<![CDATA[Learn a multidimensional approach to suicide prevention in schizophrenia—screening, insight paradox, cannabis, cognition, trauma, and ethical AI monitoring.]]>
<![CDATA[New spectral sleep analysis and HD‑tDCS aim to strengthen restorative sleep and memory, offering promise for schizophrenia-related cognitive deficits.]]>
<![CDATA[Welcome back to another Psychiatric Times Insights series. In this episode titled, “The Switch Study: Why a New Mechanism Demands a New Approach,” David Walling, PhD, address the unmet clinical needs in schizophrenia treatment that motivated the development of the study evaluating the transition from atypical antipsychotics to Cobenfy?]]>

Using AI to Detect Psychosis Relapse: Scoping Review

Background: Psychotic disorder represents a leading cause of disability worldwide, and relapse in psychosis is common. Artificial intelligence (AI) is increasingly recognized as a method that could aid clinical monitoring for individuals experiencing psychosis. Objective: This review aims to map the existing literature on AI-based approaches—including machine learning, deep learning, and natural language processing—used to detect relapse in individuals with psychotic disorders. Methods: A systematic search strategy was conducted on PubMed, PsycINFO, and Embase up to January 7, 2026. Observational studies, randomized controlled trials, and quasi-experimental studies that used AI methods to detect relapse in psychosis were eligible for inclusion. Screening and data extraction procedures were conducted by at least 2 reviewers working independently. Findings were extracted, charted, and described using narrative synthesis based on data extraction and consensus meetings with the research team. The scoping review was prospectively registered with the Open Science Framework. Results: Relevant studies identified (N=10) included the use of digital tools such as smartphone- and smartwatch-based monitoring, ecological momentary assessment tools, social media activity, and internet searches. Digital phenotyping via smartphones and wearables emerged as the most common method for data collection. The efficacy of AI models varied with sensitivity (or recall) ranging from 0.25 to 0.77 and specificity (or precision) ranging from 0.06 to 0.88. The reported area under the receiver operating characteristic curve for models ranged from 0.63 to 0.78. AI models were heterogeneous across studies, and most study findings were not replicated. Conclusions: This scoping review highlights both the promise and the current limitations of AI in psychosis relapse detection. Passive digital phenotyping research in the detection of psychosis relapse has progressed, and personalized approaches with individual-level modeling show promise; however, further studies need to include larger numbers of participants and should incorporate methods such as large language models. Future studies will require large collaborations aimed at delivering AI methods for use in real-world clinical practice.
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<![CDATA[Explore today’s schizophrenia treatments, comparing first-, second-, and newer antipsychotics, clozapine’s role, and how side effects and adherence drive medication choices.]]>

Sense of Coherence in patients with stable schizophrenia: multidimensional determinants from a cross-sectional study of 438 inpatients in Guangzhou, China

IntroductionSchizophrenia, a chronic psychiatric disorder, profoundly affects social functioning and quality of life. The Sense of Coherence (SOC), a core construct in salutogenic theory, plays a pivotal role in stress coping and recovery outcomes. While SOC has been studied in general populations, its determinants in stabilized schizophrenia inpatients remain underexplored. This study comprehensively investigated the Sense of Coherence (SOC) profile among stabilized schizophrenia inpatients by assessing its current status, exploring the interrelationships between SOC and demographic factors/Big Five personality traits (CBF-PI)/social support (SSRS), and identifying factors associated with SOC to inform potential personalized resilience-enhancing interventions. Findings may guide clinical strategies to enhance resilience and mental health recovery and potentially facilitate functional recovery in this population.Patients and methodsA cross-sectional study was conducted at a Guangzhou tertiary psychiatric hospital with 438 stabilized schizophrenia inpatients from November 2024 to February 2025. Data collection included demographic/clinical variables, Chinese Big Five Personality Inventory (CBF-PI), 13-item Sense of Coherence Scale (SOC-13), and the Social Support Rating Scale (SSRS). Statistical analyses were performed using SPSS version 27.0. Descriptive statistics were used to summarize the data and independent-samples t-tests and one-way analysis of variance (ANOVA) were employed to compare SOC scores across demographic and clinical groups. Pearson correlation analysis was conducted to examine the relationships between personality traits, social support, and sense of coherence and multiple linear regression analysis was utilized to identify independent factors influencing sense of coherence (P < 0.05).ResultThe mean SOC score among the 438 stabilized schizophrenia inpatients was 53.35 ± 11.81. SOC correlated positively with all five dimensions of CBF-PI-B (P < 0.05), as well as subjective social support (P < 0.01), while showing inverse correlations with objective support (p < 0.01) and support utilization subscales (P < 0.01), Multiple regression identified personal income(yes), frequency of physical activity (≥3 times/week), and primary caregiver (Children) as significant SOC predictors.ConclusionThis study demonstrates that stabilized schizophrenia inpatients in a tertiary psychiatric setting exhibit generally lower Sense of Coherence (SOC) levels compared with healthy normative values and moderately lower than several outpatient schizophrenia samples. Higher SOC was significantly associated with having a personal income, frequent physical activity (≥3 times/week), having children as primary caregivers, and Chinese Big Five Personality traits. Notably, while subjective support was positively correlated with SOC, objective support and support utilization showed negative associations. Given the moderate internal consistency of the SSRS (Cronbach’s α = 0.626), the differential associations with social support dimensions should be interpreted cautiously. Concurrently, personality traits are relatively stable, their associations with SOC may inform indirect supportive strategies leveraging existing strengths rather than direct modification. These findings underscore the potential value of tailored interventions that enhance subjective support, promote structured recreational activities, and foster patients’ perceived coping resources. Further cross-sectional and longitudinal outpatient studies are warranted to validate these results and elucidate the mechanisms underlying the support paradox.
<![CDATA[Delphi panel reveals how NPs and PAs can talk about long-acting injectables earlier in schizophrenia, boosting adherence and cutting relapse.]]>