Personalized Peer-Group Messaging for Mobile Health
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Solution Overview
Problem
Current mobile health intervention messaging lacks personalization, which is desired by patients to help motivate them in reaching their health goals, as medical providers often lack the time and resources to create personalized messages for hundreds or thousands of patients.
Innovation Solution
A system and method for automatically constructing personalized, peer-driven messages for mobile health applications by forming peer groups based on relevant attributes and calculating representative metrics to create messages that compare a user's health achievements to those of their peers, leveraging the psychology of peer-challenge and peer-pressure to motivate users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic pre-written messages are used for all patients, then the system can serve hundreds or thousands of patients with minimal resources, but the messages lack personalization which reduces patient motivation
Solution Approach 1:
The system creates personalized messages by copying and adapting message templates for each patient based on their profile attributes (age, gender, condition, goals) rather than writing unique messages from scratch. This allows scalable personalization where each patient receives customized content derived from standardized template structures
Solution Approach 2:
The system applies different message content and styling to different patient segments based on their specific attributes. Each patient receives messages tailored to their local context (their specific health condition, demographics, and goals) rather than a uniform message for all patients, achieving personalization at scale
2Adaptability or versatility
If medical providers write personalized messages for each patient, then message personalization increases patient motivation, but providers lack the time and resources to create personalized messages for hundreds or thousands of patients
Solution Approach 1:
The system enables automatic generation of personalized messages without requiring provider intervention. The computer system autonomously processes patient data, selects appropriate templates, and generates customized messages, making the message creation process self-service rather than provider-dependent
Solution Approach 2:
The system replaces the manual mechanical process of providers writing messages with an automated computer-based system that uses algorithms and templates to generate personalized content. This substitution eliminates the time-consuming manual work while maintaining personalization quality
Data Source
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AI summary
A method is implemented by at least one server. The method includes obtaining a set of users as a function of profile records regarding a plurality of users. The method includes defining a subset of the users as a peer group based on peer group formation features by applying a clustering algorithm to profile records of the set of users. The method includes calculating a peer group metric value for the peer group based on behaviors of the subset of users in the peer group. The method includes calculating a user metric value for a target-user included in the subset based on behavior of the target-user. The method includes constructing a message relating the patient metric value to the peer group metric value.