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Published on: May 10, 2024
Exploring Therapeutic Outcomes Through Dyadic Interactions: The Role of Patient-Avatar Dynamics in Avatar Therapy
Alexandre Hudon1, Sabrina Quilliam1, Kingsada Phraxayavong1
1Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal (Hudon, Quilliam, Potvin, Dumais); Services et Recherches Psychiatriques AD, Montreal (Phraxayavong).
Objective:
Despite the efficacy of current therapies, a significant proportion of patients with schizophrenia, a complex mental disorder marked by both positive (present) and negative (absent) symptoms, are considered to have treatment-resistant schizophrenia. Avatar therapy (AT) allows patients to interact with a three-dimensional representation of their most distressing voices in a virtual reality setting. The therapy shows promise in reducing impairments and improving quality of life through the establishment of a therapeutic alliance and the exploration of dyadic interactions (verbal exchanges) between patients and their avatar. The purpose of this study was to investigate differences in dyadic interactions throughout the immersive sessions of AT and to clarify the relationship between these interactions and therapeutic success by analyzing dyads as predictive indicators of positive outcomes in AT.
Methods:
Mean frequencies for the 10 most prevalent dyads identified in previous AT research were reported for 35 patients. A logistic regression model was implemented, and these dyads were used to predict variances in Psychotic Symptom Rating Scales-auditory hallucination scores 1 month after the completion of AT.
Results:
Variances in mean frequencies were reported for the dyads. A positive relation between the avatar (provocation)-patient (self-affirmation) dyad and the therapeutic outcome was found to be significant (OR=2.29, p=0.049).
Conclusions:
This research is pioneering in its in-depth examination of therapeutic interactions in AT, with a particular focus on dyadic interactions. Future studies should prioritize the quality rather than quantity of these interactions to more accurately forecast their effects on potential indicators of positive outcomes in AT.
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