Biobehavioral Monitoring System for Adaptive Relapse Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Individuals post-inpatient drug treatment experience high relapse rates due to environmental stimuli triggering implicit drug-related cues, which existing cognitive-oriented relapse prevention strategies fail to effectively manage in real-time.
Innovation Solution
A system that monitors biobehavioral data using sensors and delivers personalized recovery cues in real-time to users when their data exceeds predetermined risk thresholds, employing a recovery-adaptive approach to mitigate relapse risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cognitive-oriented relapse prevention strategies are used, then individuals can be provided with relapse prevention education, but these strategies fail to effectively manage implicit drug-related cues in real-time when individuals are exposed to environmental stimuli
Solution Approach 1:
The system pre-loads personalized recovery cues into a cache before the user is exposed to triggering stimuli. When biobehavioral data indicates approaching risk thresholds, the system has recovery cues ready for immediate delivery, eliminating the time lag associated with cognitive processing of relapse prevention strategies.
Solution Approach 2:
The system introduces an intermediary layer between environmental drug cues and the user's cognitive processing. Sensors detect biobehavioral changes, the system processes this data against risk thresholds, and delivers personalized recovery cues as an intermediary intervention that bypasses the need for the user to consciously recognize and respond to implicit drug-related cues.
2Ease of operation
If real-time monitoring of biobehavioral data is implemented, then personalized recovery cues can be delivered at the point of exposure to relapse triggers, but this requires continuous data collection and processing infrastructure
Solution Approach 1:
The system utilizes a smartphone application as a multi-functional platform that combines sensor data collection, biobehavioral data processing, risk threshold evaluation, personalized cue delivery, and relapse risk monitoring all in one device. This reduces the need for separate complex monitoring infrastructure while providing comprehensive automatic relapse risk management.
3Adaptability or versatility
If personalized recovery cues are cached and delivered based on individual risk thresholds, then interventions can be tailored to each user's specific triggers and responses, but this requires individual calibration and data processing
Solution Approach 1:
The system implements a feedback loop where sensors continuously collect biobehavioral data, the system processes this data against calibrated risk thresholds, delivers personalized recovery cues when thresholds are exceeded, and uses the user's response to refine future interventions. This feedback mechanism enables both personalization and ongoing calibration of risk thresholds based on individual user responses.
Data Source
AI summary
Methods and systems for recovery-adaptive biobehavioral-regulation treatments for relapse. In various embodiments, a user wears a sensor that collects biobehavioral data regarding the user and is operatively connected to an electronic computing device associated with the user. Generally, the disclosed system monitors the collected biobehavioral data to mitigate relapse of a previous disease and/or disorder (e.g., addiction, depression, post-traumatic stress disorder, etc.) by presenting personalized recovery cues (e.g., images, videos, etc.) to the user when the user is experiencing stimuli that increase the risk of relapse. In addition, the user has the ability to modulate the implicated brain structure image interactively on the smartphone interface (e.g., changing the size, texture, color, image), in order to change its functionality in real-time and improve in-the-moment emotional regulation. By presenting the personalized recovery cues, in various embodiments, the risk of relapse in the user can be reduced.