Non-enzymatic sweat sensors for pediatric continuous glucose monitoring: a systematic review of engineering readiness and the cost-accuracy trade-off.

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.

The high need for optimal diabetes management among an ever-increasing number of patients dictates the development and implementation of new digital sensors for continuous glucose monitoring. The purpose of this work is to systematize the global patenting trends of digital sensors for continuous glucose monitoring and analyze their effectiveness in controlling the treatment of diabetes patients of different ages and risk groups. The Lens database was used to build the patent landscape of sensors for continuous glucose monitoring. Retrospective analysis showed that the patenting of sensors for continuous glucose monitoring had positive trend over the analyzed period (2000–2022). Leading development companies are Dexcom Inc., Abbott Diabetes Care Inc., Medtronic Minimed Inc., Roche Diabetes Care Inc., Roche Diagnostics Operations Inc., Roche Diabetes Care Gmbh, and Ascensia Diabetes Care Holdings Ag, among others. Since 2006, a new approach has emerged where digital sensors are used for continuous glucose monitoring, and smartphones act as receivers for the data. Additionally, telemedicine communication is employed to facilitate this process. This opens up new opportunities for assessing the glycemic profile (glycemic curve information, quantitative assessment of the duration and amplitude of glucose fluctuations, and so on), which may contribute to improved diabetes management. A number of digital sensors for minimally invasive glucose monitoring are patented, have received FDA approval, and have been on the market for over 10 years. Their effectiveness in the clinic has been proven, and advantages and disadvantages have been clarified. Digital sensors offer a non-invasive option for monitoring blood glucose levels, providing an alternative to traditional invasive methods. This is particularly useful for patients with diabetes who require frequent monitoring, including before and after meals, during and after exercise, and in other scenarios where glucose levels can fluctuate. However, non-invasive glucose measurements can also benefit patients without diabetes, such as those following a dietary treatment plan, pregnant women, and individuals during fasting periods like Ramadan. The availability of non-invasive monitoring is especially valuable for patients in high-risk groups and across different age ranges. New world trends have been identified in the patenting of digital sensors for non-invasive glucose monitoring in interstitial skin fluid, saliva, sweat, tear fluid, and exhaled air. A number of non-invasive devices have received the CE mark approval, which confirms that the items meet European health, safety, and environmental protection standards (TensorTip Combo-Glucometer, Cnoga Medical Ltd.; SugarBEAT, Nemaura Medical; GlucoTrack, GlucoTrack Inc.), but are not FDA-approved yet. The above-mentioned sensors have characteristics that make them popular in the treatment of diabetes: they do not require implantation, do not cause an organism reaction to a foreign body, and are convenient to use. In the EU, in order to increase clinical safety and the level of transparency about medical devices, manufacturers must obtain certificates in accordance with Regulation (EU) 2017/745, taking into account the transition period. The development of systems, which include digital sensors for continuous glucose monitoring, mobile applications, and web platforms for professional analysis of glycemic control and implementation of unified glycemic assessment principles in mobile healthcare, represent promising approaches for controlling glycaemia in patients.

Continuous glucose monitoring (CGM) sensors have led a paradigm shift to painless, continuous, zero-finger pricking measurement in blood glucose monitoring. Recent electrochemical CGM sensors have reached two-week lifespans and no calibration with clinically acceptable accuracy. The system with the recent CGM sensors is identified as an “integrated glucose monitoring system,” which can replace finger-pricking glucose-testing for diabetes treatment decisions. Although such innovation has brought CGM technology closer to realizing the artificial pancreas, discomfort and infection problems have arisen from short lifespans and open wounds. A fully implantable sensor with a longer-term lifespan (90 days) is considered as an alternative CGM sensor with high comfort and low running cost. However, it still has barriers, including surgery for applying and replacing and frequent calibration. If technical refinement is conducted (e.g., stability and reproducibility of sensor fabrication), fully implantable, long-term CGM sensors can open the new era of continuous glucose monitoring.

Continuous glucose monitors are crucial for diabetes management, but invasive sampling, signal drift and frequent calibrations restrict their widespread usage. Microneedle sensors are emerging as a minimally-invasive platform for real-time monitoring of clinical parameters in interstitial fluid. Herein, a painless and flexible microneedle sensing patch is constructed by a mechanically-strong microneedle base and a thin layer of fluorescent hydrogel sensor for on-site, accurate, and continuous glucose monitoring. The Förster resonance energy transfer (FRET)-based hydrogel sensors are fabricated by facile photopolymerizations of acryloylated FRET pairs and glucose-specific phenylboronic acid. The optimized hydrogel sensor enables quantification of glucose with reversibility, high selectivity, and signal stability against photobleaching. Poly (ethylene glycol diacrylate)-co-polyacrylamide hydrogel is utilized as the microneedle base, facilitating effective skin piercing and biofluid extraction. The integrated microneedle sensor patch displays a sensitivity of 0.029 mM−1 in the (patho)physiological range, a low detection limit of 0.193 mM, and a response time of 7.7 min in human serum. Hypoglycemia, euglycemia and hyperglycemia are continuously monitored over 6 h simulated meal and rest activities in a porcine skin model. This microneedle sensor with high transdermal analytical performance offers a powerful tool for continuous diabetes monitoring at point-of-care settings.

The purpose of this investigation was to evaluate whether continuous glucose monitoring (CGM) sensors worn on the active muscle may provide enhanced insight into glucose control in non-diabetic participants during cycling exercise compared to traditional sensor placement on the arm. Data from 9 healthy participants (F:3) was recorded using CGM sensors on the arm (triceps brachii) and leg (vastus medialis) following 100 g glucose ingestion during 30 min experimental visits of: resting control, graded cycling, electrically stimulated quadriceps contractions, and passive whole-body heating. Finger capillary glucose was used to assess sensor accuracy. Under control conditions, the traditional arm sensor better reflected capillary glucose, with a mean absolute relative difference (MARD) of 12.4 ± 9.3% versus 18.3 ± 11.4% in the leg (P = 0.02). For the intended use during exercise, the sensor-site difference was attenuated, with similar MARDs during cycling (arm:15.5 ± 12% versus leg:16.7 ± 10.8%, P = 0.96) and quadriceps stimulation (arm:15.5 ± 14.8% versus leg:13.9 ± 9.5%, P = 0.9). At rest, glucose at the leg was consistently lower than the arm (P = 0.01); whereas, during graded cycling, the leg-glucose was lower only after maximal intensity exercise (P = 0.02). There was no difference between sensors during quadriceps stimulation (P = 0.8). Passive heating caused leg-skin temperature to increase by 3.1 ± 1.8°C versus 1.1 ± 0.72°C at the arm (P = 0.002), elevating MARD in the leg (23.5 ± 16.2%) and lowering glucose in the leg (P < 0.001). At rest, traditional placement of CGM sensors on the arm may best reflect blood glucose; however, during cycling, placement on the leg may offer greater insight to working muscle glucose concentrations, and this is likely due to greater blood-flow rather than muscle contractions. Highlights Wearing a continuous glucose monitoring (CGM) sensor on the arm may better reflect capillary glucose concentrations compared to wearing a sensor on the inner thigh at rest. With passive or active leg-muscle contractions, site-specific differences compared to capillary samples are attenuated; therefore, wearing a CGM sensor on the active-muscle during exercise may provide greater information to non-diabetic athletes regarding glucose flux at the active muscle. Discrepancies in CGM sensors worn at different sites likely primarily reflects differences in blood flow, as passive skin heating caused the largest magnitude difference between arm and leg sensor readings compared to the other experimental conditions (control, electric muscle stimulation, and cycling exercise).

Continuous glucose monitoring (CGM) sensors are portable devices, employed in the treatment of diabetes, able to measure glucose concentration in the interstitium almost continuously for several days. However, CGM sensors are not as accurate as standard blood glucose (BG) meters. Studies comparing CGM versus BG demonstrated that CGM is affected by distortion due to diffusion processes and by time-varying systematic under/overestimations due to calibrations and sensor drifts. In addition, measurement noise is also present in CGM data. A reliable model of the different components of CGM inaccuracy with respect to BG (briefly, "sensor error") is important in several applications, e.g., design of optimal digital filters for denoising of CGM data, real-time glucose prediction, insulin dosing, and artificial pancreas control algorithms. The aim of this paper is to propose an approach to describe CGM sensor error by exploiting n multiple simultaneous CGM recordings. The model of sensor error description includes a model of blood-to-interstitial glucose diffusion process, a linear time-varying model to account for calibration and sensor drift-in-time, and an autoregressive model to describe the additive measurement noise. Model orders and parameters are identified from the n simultaneous CGM sensor recordings and BG references. While the model is applicable to any CGM sensor, here, it is used on a database of 36 datasets of type 1 diabetic adults in which n = 4 Dexcom SEVEN Plus CGM time series and frequent BG references were available simultaneously. Results demonstrates that multiple simultaneous sensor data and proper modeling allow dissecting the sensor error into its different components, distinguishing those related to physiology from those related to technology.

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones.

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a “raw” current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient’s discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

We report results of a pilot clinical study of a subcutaneous fluorescence affinity sensor (FAS) for continuous glucose monitoring conducted in people with type 1 and type 2 diabetes. The device was assessed based on performance, safety, and comfort level under acute conditions (4 h). A second-generation FAS (BioTex Inc., Houston, TX) was subcutaneously implanted in the abdomens of 12 people with diabetes, and its acute performance to excursions in blood glucose was monitored over 4 h. After 30-60 min the subjects, who all had fasting blood glucose levels of less than 200 mg/dl, received a glucose bolus of 75 g/liter dextrose by oral administration. Capillary blood glucose samples were obtained from the finger tip. The FAS data were retrospectively evaluated by linear least squares regression analysis and by the Clarke error grid method. Comfort levels during insertion, operation, and sensor removal were scored by the subjects using an analog pain scale. After retrospective calibration of 17 sensors implanted in 12 subjects, error grid analysis showed 97% of the paired values in zones A and B and 1.5% in zones C and D, respectively. The mean absolute relative error between sensor signal and capillary blood glucose was 13% [±15% standard deviation (SD), 100-350 mg/dl] with an average correlation coefficient of 0.84 (±0.24 SD). The actual average "warm-up" time for the FAS readings, at which highest correlation with glucose readings was determined, was 65 (±32 SD) min. Mean time lag was 4 (±5 SD) min during the initial operational hours. Pain levels during insertion and operation were modest. The in vivo performance of the FAS demonstrates feasibility of the fluorescence affinity technology to determine blood glucose excursions accurately and safely under acute dynamic conditions in humans with type 1 and type 2 diabetes. Specific engineering challenges to sensor and instrumentation robustness remain. Further studies will be required to validate its promising performance over longer implantation duration (5-7 days) in people with diabetes.

Timely non-invasive monitoring of physiology is an emerging field in farm animal research, providing new insights on how animals adapt to environmental challenges and how we can manage performance, health, and welfare. In this study, continuous glucose monitoring (CGM) sensors were used to document glycemic excursions in lean growing pigs. Crossbred pigs (49 ± 3 kg, n = 8) were fitted with CGM sensors and indwelling venous catheters, and followed in a five-day feeding trial involving a progressive change in diet composition from high starch (HS) to high fat (HF). At days 2 and 5, time-series blood samples were collected after the first morning meal and used for time-matched chemistry analysis. During the postprandial period, the average CGM reading was higher (P = 0.01) on the HS diet than on the HF diet, primarily due to the fact that maximum glucose value at peak was higher (P < 0.05) after consuming the HS test meal than the HF meal. During night when the pigs had no access to feed, the period with glucose concentrations above baseline was twice longer (P < 0.05) with the HS diet than the HF diet. Various CGM metrics at night were or tended to be correlated with postprandial CGM metrics (P < 0.10). Finally, large inter-individual variability was observed in glycemic metrics, larger during day than during the night (P < 0.05). To conclude, this study highlights the importance of considering night-time events to inform about animal physiology and further improve precision feeding in pigs.

Aims: The abdominal region is the most common location for continuous glucose monitor (CGM) sensor insertion. However, a paucity of post-marketing data is available to demonstrate intra-individual consistency of CGM readings at different abdominal insertion sites. Methods: Healthy adults (fasting glucose (FG) < 5.5 mmol/L; BMI < 30 kg/m2) were recruited and a CGM sensor was placed on each side of the abdomen. Postprandial and continuous 48-h interstitial glucose levels were analyzed. Results: There was no significant difference in the 3-h postprandial glucose (PPG) level derived from the left versus right CGM, which remained non-significant after adjusting for waist circumference or FG. Among the glucose levels recorded over 48-h, values on the left site were greater in 3.6% of the data points (p < 0.05). After adjusting for waist circumference, only 0.5% of the glucose values remained significantly greater on the left (p < 0.05). When adjusted for FG, similar results were observed. For both PPG and 48-h readings, the mean absolute relative difference was not significant between the two abdominal sites. Conclusions: CGM-derived glucose measures were highly consistent between the left and right abdomen during both the postprandial and post-absorptive periods.

Comparison of the stability of Mucor-derived flavin adenine dinucleotide-dependent glucose dehydrogenase and glucose oxidase

Knowing the statistical properties of continuous glucose monitoring (CGM) sensor errors can be important in several practical applications, e.g., in both open- and closed-loop control algorithms. Unfortunately, modeling the accuracy of CGM sensors is very difficult for both experimental and methodological reasons. It has been suggested that the time series of CGM sensor errors can be described as realization of the output of an autoregressive (AR) model of first order driven by a white noise process. The AR model was identified exploiting several reference blood glucose (BG) samples (collected frequently in parallel to the CGM signal), a procedure to recalibrate CGM data, and a linear time-invariant model of blood-to-interstitium glucose (BG-to-IG) kinetics. By resorting to simulation, this work shows that some assumptions made in the Breton and Kovatchev modeling approach may significantly affect the estimated sensor error and its statistical properties. Three simulation studies were performed. The first simulation was devoted to assessing the influence of CGM data recalibration, whereas the second and third simulations examined the role of the BG-to-IG kinetic model. Analysis was performed by comparing the "original" (synthetically generated) time series of sensor errors vs its "reconstructed" version in both time and frequency domains. Even small errors either in CGM data recalibration or in the description of BG-to-IG dynamics can severely affect the possibility of correctly reconstructing the statistical properties of sensor error. In particular, even if CGM sensor error is a white noise process, a spurious correlation among its samples originates from suboptimal recalibration or from imperfect knowledge of the BG-to-IG kinetics. Modeling the statistical properties of CGM sensor errors from data collected in vivo is difficult because it requires perfect calibration and perfect knowledge of BG-to-IG dynamics. Results suggest that correct characterization of CGM sensor error is still an open issue and requires further development upon the pioneering contribution of Breton and Kovatchev.

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm. Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data. Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively. The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.