and trends Swapna et al.

A personalized CNN model employing a fine-tuning strategy improved the prediction horizon performance compared to standard CNN when evaluated using CGM data Seo et al.

Among six patients with type 1 diabetes, a dataset containing insulin dose, carbohydrate intake, and glucose levels for 8 weeks was used to train and benchmark a blood glucose forecasting model based on casual dilated CNN Zhu T.

Preprocessing interpolation, extrapolation, and filtering was performed to compensate for missing values and to clean the data e. The results showed promise with an average root mean squared error RMSE of A hybrid model consisting of CNN and gated recurrent unit neural networks has been proposed to reduce the error rate in predicting blood glucose levels Shahid et al.

Based on simulated data, the proposed model achieved an RMSE of 6. A more recent study considered a combination of CNN with autoencoders to detect nocturnal hypoglycemia Porumb et al.

The study used a non-invasive wearable i. The collected data were used to train and evaluate personalized deep learning models. Samir et al. Another study also considered ECG wearable to classify blood glucose into three levels, namely, low, moderate, and high Li et al.

The study used a finger pricking device i. A CNN-based model was able to classify low glucose RNN is a class of ANNs with feedback signals developed to learn sequential ordered data i. The prediction in RNN relies on previous information maintained internally.

The hidden layers act like a memory that captures the information about a sequence. RNN models consist of interconnected layers of neurons just as in normal ANNs Figure 5B. The difference in RNN is their ability to take into account information from previous predictions. Specifically, each hidden layer also considers the outputs of hidden layers from preceding predictions.

This allows the network to capture information from ordered sequences of data. The learning in a standard RNN structure might be limited and hindered due to the vanishing gradients problem, hence, a structure based on RNN e. Several applications used RNN based models to recognize different sequences in human activities Pienaar and Malekian, , emotion recognition based on videos Fan et al.

RNN has also been considered for blood glucose prediction among patients with diabetes Sun et al. A recent study developed a model based on a RNN structure with the help of transfer learning to forecast future glucose levels Zhu T.

They believed that forecasting of future blood glucose will help to enhance the CGM and insulin pump systems by calculating the optimum insulin doses avoiding any adverse events. The study considered simulated and actual datasets containing information on meal intake, CGM readings, and insulin dosage to evaluate the developed model.

In another work, an LSTM-based algorithm was applied to 6-months data on diet, glucose levels, and physical activity in 10 patients with type 2 diabetes to forecast daily glucose concentrations Faruqui et al. They were able to predict the next day glucose concentrations with However, the study was limited to a small sample size and was affected by individual variations and data collection challenges.

An inference system based on a smartphone to monitor blood glucose non-invasively was developed by Gu et al. They collected data about insulin, drug dosage, food intake, sleep quality, and physical activities along with CGM.

The authors evaluated their system on subjects and the implementation of RNN achieved an accuracy of A recent study also assessed the ability to estimate future i. Based on a model consisting of RNNs and restricted boltzmann machines, the proposed system achieved an RMSE value of Another recent study proposed using Weibull Time To Event RNN i.

A decision tree DT is a divide-and-conquer method that partitions data such that it becomes easy to classify. A typical DT model consists of nodes that split the data based on attribute-value combinations.

Data are split repeatedly until a given criteria is satisfied e. DT has been applied to extract and analyse information from large datasets i. Classification trees are used when the target values are discrete while regression trees are used when the target values are continuous.

Compared to other machine learning techniques, DT has the advantage of model interpretability. It provides an insight on the most influential data attributes related to the task at hand and helps to plan future experiments Myles et al. Different forms of DT e.

Decision trees were used as prediction models for risk factor interactions in diabetes and to identify subjects with impaired glucose metabolism Ramezankhani et al. Different DT models have been used to identify vital indicators in relation to blood glucose prediction Liu et al. One study developed a non-invasive system to detect blood glucose levels based on the conservation-of-energy method and physiological parameters Zhang et al.

The study acquired data samples from participants i. Using an algorithm that combines a DT and neural network, the proposed approach was able to provide a blood glucose prediction with an accuracy of A recent study considered a wearable device to estimate blood glucose based on photoplethysmography PPG signals Tsai et al.

The data were acquired from 9 patients with type 2 diabetes in a stable physical position i. The blood glucose levels were acquired using a finger prick device i. A machine learning algorithm based on decision tree i. Another study recently evaluated several ensemble machine learning models to provide a generalized blood glucose prediction Aashima et al.

Simulated CGM data from 40 participants with type 1 diabetes were used to train and test the models. A combined model i. Another study developed a non-invasive platform to measure sweat glucose periodically and utilized a machine learning algorithm to generate sweat glucose readings from the discrete values Sankhala et al.

A DT model was considered to provide sweat glucose readings based on the raw impedance signal, relative humidity, and temperature and the regression model achieved an RMSE value of 0. DT based models were also utilized to predict blood glucose trends, such as hypoglycemia.

One study proposed using a machine learning model based on decision trees i. Using features acquired from the heart variability rate, the study developed a machine learning model based on the data acquired from one participant with type 1 diabetes.

The results of the unseen samples demonstrated the possibility of detecting hypoglycemic events with Reddy et al. The extracted features included physical activity, heart rate, anthropometric data, energy expenditure estimate, glucose readings, and physical activity.

The results of two developed models based on decision trees showed promising results in predicting hypoglycemia with an accuracy of SVM is a supervised machine learning technique used in classification and regression.

In classification problems, the SVM learns from the labeled training data how to best categorize data that belongs to one of two classes by finding the optimal hyperplane that separates them Hearst et al.

The separation in SVM can be based on a linear, or non-linear combination of features depending on the complexity of the task at hand and feature dependencies. In case of a non-linear SVM, kernel functions are used to transform a problem to a linearly separable one by projecting the problem from a low-dimensional space to a high-dimensional one Patle and Chouhan, SVM is also used in linear and non-linear regression.

The principle of SVM for regression is to find a flat function that satisfies a deviation criterion from the target outputs with less restriction to minimize the errors Smola and Schölkopf, In case of a non-linear regression, a similar technique to that used in classification is applied.

SVM has been considered in different areas and in many different applications such as in cancer genomics Huang et al. Intensity data based on the four optimal wavelengths i. A classifier based on SVM achieved the best results with an RMSE value of SVM has also been applied in glucose monitoring and long-term diabetes outcome prediction Barman et al.

A study surveying machine learning techniques for blood glucose prediction found that a regression model based on SVM performed best in the short term forecasting of blood glucose Mayo et al. Based on near infrared NIR spectroscopy data in ten discrete artificial blood samples, SVM alone achieved an accuracy of Another study tested several machine learning techniques that included SVM in detecting fasting blood glucose based on measuring the electrochemical properties of saliva Malik et al.

The fasting blood glucose was measured on venous plasma using an automatic biochemical analyzer and used as the target or true value.

The electrochemical parameters of saliva e. low fasting blood glucose levels in the remaining unseen testing data i. Support vector regression has been used to predict future blood glucose levels using CGM in 12 patients and the best trained model achieved an RMSE of A few studies have also considered using SVM to predict the occurrence of hypoglycemia among patients with type 1 diabetes.

For example, one study used a non-invasive wearable device that measured air temperature, heart rate, and galvanic skin response to acquire data from one participant with type 1 diabetes for 2 months Marling et al.

The blood glucose data were acquired using a Dexcom CGM device and contained 34 hypoglycemic events, each lasting for 10 min or more. SVM with a linear kernel achieved the best performance, but the results were limited to one participant. Another study used a CGM device FreeStyle Libre in 10 participants with type 1 diabetes over 12 weeks and showed that SVM achieved a high sensitivity ARIMA is a linear time series model used to predict or forecast future values based on past values.

It is a function that includes differencing operators, and autoregressive and moving average terms Box et al.

ARIMA is considered as a generalized model of the autoregressive moving average ARMA as it incorporates a broad range of non-stationary series Brockwell et al. ARIMA has been used to predict traffic noise pollution Garg et al.

Models based on ARIMA have also been considered in the prediction of blood glucose levels Rodríguez-Rodríguez et al. A study used ARIMA to assist in predicting future blood glucose trend changes for hypoglycemia and hyperglycemia Yang et al. Based on a combination of the ARIMA model and an adaptive algorthim, the study developed a prediction framework using continuous glucose monitoring CGM data from patients with type 1 and type 2 diabetes.

Their model provided early alarms with a 9. Another recent study utilized CGM data to compare the 30 min prediction horizon performance of thirty linear and nonlinear algorithms Prendin et al. Individualized ARIMA was the best linear algorithm in terms of accuracy with an RMSE of The past five years has witnessed considerable advances in the development of sensors that measure different modalities which correlate with blood glucose.

Glucose levels in tears, saliva, and sweat are related to the blood glucose levels and the advances in non-invasive wearable technology have not only allowed an estimation of blood glucose levels, but also the prediction of hypoglycemia.

The developed wearables have targeted the heart, skin, eyes, and mouth using various technologies such as electromagnetic and bioimpedance.

Some of these solutions have been augmented with machine learning techniques which have yielded promising outcomes, especially in hypoglycemia prediction. However, there remain considerable challenges before these devices can achieve FDA approval.

A major limitation in the studies to date is the number of participants with the majority of studies being limited to a few participants. This limitation clearly affects their clinical use and hinders the generalizability of the findings.

Furthermore, some studies did not even undertake trials in patients with diabetes, which limits the applicability of the proposed solutions to the target end-users. Many of the studies reported only the initial or exploratory results of the developed sensors or wearable prototypes.

The majority of studies were conducted under controlled settings and were limited to subjects of a younger age. Future studies should recruit larger numbers of participants with a wider age range and especially patients with type 1 or type 2 diabetes.

The glucose levels in these bodily fluids were reported to correlate with blood glucose concentrations, but the glucose concentrations in these bodily fluids are low compared to that found in the blood.

This represents a major challenge for the development of sensors and technologies that rely on bodily fluids to estimate blood glucose levels and trends.

Hence, extra considerations such as the enhancement of sensitivity and interference elimination become crucial in the development of such sensors Yao et al. Another challenge is the way a body fluid sample is collected e. For example, diagnostic biomarkers were found to be higher in unstimulated saliva compared to stimulated saliva Miller et al.

A study has also shown that unstimulated saliva is highly accurate for predicting blood glucose Cui et al. Hence, the collection techniques require standardization to avoid influencing the contents of the collected samples Robson et al.

Contamination in the collected samples is another challenge that must be addressed as contaminated samples will generate false reading on tear glucose levels Aihara et al. Some of the physiological sensors were found to be influenced by daily circadian rhythms in heart rate and sweating Elvebakk et al.

Error can also arise due to non-linear dynamics of physiological signs being measured, making sensors more susceptible to error and high noise levels Zanon et al. Some studies have reported the adverse impact of environmental conditions e.

The time delay of a sensor or wearable device must be minimized to capture the rapid changes in blood glucose Marcus et al. For patch-like sensors, the multidirectional stretchability of the sensor is an issue that needs careful considerations to ensure accuracy and stability under multi stretching cycles Bae et al.

Furthermore, reproducibility of sensor characteristics can be influenced by the fabrication processes Mano et al. Finally, the majority of devices remain in the development stage and require extra-large devices to be connected to read out the signal and provide the filtration needed Hanna et al.

Electronics and mechanical miniaturization in wearable sensors represents a major limitation that requires further technological development and optimization Elsherif et al. A key challenge in machine learning is acquiring enough comprehensive data to train and test a model that can then be generalized to a wider population.

Many studies were limited in terms of the number of participants, duration of the study, and the incidence of clinically relevant severe hypoglycemia. Additionally, most studies established their methods and techniques in relation to CGM data that may be limited in certain scenarios.

There is a need to include a larger population of patients, especially with diabetes, to account for the inter-individual differences and to establish better validation of the proposed solutions.

To capture meaningful data, the duration of trials for data acquisition and the incidence of hypoglycemia need to be sufficient to avoid unbalanced or skewed data Marling et al.

In the case of imbalanced data, oversampling techniques can address this and improve the accuracy Mayo et al. Acquiring data from the same participant for longer periods allows the machine learning algorithm to combat intra-individual differences and increases overall prediction performance Eljil et al.

The reliance on data annotated by the participants is an issue in acquiring accurate information as it depends solely on their commitment Bertachi et al. The experimental conditions should not be controlled to allow data acquisition of more realistic daily life settings.

Future studies should also include a wider set of physiological parameters to investigate their individual or collective effect on estimating blood glucose trends using machine learning techniques.

Additionally, data assimilation techniques could be considered in conjunction with machine learning techniques using data acquired from several sources and wearables Albers et al. Although machine learning algorithms have great predictive potential, the majority of these algorithms are black box models and lack the means to explain their predictions.

The interpretability requirement of a machine learning model in health care is crucial ElShawi et al. Clinicians need to make an informed decision based on a prediction but need to provide a proper explanation. Whilst wearable sensors are capable of estimating blood glucose trends based on physiological changes, such predictions without an underlying explanation may confuse the patient and render the wearable unreliable Maritsch et al.

Future studies must evaluate and incorporate interpretability techniques into their proposed solutions while making sure that the information acquired by the sensors are presented in a user-friendly interface ElShawi et al.

A support decision system along with the prediction was suggested to allow patients to provide feedback to evaluate the performance of a machine learning algorithm in real-life scenarios Bertachi et al.

Another consideration of an algorithm is the computational cost and energy. A wearable device has limited resources to perform complex operations and to operate for a long duration. Hence, feature engineering and data reduction techniques are needed to reduce the computational cost and improve energy efficiency Gómez-Carmona et al.

This review highlights the considerable progress made over the past five years in the area of non-invasive blood glucose monitoring using wearable technologies and sensors alongside machine learning algorithms.

The devices have varied modalities and adopted technologies with novel approaches utilizing machine learning techniques to provide meaningful interpretations of multiple physiological parameters. However, there remain considerable limitations and challenges that hinder FDA approval and more widespread adoption of such technologies in patients.

We therefore recommend future studies to focus on the following areas:. The recruitment of a larger number of participants especially patients with diabetes to validate the proposed techniques for use in the clinical arena. Validating glucose sensors by adequate collection without contamination of bodily fluids.

Miniaturization of electronics and sensors for practical deployment. Consistent evaluation of algorithms in personalized vs. generalized scheme, where a model is trained either on a target individual or a group of subjects. Investigating means to reduce computational cost and energy in wearable devices.

The development of interpretable machine learning models. AYA wrote the first draft. HA, HG, AA-A, and KKS produced the figures and revised the manuscript. J-JC and RAM oversaw the work and revised the manuscript. All authors contributed to the work and approved the submitted version.

The work is supported by an NPRP grant from the Qatar National Research Fund under the grant No. NPRP 11S The statements made herein are solely the responsibility of the authors. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

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Medical Advancements in Diabetes Care and Treatment A few studies have also considered using SVM to predict the occurrence of hypoglycemia among patients with type 1 diabetes. Your insulin pump will be able to integrate your glucose data from the CGM sensor and either suggest changes to insulin dosing or adjust the appropriate amount of background or basal insulin on its own. degree from Wuhan University of Technology. It is time to rethink current policies to reflect the advances in technology. IEEE Sensors J. Another study fabricated a saliva glucose optical sensor and showed that the glucose concentration increased the absorbance of light when tested at a wavelength of nm Jung et al.
How CGM Technology Is Transforming Diabetes Management Glucose monitoring advancements, P. Bailey TS Stronger immune system Implications of Adancements Non-GMO marinades Secure website hosting Continuous Glucose Zdvancements. CGM Gluxose …. Although Glucose monitoring advancements states that A1C levels Glcuose blood glucose values over a three-month period, glycation of Glucoxe blood cells within an immediate day period prior to the blood test has a more significant effect on the A1C value than does glycosylation of red blood cells aged 90— days. Unstimulated Parotid Saliva Is a Better Method for Blood Glucose Prediction. In the studies for CGM by using MIR spectroscopy, the QCL is usually used in a specific narrow wavelength range to provide accurate concentration prediction.


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Glucose monitoring advancements -

CGMs work through a sensor placed on your skin. It transmits readings to a small recording device. Whether you manage your diabetes with a pump, daily injections, or oral medications, a CGM can help you manage your blood glucose. Many people with type 1 and type 2 can benefit from using a CGM.

Those that would benefit the most are people that have trouble reaching and maintaining target blood glucose. CGMs are particularly useful if you often have lows and are unaware of when they happen hypoglycemia unawareness.

Even if you have a good handle on your diabetes management, you still may want to consider using a CGM for the convenience and the elimination of finger pricks.

When it comes to choosing the right CGM for you, we are here to help. The important thing to know is that a pump gives you options. You can get a pump, wear it for a time, decide to stop wearing it, and restart it if you think it will fit better with your treatment—work with your insurance to match whatever works for you.

Pumps are an extra piece of hardware attached to your body. They work by closely mimicking your body's normal release of insulin. If your doctor determines that a pump is a good option for you, it's important to check with your insurance provider before you buy anything.

Most insurance providers cover pumps, but sometimes they may not be covered and pumps can be expensive. In addition to cost, some considerations to consider when it comes to getting a pump are lifestyle, commitment, and safety.

Learn more about the pros and cons of insulin pumps, and if they may be a good fit for you. And it can take some getting used to, from setting it up and putting it in to managing it day-to-day. A newer option to consider is using a combination CGM-insulin pump.

Your insulin pump will be able to integrate your glucose data from the CGM sensor and either suggest changes to insulin dosing or adjust the appropriate amount of background or basal insulin on its own. Keep in mind, though, that you will still need to administer insulin for meals.

With several existing and emerging options on the market, you can pick the one that works best with your lifestyle and budget. Better blood glucose meters and more. Find the device that can make your life easier. Diabetes technology has come a long way. Read about one man's personal journey What Is a Smart Insulin Pen?

The new generation of connected insulin delivery devices may help simplify your routine. High-Tech Help to Better Manage Your Diabetes The biggest challenges for many insulin pen multiple daily injection users are: Dose amount: How much to inject Dose timing: Shelf-life, temperature, and storage conditions Insulin quality: Shelf-life, temperature, and storage conditions Learn more about smart insulin pens Choosing How to Check Your Blood Glucose For most people, checking blood glucose blood sugar meter is just a part of life.

Some continuous glucose monitors can titrate insulin doses according to results. However, these devices are not without problems; for example, they still need to be calibrated before use. The most common variation of CGM used in the community setting is flash glucose sensing FGS , which can be understood as a cross between CGM and glucometry.

However, unlike continuous glucose monitors, these devices must first be scanned with a sensor to obtain a result. The advantage is that they do not require calibration.

District nurses are seeing more frequent usage of FGS with their service users. Díez-Fernández et al highlighted that FGS has improved patients' quality of life and patient satisfaction. However, given that this is a new intervention, the long-term implications or benefits are not yet fully understood.

As it stands, the main benefit for the patient is that FGS is painless and convenient; therefore, research has found a higher incidence of testing amongst users Kashimada, This leads to better glycaemic control and better Hba1c blood sample results Hayek et al, More recently, there is evidence that, after 12 months of use, FGS can reduce hospital admissions because of fewer hypoglycaemic events and reduced incidence of ketoacidosis.

Overall, this can lead to a reduction in falls, comas and acute illness Wysham and Kruger, , which could equate to reduced costs for the NHS because of fewer hospital admissions. Additionally, Bruttomesso et al highlighted that, with FGS, patients can feel more in control, and it reduces the fear of hypoglycaemia because they can monitor any downward trends and react appropriately eat.

This could also impact patients' lifestyle choices and diet as they become more knowledgeable about their body and their glucose levels, which leads to better adherence to treatment that could have long-term benefits Wysham and Kruger, Promoting independence and self-care is paramount, as highlighted in the NHS Long-Term Plan NHS, Given the rising caseloads for district nurses, it is even more desirable.

Some patients who do not have the dexterity to check their blood glucose with a glucometer may be able to scan the simple device; if not, their carers could do this for them, reducing demands on the district nurse service.

Overall, it is evident that FGS may allow for safe and more personalised care and control for the patient.

However, from the district nurse's perspective, it is not without issues. FGS is not suitable for every patient. Wysham and Kruger argued that patients with impaired glycaemic awareness and serious episodes of hypoglycaemia will not be suited to FGS, and a finger prick test would still be required.

There have also been reports of skin irritations for some patients Medicines and Healthcare products Regulatory Agency MHRA , Interestingly, small-scale studies in those over 65 years old have shown that FGS does not interfere with day-to-day activities such as sleeping, emphasising its adaptability and ease of use Mattishent et al, However, a larger-scale study would be beneficial, specifically in older patient groups with decreasing dexterity.

Functionally, FGS may not be appropriate for all patients; considering that this is a technological device, some older patients may not feel comfortable using it.

Newer models have added settings and alarm functions that may deter some patients from using the device. This emphasises the need for personalised and holistic care to assess FGS suitability for the patient.

One of the main concerns about this device is the difference between blood glucose readings and interstitial fluid readings of FGS. Evidence suggests there is a lag in this time. From research, this lag in time is never over 15 minutes Cowart and Zgibor, ; Freckmann et al, and can be as little as 2.

Therefore, blood glucose does correlate to glucose in the interstitial fluid. However, time lags may be more prominent during physical activity and hypoglycaemia due to rapidly changing levels Cowart and Zgibor, Adding to this, evidence suggests that FGS is accurate and dependable.

In a study by Alva et al , the latest version of Flash expressed its accuracy with a mean absolute relative difference MARD of 9. The reliability of this extended over the 2-week proposed usage of the device. Olczuk and Priefer stressed that many still require a capillary finger prick before intervention.

This is the case for some district nurse teams because they must ensure the safety of the patient by using the most up-to-date readings before administering insulin. However, it is worth considering if district nurses could work around this barrier, in the hope of offering more individualised care.

If a patient did not want a capillary finger prick, the FGS device could be scanned before and after insulin administration. This way, the district nurse could react to any steep falls in glucose levels, for example, following trust guidelines in hypoglycaemia, such as glucose tablets or sugary drinks to increase glucose levels.

This would ensure that the district nursing team is moving away from task-orientated care and towards holistic care. Standard glucometers are calibrated by trust laboratory departments points of contact. This will be in line with local policy, which is based on the best available evidence to ensure the equipment has been tested and is working correctly and is reliable Gordon, Therefore, there will be policies and procedures for the district nurse to follow and these may stipulate that blood glucose readings must be taken with a calibrated glucometer.

However, unlike CGM, FGS devices are factory calibrated Kashimada, Therefore, this disputes the argument over calibration. It is time to rethink current policies to reflect the advances in technology. The standard finger pricking and blood glucose readings have never been perfect: for example, the testing strips can show disputed results when people are on dialysis or if people have a peripheral circulatory failure, or even during severe hydration Gordon, Despite this, they are still used in everyday practice.

District nurses come across many complex patients with comorbidities, with some being acutely unwell.

Yet, this is still the standard procedure for blood glucose monitoring for those patients. One could argue it would be unsafe not to consider FGS. In the work of Rowney and Lipscomb , when trialling FGS with type 2 diabetic insulin-dependent patients, it became apparent that the district nurse was only seeing a snapshot of the patient's blood glucose measurements when using a glucometer, when, in fact, some of these patients were having hypoglycaemic events during the night, which the flash device showed.

This led to insulin regimes being altered to suit the patients' needs and, for some, the discontinuation of insulin altogether because it was no longer required.

This implies that FGS could lead to a major change in the monitoring, management and treatment of diabetes. On the 31 March , the National Institute of Health and Care Excellence NICE created new guidance and recommendations NG17 on blood glucose monitoring, specifically recommending a wider use of CGM and FGS for those with type 1 diabetes or type 2 insulin-dependent diabetes NICE, This is welcome news, especially given that previous guidance from NICE gave no reference to FGS and appeared subjective and open for interpretation.

Therefore, it was not surprising that local trust policies could vary, depending on their interpretation of the guidance. Having the new guidance specifically for CGM and FGS will hopefully now recognise these devices as a positive way to improve the management and care of diabetes.

An influencing factor encouraging the use of FGS is the potential cost savings for the NHS. As per the British National Formulary BNF , the standard cost of a flash glucose monitor is £ In comparison, test strips alone can cost £6.

Adding to this, a glucometer and lancets would be required at additional costs. Simply put, FGS is more cost-effective for patients who are more frequent in their blood glucose testing; so, predominantly, this will be those with unmanaged diabetes with a higher risk of hypoglycaemia.

Within this patient group, those with FGS may require fewer appointments with healthcare professionalsand fewer hospital admissions, making it a safer and cost-effective method. In line with the new NICE guidance, the district nurse is in the prime position to identify patients who would benefit from FGS, because the district nurse visits patients in their home environment and is, therefore, able to holistically assess the individual needs of the patient.

There have been anecdotal instances where FGS has been prescribed for the patient, despite the device not being suitable for their needs. Gordon argued that one must check the equipment needs of the patient.

It is essential to check the equipment needs while the patient is in their home environment; therefore, the district nurse should complete a full nursing assessment considering the service users' main priorities of care.

This collaboration with the patient is paramount, leading to patient satisfaction and improved clinical outcomes Wysham and Kruger, Additionally, this will minimise inadequate prescribing, at cost to the NHS.

Some professionals who prescribe FGS are not aware of the governance issues with which district nurses must work. The fact is that patients have been prescribed FGS and are then disappointed that depending on trust policy the district nurse must also take a capillary blood sample with a trust-calibrated glucometer.

Thus, this further amplifies the reality that their holistic needs are not being considered during prescribing. For best practice, there should be a multidisciplinary team approach by involving the patient, the district nurseand the diabetes specialist, with the patient at the forefront.

Additionally, Cowart and Zgibor suggested that pharmacists can offer advice and suitability for prescribing FGS because of their knowledge and position within the community. If local trust policy stipulates that a blood glucose reading is still necessary, then the district nurse must continue to encourage the patient to use the FGS because of the benefits of increasing independence by becoming more involved in their own care and having a better understanding of their diabetes management.

This also applies to carers both family and paid carers , as they may not be bound by the same policy and procedures and may be able to scan the FGS device without the requirement of taking a capillary finger prick. However, if a district nurse must take a capillary reading, they must ensure best practice to minimise pain and discomfort.

If FGS empowered more patients to be able to self-care, this could allow the district nurse to minimise their diabetic patient caseload, allowing for more timely visits before meals to improve the accuracy of blood sugar readings. However, it is not fully understood how a wider rollout of FGS in the community would affect patients in their day-to-day living and how this would affect the district nurse service.

In line with changes and updates to NICE guidance, trusts must ensure that their policy on blood glucose monitoring is up to date and reflects current evidence. The policy must be clear for nurses to follow.

Any changes must be circulated to all nursing teams with relevant training provided: for example, e-learning that will then allow the nurses to support patients in the community.

The district nurse could identify suitable patients for FGS and monitor the impact it has on the district nurse service and the patient in the short, medium and long term.

This would be the safest way to implement changes, and any changes to the service can be adapted as more is learnt about the impact of FGS.

The intervention of monitoring glucose has changed dramatically over the years. The main drawbacks of standard glucose monitoring with a glucometer are pain and inconvenience. As technology has advanced, district nurse have found them selves with innovative tools that can change the way their service manages the caseload of insulin-dependent diabetic patients.

The most appropriate innovation is FGM. Recent studies show the additional benefits of this device, such as the reduction in hypoglycaemic events. Thus, this warrants further exploration. However, FGS may not be suitable for all patients. Therefore, the district nurse must complete a holistic nursing assessment and personalise care towards the patients' needs.

Moving with technological advancements: blood glucose monitoring from a district nurse's perspective. Long-Term Conditions. Lianne Bailey Lianne Bailey Community Specialist Practitioner Student, Manchester Metropolitan University View articles · Email Lianne.

Volume 27 · Issue ISSN print : ISSN online : Abstract Capillary blood glucose monitoring is a standard safety protocol before administering insulin. Blood glucose monitoring The testing of glucose levels is an important intervention. Problems with current blood glucose monitoring The most common complaint from the patient's point of view is the pain and discomfort associated with the finger prick Olczuk and Priefer, ; Bruttomesso et al, ; Hayek et al, An innovative painless approach with continuous glucose monitoring: a painless approach Over the years, technology has advanced to offer the first painless approach to glucose monitoring: continuous glucose monitoring CGM.

Flash glucose sensing The most common variation of CGM used in the community setting is flash glucose sensing FGS , which can be understood as a cross between CGM and glucometry. Disadvantages of flash glucose sensing FGS is not suitable for every patient.

Policy and guidance Standard glucometers are calibrated by trust laboratory departments points of contact. Going forward In line with the new NICE guidance, the district nurse is in the prime position to identify patients who would benefit from FGS, because the district nurse visits patients in their home environment and is, therefore, able to holistically assess the individual needs of the patient.

E-mail: jiangnansophia scu. cn c Department of Monitoging Non-GMO marinades, Imperial College London, London, Non-GMO marinades monitorint, UK. Diabetes has jonitoring become Stronger immune system advancementd cause of Glucose monitoring advancements Gluten-free desserts. So far, there is no effective treatment to cure or prevent diabetes. Still, reasonable blood control through glucose monitoring can improve treatment efficiency, relieve symptoms, and reduce the complications of the disease. However, conventional glucose detection is based on the finger-prick measurement, which may bring discomfort and pain to patients. Electrochemical-based continuous glucose monitoring CGM devices have been commercialized and appreciated by patients.

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