Time in range: A best practice guide for UK diabetes healthcare professionals in the context of the COVID-19 global pandemic

Glycaemic outcomes in people living with diabetes under 65 and over 65 years old using an intermittently scanned continuous glucose monitoring system

Objective

Intermittently scanned continuous glucose monitoring (isCGM) has revolutionised the care of people with diabetes but its uptake and benefits in older adults are not well known. We examined the impact of isCGM (Freestyle Libre, FSL) on glycaemic outcomes in younger (⩽65 years) and older adults (>65 years) with diabetes.

Design and methods

In total, 2260 adult patients registered on the Libreview account at University Hospitals Birmingham NHS Foundation Trust, UK, were included. Inclusion criteria: all patients with type 1 and type 2 diabetes aged >18 years, use of isCGM >6 months, scanning at least 6 times/day. Demographics, diabetes history and glycaemic outcomes (time in range (TIR), time above range and time below range (TBR), estimated HbA1c, HbA1c at start and at end of study) were collected by accessing electronic patient records and Libreview. Outcomes were compared between age groups ⩽65 or >65 years old.

Results

Most patients were of Caucasian ethnicity (⩽65 years 68%, >65 years 73%) and had type 1 diabetes. Mean duration of diabetes was 19.5 years (range 0-65 years) and 34.5 years (range 0-79 years) for ⩽65 and >65 years, respectively. Only a quarter of those ⩽65 years achieved (219/943; 23.2%) their age specific TIR target compared to 69% (78/113) of those >65 years cohort, while 70.1% (663/946) of ⩽65 years and 40.7% (46/113) of >65 years achieved their age-specific TBR target. When the less strict ⩽65 years TBR target was applied, 75% (85/113) of >65 years cohort achieved this.

Conclusion

FSL use was associated with improved glycaemic outcomes across all age groups. Individualised targets may be needed to improve TBR in those aged >65 years.

The Health Economics of Automated Insulin Delivery Systems and the Potential Use of Time in Range in Diabetes Modeling: A Narrative Review

Intensive therapy with exogenous insulin is the treatment of choice for individuals living with type 1 diabetes (T1D) and some with type 2 diabetes, alongside regular glucose monitoring. The development of systems allowing (semi-)automated insulin delivery (AID), by connecting glucose sensors with insulin pumps and algorithms, has revolutionized insulin therapy. Indeed, AID systems have demonstrated a proven impact on overall glucose control, as indicated by effects on glycated hemoglobin (HbA1c), risk of severe hypoglycemia, and quality of life measures. An alternative endpoint for glucose control that has arisen from the use of sensor-based continuous glucose monitoring is the time in range (TIR) measure, which offers an indication of overall glucose control, while adding information on the quality of control with regard to blood glucose level stability. A review of literature on the health-economic value of AID systems was conducted, with a focus placed on the growing place of TIR as an endpoint in studies involving AID systems. Results showed that the majority of economic evaluations of AID systems focused on individuals with T1D and found AID systems to be cost-effective. Most studies incorporated HbA1c, rather than TIR, as a clinical endpoint to determine treatment effects on glucose control and subsequent quality-adjusted life year (QALY) gains. Likely reasons for the choice of HbA1c as the chosen endpoint is the use of this metric in most validated and established economic models, as well as the limited publicly available evidence on appropriate methodologies for TIR data incorporation within conventional economic evaluations. Future studies could include the novel TIR metric in health-economic evaluations as an additional measure of treatment effects and subsequent QALY gains, to facilitate a holistic representation of the impact of AID systems on glycemic control. This would provide decision makers with robust evidence to inform future recommendations for health care interventions.

The Role of Ultra-Rapid-Acting Insulin Analogs in Diabetes: An Expert Consensus

Ultra-rapid-acting insulin analogs (URAA) are a further development and refinement of rapid-acting insulin analogs. Because of their adapted formulation, URAA provide an even faster pharmacokinetics and thus an accelerated onset of insulin action than conventional rapid-acting insulin analogs, allowing for a more physiologic delivery of exogenously applied insulin. Clinical trials have confirmed the superiority of URAA in controlling postprandial glucose excursions, with a safety profile that is comparable to the rapid-acting insulins. Consequently, many individuals with diabetes mellitus may benefit from URAA in terms of prandial glycemic control. Unfortunately, there are only few available recommendations from authoritative sources for use of URAA in clinical practice. Therefore, this expert consensus report aims to define populations of people with diabetes mellitus for whom URAA may be beneficial and to provide health care professionals with concrete, practical recommendations on how best to use URAA in this context.

Expert Recommendations for Using Time-in-Range and Other Continuous Glucose Monitoring Metrics to Achieve Patient-Centered Glycemic Control in People With Diabetes

New metrics for assessing glycemic control beyond HbA1c have recently emerged due to the increasing use of continuous glucose monitoring (CGM) in diabetes clinical practice. Among them, time in range (TIR) has appeared as a simple and intuitive metric that correlates inversely with HbA1c and has also been newly linked to the risk of long-term diabetes complications. The International Consensus on Time in Range established a series of target glucose ranges (TIR, time below range and time above range) and recommendations for time spent within these ranges for different diabetes populations. These parameters should be evaluated together with the ambulatory glucose profile (AGP). Using standardized visual reporting may help people with diabetes and healthcare professionals in the evaluation of glucose control in frequent clinical situations. The objective of the present review is to provide practical insights to quick interpretation of patient-centered metrics based on flash glucose monitoring data, as well as showing some visual examples of common clinical situations and giving practical recommendations for their management.

Islet cells in human type 1 diabetes: from recent advances to novel therapies - a symposium-based roadmap for future research

There is a growing understanding that the early phases of type 1 diabetes (T1D) are characterised by a deleterious dialogue between the pancreatic beta cells and the immune system. This, combined with the urgent need to better translate this growing knowledge into novel therapies, provided the background for the JDRF-DiabetesUK-INNODIA-nPOD symposium entitled 'Islet cells in human T1D: from recent advances to novel therapies', which took place in Stockholm, Sweden, in September 2022. We provide in this article an overview of the main themes addressed in the symposium, pointing to both promising conclusions and key unmet needs that remain to be addressed in order to achieve better approaches to prevent or reverse T1D.

Healthcare Professionals' Knowledge of and Attitudes Towards the Use of Time in Range in Diabetes Management: Online Survey Across Seven Countries

INTRODUCTION

Time in range (TIR) is a metric of glycaemic target management derived from continuous glucose monitoring (CGM) data. This study aimed to understand knowledge of and attitudes towards use of TIR among healthcare professionals (HCPs), and gain insights into benefits and barriers to its use in clinical practice.

METHODS

An online survey was disseminated across seven countries. Participants were sampled from online HCP panels and were aware of TIR (defined as amount of time in, below, and above target range). Participants were HCPs classified as specialists (SP), generalists (GP), or allied HCPs (AP; diabetes nurse specialists, diabetes educators, general nurses, nurse practitioners/physician assistants).

RESULTS

Respondents included 741 SP, 671 GP and 307 AP. Most HCPs (approximately 90%) agreed TIR is likely/somewhat likely to become the standard of diabetes management. Perceived benefits of TIR included helping to optimise medication regimen (SP, 71%; GP, 73%; AP, 74%), giving HCPs the knowledge and insights to make informed clinical decisions (SP, 66%; GP, 61%; AP, 72%), and empowering people with diabetes with information to successfully manage their diabetes (SP, 69%; GP, 77%; AP, 78%). Barriers to wider adoption included limited CGM access (SP, 65%; GP, 74%; AP, 69%) and lack of HCP training/education (SP, 45%; GP, 59%; AP, 51%). Most participants considered integration of TIR into clinical guidelines, recognition of TIR by regulators as a primary clinical endpoint, and recognition of TIR by payers as a parameter for diabetes treatment evaluation as key factors for increased use.

CONCLUSIONS

Overall, HCPs agreed on the benefits of using TIR for diabetes management. Besides raising awareness among HCPs and people with diabetes, more training and healthcare system updates are needed to facilitate increased TIR use. In addition, integration into clinical guidelines and recognition by regulators and payers are needed.

Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels

INTRODUCTION

Time in range (TIR) as assessed by continuous glucose monitoring (CGM) measures an individual's glucose fluctuations within set limits in a time period and is increasingly used together with HbA1c in patients with diabetes. HbA1c indicates the average glucose concentration but provides no information on glucose fluctuation. However, before CGM becomes available for patients with type 2 diabetes (T2D) worldwide, especially in developing nations, fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) are still the common biomarkers used for monitoring diabetes conditions. We investigated the importance of FPG and PPG to glucose fluctuation in patients with T2D. We used machine learning to provide a new estimate of TIR based on the HbA1c, together with FPG and PPG.

METHODS

This study included 399 patients with T2D. (1) Univariate and (2) multivariate linear regression models and (3) random forest regression models were developed to predict the TIR. Subgroup analysis was performed in the newly diagnosed T2D population to explore and optimize the prediction model for patients with different disease history.

RESULTS

Regression analysis suggests that FPG was strongly linked to minimum glucose, while PPG was strongly correlated with maximum glucose. After FPG and PPG were incorporated into the multivariate linear regression model, the prediction performance of TIR was improved compared with the univariate correlation between HbA1c and TIR, and the correlation coefficient (95% CI) increased from 0.62 (0.59, 0.65) to 0.73 (0.72, 0.75) (p < 0.001). The random forest model significantly outperformed the linear model (p < 0.001) in predicting TIR through FPG, PPG and HbA1c, with a stronger correlation coefficient 0.79 (0.79, 0.80).

CONCLUSIONS

The results offered a comprehensive understanding of glucose fluctuations through FPG and PPG compared to HbA1c alone. Our novel TIR prediction model based on random forest regression with FPG, PPG, and HbA1c provides a better prediction performance than the univariate model with solely HbA1c. The results indicate a nonlinear relationship between TIR and glycaemic parameters. Our results suggest that machine learning may have the potential to be used in developing better models for understanding patients' disease status and providing necessary interventions for glycaemic control.

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

BACKGROUND

Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations.

OBJECTIVE

This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns.

METHODS

Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis.

RESULTS

In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs.

CONCLUSIONS

The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

Leveraging the future of diagnosis and management of diabetes: From old indexes to new technologies

BACKGROUND

Diabetes is a heterogeneous and multifactorial disease. However, glycemia and glycated hemoglobin have been the focus of diabetes diagnosis and management for the last decades. As diabetes management goes far beyond glucose control, it has become clear that assessment of other biochemical parameters gives a much wider view of the metabolic state of each individual, enabling a precision medicine approach.

METHODS

In this review, we summarize and discuss indexes that have been used in epidemiological studies and in the clinical practice.

RESULTS

Indexes of insulin secretion, sensitivity/resistance and metabolism have been developed and validated over the years to account also with insulin, C-peptide, triglycerides or even anthropometric measures. Nevertheless, each one has their own objective and consequently, advantages and disadvantages for specific cases. Thus, we discuss how new technologies, namely new sensors but also new softwares/applications, can improve the diagnosis and management of diabetes, both for healthcare professionals but also for caretakers and, importantly, to promote the empowerment of people living with diabetes.

CONCLUSIONS

In long-term, the solution for a better diabetes management would be a platform that allows to integrate all sorts of relevant information for the person with diabetes and for the healthcare practitioners, namely glucose, insulin and C-peptide or, in case of need, other parameters/indexes at home, sometimes more than once a day. This solution would allow a better and simpler disease management, more adequate therapeutics thereby improving patients' quality of life and reducing associated costs.

Cross-sectional association of metrics derived from continuous glucose monitoring with cognitive performance in older adults with type 2 diabetes

AIM

To examine the association between continuous glucose monitoring (CGM)-derived metrics and cognitive performance in older adults with type 2 diabetes (T2D).

MATERIALS AND METHODS

A total of 100 outpatients with T2D aged 70 years or older were analysed. Participants underwent CGM for 14 days. As CGM-derived metrics, mean sensor glucose (SG), glucose coefficient of variation (CV), time in range (TIR; 70-180 mg/dl), time above range (TAR; > 180 mg/dl) and time below range (TBR; < 70 mg/dl), were calculated. Participants underwent cognitive tests, including the Japanese version of the Montreal Cognitive Assessment (MoCA-J), a delayed word-recall test from the Alzheimer's Disease Assessment Scale-cognitive subscale, a digit symbol substitution test, a letter word fluency test, a trail-making test (TMT) and digit span test (DSP).

RESULTS

In multiple regression analyses adjusted for confounders, a higher mean SG was associated with a lower performance in MoCA-J and TMT part B (TMT-B) (P < .05). A higher TAR was associated with a lower performance in TMT-B and DSP-backward (P < .05). By contrast, a higher TIR was associated with better function in TMT-B and DSP-backward (P < .05). Furthermore, CV and TBR were not associated with any cognitive function.

CONCLUSION

Hyperglycaemia metrics and TIR derived from CGM are associated with cognitive functions, especially with executive function and working memory, in older adults with T2D.

[Insulin pump therapy and continuous glucose monitoring]

This Guideline represents the recommendations of the Austrian Diabetes Association (ÖDG) on the use of diabetes technology (insulin pump therapy; continuous glucose monitoring, CGM; hybrid closed-loop systems, HCL; diabetes apps) and access to these technological innovations for people with diabetes mellitus based on current scientific evidence.

Metabolic control and satisfaction in a diabetes education programme in flash glucose monitoring through telemedicine in type 1 diabetes

OBJECTIVE

To evaluate metabolic control and satisfaction with a telemedicine diabetes education programme for the initiation of flash glucose monitoring (FGM) in type 1 diabetes.

MATERIAL AND METHODS

Prospective study in 48 patients (52.1% women, 22.9% on insulin pump) who started FGM. They were analysed at baseline and 3 months after the beginning of the FGM. The results were compared with an on-site learning cohort matched by age, sex and HbA1c.

RESULTS

At the beginning and 3 months after the MFG, HbA1c improvement was observed (7.9±1.4 vs 7.3±1.1%), p<0.01; with a decrease in time below range - TBR - (4.7±4.9 vs 3.5±3.5%), p<0.05 and number of hypoglycaemic events (9.4±8.7 vs 6.9±5.7/15 days), p<0.05, associated with a worsening in time above range - TAR - (33.5±19.9 vs 37.0±20.9%), p<0.05. No significant differences were observed in the TIR 70-180mg/dl (61.7±18.6 vs 59.4±20.0%), glycemic variability or the use of FGM. Patient satisfaction with telemedicine training was 4.8±0.3 out of 5. No significant differences were observed in the follow-up, either in HbA1c or other glucometer parameters between on-site and online training. In a multivariate analysis adopting the HbA1c at follow-up as the dependent variable, only the TIR (β=-0.034; p<0.001) and the initial HbA1c (β=0.303; p<0.001) maintained statistical significance, unrelated to the on-site or online training (β=0.136; p=ns).

CONCLUSIONS

A telemedicine programme is an adequate tool for training in FGM, with results similar to on-site training, and it was associated with a high degree of satisfaction.

Diabetestechnologie bei Kindern und Jugendlichen mit Diabetes mellitus Typ 1

Die Behandlung des Diabetes mellitus Typ 1 (T1D) im Kindes- und Jugendalter ist komplex und stellt eine Herausforderung für die betroffenen Kinder und Jugendlichen, deren Familien und das ganze Umfeld (Schule/Kindergarten) dar. Das Ziel der Diabetestherapie besteht darin, eine möglichst normoglykämische Blutzuckerkontrolle zu erreichen, um akuten und chronischen Komplikationen vorzubeugen. Laut Registerstudien können die metabolischen Therapieziele derzeit noch nicht erreicht werden, weswegen ein Risiko für Akut- und Spätkomplikationen besteht. Weitere Therapieziele sind eine normale Entwicklung, Inklusion, Flexibilität im Alltag sowie eine hohe Lebensqualität. Abgesehen von neueren Insulinanaloga gingen auch die Entwicklungen in der Diabetestechnologie in den letzten Jahren mit großen Veränderungen und Verbesserungen in der Behandlung und Lebensqualität der betroffenen Familien einher. Die Insulinpumpentherapie, die kontinuierliche Glukosemessung sowie die automatische Insulindosierung (AID) führten zu einer signifikanten Verbesserung der metabolischen Einstellung sowie einer Reduktion der schweren Hypoglykämien und Ketoazidosen. Die Diabetestechnologie entwickelt sich ständig weiter und erfordert eine umfassende Schulung und Fortbildung der betroffenen Familien, der Betreuungseinrichtungen sowie auch des multidisziplinären Behandlungsteams. Ziel sind eine Reduktion der glykämischen Variabilität und damit ein besseres Langzeitoutcome der jungen Menschen mit T1D. Die AID ist zunehmend die Therapie der Wahl bei Kindern und Jugendlichen mit T1D. Mit weiteren Innovationen im Bereich der Diabetestechnologie ist in naher Zukunft zu rechnen.

Time in range following flash glucose monitoring: Relationship with glycaemic control, diabetes-related distress and resource utilisation in the Association of British Clinical Diabetologists national audit

AIMS

The aim of this study was to understand the relationship between time in range (TIR) achieved using the isCGM with changes in glycaemic control, diabetes-related distress (DRD) and resource utilisation in people living with diabetes.

METHODS

Clinicians from 106 National Health System (NHS) UK hospitals submitted isCGM user baseline and follow-up data in a web-based tool held within the UK NHS network. Linear regression analysis was used to identify the relationship between follow-up glucose TIR (3.9-10 mmol/L) categories (TIR% 50-70 and TIR% >70) with change in haemoglobin A1c (HbA1c), DRD and Gold score (measure of hypoglycaemia unawareness, where a score ≥4 suggests impaired awareness of hypoglycaemia).

RESULTS

Of 16,427 participants, 1241 had TIR follow-up data available. In this cohort, the mean TIR was 44.8% (±22.5). With the use of isCGM, at 7.9 months mean follow-up, improvements were observed in HbA1c (-6.9 [13.5] mmol/mol, p < 0.001), Gold score (-0.35 [1.5], p < 0.001) and Diabetes Distress Screening (-0.73 [1.23], p < 0.001). In the regression analysis restricted to people living with type 1 diabetes, TIR% 50-70 was associated with a -8.9 mmol/mol (±0.6, p < 0.001) reduction in HbA1c; TIR% >70 with a -14 mmol/mol (±0.8, p < 0.001) reduction in HbA1c. Incremental improvement in TIR% was also associated with significant improvements in Gold score and DRD. TIR% >70 was associated with no hospital admissions due to hypoglycaemia, hyperglycaemia/diabetic ketoacidosis, and a 60% reduction in the paramedic callouts and 77% reduction in the incidence of severe hypoglycaemia.

CONCLUSION

In a large cohort of UK isCGM users, we demonstrate a significant association of higher TIR% with improvement in HbA1c, hypoglycaemia awareness, DRD and resource utilisation.

Variation of glucose time in range in type 1 diabetes

INTRODUCTION

The aim of the study was to assess the variation of glucose time in range (TIR) for persons with type 1 diabetes who perform intermittently scanned continuous glucose monitoring (isCGM).

METHODS

Glucose data for 8 weeks were analysed for 166 persons. TIR was calculated over four consecutive 2 weeks periods. Sixty-one of the persons had two downloads with an interval of >3 months.

RESULTS

A total of 140 individuals (84%) used multiple daily injection, and 26 (16%) used continuous insulin infusion. The within-individual standard deviation (SD) for TIR was 6.3% corresponding to 95% limits of agreement for the difference between two TIR values of ±17.6%. Mean TIR calculated from the first and last 2 weeks was 52.2 ± 17.1% and 53.7 ± 16.4%, respectively (difference 1.5%, SD of the difference 10.4%, p = .07). For persons with two downloads separated by months, the SD of the difference in TIR was 12.6%.

CONCLUSIONS

The 95% limit of agreement for TIR is vast for persons using isCGM. It is difficult to draw firm conclusions regarding systematic differences when individual TIR from 2 weeks are compared. This may not be valid for users of insulin pumps with closed-loop insulin delivery.

A role for the diabetes nurse educator to telemedically support children with type 1 diabetes on continuous glucose monitoring? The COVID-19 lockdown experience

The role of diabetes nurse educators during the COVID-19 lockdown to telematically provide routine reviews of glycaemic control in children with type 1 diabetes (T1D) using continuous glucose monitoring systems is evaluated. Implementing these routines in the day-to-day clinical practice could reduce in-person clinic visits and improve glucose control.

Continuous glucose monitoring in primary care - are we there?

PURPOSE OF REVIEW

In this review, we examine the expanding role of continuous glucose monitoring in glycaemic management in primary care.

RECENT FINDINGS

Improving technology and decreasing cost have increased the uptake of use of continuous glucose monitoring (CGM) for glycaemic management in primary care, wherein most diabetes is managed. Optimized use of this technology, however, will require a convergence of several factors. Availability of devices for people with diabetes, availability of data at the time of clinical interactions, and expertise in interpretation of CGM and ambulatory glucose profile (AGP) data, as well as optimization of therapies, will be required. Significant progress has been made in all three areas in recent years, yet creating systems of support for widespread use of CGM in primary care remains an area of active investigation.

SUMMARY

There has been significant uptake in the use of CGM in the management of diabetes in primary care. Optimized use, however, requires both access to CGM data and the expertise to use the data. Although promising strategies have emerged, the task of generalizing these strategies to the broad population of primary care in America is ongoing. CGM technology holds significant potential for improving glycaemic management in primary care, yet important work remains to leverage the full potential of this promising technology.

Glucose control in diabetes during home confinement for the first pandemic wave of COVID-19: a meta-analysis of observational studies

AIM

To assess the effect on glycaemic control of confinement due to lockdown measures, during COVID-19 pandemic, in people with type 1 (T1DM) and type 2 (T2DM) diabetes.

METHODS

Meta-analysis of observational studies reporting measures of glucose control and variability before and during and/or after periods of confinement caused by COVID-19 in 2020 and/or 2021.

RESULTS

We included 27 studies on T1DM. No significant change in Hba1c was observed after lockdown (WMD - 1.474 [- 3.26; 0.31] mmol/mol, I² = 93.9). TIR significantly increased during and after lockdown (WMD: 2.73 1.47; 4.23 %, I² = 81% and 3.73 [1.13; 5.33] %, I² = 85%, respectively).We retrieved nine studies on T2DM patients. No significant variation in HbA1c was detected (WMD - 1.257 - 3.91; 1.39 mmol/mol, I² = 98.3%). HbA1c had a more favourable trend in studies performed in Asia than in Europe (p = 0.022 between groups).

CONCLUSION

Lockdown showed no significant detrimental effect on HbA1c in either T1DM or T2DM. Conversely, home confinement led to a reduction in mean glucose and glucose variability in T1DM, although with a high heterogeneity of results.

Design of clinical trials to assess diabetes treatment: Minimum duration of continuous glucose monitoring data to estimate time-in-ranges with the desired precision

AIM

To compute the uncertainty of time-in-ranges, such as time in range (TIR), time in tight range (TITR), time below range (TBR) and time above range (TAR), to evaluate glucose control and to determine the minimum duration of a trial to achieve the desired precision.

MATERIALS AND METHODS

Four formulas for the aforementioned time-in-ranges were obtained by estimating the equation's parameters on a training set extracted from study A (226 subjects, ~180 days, 5-minute Dexcom G4 Platinum sensor). The formulas were then validated on the remaining data. We also illustrate how to adjust the parameters for sensors with different sampling rates. Finally, we used study B (45 subjects, ~365 days, 15-minute Abbott Freestyle Libre sensor) to further validate our results.

RESULTS

Our approach was effective in predicting the uncertainty when time-in-ranges are estimated using n days of continuous glucose monitoring (CGM), matching the variability observed in the data. As an example, monitoring a population with TIR = 70%, TITR = 50%, TBR = 5% and TAR = 25% for 30 days warrants a precision of ±3.50%, ±3.68%, ±1.33% and ±3.66%, respectively.

CONCLUSIONS

The presented approach can be used to both compute the uncertainty of time-in-ranges and determine the minimum duration of a trial to achieve the desired precision. An online tool to facilitate its implementation is made freely available to the clinical investigator.

Optimizing the use of technology to support people with diabetes: research recommendations from Diabetes UK's 2019 diabetes and technology workshop

AIMS

To identify key gaps in the research evidence base that could help improve how technology supports people with diabetes, and provide recommendations to researchers and research funders on how best to address them.

METHODS

A research workshop was conducted, bringing together research experts in diabetes, research experts in technology, people living with diabetes and healthcare professionals.

RESULTS

The following key areas within this field were identified, and research recommendations for each were developed: Matching the pace of research with that of technology development Time in range as a measure Health inequalities and high-risk groups How to train people to use technology most effectively Impact of technology usage on mental health CONCLUSIONS: This position statement outlines recommendations through which research could improve how technology is employed to care for and support people living with diabetes, and calls on the research community and funders to address them in future research programmes and strategies.

Time-in-range for monitoring glucose control: Is it time for a change?

The HbA1c value has been the gold standard for evaluating glucose control for decades. However, it has limitations such as the lack of information on glycemic variability or the risk of hypoglycemia. The increasing use of continuous glucose monitoring has provided patients and healthcare professionals with a range of useful metrics for the management of diabetes. Among them, Time in Range (TIR) is a simple and intuitive metric that gives information regarding the quality of glucose control. It is defined as the time spent in an individual's target glucose range. TIR is strongly correlated with HbA1c, and it has been linked to the risk of developing microvascular and macrovascular complications. The International Consensus on Time in Range has recently set targets for different diabetes populations. For the majority of people with type 1 or type 2 diabetes, a TIR (70-180 mg/dL or 3.9-10.0 mmol/L) of >70%, a time below range (TBR) <70 mg/dL (<3.9 mmol/L) of <4% and a TBR <54 (<3.0 mmol/L) of <1% are recommended. In this review, we address the latest evidence for the use of TIR as an essential parameter in the management of diabetes.

Pediatric Endocrinology of Post-Pandemic Era

The COVID-19 pandemic has brought about significant changes in the global health and medical environment. In order to prevent viral transmission, governments implemented strong social distancing, even lockdowns. The closure of schools has reduced physical activity among children and adolescents. Children, especially those with chronic diseases, are often reluctant to visit or even give up visiting hospitals, resulting in delayed diagnosis or missing the right timing for treatment. New guidelines are needed for the management of patients with endocrine diseases such as diabetes or thyroid disease, as well as children with chronic diseases who need regular clinical monitoring. Therefore, it is necessary to analyze the previous research on pediatric endocrinology reported during COVID-19, guide the direction for future research, and establish guidelines to care for pediatric patients with endocrine diseases in the post-pandemic era.