Npj mental health research最新文献

This study explores the link between moral injury and suicidal thoughts and behaviors among US military veterans, healthcare workers, and first responders (N = 1232). Specifically, it investigates the risk associated with moral injury that is not attributable to common mental health issues. Among the participants, 12.1% reported experiencing suicidal ideation in the past two weeks, and 7.4% had attempted suicide in their lifetime. Individuals who screened positive for probable moral injury (6.0% of the sample) had significantly higher odds of current suicidal ideation (AOR = 3.38, 95% CI = 1.65, 6.96) and lifetime attempt (AOR = 6.20, 95% CI = 2.87, 13.40), even after accounting for demographic, occupational, and mental health factors. The findings highlight the need to address moral injury alongside other mental health issues in comprehensive suicide prevention programs for high-stress, service-oriented professions.

Internet gaming disorder (IGD) is recognized as a mental health issue. Traditional interventions have limitations, but mindfulness meditation (MM) shows promise due to its flexibility and social acceptance. Study 1, fMRI data from 61 IGD patients and 60 healthy controls (HCs) were compared to assess functional connectivity (FC). Study 2- a randomized clinical trial, 80 IGD patients underwent either an MM intervention (twice-weekly for 8 sessions) or progressive muscle relaxation (PMR) as a control (pre-registered-Chinese clinical trial registry, ChiCTR2300075869, September 18, 2023). Study 1 revealed abnormal FC within the executive control network (ECN) and between the ECN and reward network in IGD patients. Study 2 showed that MM enhanced FC within the ECN and frontostriatal pathway. MM refining the coupling between brain regions involved in executive control and reward processing. This enhancement improves top-down control over game craving. These findings suggest that MM can effectively treat IGD.

Linguistic features within individuals' text data may indicate their mental health. This trial examined the linguistic markers of depressive and anxiety symptoms in adults. Using a randomised cross over trial design, 218 adults provided eight different types of text data of varying frequencies and emotional valance. Linguistic features were extracted using LIWC-22 and correlated with self-reported symptoms. Machine learning was used to determine associations. No linguistic features were consistently associated with depressive or anxiety symptoms within or across all tasks. Features associated with depressive symptoms were different for each task and there was only some degree of reliability of these features within tasks. In all machine learning models, predicted values were weakly associated with actual values. Some text tasks had lower levels of engagement and negative impacts on mood. Overall, the linguistic markers of depression and anxiety shifted in response to contextual factors and the nature of the text analysed. This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (date registered: 15 September 2021, ACTRN12621001248853).

Although patients with criminal legal system involvement have among the highest rates of suicide, the model that identifies patients at high risk of suicide at the United States Veterans Health Administration (VHA) does not include predictors specific to criminal legal system involvement. We explored whether the model's predictive ability would be improved (1) by retraining the model for legal-involved veterans and (2) by adding additional predictors associated with legal-involvement. For a combined outcome of suicide attempt or suicide death, the retrained models showed a positive predictive value (PPV) of 0.124 and false negative rate (FNR) of 0.527. Adding additional predictors associated with being legal-involved did not improve predictive accuracy. Retraining the VHA suicide risk prediction model for legal-involved patients improves the model's predictive ability for this group of high-risk patients, more so than adding predictors associated with being legal-involved. A similar approach for other high-risk patients is worth exploring.

Self-harm-encompassing suicidality and nonsuicidal self-injury (NSSI)-presents a critical public health concern, particularly as it is a major risk factor of death by suicide. Understanding the psychosocial dynamics of self-harm is imperative. Accordingly, in a large-scale, naturalistic study, we leveraged modern language analysis methods to provide a comprehensive perspective on suicidality and NSSI, specifically in the context of borderline personality disorder (BPD), where self-harm is particularly prevalent. We utilised natural language processing techniques to analyse Reddit data (i.e., BPD forum posts) of 992 users with self-identified BPD (combined N posts = 66,786). The present findings generated further insight into the psychosocial dynamics of suicidality and NSSI, while also uncovering meaningful interactions between the online BPD community and these behaviours. By integrating advanced computational methods with psychological theory, our findings provide a nuanced understanding of self-harm, with implications for clinical practice, clinical and personality theory, and computational social science.

This study assessed the preliminary effectiveness of a game-based digital therapeutics (DTx) intervention for depression and anxiety using a randomized controlled trial (RCT) design to examine the role of reinforcement learning (RL) personalization. This RCT included 223 individuals with depressive symptoms, aged 18-50, divided into three groups: an RL Algorithm group (personalized treatment), an active control group (fixed treatment), and a no-intervention control group. The intervention combined cognitive bias modification and cognitive behavioral therapy, with outcomes measured by the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7. Results showed significantly higher treatment response and recovery rates in the RL Algorithm group compared to the no-intervention group. The game-based DTx intervention, enhanced by RL personalization, effectively reduced depression and anxiety symptoms, supporting its potential for mental health treatment. The study was registered at clinicaltrials.gov (NCT06301555).

We introduce an artificial intelligence model to personalize treatment in major depression, which was deployed in the Artificial Intelligence in Depression: Medication Enhancement Study. We predict probabilities of remission across multiple pharmacological treatments, validate model predictions, and examine them for biases. Data from 9042 adults with moderate to severe major depression from antidepressant clinical trials were used to train a deep learning model. On the held-out test-set, the model demonstrated an AUC of 0.65, outperformed a null model (p = 0.01). The model increased population remission rate in hypothetical and actual improvement testing. While the model identified escitalopram as generally outperforming other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. The model did not amplify potentially harmful biases. We demonstrate the first model capable of predicting outcomes for 10 treatments, intended to be used at or near the start of treatment to personalize treatment selection.

The responsible reporting of suicide in media is crucial for public health, as irresponsible coverage can potentially promote suicidal behaviors. This study examined the capability of generative artificial intelligence, specifically large language models, to evaluate news articles on suicide according to World Health Organization (WHO) guidelines, potentially offering a scalable solution to this critical issue. The research compared assessments of 40 suicide-related articles by two human reviewers and two large language models (ChatGPT-4 and Claude Opus). Results showed strong agreement between ChatGPT-4 and human reviewers (ICC = 0.81-0.87), with no significant differences in overall evaluations. Claude Opus demonstrated good agreement with human reviewers (ICC = 0.73-0.78) but tended to estimate lower compliance. These findings suggest large language models' potential in promoting responsible suicide reporting, with significant implications for public health. The technology could provide immediate feedback to journalists, encouraging adherence to best practices and potentially transforming public narratives around suicide.

This study examines the net societal impact of housing price fluctuations on mental health during a housing boom. Analyzing data from 31 Chinese provinces between 2008 and 2019, we identify a significant positive relationship between housing price returns and the rate of psychiatric outpatient visits, suggesting that rising house prices decrease mental health. The results remain robust after controlling for local firms' stock returns. Placebo tests show that mental health impacts are primarily driven by housing price changes in the patients' local neighborhoods. Moreover, using City-level data from a hospital in Shenzhen (where housing prices showed the sharpest rise between January 2015 and April 2019), we document a two-week lagged effect of housing price surges on mental health Deterioration, which takes slightly longer to manifest than the negative effect of stock market fluctuations. Overall, our findings suggest that housing booms deteriorate mental health and increase the societal burden on healthcare systems.

Recent studies suggest links between adverse childhood experiences (ACEs) and elevated cancer risk, though mechanisms remain unclear. A 2021 review by Hu et al. found a dose-dependent increase in cancer risk among adults with at least one ACE. However, individual risk varies by ACE type and cancer type. For instance, childhood abuse or neglect may heighten cancer risk, while home environment ACEs may not. Potential mechanisms include risky behaviors (e.g., smoking, alcohol use), altered healthcare engagement (e.g., cancer screenings), and biological pathways (e.g., epigenetic changes). This review highlights current findings, research gaps, and implications for cancer prevention. Comprehensive, trauma-informed strategies promoting Positive Childhood Experiences (PCEs) are crucial for reducing cancer risk linked to ACEs in adulthood.