Decoding the Physics and Algorithms of Modern CGMs

Continuous Glucose Monitoring (CGM) has evolved from a niche medical tool into a sophisticated wearable that functions as a real-time "bio-sensor". This post explores the technical architecture that allows these devices to translate chemical reactions into actionable digital data.

1. The Molecular Engine: Enzymatic Electrochemical Sensing

At the core of a CGM is a flexible filament coated with specialized enzymes. Most modern systems utilize Glucose Oxidase (GOx) to catalyze a multi-step reaction:

The Reaction: Glucose from the interstitial fluid reacts with GOx to produce gluconic acid and hydrogen peroxide.
Signal Transduction: The sensor’s platinum electrode oxidizes the hydrogen peroxide, releasing electrons. This creates a minute electrical current (measured in nanoamperes) that is directly proportional to the glucose concentration.
The "Oxygen Problem": Early sensors were sensitive to oxygen levels. Second-generation devices now use artificial electron mediators to ensure accuracy even during fluctuations in tissue oxygenation.

2. Algorithmic Intelligence: From Raw Signal to Real-Time Data

The raw electrical signal is inherently "noisy" due to body movement and sensor drift. CGMs use advanced mathematics to clean this data:

Kalman Filtering: Many systems, including those from AgaMatrix, use Kalman Filters. This recursive algorithm predicts the next glucose state and then adjusts that prediction based on the actual incoming measurement, effectively separating "physiological signal" from "electronic noise".
Lag Compensation: Glucose levels in interstitial fluid typically lag behind blood glucose by 5–15 minutes. Algorithms use the "rate of change" (velocity) to predict current blood levels and reduce this physiological delay.

3. Accuracy Benchmarks: Understanding MARD

The gold standard for CGM accuracy is the Mean Absolute Relative Difference (MARD). A lower percentage indicates higher accuracy:

Flagship Performance: Recent head-to-head studies show the FreeStyle Libre 2 and Dexcom G7 both achieving MARDs below 9%, significantly outperforming older generations that often hovered around 15–20%.
Stability: Newer sensors like the Dexcom G7 have shorter "warm-up" periods (under 30 minutes) due to improved factory calibration, eliminating the need for daily finger-prick blood tests.

4. Data Transmission: BLE vs. NFC

How the data reaches your phone depends on the wireless protocol:

Bluetooth Low Energy (BLE): Used by Dexcom and FreeStyle Libre 2, BLE allows for automated, continuous transmission every 1–5 minutes, enabling "hypo" alarms even when your phone is in your pocket.
Near Field Communication (NFC): Some models (like the original Libre) require a manual scan. While less "automated," NFC is more power-efficient and offers a smaller hardware footprint.