INTRODUCTION

Among the many glucose-sensing mechanisms tested to guarantee all the necessary requirements for long-term use of biosensor in free-living conditions, i.e., biocompatibility, lifetime, safety, sensitivity, and specificity, the most popular technique used for continuous glucose monitoring (CGM) systems relies on the glucose oxidation reaction [1]. Specifically, CGM devices based on this principle use a glucose-oxidase-doped platinum electrode deposited on a needle inserted in the subcutaneous tissue to ignite and catalyze glucose oxidation. This results in the production of gluconolactone, hydrogen peroxide, and an electrical current signal that is transformed, in the end, to a glucose concentration through a calibration process using a few self-monitoring of blood glucose (SMBG) samples collected by the patient [2].

The introduction of these “minimally invasive” needle CGM sensors in 1999 [3] revolutionized, de facto, blood glucose (BG) concentration monitoring in diabetes and opened new exciting scenarios in the daily management of diabetes [4]. CGM sensors deliver an almost continuous glucose trace providing BG readings every 1 to 5 minutes, mitigating the need of SMBG and greatly increasing the information on BG fluctuations and trend (which shows that CGM reveals hypoglycemic and hyperglycemic events not visible by SMBG) (Fig. 1A). Since the first prototype, CGM sensors have evolved remarkably. Nowadays, they are also able to provide patients with many smart features, such as arrows depicting the current glucose rate-of-change and smart alarms for impeding hypo-/hyperglycemic events, improving patient self-management. Although, mainly due to economic cost and patient acceptability of sensor devices, CGM users represent only a small part of total diabetes population, CGM sensors proved to be effective in improving patient glucose control [567] and enabling the possibility of designing new advanced technologies for diabetes management [89].

The purpose of this paper is to (1) provide an overview of the latest advances on CGM system development and glucose sensing technologies, (2) discuss about the current use of CGM sensor from patient affected by diabetes, and (3) review some recent advanced applications for diabetes treatment based on CGM. To do so, based on our experience as researchers active in the diabetes technology field, we will focus on the current literature works that we think are able to provide a general overview on the many undergoing activities involving CGM.

HISTORY OF CGM SENSORS

The first CGM prototypes

CGM devices based on glucose-oxidase have been proposed starting from the 1999, when the U.S. Food and Drug Administration (FDA) approved the first professional CGM system to be used by healthcare professionals, thus enabling the possibility of analyzing retrospective user data for review [3]. This system, however, suffered from several limitations, the most important of which was poor accuracy, i.e., a concept quantifiable through some metrics. This system, however, suffered from several limitations, the most important of which was poor accuracy, which is usually assessed by comparing the CGM trace with very accurate and precise BG concentration values, commonly collected in a hospital setting by mean of laboratory-grade medical instruments, i.e., Yellow Spring Instruments Inc. (YSI, Yellow Spring, OH, USA) (Fig. 1B). Several metrics can be computed over these differences, such as %20/20-ISO boundaries, absolute relative difference, and mean absolute relative difference (MARD) [10]. Among these quantities, MARD is the most common metric currently used in the literature to assess CGM accuracy [11], and the one on which we will focus hereafter. In 2004, Medtronic (Medtronic Minimed, Northridge, CA, USA) introduced and successfully commercialized the first real-time CGM system for personal use: the Medtronic Real-Time Guardian. This system provided patients with a glucose concentration value every 5 minutes, lasted 3 days, and it was able to sound an alarm when the glucose concentration level became either too high or too low, helping users to improve glucose control. The Medtronic Real-Time Guardian's MARD was estimated to be 15%. Dexcom Inc. (San Diego, CA, USA) commercialized the Dexcom SEVEN Plus, which had longer lifetime, lasting up to 7 consecutive days. The accuracy of the Dexcon SEVEN Plus was 16.7% [12], slightly worst of that of the Medtronic Real-Time Guardian, but significantly better than that of its predecessor. The same year, the Abbott Freestyle Navigator (Abbott Diabetes Care, Alameda, CA, USA) was marketed, featuring a glucose sensor that could be worn up to 5 days and achieving an MARD of 12.8% [13]. Compared to SMBG, whose MARD falls between 5% and 10%, the low accuracy (i.e., elevated MARD) of these “first generation” CGM systems represented one of the major barrier for the early adoption of these devices by both users, who felt unsafe in adopting CGM, and many leading diabetologists, whose reluctance to integrate CGM sensors in the daily diabetes management greatly limited the spreading of this technology.

The most recent CGM systems

In the last decade, CGM manufacturers invested many efforts to overcome the problems of lack of accuracy of their first generation devices. The first new generation product was the Medtronic Enlite CGM system. This device, besides achieving a MARD of 13.6% [14], extended the wear time up to 6 days. In addition, it improved the sensor comfortability by reducing its size and weight, it was designed to be water-proof, and it allowed the memorization of BG up to 10 hours if the receiver-transmitter connection is interrupted for any reasons. The same year, Abbott launched the Freestyle Navigator II, a newly designed CGM system that provided BG readings every minute with a 12.3% MARD [15]. In 2012, Dexcom introduced the G4 Platinum, featuring a smaller sensor, lasting for 7 days, and reducing the MARD to 13% [16], later improved to 9% in 2014 thanks to new algorithms integrated directly within the sensor [17]. In 2015, Dexcom introduced the G5 Mobile [17] achieving a MARD of 9%, a 7 days wear time, now allowing BG data to be directly transmitted to the user's cell phone without the need of a dedicated receiver.

Later on, in 2016, Abbott commercialized the Freestyle Libre featuring a MARD of 11.4% [18]. This CGM system is the first that required no fingerstick testing during wear. In addition, it extended the wear time up to 14 days. Unlike Dexcom or Medtronic CGM devices, the Freestyle Libre does not sound any alarms if BG falls out of the safe glycemic range and it requires patient to wave the receiver over the transmitter in order to get BG information, and to do so at least one time every 8 hours in order not to lose data. For this reason, the Freestyle Libre is labelled as a flash glucose monitoring (FGM) device, i.e., a device that measures BG at continuous time but displays the measured values only when scanning the sensor with the receiver. The Freestyle Libre has been the first glucose monitoring device that required no calibrations, with the additional advantage of performing similarly in terms of both accuracy and BG control compared to CGM devices that require two or more calibrations per day, e.g., the G4 Platinum and the Freestyle Navigator II [19]. Following this technological trend, Dexcom launched in 2017 the G6 [20], a CGM system that can be used without in vivo calibrations, for 10 consecutive days, ensuring the same accuracy of the G5 Mobile. In the same year, Medtronic introduced the Guardian Sensor 3, whose accuracy was quantified as 10.6% and 9.1% MARD [21], when inserted in the abdomen and in the arm, respectively. This sensor is 80% smaller than the Enlite, and it ensures up to 7 days of sensor life as well as a shorter startup time.

To summarize, in the last decade, besides achieving accuracies close to, or even within, the SMBG accuracy range, CGM systems improved also in terms of features and comfort for the patient. In Fig. 2 we reported a graphical representation of the CGM system accuracy evolution through years. In Table 1, we summarized the main characteristics in terms of accuracy, features, and limitations of the most known CGM sensor devices.

Technological challenges of next generation CGM sensors

CGM systems are now accepted as standard tools for intensive glucose control in patients with type 1 diabetes mellitus (T1DM). However, several important limitations are still present. Indeed, glucose-oxidase based electrochemical sensors suffer from several limits such as their non-linear response within the biological relevant range, the possible interference with active agents (e.g., acetaminophen, ascorbate), and most importantly, their dependence of both sensitivity and specificity on the enzyme availability on the electrode surface. Moreover, BG readings provided by glucose-oxidase based CGM sensors are affected by delay artifacts, which range from 5 to 10 minutes, due to the time lag between glucose concentration in the interstitial fluid and BG. On one hand, delay is not important when analyzing retrospective glucose data, on the other, it can be critical when CGM is used for real-time decision making.

For this reason, further research is currently undergoing to address previously listed issues and designing new CGM sensors able to better meet technological requirements such as sensor size, lifetime, and capabilities.

From this perspective, in August 2015, Dexcom started a new collaboration with Verily (Verily Life Science LLC, San Francisco, CA, USA) to develop a new miniaturized, cheaper, patch CGM sensor designed to minimize its burden to the patient and to help people with type 2 diabetes mellitus (T2DM) managing their daily routine [22]. In 2018, Abbott released the Freestyle Libre 2, which successfully secured the Conformité Européene (CE) mark in October 2018, and improved the Libre by adding smart alarms [23].

Next generation CGM system development also involves the exploration of new glucose sensing technologies beyond glucose-oxidase. In this regard, glucose sensors based on optical sensing have been recently proposed. These sensors provide an interesting alternative to traditional electrochemical sensors since they have the benefit of being free from electromagnetic interference, simple to design and handling, and characterized by low manufacturing cost. These principles have been used to design non-invasive sensors based on near infrared detection and Raman spectroscopy [24], and fully implanted CGM systems based on fluorescence [25]. In 2016, Senseonics (Senseonics Inc., Germantown, MD, USA) launched the Eversense, the first implantable CGM system to receive the CE mark. As already mentioned, it is based on fluorescence sensing, featuring a lifetime of 90 days, and an accuracy of 11.4% MARD [26]. Of course, this approach is quite demanding for the patient, who is required to undergo a, even if simple, surgical procedure, but the sensor lifetime makes this system a good and appealing alternative.

Lastly, next generation CGM systems require guaranteeing data security. Indeed, being limited by low computing capabilities, CGM transmitters, and more in general wearable devices, have been proven to suffer from several security weaknesses that make user data relatively easy to potentially be hacked [27]. Even if, to the best of our knowledge, cyberattacks to CGM sensor devices have never been publicly reported yet, these potential security flaws need to be fixed to ensure data confidentiality and integration without undermining its availability. Recently, the Diabetes Technology Society established DTSec, i.e., a new consensus-based standard thought to provide a high security and assurance level among electronic devices used in diabetes treatment including, but not limited to, insulin pumps, SMBG devices, and, of course, CGM systems [28].

CURRENT USE OF CGM TECHNOLOGIES

Professional use

CGM sensors can be used as professional or personal devices. Professional CGM systems are owned by caregivers and intermittently prescribed to patients in blinded mode. Blinded CGM devices collect glucose concentration data continuously but do not display them in real-time to the user; data can only be reviewed retrospectively by the caregiver at the end of the monitoring. Blinded data collection allows tracking patients' glucose profiles without influencing their behavior. Examples of professional CGM devices for blinded data collection include the Abbott Freestyle Libre Pro system and the Medtronic iPro2 system. The retrospective analysis of CGM data allows the caregiver to extract glycemic variability metrics, identify previously unappreciated glucose patterns and adjust therapy regimens accordingly.

Glycemic variability represents how much BG fluctuates around the average value and is considered an important glycemic target, together with glycosylated hemoglobin (HbA1c), to reduce the risk of diabetes complications [29]. Notably, glycemic variability cannot be captured by sparse SMBG measurements, but it can be detected by CGM almost continuous-time profiles. Several CGM-based glycemic variability metrics have been proposed in the literature [30]. Recently an international panel of physicians, researchers and patients expert in CGM technologies defined the key metrics for CGM data analysis and reporting [31]. The panel also recommended the Ambulatory Glucose Profile (AGP) [32], a standardized single page report with summary statistics and daily glycemic patterns developed by the International Diabetes Center, as a standard for the visualization of CGM data. The AGP report has been included in many proprietary software for retrospective CGM data analysis, e.g., Dexcom CLARITY, Diasend-Glooko, Tidepool, LibreView, and Medtronic CareLink.

Personal use

In addition to the retrospective data analysis, CGM systems for personal use allow the individual to visualize in real-time information on current BG and trend on a portable receiver or a smartphone application. Most of personal CGM systems currently on the market, including the Dexcom G5 Mobile and G6, the Medtronic Enlite and Guardian, and the Senseonics Eversense, provide high and low BG alerts that help the patient to detect hypo/hyperglycemic events. Alerts are not available with the FreeStyle Libre, which, despite considered a glucose sensor for personal use, falls within the category of FGM devices. Alerts have been introduced in a new generation FGM device, i.e., the FreeStyle Libre 2. Devices for personal use also allow data sharing with third parties, such as parents, partners and caregivers. The Dexcom G5 Mobile and G6 and the Eversense sensors can share the data in real-time, a feature that is very useful for the pediatric population, as parents are enabled to check remotely their children's BG during school, physical activity or sleep. The Freestyle Libre and the Medtronic Enlite and Guardian sensors can share the data, but not in real-time.

Until a few years ago, all CGM devices for personal use were approved to be used in adjunct to SMBG, i.e., before making the treatment decisions patients were required to check CGM readings by confirmatory fingersticks. Indeed, as described in Section “History of CGM Sensors,” past CGM sensors suffered from accuracy problems due to plasma-interstitium kinetics, imperfect calibrations, compression artefacts and sensitivity to interfering substances, such as acetaminophen. Thanks to recent technological developments and enhancement of signal processing algorithms, the accuracy of CGM sensors has been remarkably improved during the last years, reaching the range of accuracy performance of SMBG devices (MARD <10%). These improvements led to the regulatory approval of CGM nonadjunctive use, i.e., the use of CGM readings to make treatment decisions without confirmatory fingersticks, whose safety and effectiveness have been proved by simulations [8] and a randomized non-inferiority clinical trial [33]. From 2014 to 2015, three CGM sensors received the nonadjunctive label in Europe: the FreeStyle Navigator II, the FreeStyle Libre and the Dexcom G5 Mobile. The approval of nonadjunctive use by the FDA came a couple of years later: the Dexcom G5 Mobile obtained the approval in 2016, followed by the FreeStyle Libre in 2017 and the Dexcom G6 in 2018. Despite the nonadjunctive label, sensor companies still recommend the use of confirmatory fingersticks when symptoms do not match sensor's readings or a trend arrow is not displayed. With the FreeStyle Libre, confirmatory fingersticks are also recommended when glucose is rapidly changing and in order to confirm hypoglycemia or impending hypoglycemia.

Real-time CGM sensors for personal use, in particular those approved for nonadjunctive use, can be used by the patients to make therapeutic decisions, e.g., insulin dosing. Compared to conventional SMBG devices, CGM also provides information on current glucose trend that can be exploited for a more accurate calculation of insulin boluses. Clinical experts in the field have proposed some guidelines that recommend the patient to increase or decrease the insulin dose by a certain amount (either proportional to the dose or inversely proportional to the patient's correction factor) when the CGM trend arrow indicates glucose concentration is rising or decreasing [34]. Nevertheless, these guidelines have never been assessed in clinical trials and survey data evidenced that patients actually perform much greater corrections to the insulin dose than those recommended by the guidelines [35]. A recent in silico study compared three different trend adjustment guidelines in simulation [36]; the results evidenced that none of the guidelines prevailed on the others for all the pre-meal scenarios, suggesting that these simple guidelines can be further improved.

CGM uptake

Historically CGM uptake was poor. From 2010 to 2012, only 7% of the T1D Exchange Registry participants were using CGM sensors [37]. A recent study reported that the most common barriers to CGM use were related to the high cost of the device, lack of insurance coverage, the hassle of wearing devices and the dislike of having devices on the body; the most common reasons for stopping CGM were cost, alarm fatigue and perceived sensor inaccuracy [38]. Nowadays, thanks to the recent developments in CGM technology, in particular the release of new more accurate sensors with reduced sensor size and calibration requirements, most of these barriers have been overcome. After the regulatory approval of CGM therapeutic use, the United States medical insurance company Medicare has announced reimbursement criteria for therapeutic CGM devices to all T1DM and T2DM patients on intensive insulin treatment [39]. Similarly, in many European countries CGM expenses are now covered by national healthcare systems, thus increasing the accessibility of such technology.

These changes has led to a growth of CGM use both in United States and in Europe. Most recent data from the T1D Exchange Registry reported that about 30% of participants have been using CGM in the period from 2016 to 2018 [40]. Abbott reported that about 800,000 people in 43 countries worldwide are currently using the FreeStyle Libre sensor [41]. In 2017, it was estimated that 18% of German/Austrian Diabetes Patienten Verlaufsdokumentation registry participants were using a CGM device [42]. Although the use of CGM sensors has increased, the CGM market is still moving slowly. Currently, only a small portion of well-trained T1DM individuals is using CGM, which represents less than 0.5% of the global diabetic population [43]. A possible reason for the low global uptake of CGM is that there are weak evidences that CGM is beneficial for T2DM patients not on intensive insulin treatment, which actually represent the majority of the diabetic population.

To whom it is recommended

Standards of diabetes care, like the American Diabetes Association and the American Association of Clinical Endocrinologists and American College of Endocrinology, recommend CGM use in conjunction with intensive insulin treatment for T1DM subjects who do not meet the glycemic target or suffer from hypoglycemia unawareness [4445]. The Endocrine Society also recommends intermittent use of personal CGM devices for T2DM patients with poor glycemic control who are able and willing to use the device [46]. T2DM individuals were identified as possible beneficiaries of CGM use also by an international consensus on CGM, which recommended the use of CGM in conjunction with HbA1c to assess the glycemic status and adjust therapy regimen in all T1DM and T2DM patients on intensive insulin treatments [31]. Apart from T1DM and T2DM people on intensive insulin treatment, other groups may benefit from CGM use. For example, some studies have demonstrated that CGM use improves the neonatal outcomes when used in pregnant women with diabetes or gestational diabetes [4748].

THE IMPACT OF CGM ON DAILY MANAGEMENT OF DIABETES

Nowadays, the beneficial impact brought by the integration of a CGM system in diabetes management has been proven [49]. Indeed, initial reluctance from both clinicians and patients has diminished through years thanks to the constant accumulation of clinical evidence from research over adult and pediatric populations with T1DM [29]. Just to mention a few, recent research proved CGM sensors to be effective for patients with frequent hypoglycemic events [7], sensor augmented pumps [50], and gestational diabetes [47], treated with either continuous subcutaneous insulin infusion (CSII) or a multiple daily injection (MDI) insulin regimen. Published randomized controlled trials (RCTs) have been devoted to study the impact of CGM sensor systems on (1) improving glycemic control, (2) mitigating hypoglycemic episodes, (3) reducing glycemic variability. In the following, some significant literature results proving the beneficial impact of CGM are reported.

Improvement of glycemic control

Numerous RCTs have demonstrated improved glucose control in terms of reduced HbA1c in individuals using CGM compared to those using SMBG. Two major studies by Bergenstal et al. [51] and Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group et al. [5] considered T1DM individuals undergoing both MDI and CSII therapies and assessed the ability of several CGM sensors in improving the glucose control. In particular, both studies showed a significant reduction of HbA1c of 0.64% and 0.53%, respectively, in subjects adopting CGM versus a control group employing standard therapy based on SMBGs. Moreover, both studies confirmed a direct relationship between sensor usage time and average HbA1c reduction. Specifically, Bergenstal et al. [51] showed that significant HbA1c reduction was possible only for users using CGM sensor between 41% and 60% of the time, which doubled in users who used CGM more than 80% of the time. Consistently, Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group et al. [5] reported that using CGM at least 6 days per week translate to an average reduction of 0.5% of HbA1c. Similar conclusions have been reported in many other studies, spanning different population categories, e.g., from children to adult, from individuals affected by T1DM to women with gestational diabetes, elevating CGM as a particularly useful tool to achieve effective glucose control in diabetes.

Mitigation of hypoglycemic episodes

Reduction of hypoglycemia has been shown to be one of the major advantages provided by CGM use. Indeed, thanks to its intrinsically superior time resolution, CGM enables users to capture glycemic fluctuations that are invisible using SMBG only. The real-time availability of glucose concentration values as well as visual/acoustic hypoglycemic alerts allow users to act in order to mitigate, or even avoid, hypoglycemia.

In a recent multicenter clinical study involving T1DM subjects having good starting glucose control, a 50% reduction of time spent in hypoglycemia has been shown, even if no significant reduction of HbA1c was reported [52]. Another study by Haak et al. [53] analyzed the impact of CGM use on individuals affected by T2DM in free-living conditions under either MDI or CSII therapy. Results showed a significant reduction of hypoglycemia of 53%. However, no significant reduction of HbA1c has been observed although the study participants were poorly controlled T2DM subjects. The reason behind this result probably lies behind the lack of optimal education from the patients. To support this thesis, according to a recent study by Hermanns et al. [54], the usage of FGM was effective to reduce HbA1c in participants receiving proper structured education, but FGM itself without education was not effective in poorly controlled T1DM and T2DM under MDI therapy.

Summarizing, thanks to the adoption of CGM in diabetes treatment, hypoglycemia can be successfully mitigated both improving individuals' quality of life and reducing the shortcoming of dangerous short-term complications.

Reduction of glycemic variability

Several studies appear to confirm that glycemic variability can play a key-role in the appearance of vascular damages [29] and in the set-up of hypoglycemic events [55]. CGM sensors are important also in reducing glycemic variability in individuals affected by diabetes. In Jamiolkowska et al. [56], 40 subjects aged 14.6 years have been recruited to evaluate whether the use of CGM could improve glycemic variability in terms of glucose standard deviation (SD) and area under the curve (AUC). Results showed a reduction of SD from 60.7 to 51.7 mg/dL as well as a decreasing of AUC with threshold fixed at 140 mg/dL from 41.2% of the control group to 21.2% of the group that accepted the adoption of CGM use. Another study from Tumminia et al. [57] compared 10 individuals with T1DM undergoing MDI therapy. Participants were monitored for 6 months while being randomized into two groups to compare SMBG versus CGM in terms of SD of the glucose profile and range of glycemic excursions (RGEs). Results showed a reduction of both SD and RGE when using CGM compared to SMBG (62.3 mg/dL vs. 75.5 mg/dL and 132.3 mg/dL vs. 175.3 mg/dL, respectively).

In summary, the use of CGM was shown to be crucial to allow people affected by diabetes reducing glycemic variability. As a result, this unlocks the possibility of mitigating the shortcoming of both hypo and hyperglycemia as well as dangerous micro/macrovascular complications.

ADVANCED APPLICATIONS BASED ON REAL-TIME CGM

Decision support systems

The increased amount of available information brought by wearable devices, such as CGM systems and physical activity monitoring bands, has led to the development of decision-making tools and applications that can enhance the management of the disease [58]. A decision support system (DSS) gives the possibility to support users with proactive and personalized decisions in any scenario of their daily living and allows to react at shorter time scales. Over the past few years, DSSs for diabetes have been an emerging concept in health care. By means of this new technology, data can be automatically collected, transmitted, aggregated with other physiological data, analyzed, stored, and presented to the patient. By integrating e-health and tele-monitoring systems, DSSs for T1DM have the potential to improve glycemic outcomes thanks to prevention of hypo- or hyperglycemic events, reducing uncertainty when making critical self-management decisions [59]. A DSS for diabetes treatment provides an alternative to the closed-loop system, the so-called artificial pancreas (AP). Indeed, a wide range of users do not feel confident with the use of AP systems, being concerned about errors occurring in the insulin pump, and they prefer an open-loop therapy, which can be assisted by DSSs. Most of DSSs already available in the literature are composed by a predictive glucose module (which alerts the user whenever its BG is predicted to fall outside the safe range in the next future), an insulin suspension module (which temporarily suspends basal insulin delivery to avoid hypoglycemia when BG is critically low in patient using insulin pumps), and an adaptive insulin bolus calculator (which provides users with a suggestion of the correct insulin dosage to compensate the BG fluctuation due to a meal).

Many literature studies showed that DSSs are viable tools for improving diabetes treatment. A prototype bolus calculator algorithm based on neural networks providing personalized insulin recommendations has been developed and preliminarily tested in silico by Cappon et al. [60]. Breton et al. [61] proposed a DSS with automated insulin titration and dosing, proving that the use of the system results in reduced glucose variability and improved protection against hypoglycemia. Moreover, DreaMed (DreaMed Diabetes Ltd., Petah Tikva, Israel) recently introduced the Advisor Pro, a responsive application that provides real-time automated analysis of patient specific behavior to come up with personalized estimates of the optimal insulin treatment plan [62], which received FDA approval to be marketed in the United States in 2018 [63]. Finally, Patient Empowerment through Predictive PERsonalised decision support (PEPPER), a project funded by the European Community under the Horizon 2020 research program, has recently entered its final test phase, after several preliminary studies showed that it is able to improve glucose control and reducing the incidence of hypoglycemic episodes [64].

Basal insulin attenuation/suspension

Since 2006, medical devices integrating CGM sensors and insulin pumps have become commercially available, the Medtronic MiniMed Paradigm REAL-time system being the first device allowing such integration. Later in 2009, Medtronic integrated systems have been equipped with the low glucose suspend (or threshold suspend) feature that allows to automatically suspend basal insulin infusion for up to 2 hours when CGM measurements fall below a user-defined low glucose threshold. This feature, implemented in the Medtronic Paradigm Veo and MiniMed 530G, was designed to mitigate hypoglycemic events in insulin pump therapy [65]. Most recent Medtronic systems, i.e., the MiniMed 640G and 630G, implement the SmartGuard feature, which also allows to suspend basal insulin infusion when CGM measurements are predicted to fall below a preset threshold in the next 30 minutes (prediction low glucose management) [66].

Basal insulin suspension algorithms have been intensively studied also by academic research groups. In particular, the group led by Dr. Bruce Buckingham have been very active in this research line since 2009, by testing several prediction algorithms and insulin suspension criteria in both inpatient and outpatient clinical trials. Their final algorithm performs 30-minute ahead glucose prediction by a Kalman filter and suspends basal insulin when the predicted glucose concentration fall below 80 mg/dL [67]. Basal insulin delivery is restarted as soon as CGM measurements start rising or after 2 consecutive hours of suspension.

The algorithms developed by Buckingham et al. [67], as well as those implemented in commercial devices, can only turn on or off basal insulin delivery. A different approach was adopted by the group led by Prof. Kovatchev at the University of Virginia, who proposed algorithms to attenuate, rather than suspend, basal insulin delivery in presence of hypoglycemia risk. Specifically, their “power brakes” algorithm performs 15-minute ahead glucose prediction using physiological model-based Kalman filtering and applies a BG risk function in order to calculate a basal insulin attenuation factor depending on predicted risk of hypoglycemia [68].

Several clinical trials were performed to assess the safety and effectiveness of basal insulin suspension algorithms both in clinic under controlled conditions [69707172] and at home under real-life conditions [67737475]. Evidences from these trials supported the effectiveness of these algorithms in reducing hypoglycemia, at the expenses of a slight increase in hyperglycemia. Nevertheless, the use of basal insulin suspension was not associated with a significant increase of HbA1c or occurrence of ketoacidosis.

For an exhaustive review of algorithms for basal insulin suspension/attenuation, their implementation in commercial devices and clinical evidence of their effectiveness and safety, we refer the reader to [76].

Closed-loop systems

Automatic CGM-based basal insulin suspension/attenuation represents the first step towards closed-loop systems, namely AP, in which a control algorithm automatically tune insulin pump injections based on CGM readings. Research on closed-loop systems has been very intense in the last 10 years [777879]. Several control algorithms were proposed and assessed in clinical trials, including proportional-integral-derivative controller [8081], model predictive control [82], and fuzzy logic controller [83]. Most of closed-loop systems adopt the hybrid approach, in which insulin boluses are manually administered by the user (meal amount required for meal bolus computation), while basal insulin rate is automatically tuned by the control algorithm. Recently, a hybrid closed-loop system has entered the market, as in 2017 Medtronic launched the MiniMed 670G, i.e., the first commercially available hybrid closed-loop system. A retrospective analysis of 3-month real-world glucose data has shown improved clinical outcomes in patients using the Auto Mode of the MiniMed 670G, compared to patients on Manual Mode [84]. Fully closed-loop systems that do not require patients to announce meals to the controller are also under development, though an increased risk for hypoglycemia has been reported in T1DM individuals with fully closed-loop control compared to hybrid closed-loop control [85]. A fully-closed loop system has been recently tested in adult inpatients with T2DM receiving non-critical care [8687]. Results demonstrated that the closed-loop insulin delivery can greatly improve the time spent in the target range (difference between closed-loop and control groups of up to 32% points) without increasing hypoglycemia. A recent pilot study suggested that hybrid AP can be beneficial also for non-hospitalized MDI-treated T2DM subjects [88], although further investigation is needed to evaluate the cost-effectiveness of AP in this population.

While research in AP is progressively increasing the safety and effectiveness of such devices, also exploring bi-hormonal systems allowing controlled delivery of both insulin and glucagon [89], patients have shown an increased interest for the AP technology. This gave rise to the OpenAPS community, a community of patients highly interested in directly improving diabetes technologies, who have designed their own open source AP system, also called do-it-yourself closed-loop system. Although no clinical trial has ever assessed the safety and effectiveness of such open source systems, OpenAPS users self-reported an improvement in HbA1c, time in range, glycemic variability and quality of life, while perceiving the OpenAPS system as safe [9091].

For an exhaustive review of algorithms for closed-loop control and AP technologies, we refer the reader to recent reviews [777879].

FUTURE PERSPECTIVES

The advent of CGM sensors has revolutionized the glucose monitoring in T1DM. The recent approval of CGM therapeutic use and the new reimbursement policies have contributed to increase the number of CGM users worldwide, which is expected to further rise in the next years when less obtrusive and cheaper sensors will become available. Indeed, major CGM companies, like Dexcom and Medtronic, have announced the development of new products designed to be smaller and less expensive than current state-of-the-art systems, which can target not only T1DM patients, but also the much larger market of people with T2DM [2292]. Furthermore, emerging companies are working on low-cost non-invasive CGM systems [9394], which may even bring CGM technology to the consumer market.

Important advances are expected also in terms of CGM interoperability with other devices, e.g., medical devices for diabetes therapy, activity trackers and other physiological wearable sensors. Indeed, the FDA has recently defined a new class of CGM devices, i.e., integrated continuous glucose monitoring (iCGM) systems, including devices to be used as part of an integrated system with other compatible medical devices and electronic interfaces. This will enable iCGM developers to bring their device to the market more rapidly. The first CGM sensor to receive FDA approval with the iCGM label is the Dexcom G6 device, launched in 2018 [95]. The integration of CGM data with data of insulin pump and other wearable sensors, like activity trackers, will allow improving algorithms for glucose prediction and automatic basal insulin modulation, as recent studies with basal insulin suspension [96] and AP during exercise [97] have suggested. CGM data integrated with other diabetes management data (e.g., insulin pumps, SMBG and mHealth apps data) and activity data will also allow to enhance diabetes DSSs, enabling a better understanding of the causes driving to abnormal glucose events and, finally, a better tailoring of diabetes therapy to the patient's lifestyle and habits.

Finally as more broadly discussed in [9], CGM data could be integrated with other clinical data sources, including clinical registries, electronic health records, prescription registries and biomarkers collected in laboratory tests, which will provide important clinical contextualization to CGM data. This will allow the generation of a digital ecosystem of diabetes data that could be exploited to extract novel insights on the mechanisms of diabetes progression and develop cutting-edge data analytics for personalized diabetes management and prevention of related complications.

Acknowledgements

ACKNOWLEDGMENTS

None

NOTES

CONFLICTS OF INTEREST: No potential conflict of interest relevant to this article was reported.

REFERENCES

• 1. Wang J. Electrochemical glucose biosensors. Chem Rev 2008;108:814-825. ArticlePubMed
• 2. Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives. Biosensors (Basel) 2018;8:E24. Article
• 3. Clarke SF, Foster JR. A history of blood glucose meters and their role in self-monitoring of diabetes mellitus. Br J Biomed Sci 2012;69:83-93. ArticlePubMed
• 4. Cappon G, Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics 2017;6:65.Article
• 5. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase HP, Clemons R, Fiallo-Scharer R, Fox LA, Gilliam LK, Hirsch IB, Huang ES, Kollman C, Kowalski AJ, Laffel L, Lawrence JM, Lee J, Mauras N, O'Grady M, Ruedy KJ, Tansey M, Tsalikian E, Weinzimer S, Wilson DM, Wolpert H, Wysocki T, Xing D. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008;359:1464-1476. ArticlePubMed
• 6. Battelino T, Conget I, Olsen B, Schutz-Fuhrmann I, Hommel E, Hoogma R, Schierloh U, Sulli N, Bolinder J. SWITCH Study Group. The use and efficacy of continuous glucose monitoring in type 1 diabetes treated with insulin pump therapy: a randomised controlled trial. Diabetologia 2012;55:3155-3162. ArticlePubMedPMC
• 7. Battelino T, Phillip M, Bratina N, Nimri R, Oskarsson P, Bolinder J. Effect of continuous glucose monitoring on hypoglycemia in type 1 diabetes. Diabetes Care 2011;34:795-800. ArticlePubMedPMCPDF
• 8. Facchinetti A. Continuous glucose monitoring sensors: past, present and future algorithmic challenges. Sensors (Basel) 2016;16:E2093. Article
• 9. Vettoretti M, Cappon G, Acciaroli G, Facchinetti A, Sparacino G. Continuous glucose monitoring: current use in diabetes management and possible future applications. J Diabetes Sci Technol 2018;12:1064-1071. ArticlePubMedPMCPDF
• 10. ISO: ISO 15197 (2013). In vitro diagnostic test systems: requirements for blood-glucose monitoring systems for self-testing in managing diabetes mellitus cited 2019 Jul 15. Available from: https://www.iso.org/obp/ui/#iso:std:iso:15197:ed-2:v1:en.
• 11. Bailey TS. Clinical implications of accuracy measurements of continuous glucose sensors. Diabetes Technol Ther 2017;19:S51-S54. ArticlePubMed
• 12. Zisser HC, Bailey TS, Schwartz S, Ratner RE, Wise J. Accuracy of the SEVEN continuous glucose monitoring system: comparison with frequently sampled venous glucose measurements. J Diabetes Sci Technol 2009;3:1146-1154. ArticlePubMedPMCPDF
• 13. Weinstein RL, Schwartz SL, Brazg RL, Bugler JR, Peyser TA, McGarraugh GV. Accuracy of the 5-day FreeStyle Navigator Continuous Glucose Monitoring System: comparison with frequent laboratory reference measurements. Diabetes Care 2007;30:1125-1130. PubMed
• 14. Bailey TS, Ahmann A, Brazg R, Christiansen M, Garg S, Watkins E, Welsh JB, Lee SW. Accuracy and acceptability of the 6-day Enlite continuous subcutaneous glucose sensor. Diabetes Technol Ther 2014;16:277-283. ArticlePubMed
• 15. Geoffrey M, Brazg R, Richard W. FreeStyle Navigator Continuous Glucose Monitoring System with TRUstart algorithm, a 1-hour warm-up time. J Diabetes Sci Technol 2011;5:99-106. ArticlePubMedPMCPDF
• 16. Christiansen M, Bailey T, Watkins E, Liljenquist D, Price D, Nakamura K, Boock R, Peyser T. A new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system. Diabetes Technol Ther 2013;15:881-888. ArticlePubMedPMC
• 17. Bailey TS, Chang A, Christiansen M. Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm. J Diabetes Sci Technol 2015;9:209-214. ArticlePubMedPDF
• 18. Bailey T, Bode BW, Christiansen MP, Klaff LJ, Alva S. The performance and usability of a factory-calibrated flash glucose monitoring system. Diabetes Technol Ther 2015;17:787-794. ArticlePubMedPMC
• 19. Freestyle Libre. Abbot's Freestyle Libre cited 2019 Jul 15. Available from: https://www.freestylelibre.it/libre/.
• 20. Dexcom Inc.FDA authorizes marketing of the new Dexcom G6 CGM eliminating the need for fingerstick blood testing for people with diabetes cited 2019 Jul 15. Available from: https://www.dexcom.com/news/fda-authorizes-dexcom-g6.
• 21. Christiansen MP, Garg SK, Brazg R, Bode BW, Bailey TS, Slover RH, Sullivan A, Huang S, Shin J, Lee SW, Kaufman FR. Accuracy of a fourth-generation subcutaneous continuous glucose sensor. Diabetes Technol Ther 2017;19:446-456. ArticlePubMedPMC
• 22. DexCom Inc. DexCom to collaborate with the life sciences team at Google on the development of breakthrough technologies to change the future of diabetes management cited 2019 Jul 15. Available from: http://www.prnewswire.com/news-releases/dexcom-to-collaborate-with-the-life-sciences-teamat-google-on-the-development-of-breakthrough-technologies-to-change-the-future-of-diabetesmanagement-300126661.html.
• 23. Abbott. Abbott's FreeStyle Libre 2, with optional real-time alarms, secures CE mark for use in Europe cited 2019 Jul 15. Available from: https://abbott.mediaroom.com/2018-10-01-Abbott-s-FreeStyle-R-Libre-2-with-Optional-Real-Time-Alarms-Secures-CE-Mark-for-Use-in-Europe.
• 24. Vaddiraju S, Burgess DJ, Tomazos I, Jain FC, Papadimitrakopoulos F. Technologies for continuous glucose monitoring: current problems and future promises. J Diabetes Sci Technol 2010;4:1540-1562. ArticlePubMedPMCPDF
• 25. Chen C, Zhao XL, Li ZH, Zhu ZG, Qian SH, Flewitt AJ. Current and emerging technology for continuous glucose monitoring. Sensors (Basel) 2017;17:E182. Article
• 26. Dehennis A, Mortellaro MA, Ioacara S. Multisite study of an implanted continuous glucose sensor over 90 days in patients with diabetes mellitus. J Diabetes Sci Technol 2015;9:951-956. ArticlePubMedPMCPDF
• 27. Gashgari H, Attallah N, Al Muallem Y, Al Dogether M, Al Moammary E, Almeshari M, Househ M. Collecting patient data from sensor-based systems: benefits and challenges. Stud Health Technol Inform 2016;226:41-44. PubMed
• 28. Diabetes Technology Society. DTS cybersecurity standard for connected diabetes devices cited 2019 Jul 15. Available from: https://www.diabetestechnology.org/dtsec.shtml.
• 29. Hirsch IB. Glycemic variability and diabetes complications: does it matter? Of course it does! Diabetes Care 2015;38:1610-1614. ArticlePubMedPDF
• 30. Longato E, Acciaroli G, Facchinetti A, Hakaste L, Tuomi T, Maran A, Sparacino G. Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data. Comput Biol Med 2018;96:141-146. ArticlePubMed
• 31. Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, Garg S, Heinemann L, Hirsch I, Amiel SA, Beck R, Bosi E, Buckingham B, Cobelli C, Dassau E, Doyle FJ 3rd, Heller S, Hovorka R, Jia W, Jones T, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Maahs D, Murphy HR, Norgaard K, Parkin CG, Renard E, Saboo B, Scharf M, Tamborlane WV, Weinzimer SA, Phillip M. International consensus on use of continuous glucose monitoring. Diabetes Care 2017;40:1631-1640. ArticlePubMedPMCPDF
• 32. International Diabetes Center. Ambulatory Glucose Profile: AGP reports cited 2019 Jul 15. Available from: http://www.agpreport.org/agp/agpreports.
• 33. Aleppo G, Ruedy KJ, Riddlesworth TD, Kruger DF, Peters AL, Hirsch I, Bergenstal RM, Toschi E, Ahmann AJ, Shah VN, Rickels MR, Bode BW, Philis-Tsimikas A, Pop-Busui R, Rodriguez H, Eyth E, Bhargava A, Kollman C, Beck RW. REPLACE-BG Study Group. REPLACE-BG: a randomized trial comparing continuous glucose monitoring with and without routine blood glucose monitoring in adults with well-controlled type 1 diabetes. Diabetes Care 2017;40:538-545. ArticlePubMedPMCPDF
• 34. Aleppo G, Laffel LM, Ahmann AJ, Hirsch IB, Kruger DF, Peters A, Weinstock RS, Harris DR. A practical approach to using trend arrows on the Dexcom G5 CGM system for the management of adults with diabetes. J Endocr Soc 2017;1:1445-1460. ArticlePubMedPMC
• 35. Pettus J, Price DA, Edelman SV. How patients with type 1 diabetes translate continuous glucose monitoring data into diabetes management decisions. Endocr Pract 2015;21:613-620. ArticlePubMed
• 36. Cappon G, Marturano F, Vettoretti M, Facchinetti A, Sparacino G. In silico assessment of literature insulin bolus calculation methods accounting for glucose rate of change. J Diabetes Sci Technol 2019;13:103-110. ArticlePubMedPDF
• 37. Beck RW, Tamborlane WV, Bergenstal RM, Miller KM, DuBose SN, Hall CA. T1D Exchange Clinic Network. The T1D Exchange clinic registry. J Clin Endocrinol Metab 2012;97:4383-4389. ArticlePubMed
• 38. Tanenbaum ML, Hanes SJ, Miller KM, Naranjo D, Bensen R, Hood KK. Diabetes device use in adults with type 1 diabetes: barriers to uptake and potential intervention targets. Diabetes Care 2017;40:181-187. ArticlePubMedPDF
• 39. Centers for Medicare & Medicaid Services. CGM Rulings. Ruling No. [CMS-1682-R] cited 2019 Jul 15. Available from: www.cms.gov/Regulations-and-Guidance/Guidance/Rulings/Downloads/CMS1682R.pdf.
• 40. Foster NC, Miller K, Dimeglio L, Maahs DM, Tamborlane WV, Bergenstal RM, Clements MA, Rickels MR, Smith E, Olson BA, Beck R. Marked increases in CGM use has not prevented increases in HbA1c levels in participants in the T1D Exchange (T1DX) Clinic Network. Diabetes 2018;67(Suppl 1):1689P.ArticlePDF
• 41. Abbott. Abbott FreeStyle Libre 14 day flash glucose monitoring system now approved in U.S cited 2019 Jul 15. Available from: http://abbott.mediaroom.com/2018-07-27-Abbotts-FreeStyle-R-Libre-14-Day-Flash-Glucose-Monitoring-System-Now-Approved-in-U-S.
• 42. DeSalvo DJ, Miller KM, Hermann JM, Maahs DM, Hofer SE, Clements MA, Lilienthal E, Sherr JL, Tauschmann M, Holl RW. T1D Exchange and DPV Registries. Continuous glucose monitoring and glycemic control among youth with type 1 diabetes: international comparison from the T1D Exchange and DPV Initiative. Pediatr Diabetes 2018;19:1271-1275. ArticlePubMedPMCPDF
• 43. Heinemann L, Stuhr A, Brown A, Freckmann G, Breton MD, Russell S, Heinemann L. Self-measurement of blood glucose and continuous glucose monitoring: is there only one future? Eur Endocrinol 2018;14:24-29.Article
• 44. American Diabetes Association. 7. Diabetes technology: standards of medical care in diabetes. 2019. Diabetes Care 2019;42(Suppl 1):S71-S80. ArticlePubMedPDF
• 45. Bailey TS, Grunberger G, Bode BW, Handelsman Y, Hirsch IB, Jovanovic L, Roberts VL, Rodbard D, Tamborlane WV, Walsh J. American Association of Clinical Endocrinologists (AACE). American College of Endocrinology (ACE). American Association of Clinical Endocrinologists and American College of Endocrinology 2016 outpatient glucose monitoring consensus statement. Endocr Pract 2016;22:231-261. ArticlePubMed
• 46. Peters AL, Ahmann AJ, Battelino T, Evert A, Hirsch IB, Murad MH, Winter WE, Wolpert H. Diabetes technology-continuous subcutaneous insulin infusion therapy and continuous glucose monitoring in adults: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab 2016;101:3922-3937. ArticlePubMed
• 47. Jovanovic L. The role of continuous glucose monitoring in gestational diabetes mellitus. Diabetes Technol Ther 2000;2(Suppl 1):S67-S71. ArticlePubMed
• 48. Yu F, Lv L, Liang Z, Wang Y, Wen J, Lin X, Zhou Y, Mai C, Niu J. Continuous glucose monitoring effects on maternal glycemic control and pregnancy outcomes in patients with gestational diabetes mellitus: a prospective cohort study. J Clin Endocrinol Metab 2014;99:4674-4682. ArticlePubMedPDF
• 49. Rodbard D. Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther 2017;19:S25-S37. ArticlePubMed
• 50. Bergenstal RM, Klonoff DC, Garg SK, Bode BW, Meredith M, Slover RH, Ahmann AJ, Welsh JB, Lee SW, Kaufman FR. ASPIRE In-Home Study Group. Threshold-based insulin-pump interruption for reduction of hypoglycemia. N Engl J Med 2013;369:224-232. ArticlePubMed
• 51. Bergenstal RM, Tamborlane WV, Ahmann A, Buse JB, Dailey G, Davis SN, Joyce C, Peoples T, Perkins BA, Welsh JB, Willi SM, Wood MA. STAR 3 Study Group. Effectiveness of sensor-augmented insulin-pump therapy in type 1 diabetes. N Engl J Med 2010;363:311-320. ArticlePubMed
• 52. ClinicalTrials.gov. An evaluation of novel glucose sensing technology on hypoglycemia in type 1 diabetes (IMPACT) cited 2019 Jul 15. Available from: https://clinicaltrials.gov/ct2/show/NCT02232698.
• 53. Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline JP, Rayman G. Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulin-treated type 2 diabetes: a multicenter, open-label randomized controlled trial. Diabetes Ther 2017;8:55-73. ArticlePubMedPDF
• 54. Hermanns N, Ehrmann D, Schipfer M, Kroger J, Haak T, Kulzer B. The impact of a structured education and treatment programme (FLASH) for people with diabetes using a flash sensor-based glucose monitoring system: results of a randomized controlled trial. Diabetes Res Clin Pract 2019;150:111-121. ArticlePubMed
• 55. Monnier L, Colette C, Wojtusciszyn A, Dejager S, Renard E, Molinari N, Owens DR. Toward defining the threshold between low and high glucose variability in diabetes. Diabetes Care 2017;40:832-838. ArticlePubMedPDF
• 56. Jamiolkowska M, Jamiolkowska I, Luczynski W, Tolwinska J, Bossowski A, Glowinska Olszewska B. Impact of real-time continuous glucose monitoring use on glucose variability and endothelial function in adolescents with type 1 diabetes: new technology. New possibility to decrease cardiovascular risk? J Diabetes Res 2016;2016:4385312. PubMed
• 57. Tumminia A, Crimi S, Sciacca L, Buscema M, Frittitta L, Squatrito S, Vigneri R, Tomaselli L. Efficacy of real-time continuous glucose monitoring on glycaemic control and glucose variability in type 1 diabetic patients treated with either insulin pumps or multiple insulin injection therapy: a randomized controlled crossover trial. Diabetes Metab Res Rev 2015;31:61-68. ArticlePubMed
• 58. O'Connor PJ, Sperl-Hillen JM, Fazio CJ, Averbeck BM, Rank BH, Margolis KL. Outpatient diabetes clinical decision support: current status and future directions. Diabet Med 2016;33:734-741. ArticlePubMedPMC
• 59. Jia P, Zhao P, Chen J, Zhang M. Evaluation of clinical decision support systems for diabetes care: an overview of current evidence. J Eval Clin Pract 2019;25:66-77. ArticlePubMedPDF
• 60. Cappon G, Vettoretti M, Marturano F, Facchinetti A, Sparacino G. A neural-network-based approach to personalize insulin bolus calculation using continuous glucose monitoring. J Diabetes Sci Technol 2018;12:265-272. ArticlePubMedPMCPDF
• 61. Breton MD, Patek SD, Lv D, Schertz E, Robic J, Pinnata J, Kollar L, Barnett C, Wakeman C, Oliveri M, Fabris C, Chernavvsky D, Kovatchev BP, Anderson SM. Continuous glucose monitoring and insulin informed advisory system with automated titration and dosing of insulin reduces glucose variability in type 1 diabetes mellitus. Diabetes Technol Ther 2018;20:531-540. ArticlePubMedPMC
• 62. DreaMed Diabeted Ltd.On DreaMed Advisor Pro cited 2019 Jul 15. Available from: https://dreamed-diabetes.com/.
• 63. PR Newswire Association. DreaMed diabetes granted FDA authorization to market advisor pro, offering personalized optimization of insulin pump therapy cited 2019 Jul 15. Available from: https://www.prnewswire.com/news-releases/dreamed-diabetes-granted-fda-authorization-to-market-advisor-pro-offering-personalized-optimization-of-insulin-pump-therapy-300667617.html.
• 64. Herrero P, Lopez B, Martin C. PEPPER: Patient empowerment through predictive personalized decision support In: Proceedings of 1st Workshop on Artificial Intelligence for Diabetes (ECAI); 2016 Aug 30; Hague, NL: ECAI; 2016;pp 8-9.
• 65. Agrawal P, Welsh JB, Kannard B, Askari S, Yang Q, Kaufman FR. Usage and effectiveness of the low glucose suspend feature of the Medtronic Paradigm Veo insulin pump. J Diabetes Sci Technol 2011;5:1137-1141. ArticlePubMedPMCPDF
• 66. Zhong A, Choudhary P, McMahon C, Agrawal P, Welsh JB, Cordero TL, Kaufman FR. Effectiveness of automated insulin management features of the MiniMed(®) 640G sensor-augmented insulin pump. Diabetes Technol Ther 2016;18:657-663. ArticlePubMedPMC
• 67. Buckingham BA, Cameron F, Calhoun P, Maahs DM, Wilson DM, Chase HP, Bequette BW, Lum J, Sibayan J, Beck RW, Kollman C. Outpatient safety assessment of an in-home predictive low-glucose suspend system with type 1 diabetes subjects at elevated risk of nocturnal hypoglycemia. Diabetes Technol Ther 2013;15:622-627. ArticlePubMedPMC
• 68. Hughes CS, Patek SD, Breton MD, Kovatchev BP. Hypoglycemia prevention via pump attenuation and red-yellow-green “traffic” lights using continuous glucose monitoring and insulin pump data. J Diabetes Sci Technol 2010;4:1146-1155. ArticlePubMedPMCPDF
• 69. Buckingham B, Chase HP, Dassau E, Cobry E, Clinton P, Gage V, Caswell K, Wilkinson J, Cameron F, Lee H, Bequette BW, Doyle FJ 3rd. Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care 2010;33:1013-1017. ArticlePubMedPMCPDF
• 70. Cameron F, Wilson DM, Buckingham BA, Arzumanyan H, Clinton P, Chase HP, Lum J, Maahs DM, Calhoun PM, Bequette BW. Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm. J Diabetes Sci Technol 2012;6:1142-1147. ArticlePubMedPMCPDF
• 71. Abraham MB, de Bock M, Paramalingam N, O'Grady MJ, Ly TT, George C, Roy A, Spital G, Karula S, Heels K, Gebert R, Fairchild JM, King BR, Ambler GR, Cameron F, Davis EA, Jones TW. Prevention of insulin-induced hypoglycemia in type 1 diabetes with predictive low glucose management system. Diabetes Technol Ther 2016;18:436-443. ArticlePubMed
• 72. Buckingham BA, Bailey TS, Christiansen M, Garg S, Weinzimer S, Bode B, Anderson SM, Brazg R, Ly TT, Kaufman FR. Evaluation of a predictive low-glucose management system in-clinic. Diabetes Technol Ther 2017;19:288-292. ArticlePubMed
• 73. Choudhary P, Olsen BS, Conget I, Welsh JB, Vorrink L, Shin JJ. Hypoglycemia prevention and user acceptance of an insulin pump system with predictive low glucose management. Diabetes Technol Ther 2016;18:288-291. ArticlePubMedPMC
• 74. Battelino T, Nimri R, Dovc K, Phillip M, Bratina N. Prevention of hypoglycemia with predictive low glucose insulin suspension in children with type 1 diabetes: a randomized controlled trial. Diabetes Care 2017;40:764-770. ArticlePubMedPDF
• 75. Abraham MB, Nicholas JA, Smith GJ, Fairchild JM, King BR, Ambler GR, Cameron FJ, Davis EA, Jones TW. PLGM Study Group. Reduction in hypoglycemia with the predictive low-glucose management system: a long-term randomized controlled trial in adolescents with type 1 diabetes. Diabetes Care 2018;41:303-310. ArticlePubMedPDF
• 76. Vettoretti M, Facchinetti A. Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review. Biomed Eng Online 2019;18:37ArticlePubMedPMCPDF
• 77. Thabit H, Hovorka R. Coming of age: the artificial pancreas for type 1 diabetes. Diabetologia 2016;59:1795-1805. ArticlePubMedPMCPDF
• 78. Dadlani V, Pinsker JE, Dassau E, Kudva YC. Advances in closed-loop insulin delivery systems in patients with type 1 diabetes. Curr Diab Rep 2018;18:88ArticlePubMedPDF
• 79. Bertachi A, Ramkissoon CM, Bondia J, Vehi J. Automated blood glucose control in type 1 diabetes: a review of progress and challenges. Endocrinol Diabetes Nutr 2018;65:172-181. Article
• 80. Steil GM, Rebrin K, Darwin C, Hariri F, Saad MF. Feasibility of automating insulin delivery for the treatment of type 1 diabetes. Diabetes 2006;55:3344-3350. ArticlePubMedPDF
• 81. Weinzimer SA, Steil GM, Swan KL, Dziura J, Kurtz N, Tamborlane WV. Fully automated closed-loop insulin delivery versus semiautomated hybrid control in pediatric patients with type 1 diabetes using an artificial pancreas. Diabetes Care 2008;31:934-939. ArticlePubMedPDF
• 82. Kropff J, Del Favero S, Place J, Toffanin C, Visentin R, Monaro M, Messori M, Di Palma F, Lanzola G, Farret A, Boscari F, Galasso S, Magni P, Avogaro A, Keith-Hynes P, Kovatchev BP, Bruttomesso D, Cobelli C, DeVries JH, Renard E, Magni L. AP@home consortium. 2 Month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial. Lancet Diabetes Endocrinol 2015;3:939-947. ArticlePubMed
• 83. Atlas E, Nimri R, Miller S, Grunberg EA, Phillip M. MD-logic artificial pancreas system: a pilot study in adults with type 1 diabetes. Diabetes Care 2010;33:1072-1076. PubMedPMC
• 84. Stone MP, Agrawal P, Chen X, Liu M, Shin J, Cordero TL, Kaufman FR. Retrospective analysis of 3-month real-world glucose data after the MiniMed 670G system commercial launch. Diabetes Technol Ther 2018;20:689-692. ArticlePubMed
• 85. Cameron FM, Ly TT, Buckingham BA, Maahs DM, Forlenza GP, Levy CJ, Lam D, Clinton P, Messer LH, Westfall E, Levister C, Xie YY, Baysal N, Howsmon D, Patek SD, Bequette BW. Closed-loop control without meal announcement in type 1 diabetes. Diabetes Technol Ther 2017;19:527-532. ArticlePubMedPMC
• 86. Boughton CK, Bally L, Martignoni F, Hartnell S, Herzig D, Vogt A, Wertli MM, Wilinska ME, Evans ML, Coll AP, Stettler C, Hovorka R. Fully closed-loop insulin delivery in inpatients receiving nutritional support: a two-centre, open-label, randomised controlled trial. Lancet Diabetes Endocrinol 2019;7:368-377. ArticlePubMedPMC
• 87. Bally L, Thabit H, Hartnell S, Andereggen E, Ruan Y, Wilinska ME, Evans ML, Wertli MM, Coll AP, Stettler C, Hovorka R. Closed-loop insulin delivery for glycemic control in noncritical care. N Engl J Med 2018;379:547-556. ArticlePubMed
• 88. Taleb N, Carpentier AC, Messier V, Ladouceur M, Haidar A, Rabasa-Lhoret R. Efficacy of artificial pancreas use in patients with type 2 diabetes using intensive insulin therapy: a randomized crossover pilot trial. Diabetes Care 2019;42:e107-e109. ArticlePubMedPDF
• 89. Peters TM, Haidar A. Dual-hormone artificial pancreas: benefits and limitations compared with single-hormone systems. Diabet Med 2018;35:450-459. ArticlePubMedPDF
• 90. Lewis D, Leibrand S. #OpenAPS Community. OpenAPS Community. Real-world use of open source artificial pancreas systems. J Diabetes Sci Technol 2016;10:1411ArticlePubMedPMCPDF
• 91. Litchman ML, Lewis D, Kelly LA, Gee PM. Twitter analysis of #OpenAPS DIY artificial pancreas technology use suggests improved A1C and quality of life. J Diabetes Sci Technol 2019;13:164-170. ArticlePubMedPDF
• 92. Medtronic. Medtronic and Qualcomm collaborate to aim to improve care and health outcomes for people with type 2 diabetes cited 2019 Jul 15. Available from: http://newsroom.medtronic.com/phoenix.zhtml?c=251324&p=irolnewsArticle&ID=2172203.
• 93. SugarBEAT®. SugarBEAT 2017 cited 2019 Jul 15. Available from: http://sugarbeat.com.
• 94. LifePlus. LifePlus Inc; cited 2019 Jul 15. Available from: https://www.lifeplusinc.net.
• 95. U.S. Food & Drug Administration. FDA authorizes first fully interoperable continuous glucose monitoring system, streamlines review pathway for similar devices cited 2019 Jul 15. Available from: https://www.fda.gov/news-events/press-announcements/fda-authorizes-first-fully-interoperable-continuous-glucose-monitoring-system-streamlines-review.
• 96. Stenerson M, Cameron F, Wilson DM, Harris B, Payne S, Bequette BW, Buckingham BA. The impact of accelerometer and heart rate data on hypoglycemia mitigation in type 1 diabetes. J Diabetes Sci Technol 2014;8:64-69. ArticlePubMedPMCPDF
• 97. DeBoer MD, Chernavvsky DR, Topchyan K, Kovatchev BP, Francis GL, Breton MD. Heart rate informed artificial pancreas system enhances glycemic control during exercise in adolescents with T1D. Pediatr Diabetes 2017;18:540-546. ArticlePubMedPDF

Fig. 1

(A) Representative blood glucose (BG) monitoring data obtainable with self-monitoring of blood glucose (SMBG; in green) and with continuous glucose monitoring (CGM; in blue). Dotted circles denote hyperglycemic and hypoglycemic episodes that, using only SMBG measurements, are not detectable. (B) Assessment of the accuracy of a CGM sensor can be performed by comparing Yellow Spring Instruments Inc. (YSI) measurements (red stars) versus Dexcom G4 Platinum CGM (black solid line) measurements. For example, mean absolute relative difference can be calculated as the average ratio between the absolute difference between the CGM measurements and the YSI over the YSI.

Fig. 2

Accuracy evolution of state-of-the-art CGM systems through years. From the left: Medtronic Enlite, Abbott Freestyle Navigator, Dexcom G4 Platinum, Abbott Freestyle Libre, Dexcom G4 Platinum with 505 software, Senseonics Eversense, Dexcom G5, Dexcom G6. MARD, mean absolute relative difference; SMBG, self-monitoring of blood glucose.

Table 1

Summary of the main characteristics in terms of accuracy, features, and limitations of state-of-the-art CGM sensor devices

Manufacturer	CGM system	Accuracy (MARD), %	Calibrations	Sensor lifetime, day	Smart features	Main limitations
Medtronic	Enlite Sensor	13.6	Every 12 hr	6	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms, integration with Medtronic's pumps	Approved only for as an adjunctive device, acetaminophen interference
	Guardian Sensor 3	10.6 (abdomen) 9.1 (arm)	Every 12 hr	7	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms, integration with Medtronic's pumps	Approved only for as an adjunctive device, acetaminophen interference
Abbott	Freestyle Navigator II	14.5	2, 10, 24, 72 hr after insertion	5	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms	Approved only in some European countries as adjunctive device
	Freestyle Libre	11.4	No	14	Trend arrows	Sensor need to be scanned to get a glucose reading, not recommended for patient with hypoglycemic unawareness, confirmatory SMBG still recommended when specific episodes occur
	Freestyle Libre 2	Not available	No	14	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms, remote monitoring	Sensor need to be scanned to get a glucose reading, not recommended for patient with hypoglycemic unawareness, confirmatory SMBG still recommended when specific episodes occur
Dexcom	G4 Platinum	9	Every 12 hr	7	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms, remote monitoring	Approved only as an adjunctive device
	G5 Mobile	9	Every 12 hr	7	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms, remote monitoring, wireless communication with up to 5 devices	Confirmatory SMBG still recommended when specific episodes occur, acetaminophen interference
	G6	10	No	10	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms, remote monitoring, wireless communication with up to 5 devices	Confirmatory SMBG still recommended when specific episodes occur
Senseonics	Eversense	11.4	No	90	Trend arrows, rate-of-change alerts, hypo/hyperglycemic alarms	The sensor needs to be inserted and removed in doctor's office, approved as adjunctive device in Europe only

CGM, continuous glucose monitoring; MARD, mean absolute relative difference; SMBG, self-monitoring of blood glucose.

Figure & Data

References

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• Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial
  Caterina Bérubé, Vera Franziska Lehmann, Martin Maritsch, Mathias Kraus, Stefan Feuerriegel, Felix Wortmann, Thomas Züger, Christoph Stettler, Elgar Fleisch, A Baki Kocaballi, Tobias Kowatsch
  JMIR Human Factors.2024; 11: e42823.     CrossRef
• Nafion based biosensors: a review of recent advances and applications
  Roya Mohammadzadeh Kakhki
  International Journal of Polymeric Materials and Polymeric Biomaterials.2024; 73(17): 1470.     CrossRef
• Can Electrochemical Aptasensors Achieve the Commercial Success of Glucose Biosensors?
  Sina Ardalan, Anna Ignaszak
  Advanced Sensor Research.2024;[Epub]     CrossRef
• Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes
  Huiqi Y. Lu, Xiaorong Ding, Jane E. Hirst, Yang Yang, Jenny Yang, Lucy Mackillop, David A. Clifton
  IEEE Reviews in Biomedical Engineering.2024; 17: 98.     CrossRef
• Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study
  Tomoko Handa, Takeshi Onoue, Tomoko Kobayashi, Ryutaro Maeda, Keigo Mizutani, Ayana Yamagami, Tamaki Kinoshita, Yoshinori Yasuda, Shintaro Iwama, Takashi Miyata, Mariko Sugiyama, Hiroshi Takagi, Daisuke Hagiwara, Hidetaka Suga, Ryoichi Banno, Yoshinori Az
  JMIR Diabetes.2024; 9: e48019.     CrossRef
• The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study
  Shlomo Yeshurun, Tomer Cramer, Daniel Souroujon, Merav Mor
  JMIR mHealth and uHealth.2024; 12: e56083.     CrossRef
• Generative adversarial network-based data augmentation for improving hypoglycemia prediction: A proof-of-concept study
  Wonju Seo, Namho Kim, Sung-Woon Park, Sang-Man Jin, Sung-Min Park
  Biomedical Signal Processing and Control.2024; 92: 106077.     CrossRef
• Pre‐dinner walks may be superior to post‐dinner walks for glucose time in range in adults with type 1 diabetes on hybrid closed‐loop insulin delivery systems
  Lauren V. Turner, Michael C. Riddell
  Diabetes, Obesity and Metabolism.2024; 26(6): 2492.     CrossRef
• Real-world effectiveness of GLP-1 receptor agonist-based treatment strategies on “time in range” in patients with type 2 diabetes
  Yongru Chen, Jingxian Chen, Shuo Zhang, Dan Zhu, Feiying Deng, Rui Zuo, Yufei Hu, Yue Zhao, Yale Duan, Benwei Lin, Fengwu Chen, Yun Liang, Jiaxiong Zheng, Barkat Ali Khan, Kaijian Hou
  Frontiers in Pharmacology.2024;[Epub]     CrossRef
• Advancements in nanohybrid material-based acetone gas sensors relevant to diabetes diagnosis: A comprehensive review
  Arpit Verma, Deepankar Yadav, Subramanian Natesan, Monu Gupta, Bal Chandra Yadav, Yogendra Kumar Mishra
  Microchemical Journal.2024; 201: 110713.     CrossRef
• Utility of Flash Glucose Monitoring to Determine Glucose Variation Induced by Different Doughs in Persons with Type 2 Diabetes
  Maria Antonietta Taras, Sara Cherchi, Ilaria Campesi, Valentina Margarita, Gavino Carboni, Paola Rappelli, Giancarlo Tonolo
  Diabetology.2024; 5(1): 129.     CrossRef
• Facile chemiresistive biosensor functionalized with PANI/GOx and novel green synthesized silver nanoparticles for glucose sensing
  Jitendra B. Zalke, N.P. Narkhede, Dinesh R. Rotake, Shiv Govind Singh
  Microchemical Journal.2024; 200: 110339.     CrossRef
• A novel questionnaire for evaluating digital tool use (DTUQ-D) among individuals with type 2 diabetes: exploring the digital landscape
  Ora Peleg, Efrat Hadar, Meyran Boniel-Nissim
  Frontiers in Public Health.2024;[Epub]     CrossRef
• Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial
  Ji Yoon Kim, Sang-Man Jin, Kang Hee Sim, Bo-Yeon Kim, Jae Hyoung Cho, Jun Sung Moon, Soo Lim, Eun Seok Kang, Cheol-Young Park, Sin Gon Kim, Jae Hyeon Kim
  Diabetologia.2024; 67(7): 1223.     CrossRef
• Global scientific trends on the islet transplantation in the 21st century: A bibliometric and visualized analysis
  Sheng Chen, PeiZhong Wu, Ting Zhang, Jianqiang Zhang, Hongjun Gao
  Medicine.2024; 103(17): e37945.     CrossRef
• A method for evaluating the risks of glucose dysregulation in daily life by continuous measurement of glucose excursions under reduced glycemic load: a pilot study
  Yoshitada Katagiri, Hiromi Ozaku, Katsuhiko Kondo
  Frontiers in Sensors.2024;[Epub]     CrossRef
• The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis
  Najib Ur Rehman, Ivan Contreras, Aleix Beneyto, Josep Vehi
  Mathematics.2024; 12(10): 1567.     CrossRef
• Measures of nutrition status and health for weight‐inclusive patient care: A narrative review
  Kasuen Mauldin, Giselle A. P. Pignotti, John Gieng
  Nutrition in Clinical Practice.2024; 39(4): 751.     CrossRef
• Long-Term Evaluation of Inserted Nanocomposite Hydrogel-Based Phosphorescent Oxygen Biosensors: Evolution of Local Tissue Oxygen Levels and Foreign Body Response
  David Chimene, Waqas Saleem, Nichole Longbottom, Brian Ko, Ananth Soundaram Jeevarathinam, Staci Horn, Michael J. McShane
  ACS Applied Bio Materials.2024; 7(6): 3964.     CrossRef
• Comparison of Metabolic Control in Children and Adolescents Treated with Insulin Pumps
  Agnieszka Lejk, Karolina Myśliwiec, Arkadiusz Michalak, Barbara Pernak, Wojciech Fendler, Małgorzata Myśliwiec
  Children.2024; 11(7): 839.     CrossRef
• Kontinuierliche Glukosemessung bei Menschen mit Typ-2-Diabetes ohne intensivierte Insulintherapie: eine Standortbestimmung
  Jochen Seufert, Ingrid Dänschel, Stefan Gölz, Nicola Haller, Bernhard Kulzer, Susanne Tan, Oliver Schnell, Tobias Wiesner, Jens Kroeger
  Diabetologie und Stoffwechsel.2024; 19(05): 356.     CrossRef
• The Influence of Nickel Electrode’s Morphology on Glucose Detection
  Hailong Hu, Guohua Ma, Baogang Guo, Xingquan Zhang, Ruishi Xie, Haifeng Liu, Heyan Huang
  Electrocatalysis.2024; 15(5): 374.     CrossRef
• Insertable Biosensors: Combining Implanted Sensing Materials with Wearable Monitors
  David Chimene, Kirstie M.K. Queener, Brian S. Ko, Mike McShane, Michael Daniele
  Annual Review of Biomedical Engineering .2024; 26(1): 197.     CrossRef
• Development and Effectiveness of a Pattern Management Educational Program Using Continuous Glucose Monitoring for Type 2 Diabetic Patients in Korea: A Quasi-Experimental Study
  Seung-Yeon Kong, Mi-Kyoung Cho
  Healthcare.2024; 12(14): 1381.     CrossRef
• Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications
  Cristina Origlia, David O. Rodriguez-Duarte, Jorge A. Tobon Vasquez, Jean-Charles Bolomey, Francesca Vipiana
  Sensors.2024; 24(14): 4515.     CrossRef
• Diabetes Management in Transition: Market Insights and Technological Advancements in CGM and Insulin Delivery
  Tae Sang Yu, Soojeong Song, Junwoo Yea, Kyung‐In Jang
  Advanced Sensor Research.2024;[Epub]     CrossRef
• How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions
  Andreas Thomas, Thomas Haak, Astrid Tombek, Bernhard Kulzer, Dominic Ehrmann, Olga Kordonouri, Jens Kröger, Oliver Schubert-Olesen, Ralf Kolassa, Thorsten Siegmund, Nicola Haller, Lutz Heinemann
  Journal of Diabetes Science and Technology.2024;[Epub]     CrossRef
• Patterned thin film enzyme electrodes via spincoating and glutaraldehyde vapor crosslinking: towards scalable fabrication of integrated sensor-on-CMOS devices
  Dvin Adalian, Xiomi Madero, Samson Chen, Musab Jilani, Richard D. Smith, Songtai Li, Christin Ahlbrecht, Juan Cardenas, Abhinav Agarwal, Azita Emami, Oliver Plettenburg, Peter A. Petillo, Axel Scherer
  Lab on a Chip.2024; 24(17): 4172.     CrossRef
• Continuous Glucose, Insulin and Lifestyle Data Augmentation in Artificial Pancreas Using Adaptive Generative and Discriminative Models
  Deepjyoti Kalita, Hrishita Sharma, Khalid B. Mirza
  IEEE Journal of Biomedical and Health Informatics.2024; 28(8): 4963.     CrossRef
• Management of Continuous Glucose Monitors in Radiation Oncology Patients
  Johnathan Zeng, Tara Kosak, Samir Malkani, Julie C. Hudson, Neil E. Martin, Roy B. Tishler, Itai M. Pashtan
  Practical Radiation Oncology.2024;[Epub]     CrossRef
• Glycemic Outcomes and Nurse Perceptions of Continuous Glucose Monitoring for Hospitalized Patients
  Alexandra Thullen, Rebecca Gerber, Alyson Keen
  Journal of Nursing Care Quality.2024; 39(4): 310.     CrossRef
• Advances in Type 1 Diabetes Mellitus Management in Children
  Mridu Bahal, Vineeta Pande, Jasleen Dua, Shailaja Mane
  Cureus.2024;[Epub]     CrossRef
• Time‐in‐range derived from self‐measured blood glucose in people with type 2 diabetes advancing to iGlarLixi: A participant‐level pooled analysis of three phase 3 LixiLan randomized controlled trials
  Martin Haluzík, Mohammed E. Al‐Sofiani, Alice Y. Y. Cheng, Felipe Lauand, Lydie Melas‐Melt, Julio Rosenstock
  Diabetes, Obesity and Metabolism.2024; 26(11): 5046.     CrossRef
• Evaluating the Impact of Continuous Glucose Monitoring on Erectile Dysfunction in Type 1 Diabetes: A Focus on Reducing Glucose Variability and Inflammation
  Nicola Tecce, Davide Menafra, Mattia Proganò, Mario Felice Tecce, Rosario Pivonello, Annamaria Colao
  Healthcare.2024; 12(18): 1823.     CrossRef
• Indications, specifics and reimbursement of glucose sensors in type 2 diabetes patients
  Kateřina Štechová
  Vnitřní lékařství.2024; 70(6): 390.     CrossRef
• Enzyme free highly sensitive glucose biosensor based on Ag/Cu2O nanostructures deposited on TiO2 nanotube arrays
  Bittu Kumar, Sudip Kumar Sinha
  Monatshefte für Chemie - Chemical Monthly.2024; 155(11): 1095.     CrossRef
• The status of blood glucose monitoring and its influencing factors in Chinese patients with type 2 diabetes initiating premixed insulin: A prospective real-world study
  Si Chen, Jingyi Lu, Danfeng Peng, Fengjing Liu, Wei Lu, Wei Zhu, Yuqian Bao, Jian Zhou, Weiping Jia
  Diabetes Research and Clinical Practice.2024; 218: 111895.     CrossRef
• Ag@Au core–shell nanoparticles modified glassy carbon electrode synthesized by simple displacement reaction for non-enzymatic electrochemical glucose sensing
  Huiling Huang, Tianyu Chen, Xinyu Qin, Bo Quan, Sun Ha Paek, Wang Zhang, Yuanzhe Piao
  Journal of Electroanalytical Chemistry.2024; 975: 118726.     CrossRef
• Integrated Lab-on-a-Chip Optical Glucose Sensing System Using a Silicon Waveguide Bragg Grating Device
  Hongqiang Li, Fanglin Xie, Ming Han, Lizhen Zhang, Lu Cao, Zhiyue Zhu, Enbang Li
  IEEE Sensors Journal.2024; 24(20): 32034.     CrossRef
• Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach
  Jingzhen Li, Jingjing Ma, Olatunji Mumini Omisore, Yuhang Liu, Huajie Tang, Pengfei Ao, Yan Yan, Lei Wang, Zedong Nie
  IEEE Transactions on Neural Networks and Learning Systems.2024; 35(10): 14491.     CrossRef
• Technology and Continuous Glucose Monitoring Access, Literacy, and Use Among Patients at the Diabetes Center of an Inner-City Safety-Net Hospital: Mixed Methods Study
  Gaëlle Sabben, Courtney Telfort, Marissa Morales, Wenjia Stella Zhang, Juan C Espinoza, Francisco J Pasquel, Kate Winskell
  JMIR Diabetes.2024; 9: e54223.     CrossRef
• Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management
  Jiafeng Song, Jocelyn McNeany, Yifei Wang, Tanicia Daley, Arlene Stecenko, Rishikesan Kamaleswaran
  Computers in Biology and Medicine.2024; 183: 109255.     CrossRef
• Implantable Fluorogenic DNA Biosensor for Stress Detection
  Irina Drachuk, Namrata Ramani, Svetlana Harbaugh, Chad A. Mirkin, Jorge L. Chávez
  ACS Applied Materials & Interfaces.2024;[Epub]     CrossRef
• Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram
  Ho-Jung Song, Ju-Hyuck Han, Sung-Pil Cho, Sung-Il Im, Yong-Suk Kim, Jong-Uk Park
  Diagnostics.2024; 14(22): 2489.     CrossRef
• Computational Model-Assisted Development of a Nonenzymatic Fluorescent Glucose-Sensing Assay
  Lydia Colvin, Diana Al Husseini, Dandan Tu, Darin Dunlap, Tyler Lalonde, Muhammed Üçüncü, Alicia Megia-Fernandez, Mark Bradley, Wenshe Liu, Melissa A. Grunlan, Gerard L. Coté
  ACS Sensors.2024; 9(11): 6218.     CrossRef
• 당뇨병 치료의 진화: 관해를 향해가는 혁신적 약물치료와 첨단 관리기기의 결합
  종한 최, 민경 문
  Public Health Weekly Report.2024; 17(44): 1905.     CrossRef
• Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes
  Xueen Liu, Jiale Zhang
  Journal of Multidisciplinary Healthcare.2024; Volume 17: 5513.     CrossRef
• Continuous Glucose Monitoring Among Patients with Type 1 Diabetes in Rwanda (CAPT1D) Phase I: Feasibility Study (Preprint)
  Jason Baker, Giacomo Cappon, Jean Claude Habineza, Corey H Basch, Steve Mey, Diana L. Malkin-Washeim, Christian Schuetz, Simon P. Niyonsenga, Etienne Uwingabire, Alvera Mukamazimpaka, Paul Mbonyi, Sandhya Narayanan
  JMIR Formative Research.2024;[Epub]     CrossRef
• Development of a Novel Insulin Sensor for Clinical Decision-Making
  Eva Vargas, Eleonora M. Aiello, Jordan E. Pinsker, Hazhir Teymourian, Farshad Tehrani, Mei Mei Church, Lori M. Laffel, Francis J. Doyle, Mary-Elizabeth Patti, Eyal Dassau, Joseph Wang
  Journal of Diabetes Science and Technology.2023; 17(4): 1029.     CrossRef
• Diabetes technology and sexual health: which role?
  V. Zamponi, J. Haxhi, G. Pugliese, A. Faggiano, R. Mazzilli
  Journal of Endocrinological Investigation.2023; 47(6): 1315.     CrossRef
• Discordance Between Glycated Hemoglobin A1c and the Glucose Management Indicator in People With Diabetes and Chronic Kidney Disease
  Philippe Oriot, Claire Viry, Antoine Vandelaer, Sébastien Grigioni, Malanie Roy, Jean Christophe Philips, Gaëtan Prévost
  Journal of Diabetes Science and Technology.2023; 17(6): 1553.     CrossRef
• Expertenaustausch zum Einsatz von kontinuierlichem Glukosemonitoring (CGM) im Diabetesmanagement: Eine aktuelle Bestandsaufnahme und Blick in die Zukunft
  Andreas Thomas, Thomas Haak, Astrid Tombek, Bernhard Kulzer, Dominic Ehrmann, Olga Kordonouri, Jens Kroeger, Oliver Schubert-Olesen, Ralf Kolassa, Thorsten Siegmund, Nicola Haller, Lutz Heinemann
  Diabetologie und Stoffwechsel.2023; 18(01): 57.     CrossRef
• Evaluation of the performance and usability of a novel continuous glucose monitoring system
  Li Yan, Qiang Li, Qingbo Guan, Mingsong Han, Yu Zhao, Junfei Fang, Jiajun Zhao
  International Journal of Diabetes in Developing Countries.2023; 43(4): 551.     CrossRef
• Efficacy of intermittent short‐term use of a real‐time continuous glucose monitoring system in non‐insulin–treated patients with type 2 diabetes: A randomized controlled trial
  Sun Joon Moon, Kyung‐Soo Kim, Woo Je Lee, Mi Yeon Lee, Robert Vigersky, Cheol‐Young Park
  Diabetes, Obesity and Metabolism.2023; 25(1): 110.     CrossRef
• Intermittent-scanned continuous glucose monitoring with low glucose alarms decreases hypoglycemia incidence in middle-aged adults with type 1 diabetes in real-life setting
  Philippe Oriot, Michel P. Hermans
  Journal of Diabetes and its Complications.2023; 37(2): 108385.     CrossRef
• Applications of Microwaves in Medicine
  J.-C. Chiao, Changzhi Li, Jenshan Lin, Robert H. Caverly, James C. M. Hwang, Harel Rosen, Arye Rosen
  IEEE Journal of Microwaves.2023; 3(1): 134.     CrossRef
• A Double-Needle Gold-Silver Electrodes Continuous Glucose Monitoring Device
  C. Ben Ali Hassine, A. Tekin
  IRBM.2023; 44(3): 100752.     CrossRef
• Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus
  Michèle R. Weber, Matthias Diebold, Peter Wiesli, Andreas D. Kistler
  Experimental and Clinical Endocrinology & Diabetes.2023; 131(03): 132.     CrossRef
• Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective
  Yuanyuan Zou, Zhengkang Chu, Jiuchuan Guo, Shan Liu, Xing Ma, Jinhong Guo
  Biosensors and Bioelectronics.2023; 225: 115103.     CrossRef
• Continuous Glucose Monitoring in Enterally Fed Children with Severe Central Nervous System Impairment
  Marlena Górska, Joanna Kudzin, Anna Borkowska, Agnieszka Szlagatys-Sidorkiewicz, Agnieszka Szadkowska, Małgorzata Myśliwiec, Ewa Toporowska-Kowalska
  Nutrients.2023; 15(3): 513.     CrossRef
• Prevalence of type 2 diabetes complications and its association with diet knowledge and skills and self‐care barriers in Tabriz, Iran: A cross‐sectional study
  Habib Jalilian, Elnaz Javanshir, Leila Torkzadeh, Saeedeh Fehresti, Nazanin Mir, Majid Heidari‐Jamebozorgi, Somayeh Heydari
  Health Science Reports.2023;[Epub]     CrossRef
• Status of continuous glucose monitoring use and management in tertiary hospitals of China: a cross-sectional study
  Liping Chen, Xiaoqin Liu, Qin Lin, Hongmei Dai, Yong Zhao, Zumin Shi, Liping Wu
  BMJ Open.2023; 13(2): e066801.     CrossRef
• Diboronic-Acid-Based Electrochemical Sensor for Enzyme-Free Selective and Sensitive Glucose Detection
  Joong-Hyun Kim, Hongsik Choi, Chul-Soon Park, Heung-Seop Yim, Dongguk Kim, Sungmin Lee, Yeonkeong Lee
  Biosensors.2023; 13(2): 248.     CrossRef
• Artificial intelligence biosensors for continuous glucose monitoring
  Xiaofeng Jin, Andrew Cai, Tailin Xu, Xueji Zhang
  Interdisciplinary Materials.2023; 2(2): 290.     CrossRef
• Continuous Glucose Monitoring in Dogs and Cats
  Francesca Del Baldo, Federico Fracassi
  Veterinary Clinics of North America: Small Animal Practice.2023; 53(3): 591.     CrossRef
• Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy
  Anders Pors, Kaspar G. Rasmussen, Rune Inglev, Nina Jendrike, Amalie Philipps, Ajenthen G. Ranjan, Vibe Vestergaard, Jan E. Henriksen, Kirsten Nørgaard, Guido Freckmann, Karl D. Hepp, Michael C. Gerstenberg, Anders Weber
  ACS Sensors.2023; 8(3): 1272.     CrossRef
• Diabetes mellitus in der Akut- und Notfallmedizin
  Leo Benning, Julian Krehl, Felix Patricius Hans
  Notfallmedizin up2date.2023; 18(01): 45.     CrossRef
• Empowering People with Diabetes: Role of Continuous Glucose Monitor Systems
  Sneha B Srivastava
  American Journal of Lifestyle Medicine.2023; 17(3): 359.     CrossRef
• Diabétologie connectée : quelles sont les attentes des médecins et des patients ?
  Nicolas Naïditch, Jean-Pierre Riveline
  Médecine des Maladies Métaboliques.2023; 17(2): 2S3.     CrossRef
• Association of Vibrotactile Biofeedback With Reduced Ergonomic Risk for Surgeons During Tonsillectomy
  Natalie A. Kelly, Abdulrahman Althubaiti, Aashika D. Katapadi, Adam G. Smith, Sarah C. Nyirjesy, Jane H. Yu, Amanda J. Onwuka, Tendy Chiang
  JAMA Otolaryngology–Head & Neck Surgery.2023; 149(5): 397.     CrossRef
• The Evolution of Diabetes Technology – Options Toward Personalized Care
  Maleeha Zahid, Samaneh Dowlatshahi, Abhishek H. Kansara, Archana R. Sadhu
  Endocrine Practice.2023; 29(8): 653.     CrossRef
• A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q- Learning to Improve Type 1 Diabetes Management
  Giulia Noaro, Taiyu Zhu, Giacomo Cappon, Andrea Facchinetti, Pantelis Georgiou
  IEEE Journal of Biomedical and Health Informatics.2023; 27(5): 2536.     CrossRef
• Celebrating a Century of Insulin Discovery: A Critical Appraisal of the Emerging Alternative Insulin Delivery Systems
  Ntethelelo Sibiya, Bonisiwe Mbatha, Phikelelani Ngubane, Andile Khathi
  Current Drug Delivery.2023; 20(6): 656.     CrossRef
• Machine Learning–Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study
  Nicholas Berin Chan, Weizi Li, Theingi Aung, Eghosa Bazuaye, Rosa M Montero
  JMIR AI.2023; 2: e45450.     CrossRef
• Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System
  Bikram Khadka, Byeongmoon Lee, Ki-Taek Kim
  Biomolecules.2023; 13(6): 929.     CrossRef
• Wearable Electrochemical Glucose Sensors in Diabetes Management: A Comprehensive Review
  Tamoghna Saha, Rafael Del Caño, Kuldeep Mahato, Ernesto De la Paz, Chuanrui Chen, Shichao Ding, Lu Yin, Joseph Wang
  Chemical Reviews.2023; 123(12): 7854.     CrossRef
• Real-life 24-week changes in glycemic parameters among European users of flash glucose monitoring with type 1 and 2 diabetes and different levels of glycemic control
  Annel Lameijer, Julia J. Bakker, Kalvin Kao, Yongjin Xu, Rijk O.B. Gans, Henk J.G. Bilo, Timothy C. Dunn, Peter R. van Dijk
  Diabetes Research and Clinical Practice.2023; 201: 110735.     CrossRef
• Les médicaments anti-diabétiques : ce que le médecin anesthésiste réanimateur doit savoir
  Michael Joubert
  Anesthésie & Réanimation.2023; 9(3): 251.     CrossRef
• Glycemia control using remote technologies
  L. A. Suplotova, O. O. Alieva
  Meditsinskiy sovet = Medical Council.2023; 17(9): 81.     CrossRef
• Data-enabled learning and control algorithms for intelligent glucose management: The state of the art
  Deheng Cai, Wenjing Wu, Marzia Cescon, Wei Liu, Linong Ji, Dawei Shi
  Annual Reviews in Control.2023; 56: 100897.     CrossRef
• A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials
  Martina Vettoretti, Martina Drecogna, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
  Computer Methods and Programs in Biomedicine.2023; 240: 107700.     CrossRef
• Volumetric Electron Transfer from Metabolites to Chemically Doped Polymer Electrodes
  Siew Ting Melissa Tan, Gijun Lee, Kalee Rozylowicz, Adam Marks, Alberto Salleo
  Advanced Functional Materials.2023;[Epub]     CrossRef
• Diabetes and hypertension MobileHealth systems: a review of general challenges and advancements
  Bliss Utibe-Abasi Stephen, Benedicta C. Uzoewulu, Phillip Michael Asuquo, Simeon Ozuomba
  Journal of Engineering and Applied Science.2023;[Epub]     CrossRef
• THE ASSESSMENT OF COMPENSATION OF CARBOHYDRATE METABOLISM IN PATIENTS WITH TYPE 2 DIABETES MELLITUS WITH METABOLIC SYNDROME BEYOND THE LIMITS OF GLYCATED HEMOGLOBIN
  Taras V. Romaniv, Nadiya V. Skrypnyk, Ulyana V. Synko, Nataliia M. Voronych-Semchenko, Oleh V. Melnyk, Anna O. Hryb, Igor B. Boruchok
  Wiadomości Lekarskie.2023; 76(6): 1385.     CrossRef
• Pros and cons of continous glucose monitoring
  Marcin Ciechański, Edyta Witkowska, Agnieszka Ostańska, Adrianna Szafran, Klaudia Wiśniewska, Laura Piasek, Grzegorz Godek, Kacper Więcław, Katarzyna Stańko, Wiktor Terelak
  Journal of Medical Science.2023;[Epub]     CrossRef
• Continuous Glucose Monitoring by Insulin-Treated Pilots Flying Commercial Aircraft Within the ARA.MED.330 Diabetes Protocol: A Preliminary Feasibility Study
  Gillian L. Garden, Fariba Shojaee-Moradie, Ewan J. Hutchison, Brian M. Frier, Kenneth M. Shaw, Simon R. Heller, Gerd Koehler, Julia K. Mader, Declan Maher, Graham A. Roberts, David L. Russell-Jones
  Diabetes Technology & Therapeutics.2023; 25(8): 543.     CrossRef
• Importance of continuous glucose monitoring in the treatment of diabetes mellitus
  Sun Joon Moon, Won-Young Lee
  Journal of the Korean Medical Association.2023; 66(7): 432.     CrossRef
• DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions
  Temiloluwa Prioleau, Abigail Bartolome, Richard Comi, Catherine Stanger
  Scientific Data.2023;[Epub]     CrossRef
• Testing the Real-World Accuracy of the Dexcom G6 Pro CGM During the Insulin-Only Bionic Pancreas Pivotal Trial
  Martin Chase Marak, Peter Calhoun, Edward R. Damiano, Steven J. Russell, Katrina J. Ruedy, Roy W. Beck
  Diabetes Technology & Therapeutics.2023; 25(11): 817.     CrossRef
• Use of continuous glucose monitoring in pediatric gastroenterology allows for personalized nutrition support care – Potential for collaboration between pediatric endocrinologists and gastroenterologists
  Kathryn Hitchcock, Stephanie Oliveira
  Journal of Pediatric Endocrinology and Diabetes.2023; 3: 34.     CrossRef
• Anti-biofouling strategies for implantable biosensors of continuous glucose monitoring systems
  Yan Zheng, Dunyun Shi, Zheng Wang
  Frontiers of Chemical Science and Engineering.2023; 17(12): 1866.     CrossRef
• A novel strategy for therapeutic drug monitoring: application of biosensors to quantify antimicrobials in biological matrices
  Quanfang Wang, Sihan Li, Jiaojiao Chen, Luting Yang, Yulan Qiu, Qian Du, Chuhui Wang, Mengmeng Teng, Taotao Wang, Yalin Dong
  Journal of Antimicrobial Chemotherapy.2023; 78(11): 2612.     CrossRef
• Hypoglycemic Effect of an Herbal Decoction (Modified Gangsimtang) in a Patient with Severe Type 2 Diabetes Mellitus Refusing Oral Anti-Diabetic Medication: A Case Report
  Sungjun Joo, Hyonjun Chun, Jisu Lee, Seungmin Seo, Jungmin Lee, Jungtae Leem
  Medicina.2023; 59(11): 1919.     CrossRef
• GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks
  Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou
  IEEE Journal of Biomedical and Health Informatics.2023; 27(10): 5122.     CrossRef
• Millifluidic valves and pumps made of tape and plastic
  Josue U. Amador-Hernandez, Pablo E. Guevara-Pantoja, Diana F. Cedillo-Alcantar, Gabriel A. Caballero-Robledo, Jose L. Garcia-Cordero
  Lab on a Chip.2023; 23(20): 4579.     CrossRef
• Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes
  Taiyu Zhu, Kezhi Li, Pantelis Georgiou
  IEEE Journal of Biomedical and Health Informatics.2023; 27(10): 5087.     CrossRef
• Flash Glucose Monitoring in Croatia: The Optimal Number of Scans per Day to Achieve Good Glycemic Control in Type 1 Diabetes
  Silvija Canecki-Varzic, Ivana Prpic-Krizevac, Maja Cigrovski Berkovic, Dario Rahelic, Ema Schonberger, Marina Gradiser, Ines Bilic-Curcic
  Medicina.2023; 59(11): 1893.     CrossRef
• The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP
  Francesco Prendin, Jacopo Pavan, Giacomo Cappon, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti
  Scientific Reports.2023;[Epub]     CrossRef
• SHMAD: A Smart Health Care System to Monitor Alzheimer’s Disease Patients
  Shabana R. Ziyad, May Altulyan, Meshal Alharbi
  Journal of Alzheimer's Disease.2023; 95(4): 1545.     CrossRef
• Séquelles fonctionnelles après résection pancréatique carcinologique. Un sujet d’actualité pour les patients et les praticiens
  Andrea Mulliri, Michael Joubert, Marie-Astrid Piquet, Arnaud Alves, Benoît Dupont
  Journal de Chirurgie Viscérale.2023; 160(6): 470.     CrossRef
• Functional sequelae after pancreatic resection for cancer
  Andrea Mulliri, Michael Joubert, Marie-Astrid Piquet, Arnaud Alves, Benoît Dupont
  Journal of Visceral Surgery.2023; 160(6): 427.     CrossRef
• Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A preliminary study
  Yoichi Nakamura, Shinya Furukawa
  World Journal of Diabetes.2023; 14(10): 1562.     CrossRef
• Evaluating passive physiological data collection during Spravato treatment
  Todd M. Solomon, Matus Hajduk, Martin Majernik, Jamileh Jemison, Alexander Deschamps, Jenna Scoggins, Adam Kolar, Miguel Amável Pinheiro, Peter Dubec, Ondrej Skala, Owen Muir, Amanda Tinkelman, Daniel R. Karlin, Robert Barrow
  Frontiers in Digital Health.2023;[Epub]     CrossRef
• Fabrication of conductive Ag/AgCl/Ag nanorods ink on Laser-induced graphene electrodes on flexible substrates for non-enzymatic glucose detection
  Rana Bagheri, Saeid Alikhani, Ebrahim Miri-Moghaddam
  Scientific Reports.2023;[Epub]     CrossRef
• Co-design of Human-centered, Explainable AI for Clinical Decision Support
  Cecilia Panigutti, Andrea Beretta, Daniele Fadda, Fosca Giannotti, Dino Pedreschi, Alan Perotti, Salvatore Rinzivillo
  ACM Transactions on Interactive Intelligent Systems.2023; 13(4): 1.     CrossRef
• Analysis of blood glucose monitoring – a review on recent advancements and future prospects
  Gayathri Priyadarshini R, Sathiya Narayanan
  Multimedia Tools and Applications.2023; 83(20): 58375.     CrossRef
• Overview of modern sensors for continuous glucose monitoring
  K. T. Momynaliev, M. V. Prokopiev, I. V. Ivanov
  Diabetes mellitus.2023; 26(6): 575.     CrossRef
• A Prospective Multicenter Clinical Performance Evaluation of the C-CGM System
  Mihailo Rebec, Kevin Cai, Ralph Dutt-Ballerstadt, Ellen Anderson
  Journal of Diabetes Science and Technology.2022; 16(2): 390.     CrossRef
• Perceived Burdens and Benefits Associated With Continuous Glucose Monitor Use in Type 1 Diabetes Across the Lifespan
  Vidita Divan, Margaret Greenfield, Christopher P. Morley, Ruth S. Weinstock
  Journal of Diabetes Science and Technology.2022; 16(1): 88.     CrossRef
• Technologies for Diabetes Self-Monitoring: A Scoping Review and Assessment Using the REASSURED Criteria
  Jessica Hanae Zafra-Tanaka, David Beran, Beatrice Vetter, Rangarajan Sampath, Antonio Bernabe-Ortiz
  Journal of Diabetes Science and Technology.2022; 16(4): 962.     CrossRef
• Temporal Trends for Diabetes Management and Glycemic Control Between 2010 and 2019 in Korean Children and Adolescents with Type 1 Diabetes
  Jaewon Choe, Seung Hyun Won, Yunsoo Choe, Sang Hee Park, Yun Jeong Lee, Jieun Lee, Young Ah Lee, Han Hyuk Lim, Jae-Ho Yoo, Seong Yong Lee, Eun Young Kim, Choong Ho Shin, Jae Hyun Kim
  Diabetes Technology & Therapeutics.2022; 24(3): 201.     CrossRef
• International comparison of glycaemic control in people with type 1 diabetes: an update and extension
  Regina Prigge, John A. McKnight, Sarah H. Wild, Aveni Haynes, Timothy W. Jones, Elizabeth A. Davis, Birgit Rami‐Merhar, Maria Fritsch, Christine Prchla, Astrid Lavens, Kris Doggen, Suchsia Chao, Ronnie Aronson, Ruth Brown, Else H. Ibfelt, Jannet Svensson,
  Diabetic Medicine.2022;[Epub]     CrossRef
• Artificial intelligence perspective in the future of endocrine diseases
  Mandana Hasanzad, Hamid Reza Aghaei Meybodi, Negar Sarhangi, Bagher Larijani
  Journal of Diabetes & Metabolic Disorders.2022; 21(1): 971.     CrossRef
• Telehealth Technologies and Their Benefits to People With Diabetes
  Chinenye O. Usoh, Kristine Kilen, Carolyn Keyes, Crystal Paige Johnson, Joseph A. Aloi
  Diabetes Spectrum.2022; 35(1): 8.     CrossRef
• Acetylated Trifluoromethyl Diboronic Acid Anthracene with a Large Stokes Shift and Long Excitation Wavelength as a Glucose-Selective Probe
  Hongsik Choi, Inhyeok Song, Chul Soon Park, Heung-seop Yim, Joong Hyun Kim
  Applied Sciences.2022; 12(6): 2782.     CrossRef
• Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study
  Patrik Schretzlmaier, Achim Hecker, Elske Ammenwerth
  JMIR Human Factors.2022; 9(1): e34918.     CrossRef
• Continuous Glucose Monitoring System Based on Percutaneous Microneedle Array
  Ming-Nan Chien, Yu-Jen Chen, Chin-Han Bai, Jung-Tung Huang
  Micromachines.2022; 13(3): 478.     CrossRef
• Impact of COVID-19 Lockdown on the Metabolic Control Parameters in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis
  Ifan Ali Wafa, Nando Reza Pratama, Nurizzah Farahiyah Sofia, Elsha Stephanie Anastasia, Tiffany Konstantin, Maharani Ayuputeri Wijaya, M. Rifqi Wiyono, Lilik Djuari, Hermina Novida
  Diabetes & Metabolism Journal.2022; 46(2): 260.     CrossRef
• Expert Roundtable on Continuous Glucose Monitoring
  Cheryl Rosenfeld, Thomas Blevins, Grazia Aleppo, Gregory Forlenza, Diana Isaacs, Javier Morales, Jane Seley, Jeffrey Unger
  Endocrine Practice.2022; 28(6): 622.     CrossRef
• Glucose variability and predicted cardiovascular risk after gastrectomy
  Jun Shibamoto, Takeshi Kubota, Takuma Ohashi, Hirotaka Konishi, Atsushi Shiozaki, Hitoshi Fujiwara, Kazuma Okamoto, Eigo Otsuji
  Surgery Today.2022; 52(11): 1634.     CrossRef
• Efficacy of once-weekly tirzepatide versus once-daily insulin degludec on glycaemic control measured by continuous glucose monitoring in adults with type 2 diabetes (SURPASS-3 CGM): a substudy of the randomised, open-label, parallel-group, phase 3 SURPASS
  Tadej Battelino, Richard M Bergenstal, Angel Rodríguez, Laura Fernández Landó, Ross Bray, Zhentao Tong, Katelyn Brown
  The Lancet Diabetes & Endocrinology.2022; 10(6): 407.     CrossRef
• Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes
  Loïc Olçomendy, Louis Cassany, Antoine Pirog, Roberto Franco, Emilie Puginier, Manon Jaffredo, David Gucik-Derigny, Héctor Ríos, Alejandra Ferreira de Loza, Julien Gaitan, Matthieu Raoux, Yannick Bornat, Bogdan Catargi, Jochen Lang, David Henry, Sylvie Re
  Frontiers in Endocrinology.2022;[Epub]     CrossRef
• Effect of divergent continuous glucose monitoring technologies on glycaemic control in type 1 diabetes mellitus: A systematic review and meta‐analysis of randomised controlled trials
  Mona Elbalshy, Jillian Haszard, Hazel Smith, Sarahmarie Kuroko, Barbara Galland, Nick Oliver, Viral Shah, Martin I. de Bock, Benjamin J. Wheeler
  Diabetic Medicine.2022;[Epub]     CrossRef
• Novel Glycemic Index Based on Continuous Glucose Monitoring to Predict Poor Clinical Outcomes in Critically Ill Patients: A Pilot Study
  Eun Yeong Ha, Seung Min Chung, Il Rae Park, Yin Young Lee, Eun Young Choi, Jun Sung Moon
  Frontiers in Endocrinology.2022;[Epub]     CrossRef
• Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms
  Brian Bogue-Jimenez, Xiaolei Huang, Douglas Powell, Ana Doblas
  Sensors.2022; 22(9): 3534.     CrossRef
• Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach
  N. Camerlingo, M. Vettoretti, S. Del Favero, A. Facchinetti, P. Choudhary, G. Sparacino
  Computer Methods and Programs in Biomedicine.2022; 221: 106862.     CrossRef
• A Miniaturized Optofluidic Glucose Monitoring System Based on Enzyme Colorimetry
  Qingmei Xu, Chongwei Zou, Chengtao Sun, Xingguo Zhang, Haixia Yu, Dachao Li
  IEEE Sensors Journal.2022; 22(10): 9246.     CrossRef
• Use and Trends of Diabetes Self-Management Technologies: A Correlation-Based Study
  Jesús Fontecha, Iván González, Alfonso Barragán, Theodore Lim, Dario Pitocco
  Journal of Diabetes Research.2022; 2022: 1.     CrossRef
• Nanotechnology in Diabetes Mellitus: Overview for Nurses
  R Priya, Baba Vajrala
  Pondicherry Journal of Nursing.2022; 15(1): 22.     CrossRef
• Effect of Different Glucose Monitoring Methods on Bold Glucose Control: A Systematic Review and Meta-Analysis
  Yeling Wang, Congcong Zou, Han Na, Weixin Zeng, Xiaoyan Li, Xi Lou
  Computational and Mathematical Methods in Medicine.2022; 2022: 1.     CrossRef
• Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review
  Aminah Hina, Wala Saadeh
  Sensors.2022; 22(13): 4855.     CrossRef
• Performance of islets of Langerhans conformally coated via an emulsion cross-linking method in diabetic rodents and nonhuman primates
  Aaron A. Stock, Grisell C. Gonzalez, Sophia I. Pete, Teresa De Toni, Dora M. Berman, Alexander Rabassa, Waldo Diaz, James C. Geary, Melissa Willman, Joy M. Jackson, Noa H. DeHaseth, Noel M. Ziebarth, Anthony R. Hogan, Camillo Ricordi, Norma S. Kenyon, Ali
  Science Advances.2022;[Epub]     CrossRef
• Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices
  Saroj Kumar Das, Kavya K. Nayak, P. R. Krishnaswamy, Vinay Kumar, Navakanta Bhat
  ECS Sensors Plus.2022; 1(3): 031601.     CrossRef
• Design Strategies and Prospects in Developing Wearable Glucose Monitoring System Using Printable Organic Transistor and Microneedle: A Review
  Fazliyatul Azwa Md Rezali, Norhayati Soin, Sharifah Fatmadiana Wan Muhamad Hatta, Mohamad Hazwan Mohd Daut, Muhammad Hafizuddin Al-Helmy Nouxman, Hanim Hussin
  IEEE Sensors Journal.2022; 22(14): 13785.     CrossRef
• Review of Automated Insulin Delivery Systems for Type 1 Diabetes and Associated Time in Range Outcomes
  Armaan Nallicheri, Katherine M Mahoney, Hanna A Gutow, Natalie Bellini, Diana Isaacs
  Endocrinology.2022; 18(1): 27.     CrossRef
• Evaluation of Mesoporous TiO2 Layers as Glucose Optical Sensors
  David Ortiz de Zárate, Sara Serna, Salvador Ponce-Alcántara, Jaime García-Rupérez
  Sensors.2022; 22(14): 5398.     CrossRef
• A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets
  Alessandro Rossi, Annieke Venema, Petra Haarsma, Lude Feldbrugge, Rob Burghard, David Rodriguez-Buritica, Giancarlo Parenti, Maaike H Oosterveer, Terry G J Derks
  The Journal of Clinical Endocrinology & Metabolism.2022; 107(9): e3612.     CrossRef
• Continuous glucose monitoring as a close to real life alternative to meal studies – a pilot study with a functional drink containing amino acids and chromium
  Azat Samigullin, Per M. Humpert, Elin Östman
  Frontiers in Medical Technology.2022;[Epub]     CrossRef
• An overview of recent advances in insulin delivery and wearable technology for effective management of diabetes
  Sujeet Kumar Raj, M. Ravindra Babu, Sukriti Vishwas, M.V.N.L. Chaitanya, Vancha Harish, Gaurav Gupta, Dinesh Kumar Chellappan, Kamal Dua, Sachin Kumar Singh
  Journal of Drug Delivery Science and Technology.2022; 75: 103728.     CrossRef
• Medical Certification of Pilots Through the Insulin-Treated Diabetes Mellitus Protocol at the FAA
  Lynn K. Stanwyck, James R. DeVoll, Joyce Pastore, Zykevise Gamble, Anna Poe, Gabrielle V. Gui
  Aerospace Medicine and Human Performance.2022; 93(8): 627.     CrossRef
• Rate of glycaemic control and associated factors in patients with type 2 diabetes mellitus treated with insulin-based therapy at selected hospitals in Northwest Ethiopia: a multicentre cross-sectional study
  Ashenafi Kibret Sendekie, Eyayaw Ashete Belachew, Ephrem Mebratu Dagnew, Adeladlew Kassie Netere
  BMJ Open.2022; 12(9): e065250.     CrossRef
• Glucose Profiles Assessed by Intermittently Scanned Continuous Glucose Monitoring System during the Perioperative Period of Metabolic Surgery
  Kyuho Kim, Sung Hee Choi, Hak Chul Jang, Young Suk Park, Tae Jung Oh
  Diabetes & Metabolism Journal.2022; 46(5): 713.     CrossRef
• Hypoglycemic events and glycemic control effects between NPH and premixed insulin in patients with type 2 diabetes mellitus: A real-world experience at a comprehensive specialized hospital in Ethiopia
  Ashenafi Kibret Sendekie, Adeladlew Kassie Netere, Eyayaw Ashete Belachew, Rekha Samuel
  PLOS ONE.2022; 17(9): e0275032.     CrossRef
• Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study
  Daria Di Filippo, Marrwah Ahmadzai, Melissa Han Yiin Chang, Ksana Horgan, Ru Min Ong, Justine Darling, Mahmood Akhtar, Amanda Henry, Alec Welsh, Daniela Foti
  Journal of Diabetes Research.2022; 2022: 1.     CrossRef
• Caring for people with diabetes
  Martha M. Funnell, Katherine A. Kloss, Robin B. Nwankwo
  Nursing.2022; 52(11): 26.     CrossRef
• Tackling the challenges of developing microneedle-based electrochemical sensors
  Hilmee Abdullah, Tonghathai Phairatana, Itthipon Jeerapan
  Microchimica Acta.2022;[Epub]     CrossRef
• A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management
  Soumyasanta Laha, Aditi Rajput, Suvra S. Laha, Rohan Jadhav
  Biosensors.2022; 12(11): 965.     CrossRef
• Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions
  Francesco Prendin, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, Jorge Bondia
  Sensors.2022; 22(22): 8682.     CrossRef
• Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review
  Xueer Lin, Jiaying Luo, Minyan Liao, Yalan Su, Mo Lv, Qing Li, Shenglan Xiao, Jianbang Xiang
  Biosensors.2022; 12(12): 1131.     CrossRef
• Acceptability and feasibility of continuous glucose monitoring in people with diabetes: protocol for a mixed-methods systematic review of quantitative and qualitative evidence
  Jennifer V. E. Brown, Ramzi Ajjan, Najma Siddiqi, Peter A. Coventry
  Systematic Reviews.2022;[Epub]     CrossRef
• Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring
  I-Min Chiu, Chi-Yung Cheng, Po-Kai Chang, Chao-Jui Li, Fu-Jen Cheng, Chun-Hung Richard Lin
  Biosensors.2022; 13(1): 23.     CrossRef
• Effect of Hydroxychloroquine on Glycemic Variability in Type 2 Diabetes Patients Uncontrolled on Glimepiride and Metformin Therapy
  Rajesh Rajput, Suyasha Saini, Siddhant Rajput, Parankush Upadhyay
  Indian Journal of Endocrinology and Metabolism.2022; 26(6): 537.     CrossRef
• GESTATIONAL DIABETES MELLITUS: MODERN GLYCEMIA MONITORING SYSTEMS
  YU.A. DUDAREVA, V.A. GURYEVA, G.V. NEMTSEVA
  AVICENNA BULLETIN.2022; 24(1): 97.     CrossRef
• Extraction With Sweat-Sebum Emulsion as a New Test Method for Leachables in Patch-Based Medical Devices, Illustrated by Assessment of Isobornylacrylate (IBOA) in Diabetes Products
  Herbert Fink, Nuno M. de Barros Fernandes, Jörg Weissmann, Manfred Frey
  Journal of Diabetes Science and Technology.2021; 15(4): 792.     CrossRef
• Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator
  Nunzio Camerlingo, Martina Vettoretti, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
  Journal of Diabetes Science and Technology.2021; 15(2): 346.     CrossRef
• Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience
  Alan Godfrey, Benjamin Vandendriessche, Jessie P. Bakker, Cheryl Fitzer‐Attas, Ninad Gujar, Matthew Hobbs, Qi Liu, Carrie A. Northcott, Virginia Parks, William A. Wood, Vadim Zipunnikov, John A. Wagner, Elena S. Izmailova
  Clinical and Translational Science.2021; 14(1): 62.     CrossRef
• Self-charging wearables for continuous health monitoring
  Jiyong Kim, Salman Khan, Peng Wu, Sungjin Park, Hwanjoo Park, Choongho Yu, Woochul Kim
  Nano Energy.2021; 79: 105419.     CrossRef
• Impact of Switching from Intermittently Scanned to Real-Time Continuous Glucose Monitoring Systems in a Type 1 Diabetes Patient French Cohort: An Observational Study of Clinical Practices
  Yannis Préau, Martine Armand, Sébastien Galie, Pauline Schaepelynck, Denis Raccah
  Diabetes Technology & Therapeutics.2021; 23(4): 259.     CrossRef
• Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel
  Sanjay Kalra, Shehla Shaikh, Gagan Priya, Manas P. Baruah, Abhyudaya Verma, Ashok K. Das, Mona Shah, Sambit Das, Deepak Khandelwal, Debmalya Sanyal, Sujoy Ghosh, Banshi Saboo, Ganapathi Bantwal, Usha Ayyagari, Daphne Gardner, Cecilia Jimeno, Nancy E. Barb
  Diabetes Therapy.2021; 12(2): 465.     CrossRef
• Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy
  Giulia Noaro, Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Simone Del Favero, Andrea Facchinetti
  IEEE Transactions on Biomedical Engineering.2021; 68(1): 247.     CrossRef
• Efficacy of telemedicine for persons with type 1 diabetes during Covid19 lockdown
  Federico Boscari, Sara Ferretto, Ambra Uliana, Angelo Avogaro, Daniela Bruttomesso
  Nutrition & Diabetes.2021;[Epub]     CrossRef
• Technological innovation of Continuous Glucose Monitoring (CGM) as a tool for commercial aviation pilots with insulin-treated diabetes and stakeholders/regulators: A new chance to improve the directives?
  F. Strollo, A. Furia, P. Verde, A. Bellia, M. Grussu, A. Mambro, M.D. Petrelli, S. Gentile
  Diabetes Research and Clinical Practice.2021; 172: 108638.     CrossRef
• Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges
  Omer Mujahid, Ivan Contreras, Josep Vehi
  Sensors.2021; 21(2): 546.     CrossRef
• Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study
  Marina Valenzano, Ivan Cibrario Bertolotti, Adriano Valenzano, Giorgio Grassi
  BMJ Open Diabetes Research & Care.2021; 9(1): e001045.     CrossRef
• Machine learning for the diagnosis of early-stage diabetes using temporal glucose profiles
  Woo Seok Lee, Junghyo Jo, Taegeun Song
  Journal of the Korean Physical Society.2021; 78(5): 373.     CrossRef
• Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only
  Francesco Prendin, Simone Del Favero, Martina Vettoretti, Giovanni Sparacino, Andrea Facchinetti
  Sensors.2021; 21(5): 1647.     CrossRef
• A “Slide Rule” to Adjust Insulin Dose Using Trend Arrows in Adults with Type 1 Diabetes: Test in Silico and in Real Life
  Daniela Bruttomesso, Federico Boscari, Giuseppe Lepore, Giulia Noaro, Giacomo Cappon, Angela Girelli, Lutgarda Bozzetto, Andrea Tumminia, Giorgio Grassi, Giovanni Sparacino, Luigi Laviola, Andrea Facchinetti
  Diabetes Therapy.2021; 12(5): 1313.     CrossRef
• Glycemic variability and cardiovascular disease in patients with type 2 diabetes
  Marcela Martinez, Jimena Santamarina, Adrian Pavesi, Carla Musso, Guillermo E Umpierrez
  BMJ Open Diabetes Research & Care.2021; 9(1): e002032.     CrossRef
• Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments
  Omar Diouri, Monika Cigler, Martina Vettoretti, Julia K. Mader, Pratik Choudhary, Eric Renard
  Diabetes/Metabolism Research and Reviews.2021;[Epub]     CrossRef
• Smartphone-based colorimetric detection systems for glucose monitoring in the diagnosis and management of diabetes
  Özlem Kap, Volkan Kılıç, John G. Hardy, Nesrin Horzum
  The Analyst.2021; 146(9): 2784.     CrossRef
• The impact of hypoglycaemia on the quality of life of family members of adults with type 1 or type 2 diabetes: A qualitative systematic review
  Mette Valdersdorf Jensen, Melanie Broadley, Jane Speight, Alison Scope, Louise Preston, Simon Heller, Bastiaan E. de Galan, Frans Pouwer, Christel Hendrieckx
  Diabetic Medicine.2021;[Epub]     CrossRef
• A review of biosensor technology and algorithms for glucose monitoring
  Yaguang Zhang, Jingxue Sun, Liansheng Liu, Hong Qiao
  Journal of Diabetes and its Complications.2021; 35(8): 107929.     CrossRef
• Optical glucose biosensor built-in disposable strips and wearable electronic devices
  Abdullah Reda, Sherif A. El-Safty, Mahmoud M. Selim, Mohamed A. Shenashen
  Biosensors and Bioelectronics.2021; 185: 113237.     CrossRef
• Advances, Challenges, and Cost Associated with Continuous Glucose Monitor Use in Adolescents and Young Adults with Type 1 Diabetes
  Karishma A. Datye, Daniel R. Tilden, Angelee M. Parmar, Eveline R. Goethals, Sarah S. Jaser
  Current Diabetes Reports.2021;[Epub]     CrossRef
• Is HbA1c an ideal biomarker of well-controlled diabetes?
  Georgia Kaiafa, Stavroula Veneti, George Polychronopoulos, Dimitrios Pilalas, Stylianos Daios, Ilias Kanellos, Triantafyllos Didangelos, Stamatina Pagoni, Christos Savopoulos
  Postgraduate Medical Journal.2021; 97(1148): 380.     CrossRef
• Technology in the management of type 2 diabetes: Present status and future prospects
  Aideen Daly, Roman Hovorka
  Diabetes, Obesity and Metabolism.2021; 23(8): 1722.     CrossRef
• A Non-Invasive Flexible Glucose Monitoring Sensor Using a Broadband Reject Filter
  Moussa Bteich, Jessica Hanna, Joseph Costantine, Rouwaida Kanj, Youssef Tawk, Ali H. Ramadan, Assaad A. Eid
  IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology.2021; 5(2): 139.     CrossRef
• Wearable patch delivery system for artificial pancreas health diagnostic-therapeutic application: A review
  Nur Farrahain Nadia Ahmad, Nik Nazri Nik Ghazali, Yew Hoong Wong
  Biosensors and Bioelectronics.2021; 189: 113384.     CrossRef
• Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies
  Mary Katherine Ray, Alana McMichael, Maria Rivera-Santana, Jacob Noel, Tamara Hershey
  JMIR Diabetes.2021; 6(2): e27027.     CrossRef
• Designing biomaterials for the modulation of allogeneic and autoimmune responses to cellular implants in Type 1 Diabetes
  Magdalena M. Samojlik, Cherie L. Stabler
  Acta Biomaterialia.2021; 133: 87.     CrossRef
• Evaluation of a continuous glucose monitoring system in neonatal foals
  David Wong, Caitlin Malik, Katarzyna Dembek, Krista Estell, Megan Marchitello, Katie Wilson
  Journal of Veterinary Internal Medicine.2021; 35(4): 1995.     CrossRef
• Flash Glucose Monitoring in the Netherlands: Increased monitoring frequency is associated with improvement of glycemic parameters
  Annel Lameijer, Nicole Lommerde, Timothy C. Dunn, Marion J. Fokkert, Mireille A. Edens, Kalvin Kao, Yongjin Xu, R.O.B. Gans, Henk J.G. Bilo, Peter R. van Dijk
  Diabetes Research and Clinical Practice.2021; 177: 108897.     CrossRef
• Utilisation, access and recommendations regarding technologies for people living with type 1 diabetes: consensus statement of the ADS/ADEA/APEG/ADIPS Working Group
  Anthony J Pease, Sofianos Andrikopoulos, Mary B Abraham, Maria E Craig, Brett Fenton, Jane Overland, Sarah Price, David Simmons, Glynis P Ross
  Medical Journal of Australia.2021; 215(10): 473.     CrossRef
• Catalytic effects of magnetic and conductive nanoparticles on immobilized glucose oxidase in skin sensors
  Lilian C Alarcón-Segovia, Amay J Bandodkar, John A Rogers, Ignacio Rintoul
  Nanotechnology.2021; 32(37): 375101.     CrossRef
• Optical Glucose Sensor Using Pressure Sensitive Paint
  Jongwon Park
  Sensors.2021; 21(13): 4474.     CrossRef
• Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation
  Bruce A. Perkins, Jennifer L. Sherr, Chantal Mathieu
  Science.2021; 373(6554): 522.     CrossRef
• Clinical Utilities of Continuous Glucose Monitoring and Insulin Pumps in Pediatric Patients with Type 1 Diabetes
  Jieun Lee, Jae Hyun Kim
  The Ewha Medical Journal.2021; 44(3): 55.     CrossRef
• Personalized Postprandial Glucose Response–Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes
  Orly Ben-Yacov, Anastasia Godneva, Michal Rein, Smadar Shilo, Dmitry Kolobkov, Netta Koren, Noa Cohen Dolev, Tamara Travinsky Shmul, Bat Chen Wolf, Noa Kosower, Keren Sagiv, Maya Lotan-Pompan, Niv Zmora, Adina Weinberger, Eran Elinav, Eran Segal
  Diabetes Care.2021; 44(9): 1980.     CrossRef
• Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions
  Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
  Journal of Korean Medical Science.2021;[Epub]     CrossRef
• Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §
  Yannis Préau, Sébastien Galie, Pauline Schaepelynck, Martine Armand, Denis Raccah
  Sensors.2021; 21(18): 6131.     CrossRef
• A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems
  Aleix Beneyto, Vicenç Puig, B. Wayne Bequette, Josep Vehi
  Sensors.2021; 21(21): 7117.     CrossRef
• Editors’ Choice—Review—From Polarography to Electrochemical Biosensors: The 100-Year Quest for Selectivity and Sensitivity
  William R. Heineman, Peter T. Kissinger, Kenneth R. Wehmeyer
  Journal of The Electrochemical Society.2021; 168(11): 116504.     CrossRef
• Digital health and diabetes: experience from India
  Jothydev Kesavadev, Gopika Krishnan, Viswanathan Mohan
  Therapeutic Advances in Endocrinology and Metabolism.2021;[Epub]     CrossRef
• Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence
  Sun Joon Moon, Inha Jung, Cheol-Young Park
  Diabetes & Metabolism Journal.2021; 45(6): 813.     CrossRef
• Factors Associated with Adherence to Self-Monitoring of Blood Glucose Among Young People with Type 1 Diabetes in China: A Cross-Sectional Study
  Wencong Lv, Jiaxin Luo, Qing Long, Jundi Yang, Xin Wang, Jia Guo
  Patient Preference and Adherence.2021; Volume 15: 2809.     CrossRef
• Continuous glucose monitoring devices: A brief presentation (Review)
  Doina Mihai, Diana Stefan, Daniela Stegaru, Georgiana Bernea, Ileana Vacaroiu, Toma Papacocea, Mircea Lupușoru, Adriana Nica, Ovidiu Stiru, Dorin Dragos, Octavian Olaru
  Experimental and Therapeutic Medicine.2021;[Epub]     CrossRef
• Acute glycemic variability on admission predicts the prognosis in hospitalized patients with coronary artery disease: a meta-analysis
  Zhaokun Pu, Lihong Lai, Xishan Yang, Yanyu Wang, Pingshuan Dong, Dan Wang, Yingli Xie, Zesen Han
  Endocrine.2020; 67(3): 526.     CrossRef
• Glycemic profile of women with normoglycemia and gestational diabetes mellitus during early pregnancy using continuous glucose monitoring system
  Charandeep Singh, Yashdeep Gupta, Alpesh Goyal, Mani Kalaivani, Vineeta Garg, Juhi Bharti, Seema Singhal, Garima Kachhawa, Vidushi Kulshrestha, Rajesh Kumari, Reeta Mahey, Jai B Sharma, Neerja Bhatla, Rajesh Khadgawat, Nandita Gupta, Nikhil Tandon
  Diabetes Research and Clinical Practice.2020; 169: 108409.     CrossRef
• Efficacy of Intermittently Scanned Continuous Glucose Monitoring in the Prevention of Recurrent Severe Hypoglycemia
  Timothy M.E. Davis, Penny Dwyer, Michelle England, P. Gerry Fegan, Wendy A. Davis
  Diabetes Technology & Therapeutics.2020; 22(5): 367.     CrossRef
• How was the Diabetes Metabolism Journal added to MEDLINE?
  Hye Jin Yoo
  Science Editing.2020; 7(2): 201.     CrossRef
• Applying Nanomaterials to Modern Biomedical Electrochemical Detection of Metabolites, Electrolytes, and Pathogens
  Itthipon Jeerapan, Thitaporn Sonsa-ard, Duangjai Nacapricha
  Chemosensors.2020; 8(3): 71.     CrossRef
• Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies
  Jason Li, Jia Y. Liang, Steven J. Laken, Robert Langer, Giovanni Traverso
  Trends in Chemistry.2020; 2(4): 319.     CrossRef
• A single-blind, randomised, crossover study to reduce hypoglycaemia risk during postprandial exercise with closed-loop insulin delivery in adults with type 1 diabetes: announced (with or without bolus reduction) vs unannounced exercise strategies
  Sémah Tagougui, Nadine Taleb, Laurent Legault, Corinne Suppère, Virginie Messier, Inès Boukabous, Azadeh Shohoudi, Martin Ladouceur, Rémi Rabasa-Lhoret
  Diabetologia.2020; 63(11): 2282.     CrossRef
• Bimetallic PtAu alloy nanomaterials for nonenzymatic selective glucose sensing at low potential
  Lingling Lin, Shaohuang Weng, Yanjie Zheng, Xiyao Liu, Shaoming Ying, Feng Chen, Donghong You
  Journal of Electroanalytical Chemistry.2020; 865: 114147.     CrossRef
• Type 1 Diabetes in Youth and Technology-Based Advances in Management
  Christopher Ferber, Catherine S. Mao, Jennifer K. Yee
  Advances in Pediatrics.2020; 67: 73.     CrossRef
• Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
  Martina Vettoretti, Giacomo Cappon, Andrea Facchinetti, Giovanni Sparacino
  Sensors.2020; 20(14): 3870.     CrossRef
• Efficacy and safety of evogliptin treatment in patients with type 2 diabetes: A multicentre, active‐controlled, randomized, double‐blind study with open‐label extension (the EVERGREEN study)
  Gyuri Kim, Soo Lim, Hyuk‐Sang Kwon, Ie B. Park, Kyu J. Ahn, Cheol‐Young Park, Su K. Kwon, Hye S. Kim, Seok W. Park, Sin G. Kim, Min K. Moon, Eun S. Kim, Choon H. Chung, Kang S. Park, Mikyung Kim, Dong J. Chung, Chang B. Lee, Tae H. Kim, Moon‐Kyu Lee
  Diabetes, Obesity and Metabolism.2020; 22(9): 1527.     CrossRef
• Association Between Continuous Glucose Monitoring-Derived Time in Range, Other Core Metrics, and Albuminuria in Type 2 Diabetes
  Jee Hee Yoo, Min Sun Choi, Jiyeon Ahn, Sung Woon Park, Yejin Kim, Kyu Yeon Hur, Sang-Man Jin, Gyuri Kim, Jae Hyeon Kim
  Diabetes Technology & Therapeutics.2020; 22(10): 768.     CrossRef
• A New Approach to Determining Liquid Concentration Using Multiband Annular Ring Microwave Sensor and Polarity Correlator
  Waleed Sethi, Ahmed Ibrahim, Khaled Issa, Ali Albishi, Saleh Alshebeili
  Electronics.2020; 9(10): 1616.     CrossRef
• Estrategia terapéutica en el paciente diabético (I). Empoderamiento del paciente y formación. Objetivos terapéuticos. Estilo de vida, alimentación, vacunación y consejos al paciente diabético
  F.B. Rivas Sánchez, J. Sanz Cánovas, J. Martín Carmona, S. Jansen Chaparro
  Medicine - Programa de Formación Médica Continuada Acreditado.2020; 13(17): 943.     CrossRef
• Current status of continuous glucose monitoring among Korean children and adolescents with type 1 diabetes mellitus
  Jae Hyun Kim
  Annals of Pediatric Endocrinology & Metabolism.2020; 25(3): 145.     CrossRef
• Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators
  Anita Z Chang, David L Swain, Mark G Trotter
  Translational Animal Science.2020;[Epub]     CrossRef
• Electrochemical glucose sensors in diabetes management: an updated review (2010–2020)
  Hazhir Teymourian, Abbas Barfidokht, Joseph Wang
  Chemical Society Reviews.2020; 49(21): 7671.     CrossRef
• An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia
  Nunzio Camerlingo, Martina Vettoretti, Andrea Facchinetti, Giovanni Sparacino, Julia K. Mader, Pratik Choudhary, Simone Del Favero
  Scientific Reports.2020;[Epub]     CrossRef
• Smartphone-Based Data Collection in Ophthalmology
  Florian Philipp Raber, Rokas Gerbutavicius, Armin Wolf, Karsten Kortüm
  Klinische Monatsblätter für Augenheilkunde.2020; 237(12): 1420.     CrossRef
• Glycemic Status Assessment by the Latest Glucose Monitoring Technologies
  Ilaria Malandrucco, Benedetta Russo, Fabiana Picconi, Marika Menduni, Simona Frontoni
  International Journal of Molecular Sciences.2020; 21(21): 8243.     CrossRef
• Medical Nutrition Therapy Using Continuous Glucose Monitoring System
  Mee Ra Kweon
  The Journal of Korean Diabetes.2020; 21(4): 216.     CrossRef
• Use of Flash Glucose Monitoring in Patients on Intensive Insulin Treatment
  Jun Sung Moon
  The Journal of Korean Diabetes.2020; 21(4): 184.     CrossRef
• Data Analysis and Accuracy Evaluation of a Continuous Glucose-Monitoring Device
  Lijun Cai, Wancheng Ge, Zhigang Zhu, Xueling Zhao, Zhanhong Li
  Journal of Sensors.2019; 2019: 1.     CrossRef
• Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime
  Martina Vettoretti, Cristina Battocchio, Giovanni Sparacino, Andrea Facchinetti
  Sensors.2019; 19(23): 5320.     CrossRef

About this article

Cappon G, Vettoretti M, Sparacino G, Facchinetti A. Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications. Diabetes Metab J. 2019;43(4):383-397.

Received: Jun 17, 2019; Accepted: Jul 10, 2019

DOI: https://doi.org/10.4093/dmj.2019.0121.

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