Journal of Diabetes Science and Technology最新文献

Background: Glycated hemoglobin (HbA1c) is the gold standard to assess glycemic control in patients with diabetes. Glucose management indicator (GMI), a metric generated by continuous glucose monitoring (CGM), has been proposed as an alternative to HbA1c, but the two values may differ, complicating clinical decision-making. This study aimed to identify the factors that may explain the discrepancy between them.

Methods: Subjects were patients with type 1 diabetes, with one or more HbA1c measurements after starting the use of the Freestyle Libre 2 intermittent CGM, who shared their data with the center on the Libreview platform. The 14-day glucometric reports were retrieved, with the end date coinciding with the date of each HbA1c measurement, and those with sensor use ≥70% were selected. Clinical data prior to the start of CGM use, glucometric data from each report, and other simultaneous laboratory measurements with HbA1c were collected.

Results: A total of 646 HbA1c values and their corresponding glucometric reports were obtained from 339 patients. The absolute difference between HbA1c and GMI was <0.3% in only 38.7% of cases. Univariate analysis showed that the HbA1c-GMI value was associated with age, diabetes duration, estimated glomerular filtration rate, mean corpuscular volume (MCV), red cell distribution width (RDW), and time with glucose between 180 and 250 mg/dL. In a multilevel model, only age and RDW, positively, and MCV, negatively, were correlated to HbA1c-GMI.

Conclusion: The difference between HbA1c and GMI is clinically relevant in a high percentage of cases. Age and easily accessible hematological parameters (MCV and RDW) can help to interpret these differences.

Aims: The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics.

Methods: Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes.

Results: A total of 10 977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ± 1.6 mmol/l before Rhypo; 12.8 ± 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly (P < .001) correlated with Rhypo (Spearman's rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13).

Conclusions: The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions.

Background: The correct injection technique is crucial for people with insulin therapy. However, barriers to insulin injections exist, which can lead to problems with injections. In addition, injection behavior may deviate from recommendations leading to lower adherence to the correct injection technique. We developed two scales to assess barriers and adherence to the correct technique.

Methods: Two item pools were created to assess barriers to insulin injections (barriers scale) and adherence to the correct technique (adherence scale). In an evaluation study, participants completed the two newly created scales, as well as other questionnaires used for criterion validity. Exploratory factor analysis, correlational analysis, and receiver operating characteristics analysis were computed to analyze the validity of the scales.

Results: A total of 313 people with type 1 and type 2 diabetes using an insulin pen for insulin injections participated. For the barriers scale, 12 items were selected achieving a reliability of 0.74. The factor analysis revealed three factors namely emotional, cognitive, and behavioral barriers. For the adherence scale, nine items were selected achieving a reliability of 0.78. Both scales showed significant associations with diabetes self-management, diabetes distress, diabetes acceptance, and diabetes empowerment. Receiver operating characteristics analysis showed significant area under the curves for both scales in classifying people with current skin irritations.

Conclusions: Reliability and validity of the two scales assessing barriers and adherence to insulin injection technique were demonstrated. The two scales can be used in clinical practice to identify persons in need of education in insulin injection technique.

Aim: We added items relevant to continuous glucose monitoring (CGM) to the Diabetes Family Conflict Scale (DFC), Diabetes Family Responsibility Questionnaire (DFR), and Blood Glucose Monitoring Communication Questionnaire (GMC) and evaluated the psychometric properties of the updated surveys.

Research design and methods: Youth with type 1 diabetes who recently started CGM and their parents completed the updated surveys and additional psychosocial surveys. Medical data were collected from self-reports and review of the medical record.

Results: Youth (N = 114, 49% adolescent girls) were aged 13.3 ± 2.7 years and had mean glycated hemoglobin (HbA1c) 7.9 ± 0.9%; 87% of them used pump therapy. The updated surveys demonstrated high internal consistency (DFC youth: α = .91, parent: α = .81; DFR youth: α = .88, parent: α = .93; and GMC youth: α = .88, parent: α = .86). Higher youth and parent DFC scores (more diabetes-specific family conflict) and GMC scores (more negative affect related to glucose monitoring) were associated with more youth and parent depressive symptoms (r = 0.28-0.60, P ≤ .003), more diabetes burden (r = 0.31-0.71, P ≤ .0009), more state anxiety (r = 0.24 to r = 0.46, P ≤ .01), and lower youth quality of life (r = -0.29 to -0.50, P ≤ .002). Higher youth and parent DFR scores (more parent involvement in diabetes management) were associated with younger youth age (youth: r = -0.76, P < .0001; parent: r = -0.81, P < .0001) and more frequent blood glucose monitoring (youth: r = 0.27, P = .003; parent: r = 0.35, P = .0002).

Conclusions: The updated DFC, DFR, and GMC surveys maintain good psychometric properties. The addition of CGM items expands the relevance of these surveys for youth with type 1 diabetes who are using CGM and other diabetes technologies.

Background: FIND, the global alliance for diagnostics, identified the nonmarket-approved continuous glucose monitoring (CGM) system, FiberSense system (FBS), as a potential device for use in low- and middle-income countries. Together with two market-approved, factory-calibrated CGM systems, namely, the FreeStyle Libre 2 (FL2) and the GlucoRx AiDEX (ADX), the FBS was subjected to a clinical performance evaluation.

Methods: Thirty adult participants with type 1 diabetes were enrolled. The study was mainly conducted at home, with three in-clinic sessions conducted over the study period of 28 days. Comparator measurements were collected from capillary samples, using a high-quality blood glucose monitoring system.

Results: Data from 31, 70, and 78 sensors of FBS, FL2, and ADX, respectively, were included in the performance analysis. The mean absolute relative differences between CGM and comparator data for FBS, FL2, and ADX were 14.7%, 9.2%, and 21.9%, and relative biases were -2.1%, -2.5%, and -18.5%, respectively. Analysis of individual sensor accuracy revealed low, moderate, and high sensor-to-sensor variability for FBS, FL2, and ADX, respectively. Sensor survival probabilities until the end of sensor life were 47.2% for FBS (28 days), 71.3% for FL2 (14 days), and 48.4% for ADX (14 days).

Conclusions: The results of FBS were encouraging enough to conduct further performance and usability evaluations in a low- and middle-income country. The results of FL2 mainly agreed with existing studies, whereas ADX showed substantial deviations from previously reported results.

Background: The COVID-19 pandemic has added to the pre-existing challenges of diabetes management in many countries. It has accelerated the wider use of digital health solutions which have tremendous potential to improve health outcomes for people with diabetes. However, little is known about the attributes and the implementation of these solutions.

Objective: To identify and describe digital health solutions for community-based diabetes management and to highlight their key implementation outcomes.

Methods: We searched Ovid Medline, CINAHL, Embase, PsycINFO, and Web of Science for relevant articles. A purposive search was also used to identify grey literature. Articles that described digital health solutions that aimed to improve community-based diabetes management were included in this review. We applied a thematic synthesis of evidence to describe the characteristics of digital health solutions, and to summarize their key implementation outcomes.

Results: We included 15 articles that reported digital health solutions that primarily focused on community-based diabetes management. Nine of the 15 innovations involved were mobile applications and/or web-based platforms, and five were based on social media platforms. The majority of the digital health solutions were used for diabetes education and support. High engagement, utilization, and satisfaction rates with digital health solutions were observed. The use of digital health solutions was also associated with improvement in self-management, taking medication, and reduction in glycated hemoglobin (HbA1c) levels.

Conclusion: COVID-19 triggered digital health solutions have tremendous potential to improve health outcomes for people with diabetes. Further studies are needed to evaluate the sustainability and scale-up of these solutions.

This narrative review assesses the use of automated insulin delivery (AID) systems in managing persons with type 1 diabetes (PWD) in the pediatric population. It outlines current research, the differences between various AID systems currently on the market and the challenges faced, and discusses potential opportunities for further advancements within this field. Furthermore, the narrative review includes various expert opinions on how different AID systems can be used in the event of challenges with rapidly changing insulin requirements. These include examples, such as during illness with increased or decreased insulin requirements and during physical activity of different intensities or durations. Case descriptions give examples of scenarios with added user-initiated actions depending on the type of AID system used. The authors also discuss how another AID system could have been used in these situations.