Optical blood glucose non-invasive detection and its research progress.

Blood glucose concentration is an important index for the diagnosis of diabetes, its self-monitoring technology is the method for scientific diabetes management. Currently, the typical household blood glucose meters have achieved great success in diabetes management, but they are discrete detection methods, and involve invasive blood sampling procedures. Optical detection technologies, which use the physical properties of light to detect the glucose concentration in body fluids non-invasively, have shown great potential in non-invasive blood glucose detection. This article summarized and analyzed the basic principles, research status, existing problems, and application prospects of different optical glucose detection technologies. In addition, this article also discusses the problems of optical detection technology in wearable sensors and perspectives on the future of non-invasive blood glucose detection technology to improve blood glucose monitoring in diabetic patients.

Evaluation of New Blood Glucose Monitoring System According to ISO 15197 and the Food and Drug Administration Standard.

OBJECTIVE: Self-monitoring blood glucose levels using a blood glucose monitoring system (BGMS) helps minimize mortality and morbidity in patients with diabetes. We evaluated the accuracy of a new BGMS (CareSens S Fit [CaseSens; i-SENS Inc., Seoul, Korea]). METHODS: Patients who visited the Endocrinology Department at Kangbuk Samsung Hospital between July 13, 2021, and August 18, 2021 were included. CareSens was evaluated in accordance with the International Organization for Standardization 2013 (ISO 15197:2013) and the Food and Drug Administration (FDA) standard 2020. RESULTS: The study included 350 patients. The accuracy evaluation for CareSens, according to ISO 15197:2013 and FDA standard 2020, revealed that >99% of test results are within the allowed range. CONCLUSIONS: The CareSens performed excellently, meeting ISO 15197:2013 and FDA Standard 2020.

Analytical Performance of the FreeStyle Libre 2 Glucose Sensor in Healthy Male Adults.

Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects plasma glucose concentration in a healthy population in the absence of chronic diseases. In an oral glucose tolerance test (OGTT) with forty-four healthy male subjects (25.5 ± 4.5 years), the interstitial fluid glucose (ISFG) concentration obtained by a CGM sensor was compared against finger-prick capillary plasma glucose (CPG) concentration at fasting baseline (T0) and 30 (T30), 60 (T60), 90 (T90), and 120 (T120) min post OGTT to investigate differences in measurement accuracy. The overall mean absolute relative difference (MARD) was 12.9% (95%-CI: 11.8-14.0%). Approximately 100% of the ISFG values were within zones A and B in the Consensus Error Grid, indicating clinical accuracy. A paired t-test revealed statistically significant differences between CPG and ISFG at all time points (T0: 97.3 mg/dL vs. 89.7 mg/dL, T30: 159.9 mg/dL vs. 144.3 mg/dL, T60: 134.8 mg/dL vs. 126.2 mg/dL, T90: 113.7 mg/dL vs. 99.3 mg/dL, and T120: 91.8 mg/dL vs. 82.6 mg/dL; p < 0.001) with medium to large effect sizes (d = 0.57-1.02) and with ISFG systematically under-reporting the reference system CPG. CGM sensors provide a convenient and reliable method for monitoring blood glucose in the everyday lives of healthy adults. Nonetheless, their use in clinical settings wherein implications are drawn from CGM readings should be handled carefully.

Microvolumetric determination of thrombomodulin based on competitive immunoreaction using a portable glucometer.

A new method of reducing the amount of reagent and sample for determination of thrombomodulin (TM) was developed based on competitive immunoreaction using a portable glucometer (PGM). Two types of nanocomposites, TM protein-modified magnetic nanoparticles (MNPs-TM) and TM antibody-/glucose oxidase-modified gold nanoparticles (Ab-GNPs-GOx), were prepared. Their binding product, MNPs-TM-Ab-GNPs-GOx, in the microvolumetric solution was used to catalyze the oxidation of glucose, leading to a decline of the glucose content. The TM-involved competitive immunoreaction had a negative effect on the generation of MNPs-/GNPs-based nanocomposites and inhibited the catalytic oxidation of glucose. The glucose content difference in the microvolumetric solution, which was revealed by a PGM, was in proportion to the logarithm of the TM concentration from 25 ng mL-1 to 2.5 µg mL-1. The limit of detection was 5.7 ng mL-1. Microvolumetric solution and a PGM were used in the measurement, which overcame some deficiencies of classical methods in chemo/biosensing, for example, special instrument, complicated measurement procedure, and high cost.

A hybrid Transformer-LSTM model apply to glucose prediction.

The global prevalence of diabetes is escalating, with estimates indicating that over 536.6 million individuals were afflicted by 2021, accounting for approximately 10.5% of the world's population. Effective management of diabetes, particularly monitoring and prediction of blood glucose levels, remains a significant challenge due to the severe health risks associated with inaccuracies, such as hypoglycemia and hyperglycemia. This study addresses this critical issue by employing a hybrid Transformer-LSTM (Long Short-Term Memory) model designed to enhance the accuracy of future glucose level predictions based on data from Continuous Glucose Monitoring (CGM) systems. This innovative approach aims to reduce the risk of diabetic complications and improve patient outcomes. We utilized a dataset which contain more than 32000 data points comprising CGM data from eight patients collected by Suzhou Municipal Hospital in Jiangsu Province, China. This dataset includes historical glucose readings and equipment calibration values, making it highly suitable for developing predictive models due to its richness and real-time applicability. Our findings demonstrate that the hybrid Transformer-LSTM model significantly outperforms the standard LSTM model, achieving Mean Square Error (MSE) values of 1.18, 1.70, and 2.00 at forecasting intervals of 15, 30, and 45 minutes, respectively. This research underscores the potential of advanced machine learning techniques in the proactive management of diabetes, a critical step toward mitigating its impact.

[Continuous glucose monitoring for better glucose control in type 2 diabetes?] / Diabète de type 2 : vers un contrôle opti­mal grâce au ­suivi continu du glucose ?

This article reviews the use of continuous glucose monitoring (CGM) devices in the management of type 2 diabetes (T2D). Study results show that continuous CGM use improves glycemic control, lowers glycated hemoglobin (HbA1c) levels, and reduces hypoglycemic episodes compared with traditional monitoring methods. -Observational studies also suggest a reduction in diabetes-related emergencies and hospitalizations. In addition, sporadic use of CGM appears to be beneficial for certain groups of people with T2D. However, more research is needed to fully understand the long-term effects and limitations of this technology. This article discusses -innovative perspectives on T2D management.

Cet article explore l'impact des dispositifs de mesure continue du glucose (MCG) dans la gestion du diabète de type 2 (DT2). Les ­résultats des études démontrent que l'utilisation régulière de la MCG améliore le contrôle de la glycémie, réduit les taux d'hémoglobine glyquée (HbA1c) et diminue les épisodes hypoglycémiques par rapport à la surveillance traditionnelle. Les données issues d'études observationnelles mettent également en évidence une réduction des événements aigus liée au diabète et des hospitalisations. De plus, l'emploi occasionnel de la MCG semble davantage bénéfique pour certains groupes de patients DT2. Toutefois, des recherches supplémentaires sont nécessaires pour clarifier les effets à long terme et les limites de cette technologie. Cet article discute les perspectives innovantes pour la gestion du DT2.

Performance of a Novel Continuous Glucose Monitoring Device in People With Diabetes.

BACKGROUND: In this multicenter study, performance of a novel continuous glucose monitoring (CGM) system was evaluated. METHODS: Adult participants with diabetes were included in the study. They each wore three sensors of the CGM system on the upper arms for up to 14 days. During four in-clinic visits, frequent comparison measurements with capillary blood glucose (BG) samples were performed. The primary endpoint was the 20/20 agreement rate (AR): the percentage of CGM readings within ±20 mg/dL (at BG values <100 mg/dL) or ±20% (at BG values ≥100 mg/dL) of the comparator. Further evaluations included mean absolute relative difference (MARD) and 20/20 AR in different BG ranges and across the wear time. RESULTS: Data from 48 participants and 139 sensors were analyzed. During in-clinic sessions the 20/20 AR was 90.5% and the MARD was 9.2%. For BG ranges <70, 70-180, and >180 mg/dL, 20/20 AR was 94.3%, 89.0%, and 92.5%, respectively. At the beginning, middle, and end of sensor wear time, 20/20 AR was 92.8%, 91.5%, and 85.9%, respectively. The 14-day survival probability was 82.4%. Pain and bleeding after sensor insertion were within the expected range. Based on the study outcome, the use of the device is regarded as safe. CONCLUSIONS: The system showed a good performance compared to capillary BG measurements. This level of accuracy could be shown over the entire measurement range, especially in the low glycemic range, and the whole wear time of the sensors. The results of this study are supporting a non-adjunctive use of the device.

Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.

BACKGROUND: Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations. METHODS: The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226). RESULTS: On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively. CONCLUSIONS: The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board.

The recently CE-marked continuous real-time glucose monitoring (rtCGM) solution Accu-Chek® (AC) SmartGuide Solution was developed to enable people with diabetes mellitus (DM) to proactively control their glucose levels using predictive technologies. The comprehensive solution consists of three components that harmonize well with each other. The CGM device is composed of a sensor applicator and a glucose sensor patch whose data are transferred to the connected smartphone by Bluetooth® Low Energy. The user interface of the CGM solution is powered by the AC SmartGuide app delivering current and past glucose metrics, and the AC SmartGuide Predict app providing a glucose prediction suite enabled by artificial intelligence (AI). This article describes the innovative CGM solution.

Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.

The last 25 years of CGM have been characterized above all by providing better and more accurate glucose values in real time and analyzing the measured glucose values. Trend arrows are the only way to look into the future, but they are often too imprecise for therapy adjustment. While AID systems provide algorithms to use glucose values for glucose control, this has not been possible with stand-alone CGM systems, which are most used by people with diabetes. By analyzing the measured values with algorithms, often supported by AI, this should be possible in the future. This provides the user with important information about the further course of the glucose level, such as during the night. Predictive approaches can be used by next-generation CGM systems. These systems can proactively prevent glucose events such as hypo- or hyperglycemia. With the Accu-Chek® SmartGuide Predict app, an integral part of a novel CGM system, and the Glucose Predict (GP) feature, people with diabetes have the first commercially available CGM system with predictive algorithms. It characterizes the CGM systems of the future, which not only analyze past values and current glucose values in the future, but also use these values to predict future glucose progression.

Paper-based analytical device for point-of-care nucleic acid quantification combining CRISPR/Cas12a and a personal glucose meter.

Although CRISPR-based nucleic acid detection has great potential in point-of-care testing due to its simplicity, it has been rarely integrated into paper-based analytical devices (PADs), which are attractive platforms to simplify assays. This work introduces a CRISPR-assisted nucleic acid quantification approach integrated into a PAD with signal readout by a personal glucose meter (PGM). Retention of magnetic beads by filter paper and pre-deposition of all required reagents by freeze-drying stabilized with trehalose enabled the indirect quantification of human papilloma virus (HPV) DNA through a PGM readout without complicated user intervention and complex reagent handling. The calculated limit of detection was 57 pM, which is comparable with other amplification-free CRISPR-based assays detecting nucleic acids. The fully integrated device exhibited good storage stability for up to 4 weeks, suggesting its applicability toward practical point-of-care nucleic acid quantification.

Insertable Glucose Sensor Using a Compact and Cost-Effective Phosphorescence Lifetime Imager and Machine Learning.

Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durability compared to conventional electrochemical CGMs. Here, we report a computational CGM system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI). This compact and cost-effective PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local concentration of glucose. Because this phosphorescence signal has a very long lifetime compared to tissue autofluorescence or excitation leakage processes, it completely bypasses these noise sources by measuring the sensor emission over several tens of microseconds after the excitation light is turned off. The lifetime images acquired through the skin are processed by neural network-based models for misalignment-tolerant inference of glucose levels, accurately revealing normal, low (hypoglycemia) and high (hyperglycemia) concentration ranges. Using a 1 mm thick skin phantom mimicking the optical properties of human skin, we performed in vitro testing of the PLI using glucose-spiked samples, yielding 88.8% inference accuracy, also showing resilience to random and unknown misalignments within a lateral distance of ∼4.7 mm with respect to the position of the insertable sensor underneath the skin phantom. Furthermore, the PLI accurately identified larger lateral misalignments beyond 5 mm, prompting user intervention for realignment. The misalignment-resilient glucose concentration inference capability of this compact and cost-effective PLI makes it an appealing wearable diagnostics tool for real-time tracking of glucose and other biomarkers.

Clinical evidence for high-risk CE-marked medical devices for glucose management: A systematic review and meta-analysis.

AIMS: To conduct a systematic review and meta-analysis, within the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, evaluating CE-marked high-risk devices for glucose management. MATERIALS AND METHODS: We identified interventional and observational studies evaluating the efficacy and safety of eight automated insulin delivery (AID) systems, two implantable insulin pumps, and three implantable continuous glucose monitoring (CGM) devices. We meta-analysed randomized controlled trials (RCTs) comparing AID systems with other treatments. RESULTS: A total of 182 studies published between 2009 and 2024 were included, comprising 166 studies on AID systems, six on insulin pumps, and 10 on CGM devices; 26% reported industry funding; 18% were pre-market; 37% had a comparator group. Of the studies identified, 29% were RCTs, 24% were non-randomized trials, and 47% were observational studies. The median (interquartile range) sample size was 48 (28-102), age 34.8 (14-44.2) years, and study duration 17.5 (12-26) weeks. AID systems lowered glycated haemoglobin by 0.5 percentage points (absolute mean difference [MD] = -0.5; 21 RCTs; I2 = 86%) and increased time in target range for sensor glucose level by 13.4 percentage points (MD = 13.4; 14 RCTs; I2 = 90%). At least one safety outcome was assessed in 71% of studies. CONCLUSIONS: High-risk devices for glucose monitoring or insulin dosing, in particular AID systems, improve glucose control safely, but evidence on diabetes-related end-organ damage is lacking due to short study durations. Methodological heterogeneity highlights the need for developing standards for future pre- and post-market investigations of diabetes-specific high-risk medical devices.

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants.

BACKGROUND: Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. METHODS: This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. RESULTS: Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor. CONCLUSIONS: Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis.

Excellence in the management of Advanced Hybrid Closed-Loop Systems: Lessons from the Polish cohort.

BACKGROUND: The aim of the study was to analyze the real-world performance of MiniMed 780G (MM780G) Advanced Hybrid Closed Loop (AHCL) system users from Poland (PL) and compare it to the European region excluding Poland (EU-PL) in order to identify factors contributing to potential differences. The former achieved some of the best Time in Range (TIR) results globally using this technology. METHODS: CareLink Personal data uploaded by MM780G system users from August 2020 to December 2022 were analyzed. RESULTS: The Polish users (N=1304) on average reached to TIR of 79.1 ± 8.7 % (vs 73.0 ± 10.0 % for EU-PL, N=55659), a TBR<54 mg/dL of 0.6 ± 0.7 % (vs 0.4 ± 0.6 %) and a TBR<70 mg/dL of 2.9 ± 2.1 % (vs 2.1 ± 1.8 %). The adoption rate of optimal settings (i.e, GT=100 mg/dL, AIT=2hr) in PL was high (19.7 % vs 6.3 %), and filtering on optimal setting users led to less pronounced differences in glycemic control between PL and EU-PL. A univariable analysis with post-AHCL TIR showed that geography itself (PL vs EU-PL) is not a significant contributor to a high post-AHCL TIR (p = 0.15), and that much of the Polish post-AHCL TIR can be explained by the high pre-AHCL TIR. CONCLUSION: The Polish MM780G users achieved better glycemic control than the general European population (excluding Poland). This is largely attributable to the adoption of optimal settings in Poland and the already high glycemic outcomes at system start. As these characteristics can be implemented elsewhere, we believe this outstanding result can be obtained in other countries as well.

Ultra-Wideband Circular Polarized Implantable Patch Antenna for Implantable Blood Glucose Detection System Applications.

To address the current demands for antenna miniaturization, ultra-bandwidth, and circular polarization in advanced medical devices, a novel ISM band implantable antenna for blood glucose monitoring has been developed. This antenna achieves miniaturization by incorporating slots in the radiation patch and adding symmetric short-circuit probes, resulting in a compact size of only 0.054λ0 × 0.054λ0 × 0.005λ0 (λ0 is the wavelength in free space in respect of the lowest working frequency). By combining two resonance points and utilizing a differential feed structure, the antenna achieves ultra-broadband and circular polarization. Simulations indicate a |S11| bandwidth of 1.1 GHz (1.65-2.75 GHz) and an effective axial ratio (based on 3 dB axis ratio) bandwidth of 590 MHz (1.89-2.48 GHz), able to cover both the ISM frequency band (2.45 GHz) and the mid-field frequency band (1.9 GHz). The antenna exhibits CP gains of -20.04 dBi at a frequency of 2.45 GHz, while it shows gains of -24.64 dBi at 1.9 GHz. Furthermore, a superstrate layer on the antenna's radiating surface enhances its biocompatibility and minimizes its impact on the human body. Simulation and experimental results indicate that the antenna can establish a stable wireless communication link for implantable continuous blood glucose monitoring systems.

Functionalized primer initiated signal cycles and personal glucose meter for sensitive and portable miRNA analysis.

MicroRNA (miRNA) has garnered considerable attention due to its diagnostic capabilities, such as in hypoxic cognitive impairment and cancers. However, the existing miRNA detection methods are commonly criticized for the drawbacks of low sensitivity and false-positive detection derived from interfering molecules. Here, we provide a novel, sensitive and portable method for miRNA detection by combining target identification based cyclization of padlocks, immobilized primer-based signal amplification and a personal glucose meter. The proposed method exhibits several advantages, including precise identification of specific sites, exceptional sensitivity and instrument-free feature. These attributes hold great promise for the diagnosis and clinical investigation of various diseases, such as cancer and hypoxic cognitive impairment, enabling a deeper understanding of their pathological and physiological aspects.

With miRNA-155 as detective target, the feasibility of the method has been demonstrated. The padlock sequences are cyclized by miRNA-155, which subsequently hybridize with primer sequence that is immobilized on the surface of a 96-well plate, and the interfering molecules are removed. This DNA polymerase triggers a chain extension process on the terminus of primer sequence, activating DNAzyme based cleavage. Consequently, a multitude of linker sequences are generated to facilitate the formation of the 'e/linker/f/sucrase' on magnetic bead, thereby enabling the catalysis of sucrose into glucose. This enzymatic reaction may be identified and measured using the personal glucose meter.

Development of a target product profile for new glucose self-monitoring technologies for use in low- and middle-income countries.

AIMS: Most glucose self-monitoring devices have been developed with high-income countries in mind. We developed a target product profile (TPP) for new glucose self-monitoring technologies for users in low- and middle-income countries (LMICs). METHODS: A draft TPP including 39 characteristics was developed by an expert group including diabetes specialists, device specialists, and people with diabetes, incorporating findings from qualitative research in LMICs. Each characteristic had minimal and optimal requirements for two use cases, frequent and sporadic use. Characteristics requiring refinement were identified via online survey. Characteristics with agreement level <90% for any requirement were reviewed by the expert group and amended as appropriate. RESULTS: One characteristic (shelf life) had agreement <75% (both requirements for both use cases). Characteristics with agreement ≥75% and <90% for the frequent use case included infrastructure level, measurement cycle, duration of use before replacement, interchangeability, and calibration (both requirements), and activity log and price per month to end payer (minimal requirement). Intended use (both requirements), accuracy, and price per month to end payer (optimal requirement) had agreement ≥75% and <90% for the sporadic use case. CONCLUSIONS: This TPP will inform developers on requirements for glucose self-monitoring technologies for LMICs, and support decision-makers in evaluating existing devices.

Comparison of laser and traditional lancing devices for capillary blood sampling in patients with diabetes mellitus and high bleeding risk.

PURPOSE: Despite the importance of self-monitoring blood glucose (SMBG) for management of diabetes mellitus (DM), frequent blood sampling is discouraged by bleeding risk due to dual-antiplatelet agent therapy (DAPT) or thrombocytopenia. METHODS: We compared the bleeding time (BT) of sampling by using a laser-lancing-device (LMT-1000) and a conventional lancet in patients with DM and thrombocytopenia or patients undergoing DAPT. BT was measured using the Duke method, and pain and satisfaction scores were assessed using numeric rating scale (NRS) and visual analog scale (VAS). The consistency in the values of glucose and glycated-hemoglobin (HbA1c) sampled using the LMT-1000 or lancet were compared. RESULTS: The BT of sampling with the LMT-1000 was shorter than that with the lancet in patients with thrombocytopenia (60s vs. 85s, P = 0.024). The NRS was lower and the VAS was higher in laser-applied-sampling than lancet-applied sampling in the DAPT-user group (NRS: 1 vs. 2, P = 0.010; VAS: 7 vs. 6, P = 0.003), whereas the group with thrombocytopenia only showed improvement in the VAS score (8 vs. 7, P = 0.049). Glucose and HbA1c sampled by the LMT-1000 and lancet were significantly correlated in both the DAPT-user and the thrombocytopenia groups. CONCLUSION: The LMT-1000 can promote SMBG by shortening BT in subject with thrombocytopenia and by increasing satisfaction score, as well as by showing reliable glucose and HbA1c value.

Enhanced blood glucose levels prediction with a smartwatch.

Ensuring stable blood glucose (BG) levels within the norm is crucial for potential long-term health complications prevention when managing a chronic disease like Type 1 diabetes (T1D), as well as body weight. Therefore, accurately forecasting blood sugar levels holds significant importance for clinicians and specific users, such as type one diabetic patients. In recent years, Continuous Glucose Monitoring (CGM) devices have been developed and are now in use. However, the ability to forecast future blood glucose values is essential for better management. Previous studies proposed the use of food intake documentation in order to enhance the forecasting accuracy. Unfortunately, these methods require the participants to manually record their daily activities such as food intake, drink and exercise, which creates somewhat inaccurate data, and is hard to maintain along time. To reduce the burden on participants and improve the accuracy of BG level predictions, as well as optimize training and prediction times, this study proposes a framework that continuously tracks participants' movements using a smartwatch. The framework analyzes sensor data and allows users to document their activities. We developed a model incorporating BG data, smartwatch sensor data, and user-documented activities. This model was applied to a dataset we collected from a dozen participants. Our study's results indicate that documented activities did not enhance BG level predictions. However, using smartwatch sensors, such as heart rate and step detector data, in addition to blood glucose measurements from the last sixty minutes, significantly improved the predictions.