Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices

Das, Saroj Kumar; Nayak, Kavya K; Krishnaswamy, P. R.; Kumar, Vanita; Bhat, Navakanta

doi:10.1149/2754-2726/ac7abb

Cited by 108 publications

(73 citation statements)

References 103 publications

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“…Second, microfluidic flow dynamics are susceptible to disruption from bubble generation induced by high power sources or thermal energy; leakage, variation in pump volume stroke; gravitational; and motion flow effects; therefore, flow control is crucial and must be real-time and consistent in rate. Basically, for sensing detection, electron movement due to a chemical reaction on the working conductive electrode and a reduction reaction at another electrode is the basic mechanism by which glucose sensors measure current flow and convert to a measurable signal [143]. However, microfluidic work under low flow rates and small volumes is very challenging, especially at the micro-or nanoscale.…”

Section: Discussionmentioning

confidence: 99%

Finger-Actuated Micropump of Constant Flow Rate without Backflow

et al. 2023

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This paper presents a finger-actuated micropump with a consistent flow rate and no backflow. The fluid dynamics in interstitial fluid (ISF) extraction microfluidics are studied through analytical, simulation, and experimental methods. Head losses, pressure drop, diodocity, hydrogel swelling, criteria for hydrogel absorption, and consistency flow rate are examined in order to access microfluidic performance. In terms of consistency, the experimental result revealed that after 20 s of duty cycles with full deformation on the flexible diaphragm, the output pressure became uniform and the flow rate remained at nearly constant levels of 2.2 μL/min. The flow rate discrepancy between the experimental and predicted flow rates is around 22%. In terms of diodicity, when the serpentine microchannel and hydrogel-assisted reservoir are added to the microfluidic system integration, the diodicity increases by 2% (Di = 1.48) and 34% (Di = 1.96), respectively, compared to when the Tesla integration (Di = 1.45) is used alone. A visual and experimentally weighted analysis finds no signs of backflow. These significant flow characteristics demonstrate their potential usage in many low-cost and portable microfluidic applications.

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Section: Discussionmentioning

confidence: 99%

Finger-Actuated Micropump of Constant Flow Rate without Backflow

et al. 2023

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“…There has also been significant interest in the last few years on optical sensors that detect photons to determine the glucose concentration via the interaction between glucose molecules and different wavelengths of light ( 61 ). Other sensor developments focus on the non-invasive measurement of sweat, urine, saliva, tears ( 62 ) and even thermal monitoring ( 63 ); however, these ideas have not yet translated to a commercially viable stage.…”

Section: Updates On Previously Suggested Developmentsmentioning

confidence: 99%

Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023

et al. 2023

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In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These tasks are increasingly served by Continuous Glucose Monitoring (CGM) devices that measure subcutaneous glucose at near-continuous frequency. While the use of CGM in type 1 diabetes is well established, the evidence for widespread use in rare hypoglycaemia disorders is less than convincing. However, in the few years since our last review there have been multiple developments and increased user feedback, requiring a review of clinical application. Despite advances in device technology, point accuracy of CGM remains low for children with non-diabetes hypoglycaemia. Simple provision of CGM devices has not replicated the efficacy seen in those with diabetes and is yet to show benefit. Machine learning techniques for hypoglycaemia prevention have so far failed to demonstrate sufficient prediction accuracy for real world use even in those with diabetes. Furthermore, access to CGM globally is restricted by costs kept high by the commercially-driven speed of technical innovation. Nonetheless, the ability of CGM to digitally phenotype disease groups has led to a better understanding of natural history of disease, facilitated diagnoses and informed changes in clinical management. Large CGM datasets have prompted re-evaluation of hypoglycaemia incidence and facilitated improved trial design. Importantly, an individualised approach and focus on the behavioural determinants of hypoglycaemia has led to real world reduction in hypoglycaemia. In this state of the art review, we critically analyse the updated evidence for use of CGM in non-diabetic childhood hypoglycaemia disorders since 2020 and provide suggestions for qualified use.

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“…The CGM sensor can measure blood glucose (milligram/deciliter, mg/dL) every min [ 42 ] by inserting a sensor in the patient. With an average absolute relative subtraction (MARD) of 11.4%, the value of the CGM sensor is an estimation of the real sugar level in the blood [ 43 , 44 ], as specified by the manufacturer. The maximum lifespan of the CGM sensor is fourteen days.…”

Section: Monitoring Campaignmentioning

confidence: 99%

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

Rodríguez‐Rodríguez

Campo-Valera

Rodríguez

et al. 2023

Sensors

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Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.

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Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices

Cited by 108 publications

References 103 publications

Finger-Actuated Micropump of Constant Flow Rate without Backflow

Finger-Actuated Micropump of Constant Flow Rate without Backflow

Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

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