Automated insulin delivery: benefits, challenges, and recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association.

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialised for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organisations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organisation has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonisation of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organisations for confirmation.

Friend or Foe: a Narrative Review of the Impact of Diabetes Technology on Sleep.

PURPOSE OF REVIEW: The purpose of this review is to present a review of sleep science, the relationship between sleep and type 1 diabetes, and highlight the current literature on sleep outcomes in adult and pediatric diabetes technology research. RECENT FINDINGS: Sleep quality is associated with glycemic outcomes, diabetes self-management, and mental health in people with type 1 diabetes. Diabetes technologies, including insulin pumps, continuous glucose monitors, and hybrid closed-loop systems improve glycemic outcomes. However, many people find this technology challenging for a variety of reasons, including increased burden and frequent alarms, especially during the night. The impact of different devices on sleep quality and quantity has been mixed. The newest technology, the hybrid closed-loop systems, offers the best opportunity for nocturnal glycemic regulation and has improved patient and family perspectives on sleep quality. However, objective sleep assessment has not shown significant improvement on sleep duration. Sleep quality and quantity in people with type 1 diabetes are widely recognized as an important component of health care, and the literature regarding the impact of diabetes devices on sleep is increasing. However, sleep disruptions are common and a barrier to device use. Despite finding minimal changes to sleep duration with device use, subjective accounts of sleep quality are overall positive, especially in those using hybrid closed-loop systems. Sleep quantity and quality are important outcomes to consider as diabetes technology continues to evolve.

Potentio-tunable FET sensor having a redox-polarizable single electrode for the implementation of a wearable, continuous multi-analyte monitoring device.

The emerging field of wearable devices for monitoring bioanalytes calls for the miniaturization of biochemical sensors. The only commercially available electrochemical wearable monitoring medical devices for bioanalytes are the amperometric continuous glucose monitoring (CGM) systems. The use of such amperometric methods to monitor glucose levels requires a relatively large electrode surface area for sufficient redox species collection, allowing accurate measurements to be made. Consequently, miniaturization of such sensors bearing large electrodes is challenging. Furthermore, it is difficult to introduce and deploy more than one electrode-based sensor per device, thereby limiting the number of analytes that can be monitored in parallel. To address these limitations, we have employed a non-referenced, single polarizable electrode coupled to a fin-shaped field-effect transistor (Fin-FET). We have discovered that by passivating the FET area by a relatively thick oxide and/or polytetrafluoroethylene (PTFE) polymer, leaving only the polarizable working electrode (WE) exposed, we can monitor redox analytes at the micromolar to millimolar concentration range. We attribute this effect to the WE polarization by the solution redox species. We have exploited the superior sensitivity of the adjacent silicon-based Fin-FET to detect changes in sensor electrode potentials induced by the redox species. Furthermore, we demonstrated the correlation between a specific analyte and the biasing WE potential on the accumulation/depletion of the coupled Fin-FET channel as manifested by the transistor source-drain current. Moreover, we utilized the analyte-electrode potential interaction, which is analyte-specific, to tune the specificity of the sensor towards an analyte of choice. In addition, we demonstrated the use of a single-electrode potentiometric sweep to assist in identifying the accumulation/depletion as a result of analyte-WE state. Collectively, the tiny potentio-tunable electrochemical sensor (PTEchem sensor) area is ~50 × 50 µm, and dedicated wireless transducer facilitates the use of this sensor for wearable continuous, multi-metabolite monitoring.

Should continuous glucose monitoring be used to manage neonates at risk of hypoglycaemia?

The National Institute for Clinical Excellence (NICE) now recommends that continuous glucose monitoring (CGM) be offered to adults and children with diabetes who are at risk from hypoglycaemia. Hypoglycaemia is common in the neonatal period, and is a preventable cause of poor neurodevelopmental outcome, but is CGM helpful in the management of neonates at risk of hypoglycaemia? Neonatal studies have shown that CGM can detect clinically silent hypoglycaemia, which has been associated with reduced executive and visual function in early childhood. Intervention trials have further shown CGM can support the targeting of glucose levels in high-risk extremely preterm neonates. In spite of significant advances in technology, including smaller sensors, better accuracy and factory calibration, further progress and adoption into clinical practice has been limited as current devices are not designed nor have regulatory approval for the specific needs of the newborn. The use of CGM has the potential to support clinical management, and prevention of hypoglycaemia but must be set within its current limitations. The data CGM provides however also provides an important opportunity to improve our understanding of potential risks of hypoglycaemia and the impact of clinical interventions to prevent it.

Continuous glucose monitors and virtual care in high-risk, racial and ethnic minority populations: Toward promoting health equity.

Continuous glucose monitors (CGMs) have become an important tool to aid self-management of blood glucose for many patients with diabetes in the U.S., and the benefits of CGM use are well-documented. However, disparities in CGM use exist, with lower use in certain marginalized racial and ethnic groups. CGM may be an important and underutilized tool to help reduce inequities. Evidence supporting the use of CGMs as a part of virtual care is discussed, with an emphasis on designing virtual diabetes care programs to promote health equity. Recommendations for clinical practice and research are presented. In clinical practice, CGM should be an option for all people with diabetes who qualify based on clinical practice guidelines, regardless of race, ethnicity, or other individual characteristics. Future research should characterize the use of, benefit from, and preferences for CGM among individuals from racial and ethnic groups to guide interventions at the health system, clinic, provider, and patient levels to promote equitable, evidence-based, and guideline-directed CGM use in marginalized racial and ethnic groups with diabetes.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling.

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

Low carbohydrate high fat ketogenic diets on the exercise crossover point and glucose homeostasis.

In exercise science, the crossover effect denotes that fat oxidation is the primary fuel at rest and during low-intensity exercise with a shift towards an increased reliance on carbohydrate oxidation at moderate to high exercise intensities. This model makes four predictions: First, >50% of energy comes from carbohydrate oxidation at ≥60% of maximum oxygen consumption (VO2max), termed the crossover point. Second, each individual has a maximum fat oxidation capacity (FATMAX) at an exercise intensity lower than the crossover point. FATMAX values are typically 0.3-0.6 g/min. Third, fat oxidation is minimized during exercise ≥85%VO2max, making carbohydrates the predominant energetic substrate during high-intensity exercise, especially at >85%VO2max. Fourth, high-carbohydrate low-fat (HCLF) diets will produce superior exercise performances via maximizing pre-exercise storage of this predominant exercise substrate. In a series of recent publications evaluating the metabolic and performance effects of low-carbohydrate high-fat (LCHF/ketogenic) diet adaptations during exercise of different intensities, we provide findings that challenge this model and these four predictions. First, we show that adaptation to the LCHF diet shifts the crossover point to a higher %VO2max (>80%VO2max) than previously reported. Second, substantially higher FATMAX values (>1.5 g/min) can be measured in athletes adapted to the LCHF diet. Third, endurance athletes exercising at >85%VO2max, whilst performing 6 × 800 m running intervals, measured the highest rates of fat oxidation yet reported in humans. Peak fat oxidation rates measured at 86.4 ± 6.2%VO2max were 1.58 ± 0.33 g/min with 30% of subjects achieving >1.85 g/min. These studies challenge the prevailing doctrine that carbohydrates are the predominant oxidized fuel during high-intensity exercise. We recently found that 30% of middle-aged competitive athletes presented with pre-diabetic glycemic values while on an HCLF diet, which was reversed on LCHF. We speculate that these rapid changes between diet, insulin, glucose homeostasis, and fat oxidation might be linked by diet-induced changes in mitochondrial function and insulin action. Together, we demonstrate evidence that challenges the current crossover concept and demonstrate evidence that a LCHF diet may also reverse features of pre-diabetes and future metabolic disease risk, demonstrating the impact of dietary choice has extended beyond physical performance even in athletic populations.

Evaluating a systematic intensive therapy using continuous glucose monitoring and intermittent scanning glucose monitoring in clinical diabetes care: a protocol for a multi-center randomized clinical trial.

Introduction: As many people with type 1 diabetes find it hard to reach the recommended glycemic goals, even with CGM, this study aims to determine if a closer, digitally supported collaboration on interpreting CGM data together with a diabetes nurse can improve glycemic control. Methods and analysis: A total of 120 individuals, 18 years and older and with HbA1c ≥ 58 mmol/mol will be included in the study at 8 different sites in Sweden and Norway. To be included, the participants must use a CGM or isCGM and be able to upload the data to the appropriate online service for their clinic and sensor. Both those with insulin pumps and insulin pens will be included in the study. Participants will be randomized into two different groups, that is, the intensive therapy group and the control group. The intensive therapy group will upload their glucose data weekly for the first 4 months and have telephone contact with their diabetes care team to receive support in interpreting CGM data and taking appropriate actions if their mean blood glucose level is above 8.4 mmol/L. After the 4-month-long intensive treatment phase, both randomized groups will have the same number of clinical visits and receive the same type of diabetes support. Discussion: It is of great importance to find new ways to help people with type 1 diabetes manage their condition as well as they can to help them achieve better glycemic control so that hopefully more people can achieve the recommended glycemic goals, which are associated with fewer diabetes complications. If it is shown that people with type 1 diabetes achieve better glycemic control with intensive therapy, then this can be incorporated into clinical praxis as an option for those not currently reaching the recommended glycemic goals. Clinical Trial Registration: https://clinicaltrials.gov/study/NCT03474393?locStr=Uddevalla,%20Sweden&country=Sweden&distance=50&cond=Diabetes&aggFilters=ages:adult%20older&state=V%C3%A4stra%20G%C3%B6taland%20County&city=Uddevalla&page=4&rank=34, identifier 03474393.

The Benefits of Utilizing Continuous Glucose Monitoring of Diabetes Mellitus in Primary Care: A Systematic Review.

BACKGROUND: Continuous glucose monitoring (CGM) and intermittently scanned CGM (is-CGM) have shown to effectively manage diabetes in the specialty setting, but their efficacy in the primary care setting remains unknown. Does CGM/is-CGM improve glycemic control, decrease rates of hypoglycemia, and improve staff/physician satisfaction in primary care? If so, what subgroups of patients with diabetes are most likely to benefit? METHODS: A comprehensive search in seven databases was performed in June 2021 for primary studies examining any continuous glucose monitoring system in primary care. We excluded studies with fewer than 20 participants, specialty care only, or hospitalized participants. The National Heart, Lung and Blood Institute and Grading of Recommendations Assessment, Development and Evaluation were used for the quality assessment. The weighted mean difference (WMD) of HbA1c between CGM/is-CGM and usual care with 95% confidence interval was calculated. A narrative synthesis was conducted for change of time in, above, or below range (TIR, TAR, and TBR) hypoglycemic events and staff/patient satisfaction. RESULTS: From ten studies and 4006 participants reviewed, CGM was more effective at reducing HbA1c compared with usual care (WMD -0.43%). There is low certainty of evidence that CGM/is-CGM improves TIR, TAR, or TBR over usual care. The CGM can reduce hypoglycemic events and staff/patient satisfaction is high. Patients with intensive insulin therapy may benefit more from CGM/is-CGM. CONCLUSIONS: Compared with usual care, CGM/is-CGM can reduce HbA1c, but most studies had notable biases, were short duration, unmasked, and were sponsored by industry. Further research needs to confirm the long-term benefits of CGM/is-CGM in primary care.

Accuracy of Continuous Glucose Monitors for Inpatient Diabetes Management.

INTRODUCTION: In hospitalized patients, continuous glucose monitoring (CGM) may improve glycemic control, prevent hypoglycemic events, and reduce staff workload compared with point-of-care (POC) capillary glucose monitoring. METHODS: To evaluate CGM accuracy and safety of use in the inpatient setting, two versions of CGM sensors were placed on 43 and 34 adult patients with diabetes admitted to non-intensive care unit (ICU) medical wards, respectively. CGM accuracy relative to POC and safety of use were measured by calculating mean absolute relative difference (MARD) and by Clarke Error Grid (CEG) analysis. RESULTS: CGM version 2 had improved accuracy compared with CGM version 1 with MARD 17.7 compared with 21.4%. CGM accuracy did not change with POC value or with time of sensor wear. On CEG, 98.8% of paired values fell within acceptable zones A and B. CONCLUSION: Despite reduced accuracy compared with the outpatient setting, both versions of CGMs had acceptable safety profiles in the inpatient setting.

DiabetesWise: An innovative approach to promoting diabetes device awareness.

BACKGROUND: DiabetesWise is an unbranded, data-driven online resource that tailors device recommendations based on preferences and priorities of people with insulin-requiring diabetes. The objective of this study is to examine whether DiabetesWise increases uptake of diabetes devices, which are empirically supported to improve glycemic and psychosocial outcomes. METHODS: The sample included 458 participants (Mage = 37.1, SD = 9.73; 66% female; 81% type 1 diabetes) with insulin-requiring diabetes and minimal diabetes device use at enrollment. Participants used DiabetesWise and completed online surveys. Chi-square and t tests evaluated requests for a device prescription, receiving a prescription, and starting a new device at 1 and 3 months post use. Baseline predictors of these variables and past use of continuous glucose monitors (CGMs) and changes in diabetes distress post use were also examined. RESULTS: Within the first month of interacting with DiabetesWise 19% of participants asked for a prescription for a diabetes device. This rate rose to 31% in the first 3 months. These requests resulted in 16% of the sample starting a new device within the first 3 months. Whereas several factors were associated with prior CGM use, receiving a prescription, and starting a new device, more diabetes distress (t(343) = -3.13, p = .002) was the only factor associated with asking for a prescription. Diabetes distress decreased after interacting with DiabetesWise within 1 month (t(193) = 3.51, p < .001) and 3 months (t(180) = 5.23, p < .001). CONCLUSIONS: Within 3 months of interacting with DiabetesWise, one in three participants had requested a prescription for a new diabetes device and average distress levels were reduced, indicating benefit from this low-intensity online platform.

Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies.

Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2-96%) and 76.8% (74.3-81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/-0.04) mmol/L for the training data and 0.62 (+/-0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.

Continuous Glucose Monitoring in Adolescents With Obesity: Monitoring of Glucose Profiles, Glycemic Excursions, and Adherence to Time Restricted Eating Programs.

Background: Randomized controlled trials of time restricted eating (TRE) in adults have demonstrated improvements in glucose variability as captured by continuous glucose monitors (CGM). However, little is known about the feasibility of CGM use in TRE interventions in adolescents, or the expected changes in glycemic profiles in response to changes in meal-timing. As part of a pilot trial of TRE in adolescents with obesity, this study aimed to 1) assess the feasibility of CGM use, 2) describe baseline glycemic profiles in adolescents with obesity, without diabetes, and 3) compare the difference between glycemic profiles in groups practicing TRE versus control. Methods: This study leverages data from a 12-week pilot trial (ClinicalTrials.gov Identifier: NCT03954223) of late TRE in adolescents with obesity compared to a prolonged eating window. Feasibility of CGM use was assessed by monitoring 1) the percent wear time of the CGM and 2) responses to satisfaction questionnaires. A computation of summary measures of all glycemic data prior to randomization was done using EasyGV and R. Repeat measures analysis was conducted to assess the change in glycemic variability over time between groups. Review of CGM tracings during periods of 24-hour dietary recall was utilized to describe glycemic excursions. Results: Fifty participants were enrolled in the study and 43 had CGM and dietary recall data available (16.4 + 1.3 years, 64% female, 64% Hispanic, 74% public insurance). There was high adherence to daily CGM wear (96.4%) without negative impacts on daily functioning. There was no significant change in the glycemic variability as measured by standard deviation, mean amplitude glycemic excursion, and glucose area under the curve over the study period between groups. Conclusions: CGM use appears to be a feasible and acceptable tool to monitor glycemic profiles in adolescents with obesity and may be a helpful strategy to confirm TRE dosage by capturing glycemic excursions compared to self-reported meal timing. There was no effect of TRE on glucose profiles in this study. Further research is needed to investigate how TRE impacts glycemic variability in this age group and to explore if timing of eating window effects these findings.

Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes.

Diabetes is a chronic disease affecting 415 million people worldwide. People with type 1 diabetes mellitus (T1DM) need to self-administer insulin to maintain blood glucose (BG) levels in a normal range, which is usually a very challenging task. Developing a reliable glucose forecasting model would have a profound impact on diabetes management, since it could provide predictive glucose alarms or insulin suspension at low-glucose for hypoglycemia minimisation. Recently, deep learning has shown great potential in healthcare and medical research for diagnosis, forecasting and decision-making. In this work, we introduce a deep learning model based on a dilated recurrent neural network (DRNN) to provide 30-min forecasts of future glucose levels. Using dilation, the DRNN model gains a much larger receptive field in terms of neurons aiming at capturing long-term dependencies. A transfer learning technique is also applied to make use of the data from multiple subjects. The proposed approach outperforms existing glucose forecasting algorithms, including autoregressive models (ARX), support vector regression (SVR) and conventional neural networks for predicting glucose (NNPG) (e.g. RMSE = NNPG, 22.9 mg/dL; SVR, 21.7 mg/dL; ARX, 20.1 mg/dl; DRNN, 18.9 mg/dL on the OhioT1DM dataset). The results suggest that dilated connections can improve glucose forecasting performance efficiently.

Parental Perspectives: Identifying Profiles of Parental Attitudes and Barriers Related to Diabetes Device Use.

Background: Despite the demonstrated benefits of diabetes device use, uptake of insulin pumps and continuous glucose monitors (CGMs) remains quite low. The current study aimed to identify profiles of parents of youth with type 1 diabetes based on their attitudes toward diabetes-specific technology and barriers to diabetes technology uptake. Methods: Online survey data were collected from 471 parents in the T1D Exchange Clinic Network (child's age = 12.0 ± 3.2 years; diabetes duration = 7.0 ± 2.9 years; A1c = 8.4% ± 1.3; 75% using insulin pump; 27% using CGM). Results: K-means cluster analyses revealed five parent profiles: Embracers (50.7%), Burdened (15.7%), Hopeful but Hassled (14.2%), Distrusting (12.7%), and Data Minimalists (6.6%). ANOVAs and chi-square tests identified differences between groups based on diabetes distress, worry over hypoglycemia, device use, and demographic characteristics. Conclusions: Providers encouraging device uptake may benefit from tailoring their approaches based on these distinct groups and their corresponding concerns and needs.

A Feasibility Study to Detect Neonatal Hypoglycemia in Infants of Diabetic Mothers Using Real-Time Continuous Glucose Monitoring.

BACKGROUND: Infants born to mothers with diabetes commonly experience asymptomatic hypoglycemia after birth. Continuous glucose monitors (CGM) can detect asymptomatic hypoglycemia in this population without the need for painful glucose checks. METHODS: Infants born after 34 weeks of gestation to mothers with diabetes had a CGM placed after birth. One group of infants was remotely monitored in real-time by research staff during the hospitalization, whereas another group wore a blinded CGM. In both groups, hospital standard-of-care (SOC) glucose checks were performed. Clinical staff and families were blinded to CGM data. For CGM readings <45 mg/dL, research staff requested a verification blood glucose (BG) using the point-of-care glucometer. RESULTS: Sixteen infants were studied; 4 with a blinded CGM and 12 with remote monitoring (RM). When there were confirmatory hospital glucometer readings, the sensitivity of the CGM to detect hypoglycemia was 86% and the specificity was 91%. The positive predictive value was 55% and the negative predictive value was 98%. In the full cohort, hypoglycemia (<45 mg/dL) was confirmed in 12 of 16 infants with 30 events at <12 hours of life (HOL), 3 events between 12 and 24 HOL, and 1 event at >48 HOL. In the RM group, CGM detected hypoglycemia five times when the infant was not due for a BG check based on the SOC. Overall, the CGM detected five false-positive alerts and six true-positive alerts for hypoglycemia. Only one hypoglycemic episode was missed by CGM in the RM group. Barriers to recruitment included fear of pain with glucose checks, concerns with CGM use, satisfaction with the hospital SOC, personal reasons independent of the study, and lack of interest in participating in research. CONCLUSIONS: Although there were barriers to recruitment and retention in the study, we conclude that CGM can provide added benefit for detecting hypoglycemia when used early after birth.

MiniMed 670G hybrid closed loop artificial pancreas system for the treatment of type 1 diabetes mellitus: overview of its safety and efficacy.

Introduction: Automated insulin delivery for people with type 1 diabetes has been a major goal in the diabetes technology field for many years. While a fully automated system has not yet been accomplished, the MiniMed™ 670G artificial pancreas (AP) system is the first commercially available insulin pump that automates basal insulin delivery, while still requiring user input for insulin boluses. Determining the safety and efficacy of this system is essential to the development of future devices striving for more automation. Areas Covered: This review will provide an overview of how the MiniMed 670G system works including its safety and efficacy, how it compares to similar devices, and anticipated future advances in diabetes technology currently under development. Expert Opinion: The ultimate goal of advanced diabetes technologies is to reduce the burden and amount of management required of patients with diabetes. In addition to reducing patient workload, achieving better glucose control and improving hemoglobin A1c (HbA1c) values are essential for reducing the threat of diabetes-related complications further down the road. Current devices come close to reaching these goals, but understanding the unmet needs of patients with diabetes will allow future technologies to achieve these goals more quickly.

An Expert Opinion on Advanced Insulin Pump Use in Youth with Type 1 Diabetes.

Among children and adolescents with type 1 diabetes mellitus, the use of insulin pump therapy has increased since its introduction in the early 1980s. Optimal management of type 1 diabetes mellitus depends on sufficient understanding by patients, their families, and healthcare providers on how to use pump technology. The goal for the use of insulin pump therapy should be to advance proficiency over time from the basics taught at the initiation of pump therapy to utilizing advanced settings to obtain optimal glycemic control. However, this goal is often not met, and appropriate understanding of the full features of pump technology can be lacking. The objective of this review is to provide an expert perspective on the advanced features and use of insulin pump therapy, including practical guidelines for the successful use of insulin pump technology, and other considerations specific to patients and healthcare providers.