Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 111 appraisals
Current psychiatry reports
Is Technology Remaking Therapy: The Screen and the Self: Telepsychiatry, AI Therapy, and the Defense Against Intimacy
PURPOSE OF REVIEW: This paper examines several methods of technology that have challenged traditional expectations of the meaning of psychotherapy, from the widespread adoption of telepsychiatry to the subsequent emergence of AI-driven therapeutic agents (Therabots). RECENT FINDINGS: Widespread usage of new technology that impacts the therapeutic process has outpaced an analysis of how that technology might affect the meaning and effectiveness of that process. Lawsuits assume such technology causes harm, while limited data and the literature has been more mixed. From Frankenstein to CRISPR, new technology always has its cheerleaders and its detractors. The more the technology seems to impact a topic especially connected to our humanity, the deeper the convictions will be on both sides. Certainly, when it comes to psychotherapy, the introduction of new technologies such as telepsychiatry to Therabots has provoked discussion. We argue that while new technologies offer practical advantages, they risk functioning as structural defenses against the vulnerability and authentic intimacy essential to transformative psychotherapy. Through analysis of the therapeutic alliance, relational dynamics, and the psychology of vulnerability, this paper contends that the structural form of telepsychiatry does not alter the inherent nature of the therapeutic experience, whereas AI-mediated therapy may collude with maladaptive defenses, fundamentally altering the nature of the therapeutic encounter.
1 July 2026
Read appraisal →Journal of affective disorders
The role of treatment expectancy in lifestyle interventions for depression.
BACKGROUND: Treatment expectancy is a key mechanism underlying placebo. While well-documented in psychopharmacology and psychotherapy, its role in lifestyle-based interventions for mental disorders is uncertain. Given the emerging role of lifestyle-based mental health care, a comprehensive evaluation of the role of treatment expectancy has not yet been explored, especially when compared to other more established therapies. The potential for genuine treatment efficacy to be masked by placebo response may bring into question the magnitude of effects of this treatment. METHODS: We sought to: 1) investigate the role of treatment expectancy as a moderator of depression treatment outcomes in a completed randomised controlled trial of psychotherapy versus lifestyle therapy (the CALM trial); and 2) provide guidance on measuring and reporting expectancy effects in future trials. CALM was a two-arm, parallel-group, non-inferiority randomised controlled trial, comparing lifestyle therapy (delivered by accredited dietitians and exercise physiologists) and psychotherapy (facilitated by psychologists). A total of 182 participants (91 per arm) experiencing psychological distress (equivalent to 'indicative depression') during the COVID-19 pandemic were randomised. We assessed associations of expectancy (measured prior to randomisation using the Credibility-Expectancy Questionnaire) with reductions in depressive symptoms (as measured by the PHQ-9). RESULTS: Expectancy scores did not differ between treatment groups at baseline (β = 1.14; 95%CI = -1.93, 4.22). Both arms reduced PHQ-9 between baseline and 8-weeks (lifestyle mean difference = -30.25%, SD = 58.23%; psychotherapy mean difference = -22.46%, SD = 66.87%). However, higher expectancy was associated with a greater reduction in depression only in the psychotherapy arm: for every 1-unit increase in expectancy score depressive symptoms reduced by an additional 3.21% for psychotherapy compared to lifestyle therapy (95%CI = -5.77, -0.65). CONCLUSIONS: In this study we showed that participants' expectations about treatment at enrolment were only associated with treatment response in psychotherapy. These findings highlight the need to account for modality-specific drivers of expectancy when evaluating all treatments for depression, and support the inclusion of expectancy in reporting guidelines for future trials. REGISTRATION: The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12621000387820; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380897).
16 June 2026
Read appraisal →Journal of affective disorders
Prediction models for suicide reattempts by lasso regression through machine learning models: Single versus multiple suicide attempters.
The best predictor of a suicide attempt is a previous attempt, apart from psychiatric diagnoses also associated. Some studies found other indicators of great risk for suicide reattempts. Machine Learning algorithms offer the potential for systematic detection of features that carry greater risk for an event. This study sought to develop a classification algorithm distinguishing between Single Suicide Attempters (SSA) and Multiple Suicide Attempters (MSA) in a Spanish multicentre national cohort to explore prediction of subsequent attempts in suicidal patients. Two models including the same sociodemographic and clinical variables grouped in more specific (Model I) or broad (Model II) categories were developed to explore risk factors for suicide reattempts. A Least Absolute Shrinkage and Regression Operator logistic regression with a 10-fold cross-validation was adopted. 1443 adult patients from the SURVIVE cohort were included (582 SSA and 861 MSA). Both Model I (AUC = 0.696; BAC = 0.644) and Model II (AUC = 0.678; BAC = 0.621) outperformed naïve majority-class classification for SSA and MSA. Bipolar disorder type II, binge-eating disorder, and schizophrenia variables weighted heavier on Model I for suicide reattempt-related; while eating disorder diagnosis, Africa as birthplace, affective disorder diagnosis, being employed, schizophrenia-spectrum disorder and substance use disorder diagnoses were the most important suicide reattempt-related of Model II. Affective disorders, eating disorders and schizophrenia-spectrum disorders emerged as the most important variables in predicting reattempts. Both models showed similar sensitivity and specificity when discriminating between SSA and MSA. Identifying specific risk factors for reattempts could have a significant impact on tailoring prevention strategies and interventions.
16 June 2026
Read appraisal →Journal of affective disorders
Heart rate variability as a predictor of future depressive symptoms: A systematic review and meta-analysis
Depression has limited early detection strategies. Heart rate variability (HRV), an indicator of autonomic regulation, has been linked to depression cross-sectionally, but its prospective value remains uncertain. We performed a meta-analysis and systematic review examining whether HRV is prospectively associated with later depressive symptom levels. PubMed, Embase, Cochrane, and PsycINFO (inception November 3, 2024) were searched for longitudinal studies of initially non-depressed baseline HRV and at least one-month follow-up assessing depressive or internalizing symptoms. Meta-analyses used multilevel random-effects models; prediction models were synthesized by HRV's statistical role. From 12,023 records, 56 studies entered the meta-analysis, and 62 the qualitative synthesis. HRV showed small but significant negative correlations with later depressive symptoms for frequency-domain indices (HF-HRV (r = -0.16, 95% CI -0.22 to -0.11), LF-HRV (r = -0.19, 95% CI -0.27 to -0.10), and VLF-HRV (r = -0.19, 95% CI -0.28 to -0.11)), and time-domain indices (RMSSD (r = -0.17, 95% CI -0.28 to -0.05) and SDNN (r = -0.15, 95% CI -0.26 to -0.03)). Across prediction-models, HRV showed direct effects in 43% and conditional effects in 57%, involving RSA and nonlinear patterns. HRV reflects psychophysiological mechanisms of depression vulnerability, but its prospective effects are small and context-dependent. Clinically, HRV may function as an adjunctive tool for identifying individuals who are more vulnerable to adversity and for informing preventive strategies, including the personalization of biofeedback and other regulation-focused interventions.
16 June 2026
Read appraisal →Biological psychiatry
Systematic Review and Mega-Analysis of the Peripheral Blood Transcriptome in Depression Implicates Dysregulation of Lymphoid Cells and Histones
BACKGROUND: Depression has been associated with transcriptomic changes in peripheral blood. However, the contribution of specific immune cell subsets or pathways remains unclear, and findings have been variable across previous studies, which have not tended to account for sample cellular composition. METHODS: We performed a systematic review of peripheral blood transcriptome studies in depression. For the 5 datasets meeting criteria (total N = 6011), we performed harmonized reprocessing and cell composition-adjusted differential gene and transcript analyses, followed by a bias- and inflation-adjusted weighted z score mega-analysis. We investigated the biological pathways and cell subsets implicated by the results. We also performed a sex-stratified gene network mega-analysis using consensus weighted gene coexpression network analysis (WGCNA). RESULTS: Few genes showed robust differential gene expression (DGE) in depression. Depression was reproducibly associated with decreases in replication-dependent histones and with a decrease in oxidative phosphorylation pathways in females only. Cell source analyses implicated lymphoid cells (T cells and natural killer cells) as likely contributors to the depression differential expression signature. WGCNA mega-analysis revealed multiple consensus modules associated with depression, with a PUF60-related module upregulated in both female and male depression in sex-stratified analyses. Two genes predicted to be causally relevant to depression by transcriptome-wide association studies (GPX4 and GYPE) showed significant DGE. CONCLUSIONS: These results are convergent with immunogenetic evidence implicating lymphoid cell dysregulation in depression, while also highlighting histone alterations as a key molecular signature in depression. They also indicate the importance of large-scale datasets for biomarker discovery in the context of heterogeneous disorders such as depression.
16 June 2026
Read appraisal →Journal of affective disorders
Multiple peripheral inflammatory markers in adolescents with major depressive disorder treated with repeated esketamine infusions: results from a randomized controlled trial
OBJECTIVE: While inflammatory dysregulation is well-documented in major depressive disorder (MDD), its response to esketamine treatment in adolescents remains poorly understood. We aimed to examine the effects of esketamine on the inflammatory cytokines in adolescent MDD. METHODS: This randomized, double-blind trial assigned 49 adolescents with MDD to receive three infusions of esketamine or midazolam over five days between December 2020 and April 2022. Suicidal ideation (measured by Columbia Suicide Severity Rating Scale, C-SSRS), depressive symptoms (measured by Montgomery-Åsberg Depression Rating Scale, MADRS) and plasma levels of 10 inflammatory markers were assessed at baseline, Day 6, and Day 12. RESULTS: Linear mixed model revealed significant time effects for all markers, but no significant drug main effect was observed. In esketamine group, alterations in IL-8 were associated with the reduction of C-SSRS Intensity at Day 6 (r = 0.444, p = 0.026), and alterations in IFN-γ were correlated with the reduction of MADRS (r = 0.399, p = 0.048) and C-SSRS Intensity (r = 0.427, p = 0.033) at Day 12. Baseline C4 negatively related to the reductions of MADRS and C-SSRS Ideation at Day 6 and 12 (all p < 0.005). CONCLUSIONS: Our study found no significant drug main effects for inflammatory markers, with most changes representing time effects observed in both groups. Exploratory results indicate that IL-8, IFN-γ, and C4 may warrant further investigation as potential biomarkers of esketamine response in adolescent MDD.
15 June 2026
Read appraisal →Journal of affective disorders
MDD-thinker: A reasoning-enhanced large language model for diagnosis of major depressive disorder.
BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability and poses a substantial public health burden. However, current diagnostic approaches largely rely on subjective assessments and lack the ability to integrate heterogeneous clinical and sociodemographic information. Recent advances in large language models (LLMs) offer new opportunities to support MDD diagnosis through reasoning over complex data, yet their clinical applicability is constrained by challenges related to interpretability, hallucinations, and reliance on synthetic data. METHODS: We propose MDD-Thinker, an LLM-based diagnostic system that integrates supervised fine-tuning (SFT) and reinforcement learning (RL) to enhance reasoning and interpretability under the evaluated conditions. Using the UK Biobank dataset, we constructed 40,000 structured reasoning samples and incorporated an additional 10,000 records from publicly available mental health datasets. MDD-Thinker was trained on these heterogeneous textual data and evaluated against conventional machine learning models, deep learning methods, and representative LLM baselines in terms of diagnostic performance and interpretability. RESULTS: MDD-Thinker achieved high performance in MDD diagnosis, with an accuracy of 0.8268 and an F1-score of 0.8081, showing better performance than conventional machine learning models, deep learning approaches, and representative LLM baselines on the evaluated dataset. Beyond predictive accuracy, it consistently produced structured reasoning paths that were clinically coherent, enabling transparent interpretation of diagnostic decisions in the evaluated experiments. The integration of SFT and RL contributed to notable improvements in both diagnostic reliability and reasoning quality. CONCLUSION: MDD-Thinker demonstrates the potential of reasoning-enhanced LLMs for large-scale MDD diagnosis under the evaluated settings. By jointly optimizing accuracy, interpretability, and efficiency, the proposed system provides a scalable and explainable approach for intelligent psychiatric assessment within the scope of the study, highlighting the potential of reasoning-oriented LLMs in mental health care.
15 June 2026
Read appraisal →Journal of affective disorders
Implementing artificial intelligence (AI) to facilitate health outcomes in mood disorders: Application versus aspiration.
The burden of depressive and bipolar disorders at the individual and societal level are extraordinary and increasing. For decades, evidence-based treatments for these conditions have been established but outcomes amongst individuals with lived experience remains suboptimal. Notwithstanding calls to close gaps between current practice and best practice, there is an absence of evidence that overall health outcomes are significantly improving. Artificial Intelligence (AI) is the cornerstone of the digital revolution. Currently, AI sources (e.g., Open Source) are widely accessed by healthcare providers and persons with lived experience for search queries and decision support. The aspiration for AI-informed medical practice is to improve health outcomes by assisting in timely diagnostic detection, illness monitoring, informing treatment selection, integrating multimodality care, decreasing barriers to access and facilitating scalability to psychosocial interventions. Against this background, rigorous evidence is still needed to empirically demonstrate transformative improvement in each of the aforementioned areas. In addition, multiple ethical, technical, scientific and economic questions are not adequately answered including aspects of confidentiality and patient engagement. This short commentary endeavors to succinctly summarize the evidentiary base as it relates to the capabilities that AI offers currently, in the near and more intermediate term. The overarching aim is to provide readers with an up-to-date understanding of what aspects of AI are currently applicable versus those that are aspirational.
15 June 2026
Read appraisal →JMIR mental health
Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials
BACKGROUND: Digital meditation-based interventions (MBIs) reach vast global audiences with millions of active users, yet concerns persist about the frequency and nature of adverse experiences (ie, AExs) occurring during meditation training. Some researchers have argued that AExs are substantially underdetected and reflect iatrogenic harm caused by meditation (ie, adverse effects [AEfs]). Others contend that these experiences largely reflect common stressors that would be experienced without meditation. These competing perspectives underscore the need for further research, particularly in the context of digital MBIs, the most widely used form of meditation training. OBJECTIVE: This study examined the prevalence, predictors, and subjective evaluations of AExs during a digital MBI and tested whether reported experiences may be caused by meditation practice via comparisons between meditation-exposed and nonexposed participants. METHODS: Data were drawn from 2 trials of the Healthy Minds Program. Exploratory study 1 (n=315) consisted of a sample of distressed US undergraduate students to estimate the prevalence of AExs and identify baseline predictors. Preregistered confirmatory study 2 (n=594) sampled distressed US adults from all 50 states to replicate findings from study 1 and to examine participants' subjective evaluations of AExs. Study 2 additionally compared AEx rates between participants who did and did not complete guided meditations to assess whether AExs could be caused by meditation exposure. Study 3 (n=87) used qualitative methods to analyze study 1 participants' responses to an open-ended question regarding their strategies for coping with AExs. RESULTS: In studies 1 and 2, 27.9% (88/315) and 10.1% (40/396) of participants, respectively, reported at least one AEx during the study period, with 6.7% (21/315) and 3% (12/396) reporting functional impairment, largely aligning with previous research. Critically, in study 2, rates of AExs did not significantly differ between participants who did and did not complete guided meditations, suggesting that these experiences were not caused by meditation practice. Higher baseline depression, anxiety, loneliness, experiential avoidance, and perceived barriers to meditation predicted more frequent AExs. In studies 1 and 2, 89.8% (79/88) and 90% (36/40) of participants who reported AExs, respectively, indicated that they were glad to have learned to meditate. Qualitative analyses showed that participants used diverse coping strategies, often using skills learned through the Healthy Minds Program. CONCLUSIONS: AExs were relatively common but occurred at comparable rates among participants who did and did not meditate, challenging claims that such experiences were caused by meditation practice in distressed individuals. Although a small subset of participants reported some degree of functional impairment, most evaluated their AExs as tolerable and described their overall MBI experience as positive. Together, these findings highlight the importance of distinguishing AExs that likely reflect epiphenomena of preexisting distress or symptoms from iatrogenic harm attributable to MBIs.
14 June 2026
Read appraisal →Behavioural brain research
Neural correlates of rumination in psychiatric disorders: A systematic review of fMRI evidence
BACKGROUND: Rumination is a transdiagnostic, persistent factor across many psychiatric conditions. Mapping its neural mechanisms may help differentiate ruminative profiles. OBJECTIVE: To systematically review fMRI studies that evaluated rumination in psychiatric populations METHODS: We searched four databases (PubMed, EMBASE, Web of Science, Scopus) and included studies that integrated rumination measures with fMRI data. RESULTS: Thirty-two articles met criteria; most were non-randomized (n = 31, 97%) and focused on depression (n = 24, 75%). Across disorders, clinical groups showed higher rumination than healthy controls, including in euthymic participants. In major depressive disorder, neural alterations implicated the default mode network, limbic and striatal regions, dorsolateral prefrontal cortex, and anterior cingulate cortex consistent with maladaptive ongoing processing. Neural signatures often persisted in remitted patients, suggesting possible trait markers of vulnerability. Intervention studies (n = 7) support using neuroimaging to track ruminative profiles and to inform more personalized interventions. CONCLUSION: This review highlights the importance of examining rumination, here measured via trait questionnaires, state-induction paradigms, cognitive tasks, or resting-state fMRI, using more consistent conceptual and methodological approaches. Although rumination is transdiagnostic, implicated brain regions and networks may show disorder-specific patterns. Given heterogeneity in the literature, future work should prioritize controlled and interventional studies to clarify neural mechanisms and guide circuit-based, targeted treatments. PROSPERO REGISTRATION: #CRD532512.
7 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cancer patient distress and health service use is linked with carer distress: evidence from a systematic review and meta-analysis
BACKGROUND: Family carers (caregivers) of cancer patients often have poor mental health, which may adversely affect patient wellbeing and health service use. We examined the evidence for this using a systematic review and meta-analyses. METHODS: We systematically searched online databases for studies reporting associations between carer psychological health and either: (a) psychological health of patients with cancer they cared for; or (b) health service use in carers or patients. Studies' risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tool. We undertook meta-analyses to estimate pooled correlations for the most commonly reported associations between carer and patient psychological health. For associations between carer psychological health and carer and/or patient health service use, we conducted a narrative synthesis. RESULTS: Our search identified 11,911 records, 169 of which were eligible. The majority of studies were of high-to-moderate quality. Carers who were distressed, depressed, anxious, or had poor scores in the mental component of quality of life were significantly more likely to be caring for patients with these same outcomes (pooled effect sizes ranged from 0.28 to 0.42; all p < 0.001). Subgroup analyses by gender, disease stage, and study quality revealed no substantial differences. Carers with poor psychological health used general practice, mental healthcare, and hospital services more frequently than those who were psychologically healthy, and the patients they cared for used more medications and had more frequent emergency presentations. CONCLUSION: The inclusion of carers alongside patients in early psychosocial care may improve family outcomes and reduce health service use.
1 June 2026
Read appraisal →Medicine
Predicting depressive symptoms in older adults with hearing impairment development and validation of NHANES-based machine-learning models: A cross-sectional study
Depressive symptoms frequently co-occur with hearing impairment in older adults, yet available prediction tools for this high-risk subgroup remain limited in practicality and applicability. We developed and internally validated prediction models for clinically relevant depressive symptoms among older adults with hearing impairment. We analyzed cross-sectional data from 5 National Health and Nutrition Examination Survey cycles (2005-2006, 2009-2010, 2011-2012, 2015-2016, and 2017-2018). Adults aged ≥60 years with hearing impairment were identified using the 2021 World Health Organization criterion (better-ear pure-tone average at 0.5/1/2/4 kHz ≥20 dB). Depressive symptoms were defined as a Patient Health Questionnaire-9 score ≥10. Candidate predictors included sociodemographic factors, comorbidities, lifestyle behaviors, and sensory/functional limitations; missing covariates were handled using multiple imputation. Participants were randomly split into training and validation sets (7:3). Predictors were selected using the Boruta algorithm followed by least absolute shrinkage and selection operator regression, yielding 6 predictors (age, poverty-income ratio, self-rated health, sleep disorder, memory difficulty, and physical functional limitation). Logistic regression and 6 machine-learning algorithms were developed. Model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration (calibration curves and Brier score), and clinical utility (decision-curve analysis). The analytic sample included 2123 participants, of whom 154 (7.25%) had depressive symptoms. In the validation set, AUCs across models ranged from 0.799 to 0.862. Logistic regression achieved the highest discrimination (AUC 0.862, 95% CI 0.800-0.911) with good calibration (Brier score 0.053) and favorable net benefit across clinically relevant thresholds. A nomogram, a dynamic nomogram, and an online calculator were developed to facilitate individualized risk estimation. Sensitivity analyses showed broadly consistent results, except for attenuated discrimination in the severe hearing-loss subgroup. A parsimonious 6-variable model demonstrated strong discrimination, good calibration, and potential clinical utility for risk stratification and targeted screening among older adults with hearing impairment. External validation and prospective evaluation are warranted before clinical implementation.
31 May 2026
Read appraisal →JMIR research protocols
The Effectiveness of Art Activities and Peer Group Participation on Psychological Well-Being Among Older Adult Center Residents: Protocol for a Randomized Controlled Trial
BACKGROUND: Transitioning to residential centers often induces significant psychological stress in older adults, adversely affecting their psychological well-being (PWB) and ability to age healthily. Chinese art activities and their combination with peer group participation have demonstrated efficacy in improving emotional well-being and fostering social connectivity among younger populations but have not been examined in older adult populations to date. OBJECTIVE: This study aims to assess the efficacy of Chinese art activities or their combination with peer group participation in enhancing PWB among Chinese older adults living in a residential center. METHODS: This multicenter randomized, assessor- and analyst-blinded trial uses a "max-min-con" design with 3 groups: control, Chinese art activities, and Chinese art activities plus peer group participation (1:1:1 allocation). A total of 90 participants aged 60 to 85 years and with a stay of 1 year or less will be recruited from 3 older adult centers (30 from each center) and assigned via minimized randomization to the groups. Interventions will consist of 3 sessions within a single week (Monday, Wednesday, and Friday). Baseline evaluations will be conducted before randomization to assess PWB, loneliness, happiness, relaxation, and salivary cortisol levels. Outcome evaluations will be conducted 30 minutes prior to and following each intervention session to assess immediate effects. A follow-up evaluation is planned 1 week after the final intervention to measure sustained outcomes. RESULTS: The study was funded in September 2024. Participant recruitment commenced on May 9, 2025, and ended on August 20, 2025, with the recruitment of 90 participants. Data collection ended on October 31, 2025, and data analysis is expected to conclude in April 2026, with the anticipated publication of results in 2026. CONCLUSIONS: This study is the first randomized controlled trial to evaluate the effects of Chinese art activities, either alone or combined with peer group participation, on enhancing PWB in older people and the efficacy of salivary cortisol as a biomarker for the assessment of PWB. The findings will provide empirical evidence to assist policymakers and older adult centers in selecting appropriate interventions, with the long-term potential to improve psychological health in older adults.
31 May 2026
Read appraisal →JMIR infodemiology
Early Depression Detection in Social Media: Monitoring of Individual Nighttime Dynamics and Large Language Model Analysis
BACKGROUND: Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social media data (traditional risk detection) rely heavily on users' complete historical information, which cannot meet the timeliness requirements of early intervention. This underscores the need for early risk detection (ERD) methods emphasizing early-stage, real-time warning. However, existing ERD studies have notable limitations such as (1) they overlook temporal activity patterns hidden in posting time stamps, missing vital warning signals; and (2) they depend on static templates or resource-intensive sequence models, resulting in limited interpretability and inefficient use of early data, ultimately constraining their clinical applicability for early intervention. OBJECTIVE: This study aims to develop an efficient, reliable, and interpretable ERD model. The core objectives are to extract temporal activity patterns features from posting time stamps, thereby enriching feature dimensions for risk detection; to leverage large language models (LLMs) for improved text filtering precision and depression-related factor analysis; and to achieve accurate early detection of depression to support clinical intervention. METHODS: We propose the Monitoring of Individual Nighttime Dynamics (MIND) and LLM analysis model, which integrates two key innovations: (1) circadian activity dynamics: posting time stamps are transformed into temporal activity patterns, analyzing fluctuations in posting frequency and timing to derive sleep-related features, thereby compensating for the limitations of text-only approaches, and (2) LLM depression profiler: LLMs are used for dynamic text filtering, automatically removing irrelevant noise and focusing on potential depression-related cues. Based on LLM semantic understanding, latent depression risk factors are identified, enhancing interpretability for clinical treatment and robustness to noise. RESULTS: Experiments on the eRisk2017 (data source: Reddit [Reddit Inc]) benchmark dataset demonstrated that MIND significantly outperformed existing baseline models in early detection sensitivity, specificity, and accuracy. By combining sleep-related features with text analysis, the model achieved interpretable, traceable predictions that can support clinical treatment. ALL relevant experimental code is publicly available. CONCLUSIONS: The MIND model combines temporal activity pattern features with LLM-based text analysis, addressing the challenges of poor interpretability and inefficient use of early-stage data in existing ERD methods. It significantly enhances early detection performance, offering a new paradigm for applying social media data in ERD, thereby enabling earlier intervention and reducing the public health burden of depression.
31 May 2026
Read appraisal →The journal of ECT
Comparison of the Effects of Dexmedetomidine and Remifentanil on Seizure Duration, Hemodynamics, and Recovery Time in Electroconvulsive Therapy
OBJECTIVES: Electroconvulsive therapy (ECT) is an important treatment method that is widely used in psychiatric conditions resistant to pharmacological treatments. METHODS: This prospective, randomized study included 100 patients undergoing ECT. Group D received dexmedetomidine and propofol, while group R received remifentanil and propofol. In this study, key parameters such as seizure duration, hemodynamic changes, and recovery time were analyzed. RESULTS: The investigation revealed no statistically significant differences between the groups with regard to mean age, gender distribution, or seizure duration ( P > 0.05). Subsequent to induction, group R exhibited statistically significantly lower systolic blood pressure, diastolic blood pressure, and mean arterial pressures in comparison to group D ( P < 0.05). Conversely, heart rate, systolic blood pressure, diastolic blood pressure, and mean arterial pressure levels in group R were found to be statistically significantly higher than in group D ( P < 0.05). The recovery time in group D was found to be statistically significantly longer than in group R ( P < 0.05). CONCLUSIONS: Dexmedetomidine provides superior hemodynamic stability, while remifentanil ensures faster recovery. Dexmedetomidine has been shown to be more advantageous in terms of hemodynamic stability, while remifentanil has been demonstrated to result in shorter recovery times. Clinicians should tailor anesthetic choices based on patient profiles and therapeutic goals.
30 May 2026
Read appraisal →The Cochrane database of systematic reviews
Pharmacotherapy for post traumatic stress disorder (PTSD)
BACKGROUND: Posttraumatic stress disorder (PTSD) is a prevalent and disabling disorder. Evidence that PTSD is characterised by specific psychobiological dysfunctions has contributed to a growing interest in the use of medication in its treatment. OBJECTIVES: To assess the effects of medication for reducing PTSD symptoms in adults with PTSD. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL; Issue 11, November 2020); MEDLINE (1946-), Embase (1974-), PsycINFO (1967-) and PTSDPubs (all available years) either directly or via the Cochrane Common Mental Disorders Controlled Trials Register (CCMDCTR). We also searched international trial registers. The date of the latest search was 13 November 2020. SELECTION CRITERIA: All randomised controlled trials (RCTs) of pharmacotherapy for adults with PTSD. DATA COLLECTION AND ANALYSIS: Three review authors (TW, JI, and NP) independently assessed RCTs for inclusion in the review, collated trial data, and assessed trial quality. We contacted investigators to obtain missing data. We stratified summary statistics by medication class, and by medication agent for all medications. We calculated dichotomous and continuous measures using a random-effects model, and assessed heterogeneity. MAIN RESULTS: We include 66 RCTs in the review (range: 13 days to 28 weeks; 7442 participants; age range 18 to 85 years) and 54 in the meta-analysis. For the primary outcome of treatment response, we found evidence of beneficial effect for selective serotonin reuptake inhibitors (SSRIs) compared with placebo (risk ratio (RR) 0.66, 95% confidence interval (CI) 0.59 to 0.74; 8 studies, 1078 participants), which improved PTSD symptoms in 58% of SSRI participants compared with 35% of placebo participants, based on moderate-certainty evidence. For this outcome we also found evidence of beneficial effect for the noradrenergic and specific serotonergic antidepressant (NaSSA) mirtazapine: (RR 0.45, 95% CI 0.22 to 0.94; 1 study, 26 participants) in 65% of people on mirtazapine compared with 22% of placebo participants, and for the tricyclic antidepressant (TCA) amitriptyline (RR 0.60, 95% CI 0.38 to 0.96; 1 study, 40 participants) in 50% of amitriptyline participants compared with 17% of placebo participants, which improved PTSD symptoms. These outcomes are based on low-certainty evidence. There was however no evidence of beneficial effect for the number of participants who improved with the antipsychotics (RR 0.51, 95% CI 0.16 to 1.67; 2 studies, 43 participants) compared to placebo, based on very low-certainty evidence. For the outcome of treatment withdrawal, we found evidence of a harm for the individual SSRI agents compared with placebo (RR 1.41, 95% CI 1.07 to 1.87; 14 studies, 2399 participants). Withdrawals were also higher for the separate SSRI paroxetine group compared to the placebo group (RR 1.55, 95% CI 1.05 to 2.29; 5 studies, 1101 participants). Nonetheless, the absolute proportion of individuals dropping out from treatment due to adverse events in the SSRI groups was low (9%), based on moderate-certainty evidence. For the rest of the medications compared to placebo, we did not find evidence of harm for individuals dropping out from treatment due to adverse events. AUTHORS' CONCLUSIONS: The findings of this review support the conclusion that SSRIs improve PTSD symptoms; they are first-line agents for the pharmacotherapy of PTSD, based on moderate-certainty evidence. The NaSSA mirtazapine and the TCA amitriptyline may also improve PTSD symptoms, but this is based on low-certainty evidence. In addition, we found no evidence of benefit for the number of participants who improved following treatment with the antipsychotic group compared to placebo, based on very low-certainty evidence. There remain important gaps in the evidence base, and a continued need for more effective agents in the management of PTSD.
30 May 2026
Read appraisal →Journal of medical Internet research
Can Digital Tools Fix Bias in Mental Health Triage?
Recent advances in digital technologies hold both promise and potential pitfalls for mental health triage. In this News and Perspectives article, JMIR Correspondent Beth Rush reports on the issue of bias and the role of digital tools in the mental health context.
29 May 2026
Read appraisal →Journal of medical Internet research
Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis
BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). However, assessing psychological distress in this population remains challenging because of stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors such as facial expressions and gaze hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on microbehavior intervals-brief, involuntary changes in multimodal nonverbal signals-that emerge during emotion-eliciting interviews. OBJECTIVE: This study aimed to determine whether microbehavior intervals improve the discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD. METHODS: HCWs participated in a semistructured interview that included 5 work-related, emotionally charged questions and that was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (Maslach Burnout Inventory General Survey 9-item) and PTSD (PTSD checklist for Diagnostic and Statistical Manual, 5th edition). Recordings were analyzed with computer vision models to generate time-series data of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT [a Family of Open Time-Series Foundation Models]) isolated microbehavior intervals without requiring manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted 4 symptom classes of psychological distress: "moderate-severe burnout," "subthreshold-provisional PTSD," "burnout+PTSD," and "resilient." We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions. RESULTS: We analyzed 258 interview recordings from 151 HCWs. Per interview, an average of 19.65 (SD 6.01) microbehavior intervals were detected, each lasting an average of 1.31 (SD 1.10) seconds. The classifier demonstrated robust performance across classes, achieving a macro- F1-score of 0.75 and a macro area under the receiver operating characteristic curve of 0.80 on held-out data. Ablation analysis showed that excluding gaze or arousal-valence signals caused the largest performance declines, particularly in recall and F1-score. The explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in microbehaviors emerging as key predictors. CONCLUSIONS: Focusing on microbehavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables an objective, robust, and explainable assessment of psychological distress and offers a promising complement to conventional psychometric assessments.
29 May 2026
Read appraisal →Journal of medical Internet research
Using the NASSS-Complexity Assessment Tool to Evaluate the Implementation of "Cadê O Kauê?": Chat-Story Intervention for Youth Participation in Mental Health Promotion in Brazil
BACKGROUND: The global public health challenge of young people's mental health is particularly evident in Brazil, which is considered the most anxious and the fifth most depressed country globally. In response to this, a digital intervention, that takes the form of a storytelling chatbot named "Cadê o Kauê?" (translation: "Where is Kauê?"), was coproduced by an interdisciplinary team of researchers, young people, and narrative designers as part of a project titled Engajadamente. The intervention was designed to strengthen Brazilian adolescents' skills in promoting their peers' mental health through direct support and collective action for mental health. The intervention was rolled out in select schools in Brazil and online via social media, but it encountered challenges during the initial implementation phase in school settings. OBJECTIVE: This project's primary objective is to evaluate the implementation of "Cadê o Kauê" in school settings in Brazil, assess current and potential complexities, and formulate recommendations to reduce or manage these complexities. The secondary objective of this project is to understand the current and potential sociotechnical change resulting from the implementation of the "Cadê o Kauê" in a real-world setting. METHODS: The Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework was selected as the theoretical framework to identify different areas of complexity across domains and further thinking of ways to reduce or manage these complexities. Data were gathered by conducting semistructured online interviews with the Engajadamente team members based in the United Kingdom and Brazil. The NASSS-Complexity Assessment Tool was used as a sensemaking device to facilitate a collective understanding of the implementation, and narratives were mapped onto specific NASSS domains. Key themes were identified across overarching domains using thematic analysis to meet the objectives of this evaluation. RESULTS: The results mapped onto the NASSS domains generated a narrative about the initial implementation of "Cadê o Kauê?" in schools. Existing and potential complexities across technology, organization, and wider system domains were characterized by interdependencies, unintended consequences, and uncertainties. The implementation of "Cadê o Kauê?" highlighted challenges related to internet dependency, infrastructure limitations, and varying levels of organizational readiness, teacher preparedness, and school culture. Addressing these through offline solutions, additional teacher training, whole-school engagement strategies, and a value-informed complexity approach can enhance accessibility, alignment with school priorities, and adaptability within Brazil's dynamic sociopolitical context. CONCLUSIONS: Using the NASSS framework, the evaluation captured both current and emerging complexities in implementing "Cadê o Kauê?" in schools. By engaging with theory-informed approaches to technology implementation projects and deep-diving into discursive dynamic interactions between technology and its "context" of implementation, we have explored ways to manage these complexities and developed suitable recommendations to guide the Engadajamente team and support similar projects in the future.
29 May 2026
Read appraisal →Psychological medicine
Effectiveness of 13-week dialectical behavior therapy skills training for non-suicidal self-injury: a multi-center randomized clinical trial in China
BACKGROUND: Dialectical behavior therapy skills training (DBT-ST) has received empirical evidence for treating non-suicidal self-injury (NSSI). However, its effectiveness in Chinese contexts remains understudied. This study aims to explore the effectiveness of DBT-ST for NSSI in a clinical sample of individuals with NSSI. METHODS: We conducted a multicenter, randomized clinical trial in China to evaluate the effectiveness of a 13-week DBT-ST compared with social support group therapy (SSGT). A total of 196 individuals with mental disorders and NSSI were recruited. The primary outcome was the frequency of NSSI thoughts and behaviors, assessed using the Ottawa Self-Injury Inventory. RESULTS: Participants were randomized to the DBT-ST group (n = 97) or the SSGT group (n = 99). DBT-ST was significantly more effective than SSGT in reducing the frequency of NSSI behaviors. It also showed superior effects on borderline symptoms and multiple domains of psychological functioning, including emotion dysregulation, impulsivity, alexithymia, mindfulness, psychological resilience, and emotional intelligence. Both groups exhibited significant reductions in the frequency of NSSI thoughts, suicidal ideation and attempts, and depression, with no significant between-group differences in these outcomes. CONCLUSIONS: These findings confirm that the 13-week DBT-ST intervention is an effective treatment for Chinese patients with NSSI in clinical settings.
29 May 2026
Read appraisal →BMC medicine
WISE-Therapy (What's Important: Schedule and Engage) and bouldering psychotherapy for depression: A randomized clinical trial
BACKGROUND: Group therapies address the growing treatment demand-supply gap for depression. This study evaluated whether the novel WISE-Therapy (WISE-T; What's Important: Schedule and Engage) and Bouldering Psychotherapy (BPT) are more effective in reducing depressive symptoms in an outpatient group therapy setting than treatment as usual (TAU). METHODS: This randomized, controlled, assessor-blinded, longitudinal, clinical trial was conducted in a naturalistic monocentric setting in Erlangen (Germany) between April 2022 and March 2023, with follow-up until July 2024. 128 adults were randomly assigned to WISE-T (44), BPT (40), or TAU (44). In 10 weekly 2-hour group sessions, WISE-T focused on identifying what is important, scheduling meaningful activities, and engaging in them for long-term mental health, and BPT combined psychotherapy with bouldering, while the control group received TAU. Depression severity was measured with the Montgomery-Asberg Depression Rating Scale at baseline, post-intervention, and one-year follow-up, and analyzed with a hybrid marginal model. RESULTS: Both intervention groups showed significantly greater reduction in depressive symptoms than controls at post-intervention (WISE-T: -5.5 points, 95%-CI [-9.5 to -1.5], P = .007, d = 0.62; BPT: -4.2 points, 95%-CI [-8.2 to -0.3], P = .037, d = 0.48). Response rates were 35.3% for WISE-T, 29.7% for BPT, and 21.1% for controls. Remission rates were significantly higher in WISE-T (17.6%) than in controls (2.6%, P = .047). Treatment adherence was good with minimal adverse events reported. The secondary outcomes showed significant results for anxiety, sense of coherence, self-efficacy, and mindfulness during physical activity. CONCLUSIONS: Both interventions demonstrated efficacy in reducing depressive symptoms compared with controls in this single-center setting. The study supports the integration of both therapies into outpatient care settings. TRIAL REGISTRATION: Study procedures received approval from the Friedrich-Alexander-Universität Erlangen-Nürnberg Ethics Committee (Ref. 21-332-B) on December 13, 2021. The Trial was preregistered in March 2022 via ISRCTN12347878 .
29 May 2026
Read appraisal →Journal of medical Internet research
Virtual Exposure With Response Prevention for Obsessive-Compulsive Disorder: Randomized Controlled Trial
BACKGROUND: Despite being highly effective and guideline-recommended for obsessive-compulsive disorder, exposure with response prevention is often underused due to barriers from both therapists and patients. Virtual reality may help address these barriers. OBJECTIVE: This study aimed to assess the efficacy and acceptance of virtual exposure with response prevention (VERP) in patients with contamination obsessive-compulsive disorder (conOCD) and checking obsessive-compulsive disorder (checkOCD) and to identify factors influencing treatment success. METHODS: Within a randomized controlled trial, 80 participants (40 with conOCD and 40 with checkOCD) were allocated to an intervention group, which received outpatient care as usual (CAU) supplemented by 6 weekly VERP sessions (VERP group), or to a control group receiving CAU alone (CAU group). Assessments occurred at baseline (t0), postintervention (t1), and at 3-month follow-up (t2). The reduction in obsessive-compulsive symptoms, as measured with the Yale-Brown Obsessive-Compulsive Scale, served as the primary outcome. RESULTS: Overall, obsessive-compulsive symptoms showed no greater improvement in the VERP group compared to the CAU group from t0 to t1 or t2 (ηp2=0.003-0.014). In exploratory subsample analyses, we observed a trend toward an interaction effect, showing greater reduction in obsessions among participants with checkOCD from t0 to t1 (F1,52=3.91; P=.05; ηp2=0.070) and greater anxiety reduction among participants with conOCD from t0 to t2 (F1,45=4.11; P=.05; ηp2=0.084). Exploratory moderation analysis suggested that adding VERP to CAU was more effective for those in the VERP group who had not prematurely discontinued prior psychological treatment and those with checking behavior. Sense of presence was only moderate, unrelated to outcome, and patient satisfaction with VERP was positive overall. CONCLUSIONS: While overall symptom reduction did not significantly differ between groups, exploratory analyses suggested trend-level differences in treatment response across obsessive-compulsive disorder subtypes and patient characteristics. These findings should be interpreted cautiously and are best viewed as hypothesis-generating.
28 May 2026
Read appraisal →European journal of clinical pharmacology
Ondansetron augmentation for obsessive-compulsive disorder: A systematic review and meta-analysis of randomized placebo-controlled trials
BACKGROUND: A substantial proportion of adults with obsessive-compulsive disorder (OCD) experience persistent symptoms despite optimized first-line treatment with exposure and response prevention and/or serotonin reuptake inhibitors (SRIs). Ondansetron, a selective 5-hydroxytryptamine-3 (5-HT3) receptor antagonist with plausible circuit-level relevance to cortico-striato-thalamo-cortical dysfunction, has been evaluated as an augmentation strategy. Still, evidence is dispersed across small randomized trials. METHODS: We conducted a PRISMA 2020-adherent systematic review and random-effects meta-analysis (PROSPERO: CRD420261302132). Embase (Ovid and Embase.com), MEDLINE, PubMed, and Scopus were searched from inception to 12 February 2026. Eligible studies were randomized, double-blind, placebo-controlled trials of oral ondansetron augmentation in adults (≥ 18 years) with OCD on stable background treatment. The primary outcome was change in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) total score, pooled at prespecified acute windows (week 4, ~week 8 primary, week 12). Secondary outcomes included Y-BOCS obsession and compulsion subscales. Risk of bias was assessed using RoB 2. Random-effects models (DerSimonian-Laird) were used; Wan's method was applied when required. RESULTS: Five trials (total N = 289) were included. Ondansetron augmentation was associated with greater improvement in Y-BOCS total score versus placebo at week 4 (MD 2.63; 95% CI 0.87-4.39; I²=62%); heterogeneity resolved after excluding one influential study (MD 1.94; 0.37-3.51; I²=0%). At week 8 (four trials; N = 244), benefit was larger (MD 5.53; 3.45-7.60; I²=48%) and remained robust in sensitivity analysis (MD 6.76; 5.81-7.71; I²=0%). At week 12 (three trials; N = 200), effects persisted (MD 6.41; 4.28-8.53; I²=55%) with reduced heterogeneity after excluding one study (MD 5.06; 2.82-7.30; I²=0%). In two trials reporting subscales (N = 214), obsessions improved at weeks 4 and 8 (overall MD 2.67; 1.66-3.67; I²=0%), while compulsions showed a smaller, later signal (overall MD 1.02; 0.19-1.86; I²=0%). Safety reporting was variably detailed across trials; known QT-prolongation considerations remain relevant for off-label use. CONCLUSIONS: In randomized, placebo-controlled trials, ondansetron augmentation was associated with greater improvement in clinician-rated OCD severity than placebo over short-term follow-up, with signals emerging by week 4 and persisting through 8-12 weeks. However, these findings should be interpreted cautiously because the evidence base is small, geographically concentrated, and partly influenced by individual studies. The current evidence should therefore be considered preliminary and hypothesis-generating rather than definitive. Larger multicenter trials with standardized background SRI optimization, preregistered outcomes, and rigorous safety monitoring are required before routine clinical adoption can be considered.
25 May 2026
Read appraisal →Neuroscience
The effects of acute psychosocial stress on binge urges, inhibitory control, negative urgency and eating pathology in Binge Eating Disorder
Stress is frequently reported as a trigger for binge episodes, yet it remains unclear how subjective and physiological stress responses interact with cognitive and affective mechanisms to increase binge eating risk. The present study investigated the effects of acute psychosocial stress on binge urges, focusing on the role of state- and trait-level vulnerabilities. Twenty-eight individuals with BED were randomized to either a Stress condition (n = 14) or No-Stress condition (n = 14). Following an acute psychosocial stressor, participants completed the Stop-Signal Task to behaviourally assess inhibitory control. Self-reported mood, binge urge ratings and saliva samples for stress biomarker detection were collected at three timepoints throughout the experimental session. Results demonstrated that acute psychosocial stress elicited marked increases in total mood disturbance, without corresponding physiological stress responses and significantly impaired inhibitory control. Notably, only subjective stress responses were associated with greater binge urges, whereas physiological stress responses were not. This dissociation highlights subjective stress reactivity as a clinically meaningful mechanism of risk in BED, underscoring the potential value of interventions that target stress perception and regulation to reduce binge eating vulnerability.
25 May 2026
Read appraisal →Lancet (London, England)
Updated trends in the global prevalence and burden of mental disorders, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023
BACKGROUND: The 2023 iteration of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated prevalence, incidence, and health burden for 375 diseases and injuries, including 12 mental disorders. We assess past, current, and emerging trends in the prevalence and burden of mental disorders across sexes and age groups, for 21 regions, 204 countries and territories, and by Socio-demographic Index (SDI) quintile, from 1990 to 2023. METHODS: Mental disorders included in GBD 2023 were anxiety disorders, major depressive disorder, dysthymia, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, anorexia nervosa, bulimia nervosa, idiopathic developmental intellectual disability, and a residual category of other mental disorders. A literature review identified epidemiological data for each disorder. These were analysed via a Bayesian meta-regression to estimate prevalence by disorder, sex, age, location, and year. Disorder-specific prevalence was multiplied by disability weights representing the severity of health loss associated with each disorder to estimate years lived with disability (YLDs). Deaths due to anorexia nervosa were assessed with a Cause of Death Ensemble modelling strategy to estimate deaths by sex, age, location, and year, and then multiplied by the standard life expectancy at age of death to estimate years of life lost (YLLs). YLDs equalled disability-adjusted life-years (DALYs) for all mental disorders except anorexia nervosa (the only mental disorder considered as an underlying cause of death in GBD), for which DALYs represented the sum of YLDs and YLLs. We presented prevalence, deaths, YLDs, YLLs, and DALYs as counts, age-specific rates per 100 000 population, and age-standardised rates per 100 000 population. FINDINGS: We estimated 1·17 billion (95% uncertainty interval 1·06-1·31) prevalent cases of mental disorders globally in 2023, equivalent to an age-standardised prevalence rate of 14 210·7 cases (12 849·5-15 940·1) per 100 000 population. These estimates represented a 95·5% (75·0-121·2) increase in prevalent cases and 24·2% (11·4-41·4) increase in age-standardised prevalence rate between 1990 and 2023. All mental disorders showed increases in prevalent cases between 1990 and 2023, while notable increases were seen in age-standardised prevalence rates for anxiety disorders, major depressive disorder, dysthymia, anorexia nervosa, bulimia nervosa, schizophrenia, and conduct disorder. There were an estimated 171 million (127-228) DALYs due to mental disorders globally across sex and age in 2023, equivalent to an age-standardised DALY rate of 2070·5 DALYs (1519·1-2750·5) per 100 000 population. Mental disorders contributed to 6·1% (4·8-7·6) of all-cause DALYs in 2023, making them the fifth leading cause of global DALYs (up from 12th in 1990). DALYs were almost entirely composed of YLDs. Mental disorders were the leading cause of YLDs in 2023 (up from second in 1990), explaining 17·3% (14·8-20·6) of all-cause global YLDs. Leading causes of mental disorder DALYs were anxiety disorders (ranked 11th among the 304 diseases and injuries at Level 4 of the GBD cause hierarchy), major depressive disorder (15th), and schizophrenia (41st). Globally in 2023, mental disorder age-standardised DALY rates were higher among females (2239·6 [1643·7-3014·1] per 100 000) than among males (1900·2 [1399·8-2510·8] per 100 000), and peaked in the 15-19 years age group (2617·3 [1850·6-3696·8] per 100 000). All locations showed increased mental disorder DALY rates in 2023 compared with 1990, ranging across countries and territories from 1302·4 (952·7-1683·7) per 100 000 in Viet Nam to 3555·8 (2661·9-4715·0) per 100 000 in the Netherlands. Across SDI quintiles, DALY rates ranged from 1853·0 (1352·1-2469·3) per 100 000 for middle SDI to 2184·1 (1606·1-2890·3) per 100 000 for high SDI. INTERPRETATION: A significant health burden was imposed by mental disorders in all countries and territories in 2023, irrespective of the health resources available. In some instances, this burden has increased over time and is unevenly distributed across populations. Stronger surveillance systems, particularly in low-income and middle-income countries, are required. Additionally, we need more coordinated and inclusive policies to reduce the burden through early treatment and prevention, tailored to sex and age differences across locations. Responding to the mental health needs of our global population, especially those most vulnerable, is an obligation, not a choice. FUNDING: Gates Foundation, Queensland Health, and University of Queensland.
25 May 2026
Read appraisal →International journal of yoga therapy
Systematic Review on the Effect of Yoga Nidra on Menstrual Disorders Among Adult Women
Menstrual disorders including amenorrhea, premenstrual syndrome, and dysmenorrhea significantly disrupt the normal menstrual cycle and negatively impact quality of life. Yoga nidra, a guided meditation technique, has been explored for its potential therapeutic benefits in menstrual health, although comprehensive evidence remains limited. The present systematic review aimed to assess the effect of yoga nidra on menstrual disorders by analyzing existing research and synthesizing findings. The review followed PRISMA guidelines and involved searches in PubMed/MEDLINE, Cochrane Library, Science Direct, and Google Scholar using keywords related to yoga nidra and menstrual disorders. Studies were selected based on predefined inclusion criteria. A total of seven randomized controlled trials were included. The findings indicated improvements across four key areas of women's health: (1) psychological well-being, (2) hormonal balance, (3) hematological profile, and (4) physiological parameters. These results suggest that yoga nidra may be an effective complementary therapy for managing menstrual disorders because of its noninvasive and low-cost nature. However, further high-quality randomized controlled trials are necessary to confirm its efficacy.
24 May 2026
Read appraisal →Studies in health technology and informatics
Usability of Mobile Health Apps from the Patient Perspective: A Systematic Review
Mobile Health (mHealth) apps are widely used to support care, but patient-focused usability evidence remains fragmented. We conducted a systematic review (January 2012-September 2025) of PubMed, IEEE Xplore, and Scopus to identify studies assessing usability from the patient perspective using standardized questionnaires or structured interviews. Of 490 records retrieved, 146 studies met the inclusion criteria. Fifteen disease categories were represented, most commonly mental health, neurology, metabolic/endocrine, and oncology. Overall, 24 distinct usability assessment methods were reported, and most studies relied on a single method. Among standardized instruments, the System Usability Scale (SUS) was most common (50 studies, 34.3%); the mean SUS score across these studies was 76.37, above the acceptability benchmark of 68. Overall, mHealth apps were generally rated as usable by patients, but future work should adopt mHealth-specific standardized measures, enroll larger samples, and report methods transparently to enable benchmarking and inform better app design.
24 May 2026
Read appraisal →Journal of medical Internet research
Toward Participatory Precision Health With Co-Designed Recommendations: Systematic Review of Just-in-Time Adaptive Interventions in Adolescents and Young Adults
BACKGROUND: The transition from adolescence to young adulthood (age 10-25 years) constitutes a sensitive developmental period marked by rapid biological, psychological, and social change, during which preventive health interventions can shape long-term outcomes. Mobile health tools offer accessible opportunities for tailored support for this population, but often adapt poorly to dynamic contexts, resulting in inconsistent engagement and effects. Just-in-time adaptive interventions (JITAIs), which tailor support in real time using ongoing data, are increasingly explored as precision health strategies. However, how these mechanisms are designed, implemented, and evaluated for adolescents and young adults (AYAs) has not been systematically reviewed. OBJECTIVE: This review aimed to synthesize the evidence on JITAIs developed for AYAs, examine how their adaptive mechanisms have been designed to support specific health goals and changing AYA contexts, and assess methodological reporting quality to inform future precision health intervention development. METHODS: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and SWiM (Synthesis Without Meta-Analysis) reporting guidelines. Twelve databases were searched for peer-reviewed studies published from 2013 to 2025. Eligible studies focused on participants aged 10 to 25 years and reported real-time adaptive mobile health interventions consistent with JITAI design principles. Two reviewers independently conducted screening, data extraction, and methodological quality appraisal using the Joanna Briggs Institute checklists. AYA coauthors contributed to all phases. Due to substantial heterogeneity in study populations, intervention content, adaptive mechanisms, comparators, and outcome measurements, findings were synthesized narratively, and no meta-analysis was conducted. RESULTS: A total of 61 unique interventions were included. JITAIs for AYAs addressed substance use (n=24, 39.3%), mental health (n=23, 37.7%), and physical health or chronic conditions (n=14, 23%). JITAI tailoring mechanisms relied predominantly on self-reported behavioral data. Decision rules were typically symptom threshold-based, and decision points were commonly daily or event-triggered. Methodological concerns with reporting on intervention administration, participant selection, and outcome measurement reliability were pervasive across all studies, limiting the interpretability of observed effects and cross-study comparisons. Ethical considerations, including researcher positioning and reflexivity, alongside the depth of reporting around participatory AYA engagement in design and implementation, were also inconsistent. CONCLUSIONS: This review contributes a novel perspective to AYA digital health by moving beyond intervention outcomes to examine how core adaptive mechanisms are operationalized for AYAs across multiple health domains, while also integrating AYA perspectives into the interpretation of findings and recommendations. Unlike prior reviews focused primarily on adults or specific conditions, it identifies broader contextual, methodological, and ethical considerations relevant to AYA precision health. These findings highlight the need for more transparent, contextually responsive, and youth-centered adaptive interventions, alongside more rigorous designs for evaluating adaptive intervention components in daily life contexts.
23 May 2026
Read appraisal →BMJ open
Toolkit to prompt and support stopping antidepressants in general practice: an interview study exploring patient participants' experiences in the RELEASE trial.
OBJECTIVE: To explore patient experiences with the RELEASE (Redressing long-term antidepressant use) toolkit resources, including antidepressant hyperbolic tapering plans designed to minimise withdrawal symptoms as part of the RELEASE trial's implementation evaluation to understand what aspects of the toolkit work well and identify areas where refinement could enhance future implementation. The RELEASE intervention, a multistrategy intervention codesigned with general practitioners (GPs) and patients to prompt and support stopping long-term (>12 months) antidepressants, is being evaluated in an effectiveness-implementation hybrid type-1 cluster randomised controlled trial in general practice. STUDY DESIGN: One-to-one semistructured qualitative interviews using reflexive thematic analysis to identify patterns of shared meaning. SETTING: General practice, south-east Queensland, Australia. PARTICIPANTS: Adult participants (aged 18 years or older) taking an antidepressant long-term allocated to the intervention arm of the RELEASE trial. RESULTS: 31 participants with an average duration of antidepressant use of 16.5 years (range 1-30 years) were interviewed. Seven themes were identified. Across seven themes, participants described how the RELEASE toolkit resources prompted reflection on their antidepressant use, supported informed conversations with GPs, and offered tapering plans that were both structured enough to instil confidence and flexible enough to help participants feel in control of their tapering journey. They also identified barriers, including beliefs that very small tapering doses were unnecessary, difficulty accessing the mini-doses required for hyperbolic tapering and the challenge of managing withdrawal symptoms. For those who successfully stopped, the transition was associated with a renewed sense of autonomy. Participants also offered practical suggestions to strengthen the toolkit and its broader implementation. CONCLUSIONS: The RELEASE toolkit resources played a meaningful role in prompting and supporting informed shared decision-making and guiding attempts to stop antidepressants. Clearer messaging about the potential for withdrawal symptoms and access to mini-doses for tapering are needed to support patients to safely stop antidepressants. TRIAL REGISTRATION NUMBER: ACTRN12622001379707p; Pre-results.
23 May 2026
Read appraisal →JMIR mental health
Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study
BACKGROUND: Artificial intelligence (AI)-based conversational tools are rapidly expanding within mental health care as a means of increasing access and scalability. At the same time, these systems introduce distinct safety risks arising from both user disclosures (eg, self-harm ideation) and inappropriate or inadequate AI responses. OBJECTIVE: This study aimed to develop and evaluate the Automated Safety Testing and Reporting Application (ASTRA), an external system intended to identify clinically relevant risk behaviors across entire AI-mediated mental health conversations. METHODS: ASTRA was tested on a dataset of 100 synthetic therapeutic conversations written by licensed clinicians to reflect risk behaviors and harmful responses between users and AI tools. Conversations varied in length and included both subtle and overt risk behavior examples across 8 predefined categories. Human coder consensus ratings served as the reference standard. ASTRA's classifications were evaluated across 2 prompt iterations using standard diagnostic performance metrics and agreement statistics. RESULTS: ASTRA demonstrated consistently high concordance with expert human ratings across all categories. Accuracy exceeded 0.90 for all risk behavior categories examined, with specificity uniformly high and sensitivity varying by category (range 0.55-1.00). Agreement beyond chance was substantial to almost perfect between ASTRA and human raters (κ=0.65-1.00). Detection of user self-harm indicators was particularly accurate, even in conversations where risk was expressed subtly. CONCLUSIONS: In this initial validation study, ASTRA reliably identified multiple forms of mental health-related risk behaviors at the conversation level. These findings support the feasibility of independent safety monitoring systems as a complement to AI tools used in mental health contexts and underscore the need for further evaluation using larger and real-world datasets.
21 May 2026
Read appraisal →BMC psychiatry
Olanzapine effectiveness on weight gain in anorexia nervosa patients: systematic review and meta-analysis
BACKGROUND: Anorexia nervosa (AN) is a complex psychiatric disorder marked by extreme caloric restriction, intense fear of weight gain, and distorted body image perception. Not only influence physical health, but it also creates serious psychological burdens. Treatment of AN is especially daunting due to its multi-dimensional nature. Recent research indicates that a pharmacological approach, such as the prescription of atypical antipsychotics like Olanzapine, may help treat both the nutritional deficiency and psychological distress related to the disorder. By allowing an individual gain weight and potentially reducing comorbid depression and anxiety, Olanzapine can be a valuable add-on to standard treatments. AIM: This study aims to investigate the effectiveness of Olanzapine in causing weight gain among anorexia nervosa patients. Based on comparisons of results in a number of clinical trials, the study aims at establishing the effect of Olanzapine on weight restoration and investigating any accompanying shift in the psychological state of the patients. METHOD: A comprehensive literature search was performed on Medline, ClinicalTrials.gov, Scopus, and the Cochrane Database of Systematic Reviews for all records from the earliest date available through to August 22, 2024. Randomized controlled trials of olanzapine treatment in anorexia nervosa were included. Studies were screened, and data were extracted. Risk of bias was assessed with the Cochrane risk-of-bias tool. Records for meta-analysis were combined in a random-effects model to calculate effect sizes, and heterogeneity was quantified with the I2 statistic. RESULT: 207 records were found, 159 of which were excluded after title and abstract screening. After full-text review, seven RCTs were found. These trials involving 152 patients assessed the impact of olanzapine on weight gain in anorexia nervosa. Meta-analysis of 4 studies (91 patients) showed a mean difference in BMI of 1.68 kg/m² (95% CI: 0.82, 4.18) in favor of olanzapine but not statistically significant (p = 0.19), with high heterogeneity (I² = 94%). Sensitivity analysis reduced the heterogeneity to 28% but not the effect size. CONCLUSION: Although olanzapine showed a numerically greater weight gain compared to control conditions, the pooled effect did not reach statistical significance and was associated with substantial heterogeneity. Therefore, current evidence remains inconclusive. Olanzapine may be considered in selected cases, particularly where psychological comorbidities are prominent, but larger and methodologically robust trials are needed before routine clinical use can be recommended. CLINICAL TRIAL NUMBER: Not Applicable.
21 May 2026
Read appraisal →The Journal of clinical psychiatry
Systematic Review and Meta-Analysis of Mood Stabilizers in the Treatment of Adolescent Patients With Nonsuicidal Self-Injury in China
Objective: Nonsuicidal self-injury (NSSI) refers to deliberate, repeated acts of damaging one's own body tissue without suicidal intent. The aim of this systematic review and meta-analysis was to explore the effectiveness of mood stabilizers in treating depressive adolescent patients with NSSI in China. Data Sources: Literature published before June 30, 2025, was included. Chinese databases searched included CBM (Chinese Biomedical Database), CNKI (China National Knowledge Infrastructure), WANFANG (Wanfang Database), and CSSCI (Chinese Social Sciences Citation Index); English databases included MEDLINE, Embase, and the Cochrane Library. Searches used the following keywords: [Bipolar disorder OR depression OR NSSI] AND [Mood stabilizer OR Lithium OR Valproate OR Carbamazepine OR Oxcarbazepine OR Lamotrigine OR Antipsychotic OR Antidepressant]. Study Selection: Chinese and English literature related to the treatment of NSSI with mood stabilizers in adolescent patients in China was searched. Editorials, comments, letters, reviews, and case reports were excluded. Randomized controlled trials conducted in China comparing mood stabilizer augmentation of basic therapy versus basic therapy alone for treating NSSI were included, and studies evaluating mood stabilizer treatment for NSSI using pre-post designs were also included. A total of 9 studies were included, 4 comparative studies by control group and 5 comparative studies of before and after treatment. Data Extraction and Synthesis: Indices of depression, anxiety, impulsivity, self-injury severity, and treatment response were synthesized and analyzed. Mean differences or standardized mean differences and 95% confidence intervals (CIs) were used for symptom change. Odds ratios and their 95% CIs were also used for binary outcomes. Results: Severity of anxiety, depression, impulsivity, and self-injury was significantly reduced following treatment with mood stabilizers. Compared to treatment without mood stabilizers, anxiety, depression, and self-injury were also significantly reduced, and the response rate was significantly improved in adolescents with depression and NSSI. The incidence of self-injury in these patients was significantly reduced by mood stabilizers. Conclusion: These results demonstrate the important role of mood stabilizers in treating NSSI and suggest that mood stabilization may be more critical than mere improvement of affective symptoms.
21 May 2026
Read appraisal →BMC public health
Interventions against loneliness and social isolation in older adults- a systematic review.
BACKGROUND: Loneliness is a growing health and social challenge. The COVID-19 pandemic boosted feelings of loneliness for many people. To combat loneliness, a wide range of interventions have been developed and evaluated for their efficacy and effectiveness. As a result, many new interventions to alleviate loneliness have been developed, especially technological interventions. Therefore, the objective of this study was to conduct a comprehensive and up-to-date systematic review to identify interventions aimed at loneliness and social isolation among adults, 60 years and older published during or post-pandemic. METHODS: A comprehensive literature search was conducted for studies between 2020 and 2024 across five online databases: MEDLINE via PubMed, Web of Science, Scopus, Cochrane Library, and CINAHL. Title and abstract screening, critical appraisal of the studies, and data extraction were performed by two independent reviewers. A narrative approach was adopted to assess and integrate the diverse findings from the research. RESULTS: Ultimately, 79 studies were included in this systematic review. The results are structured based on the categorization of the interventions, which includes differentiation between analogue interventions, technological interventions and multicomponent (analogue and technological) interventions. The effectiveness of analogue interventions, particularly community-based interventions such as group meetings, social participation programs, and educational or psychological interventions, tended to be superior to that of technological interventions. CONCLUSIONS: The combination of analogue and technological interventions in particular produced promising results regarding a decrease of loneliness. Specific interventions must be tailored to the target group and setting and regularly reevaluated. The aim now should be to implement these interventions comprehensively and monitor their effectiveness over several years. Future research should focus on differentiating the circumstances under which various forms of intervention are effective. TRIAL REGISTRATION: PROSPERO systematic review registration: CRD42024538755.
20 May 2026
Read appraisal →Journal of affective disorders
Biological age acceleration associated with mental and behavioural disorders: Evidence from the UK biobank cohort.
BACKGROUND: Mental and behavioural disorders may accelerate biological ageing, but comprehensive evidence integrating observational and causal approaches is lacking. METHODS: We performed observational and Mendelian randomisation (MR) analyses. First, 502,411 UK Biobank participants (78,674 with ICD-10 Mental and Behavioural Disorders) were propensity score matched (1:1). Biological Age Acceleration (BAA = biological age - chronological age) was regressed on mental and behavioural disorder status, and robustness checks were conducted using heteroscedasticity-robust and cluster-robust standard errors. Second, two-sample MR assessed causal effects of genetic liability to six mental and behavioural disorders on four ageing biomarkers, using strong genetic instruments (F > 10) and validated through sensitivity analyses including MR-Egger, Cochran's Q, and leave-one-out tests. RESULTS: Ever having a diagnosis of a mental and behavioural disorder was associated with higher BAA (β = 0.261, 95% CI: 0.229-0.293, P < 0.001). Strongest associations were observed for disorders of psychological development (β = 0.717), organic mental and behavioural disorders (β = 0.395), and mental and behavioural disorders due to psychoactive substance use (β = 0.338). BAA increased with comorbidity count (P-trend <0.001). MR indicated potential causal effects of depression (OR = 5.12 for frailty index), insomnia (OR = 2.04 for frailty index), and anxiety (OR = 1.06 for frailty index) on accelerated ageing, with no evidence of horizontal pleiotropy (MR-Egger intercept P > 0.05). Bipolar disorder was associated with slower epigenetic ageing (OR = 0.71 for GrimAge). Alcohol use disorder increased facial ageing (OR = 1.02). Schizophrenia showed no significant associations. CONCLUSIONS: Mental and behavioural disorders are observationally and robustly linked to accelerated biological ageing, with a dose-response by comorbidity. Genetic evidence supports potential causal roles for depression, insomnia, and anxiety in promoting ageing, while bipolar disorder may relate to slower epigenetic ageing. Biological ageing should be considered in the long-term management of mental health.
17 May 2026
Read appraisal →Journal of affective disorders
Adverse events related to buspirone: a real-world pharmacovigilance study using the FAERS database
Buspirone, a widely used anxiolytic, has incompletely characterized adverse effects. This study aimed to comprehensively characterize buspirone's safety profile by a large-scale analysis of real-world data from the Food and Drug Administration Adverse Event Reporting System (FAERS) database, covering Q1 2006 to Q1 2025. We collected 1744 buspirone-related adverse event reports and analyzed them utilizing various signal detection techniques, including proportional reporting ratio (PRR), reporting odds ratio (ROR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN). The most common adverse events were categorized into nervous system disorders and psychiatric disorders, with "dizziness" (n = 141), "anxiety" (n = 133), "drug interaction" (n = 86), "serotonin syndrome" (n = 83), and "tremor" (n = 71) being the most frequently reported. Notably, several safety issues linked to suicide exhibited considerable signals. In addition to the adverse events already listed on the package insert, our research identified unanticipated adverse events associated with improper buspirone use, including "drug abuse" (n = 43), "intentional overdose" (n = 30), and "drug level increased" (n = 17). These findings indicated a potential risk of abuse with buspirone exposure during therapeutic use. Moreover, the study detected a relatively rare but noteworthy signal associated with abnormal body weight, such as "obesity" (n = 19), which merits further attention. The median time to onset for adverse events was 10 days (IQR:16). In conclusion, this pharmacovigilance research underscored the safety hazards related to prolonged buspirone usage, confirmed known risks and identified novel safety signals. These findings enriched the awareness of buspirone's safety profile and provided evidence-based guidance in clinical settings.
17 May 2026
Read appraisal →Journal of affective disorders
Right orbitofrontal cortex rTMS targets anxiety, not depressive, symptoms in first-episode schizophrenia.
BACKGROUND: Anxiety is highly prevalent and undertreated in first-episode schizophrenia(FES), but repetitive transcranial magnetic stimulation(rTMS) data for this comorbidity are scarce. The orbitofrontal cortex(OFC) is a key anxiety-regulating node, supporting its potential as a target. This study aimed to explore 1-Hz right OFC-rTMS's symptom-specific effects on FES mood. METHODS: This is a secondary analysis of a randomized controlled trial. Participants were drug-naive FES patients randomized to the active rTMS group(n = 51) or sham group(n = 45). All completed 20 intervention sessions, 20 sessions of active OFC-rTMS or sham, with 4-week follow-up, initiating oral olanzapine(10-20 mg/day) concurrently with the first rTMS session. Mood symptoms were assessed using the 24-item Hamilton Depression Rating Scale(HAMD) and the 14-item Hamilton Anxiety Rating Scale(HAMA). Psychopathological symptoms were assessed using the Positive and Negative Syndrome Scale(PANSS). The main outcome was the changes in HAMD and HAMA scores from baseline to 2 weeks and 4 weeks. RESULTS: The active group showed greater PANSS total(t = -3.260, p = 0.002; Cohen's d = 0.672) and subscale improvements vs. the sham group. Repeated-measures ANOVA(controlling for covariates) revealed significant Time×Group interactions for HAMA total(F = 4.698, p = 0.010; partial η2 = 0.059) and psychic anxiety(F = 5.735, p = 0.004; partial η2 = 0.072), but not somatic anxiety. For HAMD, only anxiety/somatization(F = 8.397, p = 0.031; partial η2 = 0.099) and cognitive impairment(F = 6.240, p = 0.002; partial η2 = 0.076) showed interactions, with no specific effects on overall depressive symptoms. In the sham group, HAMD anxiety/somatization correlated with all PANSS subscales(r = 0.311-0.477, p < 0.05), but this correlation was absent in the active group(all p > 0.05). CONCLUSIONS: Right OFC-rTMS improves FES anxiety (not depression), supporting it as a targeted non-pharmacological option.
17 May 2026
Read appraisal →JMIR mental health
Factors Influencing the Initiation and Continued Engagement of Digital Mental Health Tools Among Adults: Theory of Planned Behavior-Informed Systematic Review
BACKGROUND: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding the shapes of initiation and ongoing use is essential for effective design and implementation. OBJECTIVE: This study aims to synthesize determinants of adults' initiation and engagement with DMHTs, organized through two lenses: (1) psychological factors aligned with the theory of planned behavior (TPB) and (2) design and access features. METHODS: A systematic search of 9 databases (June 2025) identified qualitative and mixed methods primary studies reporting end-users' experiences with DMHTs. Studies were screened and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality appraisal used quality assessment with diverse studies (QuADS). Data were synthesized using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design and access domains. RESULTS: A total of 22 studies met inclusion criteria. Findings clustered into 2 interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control (PBC), with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural or linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally; depending on context, the same feature could facilitate or hinder engagement. CONCLUSIONS: Engagement with DMHTs is jointly determined by users' beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthening willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, and connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
17 May 2026
Read appraisal →Journal of affective disorders
Intolerance of uncertainty and depression in children and adolescents: Systematic review and meta-analysis
BACKGROUND: Intolerance of uncertainty (IU) is a development and maintenance factor for depression and anxiety. Previous reviews found that it associated with anxiety in children and adolescents. However, there was insufficient evidence at the time to review its association with depression in this population. METHOD: 20 studies investigating the relationship between IU and depression in children and adolescents were identified. Random effects meta-analysis was used to aggregate cross-sectional associations, with age, gender, and measure as moderators. A narrative review of longitudinal findings from relevant studies (K = 6) was also conducted. RESULTS: Meta-analysis of cross-sectional associations found an overall effect size of r = 0.47. There was limited evidence that age or gender moderated the effect, however there was a significant moderating effect of measure. Namely, when including studies using 'child-friendly' measures (i.e., developed and normed with children as opposed to adults), the effect size was r = 0.37. Another analysis revealed that the inhibitory IU subscale was more strongly associated with depression than prospective IU. Longitudinal findings suggested that the association between IU and depression persists over time. LIMITATIONS: Studies were limited to those published in English and including general population samples (e.g., typically developing, without significant medical needs). CONCLUSIONS: There is a medium-strong association between IU and depression symptoms in children and adolescents. However, it may be overestimated by the use of IU measures that are not child-friendly. Future research can further extend the evidence-base through careful measure selection, as well as longitudinal and experimental designs.
17 May 2026
Read appraisal →JMIR mental health
Large Language Models and Their Applications in Mental Health: Scoping Review
BACKGROUND: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. OBJECTIVE: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. METHODS: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. RESULTS: In total, 41 papers were selected. Many studies included evaluations on OpenAI's GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. CONCLUSIONS: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
16 May 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Comparison of neuropsychological side effects between contemporary radiofrequency ablative neurosurgery for psychiatric disorders and conventional neurosurgical procedures: systematic review and meta-analysis
BACKGROUND: Psychiatric disorders are increasingly contributing to global disability. Despite advances in conservative management, the prevalence of treatment-resistant cases remains high. Meanwhile, neurosurgery for psychiatric disorders (NPD) remains underused, largely due to strict regulations and historical concerns, particularly those related to neuropsychological side effects (NPSE). OBJECTIVE: To address this issue, we conducted a systematic review with meta-analysis to compare NPSE associated with radiofrequency ablative NPD to those observed in neuro-oncological, neurovascular and epilepsy surgeries. METHODS: PubMed, Embase and LILACS databases were searched in April 2024 for articles published in English/Spanish from 1990 to 2022, following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS: A total of 48 articles with 2678 participants were included. The frequency of transient and permanent NPSE in the NPD group ranged from 0.94% to 11.50% and 0.94% to 2.03%, respectively, comparable to the other surgical groups (epilepsy: 0.31-11.70%; vascular: 0.52-22.90%; oncology: 0.94-17.60% for transient NPSE; epilepsy: 0.31-12%; vascular: 0.40-1.96% and oncology: 0.84-1.48% for permanent NPSE). Regarding permanent NPSE, arguably the most critical consideration, the NPD group showed better outcomes in memory, language and social cognition than the epilepsy group, but worse outcomes in executive and perceptual-motor functions. Compared with the vascular group, the NPD group had better executive function but worse complex attention. Finally, the NPD group had fewer permanent deficits than the oncology group in executive function, complex attention and perceptual-motor domains, although language performance was lower. CONCLUSIONS: Contemporary NPD apparently carries a similar risk of NPSE as other conventional neurosurgical procedures, challenging misconceptions and this unjustified barrier to its broader use.
16 May 2026
Read appraisal →Journal of affective disorders
An exploratory randomized controlled trial of an AI-enabled mental health intervention for generalized anxiety.
Generalized Anxiety Disorder (GAD) is prevalent and often co-occurs with depression, contributing to significant disability and healthcare burden. Although treatments such as CBT and SSRIs are effective, access remains limited. This exploratory, randomized controlled trial evaluated the effectiveness of an AI-powered mental health app (PATH) in reducing symptoms of anxiety and depression. A total of 316 UK-based participants (aged 19-70) were randomized to either the intervention group (PATH) or a control group (NHS self-help website). The intervention provided evidence-based strategies including CBT-informed chat therapy and interactive tools. Anxiety (GAD-7) and depression (PHQ-9) scores were measured at baseline, two, eight, and twelve weeks. Of the 316 randomized participants, 235 completed the post-intervention assessment (33.0% attrition in the intervention group vs. 18.5% in the control group). Retention at 8-week and 12-week follow-ups was 77.4% and 54.0%, respectively. At two weeks, the intervention group showed significantly lower GAD-7 and PHQ-9 scores compared to control, with medium effect sizes. At eight weeks, those who continued using the app showed significant reductions in both anxiety and depression, while those who discontinued still showed medium improvements in anxiety. Effects were maintained at twelve weeks, with moderate to large effect sizes. Findings suggest that PATH significantly reduces anxiety and depressive symptoms, particularly with continued use. These results support the app's potential as a scalable, accessible digital intervention to address mental health treatment gaps.
16 May 2026
Read appraisal →Journal of affective disorders
Sociodemographic and clinical predictors of digital mental health intervention engagement among treatment-seeking psychiatric outpatients
BACKGROUND: Digital mental health interventions (DMHIs) have shown promise improving depression, anxiety, and psychiatric distress, yet real-world engagement remains low. Increasing engagement has great potential to improve the impact of DMHIs, but little is known about the drivers of engagement in naturalistic settings. To better understand predictors of engagement, we examined sociodemographic and clinical characteristics associated with DMHI usage among a large clinical sample of adults. METHOD: 1223 adults (74% White, 68% women, Mage = 36.8 years) with scheduled intake appointments for outpatient psychiatric services were randomized to either a mindfulness-based app (Headspace) or a CBT-based app (SilverCloud). Usage data were automatically collected, and participants were neither required nor compensated to use the apps. RESULTS: Participants engaged with their assigned DMHIs a median of 8 days, with 88.2% of participants using their assigned DMHI at least once. Participants engaged with Headspace for more than twice as many days [IRR (95% CI) = 2.4 (2.1, 2.7)] as SilverCloud. Female sex, white race, a college degree, and older age up to 60 predicted greater engagement. Further, depression severity was associated with engagement in a non-linear manner for those assigned to Headspace, with less engagement at minimal/mild and severe symptoms compared to moderate and moderately-severe symptoms. CONCLUSIONS: These findings indicate meaningful differences in engagement between DMHIs based on sociodemographic and clinical characteristics. There may be opportunities to improve engagement by tailoring DMHI offerings, with a particular emphasis on meeting the needs of less-engaged populations.
16 May 2026
Read appraisal →Journal of affective disorders
Machine learning and SHAP-based interpretation of dietary antioxidants' impact on comorbid MDD and suicidal ideation.
OBJECTIVE: To develop and validate a machine learning model for predicting major depressive disorder (MDD) with suicidal ideation (SI) by incorporating dietary antioxidants, and to elucidate the specific contribution of these antioxidants in the prediction. METHODS: Data were obtained from NHANES 2007-2010. Dietary antioxidants, including vitamins, minerals, and polyphenols, were the primary predictors. Demographic, lifestyle, and health-related variables were also included. Collinear variables were removed, class imbalance was corrected, and data were normalized prior to modeling. Twelve machine learning algorithms were compared using a systematic benchmarking protocol within the sklearn framework: Random Forest (RF), LightGBM, AdaBoost, XGBoost, Extra Trees, CatBoost, Gradient Boosting Decision Trees, Support Vector Machine, Decision Tree, Gaussian Naïve Bayes, Stochastic Gradient Descent, and K-Nearest Neighbors. SHapley Additive exPlanation (SHAP) values were calculated to interpret feature contributions within the best-performing model. RESULTS: A total of 9306 participants were included, of whom 247 with MDD comorbid with SI. After preprocessing, 31 dietary antioxidants and 9 demographic and health-related variables were retained for modeling. The RF classifier achieved the highest performance with an accuracy of 83%, precision of 0.825, recall of 0.838, area under the ROC curve of 0.927, and F1 score of 0.831. SHAP analysis identified vitamin C, kaempferol, myricetin, peonidin, luteolin, eriodictyol, hesperetin, pelargonidin, and zinc as the most influential predictors. CONCLUSION: The RF model exhibited superior predictive capability for comorbid MDD and SI. SHAP analysis highlighted the critical roles of dietary antioxidants, particularly vitamin C and kaempferol, in predicting these outcomes.
15 May 2026
Read appraisal →Journal of affective disorders
Systematic review and meta-analysis of intranasal esketamine for treatment-resistant depression: Evidence from real-world studies
BACKGROUND: Intranasal esketamine has demonstrated efficacy and safety for treatment-resistant depression in randomized controlled trials, however the generalizability of these findings to clinical practice remains unclear. This systematic review and meta-analysis aimed to synthesize real-world evidence of intranasal esketamine in patients with treatment-resistant depression. METHODS: A systematic search was conducted in PubMed, Embase, and PsycINFO up to April 2025 to identify observational studies reporting clinical outcomes of intranasal esketamine in adults with treatment-resistant depression. Standardized mean changes in depressive symptoms and odds ratios for remission were calculated. Random-effects meta-analyses were performed, followed by meta-regression to explore potential moderators. The study protocol was prospectively registered in PROSPERO (CRD420251025075). RESULTS: Nine studies were included in the final synthesis. The analyses demonstrated that intranasal esketamine showed substantial effectiveness, with an effect size of Hedges' g = -1.98 (95% CI -2.94 to -1.03; p < 0.0001). Patients were about five times more likely to achieve remission at three months compared with the induction phase (OR = 5.1, 95% CI 2.29-11.39). Treatment effectiveness was not influenced by factors such as gender or the presence of comorbidities, such as anxiety, personality, or substance use disorders. The pooled prevalence of any adverse event, based on studies that reported safety data, was 82% (95% CI 69-92). The most frequent was dissociation, reported in 49% of patients (95% CI 37-62). CONCLUSIONS: This study provides consistent real-world evidence supporting that Intranasal esketamine was associated with meaningful symptom reduction and rising remission rates over time in real-world settings. Given the observational nature of the evidence and the absence of control groups, effect sizes should be interpreted with caution. Nonetheless, these findings provide valuable insight into esketamine's effectiveness and tolerability in routine practice and highlight the need for controlled comparative studies to guide long-term treatment strategies.
15 May 2026
Read appraisal →Translational psychiatry
Acute placebo responsiveness predicts longitudinal expectation effects in antidepressant treatment
Positive treatment expectations are known to enhance antidepressant efficacy. Experimental placebo studies aim to provide mechanistic insights into this association, yet their findings have not been clearly linked to clinical expectation effects within individual patients. In a randomized crossover trial, 61 participants with major depressive disorder (31 women, 30 men) completed an emotion classification task and mood assessments after administration of a saline nasal spray labeled as either oxytocin (sham oxytocin treatment, deceptive placebo) or saline (control). Forty-five participants subsequently underwent antidepressant treatment and were monitored weekly for treatment expectations and depressive symptoms, with follow-ups extending to three months. Using linear mixed-effect models, we examined trajectories of clinical expectation effects and their prediction by experimental placebo-induced changes in emotional processing. Sham oxytocin treatment acutely induced positive expectations, improved mood, and shifted emotional processing toward positivity. Longitudinal modeling revealed that weekly reductions in depressive symptoms were predicted by higher treatment expectations reported the preceding week. These clinical expectation effects were most pronounced in individuals exhibiting strong acute placebo responses in emotional processing. Overall, our findings indicate that positive treatment expectations facilitate critical aspects of emotional processing in depression and that individual responsiveness generalizes across different treatments and time windows. Experimental placebo assessments offer an ecologically valid model for elucidating mechanisms and predictors of clinical expectation effects and highlight expectation sensitivity as a promising target for treatment optimization.
15 May 2026
Read appraisal →BMJ mental health
Emulated trial of artificial intelligence use and subsequent depressive outcomes in a survey of US adults
BACKGROUND: Generative artificial intelligence (AI) use has been suggested to have adverse mental health consequences but a causal relationship has not been examined. OBJECTIVE: To simulate a randomised controlled trial of AI use in a work, school or personal context by applying target trial emulation to multiple waves of data from a nationally representative survey. METHODS: We conducted a target trial emulation using non-probability survey data from three waves of a nationally representative survey conducted between 18 June 2024 and 8 January 2025. Participants aged ≥18 years reported generative AI use frequency at baseline. High-frequency use was defined as multiple times per week or more. The primary outcome was depressive symptom severity measured using the Patient Health Questionnaire 9-item (PHQ-9) at follow-up. Generalised causal forests assessed heterogeneity of treatment effects. FINDINGS: Among 19 099 participants assessed at baseline, 2862 (15.0%) reported AI use at least multiple times per week. A subset of 3109 (16.3%) returned for follow-up. In the primary weighted analysis, high-frequency use was not significantly associated with change in PHQ-9 score at follow-up (mean difference -0.18, 95% CI -0.94 to 0.59; p=0.65). Multiple sensitivity analyses using alternate outcome definitions also did not identify significant causal effects. Generalised causal forests yielded no significant evidence of heterogeneity of effect (p=0.81). CONCLUSIONS: In an emulated randomised trial among US adults, generative AI use was not associated with subsequent depressive symptoms. This result does not support the premise that AI use causes greater depressive symptoms, although adverse outcomes among vulnerable individuals cannot be excluded. CLINICAL IMPLICATIONS: AI use is unlikely to cause increased depressive symptoms among most US adults. Continued monitoring should clarify potential risks among vulnerable populations.
15 May 2026
Read appraisal →JMIR mental health
Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review
BACKGROUND: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. OBJECTIVE: This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. RESULTS: From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. CONCLUSIONS: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.
14 May 2026
Read appraisal →PloS one
Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data
OBJECTIVE: This retrospective, case-control study with internal validation evaluates the performance of machine learning (ML) and deep learning (DL) models in classifying pediatric patients at risk for anxiety disorders using structured electronic health records (EHRs) and area-based measures of health (ABMH). The aim is to enable proactive care by monitoring potential anxiety onset across developmental stages. METHODS: We trained a series of ML models (Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, XGBoost) and DL models (LSTM, GRU, RETAIN, Dipole) using structured EHR data from 30-day windows prior to diagnosis. Two datasets were used per age group: one with structured EHR data only, and another including both EHR and ABMH data. ML models were trained using short-term cross-sectional features, while DL models leveraged full longitudinal patient histories. Performance was assessed using AUROC, AUPRC, PPV, NPV, F1 score, and accuracy. Due to differences in input scope, model performance reflects both algorithmic and temporal design differences and is not intended as a direct comparison between ML and DL. RESULTS: ML models offered strong baseline performance, with XGBoost achieving AUROC scores of 0.817 (EHR) and 0.816 (EHR+ABMH) for 8-year-olds. Adding ABMH features did not significantly improve performance. DL models, particularly RETAIN and Dipole, achieved the highest AUROC values (e.g., Dipole: 0.853 with EHR, 0.857 with EHR+ABMH for 8-year-olds), outperforming other DL and ML models within their respective design constraints. CONCLUSION: Both ML and DL models successfully identified likely anxiety onset using structured EHR data. DL models using longitudinal data achieved the highest performance, while XGBoost provided a robust ML baseline. The minimal impact of ABMH features highlights integration challenges, and performance variation across ages emphasizes the need for age-stratified modeling approaches.
14 May 2026
Read appraisal →PloS one
Narrative-affect discrepancy as a regulated degree of freedom in 351,734 relationship narratives
In naturalistic emotional narratives, the intensity of expressed affect does not scale proportionally with narrative structure. Using 351,734 English-language relationship narratives from online support communities (the ANEST Narrative-Affect Dataset, ANAD v1.1.0), we constructed a two-dimensional expressive space defined by narrative complexity (N) and linguistically inferred affective intensity (A), with their signed discrepancy ([Formula: see text]) treated as a derived coordinate. Rather than converging toward low discrepancy, human narratives occupied a broad but structured space consistent with trade-offs between relational exposure and cognitive effort. We identified four empirically separable regimes of expressive organization: coupled expression (non-extreme discrepancy; the complement of the extreme regimes), strategic understatement (high A, low N, D < 0), strategic overstatement (high N, low A, D > 0), and collapse (high A with limited narrative scaffolding). A data-anchored cost model (NCS) formalized these regimes as arising from the interaction of exposure risk and cognitive effort, not from discrepancy minimization per se. Coupled expression dominated the corpus (91.3%), while the remaining regimes formed smaller but non-negligible subpopulations (Understatement: n = 20,223; Collapse: n = 8,040; Overstatement: n = 2,223), indicating that extreme discrepancy configurations occur systematically rather than as isolated outliers. As a comparative probe, we projected an RLHF-aligned large language model into the same space using matched prompts and identical feature extraction. Because D is a deterministic function of N and A, expressive extent was quantified as convex hull area in the clipped [Formula: see text] plane. Under this procedure, the model occupied a markedly smaller region (approximately [Formula: see text] smaller hull area; bootstrap 95% CI [1.68, 1.70]; permutation p < 0.0001) and concentrated near low-discrepancy configurations, with sparse occupancy of extreme under- and overstatement regimes. Together, these findings suggest that narrative-affect discrepancy is a measurable and regulated dimension of emotional expression and provide a reproducible geometric basis for comparing expressive degrees of freedom across populations and systems.
14 May 2026
Read appraisal →Sleep
Testing affect regulation theories of dreaming
STUDY OBJECTIVES: Dreaming is theorized to regulate waking affect via fear extinction learning, similar to exposure therapy. We tested whether fear in dreams was associated with lower negative affect (NA) and higher positive affect (PA) on subsequent mornings and whether these effects were moderated by emotion regulation strategies or mixed dream emotion states. Additionally, we examined whether individuals with higher average dream fear showed more adaptive emotion regulation. METHODS: We employed a natural language processing tool and Bayesian multilevel modeling using online daily survey data in a community sample (n = 536; 85.6% women; Mage = 39.3 ± 17.3; days = 4715). RESULTS: Frightening dreams were associated with more negative affect the following morning (+7% NA, pd = 100%), which was amplified for individuals with more adaptive emotion regulation (+3% NA, pd = 98.69). Dreams concurrently high in fear and joy were related to 20% higher odds of zero NA the next morning (pd = 97.82%). Higher average dream joy was related to 9% higher morning PA (pd = 98.79%). Individuals with higher average dream fear showed greater adaptive emotion regulation (β = 0.18, pd = 99.93%). CONCLUSIONS: Findings supported affect continuity from dream to wake, yet affect regulation at the individual level. Frightening dreams were related to worse morning affect, with a more pronounced effect among high-regulation individuals. High-regulation individuals tended to experience more fearful dreams. Mixed dream emotions were related to mornings without NA, and dream joy was related to higher morning PA. Our findings suggest adaptive, though dynamic, effects of dream emotions on psychological functioning.
14 May 2026
Read appraisal →Biomedical physics & engineering express
Differentiating depression subtypes through three-electrode EEG and emotional stimuli: a machine learning approach
We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR). EEG was recorded from 70 participants (31 first depressive episode, FDE; 18 recurrent depressive episode, RDE; 21 control participants, HC) using a compact frontal montage (Fp1/Fpz/Fp2) during positive and negative VR conditions, focusing on the immediate post-stimulus regulation period. Subject-level features were analyzed with a linear mixed-effects model to probe group-by-condition interaction patterns, and a Bi-Emotional Siamese Network (BESN) was developed to model baseline-anchored within-subject deviations by fusing positive/negative reactivity streams with path-signature-based temporal encoding. Feature-level analyses revealed interaction signals suggestive of differential emotion-related modulation across groups. In subject-wise evaluation, BESN achieved 83.1% accuracy for HC versus FDE discrimination and 70.6% accuracy for three-class classification (HC versus FDE versus RDE), outperforming conventional machine-learning baselines. Robustness was further supported on an external public resting-state EEG dataset (MODMA), achieving 78.2% accuracy. These results suggest that baseline-anchored, emotion-modulated EEG dynamics combined with path-signature modeling provide an objective and generalizable computational approach for depression-related group discrimination.
14 May 2026
Read appraisal →BMC psychiatry
N-acetylcysteine for patients with alcohol use disorder, post-traumatic stress disorder, and their co-occurrence: a systematic review of placebo-controlled randomized trials
INTRODUCTION: Alcohol use disorder (AUD) and post-traumatic stress disorder (PTSD) are thought to be significant global contributors to the burden of mental illness. According to recent WHO epidemiological estimates, 3.9% of people worldwide have experienced PTSD at some point in their lives; this number rises to roughly 5.6% among those who have experienced trauma. NAC has shown promise in reducing PTSD symptoms and cravings in veterans, according to recent trials. Our analysis aims to resolve the conflict between results and determine whether NAC is an effective add-on therapy for PTSD, AUD, and co-occurring PTSD/AUD. METHODS: We conducted this study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, following our protocol (CRD420251170956). We searched PubMed, Scopus, Cochrane CENTRAL, Embase, EBSCO, and the Web of Science for relevant studies. Only randomized placebo-controlled trials were included. The primary outcomes were severity of PTSD and alcohol craving, while secondary outcomes were anxiety, depression, and alcohol use patterns and biomarkers. RESULTS: Seven randomized, double-blind, placebo-controlled trials were included in this meta-analysis, enrolling a total of 566 participants. There were no significant differences between NAC and placebo in craving (SMD= -0.40, 95% CI [-1.05, 0.25], P = 0.22, I² = 56.1%). Regarding PTSD severity, the PCL scale pooled analysis demonstrated no significant difference between NAC and placebo (SMD = 0.04, 95% CI [-0.40, 0.48], P = 0.8) (I² = 65.9%, P = 0.05), while the CAPS scale pooled analysis demonstrated no significant difference between NAC and placebo (SMD = -0.13, 95% CI [-0.50, 0.24], P = 0.49) (I² = 54.8%, P = 0.109). Leave-one-out sensitivity analysis showed that exclusion of the Back et al. (2025) study resulted in a significant effect favoring NAC (SMD = -0.35, 95% CI [-0.70, -0.01], P = 0.04), with no observed heterogeneity (I² = 0%). CONCLUSION: Our meta-analysis indicates that N-acetylcysteine, when compared to placebo, does not demonstrate consistent efficacy in reducing the primary symptoms of AUD or PTSD in broad patient populations. However, the treatment is relatively safe. A noteworthy signal of potential efficacy for PTSD symptoms emerged in a sensitivity analysis, suggesting that the therapeutic promise of NAC should not be entirely dismissed. CLINICAL TRIAL NUMBER: Not applicable.
14 May 2026
Read appraisal →JMIR mHealth and uHealth
Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning
BACKGROUND: Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling. OBJECTIVE: This study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures. METHODS: A total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features. RESULTS: Based on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the k-means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance. CONCLUSIONS: Longitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.
13 May 2026
Read appraisal →Journal of medical Internet research
Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study
BACKGROUND: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leveraging electronic health records have emerged as promising tools for suicide risk identification, the optimal integration of these models with existing screening practices remains unclear. OBJECTIVE: The objective of this study was to compare parallel and serial testing strategies that combine an ML suicide risk model and the Ask Suicide-Screening Questions (ASQ) against using the ASQ alone. To achieve this, we conducted a retrospective secondary analysis of electronic health record data. The cohort consisted of adult emergency department visits at an Indian Health Service facility between October 1, 2019, and October 2, 2021. METHODS: Sensitivity, specificity, predictive values, and 95% CIs were averaged across 10 cross-validated patient-level folds. The final sample included 7897 American Indian patients with 26,896 visits, 824 (3.1%) of which had a positive ASQ result and 102 (0.4%) of which had the outcome of suicide attempt or death within 90 days of the visit. The logistic regression ML model previously developed using Indian Health Service-specific data was operationalized at the 95th and 75th percentiles to evaluate high-risk and medium-risk thresholds, respectively. A sensitivity analysis was performed to evaluate identification approaches across all emergency department visits during this period. RESULTS: The ML medium-risk threshold alone identified the most true positives (sensitivity: 0.782, 95% CI 0.648-0.915; specificity: 0.751, 95% CI 0.725-0.777; positive predictive value [PPV]: 0.012, 95% CI 0.009-0.014; negative predictive value [NPV]: 0.999, 95% CI 0.998-0.999) in comparison to the ML high-risk threshold alone (sensitivity: 0.429, 95% CI 0.287-0.572; specificity: 0.955, 95% CI 0.948-0.961; PPV: 0.035, 95% CI 0.022-0.048; NPV: 0.998, 95% CI 0.997-0.999) or the ASQ alone (sensitivity: 0.178, 95% CI 0.073-0.282; specificity: 0.970, 95% CI 0.968-0.971; PPV: 0.022, 95% CI 0.010-0.034; NPV: 0.997, 95% CI 0.996-0.998). Combining the ML high-risk threshold with the ASQ in series yielded the greatest positive predictive ability (PPV: 0.050, 95% CI 0.014-0.086) at the cost of reduced sensitivity (0.129, 95% CI 0.036-0.221). Finally, the parallel testing approach using the ML medium-risk threshold yielded the greatest sensitivity (0.795, 95% CI 0.671-0.920; specificity: 0.742, 95% CI 0.716-0.767; PPV: 0.012, 95% CI 0.009-0.014; NPV: 0.999, 95% CI 0.998-0.999) without missing any cases identified through screening. CONCLUSIONS: Unlike existing studies that evaluate ML and screening tools in isolation, this study innovates by assessing combined parallel and serial testing strategies in a real-world setting. We demonstrated that, while serial testing maximizes predictive accuracy, it is often infeasible. Instead, parallel testing brings value as a clinical "safety net" to catch at-risk patients missed by standard practices. Ultimately, integrating ML in suicide prevention requires balancing statistical accuracy with setting-specific, real-world workflows.
13 May 2026
Read appraisal →JMIR human factors
The Quality and Characteristics of Digital Mental Health Apps: Mixed Methods Study
BACKGROUND: There are around 20,000 mental health apps available in app stores. The Organisation for the Review of Care and Health Apps (ORCHA), a United Kingdom digital health compliance company, has assessed a number of digital mental health apps with regard to their quality, professional and clinical assurance, data privacy, and user experience. This study analyzes the data that were collected by ORCHA when they assessed mental health apps. OBJECTIVE: This study aimed to examine the characteristics of mental health apps regarding their quality, target users, features, underpinning evidence, and data privacy. METHODS: A dataset comprising ORCHA Baseline Review assessments of over 2000 digital health apps, including 436 mental health apps, was used. This study uses exploratory data analysis to gain insight into the quality and characteristics of mental health apps. Methods such as descriptive and inferential statistics, k-modes clustering, and association rule mining were used to explore the quality of mental health apps as well as reveal insights into the different cost types, target users, app features, data types, and evidence of app content. RESULTS: Information provision, data capture, and data sharing were the most common features within the 436 mental health apps. The examined apps primarily targeted the following groups: adults (n=229, 52.5%), everyone (n=184, 42.2%), and teens (n=135, 31%). The cost of apps has not been linked to the quality of mental health apps, although paid apps or apps with in-app purchases may include additional services. Indicated user acceptance or benefit is the most common type of evidence provided by these mental health apps. A total of 241 (55.3%) apps included a qualified professional in app development, and 251 (57.6%) apps provided evidence within the app that the developer validated any guidance with relevant reliable information sources or references. Usage data and email were the most commonly collected data types. Association rule mining showed that email, IP address, name, and usage data are often co-collected by the same apps. K-modes cluster analysis showed that mental health apps can be categorized into 2 clusters, where one cluster of apps (n=182, 41.7%) collected more data than apps in the other cluster. CONCLUSIONS: Mental health apps are commonly targeted for everyone to use, but many apps are targeted toward teens or adults. Our study suggests that many publicly available mental health apps did not take the precautions (such as the involvement of appropriate health professionals, literature references, or conducting tests) to ensure that their content is valid and research based. Greater effort on behalf of mental health app developers is needed to ensure that the public is provided with high-quality apps. Moreover, our study indicates that the mental health apps that collect more data tend to score better on the ORCHA Baseline Review assessment.
13 May 2026
Read appraisal →Psychological medicine
Risk factors associated with blood dyscrasia during clozapine treatment: a systematic review and meta-analysis
Clozapine is the only effective treatment for treatment-resistant schizophrenia, but concerns over blood dyscrasia and need for monitoring limit its use. Evidence suggests many hematological abnormalities may result from surveillance bias, with agranulocytosis being the primary adverse effect directly induced by clozapine. This systematic review (PROSPERO: CRD42024487199) investigates non-genetic risk factors associated with blood dyscrasia during clozapine treatment, focusing on neutropenia and agranulocytosis. Random-effect meta-analyses were performed on studies reporting quantitative risk data. Due to inconsistent neutropenia definitions, analyses used absolute neutrophil count (ANC) thresholds of <2000/mm3, <1500/mm3, and <500/mm3. Forty-four studies were included in the systematic review, 15 in meta-analyses. No significant association was found between agranulocytosis and female gender (OR = 1.48, 95% CI: 0.92-2.38; p = 0.106) or age (pooled standardized mean difference [SMD] = 0.32, 95% CI: -0.26 to 0.90, p = 0.285), whilst a modest inverse association with clozapine dose (SMD = -0.32, 95%CI: -0.50 to -0.14, p < 0.001) and baseline white cell count (SMD = -0.21, 95%CI: -0.40 to -0.03, p = 0.026) was found. Neutropenia (ANC < 2000/mm3) was positively associated with concomitant psychotropic use (OR = 2.15, 95% CI: 1.13-4.07, p = 0.019). Clozapine rechallenge studies revealed no significant associations with gender, age, duration of initial clozapine trial, or length of discontinuation period prior to rechallenge. No strong predictors of clozapine-associated blood dyscrasia were identified. Findings may be limited by study variability, surveillance bias, and lack of consistent differentiation between agranulocytosis and milder neutropenia, highlighting limitations in current evidence.
13 May 2026
Read appraisal →Journal of medical Internet research
Efficacy, User Engagement, and Acceptability of Cognitive Behavioral Therapy-Oriented Psychological Chatbots for Adults With Depressive and/or Anxiety Symptoms: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Cognitive behavioral therapy (CBT) is the most examined psychotherapy for depression and anxiety, but delivery faces significant barriers such as limited access, cost, and time constraints. CBT-oriented psychological chatbots offer a promising means of addressing these challenges. Yet, their overall efficacy, user engagement, and acceptability have not been systematically synthesized. OBJECTIVE: This study aimed to evaluate the efficacy, user engagement, and acceptability of CBT-oriented chatbots for adults with depressive and/or anxiety symptoms. METHODS: A systematic search of 9 databases, including PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, PsycINFO, CINAHL, China National Knowledge Infrastructure, WanFang, and VIP Databases, was conducted from inception to February 2026. Eligibility criteria included randomized controlled trials comparing CBT-oriented chatbots with control groups in adults with depressive and/or anxiety symptoms. Risk of bias (ROB) was assessed using the Cochrane ROB tool. Random-effects meta-analyses (Hartung-Knapp-Sidik-Jonkman adjustment) calculated pooled effect sizes (Hedges g), 95% CIs, and 95% prediction intervals (PIs). Heterogeneity was evaluated using the I² statistic, and Galbraith plots were used to identify outliers for subsequent sensitivity analyses. Subgroup and meta-regression analyses examined potential moderators. The certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Data on user engagement and acceptability were extracted and synthesized using narrative and quantitative methods where available. RESULTS: Twenty-nine eligible randomized controlled trials were included. CBT-oriented psychological chatbots produced a moderate reduction in depressive symptoms at postintervention (g=-0.55, 95% CI -0.70 to -0.40, 95% PI -1.23 to 0.13) and a small reduction in anxiety symptoms (g=-0.26, 95% CI -0.37 to -0.14, 95% PI -0.67 to 0.15). At follow-up, effects were small for depression (g=-0.32, 95% CI -0.55 to -0.09, 95% PI -0.93 to 0.29) and nonsignificant for anxiety (g=-0.19, 95% CI -0.43 to 0.04, 95% PI -0.84 to 0.46). Subgroup and meta-regression analyses revealed that anxiety outcomes were significantly moderated by clinical profiles-showing distinct advantages for comorbid symptoms-and the proportion of female participants. The CBT-oriented chatbots received an adequate level of engagement that complied with digital intervention standards. Although user satisfaction ratings were generally favorable, technical limitations and repetitive interaction patterns remain to be addressed to enhance overall acceptability. Regarding the limitations of evidence, the overall certainty was rated as very low to low, predominantly driven by high ROB and substantial heterogeneity. CONCLUSIONS: This study innovatively isolates CBT-oriented chatbots from broader digital interventions, providing a precise, methodology-driven evaluation of theoretically grounded therapeutics. This review brings critical evidence to the field that these tools yield significant short-term relief, particularly for comorbid anxiety profiles. In the real world, CBT chatbots offer profound potential as scalable, low-barrier first-line tools. To sustain engagement, future developments must evolve from rigid rule-based scripts toward adaptive, large language model-driven architectures while ensuring clinical safety.
13 May 2026
Read appraisal →BMJ open
Prevalence and types of anxiety disorders among patients with cardiac conditions and elevated HADS-A scores: findings from the initial screening phase of the Heart and Mind trial in Denmark
OBJECTIVES: To identify whether patients with arrhythmia, heart failure or ischaemic heart disease presenting with anxiety symptoms measured by the Hospital Anxiety and Depression Scale (HADS) have identifiable anxiety according to the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) and, if so, which type of anxiety disorder based on the SCID. DESIGN: Initial screening data from the Heart and Mind randomised clinical trial. SETTING: Patients with arrhythmia, heart failure or ischaemic heart disease were screened using HADS, and patients with a HADS-anxiety (HADS-A) score≥8 were invited to participate. Participants were interviewed by trained cardiac nurses using the SCID to determine whether they met the criteria for anxiety and, if so, the type of anxiety disorder. RESULTS: Of the 7816 patients who completed the HADS questionnaire, 1803 (23%) had a HADS-A score≥8. Among these, 398 (22%) agreed to the SCID interview, and 336 (84%) met the diagnostic criteria for an anxiety disorder. The mean age was 61 years, with 40% being female. The mean HADS-A score was 11.3 (SD=2.7). The most common types of anxiety were generalised anxiety disorder (61%), panic disorder (23%) and specific phobia (8%). CONCLUSIONS: The majority of individuals identified by the instrument also met the diagnostic criteria for an anxiety disorder. Generalised anxiety disorder and panic disorder were the most prevalent subtypes. Anxiety was common across the cardiac population, underscoring the need for routine assessment and targeted intervention in clinical practice. TRIAL REGISTRATION NUMBER: NCT04582734.
12 May 2026
Read appraisal →BMJ mental health
Mental health in university students: an umbrella review grading the evidence for psychosocial interventions
QUESTION: Psychological distress is increasingly prevalent among university students, raising concerns about their mental health. This review aimed to assess the strength and credibility of evidence on the efficacy of psychosocial interventions for improving mental health in this population. STUDY SELECTION AND ANALYSIS: We conducted an umbrella review of systematic reviews (SRs) with meta-analyses of randomised controlled trials assessing psychosocial interventions among university students. Searches were performed in Medline, PubMed, Cochrane Central, PsycINFO, CINAHL, Epistemonikos and Campbell Collaboration from 1 January 2014 to 1 February 2026. Data on intervention type, comparator, outcome and study-level effect sizes were analysed using inverse-variance random-effects models with restricted maximum likelihood estimation of between-study heterogeneity (τ²). Strength of associations was evaluated according to umbrella review criteria, reporting quality using A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR-2) and certainty of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE). FINDINGS: A total of 34 SRs were identified. After excluding overlapping syntheses, 28 unique meta-analyses from 12 SRs were included. Mindfulness-based interventions versus inactive controls for distress (standardised mean difference (SMD) -0.40; 95% CI -0.53 to -0.28) showed highly suggestive evidence (class II; GRADE: low; AMSTAR: high). Seven associations provided suggestive evidence (Class III): mindfulness-based interventions versus inactive controls for anxiety (SMD -0.54; 95% CI -0.81 to -0.27) and depression (SMD -0.52; 95% CI -0.65 to -0.39); cognitive-based interventions versus inactive controls for anxiety (SMD -0.48; 95% CI -0.62 to -0.34) and depression (SMD -0.60; 95% CI -0.75 to -0.45); cognitive-based interventions versus active controls for well-being (SMD -0.30; 95% CI -0.42 to -0.18); positive psychology interventions versus inactive controls for distress (SMD -0.22; 95% CI -0.35 to -0.10); and multimodal interventions versus inactive controls for distress (SMD -0.85; 95% CI -1.32 to -0.87). The remaining associations were weak or non-significant, generally supported by low or very low certainty and mostly low or very low reporting quality. CONCLUSIONS: The most robust evidence emerged for mindfulness and cognitive-based interventions, with some support also for positive psychology approaches. These interventions appear to improve depression, anxiety, distress and well-being among university students, though further rigorous research is needed. PROSPERO REGISTRATION NUMBER: CRD42023493618.
12 May 2026
Read appraisal →JMIR human factors
Persuasive Systems Design Features of Smartphone Apps for Psychosis: Systematic Review
BACKGROUND: It is unclear why some smartphone apps designed for people with psychosis are engaging, while others are not. One possible explanation is the apps' persuasive features and the operationalization and implementation of these features. OBJECTIVE: This systematic review set out to quantify and describe the persuasive features used in smartphone apps for psychosis, investigate whether there was any association between persuasive features and attrition or adherence rates. and document the quality of the included apps. METHODS: We searched electronic databases PsycINFO, PubMed, and Google Scholar for eligible papers published between the years of 2013 and 2025. Hand searches of reference lists were completed. Apps were selected if they were designed for people with psychosis and there were published empirical studies investigating the apps. Two reviewers from the review team (TT, JP, DK, and SD) independently screened papers and extracted data on adherence and attrition, as well as coded papers for evidence of persuasive features according to the persuasive systems design (PSD) model. Available data were synthesized descriptively and narratively. We attempted to access apps via app stores or by correspondence with the research team. RESULTS: We found 22 apps for psychosis, with 30 associated published papers. The persuasive features were as follows: personalization (18 apps); reminders (15 apps); suggestions (11 apps); tunneling and self-monitoring (10 apps); reduction (9 apps); liking (8 apps); social role (6 apps); rehearsal, praise, and similarity (5 apps); rewards (4 apps); simulation, real-world feel, and social learning (3 apps); surface credibility and normative influence (2 apps); and trustworthiness, social comparison, and social facilitation (1 app). Expertise, authority, third-party endorsements, verifiability, cooperation, competition, and recognition were present in zero apps. Features in the categories of primary task support and dialogue support were well represented, while social support and system credibility support were underused. It was found that there was no association between the number of persuasive features and attrition; an association between persuasive features and adherence could not be assessed. The quality of the apps could not be judged due to 20 of the 22 apps being inaccessible either through the research papers' authors or through app stores. CONCLUSIONS: Our findings indicate that in psychosis apps there is potential to include a broader range of persuasive features, which might maximize engagement. Psychosis apps may benefit from incorporating more features that leverage the persuasive impact of having users interact (social support) and incorporating features that emphasize system credibility and trustworthiness. Further studies could determine whether an increase in the number of persuasive features will impact app engagement and which features are most impactful in this context.
12 May 2026
Read appraisal →Scientific reports
Hypnotic safety suggestions reduce cortisol awakening response and morning heart rate in daily life.
With stress being causally linked to the most frequently occurring mental and physical health problems, effective coping with acute stress is essential for promoting overall well-being and building long-term stress resilience. However, most interventions aimed at improving stress regulation require extended periods of training and practice. In a previous study, we demonstrated that the Jena Safety Anchor-a brief, hypnosis-based intervention-effectively reduced subjective stress responses in a controlled laboratory setting using the Trier Social Stress Test (TSST). The present study examined whether this effect extends to everyday life. A total of 80 participants (n = 40 female) were randomly assigned to either a hypnosis group or a control group (n = 40 per group). Over the course of two weeks, all participants collected saliva samples at home to assess their cortisol awakening response (CAR), an indicator of anticipatory stress, reflecting the body's preparation for expected demands upon waking. Additionally, all participants continuously recorded their heart rate via smartwatches during the two data collection weeks. The hypnosis group received a single guided session of the Jena Safety Anchor at the end of the first week, while the control group came to the laboratory but received no intervention. CAR was measured via three saliva samples taken within one hour after waking on specified days. Morning heart rate was measured between 6 and 9 a.m. Results revealed a significant reduction in CAR and morning heart rate in the hypnosis group during the second week, following the intervention. In contrast, no change in CAR and morning heart rate was observed in the control group. These findings show that a single session of the Jena Safety Anchor can effectively reduce physiological stress anticipation in the morning. This points to its potential as a rapid, low-effort intervention for enhancing stress coping and fostering resilience. It offers a promising approach to mental health promotion that is both time-efficient and easily integrated into everyday routines.
12 May 2026
Read appraisal →CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
Screening for high-risk drinking and alcohol use disorder: update of the 2023 national clinical practice guideline
BACKGROUND: Current screening tools for harmful alcohol consumption have fallen out of step with recent guidance on the health risks of alcohol. To address this gap, the Canadian Research Initiative in Substance Matters updated the screening recommendations for high-risk drinking and alcohol use disorder (AUD) in the 2023 national clinical practice guideline. METHODS: Following a systematic review of literature published between Jan. 1, 2013, and Feb. 24, 2023, that examined screening tools for high-risk drinking and AUD, the updated recommendations were developed by a multidisciplinary national committee, which included people with lived and living experience. We scored the recommendations and certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation tool. We used the Appraisal of Guidelines for Research and Evaluation II instrument and the Guidelines International Network's principles for disclosure of interests and management of conflicts to ensure the update met international standards for transparency, high quality, and methodological rigour. RECOMMENDATIONS: Acknowledging that time constraints are the most commonly reported barrier to universal alcohol screening, we developed 5 recommendations involving a simple screening method, to identify and address both unhealthy alcohol consumption and more serious problems related to alcohol. The recommendations include asking all patients about alcohol consumption and providing educational support to all those who drink above Canada's Guidance on Alcohol and Health's low-risk threshold. We propose a simple screening algorithm to optimize and tailor further intervention, including when to assess for possible AUD. INTERPRETATION: The revised screening recommendations represent a timesaving and pragmatic approach intended to be a resource for universal screening for alcohol risks and problems. The recommendations streamline the process of identifying and addressing the health needs of those who consume alcohol in a hazardous way or may have more serious problems related to alcohol.
11 May 2026
Read appraisal →Neurosurgical review
Deep brain stimulation for clozapine-resistant schizophrenia: a systematic review of target-specific outcomes and stereotactic technical considerations
Treatment-resistant schizophrenia (TRS) affects approximately 20-30% of patients, and a substantial proportion develop clozapine-resistant schizophrenia (CRS). Deep brain stimulation (DBS) has emerged as a potential neurosurgical intervention targeting dysfunctional cortico-striato-limbic circuitry. However, technical heterogeneity and limited clinical data constrain interpretation of outcomes. This systematic review was conducted in accordance with PRISMA guidelines and prospectively registered in PROSPERO (CRD420251080715). Seven electronic databases were searched from inception through January 31, 2025. Clinical studies investigating DBS in TRS or CRS were included. Methodological quality was assessed using Joanna Briggs Institute (JBI) tools. Data extraction emphasized stereotactic targeting methods, hardware configurations, stimulation parameters, and clinical outcomes. The total number of study is 6. We excluded paper by Manssuer 2023. The total study population comprises 21 patients. Seven studies involving 21 patients met inclusion criteria. Targets included the nucleus accumbens (NAcc; n = 11), substantia nigra (SNr; n = 1), habenula (HB; n = 2), subgenual cingulate (SCG; n = 4), subgenual anterior cingulate cortex (sgACC; n = 3). Reported PANSS total score changes ranged widely (11%-85.7%), reflecting substantial inter-individual variability and methodological limitations. Surgical complications occurred in 3 of 21 patients (14.2%), including infection and hemorrhage. All cases utilized open-loop stimulation and conventional cylindrical leads. Current evidence suggests a preliminary therapeutic signal for DBS in highly selected CRS patients, particularly with NAcc targeting. However, conclusions remain limited by small sample sizes, technical heterogeneity, and absence of controlled trials. Future investigations should prioritize standardized stereotactic reporting, volumetric lead reconstruction, and long-term safety assessment within specialized neurosurgical research settings. PROSPERO CRD420251080715.
10 May 2026
Read appraisal →Medical image analysis
Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping
Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.
10 May 2026
Read appraisal →Journal of affective disorders
Gaborformer: A method for depression detection through hybrid acoustic feature extraction and fusion
Depression, a complex and heterogeneous mental disorder, poses significant challenges for timely and effective detection, highlighting the need for advanced methodologies. Current technologies often fail to fully leverage deep learning's potential, potentially overlooking critical speech features. To address this, we propose Gaborformer, a novel framework for depression detection through hybrid acoustic feature extraction and fusion. Our approach introduces classified iterative neighborhood component analysis (CINCA) for feature selection, reducing dimensionality and extracting highly correlated depressive speech features. We design GaborNet, integrating learnable Gabor filters with CNN to fuse features, and incorporate a Conformer model to capture depression-related characteristics from high-dimensional signals. Extensive validation on the DAIC-WOZ and MODMA datasets demonstrates the efficacy of our approach, significantly outperforming previous studies. Our analysis underscores the importance of features such as MFCC and LSP in speech-based depression detection (SDD). Code is available at https://github.com/staygoldboy/gaborformer.
6 May 2026
Read appraisal →Psychiatry research
Physiological correlates of emotional dysregulation in youth with psychopathology: an exploratory study using wearable technology
The failure to regulate one's own emotions, defined as Emotional Dysregulation (ED), has become a diagnostic challenge in the last decades. Peripheral indexes of functioning of the autonomic nervous system (ANS) activity emerged as reliable transdiagnostic biomarkers of psychopathology. The present study aimed to evaluate the feasibility of wearable sensors in capturing ANS correlates of affective reactivity to emotionally salient videoclips in a stimulation protocol conducted in a clinical sample of youths. Thirty subjects (mean age: 13.93 ± 11.74) were recruited and presented with videoclips showing emotional content (joy, fear, anger, sadness, and anger). Before (Baseline), during (Task), and after (Recovery), the registration, electrocardiogram (ECG), and electrodermal response (EDR) were recorded using wearable technologies. Several indices related to the activation of sympathetic and parasympathetic nervous system were extracted. These indices were compared among the different phases and emotions and correlated with clinical measures. Preliminary results showed that some emotions are more able to elicit ANS response than others, and there is a correlation between physiological activation and clinical profile. These preliminary findings could help to explore the mechanisms of ED in psychopathology and can be useful to guide behavioral and pharmacological treatment. This study supports the success of wearable-based protocols for monitoring emotional reactivity in youth with psychopathology.
6 May 2026
Read appraisal →Molecular psychiatry
A five-year risk prediction model of cardiovascular disease in individuals with bipolar disorder: a nationwide register study from Sweden
Cardiovascular disease (CVD) risk prediction models for the general population may not provide accurate predictions in individuals with bipolar disorder (BD) who have elevated risks of cardiometabolic conditions and premature mortality. Therefore, we aimed to: 1) develop a five-year CVD risk prediction model in this population by using nationwide register data from Sweden, 2) investigate whether the performance improved when we considered additional risk factors, including psychiatric comorbidity, psychotropic medication, and socio-demographic variables, compared to using established CVD risk factors only, and 3) whether machine learning approach provided improvements compared to standard logistic regression models. We followed 33,933 persons with BD aged 30-82 years old, without previous CVD, from the date of BD diagnosis registered between 2007-2014, for up to five years. The logistic regression model containing only established risk factors yielded an area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval 0.74-0.78) in the test dataset, while the logistic regression model and the best performing machine learning model including additional predictors yielded similar results (AUC was 0.77 (0.75, 0.79) in both models). The performance of logistic regression models slightly improved with additional predictors when continuous risk scores were used. In conclusion, standard logistic regression and established CVD risk factors may be sufficient to predict CVD in individuals with BD when using population register-based data from Sweden. External validation across diverse healthcare settings and rigorous assessment of clinical impact will be crucial next steps before implementing these models in clinical practice.
4 May 2026
Read appraisal →Psychiatry research
A machine learning approach to predicting clinical trajectories in bipolar disorder
BACKGROUND: Bipolar disorder (BD) is characterized by significant heterogeneity in clinical presentation, age of onset, and diagnostic delays. Identifying clinical trajectories and subtypes is a central aim of precision psychiatry, with the goal of improving risk stratification and characterization of illness courses, enabling early recognition of high-risk patients and facilitating timely interventions that improve long-term outcomes. METHODS: This is a dual-site retrospective cross-sectional secondary analysis of 203 clinical datasets from BD patients to develop machine learning models: Random Forest, Weighted Logistic Regression (LR), and XGBoost, supplemented by statistical modelling, to predict age of onset timing (early/late onset) and illness severity. Survival analysis (Kaplan-Meier and Cox proportional hazards) investigated factors influencing diagnostic delay, while clustering identified patient subtypes. RESULTS: Single-split Weighted LR (AUC = 0.733) and nested cross-validation XGBoost (AUC = 0.729) achieved superior performance in predicting onset timing. Cluster analysis identified three distinct phenotypes: baseline low complexity, late-onset familial-severe, and an early-onset comorbid group. Survival analysis revealed that the time to diagnosis was independent of clinical risk profiles (Log-Rank p = 0.34), with the most severe cases experiencing significant diagnostic latency. CONCLUSIONS: Machine learning accurately predicts BD clinical trajectories using routinely collected data in an African cohort. Diagnostic delay did not differ significantly across clinical risk or severity strata, indicating that the measured variables did not explain variation in time to diagnosis.
4 May 2026
Read appraisal →PloS one
Prevalence of perinatal depression in Ethiopia: An umbrella review of systematic review and meta-analysis studies
BACKGROUND: Perinatal depression is a significant public health concern that affects women during pregnancy and the postpartum period. Despite being acknowledged globally, the burden of perinatal depression is particularly profound in low and middle-income countries, such as Ethiopia. This umbrella review is therefore intended to systematically consolidate findings on perinatal depression among Ethiopian women to better understand its prevalence, thereby highlighting the gaps in current research and informing future interventions. METHODS: This umbrella review used the PRIOR checklist for the reviews of systematic review and meta-analytic studies. The review protocol has been registered on PROSPERO: CRD42023495174. PubMed, EMBASE, and PsycINFO databases were searched for the presence of systematic review and meta-analysis studies. The quality of included articles has been evaluated with a measurement tool to assess systematic review and meta-analysis studies (AMSTAR). A novel graphic approach with an estimated corrected covered area (CCA) has been used to determine the degree of overlap of primary studies in the systematic review and meta-analysis studies. The weighted random effect model was used during the meta-analysis. RESULT: A total of 28 unique primary studies and 8 systematic reviews, and meta-analysis studies with 15,592 participants were included in this umbrella review. The pooled prevalence of perinatal depressive symptoms in the included systematic review and meta-analysis studies ranges from 20.1% to 25.8%. The pooled umbrella prevalence of perinatal depressive symptoms among women in Ethiopia was 22.49% (95 CI%:21.38, 23.59). The pooled umbrella analysis revealed that the antenatal and postnatal depressive symptoms were 22.76% (95% CI: 19.9, 25.62) and 21.75% (95% CI: 21.03, 22.48), respectively. In addition, the pooled prevalence of perinatal depression in studies that included 10 or below primary studies is 22.86% (95%CI:20.39, 25.33), and in those that included below 10 primary studies, it was 22.10% (95%CI: 21.55, 22.65). The novel graphic presentation depicted a very high degree of overlap of primary studies in the included systematic reviews and meta-analysis studies; corrected covered area (CCA) of 25.5%. Four of the included studies (fifty percent) had high methodological quality, and the remaining four relied on a moderate quality range. CONCLUSION: The pooled overall, antenatal, and postnatal prevalence of depression symptoms was high in Ethiopia, with no significant difference during the antenatal and postnatal period. An improved understanding of perinatal depression will therefore guide policymakers and health practitioners in developing targeted strategies to alleviate this mental health challenge.
3 May 2026
Read appraisal →PloS one
Enhanced graph attention network by integrating Long Short-Term Memory for artificial emotion representation in multi-modality datasets
Emotion representation is a critical aspect of artificial intelligence, particularly in human-computer interaction and affective computing. Emotion recognition from multi-modal data remains challenging due to the complex semantic relationships between textual, audio, and visual features. This study proposes a hybrid model combining Enhanced Graph Attention Networks and Bidirectional Long Short-Term Memory to address this challenge. First, E-GAT captures structural dependencies between emotional features by constructing a semantic graph from text embeddings. Second, Bi-LSTM models temporal dynamics of sequential data, enabling effective integration of contextual information. We evaluated the model on three benchmark datasets: SemEval-2018 (text-only), RAVDESS (audio-visual), and CMU-MOSEI (multi-modal). Experimental results show that the proposed model achieves state-of-the-art performance: 58.5% accuracy and 68.7% F1-score on SemEval-2018, outperforming baseline models. On multi-modal datasets, it achieves 78.9% accuracy (RAVDESS) and 82.3% accuracy (CMU-MOSEI), demonstrating robust cross-modal generalization. This work advances emotion recognition by providing a unified framework for both text-only and multi-modal scenarios, with applications in human-computer interaction and mental health monitoring.
3 May 2026
Read appraisal →Psychological medicine
Characterizing executive dysfunctions in patients with schizo-obsessive comorbidity: comparing schizophrenia with obsessive-compulsive disorder
BACKGROUND: The term 'schizo-obsessive comorbidity (SOC)' is used to describe the presence of obsessive-compulsive symptoms or obsessive-compulsive disorder (OCD) in patients with schizophrenia (SOC). Recent studies have found overlapped executive dysfunctions in SCZ and OCD implicating shared pathophysiology. However, specific deficits in the components of executive function (EF) in patients with SOC remains unclear. METHODS: We recruited 37 patients with SOC, 68 patients with SCZ, 70 patients with OCD, and 59 healthy controls (HCs). All participants completed a battery of measures for EF components, namely initiation, sustained attention, online updating, switching, disinhibition, and planning. Apart from traditional group-mean analysis, we applied machine learning approaches to identify the unique patterns of EF among different clinical groups. RESULTS: The results showed that the three clinical groups could be distinguished from HCs. The feature importance analysis showed that, to classify clinical groups from HC, online updating was the core feature of SCZ patients, whereas disinhibition and online updating jointly determine classification between OCD patients and HC. In differentiating SOC from HC, online updating, planning, and disinhibition collectively served as key features. Machine learning algorithms classified SOC and OCD with acceptable performance but classified SOC and SCZ with lower performance. CONCLUSIONS: Deficits of EF are shared features among patients with SOC, SCZ, and OCD. However, the specific components of executive dysfunction in these clinical groups appeared distinct.
2 May 2026
Read appraisal →Behavioural brain research
Influences of high-definition transcranial direct current stimulation (HD-tDCS) and cognitive reappraisal on emotions and aggression following social exclusion
Social exclusion can trigger negative emotions and aggressive behavior, while emotion regulation strategies and neuromodulation techniques may mitigate these effects. This study employed a 2 × 2 factorial design to investigate the impacts of high-definition transcranial direct current stimulation (HD-tDCS) and cognitive reappraisal on emotions and aggression following social exclusion. Thirty-two university students were randomly assigned to four groups: active HD-tDCS + cognitive reappraisal, sham HD-tDCS + cognitive reappraisal, active HD-tDCS + no reappraisal, and sham HD-tDCS + no reappraisal. Results showed that active HD-tDCS significantly reduced aggressive behavior in both the Competitive Reaction Time Task and Job-relevant Evaluations Task, likely by enhancing prefrontal regulatory control. Cognitive reappraisal significantly decreased negative emotions but did not directly reduce aggressive behavior, suggesting a dissociation between emotional and behavioral regulation pathways. No significant interaction was found between HD-tDCS and cognitive reappraisal, possibly due to their independent neural mechanisms. These findings highlight the potential of HD-tDCS and cognitive reappraisal as interventions for social exclusion-induced aggression and emotional dysregulation.
1 May 2026
Read appraisal →Scientific reports
Intelligent virtual agents in psychotherapy: a safety evaluation across high-risk mental health scenarios
The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)-driven conversational agents as potential supports for mental health care. In this exploratory pilot study, we examined the safety and feasibility of an intelligent virtual agent (IVA) designed to simulate psychotherapeutic interactions, with a focus on high-risk situations involving suicidality and substance use. Two licensed psychotherapists engaged in scripted interactions with the IVA across 12 predefined scenarios addressing suicidality and substance abuse. The IVA was powered by GPT-4omni and embedded in a Unity-based avatar. After each interaction, testers evaluated acceptance, usability, and human-robot interaction. Two independent psychotherapists rated the IVA's responses using a structured scale assessing guideline adherence, risk recognition, help provision, de-escalation, and empathy. No real patients were involved; all interactions were simulated for safety testing purposes. The IVA showed preliminary indications of good usability and generally empathic responses. However, problematic responses occurred in 29% of conversations, with 12.5% rated as highly critical. Responses rated as "critical" or "highly critical" referred to outputs that failed to provide adequate support, showed insufficient risk recognition, or included ethically problematic suggestions. Key concerns included inadequate recognition of risk, normalization of substance use, and insufficient referral to crisis resources, particularly in scenarios involving underage alcohol access and suicide-related inquiries. In this small, expert-based pilot safety evaluation, the findings suggest that although AI-based agents may improve access to mental health support, rigorous safety evaluation, clinical oversight, and robust safeguards are essential prior to clinical deployment. No clinical conclusions can be drawn from this simulated study.
1 May 2026
Read appraisal →BMJ mental health
Using electronic health records to evaluate a children and young people's social prescribing service: challenges and implications for research and practice
BACKGROUND: Preliminary evidence indicates that social prescribing (SP) can improve children and young people's (CYP) well-being but is limited by small non-representative samples and often relies on descriptive statistics. Given the wide implementation of SP in the UK, administrative records provide a unique opportunity to understand current practice and assess impacts on well-being. OBJECTIVES: (1) To understand the quality of data captured in SP administrative records. (2) To explore which CYP are currently receiving SP and what SP entails in practice. (3) To assess the impact of SP on well-being. METHODS: We used administrative records from one CYP SP service in England. Records were extracted from Joy, an online platform for managing SP. Over 18 months, 770 age-eligible CYP were referred to SP, 203 of whom were successfully discharged and completed two pre-post measures of well-being (the short Warwick-Edinburgh Mental Wellbeing Scale; SWEMWBS) at least 7 days apart. We used descriptive statistics, a paired t-test to assess changes in well-being and linear regressions with interactions to test effect modification. FINDINGS: Missing data was the largest issue, with ethnicity missing for 94% of CYP. A lack of detail and inconsistent recording for both individual characteristics and SP practices also presented challenges. Despite this, we identified that most CYP were referred by their GP, followed by their school, with 97% referred because of their mental health. The most common pathway was to receive SP for around 90 days, with 10-15 link worker contacts and 6 contact hours. Following SP, SWEMWBS scores improved by 3.72 points (t(202)=17.50, 95% CI 3.30 to 4.14, p<0.001), a 20% relative increase. Exploratory analyses suggested that this increase was greater for those with lower baseline well-being. CONCLUSIONS: Despite numerous challenges with missing data and data quality, we found that CYP well-being increases following SP (as it is currently implemented). The moderate effect size was consistent with larger studies of adults. CLINICAL IMPLICATIONS: Further development of online platforms is needed to monitor access to, nature of and efficacy of SP. For those working in SP, we recommend more training, implementation of standardised guidelines and designated time to update records.
27 Apr 2026
Read appraisal →PloS one
The stress reduction potential of Bhagavad Gita and Yoga for healthcare workers during the COVID-19 pandemic: A randomized controlled trial
BACKGROUND: Healthcare workers (HCWs) experienced elevated psychological distress during the COVID-19 pandemic. Culturally embedded, non-stigmatizing approaches may improve uptake of mental health support in this population. We aimed to evaluate the impact of Yoga, Bhagavad Gita learning, and their combination (Yoga+Gita) on psychological distress among HCWs. METHODS: We conducted a prospective, single-center, parallel-group randomized controlled trial at a secondary hospital in India. Eligible HCWs (>18 years, without pre-existing diagnosed mental health conditions) were randomized in a 1:1:1:1 ratio to: Yoga (daily 60-min sessions for one week), Gita (daily 60-min teaching of Bhagavad Gita Chapter 2 for one week), Yoga+Gita (30 min Yoga + 30 min Gita daily for one week), or Control (usual care). Psychological distress was assessed using the Generalized Anxiety Disorder-7 (GAD7) scale at baseline (B-GAD7), immediately post-intervention (IPI-GAD7), and 45 days post-intervention (DPI-GAD7). Participants were aware of group assignment, but data analysts were blinded. Group comparisons used Friedman and Kruskal-Wallis tests with Bonferroni-adjusted post-hoc analyses. We hypothesized that (1) all three interventions would reduce GAD-7 scores compared with control, (2) Gita would produce more durable effects than Yoga alone, and (3) the combined Yoga+Gita intervention would yield the greatest overall reduction in psychological distress. FINDINGS: Sixty-eight participants were enrolled (17 per group), with no losses to follow-up. B-GAD7 did not differ across groups (p = 0.908). Compared with Control, Yoga+Gita reduced anxiety immediately (p = 0.009), and Gita reduced anxiety at 45 days (p = 0.047). A clinically meaningful (≥4-point) GAD7 reduction at 45 days occurred in 17.6% (Yoga), 47.1% (Gita), 41.2% (Yoga+Gita), and 11.8% (Control) of the participants. Exploratory analyses suggested larger benefits among women and among those who continued practice. INTERPRETATION: Yoga and Gita-learning, particularly in combination, were associated with reductions in psychological distress among HCWs. TRIAL REGISTRATION: Prospectively registered with the CTRI (CTRI/2021/10/037365).
27 Apr 2026
Read appraisal →JMIR mental health
Expected Competencies and Personal Attributes of Digital Health Navigators to Support Digital Mental Health Care: Focus Group and Interview Study With Patients and Health Care Professionals
BACKGROUND: Digital mental health apps (DMHAs), and in particular digital therapeutics (DTx), offer promising opportunities to support mental health care. However, their effective use in outpatient settings in Germany remains limited. To overcome this gap, the role of digital health navigators (DHNs) has been introduced. DHNs are trained individuals who support patients and health care professionals in selecting, using, and integrating DMHAs into care. Despite increasing interest in this role, there is limited evidence on the competencies, knowledge, and personal attributes required for DHNs to work effectively in mental health settings. OBJECTIVE: The study aims to explore the expected competencies, knowledge areas, and personal attributes that DHNs need to effectively support the implementation and use of DTx in outpatient mental health care. METHODS: As part of the prestudy of the Digital Navigators for Acceptance and Competence Development with Mental Health Apps (DigiNavi) study, a qualitative study was conducted involving 35 participants (7 general practitioners, 8 patients in general practice, 11 outpatient psychiatrists/psychologists, and 9 patients in psychiatric outpatient clinics) from different general practices and psychiatric outpatient clinics in Germany. A total of 17 semistructured interviews and 4 focus groups were conducted to explore expectations of DHNs. Data were analyzed using qualitative content analysis. RESULTS: Participants emphasized that DHNs should combine strong interpersonal skills (empathy, patience, and sensitive communication) with technical and basic clinical competencies. Most favored DHNs as integrated clinical team members (eg, medical assistants), citing their existing patient relationships, but noted time and training constraints. Key expectations included the ability to support patients with DTx use, adapt communication to individual needs, and convey data privacy information clearly. Foundational knowledge of mental health conditions and sensitivity to crises were considered important for identifying warning signs and escalating concerns. While DHNs were seen as essential intermediaries between patients, health care professionals, and DTx, participants highlighted the necessity for clearly defined roles, structured training, and realistic expectations to prevent role overload and enable sustainable implementation in outpatient mental health care. CONCLUSIONS: DHNs require a specialized skill set that bridges clinical understanding, digital expertise, and interpersonal competence. Our results lay the groundwork for developing training curricula and implementation strategies that align with real-world expectations for the DHN role. Defining these core competencies is essential for supporting the sustainable and effective integration of DMHAs into mental health care.
26 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Online self-compassion-based interventions on patient outcomes in patients with cancer: a systematic review
PURPOSE: This systematic review investigates the effectiveness of online self-compassion-based interventions and their impact on the outcomes of cancer patients. METHODS: A comprehensive search was carried out in the Web of Science Core Collection, Cochrane Library, CINAHL, PubMed, ScienceDirect, Scopus, Ovid, and SpringerLink databases without any year limitation until January 01, 2026. Cochrane and PRISMA guidelines were used for systematic review and reporting. RoB2 and Joanna Briggs Institute guidelines were utilized to assess the risk of bias. RESULTS: Eight studies included in the systematic review were conducted between 2017 and 2025 in Australia, the USA, the Netherlands, China, and Türkiye. The self-compassion interventions applied in the studies included mindful self-compassion, self-compassion-focused writing, compassion mind training, and kindness interventions. Online self-compassion-based interventions have been found to increase patients' levels of self-compassion, mindfulness, body image appreciation, well-being, self-acceptance, posttraumatic growth, and positive affect while reduce their levels of self-criticism, social isolation, anxiety, stress, and depression, body image distress. CONCLUSIONS: Studies have shown the positive effects of self-compassion-based interventions on patient outcomes. A sensitivity analysis that included only randomized controlled trials demonstrated robustness in all outcomes except anxiety; however, some research results were obtained from a single randomized controlled trial. Since some study results were derived solely from quasi-experimental studies, they were not included in the analysis. Therefore, more randomized controlled research is required to integrate these interventions into clinical patient care. Online self-compassion interventions can contribute to the planning of holistic nursing interventions for patient outcomes.
25 Apr 2026
Read appraisal →BMC psychiatry
The cross-sectional relationship between body dysmorphic disorder and perfectionism: a meta-analysis
Perfectionism has been theoretically implicated in the development and maintenance of body dysmorphic disorder (BDD), but a quantitative synthesis of its cross-sectional relationship with BDD symptom severity is lacking. The present meta-analysis therefore examines the strength and consistency of this relationship across existing studies. A random-effects meta-analysis of 34 studies comprising a total of 13 107 participants was conducted. Studies were included if they assessed BDD symptoms and perfectionism using validated instruments. Subgroup analyses were used to examine perfectionism dimensions, sample characteristics, and diagnostic subtypes. Partial correlations controlling for depression were examined. The results revealed a moderate and statistically significant correlation between BDD symptoms and perfectionism (r = .32, 95% CI [0.28, 0.36]). The association remained stable across age groups, sample types, and genders, and was comparable in individuals with muscle dysmorphia (r = .34). A separate meta-analysis focusing on individuals with elevated BDD symptoms yielded a comparable effect size (r = .35, 95% CI [0.22, 0.47]). Controlling for depressive symptoms attenuated the relationship, indicating that part of the association may be attributable to shared depressive symptomatology. Significant heterogeneity (I² = 81%) and evidence of publication bias were detected, although the findings were comparable across several sensitivity analyses. The results highlight the relevance of both perfectionistic concerns and perfectionistic strivings as correlates of BDD-related distress and support its consideration within transdiagnostic models. The protocol for this meta-analysis was pre-registered on PROSPERO (Preregistration ID: CRD42024551099).
25 Apr 2026
Read appraisal →Cell reports. Medicine
Bed nucleus of the stria terminalis-nucleus accumbens stimulation for depression: A randomized, double-blind, crossover trial
Although deep brain stimulation (DBS) shows potential in treatment-resistant depression (TRD), the efficacy remains controversial. To assess the efficacy of DBS of the bed nucleus of the stria terminalis (BNST) and nucleus accumbens (NAc) for TRD, we conduct a randomized, double-blind, crossover trial after an open-label optimization stage. Stimulation leads to a mean decrease in the Hamilton Depression Scale-17 (HAMD) scores of 10.1 points (p < 0.001) with a 50% response rate at the end of the open-label stage. Anxiety, quality of life, and disability are also improved. The HAMD scores are significantly lower in patients during active DBS than during sham DBS (p < 0.001). Serious adverse events include suicide (1 patient) and seizure (1 patients). With individual stimulation analysis, we identify optimal stimulation sites, fiber tracts, and functional networks (false discovery rate [FDR] p < 0.05). This study demonstrates the efficacy and safety of BNST-NAc DBS for TRD and reveals optimal brain targets for stimulation. This study was registered at ClinicalTrials.gov (NCT04530942).
25 Apr 2026
Read appraisal →BMJ open
Course of psychological symptoms and the initial management strategies in general practice using electronic health records in the Netherlands
BACKGROUND: Patients who consult their GP about psychological complaints, such as feeling anxious or depressed, are often initially given a psychological symptom diagnosis. However, it remains unclear whether these symptoms will develop into psychiatric conditions, which is crucial for informing patients about their prognosis and guiding GPs in their management. AIMS: To explore the course of psychological symptom diagnoses and compare GPs' management strategies for (a) psychological symptom diagnoses that persisted for more than a year and (b) those that changed into psychiatric conditions during the first year of care. METHODS: We performed a retrospective cohort study using the Family-Medicine Network database. We included all episodes of care (EoC) that started with a psychological symptom diagnosis between 2008 and 2021. We performed negative binomial analyses and logistic regression analyses to compare management strategies and number of contacts between EoC that changed into psychiatric conditions and persistent psychological symptoms (>1 year) during the first year of care. RESULTS: Out of the 14 633 EoC that started with a psychological symptom diagnosis, 79.4% resolved within 1 year, 12.8% persisted as psychological symptoms and 7.8% changed into psychiatric conditions. In EoC that changed into psychiatric conditions, as compared with EoC that persisted as psychological symptoms, we observed a significantly higher number of contacts with the GP (RR=1.76, 95% CI 1.63 to 1.91) as well as an increased total number of interventions (RR=1.71, 95% CI 1.58 to 1.84). CONCLUSION: Most psychological symptoms remain for only a short period of time and only a few persist or change into psychiatric conditions. Future research should investigate the factors that influence patients' decisions to seek further help from their GPs as well as those that contribute to the transition from psychological symptom diagnoses to a psychiatric condition.
25 Apr 2026
Read appraisal →Brain imaging and behavior
Meta-analysis and meta-regression of diagnostic test accuracy of connectome-based predictive modeling in OCD
Functional magnetic resonance imaging studies have reported disruptions in functional connectivity within brain networks, known as connectomes. Researchers have tested connectomes to see whether they serve as biomarkers for various psychiatric conditions. This meta-analysis aims to evaluate the diagnostic test accuracy of predictive models of connectomes derived from resting-state fMRI in diagnosing obsessive-compulsive disorder. A systematic review and meta-analysis were conducted on previous studies assessing the sensitivity, specificity, and accuracy of connectome-based diagnostic models in obsessive-compulsive disorder and healthy controls. Eight studies were identified, comprising 563 individuals with obsessive-compulsive disorder and 564 healthy controls. The results revealed robust diagnostic performance with a pooled sensitivity of 0.827 (95% CI: 0.779-0.867) and specificity of 0.794 (95% CI: 0.759-0.826). Connectome-based diagnostic models demonstrated excellent clinical utility, with an area under the curve of 87% (95% CI: 84%-90%), and significant predictive power as indicated by positive (4.15) and negative (0.21) likelihood ratios, as well as strong diagnostic odds ratio of 18.69 (95% CI: 11.84-29.49). The results highlight the potential of functional connectome-based predictive modeling as a robust tool for accurately diagnosing obsessive-compulsive disorder, with possibility of future implications for early diagnosis, monitoring treatment-related changes, involving in decision modeling, and understanding the biological mechanisms underlying obsessive-compulsive disorder.
20 Apr 2026
Read appraisal →BMJ open
Implementation of a digital tool for monitoring and managing the emotional and cognitive sequelae of post-intensive care syndrome: ICURA study protocol for a randomised clinical trial
INTRODUCTION: Intensive care units (ICUs) can be a particularly challenging environment for patients who are mentally vulnerable. In addition to the physical stress associated with critical illness and its management, there are physiological and psychosocial factors that can negatively impact a patient's mental health. Approximately half of ICU survivors will experience post-intensive care syndrome, a set of emotional, neuropsychological and physical sequelae that can significantly affect patients' functionality and quality of life, both in the short and long term. The main objective of this study is to investigate whether the ICU Recovery Answers (ICURA) digital follow-up platform can effectively detect emotional and cognitive problems in critically ill patients and its impact on functionality and health-related quality of life during the first year after ICU discharge. METHODS AND ANALYSIS: Multicentre longitudinal prospective study involving ICU adult patients, with randomised follow-up comparing a telemedicine monitoring programme versus usual medical care during 1 year after discharge. A total of 360 participants will be recruited during their ICU admission in two hospitals in Spain. Efficacy outcomes will focus on participants' level of functioning, assessed with the WHO Short Disability Assessment Schedule, and quality of life, measured with the 12-Item Short Form Survey at 1, 6 and 12 months after ICU discharge. Emotional state and cognitive impairment will be evaluated using the Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Treatment-Outcome Post-Traumatic Stress Disorder Scale and the Montreal Cognitive Assessment by telephone at 1, 3, 6, 9 and 12 months after ICU discharge. ETHICS AND DISSEMINATION: The implementation of this project is expected to have a direct impact on the satisfaction of ICU survivors, improving their well-being, personalised follow-up and quality of life. Results from this study will be disseminated at various scientific conferences, national and international meetings, and will be shared with the general public and other relevant parties. The dissemination of these results will occur through scientific publications, allowing the medical and scientific community to benefit from the study's findings. Ethics approval from the Ethics Board of Parc Taulí Foundation and Balearic Islands with reference numbers 2022/3031 and IB 5072/22 PI: Protocol version 1 of 18 November 2022. TRIAL REGISTRATION NUMBER: NCT06504979.
20 Apr 2026
Read appraisal →PloS one
High-frequency vs. low-frequency MIDI-assisted group music therapy in psychiatric inpatients: A randomized controlled trial
BACKGROUND: Music therapy has been increasingly incorporated into psychiatric inpatient care as an adjunctive intervention; however, evidence regarding its comparative effectiveness, particularly with respect to intervention intensity during brief psychiatric hospitalizations, remains limited. Most randomized trials have evaluated interventions delivered over several weeks, leaving uncertainty about the potential differential effects of higher versus lower intervention intensity within short inpatient stays. METHODS: This randomized controlled trial with a parallel-group design was conducted in an adult psychiatric inpatient unit in Bogotá, Colombia. Participants were randomly assigned (1:1) to either a high-frequency music therapy group, which received five sessions delivered on consecutive days within a single inpatient week, or a low-frequency group, which received one session during the same period. Allocation concealment was ensured through computer-generated randomization performed by an independent researcher not involved in participant recruitment or outcome assessment. Primary outcomes were symptoms of depression, anxiety, and stress, assessed using the Depression Anxiety Stress Scale-21 (DASS-21) at baseline and at the end of the intervention week. Secondary outcomes included emotional responses to music therapy, assessed using selected items from the Questionnaire of the Impact of Music Therapy Sessions in Adults (CISMA instrument), and global life satisfaction, measured with the Satisfaction With Life Scale (SWLS). Between-group changes over time were analyzed using repeated-measures mixed-effects linear models. RESULTS: A total of 91 participants were randomized, of whom 74 (37 per group) completed post-intervention assessments and were included in the per-protocol analysis. Between-group analyses revealed a statistically significant group × time interaction for the DASS-21 stress subscale only. Participants in the high-frequency music therapy group showed a greater reduction in stress scores compared with the low-frequency group, with a statistical between-group difference (6.49 ± 5.55 vs. 7.03 ± 5.81; p = 0.023). No statistically significant between-group differences were observed for the DASS-21 anxiety subscale (p = 0.339) or the depression subscale (p = 0.270). Global life satisfaction, as measured by the SWLS, did not differ significantly between groups (p > 0.05). For secondary outcomes assessed using selected CISMA items related to emotional change, between-group differences were small and not statistically significant across all items. Overall, except for stress symptoms, outcomes did not differ significantly between the high-frequency and low-frequency intervention groups. CONCLUSIONS: In this randomized controlled trial, a higher frequency of music therapy sessions was associated with a greater reduction in stress symptoms compared with a lower-frequency intervention during a brief psychiatric hospitalization. However, no between-group differences were observed for anxiety, depression, life satisfaction, or emotional response measures. Overall, these findings indicate limited evidence of differential effectiveness by intervention intensity and should be interpreted cautiously in light of the study's methodological limitations. TRIAL REGISTRATION: ISRCTN registry: ISRCTN87861817 (https://www.isrctn.com/ISRCTN87861817).
18 Apr 2026
Read appraisal →Medicine
Effectiveness of cognitive behavioral therapy for depression in patients with multiple sclerosis: A systematic review and meta-analysis
BACKGROUND: The efficacy and methodologies of cognitive behavioral therapy (CBT) for treating depression in multiple sclerosis (MS) require further comprehensive research. METHODS: Two researchers conducted independent literature searches across 5 databases, utilizing the keywords "multiple sclerosis," "cognitive behavioral therapy," and "depression," without any restrictions on publication year. The Cochrane Risk of Bias Tool 2.0 was utilized to assess the included studies. Statistical analyses were conducted using standardized mean difference as the effect size with Stata 17.0 software. RESULTS: This meta-analysis included 22 studies. The results indicate that, regardless of whether delivered face-to-face, online, or via telephone, both individual and group CBT effectively alleviate depression in patients with MS. Significant improvements in depressive symptoms were observed starting from the second month of intervention and persisted for up to 6 months post-intervention. CONCLUSION: Patients with MS can flexibly choose the delivery modality of CBT based on their individual circumstances while engaging in treatment for around 2 months. They are encouraged to reengage in CBT within 6 months post-intervention to ensure the continuity and stability of depression treatment outcomes. Healthcare providers can offer various CBT delivery modalities based on the individual preferences and circumstances of patients with MS, while developing corresponding plans to enhance the effectiveness of depression management.
18 Apr 2026
Read appraisal →PloS one
Exploration of schizophrenia-associated gene modules using graph theory, co-expression networks, and dimensionality reduction.
The PsychENCODE consortium has generated a comprehensive multi-omic dataset from human brain samples, spanning healthy individuals and those with neuropsychiatric disorders. In this study, we focus exclusively on schizophrenia and analyze PsychENCODE transcriptomic data to construct gene regulatory and co-expression networks, aiming to uncover biologically and clinically relevant gene modules. We apply three independent analytical approaches to the same dataset. First, we preprocess the data using Surrogate Variable Analysis (SVA) to correct for latent variation and normalize gene expression. For graph-based analysis, we use Pearson correlation and the igraph package to construct gene co-expression networks and apply Prim's algorithm to generate minimum spanning trees (MST) and compute centrality measures. Next, we implement Weighted Gene Co-expression Network Analysis (WGCNA), assuming a scale-free topology, to identify modules associated with schizophrenia traits. Dimensionality reduction is performed using Principal Component Analysis (PCA), with visualization aided by t-distributed Stochastic Neighbor Embedding (t-SNE) and Multidimensional Scaling (MDS). Functional enrichment is carried out using Gene Ontology (GO) and KEGG pathway databases. Each method reveals distinct yet complementary biological signatures associated with schizophrenia. The igraph-based approach highlights differentially expressed genes (DEGs) with high centrality, uncovering hub genes involved in mRNA export, synaptic signaling, and ion transport. WGCNA identifies co-expression modules strongly correlated with schizophrenia diagnosis, enriched for immune response, histone modification, and mRNA surveillance pathways. PCA isolates key genes contributing to diagnostic variance, with enrichment in neurotransmitter release cycles and cytokine signaling. Collectively, these results underscore the involvement of immune, synaptic, and epigenetic processes in schizophrenia and demonstrate the power of using multiple, orthogonal analytical lenses.
17 Apr 2026
Read appraisal →JMIR human factors
Comparing Images of Depression in Mass Media and AI-Generated Pictures: Mixed Methods Study
BACKGROUND: Images play an important role in reducing stigma related to mental health, which often is distorted in the media. In recent years, generative artificial intelligence (AI) has been used to generate images related to mental health. However, first reports suggest that AI-generated images do not depict mental health conditions accurately. In-depth studies on the topic of mental health representations in AI-generated images are still missing. OBJECTIVE: The main objective of this study is to analyze and compare the visual representation of depression in mass media and in AI-generated images. METHODS: The methodologies used were discussion groups (15 participants) and a quasi-experimental online survey (792 interviewees), aimed at people with depression and young people. RESULTS: The results showed that both the images used in the media and those generated by AI reproduced stereotypes and stigmas about depression. However, participants considered AI-generated pictures to be more stereotypical, stigmatizing, and more likely to have a negative impact on people with depression. In contrast, media images were considered more appropriate, realistic, inclusive, and that better reflected the relationship between gender and depression. Statistically significant differences were observed between the control and test groups in both people with depression and young people (P<.001), indicating that when people were aware of what images were AI-generated, they tended to reject them to a greater extent. CONCLUSIONS: Considering the current trend toward the widespread use of AI in mental health communication, it is crucial to promote closer collaborations between science journalists, AI developers, and mental health experts, including patients' associations, as well as a shift toward user-participatory AI design.
17 Apr 2026
Read appraisal →JMIR formative research
Designing Psychologically Grounded Artificial Intelligence for Supporting Bystander-Based Cyberaggression Intervention: Mixed Methods Exploratory Study
BACKGROUND: Cyberaggression poses a growing threat to mental health, contributing to increased distress, reduced self-esteem, and other adverse psychosocial outcomes. Although bystander intervention can mitigate the escalation and impact of cyberaggression, individuals often lack the confidence, strategies, or language to respond effectively in these high-stakes online interactions. Advances in generative artificial intelligence (AI) present a novel opportunity to facilitate digital behavior change by assisting bystanders with contextually appropriate, theory-informed intervention messages that promote safer online environments and support mental well-being. OBJECTIVE: This mixed methods design study aimed to explore the feasibility of using generative AI to support bystander intervention in cyberaggression on social media. Specifically, we examined whether AI can generate effective responses aligned with established intervention strategies and how these responses are perceived in terms of their potential to de-escalate online harm and foster behavior change. METHODS: We collected 1000 real-world cyberaggression examples from public social media datasets and generated bystander intervention responses using 3 distinct prompt strategies: a generic policy reminder, a baseline GPT prompt, and a theory-driven GPT prompt (AllyGPT). To evaluate the responses, we conducted computational linguistic analyses to assess their psycholinguistic features and carried out a mixed methods evaluation. Three trained coders rated each message on favorability, conversational impact, and potential to change behavior and later participated in semistructured interviews to reflect on their evaluation process and perceptions of intervention effectiveness. RESULTS: Linguistic analyses revealed that baseline GPT responses exhibited more emotionally positive and authentic language compared to AllyGPT responses, which showed a more analytical and assertive tone. Policy reminder messages were linguistically rigid and lacked emotional nuance. Human evaluation results showed that AllyGPT responses received the highest effectiveness ratings for low-incivil cyberaggression cases in 2 dimensions (favorability and changing behavior), and baseline GPT works better for mid and high levels for all effectiveness dimensions. For medium- and high-incivility aggressions, baseline GPT responses received the highest ratings across all 3 dimensions of effectiveness (favorability, discussion-shifting potential, and likelihood of changing bullying behavior), followed by AllyGPT, with policy reminders rated lowest. Qualitative feedback further emphasized that baseline GPT responses were perceived as natural and inclusive, while AllyGPT responses, although grounded in psychological theory, were sometimes viewed as overly direct. Policy reminders were considered clear but lacked persuasive impact. CONCLUSIONS: Our work showed that designing effective AI-generated bystander interventions requires a deep sensitivity to platform culture, social context, and user expectations. By combining psychological theory with adaptive, conversational design and ongoing feedback loops, future systems can better support bystanders, delivering interventions that are not only contextually appropriate but also socially resonant and behaviorally impactful. As such, this work serves as a foundation for scalable, human-centered AI systems that promote safer online spaces and users' mental well-being.
16 Apr 2026
Read appraisal →Biological psychiatry
From Snapshots to Stable Outcomes: Resting-State Functional Magnetic Resonance Imaging-Based Prognosis of Functioning in Patients With Psychosis Risk or Recent-Onset Depression
BACKGROUND: Early recovery of functioning is critical for favorable outcomes in psychotic and affective disorders. Transdiagnostic brain activity patterns may capture pathways for poor outcomes before clinical manifestation, thereby supporting timely prevention and intervention. METHODS: Using machine learning, we evaluated the transdiagnostic prognostic value of resting-state functional magnetic resonance imaging fractional amplitude of low-frequency fluctuations (fALFF) (slow-5 and slow-4 sub-bands) for functional outcomes in patients at clinical high risk for psychosis (n = 217) or with recent-onset depression (n = 198) from the multisite PRONIA (Prognostic Tools for Early Psychosis Management) study. Leave-site-out cross-validation assessed the geographic generalizability of models across disability and symptom domains, with outcomes defined as snapshots at 9- or 18-month follow-up or across both time points. We examined diagnosis-specific performance, generalization to recent-onset psychosis (n = 140), and negative symptoms and the added value of fALFF over clinical prognostication. RESULTS: Transdiagnostic models predicting stable good functioning across follow-ups showed up to 10% higher balanced accuracy (BAC) than snapshot models. Decreased slow-5 fALFFs in the default mode network, executive control network (ECN), and dorsal attentional network (DAN) and increased fALFF in the salience network, ECN, and DAN predicted impairment with BAC = 67% (sensitivity = 65%, specificity = 70%, p < .001). This model generalized to recent-onset psychosis (BAC = 62%, sensitivity = 64%, specificity = 59%, p < .001) and predicted (BAC = 65%, sensitivity = 66%, specificity = 65%, p < .001) and was mediated by negative symptoms. Slow-5-based models improved prognostic accuracy over expert ratings in disability (BACraters = 66%, BACraters+slow-5 = 75%, W = 1680, p < .001) and symptom (BACraters = 61%, BACraters+slow-5 = 71%, W = 1444, p < .001) domains. CONCLUSIONS: We highlighted the prognostic value of fALFF for functional impairment in psychosis risk and early depression. Leveraging trajectorial information, we identified candidate imaging biomarkers to improve prognostication, thereby supporting personalized prevention and recovery strategies.
16 Apr 2026
Read appraisal →Journal of affective disorders
Comorbid clinical and biomarker outcomes in patients with obsessive-compulsive disorder: An umbrella review and evidence map
BACKGROUND: Obsessive-compulsive disorder (OCD) is a chronic psychiatric condition with diverse comorbidities, yet outcome research remains limited. Thus, we aimed to systematically review existing meta-analyses of OCD-associated comorbid outcomes and assess the strength of evidence. METHODS: We conducted an umbrella review of meta-analyses, searching PubMed/MEDLINE, Embase, CINAHL, and Google Scholar up to July 7, 2025. Although we intended to include all studies related to OCD-associated comorbid outcomes, only observational studies were available. Effect sizes were recalculated using random-effects models, and all estimates were converted to equivalent standardized mean differences (eSMD) with 95 % confidence intervals (CIs). The methodological quality of the included reviews was assessed using the AMSTAR 2 tool, and the class of evidence (CE) for each association was graded according to predefined criteria (Class I-IV). RESULTS: We identified 17 meta-analyses that included 373 primary studies, covering 52 outcomes in 156,161 participants across 40 countries. Patients with OCD were associated with autistic traits outcomes, including restricted and repetitive behaviors (eSMD, 1.66 [95 % CI, 1.25-2.07]; CE = IV), and total autism-spectrum quotient score (1.40 [1.06-1.81]; CE = IV). OCD also showed associations of varying strength with several other outcomes, including suicidal attempt (0.64 [0.47-0.81]; CE = II) and toxoplasmosis infection (0.37 [0.15-0.59]; CE = III). Furthermore, quality of life was decreased across multiple domains in individuals with OCD (emotional, -1.47 [-1.96 to -0.98]; CE = II; global, -0.79 [-1.02 to -0.55]; CE = II; work/social, -1.21 [-1.72 to -0.69]; CE = III). CONCLUSION: Our study highlights the comprehensive impact of OCD, indicating associations with autism, suicidality, social-cognitive deficits, impaired quality of life, and neurobiological markers. STUDY REGISTRATION: PROSPERO CRD420251124380.
16 Apr 2026
Read appraisal →Journal of affective disorders
The effects of Omega-3 supplementation on stress, anxiety, depression, sleep quality, and everyday memory in individuals with psychological distress: A randomized, double-blind, placebo-controlled trial
BACKGROUND/OBJECTIVES: Psychological stress, anxiety, depression, memory impairment, and poor sleep quality are prevalent issues that affect individuals' quality of life and overall health. Omega-3 polyunsaturated fatty acids (PUFAs) have been suggested to have mood-regulating effects. However, there is lack of data on the effect of omega-3 supplementation on depression, stress, anxiety, everyday memory, and sleep quality on Saudi population. Therefore, this study aimed to evaluate the impact of omega-3 supplementation on stress, anxiety, depression, everyday memory, and sleep quality in individuals with severe psychological distress. METHODS: A randomized, double-blind, placebo-controlled trial was conducted involving 64 participants with high levels of stress, anxiety, depression, memory issues and with poor quality of sleep with 32 allocated to the intervention group and 32 to the control group. The participants received either omega-3 supplementation (500 mg EPA + 250 mg DHA) or a placebo daily for three months. Pre- and post-intervention assessments were conducted using validated scales, including the Perceived Stress Scale (PSS), Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Everyday Memory Questionnaire (EMQ). RESULTS: Significant improvements were observed in the intervention group regarding stress, anxiety, depression, sleep quality, and memory outcomes (p < 0.001 for PSS, GAD-7, PHQ-9, PSQI, and EMQ). Between-group comparisons showed statistically significant reductions in post-intervention scores for the intervention group versus the control group. Regression analysis revealed strong predictive relationships between pre- and post-scores, particularly for stress and depression. CONCLUSIONS: Omega-3 supplementation resulted in notable psychological and cognitive improvements, suggesting its potential as a safe and effective adjunct for managing stress, mood disorders, and sleep disturbances among participants with psychological distress. Further research should explore how individual responses are influenced by baseline inflammation and omega-3 status. CLINICALTRIALS: gov: NCT07157241.
16 Apr 2026
Read appraisal →Journal of affective disorders
Exploring temperamental and clinical predictors of lithium treatment outcomes in bipolar disorder using diverse machine learning approaches
BACKGROUND: Lithium is a core treatment for bipolar disorder (BD), yet clinical response varies across patients. Testing accessible predictors of lithium response is critical for personalization strategies in real-world settings. METHODS: In this cross-sectional study, 179 individuals with BD were assessed using a broad range of standardized clinical instruments and the Brief TEMPS-M to evaluate affective temperaments. Lithium response was retrospectively classified via the Alda Scale. Multivariate linear regressions and three supervised machine learning classifiers (random forest, elastic net, and XGBoost) were applied to identify predictors of treatment response. Class imbalance was addressed by down-sampling during training. RESULTS: Responders (12.3 % of participants) were more likely to be employed, diagnosed with BD-II, had fewer lifetime depressive and hypomanic episodes, fewer suicide attempts, and significantly lower anxious temperament scores. In multivariable models, lifetime psychotic and mixed features, as well as higher counts of hypomanic and manic episodes, were independently associated with poorer lithium response. Machine learning models confirmed the importance of these variables, possibly with remarkably high predictive validity (random forest AUC = 1.00; elastic net and XGBoost F1 = 0.86). Hyperthymic temperament was positively associated with lithium response, whereas cyclothymic and depressive traits were associated with poorer lithium response. CONCLUSIONS: The present exploratory study should be intended as a primer for larger samples and more balanced replication studies employing reliable machine learning techniques, allowing for firm conclusions about suggestive clinical predictors of lithium response in BD, and their complex interaction.
16 Apr 2026
Read appraisal →Journal of affective disorders
Development and validation of machine learning models to predict PTSD at multiple time points in hospitalized trauma patients
BACKGROUND: Posttraumatic stress disorder (PTSD) is a severe trauma-related mental disorder with high global burden. Early identification remains challenging, particularly in developing regions. Leveraging routinely collected clinical biomarkers may offer a scalable solution for timely risk detection. METHOD: This multicenter study utilized two independent prospective trauma cohorts to develop and externally validate machine learning models for predicting PTSD at 1, 3, and 6 months post-trauma. The development cohort consecutively enrolled 382 hospitalized trauma patients (Nov 2023-Dec 2024), while the external validation cohort included 204 patients (Oct 2021-Nov 2022). Predictors comprised psychosocial variables and routine clinical biochemical markers, including inflammatory indicators, liver and renal function markers, and hematological parameters. Five models were constructed (Logistic Regression, Random Forest, SVM, XGBoost, and Neural Network), with performance evaluated by AUC and feature importance interpreted via SHAP. RESULTS: Support Vector Machine achieved the best performance at 1 month (AUC = 0.84), while Random Forest models performed best at 3 and 6 months (AUC = 0.78 and 0.87). SHAP analysis indicated that ASDS were the most important predictors at all time points, with feature importance shifting over time. Acute stress and psychological state were dominant at 1 month, fearful experiences with physiological responses at 3 months, and chronic psychological burden and emotional state at 6 months. CONCLUSIONS: PTSD can be reliably predicted using psychosocial data and routine clinical biochemical markers. These findings support the integration of clinically accessible biological data into early PTSD risk screening, particularly in resource-limited settings.
15 Apr 2026
Read appraisal →Journal of affective disorders
Adverse psychosocial work exposures and 2-year incident prescription of psychotropic medications: a prospective French study using the national healthcare reimbursement database
OBJECTIVE: Despite the abundant literature on the associations between psychosocial work exposures and mental health outcomes, prospective studies using medico-administrative data are lacking. The objective was to study the associations between various psychosocial work exposures and incident prescription of psychotropic medications. METHODS: The study relied on three waves (2010, 2012, 2014) of the French ESPS survey (Health, Health Care and Insurance Survey) (baseline data), linked to the national database of healthcare reimbursements (follow-up data). Each survey wave was conducted among a randomly selected national sample of people aged 18+. The study included a total of 8689 observations among 7695 workers without psychotropic medication prescription within the 6 months preceding survey wave. Ten psychosocial work exposures (quantitative demands, tensions with the public, freedom, possibilities for learning new things, colleague support, recognition, salary satisfaction, job insecurity, temporary contract, and redundancy plan) were assessed at each survey wave. Covariates included gender, age, marital status, employee/self-employed worker, private/public sector, occupation, and full/part-time work. The outcome was psychotropic medication prescription within the 2 years following survey wave. Mixed effects Cox regression modelling was used. RESULTS: Seven of the ten psychosocial work exposures were associated with incident psychotropic medication prescription. Associations were observed more with incident prescription of antidepressants and anxiolytics than with hypnotics/sedatives. The number of psychosocial work exposures increased the risk linearly, except for hypnotics/sedatives. No gender interaction was found for any of these associations. CONCLUSION: The findings may support that poor psychosocial working conditions increase the risk of subsequent psychotropic medication prescription.
15 Apr 2026
Read appraisal →Biomedical physics & engineering express
EEG-CMT: spatial-temporal representation of EEG for emotion recognition using convolutional neural networks and vision transformers
Background. Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional relationships of EEG acquisition electrodes. Additionally, conventional models struggled to simultaneously capture both fine-grained temporal-spatial features and long-range dependencies in EEG signals.New method. To address these limitations, we propose a novel EEG data processing method that incorporates spatial relative position encoding and a hybrid neural architecture integrating convolutional neural networks (CNNs) with self-attention mechanisms. This approach systematically encodes the spatial topology of electrodes to enhance the representation of temporal-spatial information. CNNs are employed to extract localized temporal-spatial micro-patterns, while self-attention modules model global contextual dependencies across extended sequences, thereby enhancing model's representational capacity.Results. The experimental results and feature visualizations demonstrate that our method achieves state-of-the-art (SOTA) subject-dependent performance on two benchmark emotion recognition datasets, reaching an average accuracy of 97.51% on the SEED dataset and 96.13% on the SEED-IV dataset. Moreover, the learned spatial features align well with known neuroscientific patterns of emotional processing. Comprehensive ablation studies further validate the necessity and effectiveness of both the spatial-encoded data processing strategy and the hybrid architecture design.Comparison with Existing Methods. Compared to other hybrid neural network models, our proposed method (EEG-CMT) achieves the highest classification accuracy. Specifically, it outperforms baseline algorithms by margins ranging from 0.86% to 11.43% on the SEED dataset, and from 9.49% to 39.52% on the SEED-IV dataset.Conclusions. The proposed method effectively addresses key limitations in existing EEG-based emotion recognition models by jointly leveraging spatial topology and hybrid modeling techniques. These innovations significantly improve the model's ability to recognize emotions from EEG data and provide neural interpretable insights, offering a promising direction for future research in affective brain-computer interfaces.
15 Apr 2026
Read appraisal →JAMA
A Decision-Support System to Personalize Antidepressant Treatment in Major Depressive Disorder: A Randomized Clinical Trial
IMPORTANCE: Antidepressants for moderate to severe major depressive disorder may be discontinued prematurely because the prescribed antidepressant is not always the most appropriate medication for an individual. Guidelines have recommended more precise targeting of antidepressant treatment. OBJECTIVE: To evaluate the efficacy of a web-based tool to personalize antidepressant treatment. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, randomized clinical trial included persons between the ages of 18 and 74 years with major depressive disorder. The trial was conducted at 47 sites in 3 countries (Brazil, Canada, and the UK). The first participant was screened on November 29, 2022, and the last follow-up visit occurred on January 15, 2025. INTERVENTION: A total of 540 participants were randomized (1:1) to an evidence-based clinical decision-support system (PETRUSHKA tool; n = 271) or usual care (n = 269). MAIN OUTCOMES AND MEASURES: The primary outcome was treatment discontinuation due to any cause at 8 weeks. The secondary outcomes included treatment discontinuation up to 24 weeks due to adverse events and changes in depressive symptoms (measured with the 9-item Patient Health Questionnaire [PHQ-9]; range, 0-27; higher scores indicate more severe depression) and anxiety symptoms (measured with the 7-item Generalized Anxiety Disorder [GAD-7] questionnaire; range, 0-21; higher scores indicate more severe symptoms). RESULTS: Of the 520 eligible participants, 493 were included in the primary analysis (median age, 35 [IQR, 25 to 48] years; 58% female; PHQ-9 mean score, 16.6 [SD, 5.1]; GAD-7 mean score, 11.5 [SD, 4.1]). At 8 weeks, 41 of 241 participants (17%) in the PETRUSHKA group discontinued the prescribed antidepressant due to any cause vs 69 of 252 (27%) in the usual care group (adjusted relative risk, 0.62 [95% CI, 0.44 to 0.88]; P = .007). At 8 weeks, 22 of 241 participants (9%) in the PETRUSHKA group discontinued the prescribed antidepressant due to adverse events vs 39 of 252 (16%) in the usual care group (adjusted relative risk, 0.59 [95% CI, 0.36 to 0.97]; P = .04). For the assessment of depressive symptoms at 24 weeks, the mean PHQ-9 score was 7.1 (SD, 5.4) in the PETRUSHKA group vs 9.2 (SD, 6.5) in the usual care group (n = 129 in each group; adjusted between-group mean difference, -1.92 [95% CI, -3.06 to -0.78]; P < .001). For the assessment of anxiety symptoms at 24 weeks, the mean GAD-7 score was 4.6 (SD, 4.1) in the PETRUSHKA group (n = 133) vs 5.8 (SD, 4.9) in the usual care group (n = 126) (adjusted between-group mean difference, -1.39 [95% CI, -2.26 to -0.52]; P = .002). CONCLUSIONS AND RELEVANCE: Compared with usual care, use of the PETRUSHKA tool increased the number of patients still taking their antidepressant at 8 weeks and improved depressive and anxiety symptoms at 24 weeks. However, lack of a double-blind design and the large amount of missing data limit the validity of these results. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05608330.
15 Apr 2026
Read appraisal →Journal of medical Internet research
Using Group Chats to Drive Behavior Change in Digital Health Interventions: Scoping Review and Realist Synthesis
BACKGROUND: Group chats on platforms such as WhatsApp (Meta Platforms, Inc), WeChat (Tencent Holdings Limited), and Telegram (Telegram FZ-LLC) are central to everyday communication in many settings, including across low- and middle-income countries and among groups often overlooked by one-to-one or app-based digital health tools. Yet their roles and underlying mechanisms as intentionally designed health interventions have not been comprehensively examined. OBJECTIVE: This scoping review with realist synthesis aimed to map the characteristics and reported outcomes of group chat-based health interventions and contextual conditions shaping their outcomes. It mapped intervention characteristics and identified context-mechanism-outcome (CMO) configurations driving behavior change. METHODS: We included empirical studies in which group chats were the primary digital component of the health intervention. Searches of PubMed, Embase, MEDLINE, Web of Science, and Scopus (2005-2026) and reference screening identified eligible studies. Data were extracted on study characteristics, health domains, settings, participants, platform, intervention features, and outcome domains. For the realist synthesis, CMO configurations were analyzed using reflexive thematic analysis informed by social cognitive theory and synthesized into a final program theory. RESULTS: Eighty-one studies were included. Publications increased sharply after 2020, with evidence clustering in mental health (25/81, 30.9%) and maternal and child health (20/81, 24.7%). Most studies enrolled adult participants, including pregnant or postpartum women, caregivers, and people managing chronic conditions. WhatsApp (38/81, 46.9%) and WeChat (21/81, 25.9%) were the most frequently studied platforms. Interventions were commonly delivered in clinical or community settings and typically involved mixed membership groups that included health professionals alongside participants (68/81, 84.0%). The realist synthesis identified 12 recurring CMO configurations across 5 domains: capability and actionability, confidence and motivation, modeling and norms, safe and supportive environment and access, and self-regulation and maintenance. These mechanisms were activated or suppressed by facilitation quality, group composition, cultural alignment, technological access, and social norms. The final program theory depicts group chats as dynamic systems where access, facilitation, and group structure shape how the domains amplify or dampen one another, explaining shifts from low engagement to high trust spaces sustaining behavior change. CONCLUSIONS: This review integrates descriptive mapping of interventions with realist program theory to explain how context and facilitation activate mechanisms of change, unlike prior reviews that have focused narrowly on effectiveness of digital modalities. This dual approach provides a practical, mechanism-focused basis for designing and scaling group chat interventions in practice, particularly given their potential as a low-cost, high-reach strategy embeddable within broader digital health programs. Realizing this potential depends on treating group chats as purposefully designed social environments, with deliberate attention to facilitation quality, equity-oriented access, and alignment between group structure, norms, and communication practices.
14 Apr 2026
Read appraisal →Psychological medicine
AVATAR versus cognitive-behavioral therapy for medication-resistant auditory hallucination: a systematic review and network meta-analysis
Auditory hallucinations (AH) frequently persist in schizophrenia spectrum disorder despite antipsychotic treatment. Cognitive behavioral therapy (CBT) remains an established psychological intervention, whereas AVATAR (Audio Visual Assisted Therapy Aid for Refractory auditory hallucinations) therapy has recently been introduced as a novel approach integrating interactive digital avatars. This meta-analysis compared the efficacy of AVATAR therapy with CBT for medication-resistant AH. A systematic search of five major databases up to June 1, 2025 identified randomized controlled trials (RCTs) that evaluated either therapy. The primary outcome was AH severity, and secondary outcomes included psychotic symptoms, mood measures, and all-cause discontinuation. Twenty-six RCTs (n = 2273; 65.0% male; mean age 39.3 [SD 4.1] years) met inclusion criteria. Compared with CBT, AVATAR therapy showed no significantly greater reduction in AH severity (standardized mean difference [SMD] = -0.23, 95% confidence interval [CI] = -0.55 to 0.10). However, it demonstrated superior sustained improvement three months post-treatment (SMD = -0.37, 95% CI = -0.69 to -0.05) and greater reduction in overall psychotic symptoms (SMD = -0.41, 95% CI = -0.75 to -0.06). No significant differences were observed in positive, negative, depressive, anxiety, or quality-of-life outcomes, and discontinuation rates were comparable. Interpretation should be cautious given evidence of small-study effects (Egger's tests p < 0.01 for AH severity) and predominantly moderate-to-high risk of bias across included trials. AVATAR therapy therefore shows lasting efficacy, comparable or slightly superior to CBT, and may serve as an alternative for patients with medication-resistant AH.
14 Apr 2026
Read appraisal →European journal of clinical pharmacology
Clozapine prescribing in treatment-resistant schizophrenia - an updated systematic literature review of barriers and facilitators among clinicians
BACKGROUND: Increased rates of clozapine prescribing are essential to improving timely patient access within treatment-resistant schizophrenia (TRS) management. The extent of geographical variation in its use suggests it is possible to develop interventions to increase clinician engagement. To inform intervention development, a contemporary review of barriers and facilitators to increased clozapine prescribing is required. We aimed to conduct a systematic review of research addressing barriers and facilitators to clozapine prescribing among clinicians within TRS management. METHODS: The review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Embase, CINAHL, and PsycINFO were searched from inception to July 2025. Results were synthesized qualitatively. RESULTS: Fifteen studies were included. Barriers related to clinicians, patients and carers, and healthcare institutions. Primary clinician-related barriers included insufficient knowledge of, and confidence in, managing clozapine treatment and the associated administrative burden. Primary patient-related barriers included concerns regarding patients' willingness to consistently adhere to clozapine treatment and associated monitoring requirements. A lack of dedicated systems of care to facilitate clozapine initiation and shared community care were the leading institutional barriers. Major facilitators included improved education for clinicians, access to point-of-care testing, and increased availability of dedicated clozapine clinics. CONCLUSION: Most barriers to systematically increasing clozapine prescribing rates are beyond the influence of individual prescribers. Instead, structural interventions focusing on (i) reducing the administrative burden associated with establishing clozapine treatment, (ii) increasing access to standardised training and supervision opportunities, and (iii) providing longitudinal support to clinicians when managing clozapine treatment, are required.
13 Apr 2026
Read appraisal →Journal of medical Internet research
Views of People With Psychosis About Algorithm-Based Relapse Prediction and Data Sharing: Qualitative Study
BACKGROUND: Preventing relapses of psychosis is difficult and important. Digital remote monitoring (DRM) systems are being developed and tested to support this. Increasingly, these systems use algorithm-based relapse prediction. Hence, understanding stakeholder views about algorithmic prediction is crucial. Existing qualitative work has explored health professionals' views, but very few studies have examined the perspectives of people with psychosis on this topic. OBJECTIVE: This paper aimed to provide an in-depth examination of the views of people with psychosis regarding algorithmic relapse prediction within a DRM system that incorporates active symptom monitoring and passive sensing data. METHODS: People with psychosis (n=58) were recruited from 6 geographically distinct areas of the United Kingdom. They participated in semistructured qualitative interviews exploring their views about using a DRM system that predicts psychosis relapse based on a machine learning algorithm. Transcripts were analyzed using reflexive thematic analysis. People with lived experience of psychosis were involved extensively in study design, analysis, and reporting. RESULTS: Findings were described across 4 themes. First, accuracy was a prominent theme. Participants emphasized that transparency about algorithm sensitivity and specificity is crucial and discussed the risks of the relapse prediction algorithm producing false positives (flagging that someone was relapsing when they were not) and false negatives (missing actual relapses). In both cases, participants said that errors may be partially mitigated through a human-in-the-loop approach (theme 2), with DRM blended with human oversight, from clinicians or a dedicated digital monitoring team, and calibrated based on service user, carer, and clinician feedback. The third theme, trust, fears, and choice, noted the interplay between users' trust in the DRM system and their relationship with the clinical team. This theme described participants' fears about potential overreactions (hospitalization or excessive medication) or underreactions (no additional support) from the clinical team in response to algorithm-generated relapse predictions. It emphasized the importance of retaining choice around the use of relapse detection algorithms and the sharing of personal data. The final theme described participants' views about the benefits of using a relapse prediction algorithm, including facilitating early intervention, triaging care according to need, minimizing human bias in assessment, and efficiency in saving staff time. CONCLUSIONS: People with psychosis acknowledged potential benefits of algorithm-assisted relapse prediction for receiving timely or efficient care, but with several caveats. Algorithm-generated relapse alerts need to be sufficiently accurate and must be interpreted, with understanding of their limitations, by a trustworthy human who is aware of the relevant context. Algorithm-based relapse predictions should only be used with valid consent, in a way that promotes and respects the autonomy and voice of service users and avoids increasing the use of excessive restriction.
11 Apr 2026
Read appraisal →Psychological medicine
Inflammatory cytokine alterations in genetic and clinical high risk groups of psychosis: a systematic review and network meta-analysis
BACKGROUND: Inflammation has been implicated in psychosis, but its role in individuals at clinical (CHR) and genetic (GHR) high-risk remains unclear. We therefore conducted a network meta-analysis (NMA) to compare circulating cytokine levels across CHR, GHR, and healthy control (HC) groups. METHODS: We systematically searched multiple databases up to February 2025, extracting cytokine levels (plasma/serum) from CHR, GHR, and HC groups. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were estimated using random-effects models. Given that no direct head-to-head comparisons between CHR and GHR were available, indirect comparisons were performed through the common comparator (HC). The transitivity assumption was assessed by comparing key study and participant characteristics across comparisons. RESULTS: Thirty studies were included (CHR: 1601, GHR: 675, HC: 1980). NMA estimates indicated higher IL-6 levels in CHR compared with GHR, while IL-6 and IL-1β levels were lower in GHR compared with HC. In pairwise subgroup analyses, CHR converters showed higher IL-13 levels than non-converters. The evidence network was sparse and star-shaped, with all CHR-GHR estimates relying exclusively on indirect comparisons. CONCLUSIONS: This study represents the first NMA to synthesize cytokine alterations in individuals at high risk for psychosis using indirect evidence. Elevated IL-6 in CHR individuals suggests immune activation, whereas reduced IL-6 in GHR may reflect a distinct immune profile. Increased IL-13 levels in converters highlight potential involvement of Th2-related pathways during transition to psychosis. However, the sparse nature of the evidence network necessitates cautious interpretation of the findings, and larger, standardized multi-center studies are required for confirmation.
11 Apr 2026
Read appraisal →JMIR mental health
Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study
BACKGROUND: Passive smartphone sensing shows promise for suicide prevention, but behavioral metadata (GPS, screen time, and accelerometry) often lacks the contextual information needed to detect acute psychological distress. Analyzing what people actually see, read, and type on their phones-rather than just usage patterns-may provide more proximal signals of risk. OBJECTIVE: This study aimed to test whether vision-language models (VLMs) applied to passively captured smartphone screenshots can predict momentary suicidal ideation (SI). METHODS: Seventy-nine adults with past month suicidal thoughts or behaviors completed ecological momentary assessments (EMA) over 28 days while screenshots were captured every 5 seconds during active phone use. We fine-tuned open-source VLMs (Qwen2.5-VL [Alibaba Cloud], LFM2-VL [Liquid AI]), and text-only models (Qwen3 [Alibaba Cloud]) to predict SI from screenshots captured in the 2 hours preceding each EMA. We evaluated performance with temporal and subject holdouts. RESULTS: The analytic sample comprised 2.5 million screenshots from 70 participants. Temporal holdout models achieved strong discrimination at the EMA level (AUC=0.83; AUPRC=0.77), with image-based models outperforming text-only models (AUC=0.83 vs 0.79; 95% CI 0.003-0.07). Subject holdout generalization was near chance (AUC≈0.50), though a simple lexical screening method retained modest discrimination (AUC=0.62). Smaller models performed comparably to larger models, supporting feasible on-device deployment. CONCLUSIONS: Screen content predicts short-term SI with clinically meaningful accuracy when models are personalized but does not generalize across individuals. These findings support a 2-stage clinical architecture, coarse lexical screening for new patients, with personalized VLM-based monitoring after a calibration period. On-device inference may enable privacy-preserving deployment.
10 Apr 2026
Read appraisal →European journal of psychotraumatology
Phase-based versus non-phase-based psychological interventions for complex PTSD: a systematic review and meta-analysis
Background: Since complex post-traumatic stress disorder (CPTSD) was incorporated to the International Classification of Diseases-11th revision (ICD-11), the construct has drawn increasing attention in clinical research due to its debilitating effects across multiple domains of functioning. However, whether stabilization (Phase 1) is necessary before exposure to traumatic memories (Phase 2) remains controversial.Objective: We examined whether phase-based interventions are more effective than non-phase-based interventions in reducing symptoms of CPTSD.Methods: On 17 June 2025, we searched the Cochrane Library, Embase, Medline, PTSDpubs, Scopus, and Web of Science as well as South Korean databases, including the Korean Citation Index, the Research Information Sharing Service, DataBase Periodical Information Academic (DBPia), and ScienceON. Randomized controlled trials of psychological interventions for individuals with clinically significant CPTSD symptoms or PTSD with at least two of the 'disturbances in self-organization' (DSO) symptom clusters were eligible. We used Hedges' g to pool effect sizes and performed subgroup analyses using Cochran's Q test. We assessed risk of bias using the Cochrane Risk-of-Bias Assessment 2 tool.Results: Across most outcomes, we observed no significant differences between phase- and non-phase-based interventions, between multi-phase and single-phase interventions, or between exposure and non-exposure interventions. However, multi-phase interventions had superior effects on PTSD, while phase-based, multi-phase, and exposure interventions showed greater improvements in DSO - affect regulation compared to their counterparts.Conclusions: This study was limited by the small number of available trials, single time-point, and heterogeneity in outcome measures, which constrained both the detection of subgroup differences and the precision of effect size estimates. Our findings provide empirical evidence for the current discourse, suggesting that non-phase-based and non-exposure-based interventions can be as effective as structured approaches in many contexts, while some outcomes may still benefit from phase-, multi-modular-, or exposure-based designs. This systematic review examined whether interventions that include a first phase of stabilization followed by a second phase of exposure to traumatic memories are more effective than other interventions.Across most outcomes, there were no clear differences between phase- and non-phase-based interventions, or between exposure and non-exposure interventions.For post-traumatic stress symptoms, interventions with more than one phase were more effective than those with a single-phase treatment. For difficulties with emotion regulation, multi-phase interventions that included stabilization before exposure showed greater benefits.
9 Apr 2026
Read appraisal →JMIR human factors
e-Mental Health Intervention Preferences Among Informal Caregivers of Adults Living with Chronic Kidney Disease: Semistructured Interview Study
BACKGROUND: Informal caregivers of people living with chronic kidney disease commonly experience mental health difficulties. However, there is currently limited access to scalable psychological interventions such as those using e-mental health platforms. Furthermore, there is an absence of such interventions tailored for this group of caregivers, which may limit acceptability. OBJECTIVE: To inform future e-mental health intervention development, this study aimed to explore preferences for e-mental health interventions among informal caregivers of people living with chronic kidney disease. METHODS: A total of 13 informal caregivers of people living with chronic kidney disease participated in semistructured interviews. Eligible informal caregivers lived in the United Kingdom and were recruited via nonprofit organizations and social media advertisements. Semistructured interviews followed a topic guide exploring caregivers' e-mental health intervention preferences. Data were analyzed using manifest content analysis. RESULTS: A total of 4 categories and 13 subcategories were generated. "Design preferences" illustrates key intervention design features that were important to caregivers, such as involving caregivers and other interest-holders in the design process. "Content preferences" describes the information caregivers valued in an intervention, such as information about chronic kidney disease and resources to support caregivers. "Support preferences" reflects the characteristics of support caregivers preferred if support was available as part of the intervention, such as peer support and providing support that accommodates individual preferences. Lastly, "Dissemination preferences" describes methods caregivers recommended to promote intervention uptake, such as engaging communication strategies and trusted and accessible sources of information. CONCLUSIONS: Caregivers identified a number of e-mental health intervention design and delivery preferences that should be considered when developing e-mental health interventions for caregivers of people living with chronic kidney disease. To ensure such interventions are acceptable, feasible, and relevant, co-design approaches should be adopted during the intervention development process.
8 Apr 2026
Read appraisal →Archives of endocrinology and metabolism
Cushing's syndrome: a systematic review of psychiatric and cognitive symptoms in case studies
OBJECTIVE: Cushing's syndrome, characterized by chronic hypercortisolism, is associated with various health risks, including psychiatric symptoms and cognitive impairments. This systematic review of case studies aimed to map and categorize these symptoms. MATERIALS AND METHODS: We hypothesized that a broader range of psychiatric and cognitive manifestations would be observed beyond anxiety, depression, and memory impairment. The review followed PRISMA guidelines and was preregistered in PROSPERO (CRD42024433186). We conducted searches in PsycINFO, Embase, PubMed, and Scopus, identifying 273 potentially relevant studies. After screening, 66 studies were included, comprising 74 cases (81% female; mean age 35.7 years, range 13-81). RESULTS: revealed that 93% of cases presented psychiatric complaints, including depression (39.2%), psychosis/schizophrenic symptoms (35.1%), suicidal ideation/attempts (20.3%), anxiety (17.5%), panic attacks (2.7%), and post-traumatic stress disorder (1.4%). Cognitive complaints were reported in 32% of cases, primarily as general cognitive complaints (18.9%), memory impairment (9.5%), and attentional deficits (5.4%). An overlap of psychiatric and cognitive symptoms was observed in 26% of cases. CONCLUSION: This review underscores the clinical relevance of symptoms such as mania, psychosis, and suicidal behavior in Cushing's syndrome, which are often underreported. Individualized clinical assessment informed by these case studies is crucial for comprehensive management that extends beyond the typical focus on depression and memory. Moreover, greater awareness of the full spectrum of neuropsychiatric manifestations in hypercortisolism is needed.
8 Apr 2026
Read appraisal →Psychological medicine
Sleep diary-derived parameters and sleep measures mediate the efficacy of transdiagnostic sleep and circadian intervention for depression.
The present study aimed to explore sleep diary-derived parameters and sleep measures as mediators of the effects of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) on psychological outcomes. A secondary analysis of a two-arm randomized controlled trial of a group-based TSC for major depressive disorder was conducted. The participants included 152 adults (mean age = 34.0; 79.6% female) who were randomized into either the TSC or care-as-usual group. Mediation analysis indicated that reduction in insomnia symptom severity (standardized indirect effects: -0.06 to -0.17), sleep disturbance (-0.04 to -0.22), and sleep-related impairment (-0.04 to -0.17) was significantly mediated by sleep diary-derived sleep parameters. The treatment effects on depressive symptoms (standardized indirect effects: -0.05 to -0.10), anxiety symptoms (-0.04 to -0.07), fatigue (-0.05 to -0.09), functional impairment (-0.06 to -0.09), and quality of life (0.04 to 0.08) were sequentially mediated by sleep parameters and insomnia symptom severity. However, the severity of insomnia symptoms alone (magnitudes of standardized indirect effects: 0.09-0.17) but not sleep parameters alone (0.00-0.07) mediated the treatment effects on psychological outcomes, indicating that sleep parameters need to influence subjective sleep measures to sequentially affect psychological outcomes. These results underscore the critical roles of subjective sleep measures in clinical improvements within a sleep-targeted intervention.
8 Apr 2026
Read appraisal →PloS one
Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach.
This study aimed to develop and validate a machine learning (ML) model to identify the minimal risk factors for adolescent suicidal behaviors, including suicidal ideation and suicide attempts. Data from the Korea Youth Risk Behavior Web-based Survey (2022-2023), including 90,813 adolescents aged 12-18 years, were analyzed. Using multidimensional risk factors spanning sociodemographic, physical and mental health, and behavioral domains, we applied a Random Forest model combined with recursive feature elimination to identify a minimal subset of risk factors (optimal features). Model performance for identifying suicidal ideation and predicting suicide attempts was evaluated via area under the curve (AUC), sensitivity, specificity, and accuracy metrics across the validation datasets. Sadness, loneliness, anxiety, and stress were identified as optimal features, achieving a high AUC, sensitivity, specificity, and accuracy in identifying suicidal ideation and predicting suicide attempts. Additional factors further improved the ML model's predictive performance for suicide attempts, achieving an AUC of 97.28%, sensitivity of 93.49%, specificity of 90.21%, accuracy of 91.85%, a PPV of 90.52%, and an NPV of 93.26%. This study demonstrated the efficacy of ML-driven approaches in identifying critical risk factors for adolescent suicidal behaviors. The findings highlight the potential of ML frameworks to transform suicide prevention strategies and improve mental health outcomes in adolescents.
7 Apr 2026
Read appraisal →European journal of cardiovascular nursing
Impact of home-based cardiac rehabilitation in people with heart failure with elevated depressive and anxiety symptoms: a secondary analysis of the REACH-HF randomized trials
AIMS: This study examined whether elevated baseline depression and anxiety moderated the effects of the REACH-HF home-based cardiac rehabilitation (CR) programme on patient outcomes in individuals with heart failure. METHODS AND RESULTS: Individual patient data from 263 patients with HF from the REACH-HF HFrEF and HFpEF trials were analysed. At baseline, 62 (24%) had elevated depression [Hospital Anxiety and Depression Scale (HADS) D > 7], and 86 (33%) had elevated anxiety (HADS-A > 7). Patients were randomized to home-based CR plus usual care or usual care alone. Outcomes at 6-12 months included depression and anxiety (HADS), health-related quality of life (HRQoL) [Minnesota Living with Heart Failure Questionnaire (MLHFQ), HeartQoL], and accelerometer-measured physical activity (PA). Linear regression models, adjusting for baseline values, stratification variables, and confounders, included interaction terms (baseline depression/anxiety ×intervention group) to assess moderation effects. Those with elevated depression showed greater benefits from CR effects in depressive symptoms and HRQoL, with significant interactions for HADS-D (P = 0.004), MLHFQ (P = 0.007), HeartQoL (P = 0.001). No effects were found for PA. In participants with elevated anxiety, significant interactions were observed for HADS-D (P = 0.024), HeartQoL physical (P = 0.04), inactivity (P = 0.02), and light PA (P = 0.04). No effects were found for anxiety symptoms or HRQoL measures. CONCLUSION: People with HF participating in REACH-HF with elevated depression and anxiety experienced greater improvements in their mental health and HRQoL than individuals without elevated symptoms. Diagnostic clinical interviews in future studies would allow for stronger conclusions regarding the impact of home-based CR on patients with confirmed depression or anxiety.
5 Apr 2026
Read appraisal →The pharmacogenomics journal
Economic effectiveness of pharmacogenomics-guided prescribing for psychiatric disorders: a systematic review and meta-analysis
Pharmacogenomics (PGx)-guided prescribing is a promising approach to reduce variability in drug response, although its cost-effectiveness remains uncertain. We performed a systematic review and meta-analysis evaluating the cost-effectiveness of PGx-guided prescribing compared to standard care in psychiatry. In January 2026, we searched MEDLINE, Embase and PsycINFO for studies published between 2014 and 2025. We included any peer-reviewed study that included adults with a diagnosed psychiatric disorder, comparing PGx-guided prescribing to standard care, and reported both quality-of-life and economic outcomes. Given the lack of consensus on synthesising economic evidence, both a narrative synthesis and meta-analysis were conducted. Pooled incremental net benefit (INB) was used as the effect measure for the meta-analysis and heterogeneity measures including the I2 test were used to assess heterogeneity and determine which model to use for the meta-analysis. From an initial 1 271 records, 17 studies were included. The narrative synthesis found that 88% of studies favoured PGx-guided prescribing. Meta-analyses produced positive, though non-significant, pooled Incremental Net Benefits (INBs) for the total study groups (£1 623.14, 95% CI: -£116.50 to £3 362.79, p = 0.07, I2 = 100%), and for a statistically homogeneous subgroup (£41.54, 95% CI: -£18.27 to £101.35, p = 0.17, I2 = 0%). Our review indicates that PGx-guided prescribing can be cost-effective in psychiatry but highlights the need for increased consensus in economic modelling methods.
5 Apr 2026
Read appraisal →JMIR mental health
AI Chatbots for Mental Health Self-Management: Lived Experience-Centered Qualitative Study
BACKGROUND: Large language models (LLMs) now enable chatbots to engage in sensitive mental health conversations, including depression self-management. Yet their rapid deployment often overlooks how well these tools align with the priorities of people with lived experiences, which can introduce harms such as inaccurate information, lack of empathy, or inadequate crisis support. OBJECTIVE: This study explores how people with lived experience of depression experience an LLM-based mental health chatbot in self-management contexts, and what perceived benefits, limitations, and concerns inform harm-mitigating design implications. METHODS: We developed a technology probe (a GPT-4o-based chatbot named Zenny) designed to simulate depression self-management scenarios grounded in prior research. We conducted interviews with 17 individuals with lived experiences of depression, who interacted with Zenny during the session. We applied qualitative content analysis to interview transcripts, notes, and chat logs using sensitizing concepts related to values and harms. RESULTS: We identified 3 themes shaping participants' evaluations: (1) informational accuracy and applicability, including concerns about incorrect or misleading information, vagueness, and fit with personal constraints; (2) emotional support vs need for human connection, including validation and a judgment-free space alongside perceived limits of machine empathy; and (3) a personalization-privacy dilemma, where participants wanted more tailored guidance while withholding sensitive information and using privacy-preserving tactics. CONCLUSIONS: People with lived experience of depression evaluated LLM-based mental health chatbots through intertwined priorities of actionable information, emotional validation with clear limits, and personalization that does not require unsafe data disclosure. These findings suggest concrete design strategies to mitigate harms and support LLM-based tools as complements to, rather than replacements for, human support and recovery.
4 Apr 2026
Read appraisal →Journal of medical Internet research
The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis
BACKGROUND: Digital biomarkers are increasingly being used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited. OBJECTIVE: This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and controls. METHODS: Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool and the Scottish Intercollegiate Guidelines Network checklist. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized. RESULTS: The search yielded 39,617 records, of which 132 studies involving 57,852 participants met the inclusion criteria. These studies encompassed various digital biomarkers, including sleep, physical activity, cardiac measures, smartphone-derived data, speech, GPS data, and circadian rhythms. A meta-analysis of 22 studies (6947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies; n=292; +4.75 min, 95% CI 2.46-7.04; P=.005; 95% prediction interval [PI] 0.01-10.27) and time in bed (3 studies; n=236; +31.81 min, 95% CI 18.22-45.39; P=.01; 95% PI 2.28-55.16). Physical activity counts were also significantly lower (5 studies; n=462; standardized mean difference -0.71, 95% CI -1.33 to -0.09; P=.03; 95% PI -2.18 to 0.71). Although individuals with depression showed a lower sleep efficiency, higher mean heart rate, and lower SD of normal-to-normal intervals, these differences were not statistically significant. Other digital markers yielded inconsistent results. Overall, these findings indicate that no single digital biomarker sufficiently captures depression-related changes. Instead, the results support the superiority of personalized, multimodal approaches. However, the generalizability of these findings is limited by the lack of standardized data collection protocols and high clinical heterogeneity across studies, as reflected in wide PIs. CONCLUSIONS: Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between the depression and control groups. However, wide PIs indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. This study advances the field by providing a comprehensive meta-analysis of multidimensional digital biomarkers, establishing a quantitative foundation for objective depression screening and monitoring. These findings support the use of personalized, multimodal digital phenotyping approaches and highlight the need for standardized, clinically interpretable frameworks for real-world depression monitoring.
4 Apr 2026
Read appraisal →Transl Psychiatry
Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review
Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been systematically mapped. We conducted a systematic scoping review of PubMed, Web of Science, IEEE Xplore and the ACM Digital Library (January 1, 2012-October 31, 2025; two search rounds), applying operationalized rehabilitation boundaries and excluding diagnostics-only case-control studies. We extracted data on data sources, feature engineering, model families, validation, calibration, interpretability, application domains, outcomes and implementation readiness. Eighty-three studies met inclusion criteria (median sample size 160; 55% longitudinal). Applications focused on symptom monitoring (48/83), medication management (19/83) and risk management (16/83), whereas functional training (1/83) and psychosocial support (3/83) were rarely targeted. Supervised learning predominated (53/83, 63.9%) over representation learning (20/83, 24%), most commonly using speech/text, electronic health records and smartphone sensing. Across classification tasks, the median AUC was 0.79 (IQR 0.71-0.86); relapse early-warning models showed a median sensitivity of 31.5% at 88.0% specificity. Only four studies reported external validation and three described closed-loop deployment, including one randomized trial that improved adherence. Proxy endpoints were more common than clinical endpoints, and reporting of calibration/uncertainty and fairness auditing was sparse. Overall, AI shows promise for monitoring, adherence support and relapse risk stratification, but routine-care deployment will require externally validated and calibrated human-in-the-loop decision support, privacy-preserving multimodal pipelines and pragmatic trials targeting functional outcomes and participation.
1 Mar 2026
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