AI Nutritional-CBT for Personalized Diabetes Behavior Change
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Solution Overview
Problem
Conventional medical treatments for type 2 diabetes and cardiometabolic conditions fail to address behavioral determinants effectively, leading to poor glycemic control and increased mortality risk, and lack the capability to provide comprehensive behavioral therapy at scale or on a daily/weekly basis.
Innovation Solution
A computer-implemented Nutritional Cognitive Behavioral Therapy (Nutritional-CBT) that addresses the cognitive patterns and mental structures that drive dietary patterns and associated lifestyle behaviors, using a series of therapy lessons and interactive skill-based exercises, with machine learning algorithms to dynamically adjust goals and treatment plans based on patient responses and biometric data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pharmacological treatments are used for type 2 diabetes, then glycemic control may be achieved, but behavioral determinants remain unaddressed and deleterious side effects occur
Solution Approach 1:
The patent combines pharmacological treatment with digital therapeutic interventions that deliver cognitive behavioral therapy (CBT) principles. The system integrates medication management with personalized digital content that addresses behavioral determinants, creating a hybrid treatment approach that maintains glycemic control while reducing reliance on pharmacotherapy alone and minimizing side effects.
Solution Approach 2:
The digital therapeutic system acts as an intermediary between patients and comprehensive behavioral therapy. It provides accessible, scalable CBT interventions through digital platforms, enabling patients to receive evidence-based behavioral support without requiring direct access to specialized therapists, thus addressing behavioral determinants while maintaining glycemic control.
2Reliability
If comprehensive behavioral therapy is provided, then patient outcomes improve, but the health care system lacks the organization and scale to deliver it effectively
Solution Approach 1:
The system uses digital therapeutics to replicate and scale evidence-based CBT interventions that would traditionally require intensive human therapist involvement. By codifying therapeutic principles into structured digital content, the system preserves treatment effectiveness while eliminating the complexity of organizing comprehensive behavioral therapy across large patient populations.
Solution Approach 2:
The patent replaces the mechanical system of human therapist delivery with digital therapeutic platforms. This substitution maintains the therapeutic content and effectiveness while eliminating the organizational complexity and scalability limitations inherent in human-based delivery systems, enabling comprehensive behavioral therapy at scale.
3Productivity
If behavioral therapy is delivered at scale, then patient engagement increases, but the system requires dynamic adjustment capabilities to personalize treatment
Solution Approach 1:
The digital therapeutic system incorporates dynamic adjustment capabilities that automatically adapt content, intensity, and delivery based on real-time patient responses, progress data, and engagement patterns. This enables personalized treatment at scale by continuously optimizing the therapeutic intervention based on individual patient needs and outcomes.
Solution Approach 2:
The system implements feedback loops that monitor patient engagement, progress, and responses to therapeutic content. This feedback information is used to dynamically adjust treatment parameters, ensuring that scalable delivery maintains personalization through continuous adaptation to individual patient trajectories and needs.
Data Source
AI summary
Nutritional Cognitive Behavioral Therapy (Nutritional-CBT) is provided for the treatment of patients with type 2 diabetes and other cardiometabolic diseases, addressing common mal-adaptive thinking and beliefs pertaining to diet and lifestyle in a digitally-delivered therapy personalized to the individual patient using artificial intelligence (AI)/machine learning (ML) driven feedback loops.