Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

An evaluation of how exercise position statement guidelines are being used in the real world in type 1 diabetes: Findings from the type 1 diabetes exercise initiative (T1DEXI)

AIMS

Position statement guidelines should help people with type 1 diabetes (T1D) improve glucose outcomes during exercise.

METHODS

In a 4-week observational study, continuous glucose, insulin, and nutrient data were collected from 561 adults with T1D. Glucose outcomes were calculated during exercise, post-exercise, and overnight, and were compared for sessions when participants used versus did not use exercise guidelines for open-loop (OL) and automated insulin delivery (AID) therapy.

RESULTS

Guidelines requiring behaviour modification were rarely used while guidelines not requiring modification were often used. The guideline recommending reduced meal insulin before exercise was associated with lower time <3.9 mmol/L during exercise (-2.2 %, P=0.02) for OL but not significant for AID (-1.4 %, P=0.27). Compared to exercise with low glucose (<3.9 mmol/L) in prior 24-hours, sessions without recent low glucose had lower time <3.9 mmol/L during exercise (-1.2 %, P<0.001). The AID guideline for no carbohydrates before exercise when CGM is flat, or increasing, was not associated with improved glycaemia.

CONCLUSIONS

Free-living datasets may be used to evaluate usage and benefit of position statement guidelines. Evidence suggests OL participants who reduced meal insulin prior to exercise and did not have low glucose in the prior 24 h had less time below range.

The role of automated insulin delivery technology in diabetes

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.

The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness and challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, and mental health. A thorough literature search identified studies focusing on wearable devices' impact on patient outcomes. In cardiology, wearables have proven effective for monitoring hypertension, detecting arrhythmias, and aiding cardiac rehabilitation. In respiratory health, these devices enhance asthma management and continuous monitoring of critical parameters. Neurological applications include seizure detection and Parkinson's disease management, with wearables showing promising results in improving patient outcomes. In endocrinology, wearable technology advances thyroid dysfunction monitoring, fertility tracking, and diabetes management. Orthopedic applications include improved postsurgical recovery and rehabilitation, while wearables help in early complication detection in oncology. Mental health benefits include anxiety detection, post-traumatic stress disorder management, and stress reduction through wearable biofeedback. In conclusion, wearable health devices offer transformative potential for managing chronic illnesses by enhancing real-time monitoring and patient engagement. Despite significant improvements in adherence and outcomes, challenges with data accuracy and privacy persist. However, with ongoing innovation and collaboration, we can all be part of the solution to maximize the benefits of wearable technologies in healthcare.

Simple meal announcements and pramlintide delivery versus carbohydrate counting in type 1 diabetes with automated fast-acting insulin aspart delivery: a randomised crossover trial in Montreal, Canada

BACKGROUND

In type 1 diabetes, carbohydrate counting is the standard of care to determine prandial insulin needs, but it can negatively affect quality of life. We developed a novel insulin-and-pramlintide closed-loop system that replaces carbohydrate counting with simple meal announcements.

METHODS

We performed a randomised crossover trial assessing 14 days of (1) insulin-and-pramlintide closed-loop system with simple meal announcements, (2) insulin-and-placebo closed-loop system with carbohydrate counting, and (3) insulin-and-placebo closed-loop system with simple meal announcements. Participants were recruited at McGill University Health Centre (Montreal, QC, Canada). Eligible participants were adults (aged ≥18 years) and adolescents (aged 12-17 years) with type 1 diabetes for at least 1 year. Participants were randomly assigned in a 1:1:1:1:1:1 ratio to a sequence of the three interventions, with faster insulin aspart used in all interventions. Each intervention was separated by a 14-45-day wash-out period, during which participants reverted to their usual insulin. During simple meal announcement interventions, participants triggered a prandial bolus at mealtimes based on a programmed fixed meal size, whereas during carbohydrate counting interventions, participants manually entered the carbohydrate content of the meal and an algorithm calculated the prandial bolus based on insulin-to-carbohydrate ratio. Two primary comparisons were predefined: the percentage of time in range (glucose 3·9-10·0 mmol/L) with a non-inferiority margin of 6·25% (non-inferiority comparison); and the mean Emotional Burden subscale score of the Diabetes Distress Scale (superiority comparison), comparing the insulin-and-placebo system with carbohydrate counting minus the insulin-and-pramlintide system with simple meal announcements. Analyses were performed on a modified intention-to-treat basis, excluding participants who did not complete all interventions. Serious adverse events were assessed in all participants. This trial is registered on ClinicalTrials.gov, NCT04163874.

FINDINGS

32 participants were enrolled between Feb 14, 2020, and Oct 5, 2021; two participants withdrew before study completion. 30 participants were analysed, including 15 adults (nine female, mean age 39·4 years [SD 13·8]) and 15 adolescents (eight female, mean age 15·7 years [1·3]). Non-inferiority of the insulin-and-pramlintide system with simple meal announcements relative to the insulin-and-placebo system with carbohydrate counting was reached (difference -5% [95% CI -9·0 to -0·7], non-inferiority p<0·0001). No statistically significant difference was found in the mean Emotional Burden score between the insulin-and-pramlintide system with simple meal announcements and the insulin-and-placebo system with carbohydrate counting (difference 0·01 [SD 0·82], p=0·93). With the insulin-and-pramlintide system with simple meal announcements, 14 (47%) participants reported mild gastrointestinal symptoms and two (7%) reported moderate symptoms, compared with two (7%) participants reporting mild gastrointestinal symptoms on the insulin-and-placebo system with carbohydrate counting. No serious adverse events occurred.

INTERPRETATION

The insulin-and-pramlintide system with simple meal announcements alleviated carbohydrate counting without degrading glucose control, although quality of life as measured by the Emotional Burden score was not improved. Longer and larger studies with this novel approach are warranted.

FUNDING

Juvenile Diabetes Research Foundation.

Digital twins and artificial intelligence in metabolic disease research

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.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

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.

NIH Fifth Artificial Pancreas Workshop 2023: Meeting Report: The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption

The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption was held at the National Institutes of Health (NIH) Campus in Bethesda, Maryland on May 1 to 2, 2023. The organizing Committee included representatives of NIH, the US Food and Drug Administration (FDA), Diabetes Technology Society, Juvenile Diabetes Research Foundation (JDRF), and the Leona M. and Harry B. Helmsley Charitable Trust. In previous years, the NIH Division of Diabetes, Endocrinology, and Metabolic Diseases along with other diabetes organizations had organized periodic workshops, and it had been seven years since the NIH hosted the Fourth Artificial Pancreas in July 2016. Since then, significant improvements in insulin delivery have occurred. Several automated insulin delivery (AID) systems are now commercially available. The workshop featured sessions on: (1) Lessons Learned from Recent Advanced Clinical Trials and Real-World Data Analysis, (2) Interoperability, Data Management, Integration of Systems, and Cybersecurity, Challenges and Regulatory Considerations, (3) Adaptation of Systems Through the Lifespan and Special Populations: Are Specific Algorithms Needed, (4) Development of Adaptive Algorithms for Insulin Only and for Multihormonal Systems or Combination with Adjuvant Therapies and Drugs: Clinical Expected Outcomes and Public Health Impact, (5) Novel Artificial Intelligence Strategies to Develop Smarter, More Automated, Personalized Diabetes Management Systems, (6) Novel Sensing Strategies, Hormone Formulations and Delivery to Optimize Close-loop Systems, (7) Special Topic: Clinical and Real-world Viability of IP-IP Systems. "Fully automated closed-loop insulin delivery using the IP route," (8) Round-table Panel: Closed-loop performance: What to Expect and What are the Best Metrics to Assess it, and (9) Round-table Discussion: What is Needed for More Adaptable, Accessible, and Usable Future Generation of Systems? How to Promote Equitable Innovation? This article summarizes the discussions of the Workshop.