Funding
Funding not disclosed
Founders
Product
Problem
Individuals with prediabetes often lack clear insights into how their diet and lifestyle choices impact their blood sugar levels, making it difficult to manage their condition effectively. Traditional blood glucose monitoring requires frequent finger pricks, which can be inconvenient and may not provide a complete picture of glucose fluctuations throughout the day. Interpreting continuous glucose monitoring (CGM) data can also be challenging without expert guidance.
Solution
Unswt-g provides a continuous glucose monitoring (CGM) system and AI-powered coaching service designed to help individuals with prediabetes understand and manage their blood sugar levels. The system includes a wearable,无采血 CGM sensor that continuously tracks glucose levels and transmits data to a mobile app. The app visualizes real-time glucose fluctuations, provides personalized insights into how different foods and activities affect blood sugar, and offers AI-driven coaching to promote healthier eating habits. Users receive a meal score for each meal, personalized reports, and recommendations for foods to favor or avoid, enabling proactive management of prediabetes.
Target Audience
The primary target audience is individuals with prediabetes or those at risk of developing type 2 diabetes who are seeking a convenient and data-driven approach to manage their blood sugar levels and improve their metabolic health.
Features
- Continuous, 무채혈 glucose monitoring via a wearable sensor that lasts for 14 days
- Real-time glucose data displayed on a mobile app, providing insights into blood sugar fluctuations
- AI-powered analysis of glucose data to identify patterns and provide personalized recommendations
- Meal scoring system to evaluate the impact of different foods on blood sugar levels
- Personalized reports with insights into individual glucose responses and trends
- Food recommendations to help users make informed dietary choices
- AI-driven prediction of future glucose levels based on meal logging
- Integration of meal, activity, stress, and sleep data to provide a holistic view of metabolic health