How to Spot Patterns in Your Glucose Data: Complete Pattern Recognition Guide

📊 Why Pattern Recognition Matters for Diabetes Control

Imagine you're trying to solve a puzzle, but instead of seeing the full picture, you're only looking at one piece at a time. That's what diabetes management without pattern recognition feels like – you see individual glucose readings (142 mg/dL, 98 mg/dL, 185 mg/dL) but miss the story those readings tell when viewed together.

Pattern recognition transforms reactive diabetes management into proactive optimization. Instead of wondering "Why did I spike?" after every meal, you start predicting "I always spike 80-100 mg/dL after breakfast, so I'll take a 10-minute walk beforehand to blunt that response by 30-40 mg/dL." This shift from reactive to predictive thinking is the foundation of successful long-term diabetes control.

The Research Behind Pattern-Based Management

A 2024 study published in Diabetes Care followed 487 adults with Type 2 diabetes for 12 weeks, comparing three groups:

• Group 1 (Control): Used CGM for real-time monitoring only, reacted to individual highs and lows
• Group 2 (Pattern-Aware): Received weekly pattern reports highlighting trends, made adjustments based on patterns
• Group 3 (AI-Enhanced): Used AI-powered pattern recognition with personalized recommendations

Results after 12 weeks:

• Group 1: Time in Range improved from 61% to 66% (+5 percentage points)
• Group 2: Time in Range improved from 62% to 74% (+12 percentage points)
• Group 3: Time in Range improved from 61% to 79% (+18 percentage points)

The pattern-aware groups achieved 2.4x to 3.6x better outcomes than the control group simply by identifying and responding to patterns rather than individual glucose events. This is the power of systematic pattern recognition.

What Patterns Reveal That Individual Readings Don't

Patterns reveal:

• Root causes (not just symptoms) – Why glucose behaves a certain way
• Predictable triggers – Which foods, activities, or contexts consistently affect glucose
• Intervention effectiveness – Whether your adjustments are actually working over time
• Hidden correlations – How sleep, stress, and activity interact with glucose in your unique physiology
• Optimal timing windows – When to eat, exercise, or take medications for best control

📈 Data Collection Requirements: The 14-Day Minimum Rule

Single-day glucose data is essentially useless for pattern identification. Individual days have too much variability from one-off events: unusual meals, stress, poor sleep, spontaneous exercise, or unpredictable schedule disruptions. Drawing conclusions from 1-3 days of data leads to false patterns and misguided interventions.

Why 14 Days Is the Minimum Standard

The American Diabetes Association and major CGM manufacturers recommend at minimum 14 consecutive days of data for reliable pattern analysis. Here's why:

Duration	Data Points	Pattern Reliability
1-3 days	288-864 readings	❌ Unreliable – too few repeated cycles
7 days	2,016 readings	⚠️ Marginal – captures one weekly cycle
14 days	4,032 readings	✅ Reliable – minimum recommended
21-30 days	6,048-8,640 readings	✅ Excellent – captures full monthly cycle

What 14+ days captures that shorter periods miss:

• Two complete weeks of weekday vs weekend routines
• Multiple repetitions of each daily pattern (dawn phenomenon, meal responses, exercise effects)
• Enough data to average out one-off anomalies
• Statistical significance for calculating metrics like Coefficient of Variation
• For women: Partial menstrual cycle phases (full cycle requires 28-30 days)

Data Quality Matters More Than Duration

A common mistake: "I've been wearing my CGM for 3 months, why can't I see patterns?" The answer is usually data completeness. 14 consecutive days of 95%+ complete data beats 30 days with frequent sensor failures or 8-hour gaps.

Requirements for high-quality pattern analysis data:

• 95%+ sensor uptime – Less than 1 hour of missing data per day
• Consistent calibration (if required by your CGM)
• Activity context logging – Note meals, exercise, stress events
• Sleep tracking – Simultaneous tracking reveals sleep-glucose correlations
• Medication adherence – Inconsistent dosing creates pattern noise

🔍 The 7 Critical Glucose Patterns Every Person with Diabetes Should Know

While everyone's diabetes is unique, certain glucose patterns appear consistently across different people. Learning to recognize these 7 critical patterns will dramatically improve your pattern recognition skills.

Pattern #1: Dawn Phenomenon (Morning Glucose Rise Without Food)

What it looks like: Glucose is stable overnight (midnight to 4 AM at 100-120 mg/dL), then rises steadily 20-50 mg/dL between 4 AM and 8 AM without eating, peaking around 6-7 AM.

Why it happens: Between 4-8 AM, your body naturally releases hormones (cortisol, growth hormone, glucagon) that trigger glucose production from the liver to prepare you for waking. This is a normal biological rhythm, but more pronounced in diabetes due to insulin resistance or insufficient basal insulin.

How to identify it:

• Overlay 7-14 days of overnight glucose curves (midnight to 10 AM)
• Look for consistent upward slope starting around 4-5 AM
• Verify the rise happens BEFORE breakfast (not a food response)
• Typical magnitude: 20-50 mg/dL rise in Type 2, sometimes 60-80 mg/dL in Type 1

Action steps: Adjust long-acting insulin timing (switch from morning to evening), try pre-bed protein snack, eat dinner 3+ hours before bed, or exercise in the evening to deplete glycogen stores. Always consult your healthcare provider before medication changes.

Pattern #2: Post-Meal Glucose Spikes (Reactive Hyperglycemia)

What it looks like: Glucose rises sharply 15-45 minutes after eating, peaks 60-90 minutes post-meal at 40-120 mg/dL above baseline, then gradually returns to near-baseline over 2-3 hours.

Why it happens: Carbohydrate digestion releases glucose into bloodstream faster than insulin can shuttle it into cells, especially with high glycemic index foods, large portions, insufficient insulin, or insulin resistance.

How to identify it:

• Track glucose from 30 minutes before meal to 3 hours after
• Calculate peak glucose minus pre-meal baseline = spike magnitude
• Compare spikes across different meals with similar carb content
• Look for patterns: Does breakfast always spike higher than lunch? Do evening meals spike more than morning meals?

Action steps: Adjust pre-meal insulin timing (take 15-20 min before eating instead of at mealtime), reduce portion sizes, choose lower glycemic index foods, add pre-meal walks (10 minutes), or combine carbs with protein/fat to slow digestion.

Pattern #3: Nocturnal Hypoglycemia (Overnight Lows)

What it looks like: Glucose drops below 70 mg/dL during sleep (typically 2-4 AM), sometimes without waking you. You might wake with headache, night sweats, or unusually high morning glucose (rebound from counter-regulatory hormones).

Why it happens: Excessive evening insulin, late or skipped dinner, evening exercise without carb adjustment, or alcohol consumption (which impairs liver glucose production overnight).

How to identify it:

• Review overnight glucose curves (10 PM to 8 AM) for 14+ days
• Look for dips below 70 mg/dL, especially 2-4 AM window
• Check if pattern repeats on specific days (workout days, alcohol days)
• Morning glucose >160 mg/dL after stable bedtime glucose suggests overnight low with rebound

Action steps: Reduce evening basal insulin dose, eat small protein/fat snack before bed (15g carbs + protein), avoid evening alcohol or adjust accordingly, move evening workouts earlier, or use CGM urgent low alarms. Critical: Discuss with healthcare provider – nocturnal hypoglycemia requires immediate medical guidance.

Pattern #4: High Glycemic Variability (Glucose Rollercoaster)

What it looks like: Frequent, large glucose swings throughout the day – spiking to 200+ mg/dL then crashing to 60-70 mg/dL multiple times daily. CGM graph looks like a mountain range rather than gentle rolling hills.

Why it happens: Inconsistent meal timing/content, insulin stacking (taking correction doses too frequently), emotional eating, reactive over-correction of highs/lows, or undiagnosed gastroparesis (delayed stomach emptying).

How to measure it: Calculate Coefficient of Variation (CV) from 14 days of CGM data:

• CV = (Standard Deviation ÷ Mean Glucose) × 100
• Target: CV <36% (stable control)
• Moderate variability: CV 36-50%
• High variability: CV >50% (requires intervention)

Action steps: Standardize meal timing (eat at similar times daily), implement "wait 2 hours" rule before stacking insulin corrections, address stress eating triggers, practice consistent carb counting, or investigate potential gastroparesis if digestion seems unpredictably delayed.

Pattern #5: Exercise-Induced Glucose Changes

What it looks like: Glucose drops 40-80 mg/dL during aerobic exercise, sometimes continuing to drop 2-4 hours post-exercise. OR glucose rises 20-60 mg/dL during high-intensity exercise, then drops several hours later.

Why it happens: Aerobic exercise increases insulin sensitivity and glucose uptake by muscles (causing drops). High-intensity or resistance training triggers stress hormones that temporarily raise glucose (then drops later as muscle glycogen replenishment pulls glucose from blood).

How to identify it:

• Track glucose from 30 min before exercise through 4 hours after
• Compare different exercise types: walking vs running vs weightlifting
• Note timing: morning fasted vs post-meal vs evening exercise
• Distinguish between immediate drop during exercise vs delayed drop 2-4 hours later

Action steps: Pre-exercise carb loading (15-30g if starting below 120 mg/dL), reduce pre-exercise insulin by 25-50% (with doctor approval), time exercise for 60-90 minutes post-meal to use meal-induced glucose rise, or carry fast-acting carbs during long workouts. See our complete guide to preventing exercise hypoglycemia.

Pattern #6: Stress-Induced Glucose Elevation

What it looks like: Gradual glucose elevation of 15-40 mg/dL over 30-60 minutes without food intake, sustained elevated plateau for 2-6 hours, returning to baseline when stressor resolves. Often coincides with meetings, deadlines, conflicts, or anxiety.

Why it happens: Stress triggers cortisol and adrenaline release, which signal the liver to release stored glucose (the "fight or flight" response). This is helpful for actual physical threats but problematic for mental/emotional stress where the released glucose isn't used.

How to identify it:

• Track context alongside glucose – note stressful events in CGM app
• Look for glucose rises that don't align with meals
• Compare weekday vs weekend glucose (work stress vs relaxation)
• Different curve shape than food spikes: gradual rise + sustained plateau vs sharp peak

Action steps: Practice stress management techniques (deep breathing, meditation, exercise), schedule buffer time before stressful events, take short walks during high-stress periods, or consider short-acting insulin corrections for predictable major stressors (with doctor guidance).

Pattern #7: Weekday vs Weekend Glucose Differences

What it looks like: Average glucose, Time in Range, or specific metrics differ significantly between weekdays and weekends. Example: TIR averages 68% weekdays vs 58% weekends, or vice versa.

Why it happens: Different routines create different glucose patterns. Weekdays: consistent wake time, work stress, rushed meals, scheduled exercise. Weekends: sleeping in (longer fasting triggering dawn phenomenon later), restaurant meals, alcohol, spontaneous activity, relaxation.

How to identify it:

• Segment 2-4 weeks of data into weekday averages (Mon-Fri) vs weekend averages (Sat-Sun)
• Compare: average glucose, Time in Range, Coefficient of Variation, meal spike magnitudes
• Look for 10+ mg/dL average glucose difference or 5+ percentage point TIR difference

Action steps: Standardize sleep schedule (wake within 1 hour of weekday time on weekends), plan weekend meals in advance, implement "one weekend meal exception" rule instead of multiple, or adjust weekend basal insulin if pattern is consistent.

🧠 Step-by-Step Pattern Analysis Framework

Spotting patterns isn't about staring at glucose graphs hoping for epiphanies. It's a systematic process. Here's the exact framework to follow:

Step 1: Collect High-Quality Baseline Data (14+ Days)

• Wear CGM continuously with minimal sensor failures
• Maintain consistent medication adherence
• Track sleep with Google Fit or Apple Health
• Log meals with approximate carb counts (doesn't need to be perfect)
• Note exercise, stress events, alcohol, illness

Step 2: Calculate Summary Statistics

Before looking for patterns, establish baseline metrics:

• Average glucose: All readings over 14 days
• Time in Range (70-180 mg/dL): Percentage of readings in target
• Time Below Range (<70 mg/dL): Hypoglycemia frequency
• Time Above Range (>180 mg/dL): Hyperglycemia frequency
• Coefficient of Variation: Glucose stability metric
• Glucose Management Indicator (GMI): Estimated HbA1c

Most CGM apps calculate these automatically. Write them down as your baseline.

Step 3: Perform Visual Pattern Scan

Daily Pattern Analysis:

• Overlay 14 days of glucose curves in AGP (Ambulatory Glucose Profile) view
• Identify repeating shapes: Dawn rise? Post-meal spikes? Overnight dips?
• Note timing: Do patterns cluster around specific hours?
• Assess consistency: Does pattern repeat 10+ out of 14 days?

Weekly Pattern Analysis:

• Calculate daily averages for each day of the week
• Plot weekday vs weekend TIR percentages
• Compare Monday vs Wednesday vs Friday (work stress patterns)
• Identify outlier days that drag down overall control

Step 4: Correlate Glucose with Context

This is where insights emerge. Ask:

• Food correlation: Do certain meals always spike higher? Does meal timing matter?
• Sleep correlation: Is glucose higher on days with <6 hours sleep?
• Activity correlation: Do morning workouts improve all-day glucose?
• Stress correlation: Are Mondays consistently worse than Wednesdays?

Manual spreadsheet approach: Export CGM data, add columns for sleep hours, carbs eaten, exercise minutes, stress level (1-10 scale). Run correlations.

AI approach: My Health Gheware does this automatically – analyzes glucose + sleep + activity + food together and surfaces significant correlations like "TIR improves by 14% on days with 7+ hours sleep and morning exercise."

Step 5: Formulate and Test Hypotheses

Don't just observe patterns – act on them with testable hypotheses.

Example hypothesis framework:

Step 6: Measure Impact and Iterate

After 14 days of intervention:

• Re-calculate summary statistics (average glucose, TIR, CV)
• Compare to baseline from Step 2
• If improvement is ≥50% of expected → make permanent
• If improvement is 20-49% of expected → refine and test again
• If improvement is <20% of expected → abandon hypothesis, try different intervention

📅 Daily vs Weekly Patterns: Understanding Different Timescales

One common mistake in pattern analysis: confusing daily circadian patterns with weekly behavioral patterns. These operate on different timescales and require different interventions.

Daily Patterns (Circadian/Physiological)

Timescale: Hours within a 24-hour cycle

Driven by: Biological rhythms (hormone release, insulin sensitivity changes), meal timing, sleep-wake cycles

Common daily patterns:

• Dawn phenomenon (4-8 AM): Cortisol-driven glucose rise
• Morning insulin resistance (6-10 AM): Breakfast spikes higher than identical lunch
• Post-meal peaks (60-90 min after eating): Digestion-driven glucose rise
• Exercise effects (immediate + 2-4 hours delayed): Activity-driven glucose drops
• Nocturnal stability (midnight-4 AM): Overnight basal insulin assessment window

Analysis approach: Overlay multiple days of hour-by-hour glucose curves in AGP view. Look for consistent shapes that repeat at the same clock times.

Weekly Patterns (Behavioral/Routine)

Timescale: Days across a week or multiple weeks

Driven by: Work schedule, social activities, exercise routine, meal habits, stress cycles

Common weekly patterns:

• Weekday vs weekend differences: Different sleep schedules, meal timing, stress levels
• Exercise day effects: Monday/Wednesday/Friday gym days show 10-15 mg/dL lower average glucose
• Work stress cycles: Mondays elevated from weekend disruption, Wednesday stability, Friday relaxation
• Social eating patterns: Saturday restaurant meals consistently spike higher than home cooking
• Recovery patterns: Sunday glucose averages 18 mg/dL higher due to Saturday alcohol + late meals

Analysis approach: Calculate daily-averaged glucose for each day of the week over 4+ weeks. Compare Monday average vs Tuesday average vs Wednesday average. Look for >10 mg/dL differences.

Why This Distinction Matters

Daily pattern example: Post-breakfast glucose consistently spikes to 180 mg/dL while post-lunch identical meal peaks at 145 mg/dL.

• Root cause: Morning insulin resistance (physiological)
• Solution: Pre-breakfast walk, earlier breakfast timing, or adjust morning insulin ratio (daily intervention)

Weekly pattern example: Monday average glucose is 142 mg/dL vs 128 mg/dL Tuesday-Friday.

• Root cause: Weekend late meals + sleeping in disrupts routine (behavioral)
• Solution: Standardize weekend wake time, plan weekend meals, limit Saturday night alcohol (weekly routine change)

Confusing these leads to wrong interventions. Don't try to solve a weekly behavioral pattern with daily insulin adjustments, and don't try to solve a daily physiological pattern by changing your weekly schedule.

🤖 Manual Analysis vs AI-Powered Pattern Detection

Both manual and AI-powered pattern analysis have value. Understanding when to use each approach optimizes your time and results.

Manual Analysis: Strengths and Limitations

Strengths:

• Deep personal understanding: Trains your pattern recognition "eye" over time
• Contextual awareness: You know life events AI can't see (job changes, relationship stress, illness)
• No cost: Just time and spreadsheet skills
• Educational value: Builds intuition about your unique diabetes physiology

Limitations:

• Time-intensive: 2-4 hours weekly for thorough analysis
• Prone to confirmation bias: You see patterns you expect, miss unexpected ones
• Limited to 2-3 variable correlations: Hard to spot "glucose higher on days with poor sleep AND late dinner AND skipped breakfast"
• Statistical knowledge required: Distinguishing real patterns from random noise
• Inconsistent execution: Easy to skip analysis when busy

AI-Powered Analysis: Strengths and Limitations

Strengths:

• Speed: 10 minutes of analysis vs 2-4 hours manual work
• Multi-variable correlation: Analyzes glucose + sleep + activity + food + stress simultaneously
• No confirmation bias: Surfaces unexpected patterns you wouldn't think to look for
• Statistical rigor: Distinguishes real patterns (p<0.05) from noise
• Consistent execution: Always analyzes the same way, doesn't skip weeks
• Personalized recommendations: Suggests interventions with predicted impact based on your data

Limitations:

• Requires consistent data quality: Garbage in, garbage out
• May miss context: Doesn't know about your vacation, job loss, or family crisis unless you note it
• Cost: Subscription or per-use fees (though My Health Gheware offers 500 free credits to start)
• Learning curve: Understanding how to interpret AI insights

The Optimal Hybrid Approach

Best practice: Use AI for comprehensive pattern detection, manual analysis for contextual validation.

1. Weekly AI analysis: Run comprehensive pattern detection on 14+ days of data
2. Review AI insights: Read identified patterns and correlation strengths
3. Manual context validation: For each AI-identified pattern, ask "Does this make sense given my life events?"
4. Manual deep-dive on priorities: For the 1-2 highest-impact patterns, manually review the raw data to understand nuances
5. Implement and track: Use AI to measure intervention impact over time

This hybrid approach gives you AI speed and multi-variable capabilities while maintaining human contextual intelligence.

✅ From Pattern to Action: The 5-Step Implementation Framework

Identifying patterns is useless without taking action. Here's the systematic framework for turning pattern insights into better glucose control:

Step 1: Validate the Pattern (Don't Trust Single Occurrences)

• Minimum threshold: Pattern must repeat in 10+ out of 14 days (70%+ consistency)
• Statistical check: For AI-identified patterns, look for p<0.05 or confidence >80%
• Magnitude check: Pattern should have meaningful clinical impact (>20 mg/dL glucose difference or >5 percentage point TIR difference)

If pattern doesn't meet these criteria, it's likely noise. Wait for more data before acting.

Step 2: Identify Root Cause (The "5 Whys" Technique)

Don't settle for surface-level observations. Dig deeper:

Step 3: Formulate Hypothesis with Predicted Impact

Turn root cause into testable hypothesis:

Hypothesis template:
"If I [specific intervention], then [target metric] should [improve by X amount] within [timeframe] because [mechanism]."

Example:
"If I move Sunday dinner from 9 PM to 6:30 PM for the next 4 weeks, then Monday average glucose should drop from 142 mg/dL to 130-135 mg/dL (improvement of 7-12 mg/dL) because earlier meal timing allows full digestion before sleep, preventing overnight hyperglycemia that carries into Monday morning."

Step 4: Test Single-Variable Interventions (Isolation Principle)

Critical rule: Change ONE thing at a time.

If you simultaneously move dinner earlier AND reduce portion size AND start evening walks, you won't know which intervention worked. Test sequentially:

• Weeks 1-2: Baseline measurement (no changes)
• Weeks 3-4: Test intervention #1 (earlier Sunday dinner timing)
• Weeks 5-6: If needed, test intervention #2 (reduced portion size)
• Weeks 7-8: If needed, test intervention #3 (post-dinner walk)

Each test needs 14 days minimum to account for weekly variability.

Step 5: Measure, Decide, Document

After 14-day intervention test:

Measure impact:

• Recalculate target metric (Monday average glucose, overall TIR, post-meal peaks, etc.)
• Calculate improvement: (Baseline - Post-intervention) / Baseline × 100%
• Example: (142 mg/dL - 134 mg/dL) / 142 mg/dL × 100% = 5.6% improvement

Decision criteria:

• If improvement ≥50% of predicted: Success! Make permanent.
• If improvement 20-49% of predicted: Partial success. Refine and retest.
• If improvement <20% of predicted: Intervention ineffective. Try different approach.

Documentation (critical for long-term success):

• Pattern identified + date
• Hypothesis tested
• Intervention details
• Test duration (start/end dates)
• Results (baseline → post-intervention metrics)
• Decision (adopt, refine, or abandon)
• Lessons learned

This documentation becomes your personal diabetes management knowledge base.

⚠️ Common Pattern Analysis Mistakes to Avoid

Mistake #1: Drawing Conclusions from Insufficient Data

The error: "My glucose spiked to 210 after pizza yesterday, so pizza is off-limits forever."

Why it's wrong: Single data point can't establish a pattern. That 210 spike might have been from poor sleep the night before, pre-existing high glucose, large portion, or high-fat content delaying insulin absorption.

The fix: Test pizza 3-4 times under different conditions before concluding.

Mistake #2: Confusing Correlation with Causation

The error: "My glucose is always higher on days I wear my red shirt. Red shirts must raise blood sugar."

Why it's wrong: You wear your red shirt to church on Sundays, which involve large social meals after service. The meal causes the glucose rise, not the shirt.

The fix: Always ask "What's the biological mechanism?" If you can't explain HOW something would affect glucose, it's likely a confounding variable.

Mistake #3: Changing Multiple Variables Simultaneously

The error: Starting keto diet + new exercise routine + medication change + supplement regimen all at once.

Why it's wrong: If TIR improves 15%, you won't know which intervention worked. If TIR declines 5%, you won't know which intervention backfired.

The fix: Change one variable per 14-day test period. Be patient.

Mistake #4: Ignoring Data Quality Issues

The error: Analyzing 30 days of data where CGM sensor failed 6 times and you forgot to log meals for 12 days.

Why it's wrong: Incomplete data creates false patterns. Missing context makes correlations meaningless.

The fix: If data quality is <90% complete, restart your 14-day baseline collection. Better to delay analysis than make decisions on bad data.

Mistake #5: Analysis Paralysis (Never Taking Action)

The error: Spending months analyzing, waiting for "perfect" understanding before trying anything.

Why it's wrong: You learn more from 2 weeks of testing an intervention than 2 months of additional analysis.

The fix: After identifying one reliable pattern, test an intervention within 1 week. Act, measure, learn, iterate.

Mistake #6: Expecting Overnight Transformation

The error: "I tried eating dinner earlier for 3 days and TIR didn't improve. This doesn't work."

Why it's wrong: Most interventions need 7-14 days to show consistent impact. Individual days have too much noise.

The fix: Commit to minimum 14-day test periods before evaluating success/failure.

Mistake #7: Neglecting Non-Glucose Context

The error: Analyzing only CGM data without tracking sleep, activity, stress, or life events.

Why it's wrong: Glucose is influenced by dozens of variables. Ignoring them means missing critical correlations.

The fix: Track at minimum: sleep hours (automatic via phone/watch), exercise minutes, meal timing, and major stress events (manually note in CGM app). This context dramatically improves pattern recognition accuracy.

💡 How My Health Gheware Automates Pattern Recognition

Manual pattern analysis is valuable for learning, but time-intensive and prone to human error. My Health Gheware's AI automates the entire pattern recognition workflow while maintaining accuracy and personalization.

What My Health Gheware Analyzes Automatically

Multi-Data Integration (Glucose + Sleep + Activity + Food + Medicine):

• Imports glucose data from FreeStyle Libre, Dexcom (or manual entry)
• Syncs sleep tracking from Google Fit (duration, quality, timing)
• Connects activity data from Strava, Google Fit (type, duration, intensity)
• Logs meals with macronutrient estimates
• Tracks medication timing for comprehensive correlation analysis

7 Pattern Categories Automatically Detected:

1. Dawn phenomenon: Magnitude, consistency, optimal intervention timing
2. Post-meal responses: Identifies which meals spike highest, when, why
3. Nocturnal patterns: Detects overnight lows, rebound highs, stability issues
4. Glycemic variability: Calculates CV, identifies high-variability periods
5. Exercise effects: Quantifies glucose drops during/after different activity types
6. Sleep-glucose correlation: Shows how sleep quality/duration affects next-day glucose
7. Weekly behavioral patterns: Identifies weekday vs weekend differences, specific problem days

How the AI Analysis Works (10-Minute Process)

1. Data validation (30 seconds): Confirms 14+ days of high-quality data, flags gaps
2. Statistical baseline (1 minute): Calculates average glucose, TIR, CV, GMI, time above/below range
3. Pattern detection (5 minutes): Runs correlation analysis across all data streams, identifies statistically significant patterns (p<0.05)
4. Root cause analysis (2 minutes): Determines likely mechanisms behind identified patterns
5. Personalized recommendations (90 seconds): Generates 5-7 specific, actionable interventions with predicted impact

Output: Comprehensive Health Insight Report

After 10 minutes of analysis, you receive:

• Executive summary: Top 3 patterns impacting your glucose control most
• Detailed pattern breakdown: Each pattern with frequency, magnitude, context
• Correlation strengths: Which factors (sleep, food, exercise, stress) correlate strongest with glucose
• Actionable recommendations: 5-7 prioritized interventions with expected impact ("Move dinner 2 hours earlier → predicted 8-12% TIR improvement")
• Progress tracking: Compare current analysis to previous weeks to show trends

Example AI-Generated Insight

Pricing: Accessible for Everyone

My Health Gheware offers flexible pricing to fit any budget:

• 500 Free Credits on Signup: Try 5 comprehensive AI insights risk-free (no credit card required)
• Pay-Per-Use: ₹100 per comprehensive AI insight (analyze when you need it, no commitment)
• Monthly Subscription: ₹1,490/month for unlimited comprehensive insights (best for active optimization)

Start with the free 500 credits to experience AI-powered pattern recognition. Most users run 1-2 comprehensive analyses per week during active optimization phases.