AI/ML-Personalized Nutritional-CBT Therapy for Exercise Adherence
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Solution Overview
Problem
Existing digital therapeutic platforms for type 2 diabetes and cardiometabolic disorders lack effective integration of dynamic exercise interventions, personalized body treatment recommendations, and real-time monitoring, leading to poor adherence to physical activity regimens and lifestyle modifications.
Innovation Solution
A digital therapeutic system employing Nutritional Cognitive Behavioral Therapy (Nutritional-CBT) that optimizes prompt timing and content, uses machine learning algorithms to personalize treatment, and provides adaptive feedback loops for goal setting and engagement, including interactive exercises and biometric data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a digital therapeutic platform provides static therapy content without dynamic adaptation, then the system complexity is reduced, but patient adherence to therapeutic exercises and lifestyle modifications deteriorates
Solution Approach 1:
The patent implements dynamic adaptation of therapy content based on real-time biometric data and patient responses. The system automatically adjusts exercise intensity, provides personalized feedback, and modifies treatment plans based on measured progress, transforming static content into dynamically adaptive interventions that maintain patient engagement without requiring proportional increases in system complexity
Solution Approach 2:
The system incorporates continuous feedback loops where biometric data from wearables and patient responses are processed to generate personalized feedback. This feedback mechanism enables the system to adapt therapy content in real-time, improving adherence by making patients aware of their progress and adjusting interventions based on actual performance rather than fixed protocols
2Productivity
If the system provides personalized real-time feedback and adaptive interventions, then patient engagement improves, but the computational requirements and processing complexity increase
Solution Approach 1:
The system performs preliminary processing of biometric data streams by filtering and pre-processing information before detailed analysis. Common patterns and anomalies are identified in advance using threshold-based rules, reducing the computational burden on more complex machine learning algorithms and enabling faster real-time responses without sacrificing personalization quality
Solution Approach 2:
The feedback processing system is divided into multiple independent modules: biometric data acquisition, preliminary filtering, pattern recognition, personalized feedback generation, and delivery. This segmentation allows each module to be optimized independently and enables parallel processing, reducing overall computational complexity while maintaining high patient engagement through personalized real-time feedback
3Adaptability or versatility
If the system collects and analyzes extensive biometric data and patient responses, then treatment personalization improves, but data processing time and system resource consumption increase
Solution Approach 1:
The system implements a tiered data analysis approach where essential biometric parameters are processed continuously with high priority, while secondary parameters are analyzed periodically or on-demand. This partial processing strategy ensures that critical treatment personalization decisions are made with sufficient data without requiring complete analysis of all available information, reducing processing time while maintaining effective personalization
Solution Approach 2:
The system dynamically adjusts data collection frequency and analysis depth based on treatment phase and patient needs. During stable periods, data collection frequency is reduced and analysis is performed at lower resolution. During transition phases or when anomalies are detected, the system increases sampling rates and analysis depth, optimizing the balance between personalization quality and processing time through adaptive parameter adjustment
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 maladaptive 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 feed-back loops. Systems, methods, and computer-readable media described herein can include providing, by one or more processors, a digital therapeutic application including one or more lessons or activities. The one or more processors can collect at least one response or biometric data from the user. The one or more processors can generate, using a machine-learning model, one or more goals for the user to achieve or a progress overview.