More than 400 million people are living with diabetes around the world. A preventable disease, diabetes is a global health challenge that demands innovative solutions. Together with Edith Giemula, Clinical Project Manager at Alcedis, part of the Huma family, we explore how digital tools are becoming a game-changer in diabetes research, accelerating the quest for effective treatments and empowering patients to live healthier lives. To learn more about how to run a remote clinical trials, book a meeting here.
What is diabetes, and how does it impact patients’ lives?
Diabetes is a chronic disease characterised by high blood sugar levels due to impaired insulin production or insulin resistance. It has a significant impact on patients’ lives, affecting daily activities, quality of life, and life expectancy, and requires continuous management. Patients need to monitor their blood sugar levels, take medication or insulin as prescribed, and make adjustments to their treatment plan based on their health status.
Managing diabetes often requires significant lifestyle changes, including adopting a healthy diet, regular exercise, and medication management. Diabetes can lead to various health complications, including cardiovascular disease, nephropathy, neuropathy, retinopathy, and increased risk of infections. Dealing with the demands of the condition, the risk of long-term complications and the need for constant self-management can lead to stress, anxiety and depression, potentially affecting body image, self-esteem and social interactions.
How does diabetes research contribute to improving treatment approaches and enhancing quality of life?
Research helps expand our understanding of diabetes, its causes, risk factors and mechanisms. This knowledge enables us to deduce prevention strategies, treatment approaches and management guidelines. Clinical trials lead to the development of new medications, insulin formulations and treatment modalities. It helps refine existing therapies, optimise dosages and improve treatment outcomes for patients. Understanding how individuals respond to diabetes management and treatments can help us develop personalised care plans. Factors such as genetics, lifestyle choices, and coexisting conditions play a pivotal role in tailoring treatment plans to suit each patient's unique needs. Diabetes research aims to identify strategies for preventing diabetes-related complications and to improve the overall quality of life for individuals living with the condition. This includes addressing psychosocial aspects, developing supportive tools and technologies and promoting self-management skills.
How is digital clinical trial technology enhancing diabetes research and treatment?
Digital technologies enable remote patient monitoring, allowing healthcare providers to collect real-time data on diabetes-related parameters such as blood glucose levels, physical activity and medication adherence. This continuous monitoring provides a more comprehensive understanding of patients' health status, facilitates timely interventions and helps optimise treatment plans.
Digital platforms enable virtual visits and telemedicine consultations, eliminating the need for in-person appointments. Patients can communicate with healthcare professionals, discuss treatment options, receive guidance and seek clarifications remotely. Wearable devices, such as continuous glucose monitoring (CGM) and activity trackers, coupled with mobile apps, provide patients with self-management tools. These devices enable continuous monitoring of glucose levels, physical activity, and other health metrics, empowering patients to take a proactive role in managing their diabetes while spending more time at home or doing the things they love.
How do automated insulin delivery (AID) systems work, and what benefits do they offer to patients with type 1 diabetes?
The combination of insulin pumps with continuous glucose monitoring (CGM) makes AID systems possible. The AID system continuously monitors glucose levels through the CGM sensor, which measures glucose in the interstitial fluid through a small cannula underneath the skin. The collected glucose data is then transmitted to an algorithm that calculates the appropriate insulin dose to maintain target glucose levels. The algorithm considers factors such as glucose trends, previous insulin delivery and individualised settings. Based on the algorithm's calculations, the system automatically adjusts insulin delivery through an insulin pump, delivering precise amounts of insulin as needed.
AID systems for patients with type 1 diabetes have been tested in clinical trials and are proven to be safe and effective. They have shown improvements in parameters of glycemic control such as time in range (TIR) and enable more stable and predictable glucose control. Because the systems are constantly adapting to minor fluctuations in blood sugar levels, they can more closely mimic the function of a healthy pancreas.
What are the key advantages of using digital tools for collecting and analysing data in diabetes clinical trials?
Digital tools enable the collection of real-time data on various diabetes-related parameters, such as blood glucose levels, physical activity, medication adherence and dietary habits. This real-time data provides a more accurate and comprehensive understanding of participants' day-to-day experiences and allows for prompt intervention or adjustment of treatment plans.
Digital tools also reduce the potential for human error in data collection and entry. Automated data capture minimises transcription errors and ensures the accuracy of recorded values. This improves the reliability and quality of the collected data, leading to more robust study findings and conclusions.
The interactive nature of digital tools can increase participant motivation, adherence to study protocols and overall engagement in the clinical trial process. Digital tools provide accessible and convenient resources, allowing participants to actively engage in their disease management. Participants receive feedback, guidance, and positive reinforcement through behavioural support mechanisms integrated into digital platforms. Educational materials empower individuals to make informed decisions and feel confident in managing their condition. Enhanced communication and collaboration facilitate convenient interactions between participants and healthcare providers. Data visualisation features and goal-setting mechanisms further motivate participants to adhere to treatment protocols. By leveraging digital technologies, clinical trials in diabetes encourage active patient involvement, resulting in improved treatment outcomes and overall health.
Digital tools often offer visual representations of data, such as charts, graphs, and dashboards. These visualisations make it easier for researchers to interpret the data and identify trends, patterns, and correlations. Equally, digital platforms facilitate data sharing and collaboration among research team members, enhancing efficiency in data management and analysis.
What are the challenges and limitations associated with implementing digital clinical trial technology in the context of diabetes research?
Digital clinical trials may require significant investments in technology infrastructure, software development and staff training. Participants involved in digital clinical trials also need to have a certain level of digital health literacy to effectively engage with the technology and tools involved. Patients with limited technology exposure may struggle to navigate digital platforms, input data accurately, or interpret instructions correctly. Retaining participants throughout the trial duration can be challenging due to factors such as technical issues, or lack of engagement with the digital tools.
Digital clinical trials rely heavily on collecting and analysing vast amounts of participant data, including personal health information. Ensuring the security and privacy of this data is crucial to maintain participant trust. Implementing robust data encryption, anonymisation techniques, and compliance with data protection regulations is essential but can be complex.
Incorporating digital technologies into clinical trials requires adherence to regulatory and ethical guidelines. These guidelines may not always be specifically tailored to digital health interventions, creating uncertainties and challenges for trial sponsors and investigators. Regulatory bodies are continuously adapting to address these concerns, but staying up to date with evolving regulations is crucial.
What role does artificial intelligence (AI) and Machine Learning play in leveraging the data collected through digital clinical trial technology for diabetes research and personalised treatment approaches?
AI and ML algorithms can analyse large volumes of data collected from digital clinical trial technologies, such as CGMs, wearable devices and patient-reported outcomes. These algorithms identify patterns, trends and correlations that may not be apparent through traditional analysis methods. By uncovering hidden insights, AI and ML enhance researchers' understanding of diabetes dynamics, treatment responses and disease progression.
AI and ML techniques enable predictive modelling based on the collected data. These models can forecast future blood glucose levels, detect potential complications, and predict individual responses to specific treatments or interventions. By continually analysing and updating models with real-world data, these technologies can improve accuracy, refine predictions, and enhance personalised treatment approaches over time. This iterative learning process enables AI to keep pace with evolving diabetes research and incorporate new findings into clinical practice.
AI and ML algorithms can stratify patients into different risk groups based on various factors, such as genetics, clinical characteristics, and lifestyle behaviours. By identifying high-risk individuals who would benefit most from specific treatments or targeted interventions, AI and ML can enable early intervention and preventive measures to mitigate the development or progression of diabetes-related complications.
What are the potential future advancements or innovations in digital clinical trial technology that could further advance diabetes research and treatment?
While AID systems are already able to automate basal insulin delivery, they do not address glucagon deficiency which affects type 1 diabetes patients. Glucagon is the counterpart to insulin and increases blood sugar levels. Dual-hormone pumps, that are currently under investigation, are intended to supply both insulin and glucagon, extending the benefits of an artificial pancreas to type 1 diabetes patients by preventing hypoglycemic events.
AI and ML algorithms will continue to evolve, becoming more sophisticated in analysing complex datasets. This will enable better prediction of treatment responses, identification of personalised interventions, and improved risk stratification. Virtual reality (VR) and augmented reality (AR) technologies hold promise in improving patient education, self-management, and treatment adherence. VR can simulate real-life scenarios to help patients understand the consequences of certain behaviours or provide immersive education experiences. AR can overlay real-time data on the patient's environment, enhancing self-monitoring and treatment adherence.
Digital biomarkers, derived from various digital sources like wearable devices and mobile applications, will play a significant role in diabetes research and treatment. Digital therapeutics could be designed to target specific behavioural and lifestyle modifications, supporting treatment compliance, and improving overall outcomes.
