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Computer tool to evaluate the cue reactivity of chemically dependent individuals
Meire Luci da Silva1, Annie France Frère2, Henrique Jesus Quintino de Oliveira3
1Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Filosofia e Ciências, Av. Hygino Muzzi Filho 737, Marília, São Paulo, Brazil.
Computer Methods and Programs in Biomedicine
|March 4, 2017
Summary
This study introduces a computer tool (CT) that uses physiological signals to increase relapse risk awareness in substance abuse patients. The CT helps individuals with marijuana or cocaine dependence better perceive their vulnerability to triggers.
Area of Science:
- Neuroscience
- Psychology
- Computer Science
Background:
- Anxiety significantly contributes to relapse and dropout in substance abuse treatment.
- Chemically dependent individuals (CDI) require heightened awareness of emotional states during high-risk situations.
- Discrepancies often exist between patient self-assessment and therapist diagnoses regarding vulnerability to drug-related stimuli.
Purpose of the Study:
- To introduce a novel cue reactivity detection tool (CT) for substance abuse treatment.
- To enhance patient awareness of relapse risks through physiological signal monitoring.
- To develop a therapist-independent method for identifying patient vulnerability to environmental drug cues.
Main Methods:
- A computer tool (CT) was developed using 3ds Max® software, featuring virtual environments relevant to marijuana and cocaine users.
- A Human-Computer Interface (HCI) translated physiological signals indicative of anxiety into commands that altered virtual scenes.
- Anxiety was quantified by analyzing cardiac and respiratory rate variability in 30 volunteers under stress; 50 dependent individuals were evaluated for cue reactivity.
Main Results:
- Initial assessments revealed poor agreement between therapists' and patients' predictions of vulnerability.
- Following exposure to the CT, a significant 73% increase in awareness of relapse risks was observed.
- The CT effectively detected emotional state variations through physiological signal changes.
Conclusions:
- The study confirmed that the CT, driven solely by physiological signals, enhances the perception of vulnerability to risk situations.
- This technology offers a promising approach to improving self-awareness and reducing relapse rates in individuals dependent on marijuana, cocaine, or both.
- The findings support the use of biofeedback-driven virtual environments in addiction treatment.
