View clinical trials related to Type 1 Diabetes.
Filter by:The use of continuous glucose monitoring (CGM) is becoming the new standard in glycometabolic control in patients with Type 1 Diabetes Mellitus (T1DM) even in subjects in multiple daily insulin injections (MDI). Compared to self-monitoring of blood glucose (SMBG), the CGM systems allow continuous monitoring of the glycemic trends contributing to modify the therapeutic habits of adult and pediatric patients with T1DM and allowing to better managing of critical situations such as hypoglycemia. Recently, the accuracy and reliability performance of the latest generation of CGMs using predictive alarm for hypoglycaemia and hyperglycemia has been compared to other commercially available CGM systems, showing good levels of concordance. The use of this new technology, through the continuous monitoring of the pre-and post-prandial glucose levels and the evaluation of the glycemic trends, could influence the therapeutic habits of patients and could substantially contribute to modifying insulin therapy. Furthermore, the presence of the predictive alarm technology for hypoglycemia could lead to reduce the number of hypoglycemic episodes and to modify the way these hypoglycemic episodes are managed; moreover, the use of this technology could improve the time spent in the target glycemic range [Time in Range (TIR), 70-180 mg/dl] with possible improvement also in glycemic variability control.
Prospective observational study to analyse the clinical effectiveness of a diabetes educational program to improve intermittently continuous glucose monitoring (iCGM) adherence in adult type 1 diabetes (T1D) patients
With over 6 billion mobile phone subscribers and 75% of the world having access to a device, global health communities increasingly recognize the potential for using these devices to improve access to health care and health outcomes-especially in sub-Saharan Africa (SSA), where device ownership has grown dramatically. Less attention, however, has been given to developing the research capacity to allow these countries' public health researchers to collaborate with software developers and the users of mobile health applications (henceforth apps) to develop their own interventions. If mobile health apps are to be adopted, effective, and scalable, they must be designed by and with these individuals, the people most knowledgeable about the issues affecting technology use and disease management in their countries. Human-centered design (HCD), or design thinking, is a promising design strategy that prioritizes the needs of the intended population. It has also been successfully used to develop innovative and locally relevant health interventions that improve health outcomes. The purpose of this R21 proposal is to introduce Kenyan public health researchers and software developers to the HCD process and then collaboratively develop and evaluate an mobile health app that targets a growing epidemic among middle-to-late adolescents (13-18 yrs.) in Kenya-Type 1 Diabetes (T1D). To achieve this goal, we will pursue these specific aims: (1) train Kenyan health practitioners and software developers in HCD; (2) use HCD to build a prototype mHealth intervention for adolescents in Kenya with T1D; and (3) assess the prototype's usability, accessibility, and feasibility in using it to increase adolescents' knowledge of T1D and management of the disease. Our long-term goals include: (1) building research capacity by establishing a research network between health researchers at The Kenyan Diabetes Management and Information Center (DMI-a non-profit organization that works with adolescents with T1D) and mobile software developers at Lake Hub (an innovation space) so they can design future mobile health apps; (2) developing a commercially available app that Kenyan adolescents can use to manage T1D and stay healthy; and (3) evaluating the HCD process as it applies to developing mobile health interventions that improve health outcomes.
The purpose of this study is to understand the impact of the automated priming boluses on the safety and feasibility of a new fully automated AP controller.
The main objective is to determine the efficacy of Lyumjev (insulin) in a bi-hormonal reactive closed loop system for automated glucose regulation (artificial pancreas; AP®) in patients with diabetes mellitus type 1. In addition, safety parameters, pharmacodynamics and AP-related parameters will be acquired. This study is a multicenter, open-label, randomized, cross-over trial in 12 subjects. The subjects will be randomized to receive either Lyumjev or Humalog® for a 30-day study period and will then switch to the alternate insulin treatment after a wash-out period.
The aim of the study is to examine the relationship between the nutritional content of the diet consumed by children and adolescents (0-18 years old) with the diagnosis of type 1 diabetes and metabolic control. In this cross-sectional study, 150 children and adolescents with type 1 diabetes who came to Ege University Child Health and Diseases Department, Pediatric Endocrine and Diabetes Department for regular check-ups (4 diabetes control/year) and kept food consumption records in the last 1 year will be included. Body weight and height measurements of the cases will be done by the same person in Ege University Pediatric Endocrinology and Diabetes BD Polyclinic. Turkish Standard Institute approved Baster brand weighing instrument with 0.1 kg intervals and height measurements will be made with Harpander brand stadiometer with 0.1 cm intervals. SD scores of body weight, height and body mass index for age Neyzi et al. It will be calculated using the reference values developed for Turkish children by Age, diabetes age, gender, height - body weight-body mass index SD score, HbA1c level, daily energy intake, percentage of energy from carbohydrate-protein-fat-saturated fat in energy, daily fiber intake amount of the cases will be recorded in the case report form. The biochemical values of the participants will be taken from the file records. The data will be entered into the statistical package program and the patients' 1) carbohydrate, protein, fat, saturated fat intake, 2) vitamin-mineral intake, and 3) fruit and vegetable consumption will be compared with the recommendations in national and international guidelines and their relationship with metabolic control will be investigated
Patients with type 1 diabetes (T1D) and metabolic syndrome (MS) require high doses of insulin to achieve a glycemic control, due to insulin resistance (IR). The investigators identified the presence of metabolic syndrome and insulin resistance in patients with T1D in a population of southeast Mexico. The sampled population was recruited from the Diabetes Clinic of the Regional Hospital of High Specialty "Dr. Gustavo A. Rovirosa Pérez"; from August 2021 to February 2022, graduated physicians interviewed patients previously diagnosed with T1D. This study included patients older than 18 years and at least six months after being diagnosed with T1D. The interviewers excluded patients who did not attend regular consultations and those who did not agree to participate or to give a blood sample. A diabetologist evaluated and diagnosed all patients based on the World Health Organization (WHO) criteria. Fasting glucose, total cholesterol, HDL cholesterol, LDL cholesterol, and triacylglycerols were determined in blood serum using a Clinical Chemistry System from Random Access Diagnostics. The reasearch team determined glycated hemoglobin concentrations by an enzymatic immunoassay method.
Observational study about adherence to guidelines for Covid-19, Influenza, Pneumococcal and Hepatitis B Vaccination in adult patients with type 1 Diabetes mellitus.
The purpose of the study is to examine whether a simplified meal approach (as compared to exact carbohydrate counting) can alleviate the need of carbohydrate counting without worsening postprandial control in youth and young adults with type 1 diabetes using hybrid closed-loop insulin delivery with the Cambridge Artificial Pancreas FX System (CamAPS FX system).
Type 1 Diabetes management is requiring and implies numerous lifestyle modifications. Insulin restriction to control weight is a frequent phenomenon, affecting up to 40% of PWT1D. Broadly, purging or binge eating behaviors are also frequently disordered eating behaviors (DEB) in people living with a Type 1 Diabetes (associated or not with restrictive eating behaviors). In a study on adolescents with T1D, the prevalence of moderate or high level of DEB ranged from 21% to 32%. Moreover, the presence of binge eating behavior seems to be associated with higher anxiety and depression levels. Omitting insulin for weight control has been associated with the highest rates of retinopathy and nephropathy when compared to other weight control behaviors and to increase the risk of mortality by 3.2 times and decrease life spans from an average of 58 to 44 years at 11-year follow-up. Moreover, insulin misuse may be much more complex behavior than just the need for weight control. These behaviors may also involve increased distress, loss of control, and feelings of regret, guilt, and shame. Interestingly, most studies of eating disorders and type 1 diabetes use question regarding insulin omission as a surrogate marker for eating disorders and disordered eating. For instance, the question used in the BETTER registry are: "In the past 12 months, did you intentionally omit insulin injections with the objective of losing weight?" or "In a typical week, how often do you miss an insulin dose?". However, the validity and robustness of such a marker have not been specifically investigated yet. Our study objectives are : 1) To confirm that participants who reported intentionally omitting insulin had significantly more disordered eating behavior (based on the review of food records available); 2) To compare the prevalence and the severity of physical and mental health comorbidities (e.g., diabetes micro and macrovascular complications, glycated hemoglobin levels, current and past psychiatric disorders, distress related to diabetes) in people living with diabetes having or not declared to intentionally omit insulin; 3) To establish, using machine learning techniques, the main factors associated with intentional insulin omission behavior, taking into account biological, anthropometric and psychometric factors.