Role of Caregivers in Remote Management of Patients With Type 2 Diabetes Mellitus: Systematic Review of Literature.

BACKGROUND: With the growing use of remote monitoring technologies in the management of patients with type 2 diabetes mellitus (T2DM), caregivers are becoming important resources that can be tapped into to improve patient care. OBJECTIVE: This review aims to summarize the role of caregivers in the remote monitoring of patients with T2DM. METHODS: We performed a systematic review in MEDLINE, Embase, Scopus, PsycINFO, and Web of Science up to 2022. Studies that evaluated the role of caregivers in remote management of adult patients with T2DM were included. Outcomes such as diabetes control, adherence to medication, quality of life, frequency of home glucose monitoring, and health care use were evaluated. RESULTS: Of the 1198 identified citations, 11 articles were included. The majority of studies were conducted in North America (7/11, 64%) and South America (2/11, 18%). The main types of caregivers studied were family or friends (10/11, 91%), while the most common remote monitoring modalities evaluated were interactive voice response (5/11, 45%) and phone consultations (4/11, 36%). With regard to diabetes control, 3 of 6 studies showed improvement in diabetes-related laboratory parameters. A total of 2 studies showed improvements in patients' medication adherence rates and frequency of home glucose monitoring. Studies that evaluated patients' quality of life showed mixed evidence. In 1 study, increased hospitalization rates were noted in the intervention group. CONCLUSIONS: Caregivers may play a role in improving clinical outcomes among patients with T2DM under remote monitoring. Studies on mobile health technologies are lacking to understand their impact on Asian populations and long-term patient outcomes.

Does introduction of continuous glucose monitoring at diagnosis of type 1 diabetes increase uptake in children and adolescents?

OBJECTIVE: To assess whether introduction of continuous glucose monitoring (CGM) at diagnosis of type 1 diabetes (T1D), leads to greater uptake and continuation at 12 and 24 months, in a population-based pediatric diabetes clinic. RESEARCH DESIGN AND METHODS: All T1D children and adolescents diagnosed in the 12 months following full government subsidization of CGM were offered CGM from diagnosis at Women's and Children's Hospital, SA (Cohort 1). Uptake and continuation of CGM was compared to those diagnosed in the preceding year, who were started on CGM after diagnosis, but otherwise had identical diabetes management (Cohort 2). Demographic and clinical data were collected prospectively. The primary outcome variable was CGM wear >75% of the time at 12 and 24 months. RESULTS: In Cohort 1, 84% were started on CGM at diagnosis. 88% had commenced CGM by 12 months and 90% by 24 months. In Cohort 2, CGM was started on average 10 months after diagnosis (range 1-25 months), with 81% started on CGM within 24 months of subsidization. At 24 months, 78% of Cohort 1 and 66% of Cohort 2 were wearing CGM >75% of the time (p = 0.26), higher than the WCH Clinic as a whole (58%). There was no difference in HbA1c between cohorts. CONCLUSION: Starting CGM at diagnosis of T1D is feasible and well received by families, with high uptake across all ages. Although CGM continuation (wearing CGM >75% of the time) was slightly higher in Cohort 1 than Cohort 2, this did not reach statistical significance.

Evaluation of HbA1c and glucose management indicator discordance in a population of children and adolescents with type 1 diabetes.

BACKGROUND: Glucose management indicator (GMI) is a useful metric for the clinical management of diabetic patients using continuous glucose monitoring (CGM). In adults, a marked discordance between HbA1c and GMI has been reported. To date, no studies have evaluated this discordance in children/adolescents with type 1 diabetes (T1D). METHODS: HbA1c and real-life CGM data of the 12 weeks preceding HbA1c measurement were collected from 805 children/adolescents. The absolute difference between HbA1c and GMI was calculated for both the 12-week and 4-week periods preceding HbA1c measurement and the proportion of discordant patients was defined according to specific thresholds in the entire study population and in subjects stratified by type of CGM, insulin therapy, gender, age and puberty. Regression analyses were performed with HbA1c-GMI discordance as dependent variable and patients' characteristics as independent ones. A new GMI equation for children and adolescent was derived from the linear regression analysis between mean glucose and HbA1c. RESULTS: HbA1c-GMI discordance calculated on the 12-week period was <0.1, ≥0.5 and ≥1.0 in 24.8, 33.9 and 9.2% of the subjects, respectively. No significant differences in the proportion of discordant patients were found comparing patients stratified by type of CGM, insulin therapy, gender, age and puberty. GMI-HbA1c discordance was not significantly explained by age, gender, BMI, type of CGM, insulin therapy, hemoglobin, anemia and autoimmune diseases (R2  = 0.012, p = 0.409). HbA1c-GMI discordance calculated on the 4-week period was comparable. GMI (%) equation derived for this cohort was: 3.74 + 0.022x (mean glucose in mg/dl). CONCLUSIONS: GMI could be meaningfully discordant respect to HbA1c in more than a third of children/adolescents with T1D. This discrepancy should be taken into careful consideration when the two indices are directly compared in daily clinical practice.

The utility of continuous glucose monitoring systems in the management of children with persistent hypoglycaemia.

OBJECTIVES: Glucose monitoring is vital in children with persistent hypoglycaemia to reduce the risk of adverse neuro-behavioural outcomes; especially in children with hyperinsulinism. The role of continuous glucose monitoring (CGM) systems in monitoring glucose levels in this cohort is limited. The objective of this study was to ascertain the effectiveness of CGM and to evaluate parents' experience of using CGM for monitoring glucose levels in children with hypoglycaemia. METHODS: Retrospective analysis of sensor glucose (SG) values from Dexcom G4 CGM with paired finger-prick blood glucose (BG) values was performed to determine the accuracy of CGM. The parent experience of CGM was assessed using a questionnaire administered to families of children with congenital hyperinsulinism currently attending the clinic. RESULTS: SG data from 40 children (median age 6 months) with persistent hypoglycaemia (60% Hyperinsulinism) were analysed. The mean difference between 5,650 paired BG and SG values was 0.28 mmol/L. The sensitivity and specificity of CGM to identify severe hypoglycaemia (BG < 3.0 mmol/L) were 54.3% (95% CI: 39.0%, 69.1%) and 97.4% (95% CI: 96.9%, 97.8%) respectively. Parents (n=11) reported less anxiety (n=9), better sleep at night (n=7) and preferred to use CGM for monitoring (n=9). CONCLUSIONS: Although the high number of false-positive readings precludes the routine use of CGM in the evaluation of hypoglycaemia, it avoids unnecessary BG testing during normoglycaemia. It is an acceptable tool for parents for monitoring their children who are at risk of hypoglycaemia. Newer CGM systems with improved accuracy at lower glucose levels have the potential to further improve monitoring.

Diabetes Update: Continuous Glucose Monitoring Yields Better HbA1C Control.

Research shows benefit to people with both type 1 and type 2 diabetes.

Systematic Review of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes.

BACKGROUND: The benefit of self-monitoring of blood glucose in reducing HbA1c in non-insulin-treated participants remains unclear. HbA1c may be improved in this population with said self-monitoring. OBJECTIVE: The aim of this study was to conduct meta-analyses of glycemic control in non-insulin-treated participants with Type 2 diabetes: self-monitoring of blood glucose versus usual care, structured versus unstructured self-monitoring of blood glucose, and use of self-monitoring of blood glucose readings by clinicians to adjust (or modify) therapy versus usual care. METHODS: MEDLINE, Embase, and Cochrane Central were electronically searched to identify articles published from January 1, 2000, to June 30, 2020. Trials investigating changes in HbA1c were selected. Screening was performed independently by two investigators. Two investigators extracted HbA1c at baseline and follow-up for each trial. RESULTS: Nineteen trials involving 4,965 participants were included. Overall, self-monitoring of blood glucose reduced HbA1c. Preplanned subgroup analysis showed that using self-monitoring of blood glucose readings to adjust therapy contributed significantly to the reduction. No significant improvement in HbA1c was shown in self-monitoring of blood glucose without therapy adjustment. The same difference was observed in structured versus unstructured self-monitoring of blood glucose. DISCUSSION: HbA1c is improved with clinician therapy modification based on structured self-monitoring of blood glucose readings. Implications are for clinicians to prescribe structured self-monitoring of blood glucose to modify therapy based on the readings and not prescribe unstructured self-monitoring of blood glucose. Participants with suboptimal glycemic control may benefit most. A self-monitoring of blood glucose regimen that improves clinical and cost-effectiveness is presented. Future studies can investigate this regimen specifically.

Expect the unexpected: Adolescent and pre-teens' experience of diabetes technology self-management.

OBJECTIVE: Only 17% of adolescents with type 1 diabetes (T1D) are currently meeting their glycemic targets despite advances in diabetes technologies. Self-management behaviors and challenges specific to use of diabetes technologies are insufficiently studied in adolescents. We aimed to describe the experience of diabetes technology self-management, including facilitators and barriers, among preteens/adolescents with low and high A1C. RESEARCH DESIGN AND METHODS: Youth (10-18 years of age) with T1D who use insulin pump therapy were recruited from the larger quantitative cohort of a mixed methods study for participation in semi-structured qualitative interviews. Maximum variability sampling was used to recruit youth with A1C <7.5% (n = 5) and A1C >9% (n = 5). Participants' personal insulin pump and continuous glucose monitoring data were downloaded and served as a visual reference. Interviews were analyzed using a qualitative descriptive approach. RESULTS: Participants were 50% female with a median age of 14.9 years and 80% used CGM. The sample was predominantly white (90.0%). Analysis produced four major themes, Bad Day, Expect the Unexpected, Nighttime Dependence, and Unpredictability, It's Really a Team and interconnecting subthemes. Youth characterized ''Bad Days'' as those requiring increased diabetes focus and self-management effort. The unpredictability (''Expect the Unexpected'') of glucose outcomes despite attention to self-management behaviors was considerable frustration. CONCLUSIONS: Diabetes devices such as insulin pumps are complex machines that rely heavily on individual proficiency, surveillance, and self-management behaviors to achieve clinical benefit. Our findings highlight the dynamic nature of self-management and the multitude of factors that feed youths' self-management behaviors.

Association of Real-time Continuous Glucose Monitoring With Glycemic Control and Acute Metabolic Events Among Patients With Insulin-Treated Diabetes.

Importance: Continuous glucose monitoring (CGM) is recommended for patients with type 1 diabetes; observational evidence for CGM in patients with insulin-treated type 2 diabetes is lacking. Objective: To estimate clinical outcomes of real-time CGM initiation. Design, Setting, and Participants: Exploratory retrospective cohort study of changes in outcomes associated with real-time CGM initiation, estimated using a difference-in-differences analysis. A total of 41 753 participants with insulin-treated diabetes (5673 type 1; 36 080 type 2) receiving care from a Northern California integrated health care delivery system (2014-2019), being treated with insulin, self-monitoring their blood glucose levels, and having no prior CGM use were included. Exposures: Initiation vs noninitiation of real-time CGM (reference group). Main Outcomes and Measures: Ten end points measured during the 12 months before and 12 months after baseline: hemoglobin A1c (HbA1c); hypoglycemia (emergency department or hospital utilization); hyperglycemia (emergency department or hospital utilization); HbA1c levels lower than 7%, lower than 8%, and higher than 9%; 1 emergency department encounter or more for any reason; 1 hospitalization or more for any reason; and number of outpatient visits and telephone visits. Results: The real-time CGM initiators included 3806 patients (mean age, 42.4 years [SD, 19.9 years]; 51% female; 91% type 1, 9% type 2); the noninitiators included 37 947 patients (mean age, 63.4 years [SD, 13.4 years]; 49% female; 6% type 1, 94% type 2). The prebaseline mean HbA1c was lower among real-time CGM initiators than among noninitiators, but real-time CGM initiators had higher prebaseline rates of hypoglycemia and hyperglycemia. Mean HbA1c declined among real-time CGM initiators from 8.17% to 7.76% and from 8.28% to 8.19% among noninitiators (adjusted difference-in-differences estimate, -0.40%; 95% CI, -0.48% to -0.32%; P < .001). Hypoglycemia rates declined among real-time CGM initiators from 5.1% to 3.0% and increased among noninitiators from 1.9% to 2.3% (difference-in-differences estimate, -2.7%; 95% CI, -4.4% to -1.1%; P = .001). There were also statistically significant differences in the adjusted net changes in the proportion of patients with HbA1c lower than 7% (adjusted difference-in-differences estimate, 9.6%; 95% CI, 7.1% to 12.2%; P < .001), lower than 8% (adjusted difference-in-differences estimate, 13.1%; 95% CI, 10.2% to 16.1%; P < .001), and higher than 9% (adjusted difference-in-differences estimate, -7.1%; 95% CI, -9.5% to -4.6%; P < .001) and in the number of outpatient visits (adjusted difference-in-differences estimate, -0.4; 95% CI, -0.6 to -0.2; P < .001) and telephone visits (adjusted difference-in-differences estimate, 1.1; 95% CI, 0.8 to 1.4; P < .001). Initiation of real-time CGM was not associated with statistically significant changes in rates of hyperglycemia, emergency department visits for any reason, or hospitalizations for any reason. Conclusions and Relevance: In this retrospective cohort study, insulin-treated patients with diabetes selected by physicians for real-time continuous glucose monitoring compared with noninitiators had significant improvements in hemoglobin A1c and reductions in emergency department visits and hospitalizations for hypoglycemia, but no significant change in emergency department visits or hospitalizations for hyperglycemia or for any reason. Because of the observational study design, findings may have been susceptible to selection bias.

Motivational Stage at Continuous Glucose Monitoring (CGM) Initiation in Pediatric Type 1 Diabetes Is Associated With Current Glycemic Control but Does Not Predict Future CGM Adherence or Glycemic Control.

OBJECTIVES: The Timing of Initiation of Continuous Glucose Monitoring in Established Pediatric Diabetes (CGM TIME) Trial is a multicenter, randomized controlled trial in children with type 1 diabetes, comparing simultaneous pump and CGM with CGM initiation 6 months later (Paradigm, Veo, Enlite Sensor, Medtronic Canada). This study addresses the ability of SOCRATES (Stages Of Change Readiness And Treatment Eagerness Scale) to classify children and parents into distinct motivational stages and identify the stages' association with glycated hemoglobin (A1C) at trial entry and outcomes 6 months after CGM initiation. METHODS: Ninety-eight of 99 eligible children 10 to 18 years of age and 137 of 141 eligible parents completed SOCRATES at trial entry and 6 months later. Parent-child agreement for motivational stage was determined by weighted kappa. Linear regression was used to examine association between motivational stage and i) A1C at trial entry and ii) change in A1C and CGM adherence 6 months after CGM initiation. RESULTS: More than 87% of children and 88% of parents were classified into distinct motivational stages, with weak parent-child agreement. At trial entry, motivational stage was associated with A1C, which was 1.02% higher for children in the Action stage than in the Precontemplation stage (p<0.0001). When compared with children of parents in Precontemplation, A1C for children of parents in the Maintenance and Action stages were 0.83% (p=0.02) and 0.36% (p=0.048) higher, respectively. Precontemplation was associated with shorter diabetes duration. Motivational stage at CGM initiation did not predict change in A1C or CGM adherence 6 months later. CONCLUSIONS: SOCRATES can categorize children with type 1 diabetes and their parents into motivational stages. Although motivational stage was associated with glycemic control at trial entry, it did not predict future diabetes-related behaviour or A1C.

Telemedicine and urban diabetes during COVID-19 pandemic in Milano, Italy during lock-down: epidemiological and sociodemographic picture.

BACKGROUND: Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano. METHODS: Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 < VC < 36, n. 26 patients; Low risk VC < 20, n. 4 patients. The control group was constituted by 26 diabetic patients non utilizing Flash Glucose monitoring. RESULTS: In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = - 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down. HYPOTHESIS AND DISCUSSION: The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present "second wave" lock-down.

Glycemic deviation index: a novel method of integrating glycemic numerical value and variability.

BACKGROUND: There are many continuous blood glucose monitoring (CGM) data-based indicators, and most of these focus on a single characteristic of abnormal blood glucose. An ideal index that integrates and evaluates multiple characteristics of blood glucose has not yet been established. METHODS: In this study, we proposed the glycemic deviation index (GDI) as a novel integrating characteristic, which mainly incorporates the assessment of the glycemic numerical value and variability. To verify its effectiveness, GDI was applied to the simulated 24 h glycemic profiles and the CGM data of type 2 diabetes (T2D) patients (n = 30). RESULTS: Evaluation of the GDI of the 24 h simulated glycemic profiles showed that the occurrence of hypoglycemia was numerically the same as hyperglycemia in increasing GDI. Meanwhile, glycemic variability was added as an independent factor. One-way ANOVA results showed that the application of GDI showed statistically significant differences in clinical glycemic parameters, average glycemic parameters, and glycemic variability parameters among the T2D groups with different glycemic levels. CONCLUSIONS: In conclusion, GDI integrates the characteristics of the numerical value and the variability in blood glucose levels and may be beneficial for the glycemic management of diabetic patients undergoing CGM treatment.

Diabetic ketoacidosis at type 1 diabetes diagnosis in children during the COVID-19 pandemic.

OBJECTIVE: The COVID-19 pandemic has led to significant public health measures that have resulted in decreased acute pediatric care utilization. We evaluated whether the rate of severe presentations of new onset type 1 diabetes (DM1), such as, diabetic ketoacidosis (DKA) has changed since the COVID-19 public health measures were enacted. RESEARCH DESIGN AND METHODS: A retrospective chart review of children less than 18 years of age presenting with new onset DM1 during the pandemic period of March 17, 2020 to August 31, 2020 was conducted at two tertiary care pediatric hospitals in Alberta, Canada. Rates of DKA and severe DKA were compared to the same time period in the year 2019 (pre-pandemic control). RESULTS: The number of children presenting with newly diagnosed DM1 was similar during the pandemic year of 2020 compared with 2019 (107 children in 2020 vs. 114 in 2019). The frequency of DKA at DM1 onset was significantly higher in the pandemic period (68.2% vs 45.6%; p < 0.001) and incidence of severe DKA was also higher (27.1% in 2020 vs 13.2% in 2019; p = 0.01). CONCLUSIONS: There was a significant increase in DKA and severe DKA in children presenting with new onset DM1 during the COVID-19 pandemic period. This emphasizes the need for educating health care professionals and families to be aware of the symptoms of hyperglycemia and the importance of early diagnosis and treatment even during public health measures for COVID-19.

Magnitude and predictors of poor glycemic control among patients with diabetes attending public hospitals of Western Ethiopia.

BACKGROUND: Diabetes is one of the most prevalent non-communicable diseases globally, which rapidly is increasing in developing countries. Ethiopia is also facing growing morbidity and mortality related to diabetes complications. Thus, dealing with glycemic control is essential for controlling the development of devastating acute and chronic complications related to diabetes. Therefore, this study aims to assess the magnitude and predictors of poor glycemic control among diabetic patients in western Ethiopia. METHODS: The cross-sectional study design was employed on a sample of 423 diabetic patients. A systematic random sampling method was employed. An interviewer-administered structured questionnaire was used. The data entered into Epi data version 3.1 and exported into Statistical Package for the Social Sciences window version 24 for analysis. All variables significant at p-<0.25 in bivariate were entered into multivariate analysis. The multivariable logistic regressions were used to determine predictors' poor glycemic control by considering the Adjusted Odds Ratio at CI 95% and the significance level was set at p <0.05. RESULTS: The magnitude of poor glycemic control was 64.1%. Being females (AOR = 1.684,95%CI = 1.066,2.662), duration of diabetes >8years (AOR = 2.552,95%CI = 1.397, 4.665), presence of diabetes complication (AOR = 2.806,95%CI = 1.594,4.941), negligence of blood glucose test at home (AOR = 1.720, 95%CI = 1.078, 2.743), poor self-care behavior (AOR = 1.787, 95%CI = 1.083,2.959) and poor self-efficacy (AOR = 1.934, 95%CI = 1.078,3.469) were significant predictors of poor glycemic control. CONCLUSION: The proportion of poor glycemic control was high which was nearly comparable to that reported from many countries. This could be due to factors that were significantly associated with poor glycemic control like lack of home blood glucose test, increased duration of diabetes, presence of diabetes complications, poor self-efficacy, and poor self-care behaviors. Each were significant independent predictors of poor glycemic control. Thus, we recommend patients with diabetes and health care providers enhancing self-monitoring practices, and preventing potential complications should be a priority concern to improve blood glucose levels. Further studies are also recommended to explore important factors which were not identified by the current study.

Impact of Clinical Practice Guidelines on Blood Glucose Test Strip Prescription Rates in Manitoba and Saskatchewan (Canada): An Interrupted Time-Series Analysis.

OBJECTIVES: Our aim in this study was to assess the impact of the Diabetes Canada Dissemination & Implementation strategy on population-level prescription rates of blood glucose test strips. METHODS: We extracted all diabetes-related drugs and test strip claims in Manitoba and Saskatchewan between January 1, 2000 and September 30, 2015 from the Canadian Institute for Health Information's National Prescription Drug Utilization Information System. The primary outcome was the proportion of the cohort in each quarter who had been dispensed strips in accordance with the Diabetes Canada 2013 guidelines. We conducted an interrupted time-series analysis examining prescribing trends overall and by drug groups. RESULTS: The overall average sample size per quarter was 57,576 (standard deviation [SD]=12,320) and 49,533 (SD=10,206) individuals; the average age was 62.1 (SD=0.3) and 63.8 (SD=0.3) years, and the average proportion of total beneficiaries in the sample was 12.7% (SD=1.9%) and 12.6% (SD=1.7%) for Manitoba and Saskatchewan, respectively. On average preintervention, 27.9% (SD=0.68%, Manitoba) and 31.9% (SD=0.73%, Saskatchewan) of the sampled patients used strips according to the guidelines. On average postintervention, 26.5% (SD=0.29%, Manitoba) and 30.6% (SD=0.53%, Saskatchewan) of the patients used strips according to the guidelines. None of the interrupted time-series models reached statistical significance (p values ranging from 0.44 to 0.98 for Manitoba and 0.13 to 0.81 for Saskatchewan, depending on drug group). CONCLUSIONS: The guideline and its Dissemination & Implementation strategy did not change strip prescribing. Potential reasons include complexity of the recommendations, lack of penetrance to primary care physicians and/or disagreement with recommendations.

Real-Time Continuous Glucose Monitoring During the Coronavirus Disease 2019 Pandemic and Its Impact on Time in Range.

Background: The coronavirus disease 2019 (COVID-19) pandemic disrupted the lives of people with diabetes. Use of real-time continuous glucose monitoring (rtCGM) helped manage diabetes effectively. Some of these disruptions may be reflected in population-scale changes to metrics of glycemic control, such as time-in-range (TIR). Methods: We examined data from 65,067 U.S.-based users of the G6 rtCGM System (Dexcom, Inc., San Diego, CA) who had uploaded data before and during the COVID-19 pandemic. Users associated with three counties that included the cities of Los Angeles, Chicago, and New York or with five regions designated by the Centers for Disease Control and Prevention (CDC) were compared. Public data were used to associate regions with prepandemic and intrapandemic glycemic parameters, COVID-19 mortality, and median household income. Results: Compared with an 8-week prepandemic interval before stay-at-home orders (January 6, 2020, to March 1, 2020), overall mean (standard deviation) TIR improved from 59.0 (20.1)% to 61.0 (20.4)% during the early pandemic period (April 20, 2020 to June 14, 2020, P < 0.001). TIR improvements were noted in all three counties and in all five CDC-designated regions. Higher COVID-19 mortality was associated with higher proportions of individuals experiencing TIR improvements of ≥5 percentage points. Users in economically wealthier zip codes had higher pre- and intrapandemic TIR values and greater relative improvements in TIR. TIR and pandemic-related improvements in TIR varied across CDC-designated regions. Conclusions: Population-level rtCGM data may be used to monitor changes in glycemic control with temporal and geographic specificity. The COVID-19 pandemic is associated with improvements in TIR, which were not evenly distributed across the United States.

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods.

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

Impact of COVID-19 on Health Economics and Technology of Diabetes Care: Use Cases of Real-Time Continuous Glucose Monitoring to Transform Health Care During a Global Pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has exposed vulnerabilities and placed tremendous financial pressure on nearly all aspects of the U.S. health care system. Diabetes care is an example of the confluence of the pandemic and heightened importance of technology in changing care delivery. It has been estimated the added total direct U.S. medical cost burden due to COVID-19 to range between $160B (20% of the population infected) and $650B (80% of the population infected) over the course of the pandemic. The corresponding range for the population with diabetes is between $16B and $65B, representing between 5% and 20% of overall diabetes expenditure in the United States. We examine the evidence to support allocating part of this added spend to infrastructure capabilities to accelerate remote monitoring and management of diabetes. Methods and Results: We reviewed recent topical literature and COVID-19-related analyses in the public health, health technology, and health economics fields in addition to databases and surveys from government sources and the private sector. We summarized findings on use cases for real-time continuous glucose monitoring in the community, for telehealth, and in the hospital setting to highlight the successes and challenges of accelerating the adoption of a digital technology out of necessity during the pandemic and beyond. Conclusions: One critical and lasting consequence of the pandemic will be the accelerated adoption of digital technology in health care delivery. We conclude by discussing ways in which the changes wrought by COVID-19 from a health care, policy, and economics perspective can add value and are likely to endure postpandemic.

Defining the target value of the coefficient of variation by continuous glucose monitoring in Chinese people with diabetes.

AIMS/INTRODUCTION: To define the target value for the percentage coefficient of variation for glucose (%CV) as a measure of glycemic variability (GV) in Chinese diabetes patients. MATERIALS AND METHODS: This retrospective study included 3,007 diabetes patients who underwent continuous glucose monitoring for 3 days. Type 2 diabetes was divided into groups according to the received treatment: group 1, non-insulinotropic agent (n = 138); group 2, insulinotropic agent (n = 761); group 3, basal insulin therapy (n = 100); group 4, premixed insulin (n = 784); and group 5, intensive insulin therapy (n = 612). Type 1 diabetes patients were included as group 6 (n = 612). %CV and percentage of time per day within, below (3.9mmol/L; TBR3.9 ) and above (10.0 mmol/L) the target glucose range (3.9-10.0 mmol/L) were computed. TBR3.9 ≥4% was defined as excessive hypoglycemia. RESULTS: Type 2 diabetes with a premixed or intensive insulin regimen had an increased %CV compared with those receiving oral therapy or basal insulin. The upper limit of %CV in group 1 was 33%, which was adopted as the threshold to define excessive GV. For each treatment group, the percentage of people with TBR3.9 ≥4% was significantly greater in the subgroup with %CV >33% than ≤33% (P < 0.001). In participants who achieved TBR3.9 <4%, the time per day spent within the target glucose range of 3.9-10.0 mmol/L > 70% and time per day above 10.0 mmol/L <25%, the 95th percentile of %CV was 32.70%. Further receiver operating characteristic curve analysis showed that the cut-off values of %CV for predicting TBR3.9 ≥4% varied by the type of diabetes and glycated hemoglobin categories. CONCLUSIONS: A %CV of 33% was set as the threshold for excess glucose variability in Chinese diabetes patients. Meanwhile, glycated hemoglobin and the type of diabetes should be considered for the goal-setting of %CV.

Effects of real-time continuous glucose monitoring in type 1 diabetes: a meta-analysis of randomized controlled trials.

AIMS: Self-monitoring of blood glucose (SMBG) represented a major breakthrough in the treatment of type 1 diabetes. The aim of the present meta-analysis is to assess the effect of continues glucose monitoring (CGM) and flash glucose monitoring (FGM), on glycemic control in type 1 diabetes. MATERIALS AND METHODS: The present analysis includes randomized clinical trials comparing CGM or FGM with SMBG, with a duration of at least 12 weeks, identified in Medline or clinicaltrials.gov. The principal endpoint was HbA1c at the end of the trial. A secondary endpoint was severe hypoglycemia. Mean and 95% confidence intervals for HbA1c and Mantel-Haenzel odds ratio [MH-OR] for severe hypoglycemia were calculated, using random effect models. A sensitivity analysis was performed using fixed effect models. In addition, the following secondary endpoints were explored, using the same methods: time in range, health-related quality of life, and treatment satisfaction. Separate analyses were performed for trials comparing CGM with SMBG, and those comparing CGM + CSII and SMBG + MDI and CGM-regulated insulin infusion system (CRIS) and CSII + SMBG. RESULTS: CGM was associated with a significantly lower HbA1c at endpoint in comparison with SMBG (- 0.24 [- 0.34, - 0.13]%); CGM was associated with a significantly lower risk of severe hypoglycemia than SMBG. Treatment satisfaction and quality of life were not measured, or not reported, in the majority of studies. FGM showed a significant reduction in the incidence of mild hypoglycemia and an increased treatment satisfaction, but no significant results are shown in HbA1c. CGM + CSII in comparison with SMBG + MDI was associated with a significant reduction in HbA1c. Only two trials with a duration of at least 12 weeks compared a CRIS with SMBG + CSII; HbA1c between the two treatment arms was not statistically significant (difference in means: - 0.23 [- 0.91; 0.46]%; p = 0.52). CONCLUSION: GCM compared to SMBG has showed a reduction in HbA1c and severe hypoglycemia in patient with type 1 diabetes. The comparison between CGM + CSII and SMBG + MDI showed a large reduction in HbA1c; it is conceivable that the effects of CSII + CGM on glycemic control additives. The only comparison available between FGM and SMBG was conducted in patients in good control.

Association between Flash Glucose Monitoring and Metabolic Control as well as Treatment Satisfaction in Outpatients With Diabetes Type 1.

OBJECTIVE: The aim of this study was to follow-up people with diabetes type 1 and Flash Glucose Monitoring (FGM) regarding metabolic control and treatment satisfaction. METHODS: 40 people with diabetes type 1 and FGM use of ≥6 months were included in the study (female 55%, age 50.9 y, diabetes duration 21.9 y, HbA1c 7.4%, 57.6 mmol/mol, insulin pump therapy 32.5%). The number of scans per day and time/frequency of hypoglycaemia (interstitial glucose value ≤3.9 mmol/l) was recorded from 28 days of the glucose readings. Change of treatment satisfaction was assessed with the DTSQc questionnaire at follow-up (range-18 to+18). RESULTS: Mean time of follow-up was 1.0±0.4 y. At follow-up, all participants scanned interstitial glucose 11.9±7.7 times/day. Number of self-monitoring of blood glucose decreased from 6.7±4.2 (baseline) to 0.9±1.8 (follow-up) per day (p<0.001). In individuals with baseline HbA1c ≤7.5%, HbA1c increased (from 6.6±0.7% to 7.0±0.4%, p=0.020). On the contrary, in people with HbA1c>7.5%, HbA1c decreased (from 8.2±0.7% to 7.8±0.7%, p=0.001). In all participants, there were no differences regarding insulin dosage (33.8±12.9 vs. 34.6±13.9 IU/day, p=0.679) and number of insulin injections/day (3.9±2.3 vs. 4.0±2.6, p=0.813) between baseline and follow-up. Frequency of symptomatic hypoglycaemia was at baseline 0.3±0.3 events/day and 0.48±0.36 events/day (recognised, symptomatic events) at follow-up, respectively. In addition, 0.26±0.21 unrecognised hypoglycaemic events/day occurred at follow-up. Treatment satisfaction increased by+12.6 points. CONCLUSIONS: FGM was associated with an enormous increase in treatment satisfaction and slightly improved metabolic control in people with baseline HbA1c>7.5%. The number of capillary glucose measurements decreased significantly.