Recent Articles
Basal rates (BR) adjustment is crucial for managing Type 1 Diabetes Mellitus (T1DM), accounting for 30% to 50% of Total Daily Insulin (TDI) needs. All current Closed Loop systems revert to the user’s usual pump BR (known as manual mode) in the event of closed-loop failure. Further, those in low and middle-income countries (LMICs) and those without suitable health insurance, access to Closed Loop remains relatively low. Accurately adjusting the BR remains challenging, leading to hyperglycaemia or hypoglycaemia, and research on optimizing the BR is limited.
Managing Type 1 Diabetes (T1D) requires maintaining target blood glucose levels through precise diet and insulin dosing. Predicting postprandial glycaemic responses (PPGRs) based solely on carbohydrate content is limited by factors like meal composition, individual physiology, and lifestyle. Continuous glucose monitors (CGMs) provide insights into these responses, revealing significant individual variability. The statistical clustering method propsed here balances the number of clusters formed and the glycaemic variability of the PPGRs within each cluster to offer a clustering technique on which treatment decisions could be based.
Diabetes self-management plays a major role in controlling blood sugar levels and avoiding chronic complications. Meanwhile, AI tools such as ChatGPT are becoming increasingly available to patients and are often used for disease management advice. Frontline caregivers must be aware of these tools’ strengths and weaknesses to ensure their safe use.
Despite efforts to raise glycemic targets and reduce modifiable risk factors, hypoglycemia continues to impact a large number of long-term care (LTC) residents living with diabetes mellitus and remains one of the leading causes of hospitalization in this cohort. Effective, sustainable practice strategies to monitor and maintain glycemic control in LTC are lacking. We describe the stepwise approach used by 2 LTC homes switching from traditional fingerstick testing to a continuous glucose monitoring (CGM) system as part of a quality improvement initiative to reduce nursing workload and address hypoglycemia. This was an exploratory pilot project. A working group was established at each of the 2 participating LTC homes, including representatives from management and direct care staff. Kickoff meetings were held with key direct care staff to discuss the limitations of current monitoring practices and potential solutions. The following interventions were agreed upon and implemented by the working groups: (1) initiation of structured glucose monitoring for residents using CGM (FreeStyle Libre 2), requiring scanning of sensors 4 times per day; (2) provision of staff education and training on CGM by a diabetes expert; and (3) scheduling of interdisciplinary rounds as needed to optimize diabetes management. System changes were gradually introduced in a stepwise manner over a 3-month period (intervention phase), during which the LTC homes progressed from traditional fingerstick testing to point-of-care sensor readings and then to full use of the CGM software platform. Hypoglycemia was defined as a glucose reading of ≤4mmol/L. Glucose readings were collected from 38 residents living with diabetes mellitus and receiving insulin in the 6 months before the start of the intervention phase (baseline evaluation) and in the 6 months after the end of the intervention phase (post-launch evaluation). All hypoglycemic readings detected by a sensor at a point-of-care test were validated using a fingerstick test. Nursing workload associated with glucose testing was assessed in an anonymous survey of nursing staff at baseline and post-launch.The approach resulted in a 40% reduction in nursing time required to complete a glucose reading (from 5.1 minutes per test at baseline to 3.1 minutes per test at the post-launch evaluation). The frequency of glucose monitoring increased from a total of 19,438 glucose readings in the baseline evaluation to 35,971 point-of-care sensor scans in the post-launch evaluation. The number of detected hypoglycemic events increased 12-fold, from 88 in the baseline evaluation to 1049 in the post-launch evaluation. Hypoglycemic events continue to impact a large number of LTC residents living with diabetes mellitus. CGM can improve the detection of hypoglycemic events while decreasing nursing workload. A gradual transition to CGM can help overcome underlying barriers and concerns and ensure a sustainable approach.
Exercise is an important aspect of diabetes self-management. Patients with type 1 diabetes frequently struggle with exercise-induced hyperglycemia and hypoglycemia, decreasing their willingness to exercise. Objective: We aim to build accurate and easy-to-deploy models to forecast exercise-induced glycemic events in real-world settings.
The COVID-19 pandemic led to increased patient demand for remote management of type 2 diabetes using secure messaging, or patient-provider text-based communication. Prior research on secure messaging has described the content of messages sent for type 2 diabetes management and demonstrated its impact on clinical outcomes. However, there is a gap in knowledge about how secure messaging performs as a communication medium for specific tasks in clinical care (e.g. prescription management, discussing medical questions). Additional research is needed to understand physician experiences using secure messaging to communicate with patients about clinical tasks that support diabetes management.
Clinicians currently lack an effective means for identifying youth with type 1 diabetes (T1D) who are at risk for experiencing glycemic deterioration between diabetes clinic visits. As a result, their ability to identify youth who may optimally benefit from targeted interventions designed to address rising glycemic levels is limited. Although electronic health records (EHR)-based risk predictions have been used to forecast health outcomes in T1D, no study has investigated the potential for using EHR data to identify youth with T1D who will experience a clinically significant rise in HbA1c ≥0.3% (~3 mmol/mol) between diabetes clinic visits.
The COVID-19 pandemic catalyzed the adoption of digital technologies in health care. This study assesses a digital-first integrated care model for type 2 diabetes management in Western Sydney, using continuous glucose monitoring (CGM) and virtual Diabetes Case Conferences (DCC) involving the patient, general practitioner (GP), diabetes specialist, and diabetes educator at the same time.
Inequity in diabetes technology use persists among Black and Hispanic youth with type 1 diabetes (T1D). Community health workers (CHWs) can address social and clinical barriers to diabetes device use. However, more information is needed on clinicians’ perceptions to inform the development of a CHW model for youth with T1D.
