Young adults with a psychotic disorder often experience difficulties in social functioning. We developed a modular virtual reality treatment to improve social activities and participation by targeting common causes of social functioning difficulties in patients with a psychotic disorder (VR-SOAP). This paper details the development of this intervention, encompassing a piloting phase.

Using an iterative Scrum method with software engineers, clinicians, researchers, and individuals with lived experience of psychosis, we developed a treatment protocol along with a software prototype. Subsequently five patients with a psychotic disorder, aged 18–40, and three therapists, piloted VR-SOAP. Feasibility was assessed by means of interviews and session forms. Acceptability was evaluated along the seven domains of the Theoretical Framework of Acceptability (i.e. affective attitude, burden, ethicality, intervention coherence, opportunity costs, self-efficacy, and perceived effectiveness).

The final protocol consisted of the following modules and targets: 1. Motivation and Pleasure (negative symptoms); 2. Understanding Others (social cognition); 3. Safety and Trust (paranoid ideations and social anxiety); 4. Self-Image (self-esteem and self-stigma); 5. Communication (communication and interaction skills). Modules were piloted by the participating patients and therapists. The modules proved feasible and showed a high degree of acceptability on all seven domains of the acceptability framework.

The modular VR-SOAP treatment protocol and prototype was acceptable and feasible for therapists and patients. The primary recommendation for enhancement underscores the need for flexibility regarding the number of sessions and the content.

1. (1) Understanding the development and structure of a novel modular CBT treatment in VR.

2. (2) Learning to use specific VR modules to target negative symptoms, social cognition, paranoid ideations, social anxiety, self-esteem, and communication skills.

3. (3) Gaining insights into the feasibility and acceptability assessments of a novel modular CBT treatment in VR.

Prediction models that can detect the onset of psychotic experiences are a key component of developing Just-In-Time Adaptive Interventions (JITAI). Building these models on passively collectable data could substantially reduce user burden. In this study, we developed prediction models to detect experiences of auditory verbal hallucinations (AVH) and paranoia using ambulatory sensor data and assessed their stability over 12 weeks.

Fourteen individuals diagnosed with a schizophrenia-spectrum disorder participated in a 12-day Ecological Momentary Assessment (EMA) study. They wore ambulatory sensors measuring autonomic arousal (i.e., electrodermal activity, heart rate variability) and completed questionnaires assessing the intensity/distress of AVHs and paranoia once every hour. After 12 weeks, participants repeated the EMA for four days for a follow-up assessment. We calculated prediction models to detect AVHs, paranoia, and AVH-/paranoia-related distress using random forests within nested cross-validation. Calculated prediction models were applied to the follow-up data to assess the stability of prediction models.

Prediction models calculated with physiological data achieved high accuracy both for AVH (81%) and paranoia (69%–75%). Accuracy increased by providing models with baseline information about psychotic symptom levels (AVH: 86%; paranoia: 80%–85%). During the follow-up EMA accuracy dropped slightly throughout all models but remained high (73%–84%).

Relying solely on physiological data to detect psychotic symptoms achieved substantial accuracy that remained sufficiently stable over 12 weeks. Experiences of AVHs can be predicted with higher accuracy and long-term stability than paranoia. The findings tentatively suggest that psychophysiology-based prediction models could be used to develop and enhance JITAIs for psychosis.

Individuals with a psychiatric inpatient admission in adolescence have a high risk of schizophrenia-spectrum disorders (SSDs) when followed to adulthood. Whether psychotic symptoms predict subsequent SSDs in inpatient cohorts, however, is an important unanswered question.

The sample consisted of adolescents (aged 13–17) admitted to psychiatric inpatient care (Oulu, Finland) from April 2001 to March 2006. Psychotic symptoms were assessed with the Schedule for Affective Disorders and Schizophrenia. Specialized health care use and diagnoses were followed up in national health care registers until June 2023. Cox regression was used to predict SSDs by the presence of baseline psychotic symptoms.

Of 404 adolescent inpatients admitted with non-psychotic mental disorders, 28% (n = 113) reported psychotic symptoms: 17% (n = 68) subthreshold and 11% (n = 45) full threshold. By the end of follow-up, 23% of the total cohort went on to be diagnosed with an SSD. Subthreshold psychotic symptoms did not differentiate patients who would subsequently develop SSDs (cumulative incidence 24%; HR = 1.42, 95%CI = 0.81–2.50). Full-threshold psychotic symptoms, on the other hand, were associated with an increased risk of subsequent SSDs (cumulative incidence 33%; HR = 2.00, 95%CI = 1.12–3.56). Most subsequent SSDs (83%), however, occurred in individuals who had not reported threshold psychotic symptoms during inpatient admission.

There was a high risk of subsequent SSDs among adolescent psychiatry inpatients when followed over time. SSDs were not predicted by subthreshold psychotic symptoms. Full-threshold psychotic symptoms were associated with an increased risk of subsequent SSDs, though with low sensitivity.

Findings from contemporary clinical trials suggest that psychedelics are generally safe and may be effective in the treatment of various psychiatric disorders. However, less is known about the risks associated with psychedelic use outside of medically supervised contexts, particularly in populations that are typically excluded from participation in clinical trials.

Using a preregistered longitudinal observational research design with a purposive sample of US residents between 18 and 50 years old (N=21,990), we investigated associations between self-reported naturalistic psychedelic use and psychotic and manic symptoms, with emphasis on those with psychiatric histories of schizophrenia or bipolar I disorder.

The follow-up survey was completed by 12,345 participants (56% retention), with 505 participants reporting psychedelic use during the 2-month study period. In covariate-adjusted regression models, psychedelic use during the study period was associated with increases in the severity of psychotic and manic symptoms. However, such increases were only observed for those who reported psychedelic use in an illegal context. While increases in the severity of psychotic symptoms appeared to depend on the frequency of use and the intensity of challenging psychedelic experiences, increases in the severity of manic symptoms appeared to be moderated by a personal history of schizophrenia or bipolar I disorder and the subjective experience of insight during a psychedelic experience.

The findings suggest that naturalistic psychedelic use specifically in illegal contexts may lead to increases in the severity of psychotic and manic symptoms. Such increases may depend on the frequency of use, the acute subjective psychedelic experience, and psychiatric history.

While cognitive impairment is a core feature of psychosis, significant heterogeneity in cognitive and clinical outcomes is observed.

The aim of this study was to identify cognitive and clinical subgroups in first-episode psychosis (FEP) and determine if these profiles were linked to functional outcomes over time.

A total of 323 individuals with FEP were included. Two-step hierarchical and k-means cluster analyses were performed using baseline cognitive and clinical variables. General linear mixed models were used to investigate whether baseline cognitive and clinical clusters were associated with functioning at follow-up time points (6–9, 12 and 15 months).

Three distinct cognitive clusters were identified: a cognitively intact group (N = 59), a moderately impaired group (N= 77) and a more severely impaired group (N= 122). Three distinct clinical clusters were identified: a subgroup characterised by predominant mood symptoms (N = 76), a subgroup characterised by predominant negative symptoms (N= 19) and a subgroup characterised by overall mild symptom severity (N = 94). The subgroup with more severely impaired cognition also had more severe negative symptoms at baseline. Cognitive clusters were significantly associated with later social and occupational function, and associated with changes over time. Clinical clusters were associated with later social functioning but not occupational functioning, and were not associated with changes over time.

Baseline cognitive impairments are predictive of both later social and occupational function and change over time. This suggests that cognitive profiles offer valuable information in terms of prognosis and treatment needs.

Early detection of psychosis is paramount for reducing the duration of untreated psychosis (DUP). One key factor contributing to extended DUP is service delay – the time from initial contact with psychiatric services to diagnosis. Reducing service delay depends on prompt identification of psychosis. Patients with schizophrenia and severe social impairment have been found to have prolonged DUP. Whether service delay significantly contributes to prolonged DUP in this group is unclear.

To examine and compare the course of illness for patients with schizophrenia who are homeless or domiciled, with a focus on service delay in detecting psychosis.

In this case–control study, we included out-patients with a schizophrenia spectrum diagnosis and who were homeless or domiciled but in need of an outreach team to secure continuous treatment. Interviews included psychosocial history and psychopathological and social functioning scales.

We included 85 patients with schizophrenia spectrum disorder. Mean service delay was significantly longer in the homeless group (5.5 years) compared with the domiciled group (2.5 years, P = 0.001), with a total sample mean of 3.9 years. Similarly, DUP was significantly longer in the homeless group, mean 15.5 years, versus 5.0 years in the domiciled group (P < 0.001). Furthermore, the homeless group had an earlier onset of illness than the domiciled group but were almost the same age at diagnosis.

Our findings point to the concerning circumstance that individuals with considerable risk of developing severe schizophrenia experience a substantial delay in diagnosis and do not receive timely treatment.

Schizophrenia is a chronic condition that requires long-term management. Quality of life is an important outcome measure for individuals diagnosed with schizophrenia; it can be tracked over time allowing evaluation of whether interventions lead to sustainable improvements. Nutrition and dietary interventions are an underutilized treatment for tackling the metabolic consequences of mental illness, which is now recognized as having increased importance in the management of schizophrenia. This study examines the impact of nutrition and dietary interventions on quality of life outcomes for those with schizophrenia.

A systematic review of the literature was conducted, assessing the impact of nutritional interventions on quality of life outcomes in individuals with a diagnosis of schizophrenia.

A total of 982 articles were screened, of which nine articles met the inclusion criteria. Quality of life measures varied across studies, which made comparison across studies challenging. Previous studies had relatively small sample sizes and did not have long follow-up durations. Some of the studies found that dietary interventions such as counselling, weight management programs, food diaries and nutritional education improved quality of life, whereas others did not detect any effect.

The review provides preliminary evidence that nutrition and dietary interventions may benefit quality of life among individuals with schizophrenia. There were however substantial limitations in studies highlighting the need for further research. The paper also highlights the need to standardize assessment tools for future quality-of-life research.

To improve early intervention and personalise treatment for individuals early on the psychosis continuum, a greater understanding of symptom dynamics is required. We address this by identifying and evaluating the movement between empirically derived attenuated psychotic symptomatic substates—clusters of symptoms that occur within individuals over time.

Data came from a 90-day daily diary study evaluating attenuated psychotic and affective symptoms. The sample included 96 individuals aged 18–35 on the psychosis continuum, divided into four subgroups of increasing severity based on their psychometric risk of psychosis, with the fourth meeting ultra-high risk (UHR) criteria. A multilevel hidden Markov modelling (HMM) approach was used to characterise and determine the probability of switching between symptomatic substates. Individual substate trajectories and time spent in each substate were subsequently assessed.

Four substates of increasing psychopathological severity were identified: (1) low-grade affective symptoms with negligible psychotic symptoms; (2) low levels of nonbizarre ideas with moderate affective symptoms; (3) low levels of nonbizarre ideas and unusual thought content, with moderate affective symptoms; and (4) moderate levels of nonbizarre ideas, unusual thought content, and affective symptoms. Perceptual disturbances predominantly occurred within the third and fourth substates. UHR individuals had a reduced probability of switching out of the two most severe substates.

Findings suggest that individuals reporting unusual thought content, rather than nonbizarre ideas in isolation, may exhibit symptom dynamics with greater psychopathological severity. Individuals at a higher risk of psychosis exhibited persistently severe symptom dynamics, indicating a potential reduction in psychological flexibility.

Mentalizing—our ability to make inferences about the mental states of others—is impaired across psychiatric disorders and robustly associated with functional outcomes. Mentalizing deficits have been prominently linked to aberrant activity in cortical regions considered to be part of the “social brain network” (e.g., dorsomedial prefrontal cortex, temporoparietal junction), yet emerging evidence also suggests the importance of cerebellar dysfunction. In the present study—using a transdiagnostic, clinical psychiatric sample spanning the psychosis-autism-social anxiety spectrums—we examined the role of the cerebellum in mentalizing and its unique contributions to broader social functioning.

Sixty-two participants (38 with significant social dysfunction secondary to psychiatric illness and 24 nonclinical controls without social dysfunction) completed a mentalizing task during functional magnetic resonance imaging. General linear model analysis, latent variable modeling, and regression analyses were used to examine the contribution of cerebellum activation to the prediction of group status and social functioning.

Mentalizing activated a broad set of social cognitive brain regions, including cerebral mentalizing network (MN) nodes and posterior cerebellum. Higher posterior cerebellum activation significantly predicted clinical status (i.e., individuals with psychiatric disorders versus nonclinical controls). Finally, cerebellar activation accounted for significant variance in social functioning independent of all other cerebral MN brain regions identified in a whole-brain analysis.

Findings add to an accumulating body of evidence establishing the unique role of the posterior cerebellum in mentalizing deficits and social dysfunction across psychiatric illnesses. Collectively, our results suggest that the posterior cerebellum should be considered – alongside established cerebral regions – as part of the mentalizing network.

Although cognitive remediation (CR) improves cognition and functioning, the key features that promote or inhibit its effectiveness, especially between cognitive domains, remain unknown. Discovering these key features will help to develop CR for more impact.

To identify interrelations between cognition, symptoms, and functioning, using a novel network analysis approach and how CR affects these recovery outcomes.

A secondary analysis of randomized controlled trial data (N = 165) of CR in early psychosis. Regularized partial correlation networks were estimated, including symptoms, cognition, and functioning, for pre-, post-treatment, and change over time. Pre- and post-CR networks were compared on global strength, structure, edge invariance, and centrality invariance.

Cognition, negative, and positive symptoms were separable constructs, with symptoms showing independent relationships with cognition. Negative symptoms were central to the CR networks and most strongly associated with change in functioning. Verbal and visual learning improvement showed independent relationships to improved social functioning and negative symptoms. Only visual learning improvement was positively associated with personal goal achievement. Pre- and post-CR networks did not differ in structure (M = 0.20, p = 0.45) but differed in global strength, reflecting greater overall connectivity in the post-CR network (S = 0.91, p = 0.03).

Negative symptoms influenced network changes following therapy, and their reduction was linked to improvement in verbal and visual learning following CR. Independent relationships between visual and verbal learning and functioning suggest that they may be key intervention targets to enhance social and occupational functioning.

There is currently no definitive method for identifying individuals with psychosis in secondary care on a population-level using administrative healthcare data from England.

To develop various algorithms to identify individuals with psychosis in the Mental Health Services Data Set (MHSDS), guided by national estimates of the prevalence of psychosis.

Using a combination of data elements in the MHSDS for financial years 2017–2018 and 2018–2019 (mental health cluster (a way to describe and classify a group of individuals with similar characteristics), Health of the Nation Outcome Scale (HoNOS) scores, reason for referral, primary diagnosis, first-episode psychosis flag, early intervention in psychosis team flag), we developed 12 unique algorithms to detect individuals with psychosis seen in secondary care. The resulting numbers were then compared with national estimates of the prevalence of psychosis to ascertain whether they were reasonable or not.

The 12 algorithms produced 99 204–138 516 and 107 545–134 954 cases of psychosis for financial years 2017–2018 and 2018–2019, respectively, in line with national prevalence estimates. The numbers of cases of psychosis identified by the different algorithms differed according to the type and number (3–6) of data elements used. Most algorithms identified the same core of patients.

The MHSDS can be used to identify individuals with psychosis in secondary care in England. Users can employ several algorithms to do so, depending on the objective of their analysis and their preference regarding the data elements employed. These algorithms could be used for surveillance, research and/or policy purposes.

There is evidence that attachment, trauma, and voice appraisals individually impact voice hearing in psychosis, but their intersectional relationship has not been examined. The aim of this study was to identify subgroups of individuals from the intersectional relationship between these factors and examine differences between subgroups on clinical outcomes.

A latent profile analysis was conducted on baseline data from the AVATAR2 trial (n = 345), to identify statistically distinct subgroups of individuals with psychosis who hear distressing voices based on co-occurring patterns of trauma, fearful attachment, and voice appraisals. The association between profile membership and demographic characteristics, voice severity, posttraumatic stress disorder symptoms, emotional distress, and difficulties with motivation and pleasure was then examined. Experts by experience were consulted throughout the process.

Four profiles were identified: ‘adverse voices and relational trauma’, ‘low malevolent and omnipotent voices’, ‘adverse voices yet low relational trauma’, and ‘high benevolent voices’. Negative voice appraisals occurred in the presence of high and low trauma and attachment adversities. The first profile was associated with being female and/or other non-male genders and had worse voice severity and emotional distress. High adversities and worse emotional distress occurred in the presence of voice benevolence and engagement. Black and South Asian ethnicities were not associated with specific profiles.

The identified profiles had negative and positive voice appraisals associated with higher and lower occurrence of adversities, and different clinical outcomes. These profiles could inform detailed case formulations that could tailor interventions for voice hearers.