CGM-Based Algorithm Enhances Insulin Dose Adjustment in Type 2 Diabetes

Type 2 diabetes frequently requires transition to insulin, yet adjusting doses to maintain safe glycemic control is complex and time-consuming. Variability in insulin titration can expose patients to hyperglycemia or hypoglycemia and increases clinical workload. To help address this challenge, researchers have developed a dosing algorithm linked to continuous glucose monitoring that guides weekly insulin changes. A new randomized study indicates the approach improves time in a safe blood-sugar range compared with self-directed titration.

The University of Virginia Center for Diabetes Technology developed the algorithm for adults with type 2 diabetes who use insulin. The system pairs with a continuous glucose monitor (CGM) and is designed to support clinicians and patients during dose initiation and adjustment. Its purpose is to standardize insulin titration and reduce reliance on trial-and-error methods.

The algorithm processes two weeks of CGM data to produce a single recommended change to the user’s insulin dose each week. Recommendations are based on recent glucose patterns rather than isolated measurements, offering a structured cadence for follow-up. The output is intended to be actionable during routine care visits or remote monitoring check-ins.

Investigators evaluated the tool in a 16-week randomized clinical trial that enrolled 30 participants. Subjects were assigned either to receive weekly insulin-dose recommendations generated by the algorithm and CGM data or to adjust insulin through self-monitoring of blood glucose. Participants using the algorithm increased time in a safe blood-sugar range from 54.1% to 75.3%, while participants in the self-monitoring group increased from 50.2% to 55.3%.

Findings were published in Diabetes Technology & Therapeutics. The study team reported strong user acceptance of the technology and noted the need for longer trials with larger and more diverse cohorts to confirm effectiveness. The trial was supported by a grant from Novo Nordisk.

“These results clearly show that diabetes technology and advanced algorithms can be leveraged to great effects, well beyond the classical paradigm of automated insulin delivery,” said Marc D. Breton, PhD, associate director of research at the UVA Center for Diabetes Technology.

“As continuous glucose monitoring and connected medical devices become ubiquitous, we have the opportunity to provide highly personalized advice and monitoring to people with diabetes and guide their use of insulin and medications. Showing the impact of these technologies in early insulin therapy (only one dose a day) opens the door to helping the vast majority of people using insulin, well beyond what we were able to achieve with automated insulin delivery,” said Breton.

Related Links
UVA Center for Diabetes Technology