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Use of technology in diabetes management is rapidly advancing and has the potential to help individuals with diabetes achieve optimal glycemic control. Over the past 40 years, several devices have been developed and refined, including the blood glucose meter, insulin pump, and continuous glucose monitor. When used in tandem, the insulin pump and continuous glucose monitor have prompted the Artificial Pancreas initiative, aimed at developing control system for fully automating glucose monitoring and insulin delivery. In addition to devices, modern technology, such as the Internet and mobile phone applications, have been used to promote patient education, support, and intervention to address the behavioral and emotional challenges of diabetes management. These state-of-the-art technologies not only have the potential to improve clinical outcomes, but there are possible psychological benefits, such as improved quality of life, as well. However, practical and psychosocial limitations related to advanced technology exist and, in the context of several technology-related theoretical frameworks, can influence patient adoption and continued use. It is essential for future diabetes technology research to address these barriers given that the clinical benefits appear to largely depend on patient engagement and consistence of technology use. (PsycINFO Database Record, ((c) 2016 APA, all rights reserved).)

Diabetes management depends almost entirely on behavioral self-regulation. Behavioral scientists have continued a collaboration with other health systems researchers to develop a holistic approach to this disease. The authors summarized the literature in 4 major areas: self-management of diabetes, psychosocial adjustment and quality of life, neuropsychological impact, and psychobehavioral intervention development. Progress made in each of these areas over the past decade is highlighted, as are important issues that have not yet received sufficient scientific attention. Emerging areas likely to become central in behavioral research, such as diabetes prevention, are introduced. The future of behavioral medicine in diabetes is also discussed, including topics such as the changing role of psychologists in diabetes care, the urgent need for more and better intervention research, the growing importance of incorporating a health system-public health perspective, and obstacles to the integration of psychobehavioral approaches into routine health care delivery.

Although the scientific literature contains numerous reports of the statistical accuracy of systems for self-monitoring of blood glucose (SMBG), most of these studies determine accuracy in ways that may not be clinically useful. We have developed an error grid analysis (EGA), which describes the clinical accuracy of SMBG systems over the entire range of blood glucose values, taking into account 1) the absolute value of the system-generated glucose value, 2) the absolute value of the reference blood glucose value, 3) the relative difference between these two values, and 4) the clinical significance of this difference. The EGA of accuracy of five different reflectance meters (Eyetone, Dextrometer, Glucometer I, Glucometer II, Memory Glucometer II), a visually interpretable glucose reagent strip (Glucostix), and filter-paper spot glucose determinations is presented. In addition, reanalyses of a laboratory comparison of three reflectance meters (Accucheck II, Glucometer II, Glucoscan 9000) and of two previously published studies comparing the accuracy of five different reflectance meters with EGA is described. EGA provides the practitioner and the researcher with a clinically meaningful method for evaluating the accuracy of blood glucose values generated with various monitoring systems and for analyzing the clinical implications of previously published data.