Diabetes Mellitus (DM) represents one of the major and fastest growing global health emergencies. Approximately 90% of the individuals with DM are affected by type 2 diabetes (T2D), whose management usually begins with lifestyle interventions, then progressing to one or more antihyperglycemic, while only ultimately moving to insulin therapy. Focusing on non-insulin treated T2D, evidence has shown lifestyle modifications, including exercise, healthy diet and education, to be effective in reversibility of the disease, but also in preventing the transition to T2D in high-risk (healthy) individuals. This would call for the improvement of participatory medicine, exploiting the increase in smartphone use over the past decade. In this framework, technological platforms would be key for the development of personalized, patient-centered interventions that integrate patient-reported data, tailored education, and individualized feedback. A fundamental component of these technological platforms for participatory medicine in diabetes is represented by continuous glucose monitoring (CGM) devices. These devices, usable in real-life conditions, provide prompt information on glucose trends and variability, warnings for out of-range glucose values as well as the ability for physicians to remotely review glucose profiles. Numerous studies have shown benefits of using CGM devices mainly in T1D but recently also in advanced T2D, while only limited data is available in non insulin treated T2D. The increasing accuracy of these devices paved the way for the development of tools assessing glucose tolerance in real-life conditions. The development of these tools would be highly beneficial for patients with T2D, allowing to provide information on therapy effectiveness and, possibly, disease progression. Moreover, incorporation of these tools into technological platforms, empowering participatory interventions and supporting self-management, would be fundamental for both the patients and the healthcare system.
The aim of the GluToTrack project is to combine bioengineering mathematical models and a data collection platform based on m-health solutions to develop a tool for glucose tolerance tracking, as well as evaluating the impact of individuals' daily activities like exercise on it, from PGHD and CGM data collected in real-life conditions. Data, required for the mathematical model validation phase, will be gathered, exploiting an e-health approach combining medical informatics and medical device wearables, running an in vivo clinical trial in a population of 20 patients with non-insulin treated T2D.
The clinical trial is an interrupted time series analysis. The trial seeks to enroll 20 participants with non-insulin treated T2D, 40 to 70 years old and physically inactive (less than 150 min/week of moderate physical activity).
During the first visit (visit 1, day 0) routine vital signs will be registered, and wearables (a CGM device and an activity tracker) and smartphones provided. The study is structured in two 1-week-long phases: in the first week patients will be asked to maintain their already sedentary lifestyle, while during the second week, participants should take at least 10,000 steps/day, with sitting replaced by standing and light-intensity walking, for a total of at least 150 min/week of light physical activity. At the end of the first week (visit 2, day 7) participants undergo an MMTT, which consists in the ingestion of a meal containing 75 g of carbohydrates and the drawing of 10 plasma samples for the measurement of plasma glucose, insulin and C-peptide concentrations. At the end of the second week (visit 3, day 14), subjects will repeat the MMTT to also assess the potential impact of the physical activity intervention on glucose tolerance.
