Helping diabetes patients understand their food choices through intelligent meal analysis
The Problem
Nutrition tracking was a major barrier for diabetes management. Our research with 6 diabetes patients revealed:
Tracking was too manual: "I wish I could just say what I ate instead of typing it all in"
No personalized guidance: Patients were trying multiple apps, finding none that fit
Knowledge gaps: Patients didn't understand how food choices impacted their diabetes
Core insight: To manage diabetes effectively, patients need to understand the food they consume.
The Solution
I designed a multi-modal AI nutrition feature with three input methods and intelligent feedback:
Key Features
Photo, voice, or manual entry - meet patients where they are
AI analysis - instant nutritional assessment for every meal
A-C grading - simple scoring for immediate understanding
Constructive feedback - positive reinforcement + actionable suggestions
[VISUAL: Meal summary page showing grading and feedback]
Design Philosophy
Working with a nutritionist, I created a feedback framework that:
Highlights positives first ("Keep doing")
Provides diabetes-specific suggestions
Never judges food choices
Encourages incremental improvements
Implementation & Results
Strong Engagement
66% adoption rate among patients
Weekly logging consistency maintained
Manual entry most popular despite photo/voice options