Deep Dive: CGM Interpretation Guide for Coaches¶

Reading time: ~6 minutes
Prerequisite: Chapter 2.8 (Metabolic Health & Nutrition Timing)

The Big Picture¶

Continuous glucose monitors (CGMs) are increasingly popular among health-conscious clients, not just diabetics. These devices provide real-time glucose data, which can be fascinating, and overwhelming.

As a coach, you'll encounter clients with CGM data who want help understanding it. Here's what you need to know about working with CGM data while staying firmly in your scope.

First: Your Role With CGMs¶

Let's be clear about boundaries:

What you CAN do:
- Help clients observe patterns in their data
- Support experiments with meal timing, composition, and activity
- Encourage curiosity about personal responses
- Refer to providers for interpretation of abnormal patterns

What you CANNOT do:
- Diagnose diabetes, prediabetes, or any condition
- Interpret CGM data as medical information
- Recommend medication changes based on glucose patterns
- Provide clinical management for glucose disorders

If a client has diabetes or prediabetes, their CGM use should be medically supervised. Your role is supporting the lifestyle component of their care plan, not managing their glucose.

CGM Basics: What the Numbers Mean¶

Glucose Ranges (for non-diabetic individuals):

Important: These are general reference ranges. Individual variation is significant, and "optimal" is debated even among researchers.

Time in Range

Modern CGMs track "time in range" (TIR): the percentage of readings within target zones. For non-diabetic users, common targets:
- 70-140 mg/dL: aim for >90% of readings
- <70 mg/dL: minimize (hypoglycemia)
- >180 mg/dL: minimize (hyperglycemia)

Common Patterns and What They Might Mean¶

Pattern 1: Post-Meal Spikes

What it looks like: Sharp rise (to 160-180+) after meals, especially carb-heavy ones

Possible factors:
- Large portion sizes
- Fast-eating without adequate chewing
- High glycemic load meals
- Eating carbs without protein/fat/fiber
- Individual carbohydrate sensitivity

Experiment suggestions:
- Try adding protein or fat to carb-heavy meals
- Experiment with eating vegetables first
- Take a 10-15 minute walk after meals
- Try smaller portions eaten more slowly

Pattern 2: Overnight Spikes ("Dawn Phenomenon")

What it looks like: Glucose rises in early morning hours (4-8 AM) without eating

What's happening: Normal hormonal patterns: cortisol and growth hormone rise before waking, which increases glucose production

Not necessarily a problem: This is physiologically normal. Concerning only if very high or if person has diabetes.

Pattern 3: Reactive Hypoglycemia

What it looks like: Glucose drops below 70 mg/dL a few hours after eating, often following a spike

Possible factors:
- Large insulin response to high-glycemic meals
- Eating simple carbs without protein/fat
- Individual metabolic patterns

Experiment suggestions:
- Balanced meals with protein, fat, and fiber
- Smaller, more frequent meals
- If persistent, refer to provider

Pattern 4: Flat, Stable Lines

What it looks like: Glucose stays between 80-110 mg/dL most of the time with minimal variability

Generally a good sign: Suggests good glycemic control and metabolic flexibility

Caveat: Extremely flat lines in someone eating normally might indicate sensor issues or need calibration

Using CGM Data in Coaching¶

Frame It as Experimentation

Position CGM use as curiosity-driven exploration, not medical monitoring:

• "Let's see what happens when you add a walk after lunch"
• "I'm curious how your body responds to oatmeal versus eggs for breakfast"
• "What patterns do you notice on days you sleep well versus poorly?"

This keeps the focus on behavior and personal discovery rather than medical management.

Connect to Subjective Experience

Help clients link data to how they feel:

• "You mentioned feeling tired mid-afternoon. What does your glucose look like at that time?"
• "On days you feel energized, what patterns do you see?"

This builds body awareness that persists even without the device.

Avoid Obsession

CGMs can trigger obsessive monitoring. Watch for signs:
- Checking glucose dozens of times daily
- Anxiety about every fluctuation
- Avoiding foods "because of what they do to my glucose"
- Orthorexic tendencies emerging

If you see these patterns, it's worth a conversation about whether the CGM is helping or harming.

Practical Experiments to Suggest¶

Food Order Experiment

Have client eat the same meal twice:
1. Day 1: Carbs first, then protein and vegetables
2. Day 2: Vegetables and protein first, then carbs

Compare glucose curves. Many people see flatter responses eating fiber and protein first.

Walking Experiment

Compare glucose after the same meal with and without a 15-minute post-meal walk. Most people see meaningful reduction in post-meal peaks.

Sleep Experiment

Track glucose on nights with <6 hours sleep versus >7 hours. Poor sleep often correlates with higher glucose and more variability the next day.

Stress Experiment

Note glucose during stressful periods (work deadlines, arguments). Stress hormones raise glucose independent of food. A powerful demonstration of the stress-metabolism connection.

When to Refer¶

Send clients back to their healthcare provider if you see:

• Consistent fasting glucose >100 mg/dL: May indicate prediabetes
• Frequent readings >180 mg/dL: Needs medical evaluation
• Frequent hypoglycemia (<70 mg/dL): Especially if symptomatic
• Any diabetes diagnosis or management questions: Not your scope
• Obsessive or disordered behavior around food/glucose: May need mental health support

What This Means for Coaches¶

• Stay in scope: CGMs provide fascinating data, but you're not interpreting it medically. You're supporting behavior experiments.
• Frame as exploration: Curiosity and experimentation, not diagnosis and treatment.
• Connect data to experience: Help clients build awareness that extends beyond the device.
• Watch for obsession: Some people do better without constant glucose monitoring.
• Refer appropriately: Abnormal patterns or medical questions go to providers.

Key Takeaway¶

CGMs offer valuable biofeedback for exploring how food, movement, sleep, and stress affect glucose, but coaches should frame this as behavior experimentation, not medical monitoring, and refer to providers for any interpretation of abnormal patterns.

References¶

1. Dexcom. Understanding Your CGM Data. Dexcom. 2024.
2. Abbott. FreeStyle Libre User Guide. Abbott Diabetes Care. 2024.
3. Battelino T, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation. Diabetes Care. 2019.
4. Zeevi D, et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015.
5. Meng H, et al. Effect of prior meal macronutrient composition on postprandial glycemic responses. Nutrients. 2017.
6. Reynolds AN, et al. Advice to walk after meals is more effective for lowering postprandial glycaemia than advice to exercise before meals. Diabetologia. 2016.
7. National Board for Health & Wellness Coaching. Scope of Practice. NBHWC. 2025.