New guidelines for the effectiveness of exercise in the prevention of dementia: Implications for psychiatry.

To the Editors, We recently published evidence-based guidelines for the role of exercise in the prevention of dementia.1 The guidelines combined an umbrella review and expert consensus, and has important implications for psychiatry. Evidence from published studies was evaluated using the GRADE assessment. We found scarce and relatively low-quality evidence in the literature, particularly for the primary prevention of dementia. Our GRADE-informed evidence synthesis yielded the following conclusions: For Primary prevention of dementia: Physical activity may be considered for the primary prevention of dementia. In people without dementia or MCI, exercise may be no better than health education for the primary prevention of dementia and MCI. QUALITY OF EVIDENCE: Very low for physical activity; very low for exercise. For Secondary prevention of dementia: In people with MCI there is continued uncertainty about the role of physical activity and exercise in slowing the conversion to dementia. QUALITY OF EVIDENCE: Very low for physical activity; very low for exercise. For Tertiary prevention of dementia: In people with moderate dementia, physical activity/exercise could be considered for maintaining cognition and exercise could be considered for stabilizing disability compared to usual care. QUALITY OF EVIDENCE: Exercise: very low for cognitive outcomes; low for disability. Following a consensus process, we recommended physical activity/exercise for all three purposes, namely primary, secondary, and tertiary prevention (improve cognition and reduce disability) of dementia. The recommendation of exercise was largely contingent on its positive effects on mental health,2,3 in conjunction with the extensive body of evidence linking mental disorder with dementia.4 The guidelines highlight the need for further research on multidisciplinary interventions for both the primary and secondary prevention of dementia. A question remains whether the positive effect of physical activity on mood/behaviour applies to the MCI group, as it does to the dementia group. More research is required in people with established dementia and in less common forms of dementia. The guidelines also make an implicit research recommendation in support of heurism, in the sense that they integrate the evidence-based expectation that exercise is likely to be beneficial both for mental and physical health. Indeed, employing heurism may be inherently necessary in prevention research.5 Overall, these guidelines offer an evidence-based insight into the effectiveness of physical activity/exercise for the prevention (primary, secondary, and tertiary) of dementia. Importantly, they necessitate the inclusion of mental health in a multi-component approach. In doing so, they emphasize the necessity of mental health promotion and mental illness prevention in the prevention and management of dementia.

Risk factors, prevention and treatment of weight gain associated with the use of antidepressants and antipsychotics: a state-of-the-art clinical review.

INTRODUCTION: People with severe mental illness have poor cardiometabolic health. Commonly used antidepressants and antipsychotics frequently lead to weight gain, which may further contribute to adverse cardiovascular outcomes. AREAS COVERED: We searched MEDLINE up to April 2023 for umbrella reviews, (network-)meta-analyses, trials and cohort studies on risk factors, prevention and treatment strategies of weight gain associated with antidepressants/antipsychotics. We developed 10 clinical recommendations. EXPERT OPINION: To prevent, manage, and treat antidepressant/antipsychotic-related weight gain, we recommend i) assessing risk factors for obesity before treatment, ii) monitoring metabolic health at baseline and regularly during follow-up, iii) offering lifestyle interventions including regular exercise and healthy diet based on patient preference to optimize motivation, iv) considering first-line psychotherapy for mild-moderate depression and anxiety disorders, v)choosing medications based on medications' and patient's weight gain risk, vi) choosing medications based on acute vs long-term treatment, vii) using effective, tolerated medications, viii) switching to less weight-inducing antipsychotics/antidepressants where possible, ix) using early weight gain as a predictor of further weight gain to inform the timing of intervention/switch options, and x) considering adding metformin or glucagon-like peptide-1 receptor agonists, or topiramate(second-line due to potential adverse cognitive effects) to antipsychotics, or aripiprazole to clozapine or olanzapine.

Biomarkers for Psychosis: Are We There Yet? Umbrella Review of 1478 Biomarkers.

Background and Hypothesis: This umbrella review aims to comprehensively synthesize the evidence of association between peripheral, electrophysiological, neuroimaging, neuropathological, and other biomarkers and diagnosis of psychotic disorders. Study Design: We selected systematic reviews and meta-analyses of observational studies on diagnostic biomarkers for psychotic disorders, published until February 1, 2018. Data extraction was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Evidence of association between biomarkers and psychotic disorders was classified as convincing, highly suggestive, suggestive, weak, or non-significant, using a standardized classification. Quality analyses used the Assessment of Multiple Systematic Reviews (AMSTAR) tool. Study Results: The umbrella review included 110 meta-analyses or systematic reviews corresponding to 3892 individual studies, 1478 biomarkers, and 392 210 participants. No factor showed a convincing level of evidence. Highly suggestive evidence was observed for transglutaminase autoantibodies levels (odds ratio [OR] = 7.32; 95% CI: 3.36, 15.94), mismatch negativity in auditory event-related potentials (standardized mean difference [SMD] = 0.73; 95% CI: 0.5, 0.96), P300 component latency (SMD = -0.6; 95% CI: -0.83, -0.38), ventricle-brain ratio (SMD = 0.61; 95% CI: 0.5, 0.71), and minor physical anomalies (SMD = 0.99; 95% CI: 0.64, 1.34). Suggestive evidence was observed for folate, malondialdehyde, brain-derived neurotrophic factor, homocysteine, P50 sensory gating (P50 S2/S1 ratio), frontal N-acetyl-aspartate, and high-frequency heart rate variability. Among the remaining biomarkers, weak evidence was found for 626 and a non-significant association for 833 factors. Conclusions: While several biomarkers present highly suggestive or suggestive evidence of association with psychotic disorders, methodological biases, and underpowered studies call for future higher-quality research.

Global and risk-group stratified well-being and mental health during the COVID-19 pandemic in adults: Results from the international COH-FIT Study.

International studies measuring wellbeing/multidimensional mental health before/ during the COVID-19 pandemic, including representative samples for >2 years, identifying risk groups and coping strategies are lacking. COH-FIT is an online, international, anonymous survey measuring changes in well-being (WHO-5) and a composite psychopathology P-score, and their associations with COVID-19 deaths/restrictions, 12 a-priori defined risk individual/cumulative factors, and coping strategies during COVID-19 pandemic (26/04/2020-26/06/2022) in 30 languages (representative, weighted non-representative, adults). T-test, χ2, penalized cubic splines, linear regression, correlation analyses were conducted. Analyzing 121,066/142,364 initiated surveys, WHO-5/P-score worsened intra-pandemic by 11.1±21.1/13.2±17.9 points (effect size d=0.50/0.60) (comparable results in representative/weighted non-probability samples). Persons with WHO-5 scores indicative of depression screening (<50, 13% to 32%) and major depression (<29, 3% to 12%) significantly increased. WHO-5 worsened from those with mental disorders, female sex, COVID-19-related loss, low-income country location, physical disorders, healthcare worker occupations, large city location, COVID-19 infection, unemployment, first-generation immigration, to age=18-29 with a cumulative effect. Similar findings emerged for P-score. Changes were significantly but minimally related to COVID-19 deaths, returning to near-pre-pandemic values after >2 years. The most subjectively effective coping strategies were exercise and walking, internet use, social contacts. Identified risk groups, coping strategies and outcome trajectories can inform global public health strategies.

Collaborative outcomes study on health and functioning during infection times (COH-FIT): Insights on modifiable and non-modifiable risk and protective factors for wellbeing and mental health during the COVID-19 pandemic from multivariable and network analyses.

There is no multi-country/multi-language study testing a-priori multivariable associations between non-modifiable/modifiable factors and validated wellbeing/multidimensional mental health outcomes before/during the COVID-19 pandemic. Moreover, studies during COVID-19 pandemic generally do not report on representative/weighted non-probability samples. The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is a multi-country/multi-language survey conducting multivariable/LASSO-regularized regression models and network analyses to identify modifiable/non-modifiable factors associated with wellbeing (WHO-5)/composite psychopathology (P-score) change. It enrolled general population-representative/weighted-non-probability samples (26/04/2020-19/06/2022). Participants included 121,066 adults (age=42±15.9 years, females=64 %, representative sample=29 %) WHO-5/P-score worsened (SMD=0.53/SMD=0.74), especially initially during the pandemic. We identified 15 modifiable/nine non-modifiable risk and 13 modifiable/three non-modifiable protective factors for WHO-5, 16 modifiable/11 non-modifiable risk and 10 modifiable/six non-modifiable protective factors for P-score. The 12 shared risk/protective factors with highest centrality (network-analysis) were, for non-modifiable factors, country income, ethnicity, age, gender, education, mental disorder history, COVID-19-related restrictions, urbanicity, physical disorder history, household room numbers and green space, and socioeconomic status. For modifiable factors, we identified medications, learning, internet, pet-ownership, working and religion as coping strategies, plus pre-pandemic levels of stress, fear, TV, social media or reading time, and COVID-19 information. In multivariable models, for WHO-5, additional non-modifiable factors with |B|>1 were income loss, COVID-19 deaths. For modifiable factors we identified pre-pandemic levels of social functioning, hobbies, frustration and loneliness, and social interactions as coping strategy. For P-scores, additional non-modifiable/modifiable factors were income loss, pre-pandemic infection fear, and social interactions as coping strategy. COH-FIT identified vulnerable sub-populations and actionable individual/environmental factors to protect well-being/mental health during crisis times. Results inform public health policies, and clinical practice.

Individualized pretest risk estimates to guide treatment decisions in patients with clinical high risk for psychotic disorders.

INTRODUCTION: Clinical high risk for psychosis (CHR) states are associated with an increased risk of transition to psychosis. However, the predictive value of CHR screening interviews is dependent on pretest risk enrichment in referred patients. This poses a major obstacle to CHR outreach campaigns since they invariably lead to risk dilution through enhanced awareness. A potential compensatory strategy is to use estimates of individual pretest risk as a 'gatekeeper' for specialized assessment. We aimed to test a risk stratification model previously developed in London, UK (OASIS) and to train a new predictive model for the Swiss population. METHOD: The sample was composed of 513 individuals referred for CHR assessment from six Swiss early psychosis detection services. Sociodemographic variables available at referral were used as predictors whereas the outcome variable was transition to psychosis. RESULTS: Replication of the risk stratification model developed in OASIS resulted in poor performance (Harrel's c=0.51). Retraining resulted in moderate discrimination (Harrel's c=0.67) which significantly differentiated between different risk groups. The lowest risk group had a cumulative transition incidence of 6.4% (CI: 0-23.1%) over two years. CONCLUSION: Failure to replicate the OASIS risk stratification model might reflect differences in the public health care systems and referral structures between Switzerland and London. Retraining resulted in a model with adequate discrimination performance. The developed model in combination with CHR assessment result, might be useful for identifying individuals with high pretest risk, who might benefit most from specialized intervention.

Effects of antipsychotic treatment on cardio-cerebrovascular related mortality in schizophrenia: A subanalysis of a systematic review and meta-analysis with meta-regression of moderators.

To further explore the role of different antipsychotic treatments for cardio-cerebrovascular mortality, we performed several subgroup, sensitivity and meta-regression analyses based on a large previous meta-analysis focusing on cohort studies assessing mortality relative risk (RR) for cardio-cerebrovascular disorders in people with schizophrenia, comparing antipsychotic treatment versus no antipsychotic. Quality assessment through the Newcastle-Ottawa Scale (NOS) and publication bias was measured. We meta-analyzed 53 different studies (schizophrenia patients: n = 2,513,359; controls: n = 360,504,484) to highlight the differential effects of antipsychotic treatment regimens on cardio-cerebrovascular-related mortality in incident and prevalent samples of patients with schizophrenia. We found first generation antipsychotics (FGA) to be associated with higher mortality in incident samples of schizophrenia (oral FGA [RR=2.20, 95 %CI=1.29-3.77, k = 1] and any FGA [RR=1.70, 95 %CI=1.20-2.41, k = 1]). Conversely, second generation antipsychotics (SGAs) and clozapine were associated with reduced cardio-cerebrovascular-related mortality, in prevalent samples of schizophrenia. Subgroup analyses with NOS score ≥7 (higher quality) demonstrated a significantly increased cardio-cerebrovascular disorder-related mortality, among those exposed to FGAs vs SGAs. Meta-regression analyses demonstrated a larger association between antipsychotics and decreased risk of mortality with longer follow-up, recent study year, and higher number of adjustment variables. Overall, this subanalysis of a systematic review contributes to the evolving understanding of the complex role of antipsychotic treatment for cardio-cerebrovascular mortality in schizophrenia, paving the way for more targeted interventions and improved patient outcomes.

All-cause mortality risk in long-acting injectable versus oral antipsychotics in schizophrenia: a systematic review and meta-analysis.

Patients with schizophrenia receiving antipsychotic treatment present lower mortality rates than those who do not. However, the non-adherence rate is high, which can be partially addressed using long-acting injectable (LAI) antipsychotics. The impact of LAI treatments on all-cause mortality compared to oral antipsychotics remains unclear. To fill that gap, a random effects meta-analysis was conducted to analyze the odds ratio (OR) of all-cause, suicidal, and non-suicidal mortality among patients taking LAI antipsychotics compared to oral antipsychotics (PROSPERO:CRD42023391352). Individual and pooled LAI antipsychotics were analyzed against pooled oral antipsychotics. Sensitivity analyses were performed for study design, setting, and industry sponsorship. Meta-regressions were conducted for gender, age, antipsychotic dose, and race. Seventeen articles, total sample 12,042 patients (N = 5795 oral, N = 6247 LAI) were included. Lower risk of all-cause mortality for patients receiving LAI antipsychotics vs receiving oral antipsychotics was found (OR = 0.79; 95%CI = 0.66-0.95). Statistical significance was maintained when only studies comparing the same LAI and oral antipsychotic were included (OR = 0.79; 95%CI = 0.66-0.95; p = <0.01), as well as for non-suicidal mortality (OR = 0.77: 95%CI = 0.63-0.94; p = 0.01), but not for suicidal mortality (OR = 0.86; 95%CI = 0.59-1.26; p = 0.44). Mortality reduction was more pronounced for LAI antipsychotics in first-episode psychosis (FEP) (OR = 0.79; 95%CI = 0.66-0.96) compared to chronic psychosis. No individual LAI reported statistically significant differences against all pooled oral antipsychotics. LAI antipsychotics are associated with a lower risk of all-cause and non-suicidal mortality in individuals with schizophrenia compared to oral antipsychotics. Better adherence to the medication and health services may explain this difference. Whenever possible, the use of LAIs should be considered from the FEP.

Patients' perspective on the therapeutic relationship and session quality: the central role of alliance.

In this study, we examined how four components of the therapeutic relationship-working alliance, real relationship, and positive and negative affective reactions of the patient toward their therapist-relate to each other and to the psychotherapy session outcome, from the patient's point of view. Our simple comprised 700 adult patients in individual psychotherapy who were recruited and participated online. They underwent a baseline evaluation of their most recent therapy session, which encompassed a series of validated self-report measures focused on specific elements of the therapeutic relationship. The results revealed that, from the patient's perspective, working alliance, real relationship, and positive affective reactions toward the therapist were positively correlated with session outcome, while negative affective reactions were negatively correlated. All components predicted session outcome when simultaneously included in a regression model. Collectively, these four components accounted for 30% of the variance in session outcome. Factor analysis revealed four distinct factors, underlying perceptions of the therapeutic relationship. Notably, the bond dimension of the alliance was sufficiently different from the task and goal dimensions, warranting consideration as a distinct construct. These findings, although cross-sectional, lay the groundwork for a more nuanced investigation of multiple dimensions of the therapeutic relationship.

Do Cognitive Subtypes Exist in People at Clinical High Risk for Psychosis? Results From the EU-GEI Study.

BACKGROUND AND HYPOTHESIS: Cognition has been associated with socio-occupational functioning in individuals at Clinical High Risk for Psychosis (CHR-P). The present study hypothesized that clustering CHR-P participants based on cognitive data could reveal clinically meaningful subtypes. STUDY DESIGN: A cohort of 291 CHR-P subjects was recruited through the multicentre EU-GEI high-risk study. We explored whether an underlying cluster structure was present in the cognition data. Clustering of cognition data was performed using k-means clustering and density-based spatial clustering of applications with noise. Cognitive subtypes were validated by comparing differences in functioning, psychosis symptoms, transition outcome, and grey matter volume between clusters. Network analysis was used to further examine relationships between cognition scores and clinical symptoms. STUDY RESULTS: No underlying cluster structure was found in the cognitive data. K-means clustering produced "spared" and "impaired" cognition clusters similar to those reported in previous studies. However, these clusters were not associated with differences in functioning, symptomatology, outcome, or grey matter volume. Network analysis identified cognition and symptoms/functioning measures that formed separate subnetworks of associations. CONCLUSIONS: Stratifying patients according to cognitive performance has the potential to inform clinical care. However, we did not find evidence of cognitive clusters in this CHR-P sample. We suggest that care needs to be taken in inferring the existence of distinct cognitive subtypes from unsupervised learning studies. Future research in CHR-P samples could explore the existence of cognitive subtypes across a wider range of cognitive domains.

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.

BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. METHODS: We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. RESULTS: The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). CONCLUSIONS: These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.

Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review.

Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state of the art is missing. Moreover, individual studies often target ML experts who may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review conducted in 5 psychology and 2 computer science databases. We included 128 studies that assessed the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and posttraumatic stress disorder. Most studies (n = 87) were aimed at predicting anxiety, while the remainder (n = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text (n = 72) as opposed to audio or video. Studies focused mainly on general populations (n = 92) and less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two-thirds of studies, which focused on both disorders, reported acceptable to very good predictive power (including high-quality studies only). The results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy but shows potential to contribute to diagnostics of mental disorders, such as anxiety and posttraumatic stress disorder, in the future if standardization of methods, reporting of results, and research in clinical populations are improved.

Short- and long-term neuropsychiatric outcomes in long COVID in South Korea and Japan.

We investigated whether SARS-CoV-2 infection is associated with short- and long-term neuropsychiatric sequelae. We used population-based cohorts from the Korean nationwide cohort (discovery; n = 10,027,506) and the Japanese claims-based cohort (validation; n = 12,218,680) to estimate the short-term (<30 days) and long-term (≥30 days) risks of neuropsychiatric outcomes after SARS-CoV-2 infection compared with general population groups or external comparators (people with another respiratory infection). Using exposure-driven propensity score matching, we found that both the short- and long-term risks of developing neuropsychiatric sequelae were elevated in the discovery cohort compared with the general population and those with another respiratory infection. A range of conditions including Guillain-Barré syndrome, cognitive deficit, insomnia, anxiety disorder, encephalitis, ischaemic stroke and mood disorder exhibited a pronounced increase in long-term risk. Factors such as mild severity of COVID-19, increased vaccination against COVID-19 and heterologous vaccination were associated with reduced long-term risk of adverse neuropsychiatric outcomes. The time attenuation effect was the strongest during the first six months after SARS-CoV-2 infection, and this risk remained statistically significant for up to one year in Korea but beyond one year in Japan. The associations observed were replicated in the validation cohort. Our findings contribute to the growing evidence base on long COVID by considering ethnic diversity.

A transdiagnostic prodrome for severe mental disorders: an electronic health record study.

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

Sexually transmitted infections, sexual life and risk behaviours of people living with schizophrenia: systematic review and meta-analysis.

BACKGROUND: Sexually transmitted infections (STIs), along with sexual health and behaviour, have received little attention in schizophrenia patients. AIMS: To systematically review and meta-analytically characterise the prevalence of STIs and sexual risk behaviours among schizophrenia patients. METHOD: Web of Science, PubMed, BIOSIS, KCI-Korean Journal Database, MEDLINE, Russian Science Citation Index, SciELO and Cochrane Central Register were systematically searched from inception to 6 July 2023. Studies reporting on the prevalence or odds ratio of any STI or any outcome related to sexual risk behaviours among schizophrenia samples were included. PRISMA/MOOSE-compliant (CRD42023443602) random-effects meta-analyses were used for the selected outcomes. Q-statistics, I2 index, sensitivity analyses and meta-regressions were used. Study quality and publication bias were assessed. RESULTS: Forty-eight studies (N = 2 459 456) reporting on STI prevalence (including 15 allowing for calculation of an odds ratio) and 33 studies (N = 4255) reporting on sexual risk behaviours were included. Schizophrenia samples showed a high prevalence of STIs and higher risks of HIV (odds ratio = 2.11; 95% CI 1.23-3.63), hepatitis C virus (HCV, odds ratio = 4.54; 95% CI 2.15-961) and hepatitis B virus (HBV; odds ratio = 2.42; 95% CI 1.95-3.01) infections than healthy controls. HIV prevalence was higher in Africa compared with other continents and in in-patient (rather than out-patient) settings. Finally, 37.7% (95% CI 31.5-44.4%) of patients were sexually active; 35.0% (95% CI 6.6-59.3%) reported consistent condom use, and 55.3% (95% CI 25.0-82.4%) maintained unprotected sexual relationships. CONCLUSIONS: Schizophrenia patients have high prevalence of STIs, with several-fold increased risks of HIV, HBV and HCV infection compared with the general population. Sexual health must be considered as an integral component of care.

Assessing the patient's affective perception of their psychotherapist: validation of the in-Session Patient Affective Reactions Questionnaire.

Background: Psychotherapists need effective tools to monitor changes in the patient's affective perception of the therapist and the therapeutic relationship during sessions to tailor therapeutic interventions and improve treatment outcomes. This study aims to evaluate the factor structure, reliability, and validity of the in-Session Patient Affective Reactions Questionnaire (SPARQ), a concise self-report measure designed for practical application in real-world psychotherapy settings. Methods: Validation data was gathered from (N = 700) adult patients in individual psychotherapy. These patients completed the SPARQ in conjunction with additional measures capturing sociodemographic details, characteristics of therapeutic interventions, individual personality traits, mental health symptom severity, elements of the therapeutic relationship, and session outcomes. This comprehensive approach was employed to assess the construct and criterion-related validity of the SPARQ. Results: The SPARQ has a two-factor structure: Positive Affect (k = 4, ω total = .87) and Negative Affect (k = 4, ω total = .75). Bifactor confirmatory factor analysis (CFA) yielded the following fit indices: X2[df] = 2.53, CFI = .99; TLI = .98; RMSEA = .05; and SRMR = .02. Multi-group CFAs demonstrated measurement invariance (i) across patients who attended psychotherapy sessions in person versus in remote mode, and (ii) across patients with and without psychiatric diagnoses confirmed metric invariance. Furthermore, the SPARQ showed meaningful correlations with concurrently administered measures. Discussion: The SPARQ proves to be a valuable instrument in clinical, training, and research contexts, adept at capturing patients' session-level affective responses towards their therapist and perceptions of the therapeutic alliance. Comprehensive descriptive statistics and a range of score precision indices have been reported, intended to serve as benchmarks for future research.

On the Road to Individualizing Pharmacotherapy for Adolescents and Adults with Schizophrenia - Results from an Expert Consensus Following the Delphi Method.

Introduction: Schizophrenia is a severe mental illness that usually begins in late adolescence or early adulthood. Current pharmacological treatments, while acceptably effective for many patients, are rarely clinically tailored or individualized. The lack of sufficient etiopathological knowledge of the disease, together with overall comparable effect sizes for efficacy between available antipsychotics and the absence of clinically actionable biomarkers, has hindered the advance of individualized medicine in the treatment of schizophrenia. Nevertheless, some degree of stratification based on clinical markers could guide treatment choices and help clinicians move toward individualized psychiatry. To this end, a panel of experts met to formally discuss the current approach to individualized treatment in schizophrenia and to define how treatment individualization could help improve clinical outcomes. Methods: A task force of seven experts iteratively developed, evaluated, and refined questionnaire items, which were then evaluated using the Delphi method. Descriptive statistics were used to summarize and rank expert responses. Expert discussion, informed by the results of a scoping review on personalizing the pharmacologic treatment of adults and adolescents with schizophrenia, ultimately generated recommendations to guide individualized pharmacologic treatment in this population. Results: There was substantial agreement among the expert group members, resulting in the following recommendations: 1) individualization of treatment requires consideration of the patient's diagnosis, clinical presentation, comorbidities, previous treatment response, drug tolerability, adherence patterns, and social factors; 2) patient preferences should be considered in a shared decision-making approach; 3) identified barriers to personalized care that need to be overcome include the lack of actionable biomarkers and mechanistic similarities between available treatments, but digital tools should be increasingly used to enhance individualized treatment. Conclusion: Individualized care can help provide effective, tailored treatments based on an individual's clinical characteristics, disease trajectory, family and social environment, and goals and preferences.