Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions

Modelling and optimal control of blood glucose levels in the human body

Regulating the blood glucose level is a challenging control problem for the human body. Abnormal blood glucose levels can cause serious health problems over time, including diabetes. Although several mathematical models have been proposed to describe the dynamics of glucose-insulin interaction, none of them have been universally adopted by the research community. In this paper, we consider a dynamic model of the blood glucose regulatory system originally proposed by Liu and Tang in 2008. This model consists of eight state variables naturally divided into three subsystems: the glucagon and insulin transition subsystem, the receptor binding subsystem and the glucosesubsystem. The model contains 36 model parameters, many of which are unknown and difficult to determine accurately. We formulate an optimal parameter selection problem in which optimal values for the model parameters must be selected so that the resulting model best its given experimental data.We demonstrate that this optimal parameter selection problem can be solved readily using the optimal control software MISER 3.3. Using this approach, significant improvements can be made in matching the model to the experimental data. We also investigate the sensitivity of the resulting optimizedmodel with respect to the insulin release rate. Finally, we use MISER 3.3 to determine optimal open loop controls for the optimized model

The biochemical basis of disease

Understanding Biochemistry is an essential online resource for post-16 students, teachers and undergraduates, providing up-to-date overviews of key concepts in biochemistry and molecular biosciences. The Understanding Biochemistry issues of Essays in Biochemistry are Open Access publications, meaning that these issues are freely available online to readers This article gives the reader an insight into the role of biochemistry in some of the current global health and disease problems. It surveys the biochemical causes of disease in an accessible and succinct form while also bringing in aspects of pharmacology, cell biology, pathology and physiology which are closely aligned with biochemistry. The discussion of the selected diseases highlights exciting new developments and illuminates key biochemical pathways and commonalities. The article includes coverage of diabetes, atherosclerosis, cancer, microorganisms and disease, nutrition, liver disease and Alzheimer’s disease, but does not attempt to be comprehensive in its coverage of disease, since this is beyond its remit and scope. Consequently there are many fascinating biochemical aspects of diseases, both common and rare, that are not addressed here that can be explored in the further reading cited. Techniques and biochemical procedures for studying disease are not covered in detail here, but these can be found readily in a range of biochemical methods sources