Background: Hypoglycemia is a common and serious side effect of insulin therapy in patients with diabetes. Early detection and prediction of hypoglycemia may improve treatment and avoidance of serious complications. Continuous glucose monitoring (CGM) has previously been used for detection of hypoglycemia, but with a modest accuracy. Therefore, our aim was to investigate whether a novel algorithm that adds information of the complex dynamic/pattern of heart rate variability (HRV) could improve the accuracy of hypoglycemia as detected by a CGM device. Methods: Data from 10 patients with type 1 diabetes studied during insulin-induced hypoglycemia were obtained. Blood glucose samples were used as reference. HRV patterns and CGM data were combined in a mathematical prediction algorithm. Detection of hypoglycemic periods, performed by the algorithm, was treated as a pattern recognition problem and features/patterns derived from HRV and CGM prior to each blood glucose sample were used to decide if that particular point in time was below the hypoglycemic threshold of 3.9 mmol/L. Results: A total of 903 samples were analyzed by the novel algorithm, which yielded a sensitivity of 79% and a specificity of 99%. The algorithm was able to detect 16/16 hypoglycemic events with no false positives and had a lead time of 22 minutes as compared to the CGM device. Conclusions: Detection accuracy and lead time were significantly improved by the novel algorithm compared to that of CGM alone.

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.

Introduction: Patients with diabetes often generate large amounts of data specifically related to the disease and to their general health. Cross-institutional sharing of patient health care data is complex, and as a consequence, data are not always available to the health care provider treating the patient. Accommodating this challenge could lead to better clinical effectiveness and improve clinical research. This work aims to present an approach for a blockchain-based platform for sharing health care data. The approach considers privacy concerns, data sharing, and patients as the center for governing their own data. Methods: The concept of this blockchain-based platform consists of using the NEM multi-signature blockchain contracts for access control of data management and the sharing and encryption of data to allow privacy and control of health care data. The architecture is built around cryptography, tokens, and multi-signature contracts. The multi-signature contract enables several entities to administrate the activity of an account and control the assets of one account. Multi-signature generates a contract that assigns the rights and powers of a certain account to other accounts; this contract can be edited to allow or remove entities. Discussion: Using blockchain could lead to improvements in diabetes data management. In the coming years, this technology should be implemented in existing small-scale diabetes health care system to explore its real-world benefits and challenges. Conclusion: This new approach could potentially lead to more efficient sharing of data between institutions and utilization of new types of data and research possibilities.

Continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) systems have revolutionized diabetes management by delivering real-time, dynamic insights into blood glucose levels. This article provides a concise overview of the evolution of CGM technology, highlights emerging innovations in the field and explores current and potential future applications (including insulin management, early diagnostics, predictive modeling, diabetes education and integration into automated insulin delivery (AID) systems) of CGM in healthcare.

The aim of this study was to assess the impact on health-related quality of life (HRQoL) of a telehealth care solution compared with usual practice of patients with heart failure (HF). A randomized controlled trial with a telehealth care solution (Telekit) as the intervention (with a focus on self-empowerment achieved by engaging patients in their own illness through self-monitoring) combined with usual care and usual care as the control. The primary outcome was a change in HRQoL as measured by the Short Form-36 (SF-36) questionnaire Physical Component Summary (PCS) score. Secondary outcomes were changes in HRQoL as measured by the SF-36 questionnaire Mental Component Summary (MSC) score and the HF disease-specific questionnaire Kansas City Cardiomyopathy Questionnaire 12 (KCCQ12) score, all of which were assessed from baseline to approximately 12 months’ follow-up between the two groups. Outcomes were assessed via unadjusted and adjusted analyses. At baseline, 299 (145 interventions, 154 controls) patients were enrolled. In the primary analysis (n = 299), the adjusted intervention effects were PCS –0.81 (95% CI −2.7–1.1), MCS 4.66 (95% CI 1.8–7.5) and KCCQ12 3.67 (95% CI −0.7–8.1). Only the change in MCS was statistically significant. An unadjusted analysis replicated the primary analysis. Complete case analyses (n = 193) generally resulted in a lower intervention effect on the PCS score, but the difference remained statistically insignificant. Only the MCS score was significantly higher in the telehealth care group compared to the control group. ClinicalTrials.gov (NCT02860013), July 28, 2016