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CGM data, insulin, and METs during the 2 h after the start of the primary in-clinic session structured exercise
Source publication
Background:
Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions com...
Context in source publication
Context 1
... using the exMPC had better glucose outcomes following in-clinic structured exercise as indicated by a significantly lower mean glucose (figure 4). Figure 4 also shows that although the exAPD shut off insulin completely when structured exercise was detected by the algorithm and accepted by the user prompt, the exMPC algorithm did not completely shut off insulin for all participants as the exercise data were just one input to the algorithm, and in certain cases, a complete shut-off of insulin was not necessarily indicated to maintain optimal glucose outcomes. Insulin shut-off could have been caused by either the LSTM or the exercise detection. ...
Citations
... Innovative approaches to improving performance of closed-loop systems include incorporation of signals from other wearables such as a smartwatch app that detects eating behaviour [63] or a fitness sensor for detecting exercise/ activity [64]. The potential clinical benefits of integrating these devices remains to be determined in larger and longer trials. ...
The role of automated insulin delivery systems in diabetes is expanding. Hybrid closed-loop systems are being used in routine clinical practice for treating people with type 1 diabetes. Encouragingly, real-world data reflects the performance and usability observed in clinical trials. We review the commercially available hybrid closed-loop systems, their distinctive features and the associated real-world data. We also consider emerging indications for closed-loop systems, including the treatment of type 2 diabetes where variability of day-to-day insulin requirements is high, and other challenging applications for this technology. We discuss issues around access and implementation of closed-loop technology, and consider the limitations of present closed-loop systems, as well as innovative approaches that are being evaluated to improve their performance.
Graphical Abstract
In 2023, epidemiological studies based on US cohorts revealed that the median age for onset of type 1 diabetes is 24 years. Furthermore, there was a high prevalence of obesity in people with type 1 diabetes. Moreover, a variety of studies have investigated the effects of using immunomodulatory drugs to maintain beta cell function. As a consequence, distinct pharmacological candidates are at an advanced stage of development. Clearly, insulin remains the cornerstone of treatment in patients with type 1 diabetes. Several studies recently focused on once-weekly basal insulin analogues. Automated insulin delivery (AID) systems have shown dramatic advances toward the use in routine care of type 1 diabetes. During the last year, several AID systems have been reported to contributing to an increased quality of life and significant improvements in the time spent in glucose target ranges, particularly in vulnerable patient groups with type 1 diabetes.
Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.
italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:
Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.
Methods:
Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.
Significance:
These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
