Npj mental health research最新文献

Emergency medical services (EMS) clinicians are first responders who experience recurrent occupational stressors. Cross-sectional research suggests that higher self-regulation of emotions may be related to lower stress, especially in individuals with regular substance use. However, temporal dynamics are unclear. Our objective was to identify real-time dynamics between perceived stress and emotion regulation in EMS clinicians who regularly use substances. Participants were full-time EMS clinicians reporting alcohol and/or cannabis use ≥2x/week. Participants completed five daily ecological momentary assessments (EMAs) at semi-random times for 28 days. We used a continuous-time structural equation model with Bayesian estimation to identify dynamics between perceived stress and emotion regulation (both within-person centered and standardized). The 110 participants completed 12,234 EMAs (81.3% adherence). Higher perceived stress predicted lower future emotion regulation (standardized estimate = -0.68 [-1.05, -0.31]). Inversely, higher emotion regulation predicted lower future perceived stress (standardized estimate = -2.25 [-3.38, -1.15]). We identified bidirectional relationships between perceived stress and emotion regulation in the daily lives of EMS clinicians with regular substance use. While results may not be generalizable to EMS clinicians who do not regularly use substances, we identified emotion regulation as a future interventional target to reduce real-time stress in this highest-risk group.

The involvement of microbiota-gut-brain axis in bipolar disorder (BD) has been uncovered, yet the specific tripartite interplay between the gut bacteriome, virome, and serum metabolome remains to be elucidated. We conducted a cross-sectional multi-omics analysis on 90 drug-free patients with bipolar depression and 30 healthy controls. A significant between-group difference in gut bacterial α-diversity was observed. Non-parametric test revealed 1929 bacterial and 134 viral species with significant inter-group difference, among which 249 bacterial and 7 viral species remained significant after FDR correction (P_adjusted < 0.05). Metabolomic analysis identified 261 significantly differential serum metabolites, which were enriched in 70 biological pathways and 40 pathways remained significant after correction. Integration of the datasets revealed strong cross-omic correlations, while only eight significant viral-metabolic correlations were detected. Post-FDR significant correlations with clinical features were exclusively observed between differential metabolites and scores of disease severity, with a predominance of negative correlations. Clinically, a random forest model integrating bacteriome, virome, and metabolome features achieved superior discriminative power (AUC = 0.986) compared to single-omics models (metabolites: 0.970; bacteria: 0.823; viruses: 0.732). This work demonstrated a dysregulated bacteriome-virome-metabolome network of patients with bipolar depression, providing a robust panel of candidate biomarkers for the precise diagnosis of BD.

Aged care staff are exposed to workplace risk factors that have the potential to considerably impact mental health. This study aimed to explore mental ill health, burnout, and associated occupational factors in a nationwide sample of residential aged care workers in Australia (N = 1085). Cross-sectional online survey data were collected. Rates of depression, anxiety, wellbeing, burnout, and turnover intentions were explored using descriptive statistics. Regression models were used to analyse occupational factors associated with mental ill health, wellbeing, and burnout. One quarter (24%) of participants reported symptoms indicating a probable depressive disorder, and over one third (35%) reported symptoms consistent with an anxiety disorder. Over half (56%) reported burnout at elevated levels. Lower perceived supervisor support and previous assault by a resident/client were associated with significantly higher anxiety, depression, and burnout. These findings suggest there is an urgent need for evidence-based interventions to improve conditions for residential aged care workers, including preventing staff assaults and upskilling managers in supporting the mental health of staff.

Artificial intelligence (AI) technologies in mental healthcare offer promising opportunities to reduce therapists' burden and enhance healthcare delivery, yet adoption remains challenging. This study identified key facilitators and barriers to AI adoption in mental healthcare, precisely psychotherapy, by conducting six online focus groups with patients and therapists, using a semi-structured guide based on the NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Data from N = 32 participants were analyzed using a combined deductive and inductive thematic analysis. Across the seven NASSS domains, 36 categories emerged. Sixteen categories were identified as factors facilitating adoption, including useful technology elements, the customization to user needs, and cost coverage. Eleven categories were perceived as barriers to adoption, encompassing the lack of human contact, resource constraints, and AI dependency. Further nine, such as therapeutic approach and institutional differences, acted as both facilitators and barriers depending on the context. Our findings highlight the complexity of AI adoption in mental healthcare and emphasize the importance of addressing barriers early in the development of AI technologies.

Machine learning (ML) is increasingly being developed to support individualized risk assessment and de-escalation in acute psychiatry. However, ML algorithms have been shown to exhibit unfair behavior based on protected characteristics, such as an individual's sex or ethnicity. The fairness of ML-based predictions of aggression in acute psychiatry has received limited investigation. To address this gap, we trained an ML algorithm to predict aggressive incidents from structured electronic health records corresponding to 17,703 patients at a large psychiatric hospital between January 2016 and May 2022 (n = 42,719 observation days). We analyzed predictions for fairness by assessing disparities in false positive rates (FPR) and true positive rates (TPR), based on patient race/ethnicity, gender, admission mode, citizenship, and housing status, as well as intersections of race/ethnicity and gender. A random forest algorithm attained ROC-AUC = 0.81. Fairness analyses revealed significant disparities in FPR and TPR across subgroups: FPR was higher for Middle Eastern and Black patients, men, those admitted into emergency care by the police, and those with unstable or supportive forms of housing. Our analysis demonstrates the potential for ML algorithms to reinforce and amplify known social and structural inequities, highlighting the importance of considering and addressing model fairness prior to clinical implementation.

This perspective calls behavioral healthcare leaders and providers to inform the direction and pace of value-based care (VBC). We recommend taking time to refocus on care quality metrics before payment models to engage providers and match current behavioral health system conditions. We review VBC, reasons why VBC is unique in behavioral healthcare, and key questions about VBC in behavioral health. We also feature youth behavioral health as particularly underdeveloped for VBC at this time. Finally, we propose a more gradual and inclusive approach to VBC in behavioral health, drawing on principles of quality improvement science. A stakeholder engaged process informed by the provider and patient community to identify appropriate care quality metrics in the short term may more productively drive value and facilitate innovation in the longer term.

Increasing numbers of neurodivergent students are engaging in higher education; however, support approaches vary within different institutions. Sometimes there are long waiting lists for specialised support, and most focus on academic adjustments, such as providing extra time in an assessment, rather than mental health and wellbeing. A systematic review, pre-registered on Prospero (CRD42024597980), was conducted to provide an overview of interventions supporting mental health and wellbeing of neurodivergent students in higher education. Ovid, Web of Science, and ERIC databases were searched in May 2025. Studies were included where the intervention aimed to improve mental health and/or wellbeing or improve the student experience, and the focus was on whether any strength-based approaches were used. Thirty-seven studies are included, conducted in seven countries. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of included papers. Interventions varied widely and included: coaching, cognitive behavioural therapy, self-help, peer support, psychotherapy, counselling, mentoring, mindfulness, and neuro/bio feedback. The narrative synthesis demonstrates little evidence of strength-based approaches and found that neurodivergent students were rarely involved in designing the interventions. Most commonly, studies focused on attention deficit hyperactivity disorder (ADHD) (17 studies) or Autism (14 studies), with few interventions considering co-occurrence or other neurotypes.

Digital phenotyping promises to transform psychiatry by using multimodal, densely sampled data. However, its potential is hindered by the lack of focus on identifying and validating digital biomarkers that accurately reflect mental states before evaluating their impact on outcomes. This longitudinal study used explainable machine learning to analyze multivariate, densely sampled data from 133 bipolar disorder (BD) participants over a median of 251 days, identifying robust digital biomarkers defining depressive episodes. The analysis included features from email-based daily self-reported mood, energy, and anxiety, as well as passively collected activity and sleep data using an Oura ring. The most robust descriptors of depressive episodes were lower daily mood variability, lower daily activity variability, and higher daily sleep onset latency variability. Self-reported daily mood features achieved the highest performance (AU-ROC: 0.82 ± 0.03). Our results establish the value of multimodal data and represent a critical first step toward automated detection and prediction of illness episodes in BD.

Loneliness and social (dis)connectedness are significant public health concerns, particularly among university students. Despite calls to reconceptualise loneliness as a systemic issue, interventions typically target individual students. This series of studies used a sequential mixed-methods and participatory action approach to explore students' social experiences and co-design a digital health solution. Focus groups (Study One) and a survey (Study Two) revealed that students see universities as partly responsible for their social connectedness, with perceptions of campus space being key. These insights informed the co-design of MAPP (Study Three), a preventative, system-focused digital solution. MAPP is an interactive campus map that visualises the university's living social network. It increases the visibility and accessibility of the university community to foster belonging, scaffold social engagement, and support institutional inclusivity. By shifting focus from the lonely student to the university as a social system, MAPP offers a novel, holistic response to student loneliness.

Mental health is sometimes understood as merely the absence of mental illness and sometimes more expansively as inclusive of a broader and more complete mental well-being. We present conceptual, empirical, and causal evidence for a distinction between the absence of mental illness and positive mental well-being. We discuss the implications for assessment, national tracking, research, policy, and mental healthcare. We argue for a greater clinical, policy, and public health attentiveness to positive mental well-being, to supplement work already being done on the treatment and prevention of mental illness.