Aims: To determine the impact of structured education promoting flexible intensive insulin therapy on rates of diabetic ketoacidosis, and the costs associated with emergency treatment for severe hypoglycaemia and ketoacidosis in adults with Type 1 diabetes.Methods: Using the Dose Adjustment For Normal Eating research database we compared the rates of ketoacidosis and severe hypoglycaemia during the 12 months preceding Dose Adjustment For Normal Eating training with the rates during the 12‐month follow‐up after this training. Emergency treatment costs were calculated for associated paramedic assistance, Accident and Emergency department attendance and hospital admissions.Results: Complete baseline and 1‐year data were available for 939/1651 participants (57%). The risk of ketoacidosis in the 12 months after Dose Adjustment For Normal Eating training, compared with that before training, was 0.39 (95% CI: 0.23 to 0.65, P < 0.001), reduced from 0.07 to 0.03 episodes/patient/year. For every 1 mmol/mol unit increase in HbA_1c concentration, the risk of a ketoacidosis episode increased by 6% (95% CI: 5 to 7%; 88% for a 1% increase), and for each 5‐year increase in diabetes duration, the relative risk reduced by 20% (95% CI: 19 to 22%). The number of emergency treatments decreased for ketoacidosis (P < 0.001), and also for severe hypoglycaemia, including paramedic assistance (P < 0.001), Accident and Emergency department attendance (P = 0.029) and hospital admission (P = 0.001). In the study cohort, the combined cost of emergency treatment for ketoacidosis and severe hypoglycaemia fell by 64%, from £119,470 to £42,948.Conclusions: Structured training in flexible intensive insulin therapy is associated with a 61% reduction in the risk of ketoacidosis and with 64% lower emergency treatment costs for ketoacidosis and severe hypoglycaemia.

Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.

Structured education programmes for patients with diabetes and other chronic conditions are being widely adopted. However, follow-up studies suggest that course graduates may struggle to sustain the self-care practices taught on their courses over time. This study explored the support needs of patients with type 1 diabetes after attending a structured education programme promoting an empowerment approach and training in use of flexible intensive insulin therapy, a regimen now widely advocated and used to manage this condition. The objective was to inform future support offered to course graduates.

The study investigated the feasibility of conducting a future Randomised Controlled Trial (RCT) of a mobile health (mHealth) intervention for weight loss and HbA1c reduction in Type 2 Diabetes Mellitus (T2DM). The intervention was a small wearable mHealth device used over 12 weeks by overweight people with T2DM with the intent to lose weight and reduce their HbA1c level. A 4 week maintenance period using the device followed. The device records physical activity level and information about food consumption, and provides motivational feedback based on energy balance. Twenty-seven participants were randomised to receive no intervention; intervention alone; or intervention plus weekly motivational support. All participants received advice on diet and exercise at the start of the study. Weight and HbA1c levels were recorded at baseline and weeks 6, 12, and 16. Qualitative interviews were conducted with participants who received the intervention to explore their experiences of using the device and involvement in the study including the training received. Overall the device was perceived to be well-liked, acceptable, motivational and easy to use by participants. Some logistical changes were required during the feasibility study, including shortening of the study duration and relaxation of participant inclusion criteria. Descriptive statistics of weight and HbA1c data showed promising trends of weight loss and HbA1c reduction in both intervention groups, although this should be interpreted with caution. A number of methodological recommendations for a future RCT emerged from the current feasibility study. The mHealth device was acceptable and promising for helping individuals with T2DM to reduce their HbA1c and lose weight. Devices with similar features should be tested further in larger studies which follow these methodological recommendations.

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis’s congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.