Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces

Smart and Pervasive Healthcare

Smart and pervasive healthcare aims at facilitating better healthcare access, provision, and delivery by overcoming spatial and temporal barriers. It represents a shift toward understanding what patients and clinicians really need when placed within a specific context, where traditional face-to-face encounters may not be possible or sufficient. As such, technological innovation is a necessary facilitating conduit. This book is a collection of chapters written by prominent researchers and academics worldwide that provide insights into the design and adoption of new platforms in smart and pervasive healthcare. With the COVID-19 pandemic necessitating changes to the traditional model of healthcare access and its delivery around the world, this book is a timely contribution

Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service

A personalised and adaptive insulin dosing decision support system for type 1 diabetes

People with type 1 diabetes (T1D) rely on exogenous insulin to maintain stable glucose levels. Despite the advent of diabetes technologies such as continuous glucose monitors and insulin infusion pumps, the majority of people with T1D do not manage to bring back glucose levels into a healthy target after meals. In addition to patient compliance, this is due to the complexity of the decision-making on how much insulin is required. Commercial insulin bolus calculators exist that help with the calculation of insulin for meals but these lack fine-tuning and adaptability. This thesis presents a novel insulin dosing decision support system for people with T1D that is able to provide individualised insulin dosing advice. The proposed research utilises Case-Based Reasoning (CBR), an artificial intelligence methodology, that is able to learn over time based on the behaviour of the patient and optimises the insulin therapy for various diabetes scenarios. The decision support system has been implemented into a user-friendly smartphone-based patient platform and communicates with a clinical platform for remote supervision. In-silico studies are presented demonstrating the overall performance of CBR as well as metrics used to adapt the insulin therapy. Safety and feasibility of the developed system have been assessed incrementally in clinical trials; initially during an eight-hour study in hospital settings followed by a six-week study in the home environment of the user. Human factors play an important role in the clinical adoption of technologies such as the one proposed. System usability and acceptability were evaluated during the second study phase based on feedback obtained from study participants. Results from in-silico tests show the potential of the proposed research to safely automate the process of optimising the insulin therapy for people with T1D. In the six-week study, the system demonstrated safety in maintaining glycemic control with a trend suggesting improvement in postprandial glucose outcomes. Feedback from participants showed favourable outcomes when assessing device satisfaction and usability. A six-month large-scale randomised controlled study to evaluate the efficacy of the system is currently ongoing.Open Acces

Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability

Occupational stress can cause all kinds of health problems. Resilience interventions that help employees deal with and adapt to adverse events can prevent these negative consequences. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee’s own context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. The Wearables and app-based resilience Modelling in employees (WearMe) project aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented, as the basis for the WearMe project. The included concepts are operationalized and measured using sleep tracking (Fitbit Charge 2), heart rate variability measurements (Elite HRV + Polar H7) and Ecological Momentary Assessment (mobile app), administered in the morning (7 questions) and evening (12 questions). The first (ongoing) study within the WearMe project investigates the feasibility of the developed measurement cycle and explores the development of such models in social studies students that are on their first major internship. Analyses will target the development of both within-subject (n=1) models, as well as between-subjects models. The first results will be shared at the Health By Tech 2019 conference in Groningen. If successful, future work will focus on further developing these models and eventually exploring the effectiveness of the envisioned personalized resilience system

An adaptive real-time intelligent system to enhance self-care of chronic disease (ARISES)

Diabetes mellitus is an increasingly prevalent chronic metabolic condition characterised by impaired glucose homeostasis and raised blood glucose levels (hyperglycaemia). Broadly categorised as either type 1 (T1DM) or type 2 diabetes (T2DM), people with diabetes are largely responsible for self-managing their blood glucose levels. Despite the development of diabetes technologies such as real time continuous glucose monitoring (RT-CGM), many individuals are frequently exposed to iatrogenic low blood glucose levels (hypoglycaemia). Severe hypoglycaemia is associated with an increased risk of recurrent hypoglycaemia, impaired symptomatic awareness of hypoglycaemia, and potentially death if left untreated. This thesis affirmed the existing clinical impact of severe hypoglycaemia and its recurrent risk in a six-month analysis of severe hypoglycaemia attended by the London Ambulance Service NHS Trust (LAS). Fewer incidents of severe hypoglycaemia observed in a date matched repeat analysis during the 2020 COVID-19 lockdown suggested improved self-management possibly motivated by a proximal fear of hospitalisation and improved structure at home. Finally, a 12-week randomised control trial demonstrating a significant difference in time spent in hypoglycaemia <3mmol/L, is the first study to prove the immediate provision of RT-CGM significantly reduces the risk of recurrent hypoglycaemia. Moreover, it highlighted the impact of socioeconomic disparity as a barrier to effective hypoglycaemia risk modification. This guided the design of an adaptive real time intelligent system to enhance self-care of chronic disease (ARISES) aimed to deliver therapeutic and lifestyle decision support for people with T1DM. The ARISES graphic user interface (GUI) design was a collaborative process conceived in a series of focus group meetings including people with T1DM. Finally, a 12-week observational study using RT-CGM, a physiological sensor wristband, and a mobile diary app, allowed for a sub-analysis identifying measurable physiological parameters associated with current and impending hypoglycaemia in people with T1DM.Open Acces

The appraisal of Facebook online community: An exposition of mobile commerce in social media reviews

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