Improved glycaemia during the Covid-19 pandemic lockdown is sustained post-lockdown and during the "Eat Out to Help Out" Government Scheme, in adults with Type 1 diabetes in the United Kingdom

Aims The majority of studies report that the Covid-19 pandemic lockdown did not have a detrimental effect on glycaemia. We sought to explore the impact of lockdown on glycaemia and whether this is sustained following easing of restrictions. Methods Retrospective, observational analysis in adults and children with type 1 diabetes attending a UK specialist centre, using real-time or intermittently scanned continuous glucose monitoring. Data from the following 28-day time periods were collected: (i) pre-lockdown; (ii) during lockdown; (iii) immediately after lockdown; and (iv) a month following relaxation of restrictions (coinciding with Government-subsidised restaurant food). Data were analysed for times in glycaemic ranges and are expressed as median (IQR). Results 145 adults aged 35.5 (25.8–51.3) years with diabetes duration of 19.0 (7.0–29.0) years on multiple daily injections of insulin (60%) and continuous insulin infusion (40%) were included. In adults, % time in range (70-180mg/dL) increased during lockdown (60.2 (45.2–69.3)%) compared to pre-lockdown (56.7 (43.5–65.3)%; p180mg/dL) reduced in lockdown compared to pre-lockdown (p = 0.01), which was sustained thereafter. In children, no significant changes to glycaemia were observed during lockdown. In multivariable analysis, a greater increase in %TIR 3.9-10mmol/L (70-180mg/dL) during lockdown was associated with higher levels of deprivation (coefficient: 4.208, 95% CI 0.588 to 7.828; p = 0.02). Conclusions Glycaemia in adults improved during lockdown, with people from more deprived areas most likely to benefit. This effect was sustained after easing of restrictions, with government-subsidised restaurant eating having no adverse impact on glycaemia

A modular safety system for an insulin dose recommender: A feasibility study

Background: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. Methods: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are: predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycaemia; and a personalised safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycaemic control and safety system functioning were compared between the two-weeks run-in period and the end-point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. Results: Overall, glycaemic control improved over the evaluated period. In particular, percentage time in the hypoglycaemia range (<3.0mmol/l) significantly decreased from 0.82 (0.05-4.79) % at run-in to 0.33 (0.00-0.93) % at endpoint (p=0.02). This was associated with a significant increase in percentage time in target range (3.9-10.0mmol/l) from 52.8 (38.3-61.5) % to 61.3 (47.5-71.7) % (p=0.03). There was also a reduction in number of carbohydrate recommendations. Conclusion: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes

Trust and contextual engagement with the PEPPER system: The qualitative findings of a clinical feasibility study

Background and aims. PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project which aims to improve self-management of type 1 diabetes (T1D). The system comprises an AI insulin bolus recommender, coupled with a safety system. The aim of the qualitative arm of this clinical feasibility study was to examine the context of participants’ interaction with the PEPPER system and identify incidents where bolus recommendations were trusted and accepted. Methods. This was a multicentre (UK and Spain) non-randomised open-labelled 6-week pilot study. Thirteen adults with T1D participated in weekly telephone interviews to explore the context of their interactions and responses to PEPPER. Data was thematically analysed through conceptual frameworks for engagement with healthcare digital behaviour change interventions. Results. Participants reported their key interactions as responding to PEPPER bolus recommendations, inputting carbohydrate values, interpreting continuous glucose monitoring (CGM) values through visualization of personal data and dealing with safety alarms. Two themes were associated with trust and engagement with the system; ‘feeling monitored’ and ‘feeling in control’. The incidents where participants trusted PEPPER also enhanced personal expertise of T1D through insights provided by the safety system such as low glucose basal insulin for pump users. Benefits were balanced against technical challenges of the system, which were used to improve the PEPPER application and enhance user experience. Conclusion. Some participants suggested that even access to PEPPER for a temporary period could positively influence self-management strategies. Contextual interviewing is a valuable tool in mobile application development for diabetes decision support systems

Is it possible to constantly and accurately monitor blood sugar levels, in people with Type 1 diabetes, with a discrete device (non-invasive or invasive)?

Real-time continuous glucose monitors using subcutaneous needle-type sensors continue to develop. The limitations of currently available systems, however, include time lag behind changes in blood glucose, the invasive nature of such systems, and in some cases, their accuracy. Non-invasive techniques have been developed, but, to date, no commercial device has been successful. A key research priority for people with Type 1 diabetes identified by the James Lind Alliance was to identify ways of monitoring blood glucose constantly and accurately using a discrete device, invasive or non-invasive. Integration of such a sensor is important in the development of a closed-loop system and the technology must be rapid, selective and acceptable for continuous use by individuals. The present review provides an update on existing continuous glucose-sensing technologies, and an overview of emergent techniques, including their accuracy and limitations

Rationale and protocol for the Assessment of Impact of Real-time Continuous Glucose Monitoring on people presenting with severe Hypoglycaemia (AIR-CGM) study

Background: Severe hypoglycaemia carries a significant risk of morbidity and mortality for people with type 1 diabetes. Economic costs are also high, estimated at approximately £13 million annually in England, UK. Continuous glucose monitoring (CGM) has been shown to reduce hypoglycaemia and associated fear, improve overall glycaemia and quality of life, and is cost-effective. Despite effective pathways in place with high levels of resource utilization, it has been reported there are low levels of follow-up, therapy change and specialist intervention after severe hypoglycaemia. This study is designed to assess the impact of providing real-time CGM to people with type 1 diabetes, who have had a recent episode of severe hypoglycaemia (within 72hours), compared to standard care. Methods/Design: Fifty-five participants with type 1 diabetes and a recent episode of severe hypoglycaemia, who are CGM naïve, will be recruited to the study. Participants will be randomised to CGM or standard care. The primary outcome is percentage time spent in hypoglycaemia (<3.0mmol/L, 55mg/dL). Secondary outcomes include other measures of hypoglycaemia, time in euglycaemia, overall glucose status and patient reported qualitative measures. Discussion: This study assesses the impact of providing continuous glucose monitoring at the outset in individuals at highest risk of hypoglycaemia. Changing demand means that novel approaches need to be taken to healthcare provision. This study has the potential to shape future national standards

Glycemic variability and hypoglycemic excursions with continuous glucose monitoring compared to intermittently scanned continuous glucose monitoring in adults With highest risk type 1 diabetes

BACKGROUND: The I-HART CGM study has shown that real-time continuous glucose monitoring (rtCGM) has greater beneficial impact on hypoglycemia than intermittently scanned continuous glucose monitoring (iscCGM) in adults with type 1 diabetes at high risk (Gold score ≥4 or recent severe hypoglycemia using insulin injections). In this subanalysis, we present the impact of rtCGM and iscCGM on glycemic variability (GV). METHODS: Forty participants were recruited to this parallel group study. Following two weeks of blinded rtCGM (DexcomG4), participants were randomized to rtCGM (Dexcom G5; n = 20) or iscCGM (Freestyle Libre; n = 20) for eight weeks. An open-extension phase enabled participants on rtCGM to continue for a further eight weeks and those on iscCGM to switch to rtCGM over this period. Glycemic variability measures at baseline, 8- and 16-week endpoints were compared between groups. RESULTS: At the eight-week endpoint, between-group differences demonstrated significant reduction in several GV measures with rtCGM compared to iscCGM (GRADE%hypoglycemia, index of glycemic control [IGC], and average daily risk range [ADRR]; P < .05). Intermittently scanned continuous glucose monitoring reduced mean average glucose and glycemic variability percentage and GRADE%hyperglycemia compared with rtCGM (P < .05). At 16 weeks, the iscCGM group switching to rtCGM showed significant improvement in GRADE%hypoglycemia, personal glycemic status, IGC, and ADRR. CONCLUSION: Our data suggest most, but not all, GV measures improve with rtCGM compared with iscCGM, particularly those measures associated with the risk of hypoglycemia. Selecting appropriate glucose monitoring technology to address GV in this high-risk cohort is important to minimize the risk of glucose extremes and severe hypoglycemia. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT03028220

Long-term glucose forecasting using a physiological model and deconvolution of the continuous glucose monitoring signal

(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose–insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min prediction horizon, the percentage improvement on prediction accuracy measured with the root mean square error, A-region of error grid analysis (EGA), and hypoglycaemia prediction calculated by the Matthews correlation coefficient, was 18.8% , 17.9% , and 80.9% , respectively. Although showing a trend towards improvement, the addition of meal absorption information did not provide clinically significant improvements. (4) Conclusion: The proposed glucose forecasting algorithm is potentially well-suited for T1D management applications which require long-term glucose predictions