Empowering pre-diabetic users
through behavioral nudges and personalized health insights using AI
Company
SynchNeuro, Inc.
Role
Product Design Intern
Duration
08 months
Problem Area
1 in 3 adults suffer from pre-diabetes. They have an increased risk of diabetes due to a disconnect from personalized, simple health data
Underlying technology
SynchNeuro, Inc. has developed a non-invasive CGM that converts brain signals to glucose levels
Challenge
Bringing clarity from complex information, for actionable metabolic well-being
Approach
Intense research!
Primary research
To understand the domain and current situation
Competitive Analysis
Physical + Digital
tools
Interviews
Patients + Healthcare
professionals
Systematic deconstruction
Breaking down into layers of functional clusters, which directly affect pre-diabetic health
Functions wrt UX
Metabolic health management
Actionable insights
Personalized recommendations
Micro-progress
Categories
Glucose, Stress, Sleep, Activity
Terminologies
Glucose Sensitivity, Metabolic Score
AI-generated data
Task 1/4
Task 2/4
Moderated Study
To validate early design directions
Qualitative
Focus Group
MUST
Quantitative
Real-time Survey
SUS
Insights
Shaped the direction for 5 key features and addressed 80% improvement in usability
Shaped the direction for 5 key features
63%
found health-related copy too clinical, yet approved terminologies
Behavior linked tips
were preferred over generic ones, especially for AI predictions of glucose levels
9 of 10
faced navigation issues in at least one task
70%
reported logging fatigue, especially for meals and symptoms
Only 30%
would stay over 2 weeks without progress metrics
5 of 8
were unaware of influences on categories and sensor’s role
Business Alignment
Addressed 80% improvement in usability, with the design going hand-in-hand on the company strategy and goals. Currently in production and user testing, soon to be launched!
Design Outcomes
Design
Empowering pre-diabetic users
through behavioral nudges and personalized health insights using AI
Company
SynchNeuro, Inc.
Role
Product Design Intern
Duration
08 months
Problem Area
1 in 3 adults suffer from pre-diabetes. They have an increased risk of diabetes due to a disconnect from personalized, simple health data
Underlying technology
SynchNeuro, Inc. has developed a non-invasive CGM that converts brain signals to glucose levels
Challenge
Bringing clarity from complex information, for actionable metabolic well-being
Approach
Intense research!
Primary research
To understand the domain and current situation
Competitive Analysis
Physical + Digital
tools
Interviews
Patients + Healthcare
professionals
Systematic deconstruction
Breaking down into layers of functional clusters, which directly affect pre-diabetic health
Functions wrt UX
Metabolic health management
Actionable insights
Personalized recommendations
Micro-progress
Categories
Glucose, Stress, Sleep, Activity
Terminologies
Glucose Sensitivity, Metabolic Score
AI-generated data
Task 1/4
Task 2/4
Moderated Study
To validate early design directions
Qualitative
Focus Group
MUST
Quantitative
Real-time Survey
SUS
Insights
Shaped the direction for 5 key features and addressed 80% improvement in usability
Shaped the direction for 5 key features
63%
found health-related copy too clinical, yet approved terminologies
Behavior linked tips
were preferred over generic ones, especially for AI predictions of glucose levels
9 of 10
faced navigation issues in at least one task
70%
reported logging fatigue, especially for meals and symptoms
Only 30%
would stay over 2 weeks without progress metrics
5 of 8
were unaware of influences on categories and sensor’s role
Business Alignment
Addressed 80% improvement in usability, with the design going hand-in-hand on the company strategy and goals. Currently in production and user testing, soon to be launched!