Continuous glucose monitors (CGMs) have become essential tools for managing diabetes, giving users real-time access to glucose levels and trends. They reduce the need for frequent fingerstick tests and help people make fast decisions about insulin, food, and activity. But the value of a CGM depends heavily on one thing: accuracy. If the sensor isn’t reliable, the data it provides can lead to incorrect dosing or a false sense of security. This article explains what sensor accuracy means, what factors affect it, how to interpret accuracy metrics, and how to get the most reliable readings from your device.

What Is Sensor Accuracy?

Sensor accuracy is the degree to which a CGM reading matches the actual blood glucose concentration. Because CGMs measure glucose in the interstitial fluid—not directly in the blood—there is an inherent difference between what the sensor reports and what a laboratory reference or fingerstick test would show. Accuracy matters because small errors can lead to inappropriate insulin doses, missed hypoglycemia alerts, or unnecessary corrections. For people with diabetes, especially those using automated insulin delivery systems, even a 10% deviation can have real consequences.

Manufacturers evaluate accuracy using standardized protocols during clinical trials. Regulatory agencies like the Food and Drug Administration (FDA) set performance criteria that devices must meet before they can be marketed. These criteria include the mean absolute relative difference (MARD), the percentage of readings within a certain range of the reference, and the consistency of performance over the sensor’s wear time. Understanding these metrics helps users and clinicians choose the right device and interpret its data appropriately.

Factors Affecting Sensor Accuracy

No CGM is perfectly accurate all the time. Many variables can cause readings to drift away from the true blood glucose level. Recognizing these factors can help you anticipate and mitigate accuracy issues.

Calibration

Some CGMs, such as older Medtronic models, require periodic fingerstick calibrations to align the sensor’s signal with the user’s blood glucose. Calibration updates the sensor’s algorithm, adjusting for sensor drift. Missing a calibration or performing it during a time of rapidly changing glucose can introduce errors. Newer “factory-calibrated” devices like the Dexcom G7 and Abbott FreeStyle Libre 3 require no user calibrations, which simplifies use but also means there is no opportunity for in-the-field correction if the sensor starts to drift. Studies show that factory-calibrated sensors generally have good accuracy, but users should still verify readings when symptoms don’t match the number.

Sensor Placement

The location where you insert the sensor affects how well it detects glucose changes. Most sensors are approved for the back of the upper arm or the abdomen. The arm site tends to have less movement and compression, but accuracy can vary based on body fat, muscle density, and local blood flow. Areas with scar tissue, tattoos, or frequent movement should be avoided. The insertion site also impacts the time it takes for the sensor to stabilize after placement—often called the “warm-up” period—which can last from one to 12 hours depending on the device.

Physiological Factors

Individual physiology can cause systematic deviations. Dehydration reduces blood flow to the interstitial tissue, leading to lower glucose readings than the actual blood level. Skin temperature matters too: cold environments can constrict capillaries and delay glucose equilibration, while heat can increase blood flow and accelerate it. Some users experience a “pressure artifact” when lying on the sensor, a temporary drop in readings due to reduced local blood flow. Even time of day can play a role, as fasting and postprandial states affect the relationship between blood and interstitial glucose.

Interference from Medications and Substances

Certain drugs can interfere with the enzymatic reaction in the sensor, typically glucose oxidase. Acetaminophen at therapeutic doses has been shown to cause falsely high readings on some older CGM models. Newer sensors have largely mitigated this interference, but the FDA still recommends checking for known interactions. Other substances like ascorbic acid (vitamin C), uric acid, and some antibiotics can also affect readings, though the impact is usually small. Always review your device’s labeling for a full list of interferents.

Time Since Insertion

Fresh sensors often require a stabilization period. During the first few hours after insertion, the sensor is “settling in” as the body’s immune response and local tissue fluid dynamics adjust. Many devices recommend ignoring the first 1–2 hours of data or performing a calibration if allowed. Toward the end of the wear period (usually 7–14 days), sensor accuracy can degrade as the enzyme activity declines or the sensor membrane becomes fouled. Manufacturers set wear durations based on clinical data, but individual results may vary.

Understanding Accuracy Metrics

To compare CGM accuracy, you will encounter a few key numbers. Knowing what they represent helps you evaluate product literature and clinical research.

Mean Absolute Relative Difference (MARD)

MARD is the average absolute percent difference between a set of CGM readings and reference blood glucose values. A lower MARD indicates better accuracy. For example, a MARD of 8% means the sensor readings differ from the reference by an average of 8%. However, MARD is an average—it can hide variability. A device with a MARD of 8% might have some readings that are off by 2% and others off by 20%. Look for the full distribution, not just the average. Most modern CGMs have MARD values between 7% and 10%, with the best models achieving around 7–8% in clinical trials.

Clinical Error Grid Analysis (EGA)

The Parkes Error Grid (also known as the Consensus Error Grid) classifies paired CGM/reference readings into zones A through E. Zone A represents clinically accurate readings (no error in treatment), zone B indicates benign errors that would lead to no or minor treatment changes, and zones C–E represent errors that could lead to significant harm. A goal for any CGM is to have more than 99% of readings in zones A and B, with the vast majority in zone A. The error grid gives a more clinically meaningful view of accuracy than MARD alone.

Bias and Precision

Bias is the average tendency of the sensor to read high or low compared to the reference. A positive bias means the sensor reads higher than the true blood glucose. Precision (or variability) describes how much the error fluctuates from reading to reading. An accurate sensor has both low bias and low variability. Some manufacturers report the percentage of readings within ±15 mg/dL or ±20% of the reference (whichever is larger) for values above 100 mg/dL. A good goal is 85–90% of readings within these limits.

How Different CGMs Compare

Several CGMs are widely available, each with its own accuracy profile. The following comparisons are based on published clinical data and manufacturer claims as of early 2025. Always check the most recent studies and device labeling, as accuracy improves with each new generation.

  • Dexcom G7: MARD of approximately 7.6–8.2% in adults. About 90% of readings fall within ±15 mg/dL or ±20% of the reference. No calibration required. Wear time of 10 days.
  • Abbott FreeStyle Libre 3: MARD of about 7.5–8.5%. The sensor is small, factory-calibrated, and lasts 14 days. The Libre 3 has a narrower error distribution compared to older Libre models, with a high percentage in error grid zone A.
  • Medtronic Guardian 4: MARD around 8.5–9.5%. Requires at least one calibration per day but integrates directly with Medtronic insulin pumps. The Guardian 4 is designed for use with the MiniMed 780G system.
  • Senseonics Eversense E3: An implantable sensor lasting up to 6 months. MARD reported around 9%. Requires a small surgical procedure to insert and remove. The accuracy is stable over the wear period but the sensor still needs daily calibration with fingersticks.

All these devices meet FDA accuracy standards and are considered reliable for routine diabetes management. The choice depends on factors like cost, convenience, integration with other devices, and personal comfort with calibration requirements.

Best Practices for Maximizing Accuracy

You can take several steps to improve your sensor’s performance day to day.

  • Insert each sensor at a consistent site that has adequate subcutaneous tissue. Rotate arms and abdomen but avoid areas with heavy scarring or tattoos.
  • Hydrate well. Dehydration can lower interstitial fluid glucose and cause your sensor to read low.
  • Warm the sensor if you are in a cold environment. Use an arm band or wear a sleeve to keep the insertion site warm.
  • Avoid pressing on the sensor while sleeping. If you are a side sleeper, try inserting on the opposite arm or using a patch that provides a barrier.
  • Verify unusual readings with a fingerstick before taking action, especially during rapid glucose changes, after meals, or when you feel symptoms that contradict the sensor.
  • Keep the receiver or phone app within Bluetooth range. Signal dropout can lead to data gaps and missed alarms.
  • Replace the sensor if you notice a sudden shift in readings that doesn’t match your fingerstick. A failing sensor may show erratic values or repeated “sensor error” messages.

Limitations of Continuous Glucose Monitors

Even the most accurate CGM has inherent limitations that all users should understand.

  • Lag time: Because CGMs measure glucose in the interstitial fluid, there is a delay of 5–15 minutes behind blood glucose. This delay is most noticeable during rapid rises or falls, such as after meals or during exercise. Automated insulin systems account for this lag, but manual dosing based solely on CGM trends may be imprecise.
  • No substitute for fingersticks: While CGMs reduce the need for blood glucose tests, they are not always 100% accurate. For critical decisions—like treating low blood sugar or verifying a high reading before driving—the FDA still recommends a fingerstick check.
  • Cost and access: Many patients face out-of-pocket costs, especially if their insurance doesn’t cover CGMs or if they need the higher-tier devices for the best accuracy. Sensors also have a limited wear time, so the recurring expense can be a barrier.
  • Skin reactions: Adhesive allergies or skin irritation can affect sensor adhesion and may cause users to change sensors early, breaking the wear schedule and reducing accuracy.

Troubleshooting Inaccurate Readings

If your CGM readings seem off, follow these steps before assuming the sensor is bad.

  1. Wash your hands and perform a fingerstick to get a reference reading.
  2. Check the sensor insertion site. If it’s red, swollen, or oozing, remove the sensor and insert a new one at a different location.
  3. Ensure the sensor was stored correctly (room temperature, not expired).
  4. Recheck for medications or supplements that might interfere. Look up your device’s interference list online.
  5. If using a calibration-required sensor, ensure you calibrated at the right time (not during rapid glucose change) and with a clean fingerstick.
  6. If the sensor has been worn for more than its designated period, replace it. Accuracy degrades significantly after the recommended wear time.
  7. Contact the manufacturer’s support line. Many companies will replace a sensor that fails within the wear period.

It’s also worth checking the American Diabetes Association’s monitoring guidelines for updated recommendations on CGM accuracy and troubleshooting.

The technology continues to improve. Researchers are developing sensors with longer wear times (up to 14–21 days) and wider Bluetooth range. Newer algorithms use machine learning to reduce lag and compensate for physiological noise. Some companies are working on “smart” sensors that automatically calibrate against a built-in reference, eliminating the need for external calibration entirely. Implantable sensors like the Eversense E3 are also being refined to improve stability and reduce the need for fingerstick calibration. In the regulatory space, the FDA has introduced stricter performance standards for integrated systems, which will push manufacturers to publish more transparency around accuracy data. As these advances become available, users can expect CGMs to become even more reliable, with MARD values potentially dropping below 7% across multiple devices.

For a technical overview of how CGM accuracy is evaluated in clinical trials, you can read this review article published in the Journal of Diabetes Science and Technology.

Conclusion

Sensor accuracy is the foundation of effective CGM use. While no device is perfect, modern CGMs provide actionable, near-real-time data that can dramatically improve glycemic control. Understanding the metrics that define accuracy—MARD, error grid zones, and bias—helps you choose the right sensor and interpret its readings with appropriate skepticism. By following best practices for insertion, hydration, and calibration, you can minimize errors and get the most out of your device. Always stay aware of limitations like lag time and potential interferences, and confirm suspicious readings with a fingerstick. As technology evolves, CGMs will continue to become more accurate and easier to use, making diabetes management safer and more data-driven than ever before.