CGM Knowledge Base

Continuous Glucose Monitors (CGMs) measure glucose in the interstitial fluid (ISF) rather than the blood, creating an inherent lag time of 5–20 minutes.

CGM sensor technology relies on amperometric electrochemical detection using Glucose Oxidase (GOx) to convert interstitial glucose into an electrical signal.

CGMs measure interstitial fluid rather than capillary blood, resulting in a physiological lag time of 5–15 minutes that creates discrepancies during rapid glucose changes.

Mean Absolute Relative Difference (MARD) is the gold-standard metric for CGM accuracy, with <10% being the threshold for non-adjunctive use.

In 2024, the FDA cleared the first OTC CGMs, led by Dexcom's Stelo and Abbott's Lingo and Libre Rio, democratizing access to metabolic data.

The growing OTC CGM market is making glucose monitoring accessible to consumers beyond traditional diabetes patients.

CGM insurance coverage has expanded significantly following the CMS 2023 update, with Medicare, commercial insurance, and Medicaid all providing various coverage options.

CGM access economics are split between Durable Medical Equipment (DME) and Pharmacy Benefits, each with distinct cost structures and access paradigms.

Understanding the long-term cost-effectiveness of CGM technology compared to traditional glucose monitoring methods.

Pharmacy Benefit Managers negotiate rebates with CGM manufacturers that significantly impact device pricing and patient access.

Trend arrows on CGMs visualize the Rate of Change (ROC) of glucose, allowing users to predict future levels and adjust treatment proactively.

Proper sensor insertion technique and regular site rotation are essential for accurate CGM readings and skin health.

Managing skin sensitivity and adhesive reactions is crucial for long-term CGM success and user comfort.

Navigating travel with CGMs requires understanding TSA guidelines, airline policies, and international travel considerations.

Automated Insulin Delivery (AID) systems integrate CGMs with insulin pumps to create closed-loop systems that adjust insulin delivery in real-time.

Connected CGMs and insulin pumps create cybersecurity risks including data breaches, signal jamming, and potentially dangerous integrity spoofing.

Closed-loop algorithms analyze CGM data, predict glucose trends, and automatically adjust insulin delivery for optimal glucose control.

Artificial intelligence and machine learning enhance CGM glucose predictions by learning individual patterns and improving forecast accuracy.

CGM use involves psychological trade-offs between increased safety and reduced fear versus potential for data overload, anxiety, and diabetes distress.

Children and adults experience CGM use differently, with distinct psychological challenges and benefits for each age group.

The collection and potential commercial use of CGM data raises important questions about privacy, data ownership, and surveillance capitalism.

The GlucoWatch Biographer's 2001 launch and subsequent failure offers important lessons about non-invasive glucose monitoring challenges.