bims-glumda 2025-03-30 papers

Issue of 2025–03–30
seven papers selected by
Mott Given

Nurs Rep. 2025 Mar 12. pii: 94. [Epub ahead of print]15(3):

The Impact of Self-Regulation Education Combined with Continuous Glucose Monitoring (CGM) on Diabetes Outcomes: A Randomized Controlled Study.

Hsiao-Yun Chang, Kuei-Chun Yeh, Yu-Yao Huang, Jui-Hsiang Li.

In previous studies exploring continuous glucose monitoring (CGM), there has been a limited focus on how CGM influences key behavioral outcomes such as self-efficacy, health behaviors, and medication adherence. Background/Objectives: The aim of this study was to assess the impact of combining self-regulation health education with CGM on medication adherence, diabetes self-efficacy, diabetes health behaviors, and glucose control in individuals with diabetes. Methods: A randomized controlled study, reported following the CONSORT 2010 reporting guidelines. Individuals with diabetes volunteered to participate and were randomly allocated into two groups: the CGM group (n = 34) and the control group (n = 34). The CGM group received real-time CGM devices and education on self-regulation theory to enable them to self-adjust health promotion strategies and behaviors, while the control group received routine diabetes health education focusing on self-monitoring of blood glucose. Outcome measures included medication adherence, diabetes self-efficacy, diabetes health behaviors, and glucose control. Results: The CGM group demonstrated consistent diabetes self-efficacy, significant improvements in diabetes health behaviors, and a reduction in HbA1c levels over time. However, no significant differences in outcomes were observed between the CGM group and the control group. Conclusions: The use of continuous glucose monitoring (CGM) provides continuous, real-time glucose data. When combined with self-regulation education, it may help provide personalized insights into how specific foods, activities, medications, and stress levels affect blood glucose levels. This allows individuals with diabetes to make personalized adjustments to their lifestyle and treatment plans to optimize their blood sugar control.

Keywords: continuous glucose monitoring (CGM); diabetes; glucose control; health behaviors; self-efficacy

DOI: https://doi.org/10.3390/nursrep15030094

J Clin Med. 2025 Mar 07. pii: 1794. [Epub ahead of print]14(6):

Advancing Telemedicine Using Smart Insulin Pens with Continuous Glucose Monitoring and Telecommunication Systems: A Case Series.

Lakshmi G Singh, Chikara Gothong, Garrett I Ash, Reynier Hernandez, Elias K Spanakis.

Background: Multiple daily injections (MDIs) have been a mainstay for insulin delivery by persons with type 1 diabetes mellitus (T1DM). "Smart" insulin pens (SIPs) offer several advantages over traditional insulin pens, such as a memory function, bolus calculator, and reminders for patients to take their insulin. SIPs can integrate with CGM, allowing for the collection of accurate insulin and glucose data, which can integrate into combined reports. Using these technologies along with telecommunication modalities provides an infrastructure to improve the way in which healthcare can be delivered to those with diabetes. Methods: Four cases of uncontrolled T1DM managed by MDIs (and not insulin pumps) and deemed to have plateaued in their management were selected to retrospectively review to identify potential advantages of SIP/CGM along with telemedicine as a method of care delivery. Results: This case series revealed potential benefits of this model of care delivery, such as the ability to identify dysglycemia patterns not discernible prior to the use of SIP/CGM, use combined reports as a visual education tool to provide targeted insulin and dietary education, and improve patient engagement in diabetes self-care behaviors. Conclusions: We described the benefits of using SIPs and CGM technologies along with telecommunication solutions, as a novel concept for a comprehensive telemedicine system, to improve management of glycemic control and diabetes self-management capabilities.

Keywords: diabetes mellitus; glycemic control; smart insulin pen; telemedicine

DOI: https://doi.org/10.3390/jcm14061794

Diabet Med. 2025 Mar 23. e70023

Managing discordance between HbA1c and glucose management indicator.

Erna Lenters-Westra, Marion Fokkert, Eric S Kilpatrick, Erwin Schleicher, Scott Pilla, Emma English, Peter van Dijk.

AIMS: The assessment of haemoglobin A1c (HbA1c) continues to play an essential role in diabetes care; however, major advances in new technologies widen the armament available to clinicians to further refine treatment for their patients. Whilst HbA1c remains a critical glycaemic marker, advances in technologies such as Continuous Glucose Monitoring (CGM) now offer real-time glucose monitoring, allowing a more instant assessment of glycaemic control. Discrepancies between laboratory-measured HbA1c and Glucose Management Indicator (GMI) values are a significant clinical issue. In this article, we present a checklist of potential sources of error for both GMI and HbA1c values and provide suggestions to mitigate these sources in order to continue to improve diabetes care.
METHODS: We identified key literature pertaining to GMI measurement, HbA1c measurement, and potential factors of discordance between the two. Using these sources, we explore the potential factors leading to discordance and how to mitigate these when found.
RESULTS: We have constructed a quick reference checklist covering the main sources of discordance between HbA1c and GMI, with accompanying narrative text for more detailed discussion. Discordance can arise due to various factors, including CGM accuracy, sensor calibration, red blood cell turnover and other physiological conditions.
CONCLUSIONS: GMI will likely continue to be used in the upcoming years by both persons with diabetes and their health care providers, and so it is important for users of CGM devices to be equipped with the knowledge to understand the potential causes of discordance between GMI and HbA1c values.

Keywords: GMI; HbA1c; diabetes; discordance

DOI: https://doi.org/10.1111/dme.70023

Diabetes Obes Metab. 2025 Mar 24.

Faster-acting insulin aspart versus insulin aspart for adults with type 1 diabetes treated with non-automated insulin pump and continuous glucose monitoring-A double-blind randomized controlled crossover trial.

Ajenthen G Ranjan, Signe Schmidt, Kirsten Nørgaard.

OBJECTIVE: To evaluate the efficacy and safety of faster-acting insulin aspart (faster aspart) compared with insulin aspart in adults with type 1 diabetes (T1D) using a non-automated insulin pump and continuous glucose monitoring (CGM).
METHODS: This double-blinded crossover study randomly assigned participants to start with either faster aspart or insulin aspart for 16 weeks, followed by a 3-week washout period, then switching to the alternate therapy for another 16 weeks. Insulin pump settings were adjusted every 3 weeks. The primary outcome was time in range (TIR: 3.9-10.0 mmol/L). Secondary outcomes included other CGM metrics and HbA1c.
RESULTS: Forty adults (20 males) with a median age of 54 years, T1D duration of 27 years, and HbA1c of 59 mmol/mol (7.5%) were included. At the study end, TIR was (mean ± SD) 60.6 ± 12.1% for insulin aspart and 62.5 ± 12.3% for faster aspart, p = 0.24 (primary endpoint). The baseline-adjusted estimated treatment difference (ETD) for TIR was 6.0% (95%CI: 2.2;9.9), p = 0.002; time above range (>10.0 mmol/L) was -5.7% (-9.8; -1.6), p = 0.007; and time below range (<3.9 mmol/L) was -0.4% (-1.1;0.4), p = 0.30-all in favour of faster aspart. Faster aspart significantly improved the coefficient of variation (34.0 ± 3.7% vs. 35.9 ± 4.9%, p = 0.02) and the HbA1c levels (ETD -1.9 (-3.7; -0.2) mmol/mol or - 0.18% (-0.34;-0.02), p = 0.03). No significant differences were observed in severe adverse events, including severe hypoglycaemia and diabetic ketoacidosis. Faster aspart had more injection site reactions than insulin aspart (p = 0.03).
CONCLUSION: Faster aspart improved baseline-adjusted TIR, TAR, CV and HbA1c after 16 weeks with frequent insulin pump adjustments but had a higher incidence of injection site reactions.

Keywords: CGM; CSII; Fiasp®; Iasp®; glucose control; sensor‐augmented insulin pump; time in range

DOI: https://doi.org/10.1111/dom.16326

Biosensors (Basel). 2025 Mar 16. pii: 190. [Epub ahead of print]15(3):

The Usefulness of the Glucose Management Indicator in Evaluating the Quality of Glycemic Control in Patients with Type 1 Diabetes Using Continuous Glucose Monitoring Sensors: A Cross-Sectional, Multicenter Study.

Sandra Lazar, Ovidiu Potre, Ioana Ionita, Delia-Viola Reurean-Pintilei, Romulus Timar, Andreea Herascu, Vlad Florian Avram, Bogdan Timar.

The Glucose Management Indicator (GMI) is a biomarker of glycemic control which estimates hemoglobin A1c (HbA1c) based on the average glycemia recorded by continuous glucose monitoring sensors (CGMS). The GMI provides an immediate overview of the patient's glycemic control, but it might be biased by the patient's sensor wear adherence or by the sensor's reading errors. This study aims to evaluate the GMI's performance in the assessment of glycemic control and to identify the factors leading to erroneous estimates. In this study, 147 patients with type 1 diabetes, users of CGMS, were enrolled. Their GMI was extracted from the sensor's report and HbA1c measured at certified laboratories. The median GMI value overestimated the HbA1c by 0.1 percentage points (p = 0.007). The measurements had good reliability, demonstrated by a Cronbach's alpha index of 0.74, an inter-item correlation coefficient of 0.683 and an inter-item covariance between HbA1c and GMI of 0.813. The HbA1c and the difference between GMI and HbA1c were reversely associated (Spearman's r = -0.707; p < 0.001). The GMI is a reliable tool in evaluating glycemic control in patients with diabetes. It tends to underestimate the HbA1c in patients with high HbA1c values, while it tends to overestimate the HbA1c in patients with low HbA1c.

Keywords: HbA1c; biomarker; continuous glucose monitoring system; glucose management indicator; type 1 diabetes mellitus

DOI: https://doi.org/10.3390/bios15030190

J Diabetes. 2025 Mar;17(3): e70073

The Relationship Between Glycated Albumin and Time in Tight Range in Type 2 Diabetes.

Jiaying Ni, Wenshuo Han, Yaxin Wang, Jiamin Yu, Wei Lu, Yufei Wang, Xiaojing Ma, Jingyi Lu, Jian Zhou.

AIMS: Among the new glucose metrics derived from continuous glucose monitoring, the concept of time in tight range (TITR) has gained increasing attention. We aimed to assess the association between TITR and traditional glycemic indicators, such as glycated albumin (GA).
METHODS: A total of 310 patients with type 2 diabetes on a stable glucose-lowering regimen over the previous 3 months were enrolled. TITR and time in range (TIR) were calculated using continuous glucose monitoring data collected over a minimum of 5 days. Spearman correlation analysis was performed to assess the relationships between traditional glycemic indicators, including GA and HbA1c, with TITR and TIR. Receiver operating characteristic curves were used to evaluate the predictive value of GA for TITR > 50% and TIR > 70%.
RESULTS: The median levels of GA and HbA1c were 15.6% (14.0%, 17.3%) and 6.5% (6.1%, 7.1%), respectively. Median TITR and TIR were 70.0% (56.0%, 81.0%) and 91.0% (84.0%, 96.8%), respectively. Spearman correlation analysis showed a moderate negative relationship between GA and both TITR and TIR. The optimal GA cutoff for identifying either TITR > 50% or TIR > 70% was 17.4%. Moreover, combining GA with fasting plasma glucose or 2-h postprandial glucose significantly enhanced the ability to identify TITR > 50%, achieving performance comparable to the combination of HbA1c and plasma glucose.
CONCLUSIONS: In patients with type 2 diabetes, a GA cutoff of 17.4% effectively identifies TITR > 50%.

Keywords: glycated albumin; time in tight range; type 2 diabetes

DOI: https://doi.org/10.1111/1753-0407.70073

JMIR Diabetes. 2025 Mar 25. 10 e64505

Examining How Adults With Diabetes Use Technologies to Support Diabetes Self-Management: Mixed Methods Study.

Timothy Bober, Sophia Garvin, Jodi Krall, Margaret Zupa, Carissa Low, Ann-Marie Rosland.

BACKGROUND: Technologies such as mobile apps, continuous glucose monitors (CGMs), and activity trackers are available to support adults with diabetes, but it is not clear how they are used together for diabetes self-management.
OBJECTIVE: This study aims to understand how adults with diabetes with differing clinical profiles and digital health literacy levels integrate data from multiple behavior tracking technologies for diabetes self-management.
METHODS: Adults with type 1 or 2 diabetes who used ≥1 diabetes medications responded to a web-based survey about health app and activity tracker use in 6 categories: blood glucose level, diet, exercise and activity, weight, sleep, and stress. Digital health literacy was assessed using the Digital Health Care Literacy Scale, and general health literacy was assessed using the Brief Health Literacy Screen. We analyzed descriptive statistics among respondents and compared health technology use using independent 2-tailed t tests for continuous variables, chi-square for categorical variables, and Fisher exact tests for digital health literacy levels. Semistructured interviews examined how these technologies were and could be used to support daily diabetes self-management. We summarized interview themes using content analysis.
RESULTS: Of the 61 survey respondents, 21 (34%) were Black, 23 (38%) were female, and 29 (48%) were aged ≥45 years; moreover, 44 (72%) had type 2 diabetes, 36 (59%) used insulin, and 34 (56%) currently or previously used a CGM. Respondents had high levels of digital and general health literacy: 87% (46/53) used at least 1 health app, 59% (36/61) had used an activity tracker, and 62% (33/53) used apps to track ≥1 health behaviors. CGM users and nonusers used non-CGM health apps at similar rates (16/28, 57% vs 12/20, 60%; P=.84). Activity tracker use was also similar between CGM users and nonusers (20/33, 61% vs 14/22, 64%; P=.82). Respondents reported sharing self-monitor data with health care providers at similar rates across age groups (17/32, 53% for those aged 18-44 y vs 16/29, 55% for those aged 45-70 y; P=.87). Combined activity tracker and health app use was higher among those with higher Digital Health Care Literacy Scale scores, but this difference was not statistically significant (P=.09). Interviewees (18/61, 30%) described using blood glucose level tracking apps to personalize dietary choices but less frequently used data from apps or activity trackers to meet other self-management goals. Interviewees desired data that were passively collected, easily integrated across data sources, visually presented, and tailorable to self-management priorities.
CONCLUSIONS: Adults with diabetes commonly used apps and activity trackers, often alongside CGMs, to track multiple behaviors that impact diabetes self-management but found it challenging to link tracked behaviors to glycemic and diabetes self-management goals. The findings indicate that there are untapped opportunities to integrate data from apps and activity trackers to support patient-centered diabetes self-management.

Keywords: continuous glucose monitors; diabetes; digital health literacy; health technology; mobile health; self-management

DOI: https://doi.org/10.2196/64505