What is a Continuous Glucose Monitor (CGM)?

A Continuous Glucose Monitor (CGM) is a wearable biosensor that provides real-time glucose readings 24 hours a day. Unlike conventional fingerstick tests, which capture only a single moment in time, CGMs deliver a steady stream of data—typically every one to five minutes—enabling users to observe glucose trends, detect rapid changes, and make proactive adjustments to insulin, diet, or activity. The technology has evolved rapidly over the past two decades, moving from adjunctive devices used alongside fingerstick checks to non-adjunctive systems that can be relied upon for treatment decisions without confirmatory blood tests. For people with type 1 diabetes, type 2 diabetes, or even those managing reactive hypoglycemia, CGMs offer a powerful window into how the body responds in real-world conditions.

How Do CGMs Work?

A CGM system consists of three main components: a tiny sensor filament inserted just under the skin, a transmitter attached to the sensor, and a receiver—often a smartphone app or dedicated display device. The sensor’s thin, flexible electrode is placed in the subcutaneous tissue, where it measures the concentration of glucose in the interstitial fluid—the fluid that bathes the cells. This measurement is performed via an enzymatic reaction: glucose oxidase on the sensor produces an electrical current proportional to the glucose concentration. The transmitter sends this signal wirelessly to the receiver, which applies a calibration algorithm to convert the raw current into a readable glucose value. Most modern CGMs use factory calibration, meaning they are pre-calibrated during manufacturing and require no fingerstick inputs. Others still require periodic fingerstick calibration to maintain accuracy. The receiver also tracks trends, generates alarms for impending hypo- or hyperglycemia, and stores historical data for retrospective analysis.

Understanding CGM Accuracy

Accuracy is the bedrock of CGM utility. A device that reports incorrect values can lead to missed hypoglycemia, excessive insulin dosing, or unnecessary anxiety. In the clinical world, CGM accuracy is assessed using several statistical metrics, with the most common being Mean Absolute Relative Difference (MARD). MARD is calculated by comparing each CGM reading against a reference blood glucose value—typically measured on a laboratory-grade glucose analyzer or a high-accuracy blood glucose meter. The MARD is expressed as a percentage; a lower percentage indicates better accuracy. For example, a system with a MARD of 9% means that, on average, the CGM reading is 9% different from the true blood glucose. Current generation CGMs typically achieve MARD values between 8% and 12%, with factory-calibrated sensors often performing on the lower end of that range, especially in the euglycemic and hyperglycemic ranges.

But MARD alone can be misleading. It is an average—skewed by many readings in the normal range—and does not fully capture performance at the extremes. A device might have a good MARD overall but still produce clinically significant errors during rapid glucose changes or in the hypoglycemic range. Therefore, regulatory bodies and clinicians also evaluate accuracy using consensus error grids, such as the Clarke Error Grid or the Parkes Error Grid. These grids plot CGM values against reference values and assign each point to a clinical risk zone (e.g., accurate reading, benign error, or potentially dangerous error). An ideal system places at least 95% of readings in Zone A (accurate) and no more than a tiny fraction in Zone D or E (dangerous). Users should look for devices with strong performance across all zones, not just a low MARD.

Key Factors Influencing CGM Accuracy

Sensor Placement and Insertion

Where you place the sensor matters. The abdomen and upper arm are the most common sites, but studies show that placement on the back of the upper arm (triceps area) often provides better agreement with blood glucose, especially during sleep when pressure on the sensor may be lower. The sensor should be inserted into clean, dry skin with minimal hair, away from tattoos, scars, or areas that undergo frequent bending. Sensors placed over muscle (e.g., on the thigh) may produce more variable readings due to differences in interstitial fluid turnover during exercise.

Lag Time

Perhaps the most misunderstood aspect of CGM accuracy is the physiological lag. Blood glucose changes are reflected in interstitial fluid only after a delay—typically 5 to 15 minutes. This lag is smaller during steady-state conditions but becomes more pronounced during rapid rises (such as after a meal) or falls (such as after an insulin bolus). The lag is composed of two parts: the time it takes for glucose to diffuse from capillaries into the interstitial space, and the signal processing time of the sensor itself. Some modern algorithms partially compensate for lag by applying predictive filters, but no system can eliminate it entirely. Users need to understand that a CGM reading may not yet show the full effect of a recent carbohydrate intake or insulin dose. When making decisions in fast-moving situations—like treating an impending low—it is prudent to confirm with a fingerstick if there is any doubt about the trend.

Hydration and Local Tissue Conditions

Dehydration reduces blood flow to the skin and alters the composition of interstitial fluid, potentially slowing the equilibration of glucose and causing readings to drift. Conversely, overhydration from excessive fluids or certain medical conditions can dilute the interstitial fluid and artificially lower readings. Skin temperature also plays a role: cold skin can vasoconstrict capillaries, reducing the availability of glucose to the sensor, while hot environments or fever can increase blood flow and speed equilibration. Applying a warm compress or taking a shower can temporarily shift readings as local perfusion changes. For this reason, CGMs often include a skin temperature sensor and use algorithms to correct for temperature-related errors.

Medications and Substances

Certain medications can interfere with the accuracy of CGM readings. Acetaminophen (paracetamol) is the best-known culprit—older generation sensors were susceptible to electrochemical interference from acetaminophen metabolites, causing falsely elevated glucose readings. Modern sensors use selective membranes or advanced electronics to minimize this interference, but high doses may still cause errors. Vitamin C (ascorbic acid) taken in large supplements can also affect readings, as can some antibiotics and diuretics. The effect is typically transient and dose-dependent. Users should review the package insert for their specific CGM system to understand known interferents. Additionally, extremely high or low pH levels in the blood (such as from diabetic ketoacidosis) can degrade sensor performance.

Sensor Age and Biofouling

As a sensor remains in the body, the body’s immune response gradually encapsulates it in a layer of fibrinogen and other proteins—a process known as biofouling. This reduces the sensor’s sensitivity over time, causing readings to drift downward, especially in the first 24 hours after insertion. Some CGMs manage this by calibrating out the drift, while others issue a “sensor warm-up” period of 12–24 hours during which accuracy may be lower. After that, the sensor tends to stabilize for several days before slowly declining again near the end of its wear period (7–14 days, depending on the system). Users may notice more erratic readings on the first day and the last day of sensor life.

Evaluating CGM Accuracy: What the Metrics Really Mean

When comparing CGM devices, look at multiple metrics. Besides MARD, check the percentage of readings within ±15 mg/dL (or ±15%) of the reference value—a common standard set by the FDA for non-adjunctive labeling. The best CGMs achieve over 90% of readings within this range. Also examine the system’s ability to detect hypoglycemia (defined as readings below 70 mg/dL or 54 mg/dL). This is measured by the sensitivity and specificity of hypo alarms. A system with high sensitivity catches most real lows, but high specificity minimizes false alarms. False alarms erode trust and lead to alarm fatigue. For users who rely heavily on CGM data for decision-making, particularly those with hypoglycemia unawareness, a device that performs well in the low range is critical.

Clinical studies published in peer-reviewed journals offer the most reliable assessments. Look for studies conducted under real-world conditions (free-living studies) as well as controlled in-clinic tests with frequent reference blood draws. The FDA and CE certification bodies require manufacturers to submit clinical data before approving a system for non-adjunctive use. You can often find summary reports on the manufacturer’s website or on the FDA’s public database. User testimonials from forums or product review sites are less reliable due to selection bias and uncontrolled variables.

Benefits of Using CGMs Despite Accuracy Challenges

The practical benefits of CGMs far outweigh their occasional inaccuracies for the vast majority of users. Continuous data revealing glucose patterns enables fine-tuning of insulin-to-carbohydrate ratios, basal rates, and correction factors. Users can see exactly how a morning run or a slice of pizza affects their glucose over the next four hours. Real-time alarms provide safety for those who do not feel hypoglycemic symptoms. For parents of children with diabetes, a CGM with remote monitoring brings peace of mind. The reduction in fingerstick frequency—from 6–10 times a day to near zero—frees up mental bandwidth and reduces pain and stress. Studies have shown that consistent CGM use lowers HbA1c by 0.5–1.0 percentage points on average and reduces the time spent in dangerous glycemic ranges.

Limitations and When to Be Skeptical

Even the best CGMs have limitations. Accuracy degrades at very low glucose levels; sensors tend to read slightly low during hypoglycemia, which can cause an alarm to sound for a value that is actually 60 mg/dL (CGM) vs. 55 mg/dL (blood glucose)—generally safe, but potentially annoying. At very high levels (above 400 mg/dL), most sensors become less precise. Some users experience pressure-induced sensor attenuation (PISA) during sleep, where lying on the sensor compresses the local tissue and causes a false dip in readings. This can trigger a hypoglycemia alarm in the middle of the night for no reason. Users should be aware of this phenomenon and avoid sleeping directly on the sensor site.

Cost remains a significant barrier. The sensors, transmitters, and receivers are expensive, and insurance coverage varies widely. Some users find that their copays for CGM supplies exceed those for test strips. Additionally, skin irritation from the adhesive is common; many users develop mild rashes or itching after a few days. Rotating sensor sites and using hypoallergenic barrier wipes can help, but a small percentage of individuals simply cannot tolerate wearing a CGM continuously.

Best Practices for Maximizing CGM Accuracy in Daily Life

Placement and Insertion Technique

Choose a site that is clean, dry, and free of lotion or antiperspirant. Avoid sites that are exercised frequently (e.g., if you do a lot of push-ups, avoid placing the sensor over the pectoral area). For arm placement, the back of the upper arm is best. Press the applicator firmly against the skin and follow the manufacturer’s insertion instructions exactly. After insertion, press and hold the sensor for 30 seconds to ensure the adhesive bonds properly. Use an overtape (provided or purchased separately) if you are active or sleep unpredictably.

Calibration Strategy

If your CGM requires fingerstick calibration, always calibrate when your blood glucose is stable—ideally before meals or several hours after a meal, when the rate of change is less than 1 mg/dL per minute. Avoid calibrating during rapid rises or falls, as the lag will cause the algorithm to learn a mismatched value. For factory-calibrated systems, you typically do not need to calibrate, but if you suspect the sensor is off, a single fingerstick check can be used to decide whether to replace the sensor.

Focus on the trend arrow and the rate of change. A reading of 90 mg/dL with a double-down arrow (rapidly falling) is more alarming than a flat 90 mg/dL. The trend arrow gives you predictive power: a steady single arrow down suggests it is safe to wait, while a rapid fall calls for immediate action. Most CGM apps display a three-hour trend graph; use the shape of the graph (linear, flat, dropping, rising) to anticipate what is coming next. This shift from reactive (check and treat) to proactive (look at the arrow and the line) is what makes CGMs revolutionary.

Regular Comparison with Fingerstick

Even with a non-adjunctive CGM, it is wise to confirm with a fingerstick in certain scenarios: if you feel symptoms of low or high blood sugar that are not reflected in the CGM reading, if the CGM gives a value that does not match your expectations (e.g., a high reading after a small meal that previously never caused a spike), or if the sensor has been on for more than its rated wear time. Also, check a fingerstick whenever you make a treatment decision for a value below 70 mg/dL or above 300 mg/dL, as those are the ranges where CGM error is most likely to be clinically significant.

Staying Informed About Updates and Research

CGM technology is one of the fastest-evolving areas of diabetes care. Software updates can improve calibration algorithms, reduce lag, and refine alarm thresholds. For example, Dexcom’s G7 received FDA clearance for a 12-hour warm-up time and improved accuracy in the hypoglycemic range. Abbott’s FreeStyle Libre 3 eliminated the need for scanning entirely, offering a real-time stream at a low per-sensor cost. Medtronic’s Guardian 4 is integrated with their insulin pumps for automated insulin delivery. Review the latest clinical trials on ClinicalTrials.gov to see how your device stacks up in head-to-head comparisons with competitors.

The Future of CGM Accuracy

Next-generation sensors are being designed with better membranes to resist biofouling, dual-electrode systems to cancel out electrochemical interference, and artificial intelligence that learns each user’s individual lag characteristics. The goal of achieving a MARD below 5%—equivalent to the accuracy of a high-quality blood glucose meter—is within reach. Non-invasive technology (e.g., optical or sonographic methods) is being explored, though none have yet matched the accuracy of enzymatic subcutaneous sensors. For now, users can confidently rely on current CGM technology to improve their glycemic control, provided they understand its limitations and apply the best practices outlined in this guide.