Editorial Team
CGM AFIA Editorial Team
The CGM AFIA editorial team researches continuous glucose monitoring technology, payer access, device features, and daily-use questions for people comparing or using CGMs. The team writes practical education for readers who need clear context before discussing device choices with a licensed clinician.
Reviewed and authored topics
Accuracy and Interstitial Lag Time
Continuous Glucose Monitors (CGMs) measure glucose in the interstitial fluid (ISF) rather than the blood, creating an inherent lag time of 5–20 minutes.
Sensor Chemistry and Enzymatic Reactions
CGM sensor technology relies on amperometric electrochemical detection using Glucose Oxidase (GOx) to convert interstitial glucose into an electrical signal.
Interstitial vs. Capillary Glucose Lag
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.
MARD Accuracy Metrics
Mean Absolute Relative Difference (MARD) is the gold-standard metric for CGM accuracy, with <10% being the threshold for non-adjunctive use.
Biofouling and Foreign Body Response
The Foreign Body Response (FBR) is the primary physiological limiter of CGM lifespan and accuracy, causing sensors to be walled off by fibrous tissue over time.
CGM Sensor Interference and Noise
CGM accuracy is challenged by three primary noise sources: electrochemical interference, mechanical artifacts, and biological noise from the body's immune response.
Non-Invasive Glucose Monitoring Technologies
Non-invasive glucose monitoring seeks to measure blood glucose without skin penetration using technologies like NIR spectroscopy and RF dielectric sensing.
Calibration Algorithms and Factory-Calibration
Modern CGMs use sophisticated calibration algorithms, with many now featuring factory-calibration that eliminates the need for user fingerstick calibrations.
Enzymatic Chemistry: GOx vs. GDH
Two key enzymes power CGM sensors: Glucose Oxidase (GOx) offers superior specificity while Glucose Dehydrogenase (GDH) provides different performance characteristics.
Enzymatic vs. Fluorescence Sensing Technology
CGM technologies include enzymatic sensing (traditional) and fluorescence sensing, each with distinct advantages for glucose detection.
Consensus Error Grid Analysis
Error grids evaluate CGM clinical accuracy by plotting sensor readings against reference values and categorizing deviations by clinical risk level.
iCGM Regulatory Standards
The FDA's iCGM classification establishes interoperability standards allowing CGMs to be used with automated insulin delivery systems.
Over-the-Counter (OTC) CGM Options
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.
OTC CGM Market Access
The growing OTC CGM market is making glucose monitoring accessible to consumers beyond traditional diabetes patients.
Insurance Coverage and Medicare Criteria
CGM insurance coverage has expanded significantly following the CMS 2023 update, with Medicare, commercial insurance, and Medicaid all providing various coverage options.
Pharmacy Benefit vs. DME Benefit Economics
CGM access economics are split between Durable Medical Equipment (DME) and Pharmacy Benefits, each with distinct cost structures and access paradigms.
Cost-Effectiveness Analysis of CGM
Understanding the long-term cost-effectiveness of CGM technology compared to traditional glucose monitoring methods.
The Role of PBM Rebates in CGM Pricing
Pharmacy Benefit Managers negotiate rebates with CGM manufacturers that significantly impact device pricing and patient access.
Medicare Coverage Criteria Evolution
Medicare CGM coverage has evolved significantly, expanding access from intensive insulin users to broader diabetic populations.
Economic Impact of Non-Adherence
Non-adherence to CGM use costs the healthcare system billions annually through increased complications and emergency care.
CGM Alarms and Alert Settings
CGM alerts can warn about high, low, and predicted glucose changes, but settings should balance safety with alarm fatigue.
Interpreting Trend Arrows
Trend arrows on CGMs visualize the Rate of Change (ROC) of glucose, allowing users to predict future levels and adjust treatment proactively.
Sensor Insertion and Site Rotation
Proper sensor insertion technique and regular site rotation are essential for accurate CGM readings and skin health.
CGM Sensor Lifespan and Replacement
CGM sensors are designed for fixed wear periods, but adhesion, insertion, signal quality, and support rules affect replacement.
Skin Sensitivity and Adhesive Management
Managing skin sensitivity and adhesive reactions is crucial for long-term CGM success and user comfort.
Sharing CGM Data and Remote Monitoring
CGM data sharing lets chosen caregivers follow glucose trends remotely, but setup, consent, alerts, and clinic uploads differ.
Travel and Airport Security Protocols
Navigating travel with CGMs requires understanding TSA guidelines, airline policies, and international travel considerations.
Troubleshooting Common Sensor Errors
Understanding the difference between technical failures and physiological artifacts helps users troubleshoot CGM errors effectively.
The Impact of Lipohypertrophy on Sensor Accuracy
Lipohypertrophy (fatty tissue buildup from repeated injections) significantly degrades CGM accuracy due to impaired glucose diffusion.
Impact of Hydration Status on CGM Signal
Dehydration can affect CGM sensor performance and accuracy, making proper hydration important for reliable readings.
Impact of Sensor Site Selection
Choosing the right sensor site affects CGM accuracy, comfort, and longevity—consider blood flow, activity level, and skin condition.
Dermatological Reactions to CGM Adhesives
Understanding and managing skin reactions to CGM adhesives is essential for long-term device use.
Integration with Insulin Pumps (AID Systems)
Automated Insulin Delivery (AID) systems integrate CGMs with insulin pumps to create closed-loop systems that adjust insulin delivery in real-time.
Data Privacy and Cybersecurity in CGM Devices
Connected CGMs and insulin pumps create cybersecurity risks including data breaches, signal jamming, and potentially dangerous integrity spoofing.
Closed-Loop Algorithm Signal Processing
Closed-loop algorithms analyze CGM data, predict glucose trends, and automatically adjust insulin delivery for optimal glucose control.
The Role of AI/ML in Glucose Prediction
Artificial intelligence and machine learning enhance CGM glucose predictions by learning individual patterns and improving forecast accuracy.
The Role of AI in Reducing Data Fatigue
AI helps manage the overwhelming volume of CGM data by surfacing actionable insights and reducing decision burden on users.
Predictive Algorithms vs. Sensor Noise
CGM algorithms must balance accurate glucose prediction against sensor noise and artifacts that can lead to false readings.
iCGM Interoperability and System Integration
FDA interoperability standards enable CGMs and insulin pumps from different manufacturers to work together safely.
Data Sharing Features for Caregivers
CGM sharing features allow caregivers to remotely monitor glucose data and receive alerts, enabling better support for diabetes management.
Pediatric CGM Monitoring and School Protocols
CGMs enable safe diabetes management in schools through remote monitoring by parents and established protocols for school nurses.
Psychological Impacts of Continuous Monitoring
CGM use involves psychological trade-offs between increased safety and reduced fear versus potential for data overload, anxiety, and diabetes distress.
Pediatric vs. Adult CGM Psychology
Children and adults experience CGM use differently, with distinct psychological challenges and benefits for each age group.
Metabolic Data Privacy and Surveillance
The collection and potential commercial use of CGM data raises important questions about privacy, data ownership, and surveillance capitalism.
The Failure of the GlucoWatch Biographer
The GlucoWatch Biographer's 2001 launch and subsequent failure offers important lessons about non-invasive glucose monitoring challenges.
Glucose in Tears and Sweat vs. Blood
Research explores measuring glucose in tears and sweat as potential non-invasive alternatives, but significant accuracy challenges remain.
Bio-RFID and Dielectric Spectroscopy
Emerging technologies like Bio-RFID and dielectric spectroscopy offer potential paths to non-invasive glucose monitoring.