Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 96 appraisals
International journal of yoga therapy
Randomized Controlled Trial on the Effect of Yoga on Anxiety Severity, Quality of Life, and Biomarkers in Individuals with Anxiety Disorder
Anxiety disorders are highly prevalent mental health conditions characterized by physiological dysregulation that includes heightened stress responses, low-grade systemic inflammation, and metabolic imbalance, which together contribute to impaired quality of life. Although yoga-based interventions are increasingly used as complementary therapies, evidence isolating the effects of slow pranayama (yoga breathing) and savasana (corpse pose) on psychological outcomes and biochemical markers in anxiety disorders remains limited. This randomized controlled study evaluated the effects of slow pranayama and savasana, implemented as adjuncts to routine psychiatric care, on anxiety severity, quality of life, and selected biochemical markers in individuals with anxiety disorders. A total of 150 people diagnosed with anxiety disorders were allocated to either routine psychiatric care alone (control group) or routine care plus slow pranayama and savasana (intervention group) for 8 weeks. We assessed anxiety severity using the Hamilton Anxiety Rating Scale, quality of life using the World Health Organization Quality of Life-Brief questionnaire, and biochemical markers such as serum cortisol, brain-derived neurotrophic factor (BDNF), inflammatory markers, and metabolic parameters. Outcomes were analyzed using mixed-design analysis of covariance with Bonferroni correction, and correlational analyses were performed between psychometric, quality-of-life, and biochemical variables. Of the enrolled participants, 112 completed the study. Significant Group × Time interactions were observed for anxiety severity scores and the physical, psychological, and environmental domains of quality of life, with improvements noted only in the pranayama group. Serum cortisol levels decreased and BDNF levels increased significantly in the intervention group compared with controls. Inflammatory and metabolic parameters did not show statistically significant changes after correction, although favorable directional trends were observed. Anxiety severity and quality-of-life scores demonstrated significant correlations with cortisol, BDNF, and inflammatory and metabolic markers. Slow pranayama and savasana appear to be safe, feasible, and effective adjuncts to standard psychiatric care, improving anxiety severity and quality of life while favorably modulating stress-related physiological markers in people with anxiety disorders.
31 July 2026
Read appraisal →European journal of psychotraumatology
Differential linguistic patterns across Rorschach, TAT, and SCT in individuals with PTSD
Background: While prior studies have examined linguistic markers of PTSD using everyday discourse or autobiographical narratives, comparative studies identifying specific markers across different projective personality tests remain limited.Objective: To investigate whether language features in projective personality tests differentiate individuals with PTSD from non-PTSD controls and assess their predictive value for symptom severity.Method: Verbatim transcripts from 283 participants (183 PTSD, 100 controls) across the Rorschach, TAT, and SCT were analysed using NLP across six categories: negative emotion, positive emotion, insight, causation, body-related, and self-focus. ANCOVAs with Bonferroni correction compared linguistic indicators between groups controlling for age and sex. Hierarchical regression analyses assessed predictive validity for symptom severity.Results: Following Bonferroni correction (α = .0028), four indicators showed significant group differences (ηp² = .036-.092): Rorschach Negative Emotion, SCT Positive Emotion, SCT Insight, and TAT Negative Emotion. In the PTSD group, TAT Negative Emotion was the strongest cross-symptom predictor of PTSD, depressive, and anxiety severity. Reduced Rorschach Body-related language and elevated SCT Insight were associated with greater symptom burden. TAT Positive Emotion negatively predicted symptom severity. In the non-PTSD group, SCT Self-Focus was the strongest predictor of PTSD-related severity, and SCT Positive Emotion negatively predicted anxiety. A sensitivity analysis revealed that SCT Positive Emotion and TAT Negative Emotion did not replicate under a more conservative Core Lexicon and should be treated as exploratory; only Rorschach Negative Emotion and SCT Insight were robust across lexicon configurations.Conclusions: Individuals meeting PTSD criteria showed reduced somatic language and elevated negative emotional expression, while self-referential processing and positive emotional expression emerged as salient markers in non-clinical individuals, though these differences should be interpreted cautiously given substantial demographic imbalances. These preliminary findings suggest that projective linguistic analysis may have potential as a supplementary approach in trauma-related research, pending external validation and replication in independent datasets. Language reveals trauma in distinct ways: People with post-traumatic stress show different language patterns than those without trauma-related disorders, and these patterns vary depending on the type of psychological test used.Projective tests offer supplementary linguistic insights: Responses to structurally distinct projective tests elicit different patterns of emotional, cognitive, and somatic language, providing supplementary perspectives on trauma-related psychological functioning that may not be fully captured by a single assessment paradigm.Language analysis supports clinical insight: Systematic analysis of word use helps identify subtle markers of symptom severity, suggesting potential utility as a supplementary research approach for understanding and assessing trauma-related psychological functioning.
27 July 2026
Read appraisal →Journal of behavioral addictions
Efficacy of a mobile-based approach-avoidance task training (PROTECTapp) for problematic usage of the internet in young adults: A randomized controlled trial
BACKGROUND AND AIMS: Problematic usage of the internet (PUI) has been linked to impaired mental health and academic functioning in young adults. This randomized controlled trial evaluated the efficacy of a 3-week mobile-based approach-avoidance task (AAT) training (PROTECTapp) for reducing PUI in university students. METHODS: Ninety-two participants (Mage = 22.00 years, 69.6% women) with elevated levels of PUI were randomized to the PROTECTapp intervention (n = 45) or a waitlist control group (n = 47). Primary outcomes were PUI severity and internet-related craving. Secondary outcomes included motivation to change, psychopathological symptoms and academic functioning. Participants were assessed at baseline and postintervention; the intervention group completed additional 3- and 12-week follow-ups. RESULTS: Intention-to-treat analyses indicated greater reductions in PUI following the PROTECTapp intervention compared to the waitlist (p = .003; d = -0.80, 95% CI [-1.23, -0.38]). No significant effects emerged for craving or broader psychological outcomes (ps > .05), though favorable effects were observed on motivation to change (ambivalence: p = .020; d = -0.24, 95% CI [-0.65, 0.17]; taking steps: p = .002; d = 0.45 [0.04, 0.87]). Satisfaction with the intervention was moderate (M = 18.32 of 32), and participants completed on average 35.52 training sessions. Adverse events were reported infrequent (7.1%). DISCUSSION AND CONCLUSIONS: PROTECTapp is a promising mobile-based intervention to reduce PUI and enhance motivation to change in young adults. Its brevity, scalability, and safety profile highlight its potential as a low-threshold preventive or adjunctive intervention for young individuals at-risk.
18 July 2026
Read appraisal →Journal of behavioral addictions
Problematic internet use and borderline personality features: A systematic review informed by the alternative model of personality disorders.
BACKGROUND AND AIMS: Associations between problematic internet use (PIU) and borderline personality disorder (BPD) have been reported, but the literature is highly heterogeneous, with some studies assessing overall BPD severity and others examining individual traits (e.g., emotional lability, impulsivity, dissociation). This review synthesizes these approaches using the Alternative Model of Personality Disorders (AMPD), examining how BPD-relevant pathological personality traits (Criterion B) and impairments in self and interpersonal functioning (Criterion A) relate to PIU. METHODS: Following PRISMA 2020 guidelines, we searched PsycINFO, Web of Science, PubMed, Embase, and Scopus for studies examining associations between PIU (e.g., general use, social media, gaming, smartphone use) and personality traits or functioning in adults. Study quality was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: Thirty studies (N = 21,358) met inclusion criteria. Emotional lability and impulsivity showed the most consistent associations with PIU across outcome types. Few studies examined impairments in personality functioning, but available evidence suggests that identity disturbance may predict PIU over time and that interpersonal dysfunction is associated with greater use of online environments for reassurance and emotion regulation. Associations involving dissociation and detachment were relatively weak and less consistent. DISCUSSION AND CONCLUSIONS: Studies varied widely in design, measures, and theoretical approach, limiting direct comparison across studies. Despite this heterogeneity, findings indicate that emotional lability, impulsivity, and identity disturbance appear to represent transdiagnostic vulnerabilities that increase risk for PIU and shape patterns of online engagement.
18 July 2026
Read appraisal →International journal of medical informatics
A scoping review on conversational AI in mental health: A human-centered perspective.
BACKGROUND: Conversational AI offers scalable mental health support, with large language models (LLMs) enabling personalized interactions. Human-centered design is critical in this domain, yet a comprehensive synthesis from this perspective is lacking. This review maps conversational AI research in mental health across the patient journey and develops a human-centered taxonomy to guide future design. METHODS: Following PRISMA guidelines, we conducted a comprehensive search across fifteen multidisciplinary databases. We systematically analyzed the literature across six dimensions: research foci, mental disorder types, target populations, AI technologies, data sources, and evaluation metrics. A consensual taxonomy research method was employed to develop a human-centered design framework. RESULTS: Of 10,293 identified records, 677 studies met the inclusion criteria. Analysis reveals a marked increase in publications since 2020, predominantly from computer science (449 studies), followed by medicine (148) and social sciences (80). Research is skewed toward detection (23%) and intervention (66%) stages, with prevention (8%) and maintenance (3%) receiving less attention. Mood, anxiety, and stress-related disorders are the most investigated conditions. LLMs have emerged as the predominant AI technology, particularly within intervention and maintenance stages. Data sources continue to rely heavily on text-based inputs, with multimodal approaches still limited in adoption. Evaluation metrics vary significantly by discipline, reflecting limited cross-disciplinary integration. Through thematic synthesis, we developed a human-centered taxonomy comprising four primary dimensions: Emotional Sensitivity to Users, User-Centric Interaction Design, Human-AI Collaboration and Capability Enhancement, and Ethics and Accountability, with a total of thirteen sub-dimensions. CONCLUSIONS: This review provides a comprehensive, human-centered mapping of conversational AI research in mental health across the patient journey. Critical gaps remain in stage coverage, disorder diversity, population inclusivity, multimodal data integration, and interdisciplinary evaluation. The proposed taxonomy offers a structured framework to align AI development with human-centered principles, fostering empathetic, ethical, effective, and equitable mental health support.
16 July 2026
Read appraisal →Journal of affective disorders
Ecological momentary interventions for depression: A systematic review and meta-analysis
INTRODUCTION: Depression remains a prevalent condition with many individuals experiencing residual symptoms despite standard treatments. Ecological momentary interventions (EMIs) offer real-time, ecologically-valid microinterventions, yet their clinical utility and implementation challenges in depression remain underexplored. METHOD: A systematic search of six databases (inception to 25 July 2025) identified randomised controlled trials (RCTs) evaluating EMIs for depression. Pooled effect sizes assessed differences relative to comparator conditions. RESULTS: Sixteen RCTs involving 1258 participants were included. EMIs significantly reduced depressive symptoms at posttest (Hedges' g = 0.57; 95% CI = 0.33-0.80; p < 0.001) and at follow-up ranging from 10 to 32 weeks (Hedges' g = 0.43). High heterogeneity reflected variability in EMI formats. EMIs also showed a statistically significant improvement in quality of life (Hedges' g = 0.44; 95% CI = 0.17-0.71; p < 0.01), supporting their broader impact beyond symptom reduction. Engagement averaged 64.91% (SD = 17.18), ranging 37.5%- 88.0%, with variation across reporting methods. Acceptability and usability ratings were consistently high. Intervention dosage and risk of bias were significant moderators of treatment efficacy. Subgroup analyses revealed greater benefits from EMIs incorporating psychoeducation and active practice, and fully-automated formats. CONCLUSION: The review highlights EMI efficacy in reducing depressive symptoms and improving quality of life, with intervention dosage and methodological rigor emerging as key moderating factors. Reporting engagement remains a challenge, and substantial heterogeneity underscores the need for refined trial designs to isolate the active EMI components to best optimise treatment outcomes in depression. Future studies should prioritise strategies strengthen methodological rigor and optimising user engagement. PROSPERO No. CRD420251105663.
16 July 2026
Read appraisal →Journal of affective disorders
The association between obsessive-compulsive symptoms and self-compassion: A meta-analysis and systematic review.
BACKGROUND: Self-compassion, defined as the ability to respond to personal suffering with mindful awareness, self-directed kindness, and recognition of shared humanity, has been linked to reduced risk and severity of psychopathology. No systematic reviews have examined the relationship between self-compassion and obsessive-compulsive (OC) symptoms. METHOD: We conducted a systematic review and meta-analysis of peer-reviewed quantitative studies reporting associations between validated measures of self-compassion and OC symptoms. Eleven studies (N = 3336) met inclusion criteria. Random-effects models estimated pooled correlations for total symptoms and four core OC symptom dimensions (symmetry/ordering, unacceptable thoughts, checking/responsibility, and contamination/washing). Moderator analyses examined clinical status and cultural orientation. RESULTS: Across studies, self-compassion was moderately and negatively associated with OC symptom severity (r = -0.34, 95% CI [-0.39, -0.30]), with moderate heterogeneity (τ2 = 0.002, I2 = 40%). All four symptom dimensions were inversely associated with self-compassion, with the strongest association for unacceptable thoughts (r = -0.41). Neither clinical status nor cultural orientation significantly moderated effects. Funnel plot symmetry and fail-safe N analyses indicated minimal publication bias. DISCUSSION: Findings suggest that lower self-compassion is reliably linked to greater OC symptom severity across studies, samples (clinical vs. community), and cultures (individualist vs collectivist), particularly for unacceptable thoughts. These results highlight self-compassion as a potential therapeutic target in OCD, warranting further investigation through longitudinal and interventional studies.
15 July 2026
Read appraisal →Psychological medicine
Mechanisms of mindfulness-based cognitive therapy in difficult-to-treat depression: moderation and mediation analyses from the RESPOND trial.
BACKGROUND: Mindfulness-based cognitive therapy (MBCT) was developed for relapse prevention in people with remitted depression but is increasingly used for those with difficult-to-treat depression (DTD). A key question regarding this broader application is whether ongoing depressive symptoms constrain therapeutic responsiveness or disrupt MBCT's proposed mechanism, decentering. We explored whether baseline depressive severity moderates clinical outcomes, whether changes in decentering mediate treatment effects, and whether this mediation varies by baseline severity. METHODS: Secondary moderation, mediation, and moderated mediation analyses were conducted using data from the RESPOND randomized trial (N = 234), comparing MBCT plus treatment as usual (TAU) with TAU alone in adults not remitted after high-intensity psychological therapy. Depressive symptoms (PHQ-9) and decentering (Experiences Questionnaire) were assessed at baseline, post-treatment (10 weeks), and follow-up (34 weeks). Analyses were conducted using structural equation modelling. RESULTS: Higher baseline severity predicted greater symptom improvement across both groups. Treatment-related increases in decentering partially mediated the effect of MBCT on depressive symptoms at follow-up. Although baseline severity did not moderate the treatment effect, it moderated the indirect effect, with decentering more strongly associated with symptom reduction among those with higher baseline depression. Severity did not moderate the acquisition of decentering skills. CONCLUSIONS: Concerns that more severe depressive symptoms limit the effectiveness of MBCT were not supported. MBCT's core mechanism remained operative under substantial symptom burden, with clinical impact amplified at higher severity. These findings reduce key uncertainties regarding the application of MBCT in DTD and support its use across a broad range of symptom severity.
15 July 2026
Read appraisal →Blood advances
I-STRONG: an integrative, multicomponent treatment approach for chronic pain in pediatric sickle cell disease
Chronic pain affects ∼20% of adolescents living with sickle cell disease (SCD). There is a critical unmet need for evidence-based interdisciplinary approaches for chronic SCD pain treatment. We aimed to (1) use community engagement to adapt an integrative multicomponent treatment program designed to meet the unique needs of chronic SCD pain (ie, Integrative Strong Body and Mind Training [I-STRONG] for SCD), and (2) optimize feasibility and acceptability of I-STRONG through a proof-of-concept trial. Modifications to an existing cognitive behavioral therapy (CBT) and neuromuscular treatment program for chronic widespread pain were informed by semistructured qualitative interviews with adolescents (aged 12-18 years) with chronic SCD pain (n = 12) and their caregivers (n = 12), community advisory boards, and interdisciplinary experts to develop I-STRONG. I-STRONG is a virtual 8-week, 16-session, group intervention combining CBT and neuromuscular exercise training, co-led by experts in psychology and physical therapy. A pilot clinical trial of I-STRONG (n = 12 adolescents; n = 9 caregivers) was conducted to iteratively optimize intervention feasibility and acceptability. Community engagement strategies informed systematic adaptations for access, engagement, relevance, satisfaction, and sense of belonging to meet the unique needs of youth experiencing chronic SCD pain. Pilot testing demonstrated high levels of feasibility (95% completion, 97% intervention fidelity, 92% retention), moderate to high acceptability, and safety. Over 82% of adolescents reported improvements in pain after treatment and at 3-month follow-up. Preliminary evidence suggests that I-STRONG is a promising approach for management of chronic pain in pediatric SCD. A planned multicenter, randomized controlled trial will evaluate I-STRONG's efficacy for pain reduction. This trial was registered at www.clinicaltrials.gov as #NCT06110754.
14 July 2026
Read appraisal →Current psychiatry reports
Sleep Interventions and the Prevention of Pediatric Anxiety: A Systematic Review
PURPOSE OF REVIEW: Anxiety disorders are prevalent in children, adolescents, and young adults. As many youth either do not receive adequate intervention or do not achieve remission, prevention of anxiety disorders is a public health priority. Sleep problems are a modifiable risk factor that precedes anxiety. This systematic review includes recent pediatric sleep interventions and anxiety outcomes, and introduces anxiety sensitivity as an understudied mechanism. RECENT FINDINGS: In 21 intervention and experimental studies, sleep intervention reduced anxiety symptoms/disorders and the onset of anxiety disorders. Three additional recent studies examined anxiety sensitivity in the relationship between sleep problems and anxiety, supporting the inclusion of anxiety sensitivity in future research studies. Sleep health is a scalable, accessible target for reducing anxiety and is an important part of pediatric anxiety prevention. Sleep treatments can be brief and implemented in psychiatric practice. Future research studies should consider anxiety sensitivity as a potential mechanism.
14 July 2026
Read appraisal →Depression and anxiety
Tree Imagery in Drawing Tests for Screening Mental Disorders: A Systematic Review and Meta-Analysis
Although tree imagery in projective drawing tests is a promising nonverbal tool for screening mental disorders, its clinical utility remains constrained by inconsistent predictor selection. To address this gap, the present systematic review and meta-analysis synthesizes the characteristics of tree imagery linked to mental disorders and evaluates their predictive efficacy. Following the PRISMA guidelines, we analyzed 42 studies involving 8552 participants from English and Chinese databases published between 1948 and 2024. The results showed that 24 specific characteristics significantly predicted mental disorders (p < 0.05), which were classified into five distinct categories: blackened out (e.g., blackened tree, odds ratio [OR] = 2.01), scribbled lines (e.g., weak lines, OR = 2.82), oddly shaped (e.g., flattened crown, OR = 3.10), no vitality (e.g., very small tree, OR = 3.93), and overly simple (e.g., simplified drawing, OR = 7.07). Furthermore, subgroup analyses indicated that features such as "blackened tree" (OR = 1.71), "no motion" (OR = 3.34), and "excessive separation" (OR = 2.77) were significantly associated with affective disorders, whereas the presence of "roots" (OR = 4.89) was uniquely associated with the thought-disorder subgroup. Ultimately, tree imagery may offer a valuable nonverbal, adjunctive approach to screening for mental disorders, offering several statistically supported indicators that may effectively complement traditional assessment tools.
13 July 2026
Read appraisal →JMIR medical informatics
Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review
BACKGROUND: Psychiatric clinical notes in electronic health records (EHRs) provide rich longitudinal information that can support clinical decision-making. Using historical medical data can enable earlier identification of mental illness, better characterization of disease trajectories, and more personalized treatment planning. Natural language processing (NLP) transforms these unstructured notes into analyzable representations for research and care. OBJECTIVE: This study aims to systematically summarize NLP methodologies for psychiatric clinical notes, compare major modeling paradigms and application areas, and highlight emerging large language model (LLM) trends, key challenges, and future research directions. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a literature search was conducted for articles on NLP methods based on psychiatric clinical notes published from January 2021 to December 2025 in Ovid MEDLINE, Ovid EMBASE, PubMed, Scopus, Web of Science, the ACM Digital Library, and ScienceDirect. This scoping review analyzed NLP methods applied to psychiatric clinical notes, focusing on major trends, identifying suitable features for traditional machine learning (ML)-based models, applications of pretrained language models (PLMs), and key challenges. Approaches were categorized as rule-based, traditional ML, hybrid, deep learning (DL), and LLM-based methods across information extraction and text classification tasks. RESULTS: In total, 101 studies were eligible for inclusion. Rule-based methods (n=36) and hybrid approaches (n=34) remained the most widely used techniques, largely favored for their interpretability in handling nuanced, subjective clinical notes. These were followed by DL (n=15), traditional ML (n=10), and LLM-based approaches (n=6). Traditional ML studies relied heavily on engineered features, which could be grouped into 5 broad categories: domain knowledge features, lexical and statistical features, vector-based semantic features, emotion-related features, and temporal features. PLMs improved performance mainly through domain adaptation and task-specific fine-tuning, enhancing the handling of psychiatric language, medical terminology, and clinical note structure. LLM-based studies, although still limited in number, indicated a growing shift toward generative and reasoning-based applications. CONCLUSIONS: Hybrid NLP approaches remain dominant, combining domain rules with ML for extraction and classification. DL approaches continue to advance, with domain adaptation supporting medical terminology and clinical semantics. LLMs may further automate complex workflows via zero-shot capabilities and reasoning, alongside growing interest in temporal modeling and multimodal integration. Key future needs include improved generalizability across institutions, privacy protection, and careful attention to ethical implications in clinical deployment.
13 July 2026
Read appraisal →PloS one
Optimizing treatment for depressed parents of children with emotional and behavioral disorders: A multi-site feasibility trial.
BACKGROUND: Behavioral parent training (BPT) programs are effective for reducing disruptive behavior of many children with emotional and behavioral disorders (EBD) and improving skills and competencies of their parents. However, these interventions are not sufficiently effective for parents who themselves have depression. This study assessed the feasibility of the Addressing Depression and Positive Parenting Techniques (ADAPT) Program-a novel BPT that includes aspects of cognitive behavioral therapy and dialectical behavior therapy. We identified whether recruitment was possible, attendance was acceptable, participants were satisfied, and facilitators adhered to the protocol. Additionally, we measured trends in changes in parent and child outcomes with intervention. METHODS: Participants were 28 parents and their children with EBD, referred to two hospital-based clinics. Parents were eligible if they (1) had a child aged 6-10 experiencing emotional and behavioral difficulties and (2) were experiencing elevated depressive symptoms. ADAPT took place virtually over 10 weeks. Participants completed measures before, weekly and after treatment. Clinicians recorded attendance and completed weekly treatment fidelity checklists. RESULTS: We found that recruitment was possible, the intervention showed high parental satisfaction, and facilitators reported high program fidelity. Further, secondary outcome measures showed evidence of sensitivity to change in the intended direction. CONCLUSION: Given that a priori progression criteria were met and we found preliminary indication of efficacy, we conclude that an RCT of ADAPT to determine efficacy is warranted.
11 July 2026
Read appraisal →BMJ open
The Cyber Paranoia and Fear Scale-Updated (CPFS-U): development and implications for digital health engagement
OBJECTIVES: To update and revalidate the Cyber Paranoia and Fear Scale to reflect current technological contexts and examine its relevance to digital health readiness and engagement. METHODS: Using an online community sample (n=433), exploratory factor analysis was conducted to examine the factor structure of the revised item pool. Items were refined through consultation with Patient and Public Involvement and Engagement groups to ensure contemporary relevance and clarity. RESULTS: Analysis supported a four-factor structure representing artificial intelligence (AI) and digital dependence, technological risk awareness, perceived data vulnerability and surveillance-related mistrust. The updated Cyber Paranoia and Fear Scale-Updated (CPFS-U) demonstrated good internal consistency and supported construct validity. Cyber-paranoia and fear were conceptually and empirically distinct from general paranoia and anxiety, highlighting the specific cognitive and emotional responses elicited by digital technologies. CONCLUSIONS: The CPFS-U offers a psychometrically robust, modernised measure for understanding individuals' responses to digital and AI-based technologies. Its application in digital health research and practice can inform risk communication, user engagement strategies and the design of trustworthy digital interventions. By identifying individuals who may disengage due to online mistrust, the CPFS-U has the potential to inform more inclusive and psychologically informed digital health systems.
8 July 2026
Read appraisal →European journal of psychotraumatology
Assessment of functional impairment related to post-traumatic stress in children and adolescents: a systematic review
Background: Post-traumatic stress disorder (PTSD) often impairs children's and adolescents' functioning across different life domains. Significant functional impairment is a diagnostic criterion for PTSD in both the DSM-5 and the ICD-11, but the methods in terms of domains covered and scoring rules to assess functional impairments are quite inconsistent.Objective: This systematic review evaluates instruments specifically designed to assess post-traumatic stress symptoms (PTSS), PTSS modules within diagnostic interviews, and generic functional impairment measures used to capture PTSS-related functional impairment in children and adolescents.Method: A systematic search was conducted across six databases for studies published between January 2010 and November 2025. Eligible papers included the development or psychometric evaluation of PTSS-specific measures, PTSS modules in diagnostic interview, and generic functional impairment measures validated in samples exposed to trauma or adverse childhood experiences. Data were extracted and categorized based on assessed content domains, scoring methods, and psychometric properties, with results stratified by age group (6 years and younger versus 7 years and older).Results: From 3519 records, 30 papers met the inclusion criteria. Thirteen PTSS-specific instruments, one diagnostic interview module, and one generic functional impairment measure were identified. Together, these instruments assessed 11 different life domains, although considerable variation existed in the domains covered across tools. Scoring methods also varied, with approximately half using dichotomous response formats and the remainder using multi-categorical scales. Psychometric properties of the functional impairment scales were reported in only 20.7% of publications.Conclusion: Current tools for assessing PTSS-related functional impairment in youth lack standardization. There is a critical need for well-validated instruments that comprehensively capture the presence and severity of PTSS-related functional impairment. This systematic review identified and evaluated 15 instruments, comprising PTSS-specific measures, diagnostic interview modules, and generic functional impairment tools, used to assess PTSS-related functional impairment in children and adolescents.The instruments collectively assessed 11 different life domains; however, substantial variability existed in the domains covered, and scoring methods ranged from dichotomous formats to Likert-type scales.Only 20.7% of papers reported psychometric properties for functional impairment items, highlighting the critical need for standardized, developmentally appropriate tools with robust validation in children and adolescents with potentially traumatic experiences.
8 July 2026
Read appraisal →Neuroscience and biobehavioral reviews
Anxiety detection using neural and physiological signals and artificial intelligence: A comprehensive review.
Anxiety disorders are a significant challenge to global health, yet current diagnostic methods rely mainly on subjective and episodic assessment. The application of artificial intelligence (AI) in combination with neural and physiological signals is a promising pathway toward objective and continuous monitoring. This review provides a comprehensive evaluation of machine learning (ML) and deep learning (DL) techniques applied to electroencephalography (EEG), electrocardiography (ECG), Photoplethysmography (PPG), and electrodermal activity (EDA) for anxiety detection. A systematic review of the literature covering the period 2015-2025 indicates that the field has evolved from traditional ML systems based on hand-crafted features to modern end-to-end deep learning schemes. Our review indicates that while classical models remain effective, hybrid models such as CNN-LSTM and more sophisticated architectures, like Transformers, consistently deliver state-of-the-art results, particularly in multimodal data integration scenarios. Despite the impressive accuracies reported, a critical examination identifies several key challenges including reliance on narrow, laboratory-trained datasets, a lack of standardized validation procedures, and limited transparency in complex models, collectively impeding clinical translation. Progress in this field requires the creation of large-scale, clinically-validated datasets; the development of fault-tolerant, generalizable, and interpretable schemes; and a transition from basic state classification towards longitudinal, personalized monitoring in support of just-in-time intervention. As a comprehensive and up-to-date narrative synthesis of AI-driven anxiety detection using neural and physiological signals, this review consolidates a fragmented literature, articulates the translational gaps limiting real-world deployment, and establishes a clear research agenda for the development of clinically impactful digital mental health technologies.
3 July 2026
Read appraisal →JMIR mental health
From Alliance to Nexus: Rethinking Digital Therapeutic Relationships
In traditional human psychotherapy, the therapeutic alliance (TA) is regarded as a fundamental factor that describes the client-therapist relationship, mainly due to strong evidence demonstrating its impact on treatment outcomes regardless of theoretical orientation. More recently, advances in artificial intelligence (AI) and other technologies have led to the emergence of the concept of digital TA, used to characterize the relationship between clients and AI-based therapeutic systems. This approach replicates human dynamics but overlooks key differences between human therapists and digital agents. Prematurely translating the concept of TA into the digital context fails to address issues such as the sycophantic tendencies of current systems and the inherent limitations of algorithmic interaction. We propose the digital therapeutic nexus, a framework that recognizes these differences and provides a set of structured criteria for categorizing digital interactions into 3 progressive levels. This Viewpoint argues that only at the highest level can parallels be drawn to the human TA and stratifies the main risks associated with each nexus level. Transitioning from the concept of alliance to that of a nexus offers a more precise conceptual basis for describing and evaluating digital therapeutic relationships, with implications for research, design, and the ethical development of AI-based mental health interventions.
3 July 2026
Read appraisal →Behavior research methods
A validity-guided workflow for robust large language model research in psychology.
Large language models (LLMs) are rapidly being integrated into psychological and behavioral research as research tools, evaluation targets, human simulators, and cognitive models. Yet recent evidence reveals severe measurement unreliability: personality assessments degenerate under factor analysis, moral preferences reverse with punctuation changes, and theory-of-mind accuracy varies widely with trivial rephrasing. These "measurement phantoms"-statistical artifacts masquerading as psychological phenomena-threaten the validity of a growing body of research. Guided by the dual-validity framework that integrates psychometrics with causal inference, this article presents a six-stage workflow that scales validity requirements to research ambition-using LLMs to code text requires basic reliability and accuracy, whereas claims about psychological properties demand comprehensive construct validation. Researchers must (1) explicitly define their research goal and corresponding validity requirements, (2) develop and validate computational instruments through psychometric testing, (3) design experiments that control for computational confounds, (4) execute protocols transparently, (5) analyze data with methods appropriate for nonindependent observations, and (6) report findings within boundaries and use results to refine theory. The workflow is illustrated through an example of model evaluation-"LLM selfhood"-showing how systematic validation can distinguish genuine computational phenomena from measurement artifacts. By establishing validated computational instruments and transparent practices, this workflow provides a path toward building a robust empirical foundation for AI psychology research.
3 July 2026
Read appraisal →Journal of medical Internet research
How Does That Large Language Model Make You Feel?
People are increasingly turning to commercially available large language models (LLMs) for emotional support. In this News and Perspectives article, JMIR Correspondent Simon Spichak reports on the role of LLMs in mental health, speaking with experts about safety concerns, research gaps, and next steps.
2 July 2026
Read appraisal →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 →JMIR formative research
Supporting Student Mental Health With the Safespace Generative AI Chatbot: Mixed Methods Feasibility Study
BACKGROUND: Generative artificial intelligence (GenAI) chatbots have the potential to provide personalized mental health support to individuals at scale. OBJECTIVE: This study evaluates the feasibility and usage patterns of the Safespace GenAI chatbot, an artificial intelligence (AI)-driven smartphone app that offers a large language model-powered interactive chatbot to support mental health. METHODS: Using a mixed methods approach, we explored baseline attitudes toward GenAI chatbots and chatbot usage patterns, conducted a qualitative content analysis of participants' experiences, and descriptively assessed patterns related to preintervention depressive symptoms. The study included an initial sample of 42 university students, 20 of whom actively used the chatbot over 2 to 4 weeks, generating 286 user-chatbot interactions. RESULTS: Preintervention surveys indicated that the majority of participants anticipated that the chatbot would be helpful (27/42, 64%) and that they trusted its privacy safeguards (39/42, 93%). Usage patterns suggested that the highest levels of interaction occurred early in the morning and late at night, when peer and professional support may be inaccessible. The qualitative analysis indicated that participants appreciated using the chatbot for reflection as a blended-care tool between their counseling sessions, while also naming technical barriers and specific design needs required to sustain engagement. In addition, our exploratory analyses descriptively showed that participants with elevated depression scores engaged in emotional disclosure during 99% (38 sessions with 8 participants) of their sessions, compared to 84% (26 sessions of 12 participants) of those with low symptoms. Due to the small sample size, future adequately powered studies are needed to inferentially examine these observed patterns. CONCLUSIONS: These findings provide initial insights into the usage and engagement dynamics of the Safespace GenAI chatbot and highlight directions for future research to optimize GenAI-driven mental health interventions.
27 June 2026
Read appraisal →Journal of medical Internet research
The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review
BACKGROUND: Stress is widespread and carries substantial mental health, social, and economic burdens. Yet, access to clinician-led stress management remains constrained by service capacity, cost, and stigma. In response, artificial intelligence (AI)-enabled tools have rapidly proliferated as scalable, self-directed options. However, evidence on how these systems support stress management outside formal clinical settings remains fragmented. OBJECTIVE: This systematic review aimed to synthesize empirical evidence on how AI-enabled technologies are used for self-directed stress management. We mapped the emerging functions of these tools, the psychological frameworks informing their design, the populations and settings studied, and the outcomes reported. METHODS: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-compliant systematic review of English-language studies published between 2000 and 2025. Six databases were searched (APA PsycINFO, PubMed, MEDLINE, Scopus, Web of Science Core Collection, ProQuest, and Google Scholar). RESULTS: Of 3008 records identified, 35 studies met the inclusion criteria. The methodological quality of included studies was critically appraised using the Mixed Methods Appraisal Tool (version 2018). Findings illustrated that AI-supported stress management can operate through 5 core functions, including psychological intervention, behavioral support, psychoeducation, companionship, and emotional support, and stress monitoring, detection, and triage. Across the reviewed studies, these functions supported self-directed stress management by helping users identify stress, regulate responses, and engage in coping outside formal clinical care. CONCLUSIONS: AI-enabled systems show preliminary promise for supporting self-directed stress management through multiple user-facing functions grounded in established psychological frameworks.
26 June 2026
Read appraisal →Psychological medicine
Sorting the mind: cognitive enhancement through transcutaneous auricular vagus nerve stimulation: a systematic review and meta-analysis
Transcutaneous auricular vagus nerve stimulation (taVNS) is a noninvasive technique engaging vagal afferents that may enhance cognition, but results vary across domains and samples. Following PRISMA, seven databases (inception-October 2025) plus registries and gray literature were searched. Random-effects meta-analyses (REML; Hedges' g) were complemented by Bayesian hierarchical models and sensitivity analyses. Fifty-three studies were included; 30 contributed quantitative data (>1,500 participants). taVNS was associated with improved cognitive performance overall (g = 0.41, 95% CI: 0.30-0.53; I2 = 51.4%). Effects were moderate for executive functions (g = 0.46, 95% CI: 0.27-0.65; I2 = 9.5%) and cognitive flexibility/learning (g = 0.53, 95% CI: 0.32-0.75; I2 = 52.9%), and small for working memory/attention (g = 0.19, 95% CI: 0.04-0.33; I2 = 14.9%). Social cognition/emotion regulation showed larger but imprecise effects (k = 3; g = 0.80, 95% CI: 0.07-1.52; I2 = 82.1%). Clinical samples benefited similarly (k = 7; g = 0.55, 95% CI: 0.31-0.79; I2 = 29.5%), with no difference from healthy cohorts (β = -0.001, p = .994). High-intensity protocols (>1.0 mA) yielded larger effects; mode, duration, and site were not moderators. Bayesian models supported effects (P [μ > 0] ≥ 0.93). taVNS is associated with statistically significant improvements in cognitive performance, strongest for executive control and adaptive learning. We propose a Vagal Neurocognitive Integration Model linking LC-NE arousal modulation to prefrontal control. Future diagnosis-specific, adequately powered trials with multimodal neuroimaging should refine mechanisms and dose-response.
25 June 2026
Read appraisal →PloS one
Factors associated with resilience among parents of children with autism spectrum disorder: A systematic review
Parents of children with autism spectrum disorder (ASD) experience ongoing caregiving demands and challenges that may contribute to their general well-being. Research has shown that resilience enables parents to adapt to these challenges; however, existing evidence on associated factors toward resilience among these parents remains fragmented. This systematic review aimed to identify factors associated with resilience among parents of children with ASD. We conducted a PRISMA 2020-guided systematic review on studies published between January 2019 and May 2025 using Web of Science, Scopus, and PubMed by applying predefined search terms and Boolean operators. Studies examining factors associated with resilience among parents of children with ASD aged under 18 years were included in this review. We also used the Mixed Methods Appraisal Tool 2018 to assess the quality of these studies. Findings were synthesised narratively using thematic analysis. From the 13 selected studies, five themes were identified as determinants of resilience among parents, i.e., sociodemographic and economic factors; child-related characteristics; parenting well-being and distress; psychological factors and coping styles; and social support and family relationships. Most included studies were cross-sectional and utilised different measurement tools, which may limit causal inference and comparability. As a conclusion, parental resilience was found to be influenced by psychological, social, and contextual factors. Strengthening coping resources and social support may enhance parental adaptation and family well-being. Along these lines, multi-level and culturally sensitive interventions are thus needed to inform strategies to foster resilience among parents of children with ASD. The review was registered with PROSPERO (CRD420251034348). The author(s) received no specific funding for this work.
23 June 2026
Read appraisal →Current psychiatry reports
The Digital Mirror: Clinical Potentials and Relational Risks of Generative AI in Mental Health Interventions
PURPOSE OF REVIEW: This review explores the rapidly evolving integration of Generative Artificial Intelligence (GenAI) in mental health care. It aims to evaluate current applications in assessment, treatment planning, and psychotherapeutic interventions, while critically examining the clinical risks, ethical dilemmas, and the future potential of GenAI as an adjunctive tool rather than a replacement for human-delivered therapy. RECENT FINDINGS: Recent studies indicate that AI models can effectively assist in diagnostic reasoning, biomarker identification via EEG, and the prediction of symptom trajectories from session transcripts. Randomized controlled trials (RCTs) suggest that GenAI chatbots significantly reduce anxiety and depressive symptoms in the short term, particularly in settings with limited access to clinicians. However, human-led therapy remains superior in fostering deep emotional engagement and clinical impact. Significant risks identified include the potential for GenAI to foster dependency, reinforce maladaptive schemas or delusional ideation through "sycophantic" mirroring, and raise complex ethical-legal challenges regarding the reporting of criminal disclosures. AI represents a transformative adjunctive layer in mental health, offering scalable support for assessment, training, and between-session monitoring. While technological advances in personalization, multimodality, and immersive virtual reality enhance its clinical utility, GenAI lacks the authentic relational depth and"calibrated mismatches" essential for autonomy and transformative change. Future integration must prioritize a human-centered, blended approach, where GenAI is strictly supervised by clinicians within a robust ethical and regulatory framework to preserve the essential heart of the therapeutic connection. Research priorities, interim clinical safeguards, and recommendations for navigating the gap between current evidence and real-world adoption need to be defined and implemented.
19 June 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
Assessing symptom severity in patients with markedly treatment-resistant depression: Insights from the RECOVER trial
BACKGROUND: The performances of the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Quick Inventory of Depressive Symptomatology-Clinician (QIDS-C) and Self-Report (QIDS-SR) ratings have never been evaluated in markedly treatment-resistant depression (TRD). RECOVER, a 12-month randomized, controlled trial, compared adjunctive active to adjunctive sham vagus nerve stimulation, provided data to address this knowledge gap, and to examine the basis for the differential sensitivities of these scales in detecting overall and between-treatment-group symptom changes. METHODS: We employed classical test and item response theory methods to characterize each scale. Effect sizes identified items that were most sensitive to change over time across treatments, best discriminated between the two treatments, and best detected at least a partial symptom response in clinician global ratings. RESULTS: All three inventories were unidimensional, with high retest reliability and strong internal consistency. MADRS total scores were less sensitive in detecting overall and between-treatment-group symptom differences. On each scale, five symptom domains-mood, energy/lassitude, interest/inability to feel, negative self-view/pessimism, and concentration-covaried most strongly with overall depression severity and had the largest effect sizes in detecting overall therapeutic change and between-group difference in efficacy. LIMITATIONS: These findings may not generalize to less treatment-resistant populations or to different treatments. The relative insensitivity of suicidal ideation, sleep, and appetite items may be due to study design or concomitant medications. CONCLUSION: The MADRS, QIDS-C, and QIDS-SR are suitable for use in markedly TRD. Whether sadness, energy, interest, guilt, and concentration are "core" symptoms that are especially sensitive to change deserves further study.
16 June 2026
Read appraisal →Journal of affective disorders
Emotion regulation-targeted interventions initiated during hospitalization for suicidal crisis: a systematic review and exploratory meta-analysis
INTRODUCTION: Suicide is a leading cause of premature death, with risk peaking around psychiatric hospitalization. Emotion dysregulation (ED) is recognized as a key factor contributing to suicidal ideation and behavior across psychiatric diagnoses. The efficacy of interventions that target ED for suicidality reduction remains unclear, so we aimed to assess these interventions in psychiatric inpatients. METHODS: We conducted a PRISMA-compliant, PROSPERO-registered (CRD420251140949) systematic review and meta-analysis of randomized and non-randomized trials evaluating interventions targeting emotion regulation (ER) for psychiatric inpatients with suicidal ideation and/or behavior. Searches covered PubMed, Embase, and PsycINFO to September 18, 2025. Standardized mean differences (Hedges' g) were pooled under random-effects models and heterogeneity was assessed (Q, I2, τ2). Exploratory meta-regressions examined age, sex, and study quality; small-study effects were explored with funnel plots. RESULTS: Twelve studies met inclusion (n = 1708), spanning dialectical behavior therapy (DBT), mindfulness-based interventions, acceptance and commitment therapy (ACT), and other ER-related protocols. Across modalities, feasibility and acceptability were high and interventions consistently improved suicide-related outcomes in transdiagnostic adolescents and adult populations. Suicide attempts and ideation met outcome criteria for exploratory meta-analyses, and, for both, ER-targeted interventions outperformed usual care, despite heterogeneity and small samples. Exploratory meta-regressions were non-significant; funnel plots did not suggest marked publication bias, though power was limited. CONCLUSIONS: ER-targeted interventions delivered during psychiatric hospitalization are feasible and show promising benefits on suicide-related outcomes. Given heterogeneity and limited sample sizes, larger, well-powered randomized trials using standardized suicidality endpoints are needed to establish efficacy, refine inpatient protocols, and inform acute care pathways.
16 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
On the relationships between apathy, depression and anhedonia
BACKGROUND: Apathy, depression and anhedonia are clinically overlapping constructs, which hinders diagnostic clarity and treatment development. This study aimed to comprehensively characterise these syndromes to identify a core set of non-redundant symptoms that maximally dissociate them and to investigate the psychological nature of key distinguishing features. METHODS: Data from seven datasets (N=4578) of healthy individuals and patients with major depressive disorder were analysed using the Apathy Motivation Index, Beck Depression Inventory and Snaith-Hamilton Pleasure Scale. A machine-learning algorithm identified the most informative, non-redundant items for dissociating 'pure' apathy, depression and anhedonia. The nature of emotional apathy was further investigated with follow-up studies. RESULTS: Although substantial symptom overlap existed, 'pure' syndromes were present. Factor analysis revealed a robust five-factor structure, separating depression, anhedonia and three distinct apathy domains (behavioural, social and emotional). Machine learning identified 10 core symptoms that differentiated the pure syndromes with high accuracy (area under the curve >0.90) and could also identify well the presence of each syndrome in individuals suffering from two or more syndromes. Emotional apathy negatively correlated with depression and was specifically associated with reduced affective empathy and a diminished sensitivity to the intensity of negative facial emotions, rather than with alexithymia or antidepressant-induced emotional blunting. CONCLUSIONS: Apathy, depression and anhedonia are dissociable constructs with distinct symptom signatures. Emotional apathy is a unique dimension which provides a novel target for research. A 10-item Apathy-Depression-Anhedonia Measure developed here provides a pragmatic tool for rapid, precise phenotyping to guide more personalised therapeutic strategies.
14 June 2026
Read appraisal →The Journal of clinical psychiatry
Placebo Effects in Alcohol, Cocaine, and Methamphetamine Use Disorder: A Systematic Review and Meta-Analysis
Objective: The placebo effect is established in psychiatry, yet its role in substance use disorders (SUDs) treatment remains unclear. The aim of this study was to evaluate changes in craving scores in placebo groups in randomized controlled trials (RCTs) across different SUDs in adults. Data sources: Eight different databases (PubMed, Embase, Web of Science, Cochrane Library, Emcare, MEDLINE, PsycINFO, and Academic Search) were systematically searched up to October 28, 2024, by 2 independent researchers. Study selection: Placebo-controlled, double-blind RCTs assessing craving in patients with SUDs were included based on predefined eligibility criteria, published in English, Dutch, Spanish, or German language. Search terms were a combination and synonyms of "craving," "addiction," and "placebo." From a total of 1945 unique records identified, 9 articles met our eligibility criteria. Data extraction: Data on demographic characteristics, study design, and outcome results were extracted. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated for pre-post craving scores within each placebo group; a positive difference point towards improvement in the postplacebo period. Results: A total of n=495 placebo-treated participants were included from nine RCTs in three different SUDs (n=300 in alcohol, n=88 in cocaine, and n=107 in methamphetamine use disorder). Craving scores significantly decreased over time (meaning symptom improvement) in the placebo treatment group in alcohol use disorder (5 studies, SMD=0.93 [95% CI=0.33 to 1.54]) but did not reach statistical significance in cocaine use disorder (2 studies, SMD=0.61 [95% CI=-1.41 to 2.62]) or in (meth)amphetamine use disorder (2 studies, SMD=0.44 [95% CI=-3.43 to 4.32]). Conclusions: Placebo treatment led to reductions in craving scores across all three SUDs, although statistical significance was achieved only in alcohol use disorder. Given the low number of studies for cocaine and (meth)amphetamine use disorder, these estimates were captured with much uncertainty. These findings may inform the design of future RCTs and support the development of more individualized treatment approaches in clinical settings.
13 June 2026
Read appraisal →Journal of medical Internet research
Using Ultra-Abridged Individual Difference Scales for Personalization in Digital Mental Health to Improve Uptake, Engagement, and Experiences: Three-Tiered Decision Framework for Scale Shortening
Given the diversity of human characteristics and experiences, personalization in nudges, messages, choice presentations, interventions, and overall product design has been increasingly adopted in digital health to promote engagement. Past studies on moderators and personalization in digital health and mental health services generally focused on demographic and symptom variables, with generally inconsistent findings or null findings. Cognitive, motivational, and decisional psychological attributes are largely overlooked. Psychology often uses long self-report scales to measure various psychological attributes. Although they are useful in tapping into individuals' psychological profiles, when applied in real-life, everyday settings to assess individual differences, people are most likely unwilling to complete them. With the pressing need to personalize digital health platforms to enhance uptake, retention, and engagement, ultrashort versions of these psychological scales may be considered to allow assessment of multiple attributes at the same time. Scale shortening can be achieved through regression analyses of each item, factor analyses, item response theory, ant colony optimization, and machine learning methods, with each method having advantages, disadvantages, and conditions required to make it suitable. To illustrate, we provided examples of regression analyses of each item and factor analyses, with potential implications for personalizing narrative versus research-based messages in digital mental health contexts. We present a 3-tiered decision framework for scale shortening method selection depending on goals and possible constraints, with guidelines on validation methods for ultrashort scales. Moving forward, more validation studies and field studies in digital health platforms are needed to evaluate the ecological validity, reliability, and generalizability of these methods, bearing in mind the limitations and conditions where such shortening methods may not work well. Researchers may compare the effectiveness and limitations of personalization using ultrashort scales with other commonly adopted personalization methods (eg, based on longer scales, behavioral data, and large language models). Ethical concerns need to be considered and mitigated carefully, respecting diverse preferences, informed choices, and the privacy of service users. Our viewpoint piece is primarily intended for digital mental health researchers and practitioners, but may also be informative for the fields of digital health and medicine as well as personalization (eg, personalized health care, personalized nudging, and message matching) more broadly, given the common goal of boosting uptake and engagement as well as improving service users' experiences.
10 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 →Medicine
Machine learning-based identification of multidimensional predictors of quality of life in individuals with multiple sclerosis
This study aimed to identify independent predictors of quality of life in patients with multiple sclerosis (MS) using machine learning approaches. One hundred and one individuals diagnosed with MS were included in this cross-sectional study. Quality of life was assessed using the MS Quality of Life-54 (MSQoL-54). Demographic variables, clinical characteristics, disability level (Expanded Disability Status Scale [EDSS]), fatigue severity, sleep quality (Pittsburgh Sleep Quality Index), depression level (Beck Depression Inventory), and functional mobility (Timed Up and Go test) were evaluated. Multiple machine learning regression models, including Linear Regression, Lasso, Elastic Net, Support Vector Machines, Random Forest, and XGBoost, were developed and compared. Five-times repeated five-fold cross-validation was applied for internal validation. Model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Among all models, Lasso regression demonstrated the best predictive performance (RMSE = 5.02, MAE = 4.04, R2 = 0.86). Disability level (EDSS) emerged as the strongest negative predictor of quality of life, followed by fatigue severity, sleep quality, and impaired functional mobility. Although disease duration was not significantly correlated with quality of life in univariate analyses, it showed a positive contribution in the multivariable model. Depression showed a strong bivariate association but had a relatively lower weight in the final predictive model. Quality of life in individuals with MS is strongly influenced by disability, fatigue, sleep quality, and functional mobility. Machine learning approaches provide an effective and interpretable framework for identifying key determinants of quality of life and may support personalized rehabilitation and clinical decision-making in MS.
31 May 2026
Read appraisal →BMJ open
Feasibility and acceptability of therapist-guided, asynchronous, internet-delivered trauma-focused CBT for adolescents with PTSD: a single-group feasibility trial in Sweden
OBJECTIVES: Trauma-focused cognitive behavioural therapy (TF-CBT) is the established first-line treatment for paediatric post-traumatic stress disorder (PTSD), but access to evidence-based care remains limited. This study aimed to evaluate the feasibility and acceptability of a therapist-guided, 12 week, internet-delivered TF-CBT (iTF-CBT) programme for adolescents with PTSD and to explore preliminary changes in PTSD symptoms. DESIGN: Single-group feasibility trial. SETTING: Save the Children, Sweden. PARTICIPANTS: Twenty-two adolescents (13-17 years, 82% female) with primary PTSD. INTERVENTIONS: A 12 week, therapist-guided, asynchronous, internet-delivered TF-CBT comprising eight modules and parallel caregiver modules with joint adolescent-caregiver activities. OUTCOMES: Feasibility measures included recruitment pace, participant retention, treatment adherence (module completion) and therapist time. Acceptability was evaluated through satisfaction, credibility, negative effects and reported adverse events. Within-group changes in PTSD severity using independent evaluator-rated Clinician-Administered PTSD Scale (CAPS-CA-5) and the self-reported Child and Adolescent Trauma Screen 2 (CATS-2) were used as indicators of potential clinical change. Assessments occurred at baseline, during treatment, post-treatment and at 1 month follow-up (primary endpoint). RESULTS: Recruitment was completed after 7 months of active enrolment. Retention and adherence were high, satisfaction and credibility ratings were favourable, and no intervention-related serious adverse events occurred. Within-group improvements were observed at the primary endpoint, with large reductions on CAPS-CA-5 (Cohen's d=1.27) and CATS-2 (Cohen's d=1.51). At follow-up, 47.6% of participants no longer met criteria for PTSD. CONCLUSIONS: Therapist-guided iTF-CBT for adolescents with PTSD was safe, feasible, acceptable and associated with potentially meaningful symptom improvements. These findings support further evaluation in larger, controlled trials to determine efficacy, cost-effectiveness and long-term outcomes. TRIAL REGISTRATION NUMBER: NCT06185244.
30 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 →JMIR mental health
When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion
Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion-the uncritical acceptance of an unreliable account-as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health-relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health-relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.
28 May 2026
Read appraisal →JMIR formative research
Testing a Smartphone-Based Intervention Targeting Anxiety Sensitivity Among Women Presenting for Emergency Care After Sexual Assault: Pilot Randomized Controlled Trial
BACKGROUND: Anxiety sensitivity (AS), defined as the fear of anxious arousal, is a promising therapeutic target for reducing posttraumatic stress disorder (PTSD) symptom development after trauma exposure. Initial research suggests that smartphone-based AS interventions may be acceptable to sexual assault survivors at risk for PTSD symptoms and effective for symptom reduction, but only small one-arm proof-of-concept studies have been conducted. OBJECTIVE: The goal of this study was to extend prior proof-of-concept work by conducting a pilot randomized controlled trial. The aims were to evaluate intervention efficacy, AS and PTSD symptom change, the acceptability and credibility of a control intervention, and the feasibility of a larger randomized controlled trial. METHODS: A total of 60 women with high AS presenting for emergency care after sexual assault were recruited and randomized to either the AS intervention or a control condition, and they were followed up with for over 6 months via remote self-report questionnaires. RESULTS: The findings indicated that the study population is at risk and in need of intervention: 88.8% (40/45) and 80.6% (29/36) of women sexual assault survivors in the sample met the criteria for probable PTSD at 7 weeks and 6 months post assault, respectively. Most (16/27, 59.3%) individuals receiving the AS intervention who completed it rated it as acceptable (eg, 18/21, 85.7% reported that the treatment was helpful). Early within-group reductions were not statistically significant, but by month 6, statistically significant reductions in AS and PTSD symptoms were observed in both conditions. Recruitment and retention data supported the feasibility of the study design, although some suggestions were noted for future research (eg, improving intervention and ecological momentary assessment compliance). CONCLUSIONS: This pilot study replicated the proof of concept and acceptability of a novel smartphone-based intervention targeting AS delivered to women sexual assault survivors presenting to emergency care. Intervention completion was in line with or better than traditional therapy but remained a challenge. Our selected control condition demonstrated a larger effect than expected, and we were unable to track its initiation or completion, causing difficulty in drawing conclusions. Overall, the results highlight the need for additional research.
27 May 2026
Read appraisal →Journal of medical Internet research
Training Clinicians in Private Practice in Family-Based Treatment for Anorexia Nervosa: Randomized Controlled Trial Comparing Two Online Approaches
BACKGROUND: There is a critical need to disseminate efficacious psychosocial treatments for mental disorders because there is a significant gap between evidence-based treatment (EBT) approaches and usual clinical practice. To address this gap, cost-effective, efficient, and scalable methods of training mental health clinicians in EBT are needed. One example of the need to improve dissemination and implementation of psychosocial treatments is for adolescents with anorexia nervosa (AN), a serious mental disorder with an incidence rate of about 1% that can become life-threatening. Based on outcomes from a series of randomized controlled trials (RCTs), an EBT for adolescents with AN is family-based treatment (FBT); however, few therapists are trained in the approach. Some studies suggest that online training is feasible for training clinicians treating eating disorders, including pilot data related to FBT specifically, but RCTs examining different training formats for FBT to improve fidelity and patient outcomes are needed. OBJECTIVE: This study compared 2 different formats for delivering online training in FBT to therapists treating adolescents with AN and planned to assess the feasibility of the 2 training formats, as well as to examine whether either approach was superior in improving fidelity to FBT or patient outcomes. METHODS: Participants were 123 mental health therapists licensed in the United States in private practice with no previous FBT training. Therapists were randomized to either (1) a webinar training lecture series or (2) an interactive e-training on-demand program (ET-FBT) with an additional focus on key FBT interventions. Both groups were offered up to 12 one-hour group-based clinical case consultation (CCC) sessions posttraining. We examined the feasibility and acceptability of the online training programs and posttraining outcomes in fidelity to key components of FBT, self-efficacy, and working alliance. We explored rates of patient early response (weight gain of 2.4 kg at session 4) in adolescent patients with AN treated after FBT training during CCC. RESULTS: Both online trainings had a high completion rate of 95% (117/123), with CCC completion at 38% (47/123). Both programs showed significant improvements within randomized groups in therapists' fidelity, self-efficacy, and working alliance. Working alliance improved significantly more in the ET-FBT group, but there were no other significant differences between training groups. Early response rates doubled posttraining and CCC (16%-34%), regardless of randomized training format, a rate that is similar to that achieved by therapists in RCTs examining FBT treatment outcomes. CONCLUSIONS: It is feasible to deliver online training in FBT to clinicians in private practice in 2 different formats, and both trainings led to significant improvements in fidelity, self-efficacy, working alliance, and patient outcomes. Future studies should address challenges in patient recruitment for posttraining CCC and refine CCC implementation to maximize training effects and efficiency.
26 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 →Translational psychiatry
Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial
Cognitive Bias Modification - Interpretation (CBM-I) aims to alter maladaptive interpretations in social anxiety, yet effects are often small and outcome measures are diverse. Although CBM-I has shown promise, its underlying mechanisms remain unclear and integration with psychophysiological and neural measures has been limited. In this randomized controlled trial, eighty-eight participants with high levels of social anxiety completed two lab sessions, an online training in between, and online follow-up. Participants filled out questionnaires, completed interpretation bias tasks, and underwent neuro-psychophysiological assessments. Active CBM-I trained positive resolutions of ambiguous social scenarios, while the sham version used neutral scenarios. The primary outcome, i.e., scores on the Liebowitz Social Anxiety Scale (LSAS), decreased across time in both groups, without group differences. However, the Brief Fear of Negative Evaluation decreased only in the active group. Interpretation bias shifted more strongly toward positive outcomes in the active group. Autonomic measures confirmed sensitivity to stress induction but did not differentiate between conditions. Electrophysiological results paralleled subjective ratings, as participants exhibited ambivalent responses to socially relevant stimuli but clearly differentiated responses toward neutral stimuli. Baseline correlations indicated strong convergence across self-report and interpretation tasks. Mediation analyses showed that reductions in negative interpretations mediated the effect of the training group on LSAS scores at follow-up. These findings identify interpretation bias as a modifiable mechanism underlying social anxiety and underscore its role as a transdiagnostic marker. Targeting interpretation bias through easily accessible and applicable online interventions may strengthen preventive and therapeutic approaches for social anxiety and related disorders.
25 May 2026
Read appraisal →PloS one
A systematic review and narrative synthesis of the psychometric properties and biopsychosocial correlates of the English version of the Intuitive Eating Scale-2
Intuitive eating is an adaptive eating approach characterised by having unconditional permission to eat when you are hungry, eating for physical rather than emotional reasons, relying on internal hunger and satiety cues, and honouring your health and practising gentle nutrition. The Intuitive Eating Scale-2 (IES-2) is currently the most commonly-used intuitive eating measure but, to date, there has not been a systematic review of how it performs psychometrically outside of the student sample in which it was developed. This systematic review aimed to assess the psychometric properties of the IES-2, including its associations with other variables, across all studies that used it as a measure of intuitive eating and reported psychometric data. MEDLINE, PsycINFO, Scopus and Web of Science were searched in April 2022, May 2024 and June 2025, identifying 90 papers from diverse populations including college students, people from the general population/community, and people seeking treatment for eating disorders or weight management. Results were presented and synthesised narratively, and risk of bias was assessed using two domains from the COSMIN Risk of Bias checklist. Findings suggest that the IES-2 has good construct validity but underperforms psychometrically in other areas such as response distribution, subscale inter-correlations and dimensionality, particularly in relation to the 'Unconditional permission to eat' subscale. Alternative factor structures were reported on and a three-factor solution excluding the 'Unconditional permission to eat' subscale was found to have promising results. The review contributes a comprehensive account of the biopsychosocial correlates of the IES-2, as well as identifying that studies relating to weight or eating disorders often report mean scores that tend towards the extremes of the scale. Limitations include the exclusion of non-English translations of the IES-2, and future reviews would benefit from being conducted in other languages. PROSPERO registration: CRD42022299436. Funder: ESRC (ES/P000711/1).
22 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 →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 →Journal of affective disorders
Prediction models for longitudinal trajectories of depression and anxiety: a systematic review
BACKGROUND: Prediction of atypical health trajectories may enable early intervention. We systematically reviewed the existing literature on models for predicting longitudinal depression and/or anxiety trajectories. METHODS: MEDLINE, Embase and APA PsycINFO were searched (from inception to 31-Jan-2025). We included population-based studies of children and adults (aged 3-65 years). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST-AI) tool. RESULTS: Seven of the nine included studies were in adult populations with a diagnosis of depression or anxiety at baseline; two focused on child and adolescent populations. Only one study included anxiety trajectories. Identified trajectories typically comprised three to four groups including: chronic/persistent-high, stable-low, increasing/worsening, and improved/remitted groups. Various supervised predictive modelling methods were used. The number of final predictors included in models ranged from three to 152. Family and own/personal psychiatric history were the most common predictors but were not always important for model performance. Models including more predictors did not always perform better. Overall risk of bias was high in all studies. No studies were externally validated and no studies assessed the clinical utility of models. CONCLUSION: This review highlights a need for robust, validated models that can forecast future risk of persistent or worsening anxiety and depression, especially in young people where early intervention is possible.
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 →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 →European respiratory review : an official journal of the European Respiratory Society
Evolution of body image across treatment eras: a systematic review in young people and adults living with cystic fibrosis
OBJECTIVES: Advancements in treatments for cystic fibrosis (CF) have improved prognosis, but variant-specific therapies have also driven weight gain. Traditional challenges to maintain weight are now less prevalent, with new body image concerns potentially emerging. This review aims to synthesise quantitative and qualitative evidence on body image in adults and young people (aged ≥16 years) with CF. METHODS: Systematic search of MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health, the Allied and Complementary Medicine Database and the Cochrane Library (January 2011 to August 2025) using CF-related keywords including body image and physical appearance. RESULTS: Searches retrieved 29 eligible studies (26 quantitative, two qualitative, one mixed methods). The Cystic Fibrosis Questionnaire - Revised (CFQ-R) and Cystic Fibrosis Quality of Life Questionnaire (CFQoL) questionnaires assessed body image quantitatively (0-100). 15 studies reported CFQ-R body image scores (58±19 to 78±23) and five reported CFQoL body image scores (21±26 to 76). Pre-modulator era (≤2012) scores ranged from 64±27 to 70±27; initial rollout phase (2012-2019) scores ranged from 58±19 to 81 (post-ivacaftor); and in the widespread modulator era (2020 to present) were 63±32 to 67 (31). Gender effects were inconsistent. Higher body mass index, weight, and body fat were linked to better body image in some studies; psychological wellbeing and lower anxiety/depression consistently correlated with body image. Ivacaftor and life coaching improved body image, while elexacaftor/tezacaftor/ivacaftor, cognitive behavioural therapy, and self-management programmes showed no effect. Functional health was strongly linked to body image.Qualitative findings highlighted influences of gender, weight change, functional and mental health, social support, early eating experiences, and limited clinician-initiated body image discussions. CONCLUSION: This is the first systematic review of body image in CF since new drug regimens emerged. Emerging body image disturbance patterns highlight the need for clinical strategies to screen for and address body image issues in CF care.
15 May 2026
Read appraisal →PloS one
Emotion-focused therapy and forgiveness in the older population: Protocol for a feasibility randomized controlled trial
Over recent decades, Emotion-Focused Therapy, aging, and forgiveness have garnered significant attention in the field of psychology. However, there is a lack of studies on Emotion-Focused Therapy and forgiveness specifically tailored for older adults. This article presents a protocol designed to assess the feasibility and acceptability of a randomized controlled trial of Emotion-Focused Therapy aimed at resolving emotional injuries in individuals aged 65 and older, within the context of interpersonal offenses. Feasibility will be evaluated through recruitment and retention rates, as well as adherence to the intervention protocol. Acceptability will be assessed based on perceived treatment credibility, participants' expectations regarding outcomes, levels of engagement, and overall satisfaction with the intervention. The study is structured as a two-arm, parallel-group randomized trial with a waiting list control. We propose recruiting a sample of 70 participants, randomly assigned to either an immediate intervention group, which will receive Emotion-Focused Therapy over twelve weekly sessions, or a control group that will receive the same therapy after a twelve-week waiting period. Data will be collected in the beginning, middle, and at the end of therapy, and in two planned follow-ups (three and six months after therapy). Once this protocol is implemented, if the therapy proves to be feasible, acceptable, and shows promising results, the findings will inform a large-scale randomized clinical trial to advance the understanding of this treatment for individuals aged 65 + .
15 May 2026
Read appraisal →Integrative psychological & behavioral science
The Human Mind in Dialogue With its Replica AI - a Historical Perspective
Although artificial intelligence has exploded for the last couple of years, the idea of comparing the human mind with a computer is not new. One may say that Alan Turing started this movement, and this idea got off to a flying start in psychology through the symposium arranged by the "Special Interest Group in Information Theory" at Massachusetts Institute of Technology Sept. 10-11, 1956. This symposium was sponsored by IBM, and Noam Chomsky presented for the first time his ideas about a transformational generative grammar. There were several contributors to the cognitive revolution, but Chomsky's presentation of a linguistic system with a level of precision comparable with mathematics was symptomatic for how a cognitive model for the human mind aimed to be conceptualized. This has brought the question about artificial intelligence to the core of psychology, namely how it can be understood as a model for the human mind. Psychology and its understanding of the human mind have changed along the last 500 years of the Western psychology. After a short presentation of some of the premises for artificial intelligence of today and some aspects of the idea behind the computational mind, these perspectives are compared with the very early use of the term 'psychology' in the early 16th century. The role of classical logic as a model for the human mind was at stake back then and one of the main factors that paved the way for psychology was that Aristotelian logic was criticized and questioned as a model for the human mind. On the other hand, the importance of classical logic has never been completely outdated Consequently, for the last 500 years Western psychology can in many ways be characterized as an incessant battle about the role of logic in the understanding of the human mind. However, the conclusion is that AI of today presupposes an understanding of the human mind that has been outdated for more than 500 years.
13 May 2026
Read appraisal →Stress (Amsterdam, Netherlands)
Safety and efficacy of ashwagen (a standardized withania somnifera extract) in stress and anxiety with hypertension and associated cardiometabolic risk factors: a randomized, placebo-controlled trial
Stress and anxiety are prevalent mental health conditions that often coexist with cardiovascular comorbidities, resulting in a complex interplay between psychological and physiological processes, partly mediated through activation of the hypothalamic-pituitary-adrenal (HPA) axis. Ashwagen®, a standardized extract of Withania somnifera (Ashwagandha), has demonstrated adaptogenic and anxiolytic properties in preclinical and clinical studies. This study aimed to evaluate the safety and efficacy of Ashwagen® in managing stress and anxiety in patients with pre-existing hypertension and associated cardiometabolic risk factors. A randomized, double-blind, placebo-controlled Phase III clinical trial was conducted over 60 days in 60 patients diagnosed with stress, anxiety, and pre-existing hypertension with cardiometabolic risk profiles. Participants received either Ashwagen® (300 mg capsule) or placebo twice daily. Efficacy assessments included validated psychological scales-Hamilton Anxiety Rating Scale (HAM-A), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale (PSS), Clinical Global Impression-Improvement (CGI-I), Epworth Sleepiness Scale (ESS), and Fatigue Severity Scale (FSS)-along with serum cortisol, triglycerides, LDL, and blood pressure measurements. Ashwagen® significantly reduced anxiety and stress markers compared with placebo. HAM-A, GAD-7, and PSS scores decreased by 26.94%, 50.77%, and 30.47%, respectively, in the Ashwagen® group. Cortisol levels declined by 28.99% and triglycerides by 13.00%, with favorable trends in LDL and blood pressure. No serious adverse events were reported. Ashwagen® was found to be a safe and effective adjunctive integrative intervention for stress and anxiety management in patients with pre-existing hypertension.
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 →Current heart failure reports
Face-to-Face and Internet-based Cognitive Behavioral Therapy for Patients with Heart Failure: an Umbrella Review of Systematic Reviews and Meta-analyses
PURPOSE OF REVIEW: The present study aims to provide an up-to-date umbrella review of existing meta-analyses of randomized control trials (RCT) of cognitive behavioral therapy (CBT) among patients with heart failure (HF). RECENT FINDINGS: The umbrella review covered five meta-analyses published in the past five years with 18 primary studies (conducted between 2010 and 2022) of 1,687 non-duplicated patients aged 51.63 to 77.40. Primary studies originated from the USA, Sweden, Netherlands, China, Iran, and Philippines. Common benefits of CBT included depressive symptoms (89%), quality of life (56%), anxiety symptoms (39%), self-care (33%), 6-minute walk distance (33%), and health status (17%). Evidence varied depending on the timepoint of follow-up assessment and the specific measurement used to evaluate the outcome. No meta-analyses pooled the effect sizes for iCBT and analyzed the specific therapeutic elements in CBT. No meta-regression was performed. Individual participant data meta-analyses with meta-regression should be performed to identify common moderators and factors that can influence treatment adherence. The current research would also benefit from additional RCT evidence focusing specifically on iCBT for patients with HF.
12 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 →Psychological medicine
Cognitive effects of intravenous Ketamine in treatment-resistant depression: A systematic review
Intravenous (IV) low-dose ketamine has emerged as a promising treatment for patients with treatment-resistant depression (TRD). However, its impact on cognitive functioning remains unclear. This systematic review examines the cognitive and executive effects of IV ketamine in TRD, focusing on their relationship to depressive and suicidal outcomes. A systematic search of Cochrane, MEDLINE, Embase, and PsycINFO databases was conducted up to May 15, 2025, using the terms depression, cognition, and ketamine. This review was conducted in accordance with the PRISMA guidelines, and the protocol was registered in PROSPERO (ID: 1160487). Risk of bias was evaluated using the Cochrane RoB 2 tool for randomized trials and the ROBINS-I tool for non-randomized studies. Twenty-one studies, comprising approximately 900-1,180 participants with TRD, assessed cognitive domains of processing speed, working memory, attention, verbal and visual memory, cognitive flexibility, and executive control. Procognitive effects were frequently observed in processing speed and working memory, while attention results were preserved or modestly improved, and verbal and visual memory results were heterogeneous. Executive control, particularly inhibitory performance on Stroop paradigms, improved in several trials. Two studies directly examined cognition as it relates to suicidal behaviors. No cognitive deterioration was reported. Subanesthetic IV ketamine appears to preserve and enhance specific cognitive functions in TRD, notably across processing speed, working memory, and executive control. These procognitive effects, particularly in executive control, may mediate ketamine's antisuicidal action. Standardized longitudinal studies are warranted to clarify their durability and clinical significance.
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 →American journal of epidemiology
Predicting first onset of suicide attempt among children with suicidal ideation or non-suicidal self-injury using machine learning: a prospective population-based cohort study
While children with suicidal ideation or non-suicidal self-injury (NSSI) are at high risk of suicide, most do not attempt suicide. This study aims to identify predictors of first suicide attempts among children with suicidal thoughts or NSSI. We utilized longitudinal data from the Adolescent Brain and Cognitive Development study to examine a broad range of risk factors in children reporting suicidal ideation (N = 344, mean age = 9.93) or NSSI (N = 261, mean age = 9.88) at baseline, predicting first attempt of suicide during a 4-year follow-up. Predictive models were developed through bivariate feature selection followed by Discrete-Time Random Survival Forest machine learning, identifying predictors of subsequent suicide attempts. During the follow-up, 40 of 344 children (11.6%) with suicidal ideation, and 32 of 261 children (12.3%) with NSSI at baseline initiated suicide attempts. For suicidal ideation, risk for attempting suicide increased with caregiver-reported NSSI, witnessing domestic violence, severity of suicidal thoughts, being female, online social screen use, and less parental supervision. For NSSI, risk of suicide attempt increased with witnessing domestic violence, anxiety disorders, caregiver-reported NSSI, being female, and disruptive, impulse control, and conduct disorders. Our findings shed light on the development of population-based suicide prevention strategies for children.
11 May 2026
Read appraisal →JMIR formative research
Cultural Relevance and Acceptability of Cognitive Behavioral Therapy Techniques Adapted by AI or a Human Psychologist: Experimental Study
BACKGROUND: Evidence-based psychological interventions are usually not accessed by marginalized groups such as refugees. Culturally adapted psychological interventions have reported larger effect sizes than nonadapted psychological interventions. However, the cultural adaptation of interventions is a lengthy process, entailing a challenge. One potential solution to overcome this challenge is the use of artificial intelligence (AI). OBJECTIVE: The aim of this study was to investigate and compare the perceived cultural relevance and acceptability of 2 common cognitive behavioral therapy (CBT) techniques when translated and culturally adapted by AI versus a human psychologist. METHODS: In a 2×2 factorial design, the text generator type (AI vs human psychologist) and the CBT technique (cognitive restructuring vs behavior modification) were compared. CBT technique texts translated and culturally adapted either by AI or by a human psychologist were blindly rated using the Cultural Relevance Questionnaire and the Theoretical Framework of Acceptability. Raters were Arabic-speaking refugees and immigrants, aged between 18 and 69 years, residing in Sweden, Denmark, and Germany. Raters were randomly allocated to 1 of 4 conditions. Each condition consisted of 2 stimuli. Two-factor between-subject design analyses were used to analyze the data. RESULTS: A significant main effect of the text generator domain type (P=.02; η²=0.045) was found in the first rating, with texts adapted by the AI domain perceived as more culturally relevant than those adapted by the human domain. No significant main effect of the CBT technique was found in the first rating (P=.10; η²=0.022). There were no differences in the second rating. Regarding acceptability, no significant main effects of text generator domain type (P=.09; η²=0.024) or the CBT technique (P=.88; η²=0.001) were found in either of the ratings. CONCLUSIONS: CBT technique materials adapted by AI may be perceived as similarly culturally relevant as those adapted by a human psychologist. This finding implies the potential to accelerate the cultural adaptation of psychological interventions. However, AI still needs to be used with caution and in accordance with rigorous safety standards and robust frameworks.
11 May 2026
Read appraisal →The British journal of psychiatry : the journal of mental science
Characterisation of serious mental illness trajectories through transdiagnostic clinical features
BACKGROUND: Electronic health records (EHRs), increasingly available in low- and middle-income countries (LMICs), provide an opportunity to study transdiagnostic features of serious mental illness (SMI) and its trajectories. AIMS: Characterise transdiagnostic features and diagnostic trajectories of SMI using an EHR database in an LMIC institution. METHOD: We conducted a retrospective cohort study using EHRs from 2005-2022 at Clínica San Juan de Dios Manizales, a specialised mental health facility in Colombia, including 22 447 patients with schizophrenia (SCZ), bipolar disorder (BPD) or severe/recurrent major depressive disorder (MDD). Using diagnostic codes and clinical notes, we analysed the frequency of suicidality and psychosis across diagnoses, patterns of diagnostic switching and the accumulation of comorbidities. Mixed-effect logistic regression was used to identify factors influencing diagnostic stability. RESULTS: High frequencies of suicidality and psychosis were observed across diagnoses of SCZ, BPD and MDD. Most patients (64%) received multiple diagnoses over time, including switches between primary SMI diagnoses (19%), diagnostic comorbidities (30%) or both (15%). Predictors of diagnostic switching included mentions of delusions (odds ratio = 1.47, 95% CI 1.34-1.61), prior diagnostic switching (odds ratio = 4.01, 95% CI 3.7-4.34) and time in treatment, independent of age (log of visit number; odds ratio = 0.57, 95% CI 0.54-0.61). Over 80% of patients reached diagnostic stability within 6 years of their first record. CONCLUSIONS: Integrating structured and unstructured EHR data reveals transdiagnostic patterns in SMI and predictors of disease trajectories, highlighting the potential of EHR-based tools for research and precision psychiatry in LMICs.
9 May 2026
Read appraisal →IEEE transactions on bio-medical engineering
Multilevel Correlation-Aware and Modal-Aware Graph Convolutional Network for Diagnosing Neurodevelopmental Disorders
OBJECTIVE: Graph-based methods using resting-state functional magnetic resonance imaging demonstrate strong capabilities in modeling brain networks. However, existing graph-based methods often overlook inter-graph relationships, limiting their ability to capture the intrinsic features shared across individuals. Additionally, their simplistic integration strategies may fail to take full advantage of multimodal information. To address these challenges, this paper proposes a Multilevel Correlation-aware and Modal-aware Graph Convolutional Network (MCM-GCN) for the reliable diagnosis of neurodevelopmental disorders. METHODS: At the individual level, we design a correlation-driven feature generation module that incorporates a pooling layer with external graph attention to perceive inter-graph correlations, generating discriminative brain embeddings and identifying disease-related regions. At the population level, to deeply integrate multimodal and multi-atlas information, a multimodal-decoupled feature enhancement module learns unique and shared embeddings from brain graphs and phenotypic data and then fuses them adaptively with graph channel attention for reliable disease classification. RESULTS: Extensive experiments on two public datasets for Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) demonstrate that MCM-GCN outperforms other competing methods, with an accuracy of 93.11% for ASD and 76.41% for ADHD. CONCLUSION: The MCM-GCN framework integrates individual-level and population-level analyses, offering a comprehensive perspective for neurodevelopmental disorder diagnosis, significantly improving diagnostic accuracy while identifying key indicators. SIGNIFICANCE: These findings highlight the potential of the MCM-GCN for imaging-assisted diagnosis of neurodevelopmental diseases, advancing interpretable deep learning in medical imaging analysis.
6 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 →Neurology
Hypnotic Cognitive Therapy for Chronic Pain in Spinal Cord Injury: A Randomized Controlled Trial
BACKGROUND AND OBJECTIVES: Chronic pain is highly prevalent, disabling, and difficult to treat in people with spinal cord injury (SCI). Our objective was to determine whether hypnotic cognitive therapy (HYP-CT) significantly reduces average pain intensity. METHODS: This single-blind, randomized, parallel-group trial investigated the efficacy of HYP-CT in treating moderate-to-severe chronic pain in community-residing adults with SCI. Participants were recruited nationwide through SCI organizations and randomized 1:1 to HYP-CT vs usual care (UC). We used computerized permuted block randomization with variable block sizes and stratified by sex and worst pain type. HYP-CT was delivered by a psychologist over the telephone or through Zoom and consisted of 6 weekly 60-minute sessions plus instructions to practice self-hypnosis daily independently by listening to session recordings. Controls were encouraged to continue current treatments or seek additional pain treatments. The primary outcome was average pain intensity assessed 4 times per week on a 0-10 numerical rating scale and averaged. The primary end point was at 6 weeks after randomization, with a follow-up assessment at 12 weeks. All outcomes were assessed through structured telephone interviews performed by blinded assessors. RESULTS: A total of 127 participants were randomized to HYP-CT (n = 64) vs UC (n = 63). Forty-eight percent were treated over the telephone and 52% through Zoom. The group was 42% female, had a mean age of 51.3 years (15.4 years after SCI), and had a mean baseline pain intensity of 5.93. The worst pain was neuropathic in 57% of cases. The primary outcome, average pain intensity, decreased more in the HYP-CT group compared with UC controls at 6 (-0.55, 95% CI -1.04 to -0.06) and 12 (-0.79, 95% CI -1.28 to -0.29) weeks. Depression declined significantly more in the HYP-CT vs UC groups at 6 and 12 weeks. Planned exploratory analyses suggested that the effect of HYP-CT on pain intensity was similar when delivered over the telephone vs through Zoom, and that pain intensity decreased more in those with all neuropathic pain vs those with mixed pain at 12 weeks. DISCUSSION: The study suggests that HYP-CT is an effective adjunctive treatment for SCI-related pain. Replication studies are needed. TRIAL REGISTRATION INFORMATION: The study was registered on ClinicalTrials.gov (NCT03857672) on February 28, 2019 and study enrollment commenced on April 24, 2019. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that, in patients with SCI, HYP-CT improves average pain intensity at 6 weeks compared with UC.
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 →JMIR mental health
The Effectiveness and Mechanisms of Action of App-Based Interventions for Improving Mental Health and Workplace Well-Being: Randomized Controlled Trial
BACKGROUND: Depression is the most common mental health disorder worldwide and frequently leads to workplace absence. As face-to-face treatment can be difficult to access, app-based interventions are a popular solution, although their effectiveness in working populations and their mechanisms of action are unclear. Deficits in executive function may contribute to the onset and maintenance of depression, and executive function training is proposed to improve symptoms by enhancing executive function. Responders to cognitive behavioral therapy (CBT) show improvements in executive function, suggesting that this may be one mechanism of action. OBJECTIVE: This study investigated the effectiveness of app-based interventions (executive function or CBT-based) for reducing depressive and anxiety symptoms and improving workplace well-being, and assessed whether changes in executive function mediated improvements. METHODS: A total of 228 participants (147 female participants) with mild-to-moderate symptoms of depression and anxiety were recruited online and randomly assigned to a waitlist control group, an executive function training group (NeuroNation app, Synaptikon GmbH), or a self-guided CBT group (Moodfit app, Roble Ridge LLC) for a 4-week intervention period. Participants assigned to the active intervention groups were asked to use their apps a minimum of 21 times during the intervention. Participants completed measures of depressive symptoms, anxiety symptoms, and workplace well-being, and a working memory task at baseline, postintervention, and follow-up (12 weeks). RESULTS: Executive function training reduced anxiety (β=-2.79; P=.004) and depressive (β=-2.77; P=.02) symptoms at follow-up but not at postintervention, and it did not affect workplace well-being. There were no reductions in depressive or anxiety symptoms in the self-guided CBT group, though workplace well-being was improved at postintervention (β=3.72; P=.02) and follow-up (β=4.46; P=.02). Improvements in executive function did not mediate intervention-related changes in symptoms or workplace well-being. Self-reported adherence rates were high (executive function training: 48/54, 89%; self-guided CBT: 52/54, 96%), although attrition was high at follow-up (58% missing). CONCLUSIONS: These results suggest that app-based executive function training may be effective at managing symptoms of anxiety and depression in a working population, while self-guided CBT apps may improve workplace well-being. However, improving executive function did not appear to be a mechanism of action of either intervention.
2 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 →Psychological medicine
Treatment effect modifiers of virtual reality-based versus standard cognitive behavioral therapy for paranoia in schizophrenia spectrum disorders: an exploratory moderator analysis of clinical and demographic characteristics in the FaceYourFears trial
BACKGROUND: This exploratory study examined baseline characteristics modifying treatment effects on paranoia in individuals diagnosed with schizophrenia spectrum disorders following a 10-session virtual reality-based cognitive behavioral therapy for psychosis (VR-CBTp) or standard CBTp. METHODS: All participants in the FaceYourFears trial were included (n=254; CBTp n=128; VR-CBTp n=126). General linear and logistic regression models examined baseline variables associated with end-of-treatment paranoia. In covariate analyses, regression coefficients quantified associations across treatments. In moderation analyses, interaction terms (randomization x moderator) were tested, with corresponding regression coefficients estimated and assessed at the 25th (low), 50th (medium), and 75th (high) percentiles of continuous variables. RESULTS: Across treatments, higher baseline avolition, safety behavior, delusion severity, and cognitive biases were associated with end-of-treatment paranoia. Moderation analyses revealed interactions for avolition, delusion severity, and negative other-beliefs. Although avolition and delusion severity were associated with poorer outcomes overall, individuals with high avolition and those with moderate-to-high delusion severity improved more in VR-CBTp than CBTp, whereas participants with lower delusion severity showed better outcomes in CBTp. No demographic (age, gender, education, and occupation) or other clinical characteristics (diagnosis, paranoia, social anxiety, depression, anhedonia, total negative symptoms, functioning, core beliefs, or interpersonal trauma) were significantly associated with outcome. CONCLUSIONS: This exploratory study generates hypotheses for future research, including VR-CBTp's potential to engage individuals with high avolition. Given the modest effects and largely nonsignificant findings, both CBTp and VR-CBTp appear suitable for a wide range of individuals with paranoia, highlighting the importance of considering patient preferences.
2 May 2026
Read appraisal →Behavioural brain research
Targeting the prefrontal-supplementary motor network with online and offline tDCS to modulate disgust: A single-blind and sham-controlled study
In certain individuals, disgust experience is abnormally high. This heightened sensitivity serves as a cognitive component that can lead to dysfunction in neural circuits related to emotion processing and psychological disorders such as contamination-based obsessive-compulsive disorder. In this single-blind study, 36 individuals with high disgust sensitivity were randomly assigned to three groups: online, offline, and sham. The online group received intensified and repetitive transcranial direct current stimulation (tDCS) while performing a disgust image watching task, whereas the offline and sham groups performed the task at two time points: immediately before (pre-test) and immediately after (post-test) the stimulation session. The offline group received active tDCS, whereas the sham group received placebo tDCS to control for the interval duration. Anodal and cathodal stimulation was applied to the left dorsolateral prefrontal cortex (l-DLPFC; F3) and pre-supplementary motor area (pre-SMA; FC2), respectively. The intervention consisted of 10 sessions of tDCS (2 sessions per day) over 5 days, with a stimulation intensity of 2 mA for 20 min and a 20-minute interval between sessions. The results showed a significant decrease in disgust experience in the online group compared to the offline group, while there was no significant difference between the online and sham groups. Although these findings suggest potential effects of tDCS combined with a task, further studies are necessary to confirm its efficacy.
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 →PloS one
Research on the development of an automated system for psychology questionnaire generation based on large language models
This study reimagined the psychology questionnaire development process using large language model ((LLM) technology, aiming to overcome the protracted preparation cycles and significant human bias inherent in traditional scale development. We developed a specialized fine-tuning scheme for a corpus of 169 professional psychological questionnaires. By integrating instruction fine-tuning with human feedback reinforcement, we significantly enhanced the adaptability of the Qwen-2.5 and GLM-4 models for demanding professional psychological assessment tasks. The optimized models demonstrated remarkable gains across key dimensions: text generation quality (BLEU-4 increased by 0.05, ROUGE-L by 0.057), scientific rigor (logical consistency improved by 28.6%), and cultural adaptability (achieving over 85% accuracy in cross-regional expression conversion). This research solidly supports the feasibility of leveraging LLM technology to drive research paradigm transformation in psychology, offering crucial methodological support for developing efficient, intelligent psychological measurement tools.
27 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 →Behavior research methods
A modified expectation-maximization algorithm for accelerated item response theory model estimation with large datasets
The expectation-maximization (EM) algorithm is widely used for parameter estimation in item response theory (IRT) modeling. However, when applied to datasets with large numbers of individuals and items, the standard EM algorithm can be slow to converge, with computationally expensive E-steps. We propose a modified EM algorithm to accelerate estimation for unidimensional two-parameter logistic IRT models. The modified algorithm uses a two-stage structure with partial-step updating over data subsets to reduce convergence time, while maintaining comparable accuracy and precision. The first two simulation studies evaluated its performance relative to standard EM, focusing on convergence time, parameter recovery, and standard error estimation across varying subset sizes and item counts. The third study demonstrated its scalability and runtime advantage in a large-scale testing scenario involving one million respondents and 100 items. The fourth study evaluated robustness under departures from unidimensionality. The proposed algorithm showed time reductions under smaller subsets with 36 items, consistent reductions across all subset sizes with 54 items, and the largest reduction (60%) with 40-item forms constructed from 100 items, while maintaining comparable estimation performance. These results highlight the algorithm's potential for large-scale applications involving tens of thousands of respondents and moderate-to-large item pools, with modifications that can be integrated into existing EM routines. We conclude with directions for operational use and potential extensions to multidimensional IRT estimation.
23 Apr 2026
Read appraisal →Journal of medical Internet research
Long-Term Outcomes, Moderators, and Predictors in Online Mindfulness-Based Cognitive Therapy for People With Cancer: Secondary Analysis of a Randomized Controlled Trial
BACKGROUND: A 3-armed randomized controlled trial (RCT) demonstrated that 2 formats of online mindfulness-based cognitive therapy (eMBCT)-group-blended and individual-unguided-effectively reduced psychological distress and improved positive health outcomes in people with cancer and survivors, when compared to care as usual, up to 3 months posttreatment. However, the long-term effectiveness and possible predictors and moderators of treatment outcomes remain unknown. OBJECTIVE: This study examined the long-term effects (up to 9 months posttreatment) of group-blended and individual-unguided eMBCTs on psychological distress and other mental health outcomes in people with cancer. Additionally, it explored possible predictors and moderators of treatment effects across the 2 delivery formats. METHODS: The study population consisted of people with cancer who were initially randomized to group-blended or individual-unguided eMBCT within a 3-arm RCT, augmented by those who completed the waitlist condition and were subsequently randomly allocated to one of the eMBCT formats. Both groups were assessed over a 9-month follow-up period. Outcomes completed at baseline, posttreatment, and 3-, 6-, and 9-month follow-up assessments included psychological distress (primary), fear of cancer recurrence, rumination, fatigue, mindfulness skills, decentering, self-compassion, and well-being. Linear mixed-effects models examined changes over time, while linear mixed-effects models and binary logistic regression analyzed potential predictors and moderators of psychological distress and dropout. RESULTS: Of the 186 participants enrolled in the 3-arm RCT, 161 participants were randomly assigned to either group-blended or individual-unguided eMBCTs after adding those initially assigned to the waiting condition (group-blended: n=71; individual-unguided: n=90). The majority of participants were female (n=129, 80%), diagnosed with breast cancer (n=78, 48%), and were undergoing or had completed treatment with curative intent (n=124, 77%). The mean age was 52.8 (SD 11.4) years. Both eMBCT formats resulted in significant reductions in psychological distress, fear of cancer recurrence, rumination, and fatigue, alongside improvements in mindfulness skills, decentering, and self-compassion, up to 9 months posttreatment. Higher baseline rumination, as well as lower mindfulness skills and self-compassion at baseline, predicted larger reductions in psychological distress from baseline to the 9-month follow-up period. Additionally, highly distressed participants in the group-blended eMBCT arm were more prone to dropout than those with lower distress scores, whereas psychological distress was not associated with dropout in the individual-unguided format. No other significant moderators were identified. CONCLUSIONS: Group-blended and individual-unguided eMBCTs effectively reduced psychological distress and improved well-being among people with cancer and survivors, with greater benefits for those with fewer psychological resources. However, individuals experiencing higher levels of distress were more likely to discontinue group-blended eMBCT. These findings highlight the importance of considering individual preferences and pragmatic factors in treatment decisions. Larger, fully powered RCTs are needed to confirm these results and provide more definitive guidance on treatment format selection.
20 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 →Journal of affective disorders
Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.
BACKGROUND: Although mindfulness ecological momentary interventions (MEMI) appear effective in alleviating worry symptoms, treatment engagement remains suboptimal. Determining baseline variables of MEMI over self-monitoring placebo (SM) can inform tailored interventions for individuals with generalized anxiety disorder (GAD). METHOD: Machine meta-learning methods (ML) were applied to predict two-week engagement (log-transformed number of prompts completed) among individuals randomized to MEMI or SM (N = 110). Sixteen baseline variables comprised the predictor set: clinical, demographic, process, and executive functioning (EF) factors. Random forest using a five-fold nested cross-validation approach mitigated overfitting. X-learner meta-algorithms estimated conditional average treatment engagement (CATE). Shapley additive explanations evaluated relative importance. RESULTS: The 16-predictor model displayed strong predictive performance (R-squared [R2] = 82.7%; root mean squared error [RMSE] = 0.780; mean absolute error [MAE] = 0.512). The top-10 predictor model also yielded good predictive performance (R2 = 82.1%; RMSE = 0.547; MAE = 0.307). As predicted by the CATE analysiscate, participants had the highest treatment engagement when assigned to MEMI instead of SM (d = 1.447, p < .001). The following baseline variables predicted more engagement with MEMI over SM: lower GAD severity, inhibition response time (RT), and EF errors, higher attentional control, empathy, and verbal fluency (capitalization theory); lower mindfulness, and treatment expectancy, poorer working memory, and higher set-shifting RT (compensation model). LIMITATIONS: The small sample size, single engagement metric, and brief duration might constrain generalizability. DISCUSSION: Integrating robust ML approaches could optimally identify prescriptive predictors of engagement to brief digital mental health interventions to inform targeted treatments. TRIAL REGISTRATION: ClinicalTrials.gov ID (NCT04846777).
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
What retrospective and dynamic assessments tell us about youth depression: A network analysis perspective.
PURPOSE: Traditional assessments of depressive symptoms often rely on retrospective self-reports, which may be affected by cognitive and memory biases. Few studies have compared retrospective and dynamic (real-time) assessments to examine the consistency and structure of depressive symptom reporting. This study aimed to compare retrospective and dynamic assessments of depressive symptoms in youth using network analysis to explore symptom-level associations and clustering. METHODS: Ninety Brazilian adolescents and young adults (mean age = 18 years), with and without depression, completed the Short Mood and Feelings Questionnaire (SMFQ) every other day for 14 days via a smartphone-based chatbot (dynamic assessment). At the end of the 2-week period, they completed the same questionnaire retrospectively. Network analyses were conducted using Exploratory Graph Analysis (EGA) and Dynamic Exploratory Analysis (DEA) to identify symptom communities and compare network structures across both assessment methods. RESULTS: Both retrospective and dynamic assessments revealed three symptom communities; however, the composition and structure of these communities differed. Retrospective assessments showed stronger connections among cognitive symptoms, while dynamic assessments displayed a more balanced distribution, with stronger associations between somatic and affective symptoms. IMPLICATIONS: Findings highlight significant differences in depressive symptom networks between retrospective and dynamic assessments. Dynamic methods may offer less biased and more ecologically valid insights into youth depression, underscoring the importance of real-time data collection in clinical assessment and research.
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 →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 →BMJ open
Regional disparities in insurance-covered cognitive behavioural therapy in Japan: a nationwide cross-sectional study using National Database Open Data, 2015-2023
OBJECTIVES: Cognitive behavioural therapy (CBT) is recommended as a first-line treatment for depression and anxiety disorders, but its utilisation under Japan's national health insurance remains poorly understood. This study aimed to describe CBT utilisation patterns, quantify regional disparities across prefectures and analyse temporal trends from fiscal year (FY)2015 to FY2023. DESIGN: This was a nationwide repeated cross-sectional study. SETTING: Japan's National Database of Health Insurance Claims and Specific Health Checkups Open Data (NDB Open Data), FY2015-2023. PARTICIPANTS: All patients who received insurance-covered CBT in FY2023, with a longitudinal comparison across FY2015-2023. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcomes were annual CBT claims and patient counts. The secondary outcomes included prefecture-level distribution, population-adjusted utilisation rates per 100 000 population, distribution by sex and age, monthly trends and temporal changes over 9 years. Regional variation in physician-delivered CBT was assessed using the coefficient of variation (CV) and extremal quotient (EQ). RESULTS: In FY2023, the total CBT claims numbered 38 045 with 8299 patients, representing only 0.14% of an estimated 6.03 million psychiatric patients. Physician-delivered CBT accounted for 99.6% (37 886 claims), whereas nurse-delivered CBT introduced in 2016 remained at 0.4% (159 claims). 13 of the 47 prefectures (27.7%) had zero or fewer than 10 claims. The population-adjusted physician-delivered CBT claims ranged from 370.96 per 100 000 in Okayama to 0.99 per 100 000 in Kumamoto, yielding an EQ of 375-fold. The CV among the 34 prefectures with measurable physician-delivered CBT was 174.8%. Despite indication expansions in 2016 and 2018, claims decreased by 9.9% from 42 216 in FY2015 to 38 045 in FY2023. CONCLUSIONS: Insurance-covered CBT in Japan remains severely underused, with significant regional disparities. Incremental policy measures, including indication expansions and nurse-delivered CBT, have failed to improve access. Fundamental system reforms, potentially including dedicated psychological therapy services, are needed to ensure equitable access to evidence-based psychological treatments.
15 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 →Psychological medicine
What is being measured by formal thought disorder scales? An item-level content analysis.
BACKGROUND: Formal thought disorder (FTD), characterized by disruptions in the flow and form of thought, is a core feature of psychosis. But its measurement is fragmented across numerous rating scales, leading to its continued neglect in both research and clinical practice. To determine if different FTD scales measure the same underlying construct, we need to assess the degree to which the content of commonly used FTD scales overlaps with each other. METHODS: We conducted a systematic review to identify all standardized, clinician-rated scales used to measure FTD in psychotic disorders. From this set, we extracted individual items and derived a consensus list of 56 discrete FTD phenomena. Two independent clinical experts conducted item-to-item mapping for every scale item onto these FTD phenomena. Content overlap between scales was quantified using Jaccard Similarity Index (JSI). We determined the overall coverage achieved via several combinations of FTD scales. RESULTS: The 15 scales, comprising 207 items, showed weak content overlap. The mean JSI across all scale pairs was low, and no single phenomenon featured across all scales. While some core FTD phenomena (e.g. 'incoherence', 'poor speech content', 'drifting-off') were represented in many scales, 20% of all identified features were idiosyncratic, appearing in only one scale. CONCLUSIONS: Existing FTD rating scales capture a wide but heterogeneous array of symptoms with poor content overlap. This lack of harmonization challenges the comparability of mechanistic and interventional studies. We highlight the need for a consensus-based, standardized measurement of FTD and provide a comprehensive checklist to advance the research and clinical practice.
14 Apr 2026
Read appraisal →European journal of psychotraumatology
Emotion regulation skills training as an adjunctive treatment to narrative exposure therapy for posttraumatic stress disorder (PTSD) in refugees: a pilot randomized controlled trial
Background: Responses to evidence-based interventions for posttraumatic stress disorder (PTSD) in refugees vary considerably. Emotion regulation difficulties are associated with greater PTSD severity in refugees and thus represent a potential treatment target.Objective: This study aimed to test the efficacy of Skills Training in Affective and Interpersonal Regulation for Refugees (STAIR-R) and Narrative Exposure Therapy (NET) or Supportive Problem-Solving (SPS) and NET. An exploratory aim was to examine relative efficacy in refugees with high and low levels of visa and family insecurity.Methods: Seventy-one participants were randomly assigned to STAIR-R (6 sessions) + NET (7 sessions) or SPS (6 sessions) +NET (7 sessions). Assessments occurred at baseline, post-treatment, and 3-month follow-up, with additional self-report at mid-treatment. The primary outcome was clinician-assessed PTSD symptom severity (CAPS-5) and secondary outcomes included self-reported PTSD symptoms, depression symptoms, emotion regulation difficulties, difficulties in relationships and environmental quality of life.Results: Intent-to-treat linear mixed models showed no significant between-group differences at mid-treatment, post-treatment, or follow-up. Both groups demonstrated significant improvements at 3-month follow-up in clinician-assessed (STAIR-R + NET, g = -1.41; SPS + NET, g = -1.54, p < .001) and self-reported (STAIR-R + NET, g = -0.49; SPS + NET, g = -0.44, p = .006) PTSD symptoms. Moderatorg analyses revealed that those with high insecurity (n = 16) obtained greater benefits in STAIR-R + NET from pre-treatment to follow-up on self-reported PTSD (g = 1.35), depression (g = 1.11), emotion regulation difficulties (g = 1.24), relationship difficulties (g = 1.12) and quality of life (g = -1.05).Conclusions: While there were no overall between group-differences, refugees in both conditions showed reduced PTSD symptoms. There is preliminary evidence that those with high insecurity showed a better response to STAIR-R + NET than SPS + NET across several clinical outcomes, although the small sample size necessitates replication of these. These findings highlight the potential importance of tailored intervention approaches for refugees living in different environmental circumstances. Emotion regulation difficulties represent a promising treatment target for refugees with PTSD.A randomized controlled trial comparing Skills Training in Affective and Interpersonal Regulation for Refugees + Narrative Exposure Therapy (STAIR-R + NET) to Supportive Problem Solving Training + Narrative Exposure Therapy (SPS + NET) revealed no significant differences at 3-month follow-up, with both groups improving on PTSD symptoms.Moderator analyses provided preliminary evidence that refugees living with high insecurity (i.e. insecure visa or separated from all family) showed greater improvements following STAIR-R + NET than SPS + NET.
9 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 →PloS one
Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is primarily characterized by deficits in social communication and restricted or repetitive behavioral patterns. Although psychologists contribute significantly to the understanding of ASD, offering insights into its cognitive, emotional, and behavioral dimensions through assessments, diagnoses, therapeutic approaches, and family support, the diagnostic process remains complex. This complexity arises from the diverse manifestations of the disorder and the challenges associated with data sharing. In addition, conventional machine learning approaches for ASD detection may struggle with high-dimensional neuroimaging data and may require careful feature engineering. Consequently, this motivated us to enhance ASD diagnosis by incorporating deep learning (DL) techniques for feature extraction alongside a modified exponential-trigonometric optimization (ETO) algorithm as a feature selection (FS) technique. The modified ETO integrates the Arithmetic Optimization Algorithm (AOA) and the Guided Learning Strategy (GLS) to improve diagnostic performance. To evaluate the effectiveness of the proposed model, we utilized resting-state functional MRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE I). Furthermore, the performance of the proposed model was compared with that of established models. The results indicate that the proposed model achieves competitive and, in most cases, superior performance compared with the benchmark methods, demonstrating superior accuracy, sensitivity, and AUC in diagnosing ASD. On average across the three atlas-based feature sets, the proposed model has an accuracy, sensitivity, and AUC of 73%, 78%, and 79%, respectively.
9 Apr 2026
Read appraisal →JMIR formative research
Real-World Data From a Group Parent Management Training Program Enhanced Using Artificial Intelligence: Qualitative Study
BACKGROUND: Parent management training (PMT) is an evidence-based intervention for children with disruptive behavior problems. However, access to care is often limited by cost, availability of clinicians, scheduling, and transportation barriers. Integrating artificial intelligence (AI) into group PMT may improve accessibility, personalization, and adherence, while preserving therapeutic quality. OBJECTIVE: This study explored caregivers' experiences with a hybrid PMT program that combined live therapist-led group sessions with asynchronous support from Pat, an AI conversational agent designed to augment the therapists' support to caregivers. METHODS: A total of 88 caregivers of children aged 3-14 years (mean 7.98, SD 2.45 years) from Argentina and Paraguay participated in eight weekly online group sessions led by human therapists and supplemented by Pat. Caregivers were asked to provide the net promoter score (NPS) and their perceived contribution of their experience in remote group sessions and with Pat. Caregiver perspectives were analyzed using thematic analysis by multiple coders with consensus and interrater reliability assessment. RESULTS: The average NPS was 76.92, indicating excellent satisfaction. Regarding participants' perceptions of the overall program, the most frequent theme was useful strategies (73/202, 36.1%), reflecting the value placed on clear, structured, and practical tools to address everyday parenting challenges. Most comments about Pat were positive (156/164, 95.1%), particularly highlighting its 24/7 accessibility and constant availability (69/164, 42.1%). Recommendations for improvement mainly focused on enhancing the user experience and incorporating additional functionalities. Regarding perceived contribution to progress, caregivers attributed, on average, 61% to Pat and 46% to the group sessions. CONCLUSIONS: Combining therapist-led PMT group sessions with AI support appears feasible, acceptable, and valued by caregivers and may expand reach without sacrificing quality. Overall, caregivers valued the useful strategies and the professional and peer support and reported positive changes. Regarding Pat, the most valued aspect is the constant and immediate support. This model integrates human expertise with the accessibility and continuity provided by AI, reducing barriers, such as time, cost, and clinician availability.
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 →JMIR mental health
It Is the Journey, Not the Destination: Moving From End Points to Trajectories When Assessing Chatbot Mental Health Safety
Large language models are rapidly becoming embedded in everyday life through artificial intelligence (AI) chatbots that people use for practical assistance and companionship, as well as for support with mental health and emotional well-being. Alongside clear benefits, clinicians and public reports increasingly describe a minority of users whose interactions seem to drift over days or weeks toward strongly questionable convictions, delusions, or suicidal crises. Importantly, clinically meaningful deterioration can occur even without overtly unsafe text outputs, via more insidious processes, such as compulsive use, sleep disruption, withdrawal from human contact, and progressive narrowing of attention around the chatbot relationship. In this Viewpoint, we argue that risk often arises not at a single tipping point but through trajectory effects that accumulate across extended dialogue and that prevailing safety evaluation approaches are misaligned with this reality because they primarily score risk at discrete conversational end points often reached through scripted dialogues lasting just a single turn or several turns. Mental health benchmarks and safety suites (including clinician-informed efforts) have advanced the field by testing refusal behavior, toxicity, and adversarial prompting. However, they often treat the last message as the unit of analysis and, therefore, miss when risk-relevant relational cues, signs of validation, contradiction handling, and shifts in certainty first emerge and how they compound. We propose that mental health safety assessment should shift from end points to trajectories by (1) treating the whole dialogue, not just the end result, as the focus of evaluation; (2) reporting turn-by-turn dynamics, such as delusion confirmation and harm enablement, and timing and persistence of safety interventions; and (3) calibrating short multiturn tests against longer, clinically realistic interaction sequences that can reveal context-length effects and drift. We further argue that transcript-only evaluation is insufficient in mental health contexts. Similar language can reflect very different internal states, and the relationship between expressed psychopathology and real-world harm is nonlinear. Therefore, safety research should incorporate proximal human outcomes following interactions (eg, shifts in certainty, openness to counterevidence, arousal, urge to continue, and subsequent sleep or behavior) and build a prospective clinical surveillance infrastructure that supports transcript donation with consent and linkage to health outcomes. Together, these steps would enable benchmarks that are clinically relevant and better aligned with the types of harms now being observed in real-world chatbot use.
7 Apr 2026
Read appraisal →Journal of psychosomatic obstetrics and gynaecology
The effect of Snyder's hope theory-based psychological intervention on post-traumatic growth in chinese women experiencing perinatal loss: a randomised controlled trial
OBJECTIVE: To evaluate a Snyder's hope theory-based psychological intervention for promoting post-traumatic growth (PTG) in women after perinatal loss. METHODS: This single-blind randomised controlled trial enrolled 140 women (≥20 years) who had experienced perinatal loss. Participants were randomised to an intervention group (n = 70, four weekly hope-based sessions plus routine care) or a control group (n = 70, routine care only). The primary outcome was PTG, with secondary outcomes including grief, hope and post-traumatic stress symptoms. Assessments were conducted at baseline, post intervention and at 1-month follow-up. RESULTS: The intervention group demonstrated significantly greater improvements in PTG than the control group (post intervention: 69.4 ± 10.2 vs 58.3 ± 11.9; follow-up: 71.9 ± 9.6 vs 59.5 ± 11.0; both p < 0.001), with a large effect size (Cohen's d = 1.21). Significant benefits were also observed in grief reduction, hope enhancement and reductions in trauma-related symptoms (all p < 0.001). Regression analysis identified post-intervention hope as a robust predictor of PTG (β = 0.44, p < 0.001). CONCLUSIONS: Snyder's hope theory-based intervention effectively promoted PTG and improved psychological outcomes in women experiencing perinatal loss. This approach represents a promising therapeutic framework for supporting women through the complex process of moving from trauma to growth following perinatal loss.
4 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 behavioral addictions
Gambling in women: A systematic review of interventions and prevention approaches
BACKGROUND: Gambling disorder (GD) is a behavioral addiction with significant psychological, social, and economic consequences. Despite growing female participation and distinct gambling characteristics, research and interventions largely male-centered, overlooking women-specific factors. OBJECTIVES: This systematic review synthesizes evidence on prevention and treatment interventions for women with GD, assessing their effectiveness on gambling behaviors, cognitive and psychological outcomes, and identifying barriers related to access, adherence, and gender-sensitive design. METHOD: A systematic search in PubMed, PsycINFO, and Web of Science (up to September 2025) identified quantitative studies reporting female-specific outcomes in gambling prevention or treatment. Study quality was appraised using National Heart, Lung, and Blood Institute tools, and data on intervention characteristics, outcomes, and participant profiles were extracted. RESULTS: Eighteen studies met the inclusion criteria (five prevention, thirteen treatment). Prevention programs, mostly universal and adolescent-focused, improved gambling knowledge, cognitive distortions, and short term gambling frequency; however, socioemotional effects were inconsistent. Only one study included follow-up assessment. Treatment studies, predominantly cognitive behavioral therapy (CBT)-based, reduced gambling severity and psychological distress, although dropout and relapse rates were higher among women with greater psychopathology or limited support. Person-centered approaches benefited of tailoring interventions to individual profiles. No studies systematically applied gender-sensitive frameworks or disaggregated outcomes by sex in prevention. DISCUSSION AND CONCLUSION: Prevention and treatment programs can benefit women, but standard interventions insufficiently address their unique needs. Gender-sensitive, individualized strategies, inclusion of socioemotional factors, and long-term evaluations are essential to enhance outcomes and inform inclusive public health policies.
4 Apr 2026
Read appraisal →Journal of diabetes investigation
A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.
BACKGROUND: Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate prediction models for diabetes distress in adults with T1DM using continuous glucose monitoring (CGM) metrics. METHODS: The CGM metrics were collected from 259 adults with T1DM. Severe diabetes distress was defined as 40 points on the Problem Areas in Diabetes scale. Prediction models were developed based on ten machine learning algorithms: random forest (RF), support vector machine (SVM), Naive Bayes (NB), Neural Network (NN), k-nearest neighbor (k-NN), XGBoost (XGB), SGDClassifier (SGDC), XGB_limitet_depth (CGB_ld), L1LogisticRegression (L1), and LightGBM. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: Among the ten models, accuracy in the NN model was the highest (NN: 0.744, L1: 0.731, NB: 0.718, SVM: 0.692, SGDC: 0.654, RF: 0.628, k-NN: 0.628, LightGBM: 0.615, XGBoost: 0.564, and XGB_ld: 0.564). The NN model achieved the highest AUC of 0.728 (95% confidence interval: 0.608-0.845). CONCLUSIONS: This study developed a predictive model for severe diabetes distress using machine learning, incorporating both demographic and CGM metrics in adults with type 1 diabetes mellitus. The NN model demonstrated potential as a practical tool to assist clinicians in identifying individuals at risk of severe diabetes distress.
4 Apr 2026
Read appraisal →JMIR Ment Health
Delusional Experiences Emerging From AI Chatbot Interactions or "AI Psychosis"
The integration of artificial intelligence (AI) into daily life has introduced unprecedented forms of human-machine interaction, prompting psychiatry to reconsider the boundaries between environment, cognition, and technology. This Viewpoint reviews the concept of "AI psychosis," which is a framework to understand how sustained engagement with conversational AI systems might trigger, amplify, or reshape psychotic experiences in vulnerable individuals. Drawing from phenomenological psychopathology, the stress-vulnerability model, cognitive theory, and digital mental health research, the paper situates AI psychosis at the intersection of predisposition and algorithmic environment. Rather than defining a new diagnostic entity, it examines how immersive and anthropomorphic AI technologies may modulate perception, belief, and affect, altering the prereflective sense of reality that grounds human experience. The argument unfolds through 4 complementary lenses. First, within the stress-vulnerability model, AI acts as a novel psychosocial stressor. Its 24-hour availability and emotional responsiveness may increase allostatic load, disturb sleep, and reinforce maladaptive appraisals. Second, the digital therapeutic alliance, a construct describing relational engagement with digital systems, is conceptualized as a double-edged mediator. While empathic design can enhance adherence and support, uncritical validation by AI systems may entrench delusional conviction or cognitive perseveration, reversing the corrective principles of cognitive-behavioral therapy for psychosis. Third, disturbances in theory of mind offer a cognitive pathway: individuals with impaired or hyperactive mentalization may project intentionality or empathy onto AI, perceiving chatbots as sentient interlocutors. This dyadic misattribution may form a "digital folie à deux," where the AI becomes a reinforcing partner in delusional elaboration. Fourth, emerging risk factors, including loneliness, trauma history, schizotypal traits, nocturnal or solitary AI use, and algorithmic reinforcement of belief-confirming content may play roles at the individual and environmental levels. Building on this synthesis, we advance a translational research agenda and five domains of action: (1) empirical studies using longitudinal and digital-phenotyping designs to quantify dose-response relationships between AI exposure, stress physiology, and psychotic symptomatology; (2) integration of digital phenomenology into clinical assessment and training; (3) embedding therapeutic design safeguards into AI systems, such as reflective prompts and "reality-testing" nudges; (4) creation of ethical and governance frameworks for AI-related psychiatric events, modeled on pharmacovigilance; and (5) development of environmental cognitive remediation, a preventive intervention aimed at strengthening contextual awareness and reanchoring experience in the physical and social world. By applying empirical rigor and therapeutic ethics to this emerging interface, clinicians, researchers, patients, and developers can transform a potential hazard into an opportunity to deepen understanding of human cognition, safeguard mental health, and promote responsible AI integration within society.
1 Dec 2025
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