response that targets pancreatic β -cells, leading to deterioration of β -cells function. As the disease progresses, immune cells accelerate type 1 diabetes development by furthering β -cell damage

Physical activity (PA) is a key management tool for blood glucose (BG) and health in persons with diabetes (PWD) of all types. Accurately tracking and measuring PA participation has remained a challenge, as has encouraging and maintaining regular participation. Digital support for all PA (including planned exercise sessions and other daily movement) has advanced far beyond the initial offering of step counters (pedometers). Currently, individuals can choose technologies that contain accelerometers to track both the frequency and intensity of all movement and utilize GPS capabilities to monitor distances covered. Consumer-based wearable activity trackers that allow users to objectively monitor PA levels offer an alternative method for assisting individuals to remain physically active. In PWD, electronic and mobile applications (apps) have been used to enhance a physically active lifestyle and better manage BG levels and health. Moreover, a current focus is on integrating PA and physiological monitoring devices into more complicated diabetes management systems, such as closed-loop insulin delivery systems, to better manage BG levels in individuals who use insulin. In the future, digital technologies are extremely likely to continue being recommended for individuals with all types of diabetes, and digital support for PA will undeniably be an integral part of diabetes and overall health management.

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.

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.

Authors Beverly S. Adler Angie Amado Jessica Ayers Hailey N. Barnes Kimberly Bisanz Sandra Bollinger Elizabeth Buckley Jennifer N. Clements Edward J. Colón Kailey M. Conner Jamie Cook Carla Cox Mary de Groot Patricia DeHart Angela M. Forfi a Nick Galloway Jennifer D. Goldman Jasmine D. Gonzalvo Samuel Grossman Sandra Hedin Mary E. Herman Andrea R. Hilligoss Diana isaacs Mamie Lausch Betty Lu Rachael R. Majorowicz Tamara Marini Amber McCulloch Christina McGeough Jerry Meece Chris E. Memering Mary Beth Modic Dawn Noe Bob Pang Paulushi Patel Virginia Peragallo-Dittko Lacie Peterson Robert Powell Sara (Mandy) Reece Joanne Rinker Natalie Ritchie Kellie Rodriguez Gary Scheiner Barbara Schreiner Stanley S. Schwartz Evan Sisson Barbara D. Smith Julie Stefanski Carl Stevenson Sharee Thompson Emily Timm Patti Urbanski Linda Vidone Lindsey Wahowiak Syeda Wasima Sharon Watts David Weingard Emily Weidman-Evans Jane Wendel Heather Wright Samantha M. Wright Kirsten Yehl

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.

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.