CGM data is not a diagnostic tool. This guide explains how to read device data — it is not medical guidance. Always consult your healthcare provider to interpret your glucose patterns.
This guide is based on manufacturer documentation for Dexcom, Abbott, Levels Health, and Nutrisense apps, clinical definitions of CGM metrics, and hands-on review of each platform's data presentation as of May 2026.
Reading the glucose graph
Every CGM app presents a continuous glucose graph with two axes. The x-axis (horizontal) represents time — typically the last 3 hours, 6 hours, 12 hours, or 24 hours depending on which view you select. The y-axis (vertical) represents glucose concentration, measured in mg/dL in the US or mmol/L in most other countries.
The line on the graph connects readings taken every 1–5 minutes (depending on the device) and forms the continuous curve you see. A flat line means glucose has been stable. A rising line means glucose is increasing. A descending line means it's falling.
X-axis = time. Each point on the line is a glucose reading at that moment. Y-axis = glucose level. Higher on the graph = higher glucose. Lower = lower glucose.
Most apps overlay horizontal bands or dashed lines showing your target glucose range. Any part of the line inside the bands is "in range." Parts above are elevated; parts below are low.
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What the trend arrows mean
Dexcom and most CGM apps display a trend arrow next to the current glucose reading. The arrow indicates the direction and speed at which glucose is currently moving — not where it has been.
The trend arrow is forward-looking context — it helps you understand where glucose is headed in the next 15–30 minutes based on the current rate of change. The accuracy of the arrow prediction decreases during rapid directional changes.
The metrics your app reports
Time in Range
The percentage of readings that fell within your target glucose range over a selected period. Reported as a percentage — higher is generally considered better for most users. See our full Time in Range guide.
Glucose Management Indicator
An estimate derived from average CGM glucose over the past 90 days. GMI is a device-calculated metric — it is not the same as a laboratory HbA1c test and should not be interpreted as one.
Standard Deviation
A measure of how much glucose varies around the average. A higher standard deviation means more variability in readings. Lower SD is generally associated with more stable glucose patterns.
Coefficient of Variation
Standard deviation expressed as a percentage of the mean glucose. CV is a normalized variability measure that allows comparison across different average glucose levels. Most CGM apps display CV as a percentage.
What a typical day looks like on a CGM graph
Fasting (morning)
For most users, overnight and early-morning glucose is relatively flat — this is the fasting baseline. Some users see a gradual rise between 4 and 8 AM (the dawn phenomenon) even before eating. This is a commonly observed hormonal pattern visible on the CGM graph.
After breakfast
The glucose graph typically shows a rise starting 15–30 minutes after eating, peaking somewhere between 45 minutes and 90 minutes post-meal, then declining back toward baseline over 2–3 hours. The height and duration of the post-meal rise varies significantly by individual and by what was eaten.
Exercise
Exercise can cause glucose to rise initially (especially high-intensity work) or fall during sustained aerobic activity. The graph will reflect these patterns — a rise, a drop, or both in sequence, depending on the activity type and duration. Interstitial glucose can lag behind blood glucose during rapid changes, so the graph during intense exercise should be interpreted as directional rather than exact.
Sleep
Overnight glucose is typically the most stable period of the day on a CGM graph. Most apps report overnight variability separately. Compression artefacts — sudden apparent drops caused by the sensor being pressed against a surface — can occasionally produce anomalous readings during sleep on any device.
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Common patterns and what they look like
Post-meal spike: A sharp rise after eating, typically peaking 45–90 minutes post-meal. The height depends on meal composition, individual response, and timing. The graph shows a clear upward curve followed by a gradual descent.
Gradual overnight decline: A slow downward slope during sleep as overnight fasting continues. Common and expected in most CGM data.
Exercise-induced rise: A rapid upswing during or immediately after high-intensity activity, typically followed by a decline during recovery. The graph can look sharp and then quickly reverse.
Compression lows: Sudden apparent drops during sleep that don't correlate with other data. Usually caused by the sensor being physically compressed. The reading typically recovers immediately when position changes.
CGM data shows patterns — not diagnoses. If you observe patterns that concern you, discuss them with your healthcare provider. CGM data is not a substitute for clinical evaluation.
How different apps display the same data
| App | Graph style | Key metric featured | Data export |
|---|---|---|---|
| Dexcom (G7 / Stelo) | Continuous graph + trend arrows | Time in Range | CSV + Clarity |
| LibreView (Libre 3) | AGP report + daily graph | Time in Range | PDF + CSV |
| Levels Health | Graph + Metabolic Score | Metabolic Score | Limited |
| Nutrisense | Graph + meal tags | Glucose variability | Full CSV |
| Abbott Lingo | Graph + Lingo Score | Lingo Score | Limited |
The underlying sensor data is the same — interstitial glucose measured at regular intervals. What differs is how each app processes, displays, and summarizes that data. Dexcom and LibreView present the most clinical view; Levels, Nutrisense, and Lingo layer on proprietary scoring systems designed for wellness users.
Tips for getting more from your data
Log meals and activity: All major CGM apps support event logging. Adding meal tags and activity markers makes patterns in the graph much easier to interpret — you can see which meals correlate with larger or smaller glucose responses.
Use the longer time views: The 24-hour view shows the full day's pattern. The 7-day and 14-day views reveal whether patterns are consistent or variable across days. Most apps default to shorter views — switch to longer views for broader context.
Don't over-react to single readings: A single elevated or low reading is less meaningful than a pattern over time. CGM data is most useful as a trend tool, not a moment-to-moment alarm system.
Understand sensor warmup: All sensors require a warmup period (typically 30–60 minutes) before they begin reporting readings. Readings during this period may be inaccurate — most apps indicate when the sensor is warming up.
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