Stop Guessing: CGM Data Analysis Secrets Your Doctor Uses

Understanding AGP (Ambulatory Glucose Profile) Reports

The Ambulatory Glucose Profile (AGP) is the single most powerful tool for visualizing CGM data. It's the international standard used by endocrinologists worldwide.

What is an AGP Report?

An AGP report overlays 14 days (or more) of glucose data into a single 24-hour view. Instead of looking at 14 separate daily graphs, you see:

• Median glucose line - The "typical" glucose at each time of day
• Interquartile range (IQR) - 25th to 75th percentile (where 50% of your readings fall)
• 10th to 90th percentile range - Where 80% of your readings fall
• Target range shading - Visual indicator of 70-180 mg/dL zone

How to Read an AGP Report

AGP Component	What It Shows	How to Use It
Median Line	Your "typical" glucose at each hour	Identify your baseline - is it in range most of the time?
IQR (25-75%)	Where half your readings fall	Narrow IQR = consistent control; Wide IQR = high variability
10-90% Range	Where 80% of readings fall	Identifies extreme highs/lows - if this touches <70 or >250 mg/dL, you have safety issues
Time-of-Day Spikes	Consistent patterns (dawn phenomenon, post-meal spikes)	Target intervention times - adjust insulin, meal timing, or exercise

AGP Target Zones

International consensus guidelines recommend these AGP targets:

• ✅ Median line stays in 70-180 mg/dL for >70% of the day
• ✅ IQR (dark shaded area) mostly in target range - indicates consistent control
• ✅ 10-90% range doesn't dip below 70 mg/dL - no significant hypos
• ✅ 10-90% range doesn't exceed 250 mg/dL - no dangerous highs
• ✅ Narrow IQR width - indicates low glycemic variability (good!)

But here's what Rajesh's doctor knew that most patients don't: AGP reports are just the beginning. The real magic happens when you combine them with one simple calculation that predicts complications better than HbA1c alone...

Key Statistical Metrics Beyond Time in Range

Time in Range (TIR) is the most important single metric, but it's not the whole story. Advanced CGM analysis uses these statistical measures:

1. Coefficient of Variation (CV) - The Variability King

CV measures glucose variability - how much your blood sugar bounces around. It's calculated as:

CV = (Standard Deviation ÷ Mean Glucose) × 100

Target: CV <36%

CV Value	Interpretation	Action
<36%	✅ Stable glucose control	Maintain current strategies
36-40%	⚠️ Moderate variability	Review carb consistency, insulin timing
>40%	❌ High variability (unstable)	Urgent: Review with healthcare provider, investigate root causes

Why CV matters: You can have 70% TIR but still have dangerous swings (60 mg/dL → 250 mg/dL → 80 mg/dL). High CV indicates you're riding a glucose roller coaster, which increases complication risk even if average glucose is good.

Remember Rajesh? His CV was 38%—not terrible, but not great. When his doctor looked at his 10 PM data, she found he was snacking on high-glycemic foods while watching TV. That late-night spike was crashing by 3 AM, triggering a cortisol response that spiked his morning glucose. The "dawn phenomenon" wasn't dawn at all—it was Netflix.

2. Standard Deviation (SD) - The Spread Metric

Standard deviation shows the "spread" of your glucose readings in mg/dL units.

• Target: SD <50 mg/dL (lower is better)
• Interpretation: If SD = 40 mg/dL and mean = 140 mg/dL, most readings fall between 100-180 mg/dL (mean ± SD)
• High SD (>60 mg/dL): You have frequent extreme highs and lows

3. Median vs Mean Glucose - Which Matters More?

Metric	Calculation	When to Use
Mean Glucose	Sum of all readings ÷ count	Used for GMI (estimated HbA1c) calculation
Median Glucose	Middle value when sorted	Better for "typical" glucose if you have extreme outliers

Example: Your readings: 70, 80, 90, 100, 300 mg/dL

• Mean: (70+80+90+100+300) ÷ 5 = 128 mg/dL
• Median: 90 mg/dL (middle value)

The median (90 mg/dL) better represents your "typical" glucose, while the mean (128 mg/dL) is inflated by the one 300 mg/dL spike.

4. Glucose Management Indicator (GMI)

GMI estimates your HbA1c based on CGM data:

GMI (%) = 3.31 + (0.02392 × Mean Glucose in mg/dL)

Example: Mean glucose = 154 mg/dL → GMI = 3.31 + (0.02392 × 154) = 7.0%

Target: GMI <7.0% for most adults (individualize with your doctor)

Pattern Recognition Techniques

Pattern recognition is where CGM data becomes truly actionable. You're looking for consistent trends that reveal why your glucose behaves certain ways.

1. Time-of-Day Patterns

Most people have predictable glucose patterns tied to specific times:

Time Pattern	Common Cause	Investigation Strategy
4-8 AM Spike	Dawn phenomenon (cortisol surge)	Check if happens on weekends too; adjust basal insulin or medication timing
Post-Lunch (1-3 PM) Spike	High-carb lunch, insufficient bolus, sedentary afternoon	Track lunch macros; test post-meal walk; adjust insulin-to-carb ratio
Midnight-3 AM Low	Excessive dinner insulin, late exercise effect	Reduce evening basal, have bedtime snack if exercised late
Late Afternoon (4-6 PM) Drop	Delayed lunch insulin action, increased activity	Check activity levels; adjust lunch bolus; have pre-dinner snack

How to spot these: Use AGP reports - patterns that appear in the median line for 10+ out of 14 days are real patterns, not random noise.

2. Day-of-Week Patterns

Your glucose control on weekdays vs weekends can be drastically different:

• Weekday patterns: Structured meal times, consistent wake-up, work stress, less exercise
• Weekend patterns: Late wake-up, irregular meals, more physical activity (or less), alcohol consumption

Action: Generate separate AGP reports for weekdays vs weekends. If weekend TIR is 15+ percentage points different, you need separate weekend strategies.

3. Meal-Related Patterns

Track which meals consistently cause spikes:

1. Log meals for 1-2 weeks - Note time, carb count, food type
2. Overlay on CGM graph - Identify post-meal glucose peaks
3. Calculate peak timing - Does glucose peak at 60 min or 120 min?
4. Measure spike magnitude - How much does glucose rise from pre-meal baseline?

Target: Post-meal glucose should peak <180 mg/dL and return to baseline within 3-4 hours.

4. Exercise-Related Patterns

Different exercise types have different glucose effects:

• Aerobic exercise (walking, running): Usually drops glucose during and 0-2 hours after
• Resistance training (weights): May spike glucose initially (stress hormones), then drop 4-12 hours later
• HIIT (high-intensity): Often spikes glucose initially, then drops 2-6 hours later
• Timing matters: Morning exercise has different effects than evening exercise

Strategy: Use activity tags in your CGM app or track workouts separately. Compare glucose on exercise days vs rest days.

5. Sleep-Related Patterns

Poor sleep dramatically impacts next-day glucose control:

• <6 hours sleep: Next-day insulin resistance increases 20-30%
• Interrupted sleep: Increased cortisol → morning glucose spikes
• Late bedtime: Delayed dawn phenomenon, shifted meal timing

How to correlate: Track sleep hours (manually or via Google Fit/Apple Health), then overlay on next-day glucose graphs. Look for correlation between bad sleep nights and high-glucose days.

Spotting daily patterns is powerful. But what if your patterns are changing over time—and you don't even notice? That's where trend analysis becomes critical...

Trend Analysis Across Time

Beyond daily patterns, you need to track trends over weeks and months to see if your diabetes management is improving.

Weekly Trend Tracking

Every week, calculate and log these metrics:

Metric	Target	Trend to Watch
Time in Range (TIR)	>70%	Aim for +2-3% improvement per month
Coefficient of Variation (CV)	<36%	Decreasing CV = more stable control
Time Below Range (TBR)	<4% (<70 mg/dL)	Must NOT increase - hypo risk
Mean Glucose	120-154 mg/dL	Steady decline toward target
GMI (estimated HbA1c)	<7.0%	Should track with actual HbA1c test

How to review trends: Create a simple spreadsheet:

• Column 1: Week ending date
• Column 2-6: TIR, CV, TBR, Mean Glucose, GMI
• Column 7: Notes (what you changed that week - diet, exercise, medication)

After 4-8 weeks, you'll see which interventions actually work for you.

Monthly Deep Dive

Once per month (ideally right before your endo appointment):

1. Generate 30-day AGP report - Shows your true baseline patterns
2. Compare to previous month - TIR improved? CV decreased?
3. Identify persistent problem times - Times of day that STILL aren't in range
4. Review intervention log - What you tried, what worked, what didn't
5. Set 1-2 specific goals for next month - e.g., "Reduce post-breakfast spike by 30 mg/dL"

Multi-Data Correlation (The Game-Changer)

This is where advanced CGM analysis becomes transformational. Glucose doesn't exist in isolation - it's influenced by sleep, activity, stress, nutrition, and medication.

Why Multi-Data Correlation Matters

Example scenario: Your TIR is stuck at 62% and you can't figure out why. Looking at CGM data alone, you see random spikes - some days good, some days bad, no obvious pattern.

Add sleep data: Suddenly you notice that on days with <6 hours sleep, your next-day average glucose is 165 mg/dL vs 135 mg/dL on good sleep days. That's a 30 mg/dL difference!

Add activity data: You discover that days with morning walks have 12% higher TIR than sedentary days.

Add meal timing: You realize that eating breakfast before 8 AM results in better lunch glucose control than skipping breakfast.

The insight: Your glucose variability isn't random - it's driven by sleep quality, morning activity, and meal timing. Fix these three factors, and TIR jumps from 62% to 74% in 8 weeks.

Data Sources to Correlate

1. Sleep data - Duration, quality, bedtime, wake time (Google Fit, Apple Health, Fitbit)
2. Activity data - Exercise type, duration, intensity, timing (Strava, Google Fit, manual logging)
3. Nutrition data - Meal timing, carb count, food types (manual log or photo-based tracking)
4. Stress/mood data - Self-reported stress levels (1-10 scale), major events
5. Medication/insulin data - Dosage, timing, formulation changes

How to Manually Correlate Data

The 2-Week Test Protocol:

1. Week 1: Baseline
   • Track everything: glucose, sleep, activity, meals
   • Don't change anything - just observe
   • Calculate baseline TIR, CV, mean glucose
2. Week 2: Single Variable Change
   • Change ONE thing (e.g., guarantee 7+ hours sleep every night)
   • Keep everything else constant
   • Re-calculate metrics
3. Compare results
   • Did TIR improve by ≥3%? → Intervention works for you!
   • Did CV decrease? → More stable control
   • No change? → Try a different variable next

Building Your Data Review Schedule

Consistency beats intensity. A 10-minute daily review beats a 2-hour monthly deep dive that you skip half the time.

Recommended Review Schedule

Frequency	What to Check	Time Needed
Daily (Morning)	Overnight patterns, any lows/highs, wake-up glucose	5 minutes
Weekly (Sunday)	Calculate TIR, CV, identify patterns, plan next week adjustments	15-20 minutes
Monthly (1st of month)	Generate AGP, compare to previous month, set goals, review interventions	30-45 minutes
Quarterly	3-month trend analysis, prepare for endo appointment, update strategies	60 minutes

What to Do When You Spot a Problem

If you identify a concerning pattern:

1. Document it - Screenshot the AGP section, note the time period
2. Form a hypothesis - "I think post-lunch spikes are caused by..."
3. Test with 2-Week Protocol - Change one variable, measure results
4. Consult your healthcare provider - Especially for medication/insulin adjustments
5. Track the intervention - Did it work? Document for future reference

How AI Automates Advanced Analysis (10 Minutes vs 60 Minutes)

Reality check: Everything in this guide takes 30-60 minutes to do manually. Every. Single. Week.

That's unsustainable for most people. You'll do it for 2-3 weeks, then life gets busy and you stop.

What AI-Powered Analysis Does Automatically

Platforms like My Health Gheware automate the entire process:

• ✅ Generates AGP-style visualizations - Instant median line, IQR, percentiles
• ✅ Calculates all statistical metrics - TIR, CV, SD, median, mean, GMI in seconds
• ✅ Pattern recognition across 14+ days - Identifies time-of-day, day-of-week, meal-related patterns
• ✅ Multi-data correlation - Automatically overlays glucose + sleep + activity + nutrition
• ✅ Specific, actionable insights - "Your post-lunch glucose spikes correlate with <6 hours sleep on 8 out of 10 days"
• ✅ Trend tracking over time - Week-over-week and month-over-month comparisons
• ✅ Professional reports - Suitable for sharing with your endocrinologist

How It Works (My Health Gheware Example)

1. Upload your CGM data - CSV file from Dexcom, FreeStyle Libre, etc. (30 seconds)
2. Connect integrations (optional) - Google Fit for sleep/activity, Strava for workouts (1 minute)
3. AI analyzes everything - Claude AI processes weeks of multi-source data (10 minutes)
4. Get 5-7 specific insights - With data references, not generic advice
5. Share report via email - Send to your doctor before your appointment

Your Action Plan: From Data Overwhelm to Data Mastery

Here's your step-by-step action plan for the next 7 days:

1. Day 1: Generate your first AGP report (most CGM apps have this—look for "14-day report" or "patterns")
2. Day 2: Calculate your CV (many apps show this; target <36%)
3. Day 3: Identify your worst time-of-day pattern (morning spike? post-lunch crash?)
4. Day 4: Form a hypothesis about the cause (sleep? food? timing?)
5. Day 5-6: Test ONE intervention (change only one variable)
6. Day 7: Compare your week's TIR and CV to baseline

Or skip the 60-minute manual process entirely. Health Gheware does all of this automatically—AGP visualization, statistical analysis, multi-data correlation with sleep and activity—in under 10 minutes.

Remember what Rajesh's doctor saw in 90 seconds? Now you have the same tools. The only question is: what will your data reveal?

Last Reviewed: January 19, 2026 | Medical content verified against ADA 2025 Standards of Care

📚 Related Articles

How to Read Your Glucose Data Like a Doctor → What Is Time in Range? The Most Important Diabetes Metric → The Power of Multi-Data Health Tracking →