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Refining Insulin on Board with netIOB for Automated Insulin Delivery.

Michael C RiddellDana Michelle LewisLauren V TurnerRayhan A LalArsalan ShahidDessi P Zaharieva
Published in: Journal of diabetes science and technology (2024)
Automated insulin delivery (AID) systems enhance glucose management by lowering mean glucose level, reducing hyperglycemia, and minimizing hypoglycemia. One feature of most AID systems is that they allow the user to view "insulin on board" (IOB) to help confirm a recent bolus and limit insulin stacking. This metric, along with viewing glucose concentrations from a continuous glucose monitoring system, helps the user understand bolus insulin action and the future "threat" of hypoglycemia. However, the current presentation of IOB in AID systems can be misleading, as it does not reflect true insulin action or automatic, dynamic insulin adjustments. This commentary examines the evolution of IOB from a bolus-specific metric to its contemporary use in AID systems, highlighting its limitations in capturing real-time insulin modulation during varying physiological states.
Keyphrases
  • type diabetes
  • glycemic control
  • blood glucose
  • machine learning
  • deep learning
  • high throughput
  • adipose tissue
  • oxidative stress
  • skeletal muscle
  • insulin resistance
  • metabolic syndrome
  • current status