Smartphone App for Automated CBT Exposure Therapy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The high cost of human therapists for administering cognitive behavioral therapy (CBT) programs, particularly for exposure therapy, makes it inaccessible to many due to the need for continuous monitoring and guidance.
Innovation Solution
A mobile app and server application system that uses sensors on a smartphone to detect user and situational states, allowing for automated administration of exposure treatments. The system generates verbal prompts and adjusts therapy steps based on machine learning algorithms to encourage progress and minimize the number of steps required for completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a human therapist administers CBT exposure therapy in person, then the patient receives personalized monitoring and guidance, but the cost becomes prohibitively expensive
Solution Approach 1:
The patent creates a digital copy of the therapist's functions through an AI-powered mobile application that replicates monitoring, guidance, and feedback capabilities. The system uses machine learning models trained on therapeutic interactions to simulate therapist behavior, providing personalized exposure therapy without requiring physical therapist presence. This copying approach maintains therapy quality while eliminating the need for expensive human therapist time for every session.
Solution Approach 2:
The system enables patients to conduct exposure therapy independently using the mobile application. The AI assistant provides real-time monitoring and guidance, allowing patients to perform exposure tasks on their own without requiring a therapist to be physically present. The application automatically tracks progress, adjusts exposure parameters, and provides feedback, making the patient self-sufficient in their therapeutic journey while maintaining personalized care.
2Reliability
If a human therapist is remotely present for exposure treatment, then the patient receives guidance, but the cost remains expensive
Solution Approach 1:
The patent replaces the mechanical system of human therapist presence with an automated AI-based mobile application. The system uses sensors, machine learning algorithms, and natural language processing to detect user state, monitor progress, and provide guidance automatically. This substitution eliminates the need for human therapist involvement in routine monitoring tasks while maintaining or improving the quality of guidance through consistent, data-driven decision-making.
Solution Approach 2:
The system implements continuous automated feedback loops where the mobile application monitors user responses during exposure tasks, analyzes physiological and behavioral data from sensors, and adjusts therapy parameters in real-time. The AI assistant provides immediate feedback on patient progress, automatically modifies exposure difficulty based on performance, and notifies therapists only when intervention is necessary. This automated feedback system maintains high guidance quality while reducing dependency on constant human oversight.
3Extent of automation
If an interactive computer interface administers CBT without human therapist presence, then the cost is reduced, but the ability to monitor patient progress and provide encouragement is limited
Solution Approach 1:
The patent transforms the traditional static computer interface into a dynamic, adaptive system that continuously changes parameters based on real-time user data. The mobile application adjusts exposure task difficulty, timing, and content based on physiological signals, behavioral patterns, and user feedback. The AI assistant modifies therapy parameters dynamically, providing personalized encouragement and monitoring that adapts to each patient's unique progress, thereby maintaining high monitoring reliability while keeping costs low through automation.
4Productivity
If the exposure assignment is made more difficult to accelerate progress, then the therapy completion time is reduced, but the patient may become overwhelmed and fail to proceed
Solution Approach 1:
The patent implements a dynamic exposure assignment system where task difficulty is continuously adjusted based on real-time patient performance and physiological data. The mobile application monitors user state during exposure tasks and automatically modifies subsequent assignment difficulty to maintain optimal challenge levels. This dynamic adjustment ensures tasks are challenging enough to promote rapid progress but not so difficult that they overwhelm the patient, thereby balancing therapy completion speed with success rate through adaptive parameter modification.
Data Source
Figure 1~4
Figure 2
Figure 3
AI summary
A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.