JMIR Publications

ABSTRACT

Background:

Type 1 Diabetes (T1D) is a chronic autoimmune disease in which a deficiency in insulin production impairs the glucose homeostasis of the body. Continuous Subcutaneous Infusion of Insulin (CSII) is a commonly used treatment method. Artificial Pancreas Systems (APS) utilizes Continuous Glucose Monitoring (CGM) and CSII in a closed-loop mode incorporating a controller (or control algorithm). However, the operation of APS is challenged due to complexities arising during meals, exercise, stress, sleep, illnesses, glucose sensing /insulin action delays and the cognitive burden. To overcome these challenges, options to augment APS through integration of additional inputs, creating Multi-input Artificial Pancreas Systems (MAPS), are being investigated.

Objective:

This survey identified and analyzed input data, control architectures, and validation methods of MAPS in order to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D.

Methods:

A literature survey was conducted, using the Scopus, PubMed, and IEEEXplore databases, for the period from 2005 to 2020. Based on the search criteria, 1092 articles were initially shortlisted, of which 11 were selected for an in-depth narrative analysis. Additionally, 6 clinical studies associated with the selected studies were also analysed.

Results:

Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller via activity detection, directly incorporated for decision making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through use of simulators, based on different physiological models and clinical trials.

Conclusions:

The integration of additional physiological signals with CGM, has potential to optimize glucose control in people with T1D through addressing identified limitations of APS. A majority of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator towards MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life.