From 58% to 74% TIR: How Deepti Transformed Her Diabetes Control in 8 Weeks

📊 Meet Deepti: Background and Starting Point

Deepti Mitchell was diagnosed with Type 1 diabetes at age 22 during her final semester of college. For the first decade, she managed with finger-prick glucose meters and basal-bolus insulin therapy, maintaining an HbA1c around 7.5-8.0% - acceptable but not optimal.

Two years ago, Deepti upgraded to a Dexcom G7 Continuous Glucose Monitor (CGM), excited about finally seeing her glucose 24/7. She assumed the real-time data would automatically improve her control. It didn't.

Deepti's Profile (Before Transformation)

Deepti's numbers weren't terrible - she was avoiding severe hypoglycemia and keeping her HbA1c under 8%. But she wanted better. She'd read about the ADA's 70% TIR recommendation and aspired to reach it. Despite diligently checking her Dexcom app 20+ times daily, her TIR remained stubbornly stuck at 58%.

⚠️ The Problem: Stuck at 58% TIR for 6 Months

Deepti's frustration wasn't from lack of effort. She was highly engaged with her diabetes management:

• Checked CGM 20-30 times daily - constantly monitoring her glucose trends
• Carb counted every meal - using a food scale for accuracy
• Logged insulin doses meticulously - tracking basal and bolus in a spreadsheet
• Tried various diet approaches - low-carb, Mediterranean, keto (nothing stuck)
• Downloaded 4 different diabetes apps - hoping one would provide "the answer"

Despite this effort, Deepti faced three critical problems that kept her TIR plateaued:

Problem #1: Data Without Context

Deepti's Dexcom app showed beautiful glucose curves and statistics, but provided zero context about why her glucose behaved certain ways. She knew her glucose spiked to 220 mg/dL every day around 2 PM, but couldn't figure out the cause. Was it her lunch? Stress from afternoon meetings? Sitting too long?

Problem #2: Missing the Sleep Connection

Deepti had read articles about sleep affecting blood sugar, but never analyzed her own data to confirm it. She averaged 5.5 hours of sleep per night due to work deadlines and late-night Netflix binges. She assumed this was "just how she was" and didn't connect it to her stubborn morning glucose spikes (150-170 mg/dL upon waking).

Her CGM showed the morning spikes. Her Google Fit tracked her terrible sleep. But the two never talked to each other, so Deepti never saw the correlation.

Problem #3: Manual Analysis Paralysis

Deepti attempted to correlate her glucose with other factors manually. She spent 4-5 hours every Sunday exporting Dexcom data to Excel, cross-referencing it with her food diary, trying to spot patterns. After 30 minutes of staring at spreadsheets, she'd usually give up, overwhelmed and exhausted.

"I felt like I needed a data science degree to understand my own body," Deepti recalled. "The tools gave me data, but I couldn't turn that data into actionable insights."

💡 The Discovery: Multi-Data Correlation Reveals 3 Hidden Patterns

Deepti's breakthrough came when a friend (also T1D) mentioned using My Health Gheware™ to analyze her glucose alongside sleep and activity data. Skeptical but desperate, Deepti signed up for the free trial (₹500 signup balance) and imported 2 weeks of data:

• Glucose: Dexcom G7 CGM data (CSV export)
• Sleep: Google Fit sleep tracking (automatically synced)
• Activity: Google Fit step count and workouts

She ran her first comprehensive AI analysis and waited 10 minutes. The results shocked her.

Hidden Pattern #1: Sleep Quality Dictated Morning Glucose

The AI correlation revealed a striking pattern Deepti had completely missed: On nights with <6 hours of sleep, her morning glucose averaged 168 mg/dL. On nights with 7+ hours, it averaged 122 mg/dL - a 46 mg/dL difference.

The AI insight explained why: Poor sleep increases cortisol (stress hormone), which triggers glucose release from the liver. This is called the "dawn phenomenon," and it's significantly worse with sleep deprivation. Deepti had been chasing afternoon glucose with insulin adjustments, completely missing that her mornings were sabotaging the entire day.

Hidden Pattern #2: Post-Meal Movement Was Critical

The second revelation: On days Deepti walked 15+ minutes after lunch, her 2 PM glucose averaged 148 mg/dL. On sedentary days (no post-lunch walk), it averaged 212 mg/dL - a 64 mg/dL difference.

Deepti occasionally went for walks, but never connected them to glucose control. The AI showed her that post-meal movement wasn't optional - it was her highest-leverage intervention for afternoon glucose.

Hidden Pattern #3: Meal Timing Consistency Reduced Variability

The third pattern was subtle but powerful: On days when Deepti ate meals within 1 hour of her "usual" time, her glucose variability (CV) was 38%. On days with irregular meal times (eating 2+ hours earlier/later), CV spiked to 48%.

Deepti's work schedule varied - some days she ate lunch at noon, other days at 2 PM. She never thought this mattered. The AI revealed that her body craved routine. Consistent meal timing primed her insulin sensitivity and reduced unpredictable spikes.

The "Aha!" Moment

Deepti printed the AI analysis report and read it three times. For the first time in 12 years with diabetes, she understood her glucose not as random chaos, but as predictable patterns driven by sleep, movement, and routine.

"I had all the data pieces - CGM, sleep tracker, step counter. But they lived in separate apps, never talking to each other. The multi-data correlation connected the dots. Suddenly, my 58% TIR made perfect sense: terrible sleep + sedentary afternoons + chaotic meal schedule = terrible glucose control."

🔧 The Implementation: 8-Week Timeline with Weekly Milestones

Armed with her personalized insights, Deepti designed an 8-week action plan. She didn't try to change everything at once - she implemented changes systematically, tracking progress weekly.

Week 1-2: Sleep Foundation (Target: 7 Hours/Night)

Changes Implemented:

• Set strict 10:30 PM bedtime (previously 12-1 AM)
• No screens after 10 PM (moved phone charger to bathroom)
• Added 30-minute wind-down routine (reading, chamomile tea)
• Installed blackout curtains and white noise machine

Results After 2 Weeks:

• Average sleep: 5.5 hours → 6.8 hours (+1.3 hours)
• Morning glucose: 162 mg/dL → 142 mg/dL (-20 mg/dL)
• TIR: 58% → 62% (+4% improvement)

Week 3-4: Post-Meal Movement Protocol

Changes Implemented:

• 15-minute walk immediately after lunch (non-negotiable calendar block)
• 15-minute walk after dinner (or 10 minutes if short on time)
• Set phone alarm 30 minutes after meals as reminder
• Recruited coworkers for "walking meetings" after lunch

Results After 4 Weeks (Cumulative):

• Post-lunch glucose: 212 mg/dL → 165 mg/dL (-47 mg/dL)
• Post-dinner glucose: 190 mg/dL → 152 mg/dL (-38 mg/dL)
• TIR: 62% → 67% (+5% improvement)
• Time Above Range: 38% → 30% (-8%)

Week 5-6: Meal Timing Consistency + Insulin Optimization

Changes Implemented:

• Fixed meal schedule: Breakfast 8 AM, Lunch 12:30 PM, Dinner 7 PM (±30 min window)
• Meal prepped on Sundays to remove decision fatigue
• Met with endocrinologist to adjust insulin based on new patterns
• Reduced basal insulin by 2 units (improved overnight control from better sleep)
• Adjusted bolus ratios for lunch (less insulin needed with post-meal walks)

Results After 6 Weeks (Cumulative):

• Glycemic variability (CV): 42% → 37% (-5% more stability)
• TIR: 67% → 71% (+4% improvement)
• Average glucose: 162 mg/dL → 142 mg/dL (-20 mg/dL)

Week 7-8: Optimization and Habit Solidification

Changes Implemented:

• Added strength training 2x/week (resistance improves insulin sensitivity)
• Experimented with pre-bolusing timing (15-20 min before meals)
• Fine-tuned sleep routine (achieved 7+ hours 6 nights/week)
• Created weekend protocol (80/20 rule - consistent on weekdays, flexible weekends)

Final Results After 8 Weeks:

• TIR: 58% → 74% (+16% improvement) ✓
• Average glucose: 162 mg/dL → 138 mg/dL (-24 mg/dL)
• Estimated HbA1c: 7.4% → 6.8% (-0.6%)
• Time Above Range: 38% → 22% (-16%)
• Time Below Range: 4% → 4% (maintained safety)
• Glycemic Variability (CV): 42% → 36% (-6% more stable)

📈 The Results: 58% to 74% TIR with Complete Data Breakdown

Deepti's transformation wasn't just about hitting 74% TIR - it was about achieving stable, sustainable diabetes control. Here's the complete before/after comparison:

Core Glucose Metrics

Lifestyle Metrics

• Sleep: 5.5 hours/night → 7.0 hours/night (+1.5 hours)
• Morning glucose: 162 mg/dL → 128 mg/dL (-34 mg/dL)
• Post-lunch glucose spike: 212 mg/dL → 148 mg/dL (-64 mg/dL)
• Daily steps: 3,200 steps → 8,500 steps (+5,300 steps)
• Post-meal walk compliance: 20% → 85% of meals
• Meal timing consistency: ±3 hours variance → ±45 min variance

Quality of Life Improvements (Self-Reported)

What Deepti's Endocrinologist Said

🚧 Challenges Faced and How Deepti Overcame Them

Deepti's journey wasn't perfectly smooth. She faced real challenges and setbacks. Here's what went wrong and how she adapted:

Challenge #1: Week 2 Motivation Dip

The Problem: After initial excitement in Week 1, Deepti felt overwhelmed by all the changes. She wanted to quit and revert to old habits.

The Solution: Deepti implemented "minimum viable compliance" - if she couldn't do everything, she'd at least hit her 10:30 PM bedtime and one post-meal walk. This 80/20 approach kept momentum without perfectionism burnout.

Challenge #2: Sleep Improvement Took 3 Weeks

The Problem: Deepti's body didn't adjust to the new sleep schedule immediately. For 2 weeks, she lay awake until midnight despite the 10:30 PM bedtime, feeling frustrated.

The Solution: She added melatonin (3mg, 30 min before bed) and a 30-minute wind-down routine (reading fiction, not work emails). By Week 3, her circadian rhythm adjusted.

Challenge #3: Weekend Consistency

The Problem: Deepti's weekday TIR hit 78%, but weekends dropped to 62% due to irregular meal times, social events, and sleeping in.

The Solution: She created a "weekend protocol" - allowing ±2 hours flexibility on meal times, but keeping post-meal walks and 7-hour sleep minimum. This gave her social flexibility while maintaining core habits.

Challenge #4: Social Situations (Dinner Parties, Celebrations)

The Problem: Dinner parties threw off Deepti's meal timing and made post-meal walks awkward ("Why are you leaving right after dinner?").

The Solution: She communicated openly: "I have diabetes, and a short walk after eating helps my glucose. Want to join me?" Most friends were supportive, and some joined her walks.

Challenge #5: Work Deadlines Disrupting Sleep

The Problem: A major project deadline in Week 6 pushed Deepti back to 11:30 PM-12 AM bedtimes for 4 consecutive nights. Her TIR dropped from 71% to 65%.

The Solution: Deepti had a crucial realization: "My work deadline lasted 4 days. My diabetes lasts forever. I can't sacrifice my health for a project." She set a hard boundary: 10:30 PM screen shutdown, even if work wasn't done. The world didn't end, and her TIR recovered within 3 days.

🎓 Key Learnings: What Worked and What Didn't

✅ What Worked (High-Impact Changes)

❌ What Didn't Work (Low-Impact or Unsustainable)

🔑 Deepti's Top 3 Success Factors

When asked what made the biggest difference, Deepti identified three critical factors:

1. Data-driven personalization: "I stopped following generic diabetes advice and started following MY data. What works for someone else might not work for me."
2. Focus over overwhelm: "I didn't try to change 20 things at once. I changed 3 things that the data said were my biggest levers."
3. Automation over effort: "AI analysis gave me insights in 10 minutes that would've taken me hours manually. I used that saved time to actually IMPLEMENT changes instead of analyzing spreadsheets."

🔄 How to Replicate Deepti's Success (Step-by-Step)

Want to achieve similar results? Here's Deepti's exact framework, broken down into actionable steps:

Step 1: Gather Your Data (Week 0)

Step 2: Run Multi-Data Correlation Analysis

You have two options:

Option A: Manual Analysis (Time-intensive)

• Export CGM data to Excel/Google Sheets
• Cross-reference with sleep and activity logs
• Look for patterns (e.g., glucose on low-sleep days vs. high-sleep days)
• Time required: 4-5 hours weekly

Option B: AI-Powered Analysis (Time-efficient)

• Use My Health Gheware™ to upload glucose, sleep, and activity data
• Run comprehensive AI analysis (10 minutes)
• Get personalized insights with specific action items
• Time required: 10 minutes weekly

Step 3: Identify Your Top 3 Patterns

From your analysis, identify the 3 factors with the biggest glucose impact. Common patterns:

• Sleep <6 hours → Morning glucose spikes
• No post-meal movement → Afternoon/evening spikes
• Irregular meal times → High glucose variability
• Skipped meals → Severe hunger → Overeating → Spikes
• Stress days → Elevated glucose all day

Your patterns might differ from Deepti's. Follow YOUR data, not generic advice.

Step 4: Create Your 8-Week Action Plan

Based on your top 3 patterns, design targeted interventions:

Step 5: Track Progress Weekly

Every Sunday, review:

• TIR for the week (target: +2-3% improvement every 2 weeks)
• Average glucose
• Compliance with changes (e.g., "I hit 7 hours sleep 6 out of 7 nights")
• What worked and what didn't

Adjust your plan based on data, not feelings. Some weeks will plateau - that's normal.

Step 6: Work With Your Healthcare Provider

Share your data and progress with your endocrinologist or diabetes educator. As your lifestyle improves glucose control, you may need medication/insulin adjustments. Never change medications without medical supervision.

Step 7: Celebrate Milestones

Deepti celebrated:

• First morning glucose <120 mg/dL (Week 2)
• First 70% TIR week (Week 6)
• 8-week goal achievement: 74% TIR (Week 8)

Small wins maintain motivation. Diabetes management is a marathon, not a sprint.

🤖 The Role of AI in Deepti's Transformation

Deepti is quick to clarify: "AI didn't do the work for me. I still had to change my sleep, walk after meals, and stick to meal times. But AI showed me WHAT to change and WHY it mattered."

What AI Did for Deepti

1. Pattern recognition: AI spotted correlations Deepti missed (sleep-glucose, post-meal movement, meal timing consistency)
2. Quantified impact: AI calculated exact impact (e.g., "7 hours sleep = 122 mg/dL morning glucose vs. 168 mg/dL with 5 hours")
3. Prioritization: AI ranked interventions by predicted TIR impact, so Deepti focused on high-leverage changes first
4. Time savings: AI automated analysis (4-5 hours → 10 minutes), freeing Deepti to implement changes instead of crunching numbers
5. Personalization: AI gave Deepti HER specific insights, not generic diabetes advice from articles

What Deepti Did (AI Couldn't Do This)

1. Changed bedtime to 10:30 PM (required discipline)
2. Walked 15 minutes after meals (required time commitment)
3. Stuck to meal schedule (required planning and routine)
4. Overcame Week 2 motivation dip (required mental resilience)
5. Worked with endocrinologist to adjust insulin (required medical collaboration)

Could Deepti Have Done This Without AI?

Yes, absolutely. Deepti could have manually correlated her data using Excel and achieved similar results. But it would have taken significantly longer:

• With AI: 10 minutes weekly analysis + 8 weeks implementation = results in 2 months
• Without AI: 4-5 hours weekly manual analysis + probably 4-6 months to spot patterns = results in 6+ months

AI compressed Deepti's learning curve from months to weeks. For someone with a demanding job and limited time, that acceleration was invaluable.

❓ Frequently Asked Questions