Posttraumatic Stress Related to Hyperglycemia: Prevalence in Adults with Type I Diabetes.

Prevalence of hyperglycemia-related posttraumatic stress (PTS) was assessed in 239 adults with type 1 diabetes using the posttraumatic stress diagnostic scale (PDS; Foa, Posttraumatic stress diagnostic scale manual, National Computer Systems, Inc., Minneapolis, 1995) by an anonymous online survey. Additionally, this study aimed to identify variables related to hyperglycemia-related PTS. Over 30 % of participants reported symptoms consistent with PTSD related to hyperglycemia with standard PDS scoring, and 10 % with more conservative scoring. Hierarchical multiple regression analyses indicated that diabetes self-management behavior and perceived helplessness about hyperglycemia predicted PTSD with standard scoring. Perceived death threat, self-management behavior, helplessness about hyperglycemia, and severity of hypoglycemia in past month predicted PTSD using more conservative scoring. Perceived helplessness, hypoglycemia severity, perceived death-threat, HbA1c, and self-management behavior predicted PTS severity. When fear, helplessness, and perceived death-threat were combined to represent an overall cognitive appraisal factor, this variable was the strongest predictor of PTSD and PTS severity. Scores for PTSD symptom clusters appeared similar to data on hypoglycemia-related PTS.

Open-source automated insulin delivery: international consensus statement and practical guidance for health-care professionals.

Open-source automated insulin delivery systems, commonly referred to as do-it-yourself automated insulin delivery systems, are examples of user-driven innovations that were co-created and supported by an online community who were directly affected by diabetes. Their uptake continues to increase globally, with current estimates suggesting several thousand active users worldwide. Real-world user-driven evidence is growing and provides insights into safety and effectiveness of these systems. The aim of this consensus statement is two-fold. Firstly, it provides a review of the current evidence, description of the technologies, and discusses the ethics and legal considerations for these systems from an international perspective. Secondly, it provides a much-needed international health-care consensus supporting the implementation of open-source systems in clinical settings, with detailed clinical guidance. This consensus also provides important recommendations for key stakeholders that are involved in diabetes technologies, including developers, regulators, and industry, and provides medico-legal and ethical support for patient-driven, open-source innovations.

Making sense of glucose metrics in diabetes: linkage between postprandial glucose (PPG), time in range (TIR) & hemoglobin A1c (A1C).

While A1C is the standard diagnostic test for evaluating long-term glucose management, additional glucose data, either from fingerstick blood glucose testing, or more recently, continuous glucose monitoring (CGM), is necessary for safe and effective management of diabetes, especially for individuals treated with insulin. CGM technology and retrospective pattern-based management using various CGM reports have the potential to improve glycemic management beyond what is possible with fingerstick blood glucose monitoring. CGM software can provide valuable retrospective data on Time-in-Ranges (above, below, within) metrics, the Ambulatory Glucose Profile (AGP), overlay reports, and daily views for persons with diabetes and their healthcare providers. This data can aid in glycemic pattern identification and evaluation of the impact of lifestyle factors on these patterns. Time-in-Ranges data provide an easy-to-define metric that can facilitate goal setting discussions between clinicians and persons with diabetes to improve glycemic management and can empower persons with diabetes in self-management between clinic consultation visits. Here we discuss multiple real-life scenarios from a primary care clinic for the application of CGM in persons with diabetes. Optimizing the use of the reports generated by CGM software, with attention to time in range, time below range, and postprandial glucose-induced time above range, can improve the safety and efficacy of ongoing glucose management.

Insulin pump therapy: guidelines for successful outcomes.

PURPOSE: Current forms of insulin delivery used in the treatment of diabetes mellitus (diabetes) include syringes, pens, and insulin pumps. Technical advantages of insulin pump therapy, or continuous subcutaneous insulin delivery (CSII), include precise and flexible insulin dosing. In the context of intensive diabetes management, insulin pumps can facilitate improved long-term glycemic control and reduce the risk for diabetes complications, with improved lifestyle flexibility for patients and their families. Comprehensive patient education, carbohydrate counting, and frequent self-monitoring of blood glucose or continuous glucose monitoring are necessary components of successful insulin pump therapy. Technological advances have increased the appeal of pump therapy to patients and clinicians. Physically, current insulin pumps are discreet, ergonomic, and water resistant. Meanwhile, software improvements have yielded smart pumps with features that support pump users in their daily diabetes management. Robust data analysis software packages allow patients and clinicians unprecedented insight into the quality of diabetes control. Furthermore, widespread insurance reimbursement for CSII has expanded access to therapy. CONCLUSIONS: As the number of pump users and potential users expands, diabetes educators face new challenges and opportunities to improve patients' lives with diabetes. This activity describes the rationale for insulin pump therapy, its potential advantages and disadvantages, and strategies regarding patient selection and education.

CGM Retrospective Data Analysis.

Continuous glucose monitoring (CGM) has increased in popularity as a daily management tool for people with diabetes and a diagnostic instrument for their healthcare providers. Achieving better clinical outcomes hinges on appropriate analysis and interpretation of data collected by CGM systems. This includes device downloading, qualification of data, and generation of applicable reports. An objectives-based analysis of the reports can yield valuable insight for fine-tuning treatment in several areas, including postprandial glucose patterns, overnight/basal stability, duration of bolus insulin action, timing of (and response to) hypoglycemic episodes, the efficacy of meal and correction insulin doses, and the impact of a variety of lifestyle activities.

Characteristics of basal insulin requirements by age and gender in Type-1 diabetes patients using insulin pump therapy.

Establishment of appropriate basal insulin levels is an essential component of intensive insulin therapy. While the existence of a "dawn phenomenon" is widely recognized, the present study sought to establish whether diurnal basal insulin patterns exist in Type-1 diabetes, and whether these patterns vary by age and gender. Participant data was drawn from 322 Type-1 insulin pump users treated at a private diabetes education practice in suburban Philadelphia. All participants completed a battery of fasting tests designed to match basal insulin levels to endogenous glucose production and insulin sensitivity. Analysis of resultant basal patterns revealed significant differences between juvenile (age < or =20) and adult (age >20) basal insulin patterns. The younger group exhibited a more pronounced and sustained night-time peak; the older group exhibiting a briefer and less pronounced early-morning peak. Lower overall basal insulin requirements were found in the youngest (age < or =10) and oldest (age >60) groups. No noteworthy gender differences were found. Results can serve as a guide for clinicians when initiating and fine-tuning patients who utilize basal/bolus insulin therapy.