IntroductionOpen-door policies for acute inpatient mental health units (AIMHU) have shown promising results in reducing coercive measures, but concerns remain among patients and staff regarding increased workload, constant surveillance, and potential safety risks associated with these policies. This study aims to develop a monitoring tool to facilitate safety management in AIMHUs by monitoring behavioral risks and nursing workload.MethodsThis study employed a qualitative approach using the content analysis method. Data were collected through three in-depth interviews and two focus groups involving staff (n=19) from the AIMHU of the hospital.Results34 items were identified to define behavioral risks related to self-harm and suicide, aggressiveness, and absconding, alongside factors affecting nursing workload. These items were categorized into three levels of risk: low, moderate, and high.DiscussionRutiSafeNet is a preliminary, observation-based monitoring prototype intended to support, rather than to predict, structured risk and workload monitoring in acute inpatient mental health units. As a qualitatively developed instrument, it requires psychometric validation, including inter-rater reliability, construct and criterion validity, predictive value, and clinical feasibility, before it can be implemented as a validated scale in clinical practice.
Path and Bayesian network analyses in the complex design of a well-being survey via New Zealand’s Integrated Data Infrastructure
IntroductionIn mental health research involved with sensitive features and privacy issues, using integrated data offers an efficient alternative to traditional approaches such as interviews or in−person data collection. These conventional methods frequently face logistical barriers, including low response rates among study populations. Using integrated data from multiple existing administrative and survey sources provides protection for participants and economic savings for researchers, despite being constrained by the limitations of the original data sources. Our research questions were: 1) Can we conduct path and network analyses of school absenteeism, psychosocial factors, and mental health outcomes using the integrated survey data from New Zealand’s Integrated Data Infrastructure (IDI)? and 2) How can we account for the complex design of the General Social Survey (GSS) to ensure representative inference?Materials and methodsThe study population was New Zealand youth enrolled in school, aged 15 years and older in 2018, who participated in the 2018 GSS. The study analyzed the Ministry of Education data integrated with the 2018 GSS survey data from New Zealand’s IDI. To explore the relationship between outcomes of including the WHO-5, “perceived life worthwhileness”, “perceived general health”, and predictors including school absenteeism, psychosocial factors, and family factors, quantile mixed−effects regression, path analysis, and Bayesian network (BN) analysis were used. The complex survey design was calibrated using replicated weights from the GSS, a design-based method for complex sampling, in path and regression analyses, and bootstrap resampling, in BN analysis.ResultsIn descending order from the weighted path model, overall life satisfaction (standardized path coefficient: 0.36), perceived health condition (0.34), ease in accepting cultural identity (0.12), and trust in the education system (0.07) were all significantly (positively) related to students’ mental health well-being (WHO-5). Perceived life worthwhileness correlated with the WHO-5 but was only connected to overall life satisfaction. Similar factors were identified for perceived health, with additional attributing factors, such as fear of crime in the area and the family’s overall well-being.ConclusionThe integration of Bayesian networks and path analysis offers rigorous methods for detecting complex interrelationships among integrated mental health outcomes from population data and variables from a complex survey design.
The relationship between trait mindfulness and psychotic-like experiences in a brief AI-generated music listening context: the roles of presence, perceived interactivity, and emotional arousal
Background and objectiveWithin the interdisciplinary field of cyberpsychology and mental health, trait mindfulness has been associated with lower levels of subclinical anomalous symptoms, such as Psychotic-Like Experiences (PLEs), has gained increasing attention. However, in the context of daily digital human-computer interactions (e.g., listening to AI-generated music), the specific pathways through which Mindfulness operates (the involvement with Presence and Perceived Interactivity) and the boundary conditions of physiological arousal, remain to be clarified. This study aims to explore the direct predictive relationship between mindfulness and individuals’ PLEs, and to investigate the multipath effect of brief AI-generated music listening context (with Presence and Perceived Interactivity), along with the moderating effect of Arousal.MethodsWith a cross-sectional survey design, self-reported multimodal data were collected from 527 Chinese participants. Structural equation modeling (SEM) was conducted using Mplus 8.3 to empirically test the main effects (path coefficients) of the theoretical hypotheses and the moderation model.ResultsBoth the measurement and structural models demonstrated good fit. The path analysis results indicated that: (1) trait trait mindfulness was significantly and negatively associated with PLEs (p < 0.001); (2) regarding the main effect paths of brief AI-generated music listening context, mindfulness significantly and positively predict individuals’ Presence and Perceived Interactivity, while both significantly and negatively predict PLEs; (3) Arousal played a significant moderating role in the relationship between mindfulness and brief AI-generated music listening context, exhibiting a synergistic enhancement effect. Higher levels of Arousal significantly amplified the positive prediction of Mindfulness on both Presence and Perceived Interactivity (p < 0.01).ConclusionsWith the help the SEM, this study maps out the underlying multipath network through which mindfulness is associated with lower PLEs within a brief AI-generated music listening context. The observed associations suggest that presence and perceived interactivity may function as pivotal correlational nodes relevant to mental health correlates, while these results also nuance classic cognitive load assumptions by indicating a potential synergistic association between trait mindfulness and emotional arousal. These results provide a solid empirical foundation and prospective insights, for the mental health-oriented design of AI music products, such as the immersive acoustic environment construction and dynamic, arousal-based interaction recommendations.
Psychometric properties of the Chinese version of the school refusal assessment scale–revised in a clinical sample of adolescents and their caregivers with depressive disorders
School refusal behavior (SRB) is a prevalent and functionally heterogeneous problem among children and adolescents that can lead to serious academic, social, and psychological consequences. The School Refusal Assessment Scale–Revised (SRAS-R) is the most widely used instrument for identifying the functional motivations underlying school refusal, yet its psychometric properties have not been examined in Chinese clinical populations. The present study aimed to translate and culturally adapt the SRAS-R into Chinese and to evaluate its psychometric properties in a clinical sample of adolescents with depressive disorders. A total of 171 adolescent outpatients (age range 12–19 years; M = 15.5, SD = 1.90; 67.3% female) diagnosed with DSM-5 depressive disorders and meeting criteria for school refusal behavior completed both child and parent versions of the Chinese SRAS-R. Confirmatory factor analysis (CFA) using diagonally weighted least squares estimation was conducted. After removing Items 20 and 24, the four-factor model yielded strong CFI and RMSEA values for both parent (CFI = 0.99, RMSEA = 0.021, SRMR = 0.094) and child reports (CFI = 0.98, RMSEA = 0.028, SRMR = 0.097), although SRMR values were marginally above the prespecified threshold. Internal consistency reliability ranged from marginal to good across subscales (Cronbach’s α: parent = 0.664–0.815; child = 0.700–0.864), with parent-reported Factor 4 showing the weakest reliability. Factor 1 (avoidance of aversive school situations) obtained the highest mean scores for both informants, consistent with the depression-related negative affectivity characteristic of this clinical sample. Cross-informant discrepancy analyses revealed that children reported significantly higher scores than parents on Factor 2 (escape from social/evaluative situations; p = .017, d = 0.18) and Factor 4 (pursuit of tangible reinforcement; p <.001, d = 0.27), suggesting that parents may underestimate internally driven motivations. Intraclass correlation coefficients indicated fair to good parent–child agreement (ICC = 0.45–0.62), with the highest agreement for Factor 1 and the lowest for Factor 4. The findings provide initial internal-structure and reliability evidence for the Chinese SRAS-R in this single-site clinical sample and underscore the need for multi-informant assessment, while future studies should examine convergent, discriminant, criterion-related, and predictive validity in more diverse samples.
Global economic burden of depression in 154 countries from 2025 to 2050
Nature Medicine, Published online: 28 July 2026; doi:10.1038/s41591-026-04548-7
A macroeconomic modeling analysis across 154 countries estimates that depression will impose a US$12 trillion global economic burden between 2025 and 2050, equivalent to around 0.5% of the annual global GDP.
Structuring Digital Mental Health Care Navigation: Co-Design Nominal Group Technique Study to Develop the MChart Definition and Typology of the Characteristics of Digital Mental Health Care Navigation Tools
<strong>Background:</strong> Australia’s mental health care system has been characterized by complexity and fragmentation, as highlighted by numerous reports, commissions, and inquiries. In response, digital mental health care navigation tools have emerged as a promising solution to help individuals locate appropriate mental health services. The rapid proliferation of these tools—without a clear understanding of their definitions and characteristics—risks creating confusion rather than clarity for users. Terms such as “navigation” and “navigators” are often used interchangeably, further complicating the landscape. <strong>Objective:</strong> This study addressed the need for a standardized definition and typology of the characteristics of digital mental health care navigation tools. <strong>Methods:</strong> This study was part of the development of a digital mental health care navigation tool for navigators and planners (MChart). It used a co-design approach using expert-based cooperative analysis, which is a nominal group technique to develop a definition and typology of the characteristics of digital mental health care navigation tools. This process was guided by the Technology Readiness Level for Implementation Sciences framework. The co-design process involved two 2-hour sessions with an expert panel comprising 28 participants, including representatives from mental health planning, primary health care, health care financing and delivery, community-managed organizations, clinical settings (psychiatrists, psychologists, and general practitioners), and consumers. <strong>Results:</strong> The expert panel collaboratively developed a consensus definition of digital mental health care navigation tools, outlining their scope and intended targets. Through the co-design process, the panel identified 157 characteristics of digital mental health care navigation tools. These characteristics were organized into 5 primary domains: type, management, content, design, and quality. The definition and typology characteristics provide a structured framework for understanding and evaluating the diverse range of digital mental health care navigation tools currently available. <strong>Conclusions:</strong> The co-designed definition and typology offer a foundational step toward reducing confusion in the digital mental health care navigation space. This study supports the development of quality standards that can be used to assess and compare existing and future tools. This framework has the potential to guide developers, end users, and policymakers in creating more effective, user-centered navigation solutions within Australia’s mental health care system and internationally.
Mobility Patterns and Mental Health During the COVID-19 Pandemic: Longitudinal Observational Study Using Smartphone Mobility Data
<strong>Background:</strong> The COVID-19 pandemic disrupted mobility globally, but its mental health implications remain difficult to characterize because most studies relied on lockdown status, population-level mobility indicators, or self-reported mobility. These approaches may miss individual differences in actual movement patterns and cannot fully examine bidirectional relationships between mobility and mental health. Individual-level smartphone geolocation data may provide a more objective and temporally aligned measure of mobility during periods of societal disruption. <strong>Objective:</strong> This study aimed to use individual-level Google location history (GLH) data and population-level Google community mobility reports (GCMRs) to examine concurrent and longitudinal relationships between pandemic-era mobility patterns and mental health symptoms in Hong Kong. <strong>Methods:</strong> This study analyzed data from the CU-COVID19 cohort study, an online longitudinal survey study of the psychological impact of the pandemic in Hong Kong. Mental health symptoms over the previous 14 days were assessed at baseline, 6 months, and 12 months using the 9-item Patient Health Questionnaire, the 7-item Generalized Anxiety Disorder scale, and the 4-item PTSD Checklist for DSM-5. Participants provided retrospective GLH data reflecting their mobility during the corresponding 14-day survey periods. The analytic sample included 145 participants with baseline GLH data, of whom 110 had 6-month follow-up data and 49 had data available at all 3 assessment waves. GLH data were used to derive mobility factors representing journey diversity, immobility, and remoteness. Population-level mobility during the same 14-day periods was measured using Hong Kong GCMR residential stay data. Concurrent mediation models examined whether individual mobility mediated associations between population-level residential stay and mental health symptoms. Longitudinal models examined bidirectional associations between changes in individual mobility and mental health across 6-month intervals. <strong>Results:</strong> Population-level residential stay was not directly associated with mental health. In concurrent mediation models, higher population-level residential stay was associated with lower individual journey diversity (β=–0.36; <i>P</i><.001), and lower journey diversity was associated with higher depression (β=–0.29; <i>P</i>=.02) and posttraumatic stress disorder (PTSD) (β=–0.35; <i>P</i>=.002). Bootstrapped indirect effects suggested mediation through journey diversity for depressive symptoms (β=0.11, 95% CI 0.02-0.25) and PTSD symptoms (β=0.13, 95% CI 0.05-0.27), although the depression-related indirect effect became less robust after adjustment for local and individual COVID-19 infection indicators. Longitudinally, higher baseline depressive symptoms predicted subsequent reductions in journey diversity (β=–0.15; <i>P</i>=.02), and reductions in journey diversity predicted higher subsequent depressive symptoms (β=–0.43; <i>P</i>=.008). <strong>Conclusions:</strong> Individual-level mobility patterns, particularly lower journey diversity, showed more consistent associations with mental health symptoms than population-level residential stay. Findings suggest bidirectional relationships between mobility and mental health and demonstrate the potential of smartphone geolocation data for digital phenotyping. However, the modest and self-selected sample, limited GCMR availability, and observational design require cautious interpretation.
MapLight’s Schizophrenia Candidate Has Mixed Results at Phase II
MapLight Therapeutics announced this week that its candidate drug for treatment of schizophrenia had achieved its primary endpoint in a Phase II trial, but only at the twice daily dose tested in the trial.
As reported by the California-based company, while participants of the trial who were given the candidate drug, ML-007C-MA, once a day did show some signs of improvement it was not statistically significant.
ML-007C-MA is a combined muscarinic agonist (betovumeline) and peripherally acting anticholinergic (fesoterodine). It acts by turning on two receptors in the brain, M1 and M4.
M1 is the main receptor the drug is trying to stimulate in the brain cortex and hippocampus, where it is linked to cognition, attention, and possibly some aspects of psychosis. Turning on M4 also helps by acting like a brake on the overactive signaling that contributes to hallucinations and delusions. Betovumeline activates both M1 and M4 centrally, while fesoterodine is there mainly to block unwanted side effects outside the brain, like gastrointestinal issues.
In this study, MapLight randomized 307 adults with an acute exacerbation of schizophrenia to treatment with either a twice daily or once daily treatment with ML-007C-MA or placebo for five weeks.
At five weeks, patients given the twice daily dose had a statistically significant and clinically meaningful reduction in Positive and Negative Syndrome Scale (PANSS) total score of 4.5 points compared to placebo. Cognitive scores were also better in the twice daily group versus placebo.
While this result is positive overall, the non-statistically significant result for the once daily dose proved unpopular with investors and company shares on the Nasdaq fell 40% after the announcement.
In September 2024, Cobenfy, the first muscarinic M1/M4 agonist drug for treatment of schizophrenia was approved by the FDA. Now owned by BMS, Cobenfy will be the main competitor for ML-007C-MA if approved.
Cobenfy was groundbreaking because it was the first new mechanism of action for schizophrenia in decades, moving beyond dopamine blockade to a muscarinic approach and targeting both hallucinations and delusions as well as the more cognitive aspects of the disease, which are not well treated with other drugs.
Despite the approval of Cobenfy, a number of other competitors developing treatments for schizophrenia have failed in recent years. Whether MapLight can succeed at Phase III with ML-007C-MA—which is also being tested as a treatment for psychosis linked to Alzheimer’s disease—and compete with Cobenfy, remains to be seen.
The post MapLight’s Schizophrenia Candidate Has Mixed Results at Phase II appeared first on Inside Precision Medicine.
Synaptic mechanisms for differential severity of social preference deficits in male and female mice induced by diminished activity-dependent BDNF
Males are more commonly diagnosed with autism spectrum disorder (ASD) than females with a ratio of about 4–1. However, the neural mechanisms underlying the sex differences in ASD are unknown. Social deficits are the core symptoms of patients with ASD. Previous studies showed that diminished activity-dependent brain-derived neurotrophic factor (BDNF) signaling induced differential severity of autism-like social preference deficits in male and female mice by using a mouse model with genetic knock-in of human BDNF methionine (Met) allele, which significantly decreased activity-dependent BDNF release without affecting basal BDNF secretion. Here, we investigated the synaptic mechanisms for diminished activity-dependent BDNF-induced differential severity of social preference deficits in males and females. The prefrontal cortex (PFC) is a critical brain region for social behaviors. Whole-cell patch-clamp brain slice recordings showed that diminished activity-dependent BDNF signaling differentially increased the frequency of spontaneous action potentials (sAPs) of pyramidal neurons in the PFC of male and female BDNF+/Met mice. The frequency of sAPs in male BDNF+/Met mice was higher than in female BDNF+/Met mice. Diminished activity-dependent BDNF signaling differentially enhanced excitatory synaptic transmission and dampened inhibitory synaptic transmission of pyramidal neurons at pre- and post- synapses in males and females, which were mediated by dysregulated transcriptional levels of key synaptic genes. Chemogenetic inhibition of pyramidal neurons in the PFC of BDNF+/Met mice was sufficient to ameliorate autism-like social preference deficits in males and females. This study reveals synaptic mechanisms underlying the differential severity of social preference deficit in male and female BDNF+/Met mice, which provides a potential neural basis for sex differences in male and female ASD patients with and without the BDNF Val66Met SNP.
Historical trauma as a contributor to postpartum depression among Indigenous mothers
IntroductionPostpartum depression (PPD) is a significant public health concern. Moreover, research suggests that American Indian/Alaskan Native (AI/AN) mothers experience postpartum depression at higher rates than the general population (Heck, 2021; Ko et al., 2017). As a result, understanding the potential causes of higher PPD among AI/AN mothers is helpful to the development of better interventions to reduce PPD among AI/AN mothers. A potential cause of higher PPD symptoms among AI/AN mothers is historical trauma (HT). HT refers to the cumulative psychological wounding experienced across generations due to systemic oppression, colonization, slavery, and other traumatic events experienced by a group of individuals.MethodsTo test the theorized relationships between HT and PPD symptoms, we conducted an online survey of adult women who both identified as Indigenous and who give birth in the past five years (N = 56). The survey consisted of the psychometrically suitable measures of the Historical Loss Scale (HLS: Whitbeck at al, 2004) and the Edinburgh Postnatal Depression Scale (EPDS; Cox et al., 1987). .ResultsThe results indicated that a model of HLS scores as a predictor of PPD symptoms over and above the controls of income, mental health diagnosis, and the total number of children of the birth mother fit the data well (χ2 = 13.60; df = 10; p = .192; RMSEA = .081 [90% CI: .000, .178]; CFI = .949). Moreover, of all the predictors, the dimension of the HLS measuring mothers’ endorsement of the presence of oppressive governmental and institutional policies toward AI/AN was the strongest predictor of greater PPD (β = .28, p < .05). A Bollen Stine bootstrap test was also used to confirm the stability of the model. The Bollen Stine operates by comparing the theorized model to a model that perfectly fits the data. The results of the Bollen Stine test indicated that the theorized model was not significantly different than a model of perfect fit. .DiscussionSuch results suggest that a potential explanation of higher PPD symptoms among AI/AN mothers is the effects of HT. Future interventions to reduce PPD symptoms among AI/AN mothers may benefit from an additional focus on treating HT.