Avatar Therapy for people with schizophrenia or related disorders.
Ontology highlight
ABSTRACT:
SUBMITTER: Aali G
PROVIDER: S-EPMC7387758 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Current practice guidelines for schizophrenia care recommend that antipsychotic medication is essential for patients' long-term maintenance treatment but their non-adherence to this medication is still a main obstacle to relapse prevention. This study evaluated the effects of a motivational-interviewing-based adherence therapy for people with schizophrenia spectrum disorders.This randomised controlled trial was conducted with 134 outpatients with schizophrenia spectrum disorders; 67 of them received a six-session adherence therapy (in addition to usual care) and 67 received usual psychiatric care alone. Participants' outcome measures included symptom severity, medication adherence, hospitalisation rates, insight into illness/treatment, and functioning.The adherence therapy group reported significantly greater improvements in symptom severity (p < 0.003), insight into illness/treatment (p < 0.001), functioning (p < 0.005), duration of re-hospitalisations (p < 0.005), and medication adherence (p < 0.005) over 18 months follow-up, when compared with usual care alone.Motivational-interviewing-based adherence therapy can be an effective approach to treatment for people with early stage of schizophrenia who poorly adhere to medication regimen.ClinicalTrials.gov NCT01780116, registration date January 29, 2013.
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Project description: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.
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Project description:(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.
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Project description:BackgroundSchizophrenia is considered a polygenic disorder. People with schizophrenia and those with genetic high risk of schizophrenia (GHR) have presented with similar neurodevelopmental deficits in hemispheric asymmetry. The potential associations between neurodevelopmental abnormalities and schizophrenia-related risk genes in both schizophrenia and those with GHR remains unclear.AimsTo investigate the shared and specific alternations to the structural network in people with schizophrenia and those with GHR. And to identify an association between vulnerable structural network alternation and schizophrenia-related risk genes.MethodA total of 97 participants with schizophrenia, 79 participants with GHR and 192 healthy controls, underwent diffusion tensor imaging (DTI) scans at a single site. We used graph theory to characterise hemispheric and whole-brain structural network topological metrics. For 26 people in the schizophrenia group and 48 in the GHR group with DTI scans we also calculated their schizophrenia-related polygenic risk scores (SZ-PRSs). The correlations between alterations to the structural network and SZ-PRSs were calculated. Based on the identified genetic-neural association, bioinformatics enrichment was explored.ResultsThere were significant hemispheric asymmetric deficits of nodal efficiency, global and local efficiency in the schizophrenia and GHR groups. Hemispheric asymmetric deficit of local efficiency was significantly positively correlated with SZ-PRSs in the schizophrenia and GHR groups. Bioinformatics enrichment analysis showed that these risk genes may be linked to signal transduction, neural development and neuron structure. The schizophrenia group showed a significant decrease in the whole-brain structural network.ConclusionsThe shared asymmetric deficits in people with schizophrenia and those with GHR, and the association between anomalous asymmetry and SZ-PRSs suggested a vulnerability imaging marker regulated by schizophrenia-related risk genes. Our findings provide new insights into asymmetry regulated by risk genes and provides a better understanding of the genetic-neural pathological underpinnings of schizophrenia.
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Project description:BackgroundPeople with schizophrenia-spectrum disorders (SSD) often have high levels of obesity and poor cardiometabolic health. Certain types of antipsychotics have been shown to contribute towards weight gain and there is some equivocal evidence that obesity is related to poor health-related quality of life (HRQoL) in people with SSD. It is also still uncertain if antipsychotic polypharmacy/higher doses of antipsychotics are linked with HRQoL and/or increased risk of obesity/Cardiovascular Disease (CVD). Therefore, this study aimed to examine potential relationships between prescribed antipsychotic medication regimens, cardiometabolic health risks and HRQoL in community-based Chinese people with SSD.MethodThis cross-sectional study reports the results of baseline measurements of a random sample of patients in an ongoing controlled trial of physical health intervention for people with severe mental illness. Data from these randomly-selected participants (n = 82) were analysed to calculate 10-year CVD relative-risk (using QRISK®2 score), estimate the prevalence of metabolic syndrome and contextualize patients' prescribed antipsychotics (types, combinations and Daily Defined Dose equivalent). Patients self-reported their HRQoL (SF12v2) and their obesity condition was assessed by waist-circumference and Body Mass Index (BMI).ResultsTwo-thirds of patients had a BMI ≥23 kg/m2, almost half were centrally obese and 29% met the criteria for metabolic syndrome. The individual relative-risk of CVD ranged from 0.62 to 15, and 13% had a moderate-to-high 10-year CVD risk score. Regression models showed that lower physical HRQoL was predicted by higher BMI and lower mental HRQoL. Higher Defined Daily Dose, clozapine, younger age and male gender were found to explain 40% of the variance in CVD relative risk.ConclusionThe findings indicate that cardiometabolic health risks in people with SSD may be more common than those reported in the general Hong Kong population. The results also provide further support for the need to consider antipsychotic polypharmacy and higher doses of antipsychotics as factors that may contribute towards cardiometabolic risk in Chinese patients with SSD. Clinicians in Hong Kong should consider using routine CVD risk screening, and be aware that younger male patients who are taking clozapine and prescribed higher Defined Daily Dose seem to have the highest relative-risk of CVD.Trial registrationClinicaltrials.gov NCT02453217 . Prospectively registered on 19th May 2015.
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