Schizophrenia is a severe mental disorder with a significant impact on social functioning. Social disability is common in patients, requiring a reliable prediction model for early intervention. This study aimed to develop and validate a risk prediction model for social disability in schizophrenia patients, focusing on key contributing factors.

A cross-sectional study that involved 473 schizophrenia patients was conducted between February and September 2021. Standardized assessments, including the Social Disability Screening Schedule, Brief Psychiatric Rating Scale, The Medication Adherence Report Scale, and Brief Assessment of Cognition in Schizophrenia, were administered. Logistic regression was employed to identify the independent risk factors for social disability, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test.

Among the 473 participants (56.0% male, mean age = 29.31 ± 8.7 years old), 314 (66.4%) had a social disability. Significant differences in educational level, income, residence, and clinical characteristics were observed between the social disability and non-disability groups. The multivariate logistic regression analysis identified six independent risk factors for social disability: severity of psychiatric symptoms, medication adherence, cognitive function, perceived stigma, social support, and psychological capital. The final risk prediction model demonstrated strong discriminatory ability, with an AUC of 0.860 (95% CI: 0.820-0.899). The model exhibited high sensitivity (0.873) and specificity (0.868), with good calibration, as indicated by the Hosmer-Lemeshow test (X² = 5.746, p = 0.783).

The risk prediction model can effectively identify schizophrenia patients at high risk for social disability, supporting early and targeted interventions to improve outcomes.

Approximately 50% of older adults experience sleep disturbances. The preservation of high-quality sleep is essential; consequently, there is a pressing need for effective interventions to enhance sleep quality.

This study aimed to determine the effectiveness of the Mindfulness-Based Art Therapy (MBAT) on sleep quality, depression anxiety stress, and mindful attention awareness among older adults in urban contexts.

A quasi-experimental study with a one-group pretest-posttest design was conducted from March to May 2025. A random sample of 35 older adults with sleep problems was selected from 98 volunteers in Bangkok to participate in a six-week MBAT program using Mandala coloring. Data collection included a Personal Information Questionnaire, the Pittsburgh Sleep Quality Index (PSQI), the Depression Anxiety Stress Scales (DASS-21), and the Mindful Attention Awareness Scale (MAAS) at baseline (pre-test), 6 weeks (post-test), and 10 weeks (follow-up). Data were analyzed using descriptive statistics, one-way repeated measures ANOVA, and the Friedman test.

Significant changes in PSQI scores (p < 0.001) and DASS-21 scores (p < 0.050), were observed over the 10 weeks study. Multiple comparisons revealed that participants showed significant improvements in PSQI scores from baseline to the 6 weeks and from baseline to 10 weeks (p < 0.001 and < 0.001, respectively) and DASS-21 scores were significantly different between the 6 weeks and 10 weeks (p < 0.050). Effect sizes indicated large effects for PSQI scores and DASS-21 scores. MAAS scores showed medium effects that were not statistically significant.

The MBAT program enhances relaxation for older adults, improving their sleep quality and mental health. It incorporates education on sleep hygiene, stimulus control, sleep restriction, relaxation techniques, and weekly telenursing sessions. The findings suggest it should be integrated into standard nursing care, and future research should consider extending the program's duration to better change the MAAS score.

Thai Clinical Trials Registry (TCTR20250409001).(https://www.thaiclinicaltrials.org/show/TCTR20250409001).

Psychotherapeutic consultation at work (PT-A) aims to reach employees at risk of or suffering from common mental disorders early by offering low-threshold consultation that combines person-related and work-related modules. In addition to this individual-level approach, it is suggested that the psychosocial safety climate (PSC) in an organization precedes psychosocial working conditions and impacts employees' psychological health. The PSC reflects employees' perceptions regarding the extent to which psychological health and safety are prioritized by the management. However, little is known about the role of PSC on the intervention effect in participants receiving PT-A. Therefore, this study aims to investigate whether PSC moderates the effect of PT-A on self-reported depressive symptoms, anxiety, and general health status.

As a secondary analysis, this study analyzed data on participants (n = 549) from a multicenter randomized controlled trial in Germany evaluating the effectiveness of PT-A. Participants completed a baseline questionnaire (T0) and follow-up questionnaires nine months (T1) and 15 months later (T2). They provided information on general health status (one item of the 12-items Veterans RAND health survey), depressive symptoms (Patient-Health-Questionnaire-9), anxiety symptoms (Generalized Anxiety Disorder Scale-2) and the four-item PSC questionnaire. To investigate the role of PSC for the intervention effect, an interaction term (PSC*treatment group) was included in multiple linear regression analyses estimating (mental) health indicators at either T1 (n = 411) or T2 (n = 362). Analyses were adjusted for sex, age and occupational level.

Participants in the intervention group reported a significant symptom reduction nine and 15 months after PT-A. In terms of direct effects, PSC was negatively associated with anxiety symptoms at T1 (B = 0.16, p = .04). PSC did not moderate the intervention effect on depressive symptoms, anxiety symptoms or general health status.

Findings indicated a significant symptom reduction after PT-A, and showed that PSC does not play a role in the extent of symptom reduction among employees in Germany. However, PSC may need to be improved in German workplaces and its effect on individual-level symptoms regarding common mental disorders (CMD) should be further investigated.

The RCT was registered at the German Clinical Trial Register on 01.03.2021 (DRKS00023049) https//drks.de/search/en/trial/DRKS00023049.

The online version contains supplementary material available at 10.1186/s12889-025-24394-5.

Reduced lumbar multifidus (LM) cross-sectional area (CSA) has been linked with non-specific chronic low back pain (NCLBP); hence, lumbar stabilization exercise (LSE) is typically used as treatment. Real-time ultrasound imaging (RUSI) biofeedback can enhance performance and retention in LM activation. This study aimed to compare the effects of LSE with or without RUSI-biofeedback in patients with NCLBP. A single-blind, three-arm, parallel randomized controlled trial involving 90 participants, randomized into LSE_{RUSI-biofeedback,} LSE_{no-biofeedback}, or minimal intervention (n = 30 per group) was conducted. All interventions were administered twice weekly for 8 weeks, and outcomes (LM-CSA, pain, disability, and quality of life) were evaluated before and at 8-week and 20-week follow-ups. Compared to LSE_{no-biofeedback} and minimal intervention, LSE_{RUSI-biofeedback} demonstrated a greater increase (p < 0.05) in LM-CSA at 8 weeks (mean [95%CI] difference: 0.81 [0–1.62] and 1.79 [0.91–2.65], respectively) and at 20 weeks (1.61 [0.79–2.42] and 2.66 [1.79–2.53], respectively). LSE_{RUSI-biofeedback} showed a greater improvement (p < 0.05) in mental health scores at 8 weeks (3.63 [0.32–6.94]) and 20 weeks (7.34 [4.03–10.60]) compared to LSE_{no-biofeedback}. However, LSE_{no-biofeedback} showed a greater physical health scores at 20 weeks (− 4.89 [− 7.92 to − 1.86]) compared to LSE_{RUSI-biofeedback}. No significant differences (p > 0.05) were found between LSE_{RUSI-biofeedback} and LSE_{no-biofeedback} for pain or disability. The increase in LM-CSA significantly correlated with changes in pain (r = − 0.40 to − 0.50, p < 0.05) and disability (r = − 0.38 to − 0.42, p < 0.05). In conclusion, LSE with or without RUSI-biofeedback improved LM-CSA, pain, disability, and quality of life. However, LSE_{RUSI-biofeedback} led to a greater increase in LM-CSA. Such an increase appears to be related to changes in pain and disability. Trial registration: The study was registered in the Pan African Clinical Trials Registry (16/01/2018; PACTR201801002980602).

ObjectiveCognitive impairment is a core feature of schizophrenia spectrum disorders. Our previous study on a first-episode psychosis cohort showed that symptoms related to impoverished/disorganized communication and motor impoverishment predicted verbal and working memory scores, respectively. This study aimed to explore those predictors in people across the range of illness chronicity.MethodsWe employed iterative Constrained Principal Component Analysis (iCPCA) to investigate the relationship between 15 cognitive measures from the MATRICS battery, including processing speed, attention, working, verbal and nonverbal memory, reasoning, and problem-solving, and 27 Positive and Negative Syndrome Scale (PANSS) items in 198 outpatients from two sites in Australia and one in Canada. The iCPCA method was used to determine symptoms that reliably predict specific combinations of cognitive measures while controlling Type I errors.ResultsWe found that a verbal memory and learning component was predicted by the PANSS item Lack of Spontaneity and Flow of Conversation, and a visual attention/working memory component was linked to the PANSS item Motor Retardation.ConclusionsThese accord with our previous findings in an early psychosis sample, that is, negative symptoms of diminished expression are key predictors of cognitive abilities in schizophrenia. Namely, communication and motor impoverishments predicted lower scores on tests of verbal memory, learning, visual attention, and working memory. These findings may inform personalized treatment approaches targeting cognitive deficits and negative symptoms in schizophrenia.

Metabolic dysfunction-associated steatotic liver disease (MASLD), a liver-centric condition, is associated with cognitive impairment and sensorimotor alterations. However, it remains unclear whether MASLD is sufficient to drive central nervous system deficits. Here, using diet-induced mouse models, we showed that MASLD was associated with alterations in social memory, sensorimotor processing, and hippocampal function, including decreased parvalbumin-positive interneurons, reduced dendritic spine density, and diminished dentate gyrus neurogenesis and neuronal differentiation. Then, we selectively modulated liver metabolism through N-acetylgalactosamine small interfering RNA (siRNA) therapy against Cyclin M4 (CNNM4), a magnesium transporter dysregulated in MASLD. Liver-specific intervention with siRNA-Cnnm4 reversed impaired social memory and sensorimotor processing in association with recovery of hippocampal synaptogenesis and mitochondrial function pathways, alongside activation of neurogenesis-associated transcriptional programs. Our findings demonstrate that liver pathology is sufficient to drive neurobehavioral and hippocampal dysfunction in MASLD. Hepatic-specific intervention restores brain function, strongly supporting the existence of a causal and therapeutically targetable liver-brain axis for MASLD-associated neurological complications.

Early lead exposure is associated with psychological concerns in childhood, but less is known about sensitive periods of exposure or persistence into later adulthood.

To examine the association between prenatal and early postnatal lead exposure and risk of anxiety and depression in later adulthood.

This cohort study included participants from the Saint Louis Baby Tooth-Later Life Health Study (SLBT), who donated deciduous (baby) teeth in childhood during the 1950s through 1970s. SLBT participants were recontacted beginning in 2021 to complete health surveys and were masked to lead status. These data were analyzed from February 2025 through May 2025.

Lead exposure was measured in baby teeth across prenatal (approximately second trimester to birth, also split into second and approximately third trimesters), and early postnatal (birth to approximately 6 months old) periods.

Self-reported depressive and anxiety symptoms were assessed via the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder Screener-7 (GAD-7) at a mean age of 62 (SD, 3.6) years. Outcomes were dichotomized using clinical cutoffs for major depressive disorder and generalized anxiety disorder. Secondary analyses treated outcomes as continuous symptom scores.

Of 5131 SLBT participants, 718 (13.3%) had their baby teeth analyzed for lead (381 female [53%] and 334 male [47%]). In total, 695 and 697 participants responded to the PHQ-9 and GAD-7, respectively. The median (25th-75th percentile) combined tooth lead concentration was 1.34 (95% CI, 1.02-1.82) ppm. After adjusting for covariates, an IQR increase in combined tooth lead was associated with nearly 2 times the odds of later adulthood depression (odds ratio, 1.90; 95% CI, 1.20-2.99). The late prenatal period (approximately third trimester) appeared to be the most sensitive window (odds ratio, 1.55; 95% CI, 1.23-1.97). There was no association between early lead exposure and major later adulthood generalized anxiety disorder, but late prenatal and postnatal lead were associated with greater later adulthood anxiety symptoms.

Third-trimester lead exposure was associated with higher risk of major depressive disorder and anxiety symptoms in later adulthood. These findings emphasize the importance of factoring in later life health outcomes when considering the benefits of lead exposure interventions in childhood and suggest investment in screening and mental health services may be needed to address the long-term burden of historical lead exposure.

Bipolar disorder is a chronic condition characterised by alternating periods of depression and heightened mood, leading to considerable disability and frequent relapses, often resulting in difficulties in maintaining a stable social life. This study aimed to develop a set of essential information through a structured consensus process with patients, family members and healthcare professionals, to support patients with bipolar disorder to actively participate in their treatment and maintain a social life.

A modified Delphi method was used to develop a consensus among patients, family members and healthcare professionals. The following steps were involved: a review of existing relevant literature, individual rating by experts, and multiple expert panel meetings. We conducted two rounds of individual ratings and face-to-face panel meetings following the literature review.

The literature review identified 126 candidate items. The expert panel comprised 11 members, including patients, family members and healthcare professionals. Through the consensus process, 23 items were selected to form the essential information set.

The developed 23-item essential information set provides the minimum necessary information for patients, family members and healthcare professionals to support living well with bipolar disorder. This set can serve as a basis for psychoeducation. This study provides a foundation for future research to explore its effectiveness in clinical settings.

Patients and family members participated in the expert panel, where they rated candidate items, shared their lived experiences and provided feedback on the wording of each item in the information set during the panel meetings.

Dynamic Time Warping (DTW) is an emerging analytic technique that offers a flexible approach to modeling symptom dynamics in psychological and psychiatric research. Unlike traditional network models, which often rely on linear associations, DTW aligns symptom trajectories even when changes unfold at slightly different speeds or time intervals. This tutorial offers a brief introduction into DTW and demonstrates how to apply DTW to panel or time series data. We illustrate the workflow using clinical case data from patients with eating disorders, to capture temporal patterns that cannot be detected with conventional network analysis techniques, as these require more intensive time-series data. Key advantages include its applicability to non-stationary data, flexibility in handling irregular time intervals, and reduced reliance on frequent assessments, which patients often cannot maintain due to the burden. We also discuss some of the limitations such as noise, scaling decisions and lack of Granger causality associations. Finally, we outline directions for future research. By expanding the methodological toolkit available for studying therapy processes, DTW holds promise for advancing both research and clinical practice in personalized mental health care.