Exploration of the role of emotional expression of treatment-resistant schizophrenia patients having followed virtual reality therapy: a content analysis.

BACKGROUND: Emotional responses are an important component of psychotherapeutic processes. Avatar therapy (AT) is a virtual reality-based therapy currently being developed and studied for patients suffering from treatment resistant schizophrenia. Considering the importance of identifying emotions in therapeutical processes and their impact on the therapeutic outcome, an exploration of such emotions is needed. METHODS: The aim of this study is to identify the underlying emotions at the core of the patient-Avatar interaction during AT by content analysis of immersive sessions transcripts and audio recordings. A content analysis of AT transcripts and audio recordings using iterative categorization was conducted for 16 patients suffering from TRS who underwent AT between 2017 and 2022 (128 transcripts and 128 audio recordings). An iterative categorization technique was conducted to identify the different emotions expressed by the patient and the Avatar during the immersive sessions. RESULTS: The following emotions were identified in this study: Anger, Contempt/ Disgust, Fear, Sadness, Shame/ Embarrassment, Interest, Surprise, Joy and Neutral. Patients expressed mostly neutral, joy and anger emotions whereas the Avatar expressed predominantly interest, disgust/contempt, and neutral emotions. CONCLUSIONS: This study portrays a first qualitative insight on the emotions that are expressed in AT and serves as a steppingstone for further investigation in the role of emotions in the therapeutic outcomes of AT.

Association Between Cannabis and Violence in Community-Dwelling Patients With Severe Mental Disorders: A Cross-sectional Study Using Machine Learning.

ABSTRACT: The objective of this cross-sectional study was to identify cannabis-related features and other characteristics predictive of violence using a data-driven approach in patients with severe mental disorders (SMDs). A Least Absolute Shrinkage and Selection Operator regularization regression model was used on the database consisting of 97 patients with SMD who completed questionnaires measuring substance use and violence. Cannabis use, particularly related to patients' decision to consume or time spent using, was a key predictor associated with violence. Other patterns of substance use and personality traits were identified as strong predictors. Regular patterns of cannabis use and interpersonal issues related to cannabis/stimulant abuse were inversely correlated to violence. This study identified the effect of several predictors correlated to violence in patients with SMD using a regularization regression model. Findings open the door to better identify the profiles of patients that may be more susceptible to perpetrate violent behaviors.

The therapeutic processes of avatar therapy: A content analysis of the dialogue between treatment-resistant patients with schizophrenia and their avatar.

OBJECTIVE: Because the therapeutic processes of Avatar Therapy remain equivocal, the current study aims to further extend our previous findings by analysing the evolution of the avatars' and patients' speech and changes in patient responses as sessions progressed. DESIGN: Eighteen patients with treatment-resistant schizophrenia were selected from two clinical trials on Avatar Therapy. Three coders analysed both the avatars' and patients' discourse during immersive therapy sessions using content analysis methods. RESULTS: Our analyses enabled the categorization of the avatar discourse into confrontational techniques (e.g., provocation) and positive techniques (e.g., reinforcement). Patients responded to these utterances using coping mechanism or by expressing emotions, beliefs, self-perceptions or aspirations. Through identification of mutual changes in the interaction between the patient and their avatar, a shift was observed over the sessions from confrontation to a constructive dialogue. Assertiveness, emotional responses and prevention strategies seemed to be central to the therapeutic process, and these usually occur in response to positive techniques. CONCLUSION: Investigating AT's therapeutic process may help to identify components to achieve positive outcomes and can enable the development of more effective treatments. Further studies should explore the association between these themes and therapeutic response to help predict which patients will better respond to Avatar Therapy.

Exploration of the dialogue components in Avatar Therapy for schizophrenia patients with refractory auditory hallucinations: A content analysis.

Auditory verbal hallucinations are hallmark symptoms of schizophrenia and are amongst the most disturbing symptoms of the disorder. Although not entirely understood, the relationship between the voice hearer and their voices has been shown to be an important treatment target. Understanding voice hearers' standpoints through qualitative analysis is central to apprehend a deeper comprehension of their experience and further explore the relevance of interpersonal interventions. Compared with other dialogical intervention, virtual reality-assisted therapy (Avatar Therapy) enables patients to be in a tangible relation with a representation of their persecutory voice. This novel therapy has shown favourable results, though the therapeutic processes remain equivocal. We consequently sought to begin by characterizing the main themes emerging during the therapy by exploring the hearer's discussion with their avatar. The therapy sessions of 12 of our referrals were transcribed, and the patients' responses were analysed using content analysis methods. Five themes emerged from data saturation: emotional responses to the voices, beliefs about voices and schizophrenia, self-perceptions, coping mechanisms, and aspirations. All patients had at least one element within each of these themes. Our analyses also enabled us to identify changes that were either verbalized by the patients or noted by the raters throughout therapy sessions. These findings are relevant as they allowed to identify key themes that are hypothesized to be related to therapeutic targets in a novel relational therapy using virtual reality. Future studies to further explore the processes implicated within Avatar Therapy are necessary.

The Relationship between Sense of Presence, Emotional Response, and Clinical Outcomes in Virtual Reality-Based Therapy for Treatment-Resistant Schizophrenia: An Exploratory Correlational Study.

Avatar therapy (AT) is a novel virtual reality-based psychotherapy that has been developed to treat auditory verbal hallucinations (AVH) in treatment-resistant schizophrenia. Various psychotherapeutic components, such as emotions and sense of presence, could contribute to clinical outcomes. However, the interplay between sense of presence, emotions, and clinical response has seldom been investigated. This study aimed to explore the relations between sense of presence, emotions, and clinical outcomes in AT. To conduct this investigation, data from previous and ongoing AT trials were used. Sense of presence and emotions were assessed using standardized questionnaires. AVH were evaluated using the Psychotic Symptom Rating Scales. While sense of presence was positively associated with positive emotions such as control and serenity, no significant associations were found for negative emotions. Moreover, a higher level of sense of presence was associated with a bigger decrease in AVH. Overall, positive emotions seem to be associated with sense of presence in AT. Sense of presence also seems to be involved in the therapeutic outcome, thereby suggesting that this could be an important component related to clinical response. More studies are needed to confirm these trends, which could be generalized to other virtual reality-based psychotherapies.

Changes in Quality of Life in Treatment-Resistant Schizophrenia Patients Undergoing Avatar Therapy: A Content Analysis.

Avatar Therapy has a significant impact on symptoms, beliefs, and quality of life of patients with treatment-resistant schizophrenia. However, little is known about how these changes are implemented into their lives and to which aspects of their lives these improvements relate. Ten consecutive patients enrolled in an ongoing clinical trial were assessed using semi-guided interviews before as well as three months after Avatar Therapy. These encounters have been recorded and transcribed so that the discourse could be thoroughly analyzed, leading to the generation of an extensive theme grid. As the cases were analyzed, the grid was adapted in a back-and-forth manner until data saturation occurred. The content analysis allowed the identification of nine main themes representing different aspects of the patients' lives, each of which was subdivided into more specific codes. By analyzing the evolution of their frequency, it was observed that, following therapy, patients presented with fewer psychotic symptoms, better self-esteem, more hobbies and projects, and an overall improved lifestyle and mood. Finally, investigating the impact of Avatar Therapy on quality of life allows for a deeper understanding of how people with treatment-resistant schizophrenia can achieve meaningful changes and move towards a certain recovery process.

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

(1) Background: The therapeutic mechanisms underlying psychotherapeutic interventions for individuals with treatment-resistant schizophrenia are mostly unknown. One of these treatment techniques is avatar therapy (AT), in which the patient engages in immersive sessions while interacting with an avatar representing their primary persistent auditory verbal hallucination. The aim of this study was to conduct an unsupervised machine-learning analysis of verbatims of treatment-resistant schizophrenia patients that have followed AT. The second aim of the study was to compare the data clusters obtained from the unsupervised machine-learning analysis with previously conducted qualitative analysis. (2) Methods: A k-means algorithm was performed over the immersive-session verbatims of 18 patients suffering from treatment-resistant schizophrenia who followed AT to cluster interactions of the avatar and the patient. Data were pre-processed using vectorization and data reduction. (3): Results: Three clusters of interactions were identified for the avatar's interactions whereas four clusters were identified for the patient's interactions. (4) Conclusion: This study was the first attempt to conduct unsupervised machine learning on AT and provided a quantitative insight into the inner interactions that take place during immersive sessions. The use of unsupervised machine learning could yield a better understanding of the type of interactions that take place in AT and their clinical implications.

Enhancing Predictive Power: Integrating a Linear Support Vector Classifier with Logistic Regression for Patient Outcome Prognosis in Virtual Reality Therapy for Treatment-Resistant Schizophrenia.

(1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergoing investigations to address these patients' needs. To further tailor the therapeutic trajectory of patients presenting with this complex presentation of schizophrenia, quantitative insight about the therapeutic process is warranted. The aim of the study is to combine a classification model with a regression model with the aim of predicting the therapeutic outcomes of patients based on the interactions taking place during their first immersive session of virtual reality therapy. (2) Methods: A combination of a Linear Support Vector Classifier and logistic regression was conducted over a dataset comprising 162 verbatims of the immersive sessions of 18 patients who previously underwent AT. As a testing dataset, 17 participants, unknown to the dataset, had their first immersive session presented to the combinatory model to predict their clinical outcome. (3) Results: The model accurately predicted the clinical outcome for 15 out of the 17 participants. Classification of the therapeutic interactions achieved an accuracy of 63%. (4) Conclusion: To our knowledge, this is the first attempt to predict the outcome of psychotherapy patients based on the content of their interactions with their therapist. These results are important as they open the door to personalization of psychotherapy based on quantitative information about the interactions taking place during AT.

Dyadic Interactions of Treatment-Resistant Schizophrenia Patients Having Followed Virtual Reality Therapy: A Content Analysis.

(1) Background: Very little is known about the inner therapeutic processes of psychotherapy interventions for patients suffering from treatment-resistant schizophrenia. Avatar therapy (AT) is one such modalities in which the patient is undergoing immersive sessions in which they interact with an Avatar representing their main persistent auditory verbal hallucination. The aim of this study is to identify the most prevalent dyadic interactions between the patient and the Avatar in AT for patient's suffering from TRS. (2) Methods: A content analysis of 256 verbatims originating from 32 patients who completed AT between 2017 and 2022 at the Institut universitaire en santé mentale de Montréal was conducted to identify dyadic interactions between the patients and their Avatar. (3) Results: Five key dyads were identified to occur on average more than 10 times for each participant during the immersive sessions across their AT: (Avatar: Reinforcement, Patient: Self-affirmation), (Avatar: Provocation, Patient: Self-affirmation), (Avatar: Coping mechanisms, Patient: Prevention), (Patient: Self-affirmation, Avatar: Reinforcement), and (Patient: Self-appraisal, Avatar: Reinforcement). (4) Conclusion: These dyads offer a first qualitative insight to the interpersonal dynamics and patient-avatar relationships taking place during AT. Future studies on the implication of such dyadic interactions with the therapeutic outcome of AT should be conducted considering the importance of dyadic relationships in psychotherapy.

Avatar Intervention for Cannabis Use Disorder in Individuals with Severe Mental Disorders: A Pilot Study.

Cannabis use disorder (CUD) is a complex issue, even more so when it is comorbid with a severe mental disorder (SMD). Available interventions are at best slightly effective, and their effects are not maintained over time. Therefore, the integration of virtual reality (VR) may increase efficacy; however, it has not yet been investigated in the treatment of CUD. A novel approach, avatar intervention for CUD, uses existing therapeutic techniques from other recommended therapies (e.g., cognitive behavioral methods, motivational interviewing) and allows participants to practice them in real-time. During immersive sessions, participants are invited to interact with an avatar representing a significant person related to their drug use. This pilot clinical trial aimed to evaluate the short-term efficacity of avatar intervention for CUD on 19 participants with a dual diagnosis of SMD and CUD. Results showed a significant moderate reduction in the quantity of cannabis use (Cohen's d = 0.611, p = 0.004), which was confirmed via urinary quantification of cannabis use. Overall, this unique intervention shows promising results. Longer-term results, as well as comparison with classical interventions in a larger sample, are warranted through a future single-blind randomized controlled trial.

The impacts of the COVID-19 pandemic on treatment-resistant schizophrenia patients having followed virtual reality therapy or cognitive behavioural therapy: a content analysis.

PURPOSE: The COVID-19 pandemic led to exacerbation of mental health symptoms and deterioration in psychological well-being in individuals suffering from schizophrenia. The primary objective of this study is to evaluate the impacts of the COVID-19 pandemic on patients suffering from treatment-resistant schizophrenia (TRS) with auditory verbal hallucinations (AVH) having undergone virtual reality therapy (VRT) or cognitive behavioural therapy (CBT) on their symptomatology. The secondary objective is to identify the differences and similarities in relation to the response to the COVID 19 pandemic between these two groups of patients. METHODS: Qualitative analysis of semi-structured interviews was conducted with 42 patients suffering from TRS who had previously followed VRT or CBT. All interviews were recorded, transcribed, and analysed. RESULTS: Four themes emerged in this study: Psychotherapeutic Interventions, Impact of COVID-19 and Public health and safety policies, Substance use and Psychiatric follow-up. Participants from both groups reported that their therapy was beneficial in controlling AVH. Patients having followed CBT reported more depressive symptoms whereas patients having followed VRT reported more anxious symptoms. CONCLUSIONS: This study offers a first qualitative insight in patients suffering from TRS and the impacts of COVID-19 on them and opens the door to the protective factors of CBT and VRT for this specific population.

Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector classifier approach.

Avatar Therapy (AT) is a modern therapeutic alternative for patients with schizophrenia suffering from persistent auditory verbal hallucinations. Its intrinsic therapeutical process is currently qualitatively analyzed via human coders that annotate session transcripts. This process is time and resource demanding. This creates a need to find potential algorithms that can operate on small datasets and perform such annotations. The first objective of this study is to conduct the automated text classification of interactions in AT and the second objective is to assess if this classification is comparable to the classification done by human coders. A Linear Support Vector Classifier was implemented to perform automated theme classifications on Avatar Therapy session transcripts with the use of a limited dataset with an accuracy of 66.02% and substantial classification agreement of 0.647. These results open the door to additional research such as predicting the outcome of a therapy.

One-year randomized trial comparing virtual reality-assisted therapy to cognitive-behavioral therapy for patients with treatment-resistant schizophrenia.

The gold-standard cognitive-behavioral therapy (CBT) for psychosis offers at best modest effects. With advances in technology, virtual reality (VR) therapies for auditory verbal hallucinations (AVH), such as AVATAR therapy (AT) and VR-assisted therapy (VRT), are amid a new wave of relational approaches that may heighten effects. Prior trials have shown greater effects of these therapies on AVH up to a 24-week follow-up. However, no trial has compared them to a recommended active treatment with a 1-year follow-up. We performed a pilot randomized comparative trial evaluating the short- and long-term efficacy of VRT over CBT for patients with treatment-resistant schizophrenia. Patients were randomized to VRT (n = 37) or CBT (n = 37). Clinical assessments were administered before and after each intervention and at follow-up periods up to 12 months. Between and within-group changes in psychiatric symptoms were assessed using linear mixed-effects models. Short-term findings showed that both interventions produced significant improvements in AVH severity and depressive symptoms. Although results did not show a statistically significant superiority of VRT over CBT for AVH, VRT did achieve larger effects particularly on overall AVH (d = 1.080 for VRT and d = 0.555 for CBT). Furthermore, results suggested a superiority of VRT over CBT on affective symptoms. VRT also showed significant results on persecutory beliefs and quality of life. Effects were maintained up to the 1-year follow-up. VRT highlights the future of patient-tailored approaches that may show benefits over generic CBT for voices. A fully powered single-blind randomized controlled trial comparing VRT to CBT is underway.

Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms.

BACKGROUND: A growing body of literature has detailed the use of qualitative analyses to measure the therapeutic processes and intrinsic effectiveness of psychotherapies, which yield small databases. Nonetheless, these approaches have several limitations and machine learning algorithms are needed. OBJECTIVE: The objective of this study is to conduct a systematic review of the use of machine learning for automated text classification for small data sets in the fields of psychiatry, psychology, and social sciences. This review will identify available algorithms and assess if automated classification of textual entities is comparable to the classification done by human evaluators. METHODS: A systematic search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from their inception dates to 2021. The fields of psychiatry, psychology, and social sciences were selected as they include a vast array of textual entities in the domain of mental health that can be reviewed. Additional records identified through cross-referencing were used to find other studies. RESULTS: This literature search identified 5442 articles that were eligible for our study after the removal of duplicates. Following abstract screening, 114 full articles were assessed in their entirety, of which 107 were excluded. The remaining 7 studies were analyzed. Classification algorithms such as naive Bayes, decision tree, and support vector machine classifiers were identified. Support vector machine is the most used algorithm and best performing as per the identified articles. Prediction classification scores for the identified algorithms ranged from 53%-91% for the classification of textual entities in 4-7 categories. In addition, 3 of the 7 studies reported an interjudge agreement statistic; these were consistent with agreement statistics for text classification done by human evaluators. CONCLUSIONS: A systematic review of available machine learning algorithms for automated text classification for small data sets in several fields (psychiatry, psychology, and social sciences) was conducted. We compared automated classification with classification done by human evaluators. Our results show that it is possible to automatically classify textual entities of a transcript based solely on small databases. Future studies are nevertheless needed to assess whether such algorithms can be implemented in the context of psychotherapies.

[Improving Quality of Life in Patients with Refractory Schizophrenia After Virtual Reality-assisted Therapy: A Content Analysis]. / L'amélioration de la qualité de vie chez les patients atteints d'une schizophrénie réfractaire ayant suivi la Thérapie assistée par la Réalité Virtuelle : une analyse de contenu.

Objectives Schizophrenia, particularly treatment-resistant schizophrenia (TRS), is one of the most disabling psychiatric disorders in terms of adverse effects on the quality of life (QOL) of patients. Subjective QOL has become a particularly crucial target that should be improved with treatment, since improved QOL may lead to recovery in patients with schizophrenia. However, there is little evidence on the efficacy of recommended psychosocial interventions on non-symptomatic measures such as QOL. In this regard, the treatment of schizophrenia can be enhanced if, in addition to the treatment of symptoms, therapeutic emphasis is placed on other areas of importance to patients. With advancements in technology, Virtual Reality assisted Therapy (VRT) allows voice hearers to enter in a direct dialogue with an avatar, fully animated by the therapist, who represents their most persecuting voice. This is in the aim to allow them to gain better control over their voices and to work on their self-esteem. Beyond symptomatology, the results of the pilot projects on this innovative therapy have shown significant results on QOL. Method To refine the observed quantitative results, this article will explore emerging themes from a content analysis arising from the spontaneous discourse of 10 patients who responded well to TRV. Results Four general themes emerged: (i) impact of therapy on voices, (ii) interpersonal relationships, (iii) psychological well-being, and (iv) lifestyle. This content analysis has identified several spheres of life that are further improved in patients with TRS using TRV. Conclusion TRV highlights the future of patient-oriented approaches that integrate several relevant processes to potentially improve QOL. TRV can have potentially immense implications for the health and quality of life of patients. This study was a first step towards exploring the subjective effects of TRV on the lives of patients beyond the symptoms.

Exploring the Benefits of Virtual Reality-Assisted Therapy Following Cognitive-Behavioral Therapy for Auditory Hallucinations in Patients with Treatment-Resistant Schizophrenia: A Proof of Concept.

BACKGROUND: Combining cognitive behavioral therapy (CBT) for psychosis with another psychosocial intervention comprising virtual reality (VR)-assisted therapy (VRT) may improve targeted outcomes in treatment-resistant schizophrenia patients. METHODS: Ten participants having followed CBT were part of our comparative clinical trial comparing VRT to CBT and were selected at the end of the study as they desired to continue to achieve improvements with VRT (CBT + VRT). Clinical assessments were administered before/after treatments and at follow-ups. Changes in outcomes were examined using linear mixed-effects models. To gain a more in depth understanding on CBT + VRT, therapists' notes, and open interviews on a sub-group of patients were qualitatively analyzed. RESULTS: Findings showed that the sequence of both interventions was appreciated by all patients. Several significant improvements were found throughout time points on auditory verbal hallucinations, beliefs about voices, depressive symptoms, symptoms of schizophrenia and quality of life. Although most of these improvements were in similar range to those observed in our comparative trial, effects of CBT + VRT on depressive symptoms and symptoms of schizophrenia were larger than those found for either intervention alone. CONCLUSION: This proof of concept is the first to merge gold-standard CBT with VRT for treatment refractory voices and to suggest a certain synergistic effect.

Avatar Therapy for Persistent Auditory Verbal Hallucinations in an Ultra-Resistant Schizophrenia Patient: A Case Report.

Effective treatment strategies for schizophrenia remain very challenging and many treatment-resistant patients will suffer from persistent auditory verbal hallucinations (AVH). While clozapine is the gold-standard medication for this complex population, many will not respond to this molecule. For these ultra-resistant patients, limited options are available. Cognitive-behavioral therapy (CBT) is the most widely used psychological intervention, though it offers modest effects. With the interpersonal dimension of AVH being recognized, Avatar Therapy (AT), a novel experiential treatment enabling patients to create an avatar of their persecutor and allowing them to gain control over their symptoms, was developed and tested. Results have shown significant improvements in AVH symptomatology. This paper details a case report showcasing the beneficial results of AT for even the most severe and symptomatic cases of schizophrenia. Mr. Smith has been afflicted with the persistency of all his voices for almost 20 years. To our knowledge, this patient tried almost all possible treatments with little efficacy. This case highlights the difficulty of finding an adequate treatment for ultra-resistant patients. Mr. Smith first followed CBT before initiating AT. With AT, he significantly improved in a way that was not observed with any other intervention and these improvements remained afterward. The severity of his positive symptoms as well as his depressive symptoms diminished, and his most distressing persecutory voice disappeared. He was able to regain a life. The effects of AT went well beyond the patient, the morale of the entire family improved. This ultra-resistant case suggests that AT may be a promising intervention for refractory AVH in schizophrenia.

Virtual reality therapy for refractory auditory verbal hallucinations in schizophrenia: A pilot clinical trial.

Schizophrenia is a chronic and severe mental illness that poses significant challenges. While many pharmacological and psychosocial interventions are available, many treatment-resistant schizophrenia patients continue to suffer from persistent psychotic symptoms, notably auditory verbal hallucinations (AVH), which are highly disabling. This unmet clinical need requires new innovative treatment options. Recently, a psychological therapy using computerized technology has shown large therapeutic effects on AVH severity by enabling patients to engage in a dialogue with a computerized representation of their voices. These very promising results have been extended by our team using immersive virtual reality (VR). Our study was a 7-week phase-II, randomized, partial cross-over trial. Nineteen schizophrenia patients with refractory AVH were recruited and randomly allocated to either VR-assisted therapy (VRT) or treatment-as-usual (TAU). The group allocated to TAU consisted of antipsychotic treatment and usual meetings with clinicians. The TAU group then received a delayed 7weeks of VRT. A follow-up was ensured 3months after the last VRT therapy session. Changes in psychiatric symptoms, before and after TAU or VRT, were assessed using a linear mixed-effects model. Our findings showed that VRT produced significant improvements in AVH severity, depressive symptoms and quality of life that lasted at the 3-month follow-up period. Consistent with previous research, our results suggest that VRT might be efficacious in reducing AVH related distress. The therapeutic effects of VRT on the distress associated with the voices were particularly prominent (d=1.2). VRT is a highly novel and promising intervention for refractory AVH in schizophrenia.