Wellbeing Intervention Sequence Prediction for Emotional State Transition
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Solution Overview
Problem
Existing mobile apps for mental wellbeing interventions struggle to effectively transition users from their initial emotional state to a desired emotional state due to varying user engagement and efficacy, which are influenced by individual emotional states, personality, and global wellbeing, leading to inconsistent success in achieving immediate emotional changes.
Innovation Solution
A method that predicts the efficacy and engagement of available interventions based on user-specific physiological parameters and personal characteristics, using machine learning to identify the most likely sequence of interventions that can effectively transition the user through intermediary states to the desired emotional state, by calculating weights for each transition path and recommending the most promising sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single intervention type (e.g., calming intervention) is provided to all users, then the intervention can be delivered simply and quickly, but the engagement and efficacy vary significantly across different user emotional states and personal characteristics
Solution Approach 1:
The system dynamically adapts intervention recommendations based on real-time user emotional state (detected via physiological parameters) and personal characteristics. The intervention pathway is not static but changes according to the user's current state, making the system both efficient and reliably effective across diverse users.
Solution Approach 2:
The system changes key parameters including intervention type selection, pathway sequence, and targeting based on user-specific parameters such as emotional state, personality traits, and global wellbeing. This parameter adaptation ensures high engagement and efficacy while maintaining streamlined delivery.
2Reliability
If multiple personalized intervention pathways are created for different user states, then engagement and efficacy are improved, but the system complexity increases
Solution Approach 1:
The system pre-establishes multiple intervention pathways in advance, each optimized for specific emotional states and user characteristics. During runtime, the system simply selects and follows the appropriate pre-planned pathway based on current user state, avoiding the need for complex real-time decision-making while maintaining high personalization.
Solution Approach 2:
The system introduces an intermediary computational layer that maps user state parameters to appropriate pre-defined intervention pathways. This intermediary layer simplifies the complexity by providing a structured translation between diverse user states and standardized intervention protocols.
3Reliability
If intervention pathways are optimized for specific emotional states, then the efficacy for achieving desired emotional state is improved, but the time required to determine the optimal pathway increases
Solution Approach 1:
Optimal intervention pathways for different emotional states are pre-computed and stored in advance. When a user is in a particular emotional state, the system quickly retrieves and executes the corresponding pre-optimized pathway, achieving high transition efficacy without time-consuming real-time optimization.
Solution Approach 2:
The system dynamically selects from a set of pre-computed pathways based on the user's current emotional state, achieving rapid adaptation without sacrificing the efficacy benefits of state-specific optimization.
Data Source
AI summary
A method for recommending those interventions most likely to achieve a desired state involves predicting the efficacy and engagement of interventions based on the experience of prior users who undertook the interventions. Physiological and personal parameters of the user are acquired. The user's initial state and desired state are determined. The engagement and efficacy levels of each intervention are predicted and used to determine the likelihood that the transition achieved by each intervention achieves its predicted end state. The likelihood that a second transition achieves the desired state is also determined based on efficacy and engagement for the second transition whose starting state is the end state of the first transition. The first and second interventions are identified whose associated transitions have the greatest combined likelihood of achieving the desired state compared to all other intervention combinations. The user is then prompted to engage in the first and second interventions.