Personalized Health Program System for Obesity Paradox
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for promoting weight loss and improving cardiovascular health fail to address the unique factors contributing to cardiovascular disease, particularly the 'obesity paradox,' where individuals with low BMI and high visceral fat are at increased risk, due to their failure to account for the interrelationships between genetic, medical, fitness, and nutritional data.
Innovation Solution
A system and method for implementing personalized health and wellness programs that collect and analyze user-specific data from various sources, including genetic, medical, fitness, and nutritional data, to create customized health and wellness plans using mobile health devices and applications, providing real-time monitoring and adjustments based on user progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods of treating cardiovascular disease are used, then general population health is maintained, but mortality rates do not decrease and the obesity paradox is not addressed
Solution Approach 1:
The patent segments the population into distinct health profiles based on metabolic health status, body composition, and genetic factors. Instead of applying uniform cardiovascular disease treatment protocols, the system divides users into groups such as metabolically healthy obese, metabolically unhealthy normal weight, and other specific phenotypes, allowing tailored interventions for each segment that address their unique risk factors and response patterns to treatment.
Solution Approach 2:
The patent applies local quality by providing customized health and wellness programs specific to each user's metabolic phenotype and risk profile. Rather than generic advice, the system delivers localized interventions including specific dietary recommendations, exercise prescriptions, and monitoring protocols that are optimized for each individual's metabolic characteristics, genetic predispositions, and current health status.
2Ease of operation
If BMI is used as the primary measure for health assessment, then simplicity is maintained, but the obesity paradox is missed and high-risk individuals are not identified
Solution Approach 1:
The patent merges multiple assessment dimensions including BMI, body fat distribution (waist-to-hip ratio), metabolic markers (insulin resistance, lipid profile), genetic risk scores, and lifestyle factors into a comprehensive health evaluation system. This combination allows the system to maintain ease of initial screening while achieving precise risk stratification that identifies high-risk individuals who would be missed by BMI alone, such as metabolically unhealthy normal-weight individuals.
Solution Approach 2:
The patent adds another dimension to health assessment by moving from a single-dimensional BMI metric to a multi-dimensional evaluation that includes metabolic health status, body composition analysis, genetic factors, and functional markers. This dimensional expansion enables the system to capture the complexity of the obesity paradox and identify at-risk populations across different weight categories through composite risk scoring and phenotypic classification.
3Device complexity
If generic health and wellness programs are implemented, then program simplicity is maintained, but personalized interventions needed for the obesity paradox are not provided
Solution Approach 1:
The patent implements dynamic health and wellness programs that automatically adapt to each user's progress, metabolic responses, and changing health status. The system continuously monitors biomarkers, body composition changes, and lifestyle adherence, then dynamically adjusts intervention intensity, dietary recommendations, and exercise prescriptions in real-time, providing personalized adaptation without requiring complex manual program design for each user.
Solution Approach 2:
The patent incorporates continuous feedback loops where user data from wearables, lab tests, and self-reporting are fed back into the system to refine and personalize interventions. The system uses this feedback to adjust program difficulty, provide real-time guidance, modify nutritional recommendations, and escalate or de-escalate monitoring intensity based on individual response patterns, enabling personalization while maintaining operational simplicity through automated decision algorithms.
Data Source
AI summary
A system configured to receive health data pertaining to a user; select a user health profile from a plurality of user health profiles based on the collected health data, each of the plurality of user health profiles being associated with a health and wellness program and a set of interventions; receive user activity data and updated health data pertaining to, or during the user's participation in the associated health and wellness program from health devices; select a new set of interventions based on the user activity data; and select a new user health profile from the plurality of user health profiles based on at least one of the user activity data and the updated health data.