Unsupervised Machine Learning Driven Analysis of Verbatims of Treatment-Resistant Schizophrenia Patients Having Followed Avatar Therapy

Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy

OBJECTIVES

Predicting therapy responders can significantly improve clinical outcomes. This study aims to identify predictors of response to short-term dynamic therapy.

METHODS

Data from 95 patients who underwent 16-session therapy were analyzed using machine learning. Weekly progress was monitored with the Outcome Questionnaire (OQ45) and Target Complaints (TC). A machine learning model identified change trajectories for responders and non-responders, with a random forest algorithm and elastic net modeling predicting trajectory group membership using pre-treatment data.

RESULTS

A weak positive relationship was found between the trajectories of the two outcome variables. The results of the different analysis methods were compared and discussed. Important predictors of OQ45 trajectories, based on random forest modeling, included initial symptom severity, difficulties in emotion regulation, coldness, avoidant attachment, conscientiousness, interpersonal problems, non-acceptance of negative emotion, neuroticism, emotional clarity, impulsivity, and emotion awareness (72.8% accuracy). Initial problem severity, self-scarifying extraversion, and non-assertiveness were the most dominant predictors for TC trajectories (62.8% accuracy).

CONCLUSIONS

These findings offer data-driven insights for selecting short-term dynamic therapy. Predicting response for the OQ45, a nomothetic measure, does not extend to the TC, an idiographic measure, and vice versa, highlighting the importance of multidimensional outcome evaluations for personalized treatment.

Exploring the acceptability, and feasibility of a modified virtual reality-based AVATAR therapy in schizophrenia spectrum disorders: A case series report

Schizophrenia spectrum disorder poses a complex challenge in psychiatric treatment due to its multifaceted symptomatology. Modified AVATAR therapy, an innovative virtual reality-based intervention integrating Cognitive Behavioral Therapy (CBT) techniques such as systematic desensitization, and Acceptance and Commitment Therapy (ACT) approach offers a promising avenue for addressing auditory verbal hallucinations (AVH). This case series report investigates the acceptability and feasibility of a modified AVATAR therapy in three patients diagnosed with schizophrenia spectrum disorder who experience distressing AVH. The study provides detailed demographic and clinical information, outlines the modified AVATAR therapy protocol, and presents outcomes measured through standardized assessments. The findings indicate a decrease in the severity of AVHs, along with enhancements in overall symptomatology after modified AVATAR therapy sessions. Moreover, qualitative perspectives from patients shed light on their favorable experiences and perceptions of this adapted intervention. Nonetheless, the results exhibited inconsistency across individual cases, underscoring challenges for future research and clinical applications in this domain. Nevertheless, in light of the hurdles accompanying the rehabilitation of schizophrenia patients, along with the evaluation and subsequent measurement of parameters affecting efficacy, modified AVATAR therapy could present a valuable addition to schizophrenia patient care.

Immersive virtual reality in the treatment of auditory hallucinations: A PRISMA scoping review

BACKGROUND

A large group of psychiatric patients suffer from auditory hallucinations (AH) despite relevant treatment regimens. In mental health populations, AH tend to be verbal (AVH) and the content critical or abusive. Trials employing immersive virtual reality (VR) to treat mental health disorders are emerging.

OBJECTIVE

The aim of this scoping review is to provide an overview of clinical trials utilizing VR in the treatment of AH and to document knowledge gaps in the literature.

METHODS

PubMed, Cochrane Library, and Embase were searched for studies reporting on the use of VR to target AH.

RESULTS

16 papers were included in this PRISMA scoping review (ScR). In most studies VR therapy (VRT) was employed to ameliorate treatment resistant AVH in schizophrenia spectrum disorders. Only two studies included patients with a diagnosis of affective disorders. The VRT was carried out with the use of an avatar to represent the patient's most dominant voice.

DISCUSSION

The research field employing VR to treat AH is promising but still in its infancy. Results from larger randomized clinical trials are needed to establish substantial evidence of therapy effectiveness. Additionally, the knowledge base would benefit from more profound qualitative data exploring views of patients and therapists.