Personalized Wellness Feedback Using Readiness-to-Change Tracking
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Solution Overview
Problem
Existing health and wellness tracking systems are complex and often require multiple devices or applications, failing to provide personalized feedback and reinforcement based on an individual's readiness to change behavior, and do not effectively integrate different types of reinforcement methods.
Innovation Solution
A system and method that uses a software platform to track and analyze an individual's health data, determine their readiness to change based on the transtheoretical model, and provide personalized feedback and reinforcement through a discovery experience, including movement, nutrition, and rest domains, using sensor modules and electronic devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single application aggregates data from multiple sources, then device complexity is reduced, but the system must handle greater data integration complexity
Solution Approach 1:
The patent combines multiple data sources (wearable devices, mobile devices, health systems) into a single application that aggregates and integrates health and wellness data. The system merges data from activity trackers, nutrition trackers, sleep trackers, and mindfulness applications into one unified platform, eliminating the need for users to manage multiple separate applications or devices.
Solution Approach 2:
The single application performs multiple functions by integrating data collection, analysis, and personalized feedback delivery across different health domains. It universally handles activity tracking, nutrition monitoring, sleep analysis, and mindfulness guidance, making one application serve the role of previously needed multiple specialized tools.
2Reliability
If personalized feedback is provided based on readiness to change, then behavior change effectiveness is improved, but system complexity increases
Solution Approach 1:
The system implements continuous feedback loops that monitor user progress and adjust personalized feedback in real-time. Based on the transtheoretical model assessment, the system provides tailored recommendations and reinforcement that adapt as users move through different stages of behavior change readiness, from pre-contemplation to maintenance stages.
Solution Approach 2:
The system dynamically changes feedback parameters based on user readiness stage. It adjusts the type, tone, and content of recommendations according to whether the user is in pre-contemplation, contemplation, preparation, action, or maintenance stage, transforming static feedback into adaptive, stage-specific guidance.
3Adaptability or versatility
If different reinforcement methods are integrated, then behavior change support is enhanced, but system complexity increases
Solution Approach 1:
The system segments reinforcement methods into distinct categories (positive reinforcement, negative reinforcement, extinction, punishment) and applies them separately based on user needs and readiness stage. This modular approach allows the system to select and combine appropriate reinforcement types without requiring complete integration of all methods simultaneously.
Solution Approach 2:
The reinforcement strategy is dynamic rather than static. The system automatically adjusts which reinforcement method to apply based on real-time assessment of user readiness, progress, and response to previous interventions, allowing flexible switching between different reinforcement approaches.
Data Source
AI summary
Devices, systems, and methods can be used to suggest a discovery to an individual related to their health and wellness, including receiving data about the individual from a user interface regarding a goal for the individual, querying the individual regarding their perception of the goal, determining, a likely state of the individual (e.g., readiness to change), and selecting a subset of discoveries to display to the individual from a database that correspond to both the goal for the individual and the likely state of the individual. Displaying information may include receiving motion data including duration of motion, classifying a type of activity the individual is engaged in based on the motion data and likely intensity of the activity, and displaying a graphical user interface including a color spectrum, depending on one of the type of activity, intensity of the activity, or duration of the activity.